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Diffstat (limited to 'third_party/eigen3/Eigen/src')
270 files changed, 0 insertions, 88894 deletions
diff --git a/third_party/eigen3/Eigen/src/Cholesky/LDLT.h b/third_party/eigen3/Eigen/src/Cholesky/LDLT.h deleted file mode 100644 index 6c5632d024..0000000000 --- a/third_party/eigen3/Eigen/src/Cholesky/LDLT.h +++ /dev/null @@ -1,607 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Keir Mierle <mierle@gmail.com> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com > -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_LDLT_H -#define EIGEN_LDLT_H - -namespace Eigen { - -namespace internal { - template<typename MatrixType, int UpLo> struct LDLT_Traits; - - // PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef - enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite }; -} - -/** \ingroup Cholesky_Module - * - * \class LDLT - * - * \brief Robust Cholesky decomposition of a matrix with pivoting - * - * \param MatrixType the type of the matrix of which to compute the LDL^T Cholesky decomposition - * \param UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper. - * The other triangular part won't be read. - * - * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite - * matrix \f$ A \f$ such that \f$ A = P^TLDL^*P \f$, where P is a permutation matrix, L - * is lower triangular with a unit diagonal and D is a diagonal matrix. - * - * The decomposition uses pivoting to ensure stability, so that L will have - * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root - * on D also stabilizes the computation. - * - * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky - * decomposition to determine whether a system of equations has a solution. - * - * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT - */ -template<typename _MatrixType, int _UpLo> class LDLT -{ - public: - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options & ~RowMajorBit, // these are the options for the TmpMatrixType, we need a ColMajor matrix here! - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, - UpLo = _UpLo - }; - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - typedef Matrix<Scalar, RowsAtCompileTime, 1, Options, MaxRowsAtCompileTime, 1> TmpMatrixType; - - typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime, Index> TranspositionType; - typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime, Index> PermutationType; - - typedef internal::LDLT_Traits<MatrixType,UpLo> Traits; - - /** \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via LDLT::compute(const MatrixType&). - */ - LDLT() - : m_matrix(), - m_transpositions(), - m_sign(internal::ZeroSign), - m_isInitialized(false) - {} - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa LDLT() - */ - LDLT(Index size) - : m_matrix(size, size), - m_transpositions(size), - m_temporary(size), - m_sign(internal::ZeroSign), - m_isInitialized(false) - {} - - /** \brief Constructor with decomposition - * - * This calculates the decomposition for the input \a matrix. - * \sa LDLT(Index size) - */ - LDLT(const MatrixType& matrix) - : m_matrix(matrix.rows(), matrix.cols()), - m_transpositions(matrix.rows()), - m_temporary(matrix.rows()), - m_sign(internal::ZeroSign), - m_isInitialized(false) - { - compute(matrix); - } - - /** Clear any existing decomposition - * \sa rankUpdate(w,sigma) - */ - void setZero() - { - m_isInitialized = false; - } - - /** \returns a view of the upper triangular matrix U */ - inline typename Traits::MatrixU matrixU() const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - return Traits::getU(m_matrix); - } - - /** \returns a view of the lower triangular matrix L */ - inline typename Traits::MatrixL matrixL() const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - return Traits::getL(m_matrix); - } - - /** \returns the permutation matrix P as a transposition sequence. - */ - inline const TranspositionType& transpositionsP() const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - return m_transpositions; - } - - /** \returns the coefficients of the diagonal matrix D */ - inline Diagonal<const MatrixType> vectorD() const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - return m_matrix.diagonal(); - } - - /** \returns true if the matrix is positive (semidefinite) */ - inline bool isPositive() const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign; - } - - #ifdef EIGEN2_SUPPORT - inline bool isPositiveDefinite() const - { - return isPositive(); - } - #endif - - /** \returns true if the matrix is negative (semidefinite) */ - inline bool isNegative(void) const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign; - } - - /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> . - * - * \note_about_checking_solutions - * - * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$ - * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$, - * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then - * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the - * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function - * computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular. - * - * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt() - */ - template<typename Rhs> - inline const internal::solve_retval<LDLT, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - eigen_assert(m_matrix.rows()==b.rows() - && "LDLT::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<LDLT, Rhs>(*this, b.derived()); - } - - #ifdef EIGEN2_SUPPORT - template<typename OtherDerived, typename ResultType> - bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const - { - *result = this->solve(b); - return true; - } - #endif - - template<typename Derived> - bool solveInPlace(MatrixBase<Derived> &bAndX) const; - - LDLT& compute(const MatrixType& matrix); - - template <typename Derived> - LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1); - - /** \returns the internal LDLT decomposition matrix - * - * TODO: document the storage layout - */ - inline const MatrixType& matrixLDLT() const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - return m_matrix; - } - - MatrixType reconstructedMatrix() const; - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the matrix.appears to be negative. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "LDLT is not initialized."); - return Success; - } - - protected: - - /** \internal - * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U. - * The strict upper part is used during the decomposition, the strict lower - * part correspond to the coefficients of L (its diagonal is equal to 1 and - * is not stored), and the diagonal entries correspond to D. - */ - MatrixType m_matrix; - TranspositionType m_transpositions; - TmpMatrixType m_temporary; - internal::SignMatrix m_sign; - bool m_isInitialized; -}; - -namespace internal { - -template<int UpLo> struct ldlt_inplace; - -template<> struct ldlt_inplace<Lower> -{ - template<typename MatrixType, typename TranspositionType, typename Workspace> - static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign) - { - using std::abs; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - eigen_assert(mat.rows()==mat.cols()); - const Index size = mat.rows(); - - if (size <= 1) - { - transpositions.setIdentity(); - if (numext::real(mat.coeff(0,0)) > 0) sign = PositiveSemiDef; - else if (numext::real(mat.coeff(0,0)) < 0) sign = NegativeSemiDef; - else sign = ZeroSign; - return true; - } - - RealScalar cutoff(0), biggest_in_corner; - - for (Index k = 0; k < size; ++k) - { - // Find largest diagonal element - Index index_of_biggest_in_corner; - biggest_in_corner = mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner); - index_of_biggest_in_corner += k; - - if(k == 0) - { - // The biggest overall is the point of reference to which further diagonals - // are compared; if any diagonal is negligible compared - // to the largest overall, the algorithm bails. - cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner); - } - - transpositions.coeffRef(k) = index_of_biggest_in_corner; - if(k != index_of_biggest_in_corner) - { - // apply the transposition while taking care to consider only - // the lower triangular part - Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element - mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k)); - mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s)); - std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner)); - for(Index i=k+1;i<index_of_biggest_in_corner;++i) - { - Scalar tmp = mat.coeffRef(i,k); - mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i)); - mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp); - } - if(NumTraits<Scalar>::IsComplex) - mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k)); - } - - // partition the matrix: - // A00 | - | - - // lu = A10 | A11 | - - // A20 | A21 | A22 - Index rs = size - k - 1; - Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1); - Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k); - Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k); - - if(k>0) - { - temp.head(k) = mat.diagonal().head(k).asDiagonal() * A10.adjoint(); - mat.coeffRef(k,k) -= (A10 * temp.head(k)).value(); - if(rs>0) - A21.noalias() -= A20 * temp.head(k); - } - - if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff)) - A21 /= mat.coeffRef(k,k); - - RealScalar realAkk = numext::real(mat.coeffRef(k,k)); - if (sign == PositiveSemiDef) { - if (realAkk < 0) sign = Indefinite; - } else if (sign == NegativeSemiDef) { - if (realAkk > 0) sign = Indefinite; - } else if (sign == ZeroSign) { - if (realAkk > 0) sign = PositiveSemiDef; - else if (realAkk < 0) sign = NegativeSemiDef; - } - } - - return true; - } - - // Reference for the algorithm: Davis and Hager, "Multiple Rank - // Modifications of a Sparse Cholesky Factorization" (Algorithm 1) - // Trivial rearrangements of their computations (Timothy E. Holy) - // allow their algorithm to work for rank-1 updates even if the - // original matrix is not of full rank. - // Here only rank-1 updates are implemented, to reduce the - // requirement for intermediate storage and improve accuracy - template<typename MatrixType, typename WDerived> - static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1) - { - using numext::isfinite; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - - const Index size = mat.rows(); - eigen_assert(mat.cols() == size && w.size()==size); - - RealScalar alpha = 1; - - // Apply the update - for (Index j = 0; j < size; j++) - { - // Check for termination due to an original decomposition of low-rank - if (!(isfinite)(alpha)) - break; - - // Update the diagonal terms - RealScalar dj = numext::real(mat.coeff(j,j)); - Scalar wj = w.coeff(j); - RealScalar swj2 = sigma*numext::abs2(wj); - RealScalar gamma = dj*alpha + swj2; - - mat.coeffRef(j,j) += swj2/alpha; - alpha += swj2/dj; - - - // Update the terms of L - Index rs = size-j-1; - w.tail(rs) -= wj * mat.col(j).tail(rs); - if(gamma != 0) - mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs); - } - return true; - } - - template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType> - static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1) - { - // Apply the permutation to the input w - tmp = transpositions * w; - - return ldlt_inplace<Lower>::updateInPlace(mat,tmp,sigma); - } -}; - -template<> struct ldlt_inplace<Upper> -{ - template<typename MatrixType, typename TranspositionType, typename Workspace> - static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign) - { - Transpose<MatrixType> matt(mat); - return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign); - } - - template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType> - static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1) - { - Transpose<MatrixType> matt(mat); - return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma); - } -}; - -template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower> -{ - typedef const TriangularView<const MatrixType, UnitLower> MatrixL; - typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU; - static inline MatrixL getL(const MatrixType& m) { return m; } - static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); } -}; - -template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper> -{ - typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL; - typedef const TriangularView<const MatrixType, UnitUpper> MatrixU; - static inline MatrixL getL(const MatrixType& m) { return m.adjoint(); } - static inline MatrixU getU(const MatrixType& m) { return m; } -}; - -} // end namespace internal - -/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix - */ -template<typename MatrixType, int _UpLo> -LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::compute(const MatrixType& a) -{ - eigen_assert(a.rows()==a.cols()); - const Index size = a.rows(); - - m_matrix = a; - - m_transpositions.resize(size); - m_isInitialized = false; - m_temporary.resize(size); - - internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign); - - m_isInitialized = true; - return *this; -} - -/** Update the LDLT decomposition: given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T. - * \param w a vector to be incorporated into the decomposition. - * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column vectors. Optional; default value is +1. - * \sa setZero() - */ -template<typename MatrixType, int _UpLo> -template<typename Derived> -LDLT<MatrixType,_UpLo>& LDLT<MatrixType,_UpLo>::rankUpdate(const MatrixBase<Derived>& w, const typename NumTraits<typename MatrixType::Scalar>::Real& sigma) -{ - const Index size = w.rows(); - if (m_isInitialized) - { - eigen_assert(m_matrix.rows()==size); - } - else - { - m_matrix.resize(size,size); - m_matrix.setZero(); - m_transpositions.resize(size); - for (Index i = 0; i < size; i++) - m_transpositions.coeffRef(i) = i; - m_temporary.resize(size); - m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef; - m_isInitialized = true; - } - - internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma); - - return *this; -} - -namespace internal { -template<typename _MatrixType, int _UpLo, typename Rhs> -struct solve_retval<LDLT<_MatrixType,_UpLo>, Rhs> - : solve_retval_base<LDLT<_MatrixType,_UpLo>, Rhs> -{ - typedef LDLT<_MatrixType,_UpLo> LDLTType; - EIGEN_MAKE_SOLVE_HELPERS(LDLTType,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - eigen_assert(rhs().rows() == dec().matrixLDLT().rows()); - // dst = P b - dst = dec().transpositionsP() * rhs(); - - // dst = L^-1 (P b) - dec().matrixL().solveInPlace(dst); - - // dst = D^-1 (L^-1 P b) - // more precisely, use pseudo-inverse of D (see bug 241) - using std::abs; - typedef typename LDLTType::MatrixType MatrixType; - typedef typename LDLTType::Scalar Scalar; - typedef typename LDLTType::RealScalar RealScalar; - const Diagonal<const MatrixType> vectorD = dec().vectorD(); - RealScalar tolerance = numext::maxi(vectorD.array().abs().maxCoeff() * NumTraits<Scalar>::epsilon(), - RealScalar(1) / NumTraits<RealScalar>::highest()); // motivated by LAPACK's xGELSS - for (Index i = 0; i < vectorD.size(); ++i) { - if(abs(vectorD(i)) > tolerance) - dst.row(i) /= vectorD(i); - else - dst.row(i).setZero(); - } - - // dst = L^-T (D^-1 L^-1 P b) - dec().matrixU().solveInPlace(dst); - - // dst = P^-1 (L^-T D^-1 L^-1 P b) = A^-1 b - dst = dec().transpositionsP().transpose() * dst; - } -}; -} - -/** \internal use x = ldlt_object.solve(x); - * - * This is the \em in-place version of solve(). - * - * \param bAndX represents both the right-hand side matrix b and result x. - * - * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD. - * - * This version avoids a copy when the right hand side matrix b is not - * needed anymore. - * - * \sa LDLT::solve(), MatrixBase::ldlt() - */ -template<typename MatrixType,int _UpLo> -template<typename Derived> -bool LDLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const -{ - eigen_assert(m_isInitialized && "LDLT is not initialized."); - eigen_assert(m_matrix.rows() == bAndX.rows()); - - bAndX = this->solve(bAndX); - - return true; -} - -/** \returns the matrix represented by the decomposition, - * i.e., it returns the product: P^T L D L^* P. - * This function is provided for debug purpose. */ -template<typename MatrixType, int _UpLo> -MatrixType LDLT<MatrixType,_UpLo>::reconstructedMatrix() const -{ - eigen_assert(m_isInitialized && "LDLT is not initialized."); - const Index size = m_matrix.rows(); - MatrixType res(size,size); - - // P - res.setIdentity(); - res = transpositionsP() * res; - // L^* P - res = matrixU() * res; - // D(L^*P) - res = vectorD().asDiagonal() * res; - // L(DL^*P) - res = matrixL() * res; - // P^T (LDL^*P) - res = transpositionsP().transpose() * res; - - return res; -} - -#ifndef __CUDACC__ -/** \cholesky_module - * \returns the Cholesky decomposition with full pivoting without square root of \c *this - * \sa MatrixBase::ldlt() - */ -template<typename MatrixType, unsigned int UpLo> -inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> -SelfAdjointView<MatrixType, UpLo>::ldlt() const -{ - return LDLT<PlainObject,UpLo>(m_matrix); -} - -/** \cholesky_module - * \returns the Cholesky decomposition with full pivoting without square root of \c *this - * \sa SelfAdjointView::ldlt() - */ -template<typename Derived> -inline const LDLT<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::ldlt() const -{ - return LDLT<PlainObject>(derived()); -} -#endif // __CUDACC__ - -} // end namespace Eigen - -#endif // EIGEN_LDLT_H diff --git a/third_party/eigen3/Eigen/src/Cholesky/LLT.h b/third_party/eigen3/Eigen/src/Cholesky/LLT.h deleted file mode 100644 index 45ed8438f7..0000000000 --- a/third_party/eigen3/Eigen/src/Cholesky/LLT.h +++ /dev/null @@ -1,494 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_LLT_H -#define EIGEN_LLT_H - -namespace Eigen { - -namespace internal{ -template<typename MatrixType, int UpLo> struct LLT_Traits; -} - -/** \ingroup Cholesky_Module - * - * \class LLT - * - * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features - * - * \param MatrixType the type of the matrix of which we are computing the LL^T Cholesky decomposition - * \param UpLo the triangular part that will be used for the decompositon: Lower (default) or Upper. - * The other triangular part won't be read. - * - * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite - * matrix A such that A = LL^* = U^*U, where L is lower triangular. - * - * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like D^*D x = b, - * for that purpose, we recommend the Cholesky decomposition without square root which is more stable - * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other - * situations like generalised eigen problems with hermitian matrices. - * - * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices, - * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations - * has a solution. - * - * Example: \include LLT_example.cpp - * Output: \verbinclude LLT_example.out - * - * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT - */ - /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH) - * Note that during the decomposition, only the upper triangular part of A is considered. Therefore, - * the strict lower part does not have to store correct values. - */ -template<typename _MatrixType, int _UpLo> class LLT -{ - public: - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - - enum { - PacketSize = internal::packet_traits<Scalar>::size, - AlignmentMask = int(PacketSize)-1, - UpLo = _UpLo - }; - - typedef internal::LLT_Traits<MatrixType,UpLo> Traits; - - /** - * \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via LLT::compute(const MatrixType&). - */ - LLT() : m_matrix(), m_isInitialized(false) {} - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa LLT() - */ - LLT(Index size) : m_matrix(size, size), - m_isInitialized(false) {} - - LLT(const MatrixType& matrix) - : m_matrix(matrix.rows(), matrix.cols()), - m_isInitialized(false) - { - compute(matrix); - } - - /** \returns a view of the upper triangular matrix U */ - inline typename Traits::MatrixU matrixU() const - { - eigen_assert(m_isInitialized && "LLT is not initialized."); - return Traits::getU(m_matrix); - } - - /** \returns a view of the lower triangular matrix L */ - inline typename Traits::MatrixL matrixL() const - { - eigen_assert(m_isInitialized && "LLT is not initialized."); - return Traits::getL(m_matrix); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * Since this LLT class assumes anyway that the matrix A is invertible, the solution - * theoretically exists and is unique regardless of b. - * - * Example: \include LLT_solve.cpp - * Output: \verbinclude LLT_solve.out - * - * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt() - */ - template<typename Rhs> - inline const internal::solve_retval<LLT, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "LLT is not initialized."); - eigen_assert(m_matrix.rows()==b.rows() - && "LLT::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<LLT, Rhs>(*this, b.derived()); - } - - #ifdef EIGEN2_SUPPORT - template<typename OtherDerived, typename ResultType> - bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const - { - *result = this->solve(b); - return true; - } - - bool isPositiveDefinite() const { return true; } - #endif - - template<typename Derived> - void solveInPlace(MatrixBase<Derived> &bAndX) const; - - LLT& compute(const MatrixType& matrix); - - /** \returns the LLT decomposition matrix - * - * TODO: document the storage layout - */ - inline const MatrixType& matrixLLT() const - { - eigen_assert(m_isInitialized && "LLT is not initialized."); - return m_matrix; - } - - MatrixType reconstructedMatrix() const; - - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the matrix.appears to be negative. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "LLT is not initialized."); - return m_info; - } - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - template<typename VectorType> - LLT rankUpdate(const VectorType& vec, const RealScalar& sigma = 1); - - protected: - /** \internal - * Used to compute and store L - * The strict upper part is not used and even not initialized. - */ - MatrixType m_matrix; - bool m_isInitialized; - ComputationInfo m_info; -}; - -namespace internal { - -template<typename Scalar, int UpLo> struct llt_inplace; - -template<typename MatrixType, typename VectorType> -static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) -{ - using std::sqrt; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef typename MatrixType::ColXpr ColXpr; - typedef typename internal::remove_all<ColXpr>::type ColXprCleaned; - typedef typename ColXprCleaned::SegmentReturnType ColXprSegment; - typedef Matrix<Scalar,Dynamic,1> TempVectorType; - typedef typename TempVectorType::SegmentReturnType TempVecSegment; - - Index n = mat.cols(); - eigen_assert(mat.rows()==n && vec.size()==n); - - TempVectorType temp; - - if(sigma>0) - { - // This version is based on Givens rotations. - // It is faster than the other one below, but only works for updates, - // i.e., for sigma > 0 - temp = sqrt(sigma) * vec; - - for(Index i=0; i<n; ++i) - { - JacobiRotation<Scalar> g; - g.makeGivens(mat(i,i), -temp(i), &mat(i,i)); - - Index rs = n-i-1; - if(rs>0) - { - ColXprSegment x(mat.col(i).tail(rs)); - TempVecSegment y(temp.tail(rs)); - apply_rotation_in_the_plane(x, y, g); - } - } - } - else - { - temp = vec; - RealScalar beta = 1; - for(Index j=0; j<n; ++j) - { - RealScalar Ljj = numext::real(mat.coeff(j,j)); - RealScalar dj = numext::abs2(Ljj); - Scalar wj = temp.coeff(j); - RealScalar swj2 = sigma*numext::abs2(wj); - RealScalar gamma = dj*beta + swj2; - - RealScalar x = dj + swj2/beta; - if (x<=RealScalar(0)) - return j; - RealScalar nLjj = sqrt(x); - mat.coeffRef(j,j) = nLjj; - beta += swj2/dj; - - // Update the terms of L - Index rs = n-j-1; - if(rs) - { - temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs); - if(gamma != 0) - mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs); - } - } - } - return -1; -} - -template<typename Scalar> struct llt_inplace<Scalar, Lower> -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - template<typename MatrixType> - static typename MatrixType::Index unblocked(MatrixType& mat) - { - using std::sqrt; - typedef typename MatrixType::Index Index; - - eigen_assert(mat.rows()==mat.cols()); - const Index size = mat.rows(); - for(Index k = 0; k < size; ++k) - { - Index rs = size-k-1; // remaining size - - Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1); - Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k); - Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k); - - RealScalar x = numext::real(mat.coeff(k,k)); - if (k>0) x -= A10.squaredNorm(); - if (x<=RealScalar(0)) - return k; - mat.coeffRef(k,k) = x = sqrt(x); - if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint(); - if (rs>0) A21 *= RealScalar(1)/x; - } - return -1; - } - - template<typename MatrixType> - static typename MatrixType::Index blocked(MatrixType& m) - { - typedef typename MatrixType::Index Index; - eigen_assert(m.rows()==m.cols()); - Index size = m.rows(); - if(size<32) - return unblocked(m); - - Index blockSize = size/8; - blockSize = (blockSize/16)*16; - blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128)); - - for (Index k=0; k<size; k+=blockSize) - { - // partition the matrix: - // A00 | - | - - // lu = A10 | A11 | - - // A20 | A21 | A22 - Index bs = (std::min)(blockSize, size-k); - Index rs = size - k - bs; - Block<MatrixType,Dynamic,Dynamic> A11(m,k, k, bs,bs); - Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k, rs,bs); - Block<MatrixType,Dynamic,Dynamic> A22(m,k+bs,k+bs,rs,rs); - - Index ret; - if((ret=unblocked(A11))>=0) return k+ret; - if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21); - if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,-1); // bottleneck - } - return -1; - } - - template<typename MatrixType, typename VectorType> - static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) - { - return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); - } -}; - -template<typename Scalar> struct llt_inplace<Scalar, Upper> -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - - template<typename MatrixType> - static EIGEN_STRONG_INLINE typename MatrixType::Index unblocked(MatrixType& mat) - { - Transpose<MatrixType> matt(mat); - return llt_inplace<Scalar, Lower>::unblocked(matt); - } - template<typename MatrixType> - static EIGEN_STRONG_INLINE typename MatrixType::Index blocked(MatrixType& mat) - { - Transpose<MatrixType> matt(mat); - return llt_inplace<Scalar, Lower>::blocked(matt); - } - template<typename MatrixType, typename VectorType> - static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) - { - Transpose<MatrixType> matt(mat); - return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma); - } -}; - -template<typename MatrixType> struct LLT_Traits<MatrixType,Lower> -{ - typedef const TriangularView<const MatrixType, Lower> MatrixL; - typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU; - static inline MatrixL getL(const MatrixType& m) { return m; } - static inline MatrixU getU(const MatrixType& m) { return m.adjoint(); } - static bool inplace_decomposition(MatrixType& m) - { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; } -}; - -template<typename MatrixType> struct LLT_Traits<MatrixType,Upper> -{ - typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL; - typedef const TriangularView<const MatrixType, Upper> MatrixU; - static inline MatrixL getL(const MatrixType& m) { return m.adjoint(); } - static inline MatrixU getU(const MatrixType& m) { return m; } - static bool inplace_decomposition(MatrixType& m) - { return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m)==-1; } -}; - -} // end namespace internal - -/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix - * - * \returns a reference to *this - * - * Example: \include TutorialLinAlgComputeTwice.cpp - * Output: \verbinclude TutorialLinAlgComputeTwice.out - */ -template<typename MatrixType, int _UpLo> -LLT<MatrixType,_UpLo>& LLT<MatrixType,_UpLo>::compute(const MatrixType& a) -{ - eigen_assert(a.rows()==a.cols()); - const Index size = a.rows(); - m_matrix.resize(size, size); - m_matrix = a; - - m_isInitialized = true; - bool ok = Traits::inplace_decomposition(m_matrix); - m_info = ok ? Success : NumericalIssue; - - return *this; -} - -/** Performs a rank one update (or dowdate) of the current decomposition. - * If A = LL^* before the rank one update, - * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector - * of same dimension. - */ -template<typename _MatrixType, int _UpLo> -template<typename VectorType> -LLT<_MatrixType,_UpLo> LLT<_MatrixType,_UpLo>::rankUpdate(const VectorType& v, const RealScalar& sigma) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType); - eigen_assert(v.size()==m_matrix.cols()); - eigen_assert(m_isInitialized); - if(internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix,v,sigma)>=0) - m_info = NumericalIssue; - else - m_info = Success; - - return *this; -} - -namespace internal { -template<typename _MatrixType, int UpLo, typename Rhs> -struct solve_retval<LLT<_MatrixType, UpLo>, Rhs> - : solve_retval_base<LLT<_MatrixType, UpLo>, Rhs> -{ - typedef LLT<_MatrixType,UpLo> LLTType; - EIGEN_MAKE_SOLVE_HELPERS(LLTType,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dst = rhs(); - dec().solveInPlace(dst); - } -}; -} - -/** \internal use x = llt_object.solve(x); - * - * This is the \em in-place version of solve(). - * - * \param bAndX represents both the right-hand side matrix b and result x. - * - * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD. - * - * This version avoids a copy when the right hand side matrix b is not - * needed anymore. - * - * \sa LLT::solve(), MatrixBase::llt() - */ -template<typename MatrixType, int _UpLo> -template<typename Derived> -void LLT<MatrixType,_UpLo>::solveInPlace(MatrixBase<Derived> &bAndX) const -{ - eigen_assert(m_isInitialized && "LLT is not initialized."); - eigen_assert(m_matrix.rows()==bAndX.rows()); - matrixL().solveInPlace(bAndX); - matrixU().solveInPlace(bAndX); -} - -/** \returns the matrix represented by the decomposition, - * i.e., it returns the product: L L^*. - * This function is provided for debug purpose. */ -template<typename MatrixType, int _UpLo> -MatrixType LLT<MatrixType,_UpLo>::reconstructedMatrix() const -{ - eigen_assert(m_isInitialized && "LLT is not initialized."); - return matrixL() * matrixL().adjoint().toDenseMatrix(); -} - -#ifndef __CUDACC__ -/** \cholesky_module - * \returns the LLT decomposition of \c *this - * \sa SelfAdjointView::llt() - */ -template<typename Derived> -inline const LLT<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::llt() const -{ - return LLT<PlainObject>(derived()); -} - -/** \cholesky_module - * \returns the LLT decomposition of \c *this - * \sa SelfAdjointView::llt() - */ -template<typename MatrixType, unsigned int UpLo> -inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> -SelfAdjointView<MatrixType, UpLo>::llt() const -{ - return LLT<PlainObject,UpLo>(m_matrix); -} -#endif // __CUDACC__ - -} // end namespace Eigen - -#endif // EIGEN_LLT_H diff --git a/third_party/eigen3/Eigen/src/Cholesky/LLT_MKL.h b/third_party/eigen3/Eigen/src/Cholesky/LLT_MKL.h deleted file mode 100644 index 64daa445cf..0000000000 --- a/third_party/eigen3/Eigen/src/Cholesky/LLT_MKL.h +++ /dev/null @@ -1,102 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * LLt decomposition based on LAPACKE_?potrf function. - ******************************************************************************** -*/ - -#ifndef EIGEN_LLT_MKL_H -#define EIGEN_LLT_MKL_H - -#include "Eigen/src/Core/util/MKL_support.h" -#include <iostream> - -namespace Eigen { - -namespace internal { - -template<typename Scalar> struct mkl_llt; - -#define EIGEN_MKL_LLT(EIGTYPE, MKLTYPE, MKLPREFIX) \ -template<> struct mkl_llt<EIGTYPE> \ -{ \ - template<typename MatrixType> \ - static inline typename MatrixType::Index potrf(MatrixType& m, char uplo) \ - { \ - lapack_int matrix_order; \ - lapack_int size, lda, info, StorageOrder; \ - EIGTYPE* a; \ - eigen_assert(m.rows()==m.cols()); \ - /* Set up parameters for ?potrf */ \ - size = m.rows(); \ - StorageOrder = MatrixType::Flags&RowMajorBit?RowMajor:ColMajor; \ - matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \ - a = &(m.coeffRef(0,0)); \ - lda = m.outerStride(); \ -\ - info = LAPACKE_##MKLPREFIX##potrf( matrix_order, uplo, size, (MKLTYPE*)a, lda ); \ - info = (info==0) ? Success : NumericalIssue; \ - return info; \ - } \ -}; \ -template<> struct llt_inplace<EIGTYPE, Lower> \ -{ \ - template<typename MatrixType> \ - static typename MatrixType::Index blocked(MatrixType& m) \ - { \ - return mkl_llt<EIGTYPE>::potrf(m, 'L'); \ - } \ - template<typename MatrixType, typename VectorType> \ - static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \ - { return Eigen::internal::llt_rank_update_lower(mat, vec, sigma); } \ -}; \ -template<> struct llt_inplace<EIGTYPE, Upper> \ -{ \ - template<typename MatrixType> \ - static typename MatrixType::Index blocked(MatrixType& m) \ - { \ - return mkl_llt<EIGTYPE>::potrf(m, 'U'); \ - } \ - template<typename MatrixType, typename VectorType> \ - static typename MatrixType::Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma) \ - { \ - Transpose<MatrixType> matt(mat); \ - return llt_inplace<EIGTYPE, Lower>::rankUpdate(matt, vec.conjugate(), sigma); \ - } \ -}; - -EIGEN_MKL_LLT(double, double, d) -EIGEN_MKL_LLT(float, float, s) -EIGEN_MKL_LLT(dcomplex, MKL_Complex16, z) -EIGEN_MKL_LLT(scomplex, MKL_Complex8, c) - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_LLT_MKL_H diff --git a/third_party/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h b/third_party/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h deleted file mode 100644 index c449960de4..0000000000 --- a/third_party/eigen3/Eigen/src/CholmodSupport/CholmodSupport.h +++ /dev/null @@ -1,607 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CHOLMODSUPPORT_H -#define EIGEN_CHOLMODSUPPORT_H - -namespace Eigen { - -namespace internal { - -template<typename Scalar, typename CholmodType> -void cholmod_configure_matrix(CholmodType& mat) -{ - if (internal::is_same<Scalar,float>::value) - { - mat.xtype = CHOLMOD_REAL; - mat.dtype = CHOLMOD_SINGLE; - } - else if (internal::is_same<Scalar,double>::value) - { - mat.xtype = CHOLMOD_REAL; - mat.dtype = CHOLMOD_DOUBLE; - } - else if (internal::is_same<Scalar,std::complex<float> >::value) - { - mat.xtype = CHOLMOD_COMPLEX; - mat.dtype = CHOLMOD_SINGLE; - } - else if (internal::is_same<Scalar,std::complex<double> >::value) - { - mat.xtype = CHOLMOD_COMPLEX; - mat.dtype = CHOLMOD_DOUBLE; - } - else - { - eigen_assert(false && "Scalar type not supported by CHOLMOD"); - } -} - -} // namespace internal - -/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object. - * Note that the data are shared. - */ -template<typename _Scalar, int _Options, typename _Index> -cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat) -{ - cholmod_sparse res; - res.nzmax = mat.nonZeros(); - res.nrow = mat.rows();; - res.ncol = mat.cols(); - res.p = mat.outerIndexPtr(); - res.i = mat.innerIndexPtr(); - res.x = mat.valuePtr(); - res.z = 0; - res.sorted = 1; - if(mat.isCompressed()) - { - res.packed = 1; - res.nz = 0; - } - else - { - res.packed = 0; - res.nz = mat.innerNonZeroPtr(); - } - - res.dtype = 0; - res.stype = -1; - - if (internal::is_same<_Index,int>::value) - { - res.itype = CHOLMOD_INT; - } - else if (internal::is_same<_Index,UF_long>::value) - { - res.itype = CHOLMOD_LONG; - } - else - { - eigen_assert(false && "Index type not supported yet"); - } - - // setup res.xtype - internal::cholmod_configure_matrix<_Scalar>(res); - - res.stype = 0; - - return res; -} - -template<typename _Scalar, int _Options, typename _Index> -const cholmod_sparse viewAsCholmod(const SparseMatrix<_Scalar,_Options,_Index>& mat) -{ - cholmod_sparse res = viewAsCholmod(mat.const_cast_derived()); - return res; -} - -/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix. - * The data are not copied but shared. */ -template<typename _Scalar, int _Options, typename _Index, unsigned int UpLo> -cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<SparseMatrix<_Scalar,_Options,_Index>, UpLo>& mat) -{ - cholmod_sparse res = viewAsCholmod(mat.matrix().const_cast_derived()); - - if(UpLo==Upper) res.stype = 1; - if(UpLo==Lower) res.stype = -1; - - return res; -} - -/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix. - * The data are not copied but shared. */ -template<typename Derived> -cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat) -{ - EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); - typedef typename Derived::Scalar Scalar; - - cholmod_dense res; - res.nrow = mat.rows(); - res.ncol = mat.cols(); - res.nzmax = res.nrow * res.ncol; - res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride(); - res.x = (void*)(mat.derived().data()); - res.z = 0; - - internal::cholmod_configure_matrix<Scalar>(res); - - return res; -} - -/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix. - * The data are not copied but shared. */ -template<typename Scalar, int Flags, typename Index> -MappedSparseMatrix<Scalar,Flags,Index> viewAsEigen(cholmod_sparse& cm) -{ - return MappedSparseMatrix<Scalar,Flags,Index> - (cm.nrow, cm.ncol, static_cast<Index*>(cm.p)[cm.ncol], - static_cast<Index*>(cm.p), static_cast<Index*>(cm.i),static_cast<Scalar*>(cm.x) ); -} - -enum CholmodMode { - CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt -}; - - -/** \ingroup CholmodSupport_Module - * \class CholmodBase - * \brief The base class for the direct Cholesky factorization of Cholmod - * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT - */ -template<typename _MatrixType, int _UpLo, typename Derived> -class CholmodBase : internal::noncopyable -{ - public: - typedef _MatrixType MatrixType; - enum { UpLo = _UpLo }; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef MatrixType CholMatrixType; - typedef typename MatrixType::Index Index; - - public: - - CholmodBase() - : m_cholmodFactor(0), m_info(Success), m_isInitialized(false) - { - m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0); - cholmod_start(&m_cholmod); - } - - CholmodBase(const MatrixType& matrix) - : m_cholmodFactor(0), m_info(Success), m_isInitialized(false) - { - m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0); - cholmod_start(&m_cholmod); - compute(matrix); - } - - ~CholmodBase() - { - if(m_cholmodFactor) - cholmod_free_factor(&m_cholmodFactor, &m_cholmod); - cholmod_finish(&m_cholmod); - } - - inline Index cols() const { return m_cholmodFactor->n; } - inline Index rows() const { return m_cholmodFactor->n; } - - Derived& derived() { return *static_cast<Derived*>(this); } - const Derived& derived() const { return *static_cast<const Derived*>(this); } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the matrix.appears to be negative. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_info; - } - - /** Computes the sparse Cholesky decomposition of \a matrix */ - Derived& compute(const MatrixType& matrix) - { - analyzePattern(matrix); - factorize(matrix); - return derived(); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::solve_retval<CholmodBase, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "LLT is not initialized."); - eigen_assert(rows()==b.rows() - && "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<CholmodBase, Rhs>(*this, b.derived()); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::sparse_solve_retval<CholmodBase, Rhs> - solve(const SparseMatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "LLT is not initialized."); - eigen_assert(rows()==b.rows() - && "CholmodDecomposition::solve(): invalid number of rows of the right hand side matrix b"); - return internal::sparse_solve_retval<CholmodBase, Rhs>(*this, b.derived()); - } - - /** Performs a symbolic decomposition on the sparsity pattern of \a matrix. - * - * This function is particularly useful when solving for several problems having the same structure. - * - * \sa factorize() - */ - void analyzePattern(const MatrixType& matrix) - { - if(m_cholmodFactor) - { - cholmod_free_factor(&m_cholmodFactor, &m_cholmod); - m_cholmodFactor = 0; - } - cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>()); - m_cholmodFactor = cholmod_analyze(&A, &m_cholmod); - - this->m_isInitialized = true; - this->m_info = Success; - m_analysisIsOk = true; - m_factorizationIsOk = false; - } - - /** Performs a numeric decomposition of \a matrix - * - * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed. - * - * \sa analyzePattern() - */ - void factorize(const MatrixType& matrix) - { - eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); - cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>()); - cholmod_factorize_p(&A, m_shiftOffset, 0, 0, m_cholmodFactor, &m_cholmod); - - // If the factorization failed, minor is the column at which it did. On success minor == n. - this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue); - m_factorizationIsOk = true; - } - - /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations. - * See the Cholmod user guide for details. */ - cholmod_common& cholmod() { return m_cholmod; } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** \internal */ - template<typename Rhs,typename Dest> - void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const - { - eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()"); - const Index size = m_cholmodFactor->n; - EIGEN_UNUSED_VARIABLE(size); - eigen_assert(size==b.rows()); - - // note: cd stands for Cholmod Dense - Rhs& b_ref(b.const_cast_derived()); - cholmod_dense b_cd = viewAsCholmod(b_ref); - cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod); - if(!x_cd) - { - this->m_info = NumericalIssue; - } - // TODO optimize this copy by swapping when possible (be careful with alignment, etc.) - dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols()); - cholmod_free_dense(&x_cd, &m_cholmod); - } - - /** \internal */ - template<typename RhsScalar, int RhsOptions, typename RhsIndex, typename DestScalar, int DestOptions, typename DestIndex> - void _solve(const SparseMatrix<RhsScalar,RhsOptions,RhsIndex> &b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const - { - eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()"); - const Index size = m_cholmodFactor->n; - EIGEN_UNUSED_VARIABLE(size); - eigen_assert(size==b.rows()); - - // note: cs stands for Cholmod Sparse - cholmod_sparse b_cs = viewAsCholmod(b); - cholmod_sparse* x_cs = cholmod_spsolve(CHOLMOD_A, m_cholmodFactor, &b_cs, &m_cholmod); - if(!x_cs) - { - this->m_info = NumericalIssue; - } - // TODO optimize this copy by swapping when possible (be careful with alignment, etc.) - dest = viewAsEigen<DestScalar,DestOptions,DestIndex>(*x_cs); - cholmod_free_sparse(&x_cs, &m_cholmod); - } - #endif // EIGEN_PARSED_BY_DOXYGEN - - - /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization. - * - * During the numerical factorization, an offset term is added to the diagonal coefficients:\n - * \c d_ii = \a offset + \c d_ii - * - * The default is \a offset=0. - * - * \returns a reference to \c *this. - */ - Derived& setShift(const RealScalar& offset) - { - m_shiftOffset[0] = offset; - return derived(); - } - - template<typename Stream> - void dumpMemory(Stream& /*s*/) - {} - - protected: - mutable cholmod_common m_cholmod; - cholmod_factor* m_cholmodFactor; - RealScalar m_shiftOffset[2]; - mutable ComputationInfo m_info; - bool m_isInitialized; - int m_factorizationIsOk; - int m_analysisIsOk; -}; - -/** \ingroup CholmodSupport_Module - * \class CholmodSimplicialLLT - * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod - * - * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization - * using the Cholmod library. - * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest. - * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices - * X and B can be either dense or sparse. - * - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower - * or Upper. Default is Lower. - * - * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. - * - * \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLLT - */ -template<typename _MatrixType, int _UpLo = Lower> -class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT<_MatrixType, _UpLo> > -{ - typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLLT> Base; - using Base::m_cholmod; - - public: - - typedef _MatrixType MatrixType; - - CholmodSimplicialLLT() : Base() { init(); } - - CholmodSimplicialLLT(const MatrixType& matrix) : Base() - { - init(); - compute(matrix); - } - - ~CholmodSimplicialLLT() {} - protected: - void init() - { - m_cholmod.final_asis = 0; - m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; - m_cholmod.final_ll = 1; - } -}; - - -/** \ingroup CholmodSupport_Module - * \class CholmodSimplicialLDLT - * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod - * - * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization - * using the Cholmod library. - * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest. - * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices - * X and B can be either dense or sparse. - * - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower - * or Upper. Default is Lower. - * - * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. - * - * \sa \ref TutorialSparseDirectSolvers, class CholmodSupernodalLLT, class SimplicialLDLT - */ -template<typename _MatrixType, int _UpLo = Lower> -class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT<_MatrixType, _UpLo> > -{ - typedef CholmodBase<_MatrixType, _UpLo, CholmodSimplicialLDLT> Base; - using Base::m_cholmod; - - public: - - typedef _MatrixType MatrixType; - - CholmodSimplicialLDLT() : Base() { init(); } - - CholmodSimplicialLDLT(const MatrixType& matrix) : Base() - { - init(); - compute(matrix); - } - - ~CholmodSimplicialLDLT() {} - protected: - void init() - { - m_cholmod.final_asis = 1; - m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; - } -}; - -/** \ingroup CholmodSupport_Module - * \class CholmodSupernodalLLT - * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod - * - * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization - * using the Cholmod library. - * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM. - * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices - * X and B can be either dense or sparse. - * - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower - * or Upper. Default is Lower. - * - * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename _MatrixType, int _UpLo = Lower> -class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT<_MatrixType, _UpLo> > -{ - typedef CholmodBase<_MatrixType, _UpLo, CholmodSupernodalLLT> Base; - using Base::m_cholmod; - - public: - - typedef _MatrixType MatrixType; - - CholmodSupernodalLLT() : Base() { init(); } - - CholmodSupernodalLLT(const MatrixType& matrix) : Base() - { - init(); - compute(matrix); - } - - ~CholmodSupernodalLLT() {} - protected: - void init() - { - m_cholmod.final_asis = 1; - m_cholmod.supernodal = CHOLMOD_SUPERNODAL; - } -}; - -/** \ingroup CholmodSupport_Module - * \class CholmodDecomposition - * \brief A general Cholesky factorization and solver based on Cholmod - * - * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization - * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices - * X and B can be either dense or sparse. - * - * This variant permits to change the underlying Cholesky method at runtime. - * On the other hand, it does not provide access to the result of the factorization. - * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization. - * - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower - * or Upper. Default is Lower. - * - * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed. - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename _MatrixType, int _UpLo = Lower> -class CholmodDecomposition : public CholmodBase<_MatrixType, _UpLo, CholmodDecomposition<_MatrixType, _UpLo> > -{ - typedef CholmodBase<_MatrixType, _UpLo, CholmodDecomposition> Base; - using Base::m_cholmod; - - public: - - typedef _MatrixType MatrixType; - - CholmodDecomposition() : Base() { init(); } - - CholmodDecomposition(const MatrixType& matrix) : Base() - { - init(); - compute(matrix); - } - - ~CholmodDecomposition() {} - - void setMode(CholmodMode mode) - { - switch(mode) - { - case CholmodAuto: - m_cholmod.final_asis = 1; - m_cholmod.supernodal = CHOLMOD_AUTO; - break; - case CholmodSimplicialLLt: - m_cholmod.final_asis = 0; - m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; - m_cholmod.final_ll = 1; - break; - case CholmodSupernodalLLt: - m_cholmod.final_asis = 1; - m_cholmod.supernodal = CHOLMOD_SUPERNODAL; - break; - case CholmodLDLt: - m_cholmod.final_asis = 1; - m_cholmod.supernodal = CHOLMOD_SIMPLICIAL; - break; - default: - break; - } - } - protected: - void init() - { - m_cholmod.final_asis = 1; - m_cholmod.supernodal = CHOLMOD_AUTO; - } -}; - -namespace internal { - -template<typename _MatrixType, int _UpLo, typename Derived, typename Rhs> -struct solve_retval<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs> - : solve_retval_base<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs> -{ - typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -template<typename _MatrixType, int _UpLo, typename Derived, typename Rhs> -struct sparse_solve_retval<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs> - : sparse_solve_retval_base<CholmodBase<_MatrixType,_UpLo,Derived>, Rhs> -{ - typedef CholmodBase<_MatrixType,_UpLo,Derived> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_CHOLMODSUPPORT_H diff --git a/third_party/eigen3/Eigen/src/Core/Array.h b/third_party/eigen3/Eigen/src/Core/Array.h deleted file mode 100644 index 28d6f14434..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Array.h +++ /dev/null @@ -1,338 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ARRAY_H -#define EIGEN_ARRAY_H - -namespace Eigen { - -/** \class Array - * \ingroup Core_Module - * - * \brief General-purpose arrays with easy API for coefficient-wise operations - * - * The %Array class is very similar to the Matrix class. It provides - * general-purpose one- and two-dimensional arrays. The difference between the - * %Array and the %Matrix class is primarily in the API: the API for the - * %Array class provides easy access to coefficient-wise operations, while the - * API for the %Matrix class provides easy access to linear-algebra - * operations. - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN. - * - * \sa \ref TutorialArrayClass, \ref TopicClassHierarchy - */ -namespace internal { -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -struct traits<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > : traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > -{ - typedef ArrayXpr XprKind; - typedef ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > XprBase; -}; -} - -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -class Array - : public PlainObjectBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > -{ - public: - - typedef PlainObjectBase<Array> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Array) - - enum { Options = _Options }; - typedef typename Base::PlainObject PlainObject; - - protected: - template <typename Derived, typename OtherDerived, bool IsVector> - friend struct internal::conservative_resize_like_impl; - - using Base::m_storage; - - public: - - using Base::base; - using Base::coeff; - using Base::coeffRef; - - /** - * The usage of - * using Base::operator=; - * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped - * the usage of 'using'. This should be done only for operator=. - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other) - { - return Base::operator=(other); - } - - /** Copies the value of the expression \a other into \c *this with automatic resizing. - * - * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), - * it will be initialized. - * - * Note that copying a row-vector into a vector (and conversely) is allowed. - * The resizing, if any, is then done in the appropriate way so that row-vectors - * remain row-vectors and vectors remain vectors. - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array& operator=(const ArrayBase<OtherDerived>& other) - { - return Base::_set(other); - } - - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array& operator=(const Array& other) - { - return Base::_set(other); - } - - /** Default constructor. - * - * For fixed-size matrices, does nothing. - * - * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix - * is called a null matrix. This constructor is the unique way to create null matrices: resizing - * a matrix to 0 is not supported. - * - * \sa resize(Index,Index) - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array() : Base() - { - Base::_check_template_params(); - EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED - } - -#ifndef EIGEN_PARSED_BY_DOXYGEN - // FIXME is it still needed ?? - /** \internal */ - EIGEN_DEVICE_FUNC - Array(internal::constructor_without_unaligned_array_assert) - : Base(internal::constructor_without_unaligned_array_assert()) - { - Base::_check_template_params(); - EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED - } -#endif - -#ifdef EIGEN_HAVE_RVALUE_REFERENCES - Array(Array&& other) - : Base(std::move(other)) - { - Base::_check_template_params(); - if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic) - Base::_set_noalias(other); - } - Array& operator=(Array&& other) - { - other.swap(*this); - return *this; - } -#endif - - - #ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename T> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE explicit Array(const T& x) - { - Base::_check_template_params(); - Base::template _init1<T>(x); - } - - template<typename T0, typename T1> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1) - { - Base::_check_template_params(); - this->template _init2<T0,T1>(val0, val1); - } - #else - /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */ - EIGEN_DEVICE_FUNC explicit Array(const Scalar *data); - /** Constructs a vector or row-vector with given dimension. \only_for_vectors - * - * Note that this is only useful for dynamic-size vectors. For fixed-size vectors, - * it is redundant to pass the dimension here, so it makes more sense to use the default - * constructor Array() instead. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE explicit Array(Index dim); - /** constructs an initialized 1x1 Array with the given coefficient */ - Array(const Scalar& value); - /** constructs an uninitialized array with \a rows rows and \a cols columns. - * - * This is useful for dynamic-size arrays. For fixed-size arrays, - * it is redundant to pass these parameters, so one should use the default constructor - * Array() instead. */ - Array(Index rows, Index cols); - /** constructs an initialized 2D vector with given coefficients */ - Array(const Scalar& val0, const Scalar& val1); - #endif - - /** constructs an initialized 3D vector with given coefficients */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2) - { - Base::_check_template_params(); - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3) - m_storage.data()[0] = val0; - m_storage.data()[1] = val1; - m_storage.data()[2] = val2; - } - /** constructs an initialized 4D vector with given coefficients */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3) - { - Base::_check_template_params(); - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4) - m_storage.data()[0] = val0; - m_storage.data()[1] = val1; - m_storage.data()[2] = val2; - m_storage.data()[3] = val3; - } - - /** Constructor copying the value of the expression \a other */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array(const ArrayBase<OtherDerived>& other) - : Base(other.rows() * other.cols(), other.rows(), other.cols()) - { - Base::_check_template_params(); - Base::_set_noalias(other); - } - /** Copy constructor */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array(const Array& other) - : Base(other.rows() * other.cols(), other.rows(), other.cols()) - { - Base::_check_template_params(); - Base::_set_noalias(other); - } - /** Copy constructor with in-place evaluation */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array(const ReturnByValue<OtherDerived>& other) - { - Base::_check_template_params(); - Base::resize(other.rows(), other.cols()); - other.evalTo(*this); - } - - /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other) - : Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols()) - { - Base::_check_template_params(); - Base::_resize_to_match(other); - *this = other; - } - - /** Override MatrixBase::swap() since for dynamic-sized matrices of same type it is enough to swap the - * data pointers. - */ - template<typename OtherDerived> - void swap(ArrayBase<OtherDerived> const & other) - { this->_swap(other.derived()); } - - EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; } - EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); } - - #ifdef EIGEN_ARRAY_PLUGIN - #include EIGEN_ARRAY_PLUGIN - #endif - - private: - - template<typename MatrixType, typename OtherDerived, bool SwapPointers> - friend struct internal::matrix_swap_impl; -}; - -/** \defgroup arraytypedefs Global array typedefs - * \ingroup Core_Module - * - * Eigen defines several typedef shortcuts for most common 1D and 2D array types. - * - * The general patterns are the following: - * - * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size, - * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd - * for complex double. - * - * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats. - * - * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is - * a fixed-size 1D array of 4 complex floats. - * - * \sa class Array - */ - -#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \ -/** \ingroup arraytypedefs */ \ -typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix; \ -/** \ingroup arraytypedefs */ \ -typedef Array<Type, Size, 1> Array##SizeSuffix##TypeSuffix; - -#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \ -/** \ingroup arraytypedefs */ \ -typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix; \ -/** \ingroup arraytypedefs */ \ -typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix; - -#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \ -EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \ -EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \ -EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \ -EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \ -EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \ -EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \ -EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4) - -EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int, i) -EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float, f) -EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double, d) -EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>, cf) -EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd) - -#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES -#undef EIGEN_MAKE_ARRAY_TYPEDEFS - -#undef EIGEN_MAKE_ARRAY_TYPEDEFS_LARGE - -#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \ -using Eigen::Matrix##SizeSuffix##TypeSuffix; \ -using Eigen::Vector##SizeSuffix##TypeSuffix; \ -using Eigen::RowVector##SizeSuffix##TypeSuffix; - -#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \ - -#define EIGEN_USING_ARRAY_TYPEDEFS \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \ -EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd) - -} // end namespace Eigen - -#endif // EIGEN_ARRAY_H diff --git a/third_party/eigen3/Eigen/src/Core/ArrayBase.h b/third_party/eigen3/Eigen/src/Core/ArrayBase.h deleted file mode 100644 index 2c9ace4a77..0000000000 --- a/third_party/eigen3/Eigen/src/Core/ArrayBase.h +++ /dev/null @@ -1,238 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ARRAYBASE_H -#define EIGEN_ARRAYBASE_H - -namespace Eigen { - -template<typename ExpressionType> class MatrixWrapper; - -/** \class ArrayBase - * \ingroup Core_Module - * - * \brief Base class for all 1D and 2D array, and related expressions - * - * An array is similar to a dense vector or matrix. While matrices are mathematical - * objects with well defined linear algebra operators, an array is just a collection - * of scalar values arranged in a one or two dimensionnal fashion. As the main consequence, - * all operations applied to an array are performed coefficient wise. Furthermore, - * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient - * constructors allowing to easily write generic code working for both scalar values - * and arrays. - * - * This class is the base that is inherited by all array expression types. - * - * \tparam Derived is the derived type, e.g., an array or an expression type. - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN. - * - * \sa class MatrixBase, \ref TopicClassHierarchy - */ -template<typename Derived> class ArrayBase - : public DenseBase<Derived> -{ - public: -#ifndef EIGEN_PARSED_BY_DOXYGEN - /** The base class for a given storage type. */ - typedef ArrayBase StorageBaseType; - - typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl; - - using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar, - typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*; - - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::packet_traits<Scalar>::type PacketScalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - typedef DenseBase<Derived> Base; - using Base::RowsAtCompileTime; - using Base::ColsAtCompileTime; - using Base::SizeAtCompileTime; - using Base::MaxRowsAtCompileTime; - using Base::MaxColsAtCompileTime; - using Base::MaxSizeAtCompileTime; - using Base::IsVectorAtCompileTime; - using Base::Flags; - using Base::CoeffReadCost; - - using Base::derived; - using Base::const_cast_derived; - using Base::rows; - using Base::cols; - using Base::size; - using Base::coeff; - using Base::coeffRef; - using Base::lazyAssign; - using Base::operator=; - using Base::operator+=; - using Base::operator-=; - using Base::operator*=; - using Base::operator/=; - - typedef typename Base::CoeffReturnType CoeffReturnType; - -#endif // not EIGEN_PARSED_BY_DOXYGEN - -#ifndef EIGEN_PARSED_BY_DOXYGEN - /** \internal the plain matrix type corresponding to this expression. Note that is not necessarily - * exactly the return type of eval(): in the case of plain matrices, the return type of eval() is a const - * reference to a matrix, not a matrix! It is however guaranteed that the return type of eval() is either - * PlainObject or const PlainObject&. - */ - typedef Array<typename internal::traits<Derived>::Scalar, - internal::traits<Derived>::RowsAtCompileTime, - internal::traits<Derived>::ColsAtCompileTime, - AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor), - internal::traits<Derived>::MaxRowsAtCompileTime, - internal::traits<Derived>::MaxColsAtCompileTime - > PlainObject; - - - /** \internal Represents a matrix with all coefficients equal to one another*/ - typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType; -#endif // not EIGEN_PARSED_BY_DOXYGEN - -#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase -# include "../plugins/CommonCwiseUnaryOps.h" -# include "../plugins/MatrixCwiseUnaryOps.h" -# include "../plugins/ArrayCwiseUnaryOps.h" -# include "../plugins/CommonCwiseBinaryOps.h" -# include "../plugins/MatrixCwiseBinaryOps.h" -# include "../plugins/ArrayCwiseBinaryOps.h" -# ifdef EIGEN_ARRAYBASE_PLUGIN -# include EIGEN_ARRAYBASE_PLUGIN -# endif -#undef EIGEN_CURRENT_STORAGE_BASE_CLASS - - /** Special case of the template operator=, in order to prevent the compiler - * from generating a default operator= (issue hit with g++ 4.1) - */ - EIGEN_DEVICE_FUNC - Derived& operator=(const ArrayBase& other) - { - return internal::assign_selector<Derived,Derived>::run(derived(), other.derived()); - } - - EIGEN_DEVICE_FUNC - Derived& operator+=(const Scalar& scalar); - EIGEN_DEVICE_FUNC - Derived& operator-=(const Scalar& scalar); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator+=(const ArrayBase<OtherDerived>& other); - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator-=(const ArrayBase<OtherDerived>& other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator*=(const ArrayBase<OtherDerived>& other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator/=(const ArrayBase<OtherDerived>& other); - - public: - EIGEN_DEVICE_FUNC - ArrayBase<Derived>& array() { return *this; } - EIGEN_DEVICE_FUNC - const ArrayBase<Derived>& array() const { return *this; } - - /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array - * \sa MatrixBase::array() */ - EIGEN_DEVICE_FUNC - MatrixWrapper<Derived> matrix() { return derived(); } - EIGEN_DEVICE_FUNC - const MatrixWrapper<const Derived> matrix() const { return derived(); } - -// template<typename Dest> -// inline void evalTo(Dest& dst) const { dst = matrix(); } - - protected: - EIGEN_DEVICE_FUNC - ArrayBase() : Base() {} - - private: - explicit ArrayBase(Index); - ArrayBase(Index,Index); - template<typename OtherDerived> explicit ArrayBase(const ArrayBase<OtherDerived>&); - protected: - // mixing arrays and matrices is not legal - template<typename OtherDerived> Derived& operator+=(const MatrixBase<OtherDerived>& ) - {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} - // mixing arrays and matrices is not legal - template<typename OtherDerived> Derived& operator-=(const MatrixBase<OtherDerived>& ) - {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} -}; - -/** replaces \c *this by \c *this - \a other. - * - * \returns a reference to \c *this - */ -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived & -ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other) -{ - SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived()); - tmp = other.derived(); - return derived(); -} - -/** replaces \c *this by \c *this + \a other. - * - * \returns a reference to \c *this - */ -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived & -ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other) -{ - SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived()); - tmp = other.derived(); - return derived(); -} - -/** replaces \c *this by \c *this * \a other coefficient wise. - * - * \returns a reference to \c *this - */ -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived & -ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other) -{ - SelfCwiseBinaryOp<internal::scalar_product_op<Scalar>, Derived, OtherDerived> tmp(derived()); - tmp = other.derived(); - return derived(); -} - -/** replaces \c *this by \c *this / \a other coefficient wise. - * - * \returns a reference to \c *this - */ -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived & -ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other) -{ - SelfCwiseBinaryOp<internal::scalar_quotient_op<Scalar>, Derived, OtherDerived> tmp(derived()); - tmp = other.derived(); - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_ARRAYBASE_H diff --git a/third_party/eigen3/Eigen/src/Core/ArrayWrapper.h b/third_party/eigen3/Eigen/src/Core/ArrayWrapper.h deleted file mode 100644 index 4bb6480243..0000000000 --- a/third_party/eigen3/Eigen/src/Core/ArrayWrapper.h +++ /dev/null @@ -1,287 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ARRAYWRAPPER_H -#define EIGEN_ARRAYWRAPPER_H - -namespace Eigen { - -/** \class ArrayWrapper - * \ingroup Core_Module - * - * \brief Expression of a mathematical vector or matrix as an array object - * - * This class is the return type of MatrixBase::array(), and most of the time - * this is the only way it is use. - * - * \sa MatrixBase::array(), class MatrixWrapper - */ - -namespace internal { -template<typename ExpressionType> -struct traits<ArrayWrapper<ExpressionType> > - : public traits<typename remove_all<typename ExpressionType::Nested>::type > -{ - typedef ArrayXpr XprKind; -}; -} - -template<typename ExpressionType> -class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> > -{ - public: - typedef ArrayBase<ArrayWrapper> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper) - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper) - - typedef typename internal::conditional< - internal::is_lvalue<ExpressionType>::value, - Scalar, - const Scalar - >::type ScalarWithConstIfNotLvalue; - - typedef typename internal::nested<ExpressionType>::type NestedExpressionType; - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {} - - EIGEN_DEVICE_FUNC - inline Index rows() const { return m_expression.rows(); } - EIGEN_DEVICE_FUNC - inline Index cols() const { return m_expression.cols(); } - EIGEN_DEVICE_FUNC - inline Index outerStride() const { return m_expression.outerStride(); } - EIGEN_DEVICE_FUNC - inline Index innerStride() const { return m_expression.innerStride(); } - - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); } - EIGEN_DEVICE_FUNC - inline const Scalar* data() const { return m_expression.data(); } - - EIGEN_DEVICE_FUNC - inline CoeffReturnType coeff(Index rowId, Index colId) const - { - return m_expression.coeff(rowId, colId); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index rowId, Index colId) - { - return m_expression.const_cast_derived().coeffRef(rowId, colId); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index rowId, Index colId) const - { - return m_expression.const_cast_derived().coeffRef(rowId, colId); - } - - EIGEN_DEVICE_FUNC - inline CoeffReturnType coeff(Index index) const - { - return m_expression.coeff(index); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index index) - { - return m_expression.const_cast_derived().coeffRef(index); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index index) const - { - return m_expression.const_cast_derived().coeffRef(index); - } - - template<int LoadMode> - inline const PacketScalar packet(Index rowId, Index colId) const - { - return m_expression.template packet<LoadMode>(rowId, colId); - } - - template<int LoadMode> - inline void writePacket(Index rowId, Index colId, const PacketScalar& val) - { - m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val); - } - - template<int LoadMode> - inline const PacketScalar packet(Index index) const - { - return m_expression.template packet<LoadMode>(index); - } - - template<int LoadMode> - inline void writePacket(Index index, const PacketScalar& val) - { - m_expression.const_cast_derived().template writePacket<LoadMode>(index, val); - } - - template<typename Dest> - EIGEN_DEVICE_FUNC - inline void evalTo(Dest& dst) const { dst = m_expression; } - - const typename internal::remove_all<NestedExpressionType>::type& - EIGEN_DEVICE_FUNC - nestedExpression() const - { - return m_expression; - } - - /** Forwards the resizing request to the nested expression - * \sa DenseBase::resize(Index) */ - EIGEN_DEVICE_FUNC - void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); } - /** Forwards the resizing request to the nested expression - * \sa DenseBase::resize(Index,Index)*/ - EIGEN_DEVICE_FUNC - void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); } - - protected: - NestedExpressionType m_expression; -}; - -/** \class MatrixWrapper - * \ingroup Core_Module - * - * \brief Expression of an array as a mathematical vector or matrix - * - * This class is the return type of ArrayBase::matrix(), and most of the time - * this is the only way it is use. - * - * \sa MatrixBase::matrix(), class ArrayWrapper - */ - -namespace internal { -template<typename ExpressionType> -struct traits<MatrixWrapper<ExpressionType> > - : public traits<typename remove_all<typename ExpressionType::Nested>::type > -{ - typedef MatrixXpr XprKind; -}; -} - -template<typename ExpressionType> -class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> > -{ - public: - typedef MatrixBase<MatrixWrapper<ExpressionType> > Base; - EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper) - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper) - - typedef typename internal::conditional< - internal::is_lvalue<ExpressionType>::value, - Scalar, - const Scalar - >::type ScalarWithConstIfNotLvalue; - - typedef typename internal::nested<ExpressionType>::type NestedExpressionType; - - EIGEN_DEVICE_FUNC - inline MatrixWrapper(ExpressionType& a_matrix) : m_expression(a_matrix) {} - - EIGEN_DEVICE_FUNC - inline Index rows() const { return m_expression.rows(); } - EIGEN_DEVICE_FUNC - inline Index cols() const { return m_expression.cols(); } - EIGEN_DEVICE_FUNC - inline Index outerStride() const { return m_expression.outerStride(); } - EIGEN_DEVICE_FUNC - inline Index innerStride() const { return m_expression.innerStride(); } - - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); } - EIGEN_DEVICE_FUNC - inline const Scalar* data() const { return m_expression.data(); } - - EIGEN_DEVICE_FUNC - inline CoeffReturnType coeff(Index rowId, Index colId) const - { - return m_expression.coeff(rowId, colId); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index rowId, Index colId) - { - return m_expression.const_cast_derived().coeffRef(rowId, colId); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index rowId, Index colId) const - { - return m_expression.derived().coeffRef(rowId, colId); - } - - EIGEN_DEVICE_FUNC - inline CoeffReturnType coeff(Index index) const - { - return m_expression.coeff(index); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index index) - { - return m_expression.const_cast_derived().coeffRef(index); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index index) const - { - return m_expression.const_cast_derived().coeffRef(index); - } - - template<int LoadMode> - inline const PacketScalar packet(Index rowId, Index colId) const - { - return m_expression.template packet<LoadMode>(rowId, colId); - } - - template<int LoadMode> - inline void writePacket(Index rowId, Index colId, const PacketScalar& val) - { - m_expression.const_cast_derived().template writePacket<LoadMode>(rowId, colId, val); - } - - template<int LoadMode> - inline const PacketScalar packet(Index index) const - { - return m_expression.template packet<LoadMode>(index); - } - - template<int LoadMode> - inline void writePacket(Index index, const PacketScalar& val) - { - m_expression.const_cast_derived().template writePacket<LoadMode>(index, val); - } - - EIGEN_DEVICE_FUNC - const typename internal::remove_all<NestedExpressionType>::type& - nestedExpression() const - { - return m_expression; - } - - /** Forwards the resizing request to the nested expression - * \sa DenseBase::resize(Index) */ - EIGEN_DEVICE_FUNC - void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); } - /** Forwards the resizing request to the nested expression - * \sa DenseBase::resize(Index,Index)*/ - EIGEN_DEVICE_FUNC - void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); } - - protected: - NestedExpressionType m_expression; -}; - -} // end namespace Eigen - -#endif // EIGEN_ARRAYWRAPPER_H diff --git a/third_party/eigen3/Eigen/src/Core/Assign.h b/third_party/eigen3/Eigen/src/Core/Assign.h deleted file mode 100644 index 07da2fe31d..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Assign.h +++ /dev/null @@ -1,622 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net> -// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ASSIGN_H -#define EIGEN_ASSIGN_H - -namespace Eigen { - -namespace internal { - -/*************************************************************************** -* Part 1 : the logic deciding a strategy for traversal and unrolling * -***************************************************************************/ - -template <typename Derived, typename OtherDerived> -struct assign_traits -{ -public: - enum { - DstIsAligned = Derived::Flags & AlignedBit, - DstHasDirectAccess = Derived::Flags & DirectAccessBit, - SrcIsAligned = OtherDerived::Flags & AlignedBit, - JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned - }; - -private: - enum { - InnerSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::SizeAtCompileTime) - : int(Derived::Flags)&RowMajorBit ? int(Derived::ColsAtCompileTime) - : int(Derived::RowsAtCompileTime), - InnerMaxSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::MaxSizeAtCompileTime) - : int(Derived::Flags)&RowMajorBit ? int(Derived::MaxColsAtCompileTime) - : int(Derived::MaxRowsAtCompileTime), - MaxSizeAtCompileTime = Derived::SizeAtCompileTime, - PacketSize = packet_traits<typename Derived::Scalar>::size - }; - - enum { - StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)), - MightVectorize = StorageOrdersAgree - && (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit), - MayInnerVectorize = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0 - && int(DstIsAligned) && int(SrcIsAligned), - MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit), - MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess - && (DstIsAligned || MaxSizeAtCompileTime == Dynamic), - /* If the destination isn't aligned, we have to do runtime checks and we don't unroll, - so it's only good for large enough sizes. */ - MaySliceVectorize = MightVectorize && DstHasDirectAccess - && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*PacketSize) - /* slice vectorization can be slow, so we only want it if the slices are big, which is - indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block - in a fixed-size matrix */ - }; - -public: - enum { - Traversal = int(MayInnerVectorize) ? int(InnerVectorizedTraversal) - : int(MayLinearVectorize) ? int(LinearVectorizedTraversal) - : int(MaySliceVectorize) ? int(SliceVectorizedTraversal) - : int(MayLinearize) ? int(LinearTraversal) - : int(DefaultTraversal), - Vectorized = int(Traversal) == InnerVectorizedTraversal - || int(Traversal) == LinearVectorizedTraversal - || int(Traversal) == SliceVectorizedTraversal - }; - -private: - enum { - UnrollingLimit = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1), - MayUnrollCompletely = int(Derived::SizeAtCompileTime) != Dynamic - && int(OtherDerived::CoeffReadCost) != Dynamic - && int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit), - MayUnrollInner = int(InnerSize) != Dynamic - && int(OtherDerived::CoeffReadCost) != Dynamic - && int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit) - }; - -public: - enum { - Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal)) - ? ( - int(MayUnrollCompletely) ? int(CompleteUnrolling) - : int(MayUnrollInner) ? int(InnerUnrolling) - : int(NoUnrolling) - ) - : int(Traversal) == int(LinearVectorizedTraversal) - ? ( bool(MayUnrollCompletely) && bool(DstIsAligned) ? int(CompleteUnrolling) : int(NoUnrolling) ) - : int(Traversal) == int(LinearTraversal) - ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling) : int(NoUnrolling) ) - : int(NoUnrolling) - }; - -#ifdef EIGEN_DEBUG_ASSIGN - static void debug() - { - EIGEN_DEBUG_VAR(DstIsAligned) - EIGEN_DEBUG_VAR(SrcIsAligned) - EIGEN_DEBUG_VAR(JointAlignment) - EIGEN_DEBUG_VAR(Derived::SizeAtCompileTime) - EIGEN_DEBUG_VAR(OtherDerived::CoeffReadCost) - EIGEN_DEBUG_VAR(InnerSize) - EIGEN_DEBUG_VAR(InnerMaxSize) - EIGEN_DEBUG_VAR(PacketSize) - EIGEN_DEBUG_VAR(StorageOrdersAgree) - EIGEN_DEBUG_VAR(MightVectorize) - EIGEN_DEBUG_VAR(MayLinearize) - EIGEN_DEBUG_VAR(MayInnerVectorize) - EIGEN_DEBUG_VAR(MayLinearVectorize) - EIGEN_DEBUG_VAR(MaySliceVectorize) - EIGEN_DEBUG_VAR(Traversal) - EIGEN_DEBUG_VAR(UnrollingLimit) - EIGEN_DEBUG_VAR(MayUnrollCompletely) - EIGEN_DEBUG_VAR(MayUnrollInner) - EIGEN_DEBUG_VAR(Unrolling) - } -#endif -}; - -/*************************************************************************** -* Part 2 : meta-unrollers -***************************************************************************/ - -/************************ -*** Default traversal *** -************************/ - -template<typename Derived1, typename Derived2, int Index, int Stop> -struct assign_DefaultTraversal_CompleteUnrolling -{ - enum { - outer = Index / Derived1::InnerSizeAtCompileTime, - inner = Index % Derived1::InnerSizeAtCompileTime - }; - - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - dst.copyCoeffByOuterInner(outer, inner, src); - assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src); - } -}; - -template<typename Derived1, typename Derived2, int Stop> -struct assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop> -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {} -}; - -template<typename Derived1, typename Derived2, int Index, int Stop> -struct assign_DefaultTraversal_InnerUnrolling -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, typename Derived1::Index outer) - { - dst.copyCoeffByOuterInner(outer, Index, src); - assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src, outer); - } -}; - -template<typename Derived1, typename Derived2, int Stop> -struct assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, Stop, Stop> -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, typename Derived1::Index) {} -}; - -/*********************** -*** Linear traversal *** -***********************/ - -template<typename Derived1, typename Derived2, int Index, int Stop> -struct assign_LinearTraversal_CompleteUnrolling -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - dst.copyCoeff(Index, src); - assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Index+1, Stop>::run(dst, src); - } -}; - -template<typename Derived1, typename Derived2, int Stop> -struct assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, Stop, Stop> -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {} -}; - -/************************** -*** Inner vectorization *** -**************************/ - -template<typename Derived1, typename Derived2, int Index, int Stop> -struct assign_innervec_CompleteUnrolling -{ - enum { - outer = Index / Derived1::InnerSizeAtCompileTime, - inner = Index % Derived1::InnerSizeAtCompileTime, - JointAlignment = assign_traits<Derived1,Derived2>::JointAlignment - }; - - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - dst.template copyPacketByOuterInner<Derived2, Aligned, JointAlignment>(outer, inner, src); - assign_innervec_CompleteUnrolling<Derived1, Derived2, - Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src); - } -}; - -template<typename Derived1, typename Derived2, int Stop> -struct assign_innervec_CompleteUnrolling<Derived1, Derived2, Stop, Stop> -{ - static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &) {} -}; - -template<typename Derived1, typename Derived2, int Index, int Stop> -struct assign_innervec_InnerUnrolling -{ - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src, typename Derived1::Index outer) - { - dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, Index, src); - assign_innervec_InnerUnrolling<Derived1, Derived2, - Index+packet_traits<typename Derived1::Scalar>::size, Stop>::run(dst, src, outer); - } -}; - -template<typename Derived1, typename Derived2, int Stop> -struct assign_innervec_InnerUnrolling<Derived1, Derived2, Stop, Stop> -{ - static EIGEN_STRONG_INLINE void run(Derived1 &, const Derived2 &, typename Derived1::Index) {} -}; - -/*************************************************************************** -* Part 3 : implementation of all cases -***************************************************************************/ - -template<typename Derived1, typename Derived2, - int Traversal = assign_traits<Derived1, Derived2>::Traversal, - int Unrolling = assign_traits<Derived1, Derived2>::Unrolling, - int Version = Specialized> -struct assign_impl; - -/************************ -*** Default traversal *** -************************/ - -template<typename Derived1, typename Derived2, int Unrolling, int Version> -struct assign_impl<Derived1, Derived2, InvalidTraversal, Unrolling, Version> -{ - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &, const Derived2 &) { } -}; - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, DefaultTraversal, NoUnrolling, Version> -{ - typedef typename Derived1::Index Index; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - const Index innerSize = dst.innerSize(); - const Index outerSize = dst.outerSize(); - for(Index outer = 0; outer < outerSize; ++outer) - for(Index inner = 0; inner < innerSize; ++inner) - dst.copyCoeffByOuterInner(outer, inner, src); - } -}; - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, DefaultTraversal, CompleteUnrolling, Version> -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime> - ::run(dst, src); - } -}; - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, DefaultTraversal, InnerUnrolling, Version> -{ - typedef typename Derived1::Index Index; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - const Index outerSize = dst.outerSize(); - for(Index outer = 0; outer < outerSize; ++outer) - assign_DefaultTraversal_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime> - ::run(dst, src, outer); - } -}; - -/*********************** -*** Linear traversal *** -***********************/ - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, LinearTraversal, NoUnrolling, Version> -{ - typedef typename Derived1::Index Index; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - const Index size = dst.size(); - for(Index i = 0; i < size; ++i) - dst.copyCoeff(i, src); - } -}; - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, LinearTraversal, CompleteUnrolling, Version> -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - assign_LinearTraversal_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime> - ::run(dst, src); - } -}; - -/************************** -*** Inner vectorization *** -**************************/ - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, NoUnrolling, Version> -{ - typedef typename Derived1::Index Index; - static inline void run(Derived1 &dst, const Derived2 &src) - { - const Index innerSize = dst.innerSize(); - const Index outerSize = dst.outerSize(); - const Index packetSize = packet_traits<typename Derived1::Scalar>::size; - for(Index outer = 0; outer < outerSize; ++outer) - for(Index inner = 0; inner < innerSize; inner+=packetSize) - dst.template copyPacketByOuterInner<Derived2, Aligned, Aligned>(outer, inner, src); - } -}; - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, CompleteUnrolling, Version> -{ - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, Derived1::SizeAtCompileTime> - ::run(dst, src); - } -}; - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, InnerVectorizedTraversal, InnerUnrolling, Version> -{ - typedef typename Derived1::Index Index; - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - const Index outerSize = dst.outerSize(); - for(Index outer = 0; outer < outerSize; ++outer) - assign_innervec_InnerUnrolling<Derived1, Derived2, 0, Derived1::InnerSizeAtCompileTime> - ::run(dst, src, outer); - } -}; - -/*************************** -*** Linear vectorization *** -***************************/ - -template <bool IsAligned = false> -struct unaligned_assign_impl -{ - template <typename Derived, typename OtherDerived> - static EIGEN_STRONG_INLINE void run(const Derived&, OtherDerived&, typename Derived::Index, typename Derived::Index) {} -}; - -template <> -struct unaligned_assign_impl<false> -{ - // MSVC must not inline this functions. If it does, it fails to optimize the - // packet access path. -#ifdef _MSC_VER - template <typename Derived, typename OtherDerived> - static EIGEN_DONT_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end) -#else - template <typename Derived, typename OtherDerived> - static EIGEN_STRONG_INLINE void run(const Derived& src, OtherDerived& dst, typename Derived::Index start, typename Derived::Index end) -#endif - { - for (typename Derived::Index index = start; index < end; ++index) - dst.copyCoeff(index, src); - } -}; - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, NoUnrolling, Version> -{ - typedef typename Derived1::Index Index; - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - const Index size = dst.size(); - typedef packet_traits<typename Derived1::Scalar> PacketTraits; - enum { - packetSize = PacketTraits::size, - dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) , - srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment - }; - const Index alignedStart = assign_traits<Derived1,Derived2>::DstIsAligned ? 0 - : internal::first_aligned(&dst.coeffRef(0), size); - const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize; - - unaligned_assign_impl<assign_traits<Derived1,Derived2>::DstIsAligned!=0>::run(src,dst,0,alignedStart); - - for(Index index = alignedStart; index < alignedEnd; index += packetSize) - { - dst.template copyPacket<Derived2, dstAlignment, srcAlignment>(index, src); - } - - unaligned_assign_impl<>::run(src,dst,alignedEnd,size); - } -}; - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, LinearVectorizedTraversal, CompleteUnrolling, Version> -{ - typedef typename Derived1::Index Index; - static EIGEN_STRONG_INLINE void run(Derived1 &dst, const Derived2 &src) - { - enum { size = Derived1::SizeAtCompileTime, - packetSize = packet_traits<typename Derived1::Scalar>::size, - alignedSize = (size/packetSize)*packetSize }; - - assign_innervec_CompleteUnrolling<Derived1, Derived2, 0, alignedSize>::run(dst, src); - assign_DefaultTraversal_CompleteUnrolling<Derived1, Derived2, alignedSize, size>::run(dst, src); - } -}; - -/************************** -*** Slice vectorization *** -***************************/ - -template<typename Derived1, typename Derived2, int Version> -struct assign_impl<Derived1, Derived2, SliceVectorizedTraversal, NoUnrolling, Version> -{ - typedef typename Derived1::Index Index; - static inline void run(Derived1 &dst, const Derived2 &src) - { - typedef packet_traits<typename Derived1::Scalar> PacketTraits; - enum { - packetSize = PacketTraits::size, - alignable = PacketTraits::AlignedOnScalar, - dstAlignment = alignable ? Aligned : int(assign_traits<Derived1,Derived2>::DstIsAligned) , - srcAlignment = assign_traits<Derived1,Derived2>::JointAlignment - }; - const Index packetAlignedMask = packetSize - 1; - const Index innerSize = dst.innerSize(); - const Index outerSize = dst.outerSize(); - const Index alignedStep = alignable ? (packetSize - dst.outerStride() % packetSize) & packetAlignedMask : 0; - Index alignedStart = ((!alignable) || assign_traits<Derived1,Derived2>::DstIsAligned) ? 0 - : internal::first_aligned(&dst.coeffRef(0,0), innerSize); - - for(Index outer = 0; outer < outerSize; ++outer) - { - const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask); - // do the non-vectorizable part of the assignment - for(Index inner = 0; inner<alignedStart ; ++inner) - dst.copyCoeffByOuterInner(outer, inner, src); - - // do the vectorizable part of the assignment - for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize) - dst.template copyPacketByOuterInner<Derived2, dstAlignment, Unaligned>(outer, inner, src); - - // do the non-vectorizable part of the assignment - for(Index inner = alignedEnd; inner<innerSize ; ++inner) - dst.copyCoeffByOuterInner(outer, inner, src); - - alignedStart = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize); - } - } -}; - -} // end namespace internal - -/*************************************************************************** -* Part 4 : implementation of DenseBase methods -***************************************************************************/ - -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived& DenseBase<Derived> - ::lazyAssign(const DenseBase<OtherDerived>& other) -{ - enum{ - SameType = internal::is_same<typename Derived::Scalar,typename OtherDerived::Scalar>::value - }; - - EIGEN_STATIC_ASSERT_LVALUE(Derived) - EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived) - EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - -#ifdef EIGEN_TEST_EVALUATORS - -#ifdef EIGEN_DEBUG_ASSIGN - internal::copy_using_evaluator_traits<Derived, OtherDerived>::debug(); -#endif - eigen_assert(rows() == other.rows() && cols() == other.cols()); - internal::call_dense_assignment_loop(derived(),other.derived()); - -#else // EIGEN_TEST_EVALUATORS - -#ifdef EIGEN_DEBUG_ASSIGN - internal::assign_traits<Derived, OtherDerived>::debug(); -#endif - eigen_assert(rows() == other.rows() && cols() == other.cols()); - internal::assign_impl<Derived, OtherDerived, int(SameType) ? int(internal::assign_traits<Derived, OtherDerived>::Traversal) - : int(InvalidTraversal)>::run(derived(),other.derived()); - -#endif // EIGEN_TEST_EVALUATORS - -#ifndef EIGEN_NO_DEBUG - checkTransposeAliasing(other.derived()); -#endif - return derived(); -} - -namespace internal { - -template<typename Derived, typename OtherDerived, - bool EvalBeforeAssigning = (int(internal::traits<OtherDerived>::Flags) & EvalBeforeAssigningBit) != 0, - bool NeedToTranspose = ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1) - | // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&". - // revert to || as soon as not needed anymore. - (int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1)) - && int(Derived::SizeAtCompileTime) != 1> -struct assign_selector; - -template<typename Derived, typename OtherDerived> -struct assign_selector<Derived,OtherDerived,false,false> { - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); } - template<typename ActualDerived, typename ActualOtherDerived> - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { other.evalTo(dst); return dst; } -}; -template<typename Derived, typename OtherDerived> -struct assign_selector<Derived,OtherDerived,true,false> { - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.eval()); } -}; -template<typename Derived, typename OtherDerived> -struct assign_selector<Derived,OtherDerived,false,true> { - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); } - template<typename ActualDerived, typename ActualOtherDerived> - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { Transpose<ActualDerived> dstTrans(dst); other.evalTo(dstTrans); return dst; } -}; -template<typename Derived, typename OtherDerived> -struct assign_selector<Derived,OtherDerived,true,true> { - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose().eval()); } -}; - -} // end namespace internal - -template<typename Derived> -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other) -{ - return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived()); -} - -template<typename Derived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other) -{ - return internal::assign_selector<Derived,Derived>::run(derived(), other.derived()); -} - -template<typename Derived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other) -{ - return internal::assign_selector<Derived,Derived>::run(derived(), other.derived()); -} - -template<typename Derived> -template <typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other) -{ - return internal::assign_selector<Derived,OtherDerived>::run(derived(), other.derived()); -} - -template<typename Derived> -template <typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other) -{ - return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived()); -} - -template<typename Derived> -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other) -{ - return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_ASSIGN_H diff --git a/third_party/eigen3/Eigen/src/Core/AssignEvaluator.h b/third_party/eigen3/Eigen/src/Core/AssignEvaluator.h deleted file mode 100644 index b1e304e2b1..0000000000 --- a/third_party/eigen3/Eigen/src/Core/AssignEvaluator.h +++ /dev/null @@ -1,842 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2011-2013 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ASSIGN_EVALUATOR_H -#define EIGEN_ASSIGN_EVALUATOR_H - -namespace Eigen { - -// This implementation is based on Assign.h - -namespace internal { - -/*************************************************************************** -* Part 1 : the logic deciding a strategy for traversal and unrolling * -***************************************************************************/ - -// copy_using_evaluator_traits is based on assign_traits - -template <typename Derived, typename OtherDerived> -struct copy_using_evaluator_traits -{ -public: - enum { - DstIsAligned = Derived::Flags & AlignedBit, - DstHasDirectAccess = Derived::Flags & DirectAccessBit, - SrcIsAligned = OtherDerived::Flags & AlignedBit, - JointAlignment = bool(DstIsAligned) && bool(SrcIsAligned) ? Aligned : Unaligned, - SrcEvalBeforeAssign = (evaluator_traits<OtherDerived>::HasEvalTo == 1) - }; - -private: - enum { - InnerSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::SizeAtCompileTime) - : int(Derived::Flags)&RowMajorBit ? int(Derived::ColsAtCompileTime) - : int(Derived::RowsAtCompileTime), - InnerMaxSize = int(Derived::IsVectorAtCompileTime) ? int(Derived::MaxSizeAtCompileTime) - : int(Derived::Flags)&RowMajorBit ? int(Derived::MaxColsAtCompileTime) - : int(Derived::MaxRowsAtCompileTime), - MaxSizeAtCompileTime = Derived::SizeAtCompileTime, - PacketSize = packet_traits<typename Derived::Scalar>::size - }; - - enum { - StorageOrdersAgree = (int(Derived::IsRowMajor) == int(OtherDerived::IsRowMajor)), - MightVectorize = StorageOrdersAgree - && (int(Derived::Flags) & int(OtherDerived::Flags) & ActualPacketAccessBit), - MayInnerVectorize = MightVectorize && int(InnerSize)!=Dynamic && int(InnerSize)%int(PacketSize)==0 - && int(DstIsAligned) && int(SrcIsAligned), - MayLinearize = StorageOrdersAgree && (int(Derived::Flags) & int(OtherDerived::Flags) & LinearAccessBit), - MayLinearVectorize = MightVectorize && MayLinearize && DstHasDirectAccess - && (DstIsAligned || MaxSizeAtCompileTime == Dynamic), - /* If the destination isn't aligned, we have to do runtime checks and we don't unroll, - so it's only good for large enough sizes. */ - MaySliceVectorize = MightVectorize && DstHasDirectAccess - && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=3*PacketSize) - /* slice vectorization can be slow, so we only want it if the slices are big, which is - indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block - in a fixed-size matrix */ - }; - -public: - enum { - Traversal = int(SrcEvalBeforeAssign) ? int(AllAtOnceTraversal) - : int(MayInnerVectorize) ? int(InnerVectorizedTraversal) - : int(MayLinearVectorize) ? int(LinearVectorizedTraversal) - : int(MaySliceVectorize) ? int(SliceVectorizedTraversal) - : int(MayLinearize) ? int(LinearTraversal) - : int(DefaultTraversal), - Vectorized = int(Traversal) == InnerVectorizedTraversal - || int(Traversal) == LinearVectorizedTraversal - || int(Traversal) == SliceVectorizedTraversal - }; - -private: - enum { - UnrollingLimit = EIGEN_UNROLLING_LIMIT * (Vectorized ? int(PacketSize) : 1), - MayUnrollCompletely = int(Derived::SizeAtCompileTime) != Dynamic - && int(OtherDerived::CoeffReadCost) != Dynamic - && int(Derived::SizeAtCompileTime) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit), - MayUnrollInner = int(InnerSize) != Dynamic - && int(OtherDerived::CoeffReadCost) != Dynamic - && int(InnerSize) * int(OtherDerived::CoeffReadCost) <= int(UnrollingLimit) - }; - -public: - enum { - Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal)) - ? ( - int(MayUnrollCompletely) ? int(CompleteUnrolling) - : int(MayUnrollInner) ? int(InnerUnrolling) - : int(NoUnrolling) - ) - : int(Traversal) == int(LinearVectorizedTraversal) - ? ( bool(MayUnrollCompletely) && bool(DstIsAligned) ? int(CompleteUnrolling) - : int(NoUnrolling) ) - : int(Traversal) == int(LinearTraversal) - ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling) - : int(NoUnrolling) ) - : int(NoUnrolling) - }; - -#ifdef EIGEN_DEBUG_ASSIGN - static void debug() - { - EIGEN_DEBUG_VAR(DstIsAligned) - EIGEN_DEBUG_VAR(SrcIsAligned) - EIGEN_DEBUG_VAR(JointAlignment) - EIGEN_DEBUG_VAR(InnerSize) - EIGEN_DEBUG_VAR(InnerMaxSize) - EIGEN_DEBUG_VAR(PacketSize) - EIGEN_DEBUG_VAR(StorageOrdersAgree) - EIGEN_DEBUG_VAR(MightVectorize) - EIGEN_DEBUG_VAR(MayLinearize) - EIGEN_DEBUG_VAR(MayInnerVectorize) - EIGEN_DEBUG_VAR(MayLinearVectorize) - EIGEN_DEBUG_VAR(MaySliceVectorize) - EIGEN_DEBUG_VAR(Traversal) - EIGEN_DEBUG_VAR(UnrollingLimit) - EIGEN_DEBUG_VAR(MayUnrollCompletely) - EIGEN_DEBUG_VAR(MayUnrollInner) - EIGEN_DEBUG_VAR(Unrolling) - } -#endif -}; - -/*************************************************************************** -* Part 2 : meta-unrollers -***************************************************************************/ - -/************************ -*** Default traversal *** -************************/ - -template<typename Kernel, int Index, int Stop> -struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling -{ - typedef typename Kernel::DstEvaluatorType DstEvaluatorType; - typedef typename DstEvaluatorType::XprType DstXprType; - - enum { - outer = Index / DstXprType::InnerSizeAtCompileTime, - inner = Index % DstXprType::InnerSizeAtCompileTime - }; - - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - kernel.assignCoeffByOuterInner(outer, inner); - copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel); - } -}; - -template<typename Kernel, int Stop> -struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop> -{ - static EIGEN_STRONG_INLINE void run(Kernel&) { } -}; - -template<typename Kernel, int Index, int Stop> -struct copy_using_evaluator_DefaultTraversal_InnerUnrolling -{ - static EIGEN_STRONG_INLINE void run(Kernel &kernel, int outer) - { - kernel.assignCoeffByOuterInner(outer, Index); - copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index+1, Stop>::run(kernel, outer); - } -}; - -template<typename Kernel, int Stop> -struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop> -{ - static EIGEN_STRONG_INLINE void run(Kernel&, int) { } -}; - -/*********************** -*** Linear traversal *** -***********************/ - -template<typename Kernel, int Index, int Stop> -struct copy_using_evaluator_LinearTraversal_CompleteUnrolling -{ - static EIGEN_STRONG_INLINE void run(Kernel& kernel) - { - kernel.assignCoeff(Index); - copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel); - } -}; - -template<typename Kernel, int Stop> -struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop> -{ - static EIGEN_STRONG_INLINE void run(Kernel&) { } -}; - -/************************** -*** Inner vectorization *** -**************************/ - -template<typename Kernel, int Index, int Stop> -struct copy_using_evaluator_innervec_CompleteUnrolling -{ - typedef typename Kernel::DstEvaluatorType DstEvaluatorType; - typedef typename DstEvaluatorType::XprType DstXprType; - - enum { - outer = Index / DstXprType::InnerSizeAtCompileTime, - inner = Index % DstXprType::InnerSizeAtCompileTime, - JointAlignment = Kernel::AssignmentTraits::JointAlignment - }; - - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - kernel.template assignPacketByOuterInner<Aligned, JointAlignment>(outer, inner); - enum { NextIndex = Index + packet_traits<typename DstXprType::Scalar>::size }; - copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel); - } -}; - -template<typename Kernel, int Stop> -struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop> -{ - static EIGEN_STRONG_INLINE void run(Kernel&) { } -}; - -template<typename Kernel, int Index, int Stop> -struct copy_using_evaluator_innervec_InnerUnrolling -{ - static EIGEN_STRONG_INLINE void run(Kernel &kernel, int outer) - { - kernel.template assignPacketByOuterInner<Aligned, Aligned>(outer, Index); - typedef typename Kernel::DstEvaluatorType::XprType DstXprType; - enum { NextIndex = Index + packet_traits<typename DstXprType::Scalar>::size }; - copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop>::run(kernel, outer); - } -}; - -template<typename Kernel, int Stop> -struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop> -{ - static EIGEN_STRONG_INLINE void run(Kernel &, int) { } -}; - -/*************************************************************************** -* Part 3 : implementation of all cases -***************************************************************************/ - -// dense_assignment_loop is based on assign_impl - -template<typename Kernel, - int Traversal = Kernel::AssignmentTraits::Traversal, - int Unrolling = Kernel::AssignmentTraits::Unrolling> -struct dense_assignment_loop; - -/************************ -*** Default traversal *** -************************/ - -template<typename Kernel> -struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling> -{ - static void run(Kernel &kernel) - { - typedef typename Kernel::Index Index; - - for(Index outer = 0; outer < kernel.outerSize(); ++outer) { - for(Index inner = 0; inner < kernel.innerSize(); ++inner) { - kernel.assignCoeffByOuterInner(outer, inner); - } - } - } -}; - -template<typename Kernel> -struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling> -{ - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - typedef typename Kernel::DstEvaluatorType::XprType DstXprType; - copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel); - } -}; - -template<typename Kernel> -struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling> -{ - typedef typename Kernel::Index Index; - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - typedef typename Kernel::DstEvaluatorType::XprType DstXprType; - - const Index outerSize = kernel.outerSize(); - for(Index outer = 0; outer < outerSize; ++outer) - copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer); - } -}; - -/*************************** -*** Linear vectorization *** -***************************/ - - -// The goal of unaligned_dense_assignment_loop is simply to factorize the handling -// of the non vectorizable beginning and ending parts - -template <bool IsAligned = false> -struct unaligned_dense_assignment_loop -{ - // if IsAligned = true, then do nothing - template <typename Kernel> - static EIGEN_STRONG_INLINE void run(Kernel&, typename Kernel::Index, typename Kernel::Index) {} -}; - -template <> -struct unaligned_dense_assignment_loop<false> -{ - // MSVC must not inline this functions. If it does, it fails to optimize the - // packet access path. - // FIXME check which version exhibits this issue -#if EIGEN_COMP_MSVC - template <typename Kernel> - static EIGEN_DONT_INLINE void run(Kernel &kernel, - typename Kernel::Index start, - typename Kernel::Index end) -#else - template <typename Kernel> - static EIGEN_STRONG_INLINE void run(Kernel &kernel, - typename Kernel::Index start, - typename Kernel::Index end) -#endif - { - for (typename Kernel::Index index = start; index < end; ++index) - kernel.assignCoeff(index); - } -}; - -template<typename Kernel> -struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling> -{ - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - typedef typename Kernel::Index Index; - - const Index size = kernel.size(); - typedef packet_traits<typename Kernel::Scalar> PacketTraits; - enum { - packetSize = PacketTraits::size, - dstIsAligned = int(Kernel::AssignmentTraits::DstIsAligned), - dstAlignment = PacketTraits::AlignedOnScalar ? Aligned : dstIsAligned, - srcAlignment = Kernel::AssignmentTraits::JointAlignment - }; - const Index alignedStart = dstIsAligned ? 0 : internal::first_aligned(&kernel.dstEvaluator().coeffRef(0), size); - const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize; - - unaligned_dense_assignment_loop<dstIsAligned!=0>::run(kernel, 0, alignedStart); - - for(Index index = alignedStart; index < alignedEnd; index += packetSize) - kernel.template assignPacket<dstAlignment, srcAlignment>(index); - - unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size); - } -}; - -template<typename Kernel> -struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling> -{ - typedef typename Kernel::Index Index; - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - typedef typename Kernel::DstEvaluatorType::XprType DstXprType; - - enum { size = DstXprType::SizeAtCompileTime, - packetSize = packet_traits<typename Kernel::Scalar>::size, - alignedSize = (size/packetSize)*packetSize }; - - copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel); - copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, alignedSize, size>::run(kernel); - } -}; - -/************************** -*** Inner vectorization *** -**************************/ - -template<typename Kernel> -struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling> -{ - static inline void run(Kernel &kernel) - { - typedef typename Kernel::Index Index; - - const Index innerSize = kernel.innerSize(); - const Index outerSize = kernel.outerSize(); - const Index packetSize = packet_traits<typename Kernel::Scalar>::size; - for(Index outer = 0; outer < outerSize; ++outer) - for(Index inner = 0; inner < innerSize; inner+=packetSize) - kernel.template assignPacketByOuterInner<Aligned, Aligned>(outer, inner); - } -}; - -template<typename Kernel> -struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling> -{ - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - typedef typename Kernel::DstEvaluatorType::XprType DstXprType; - copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel); - } -}; - -template<typename Kernel> -struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling> -{ - typedef typename Kernel::Index Index; - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - typedef typename Kernel::DstEvaluatorType::XprType DstXprType; - const Index outerSize = kernel.outerSize(); - for(Index outer = 0; outer < outerSize; ++outer) - copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer); - } -}; - -/*********************** -*** Linear traversal *** -***********************/ - -template<typename Kernel> -struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling> -{ - static inline void run(Kernel &kernel) - { - typedef typename Kernel::Index Index; - const Index size = kernel.size(); - for(Index i = 0; i < size; ++i) - kernel.assignCoeff(i); - } -}; - -template<typename Kernel> -struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling> -{ - static EIGEN_STRONG_INLINE void run(Kernel &kernel) - { - typedef typename Kernel::DstEvaluatorType::XprType DstXprType; - copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel); - } -}; - -/************************** -*** Slice vectorization *** -***************************/ - -template<typename Kernel> -struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling> -{ - static inline void run(Kernel &kernel) - { - typedef typename Kernel::Index Index; - typedef packet_traits<typename Kernel::Scalar> PacketTraits; - enum { - packetSize = PacketTraits::size, - alignable = PacketTraits::AlignedOnScalar, - dstAlignment = alignable ? Aligned : int(Kernel::AssignmentTraits::DstIsAligned) - }; - const Index packetAlignedMask = packetSize - 1; - const Index innerSize = kernel.innerSize(); - const Index outerSize = kernel.outerSize(); - const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0; - Index alignedStart = ((!alignable) || Kernel::AssignmentTraits::DstIsAligned) ? 0 - : internal::first_aligned(&kernel.dstEvaluator().coeffRef(0,0), innerSize); - - for(Index outer = 0; outer < outerSize; ++outer) - { - const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask); - // do the non-vectorizable part of the assignment - for(Index inner = 0; inner<alignedStart ; ++inner) - kernel.assignCoeffByOuterInner(outer, inner); - - // do the vectorizable part of the assignment - for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize) - kernel.template assignPacketByOuterInner<dstAlignment, Unaligned>(outer, inner); - - // do the non-vectorizable part of the assignment - for(Index inner = alignedEnd; inner<innerSize ; ++inner) - kernel.assignCoeffByOuterInner(outer, inner); - - alignedStart = std::min<Index>((alignedStart+alignedStep)%packetSize, innerSize); - } - } -}; - -/**************************** -*** All-at-once traversal *** -****************************/ - -// TODO: this 'AllAtOnceTraversal' should be dropped or caught earlier (Gael) -// Indeed, what to do with the kernel's functor?? -template<typename Kernel> -struct dense_assignment_loop<Kernel, AllAtOnceTraversal, NoUnrolling> -{ - static inline void run(Kernel & kernel) - { - // Evaluate rhs in temporary to prevent aliasing problems in a = a * a; - // TODO: Do not pass the xpr object to evalTo() (Jitse) - kernel.srcEvaluator().evalTo(kernel.dstEvaluator(), kernel.dstExpression()); - } -}; - -/*************************************************************************** -* Part 4 : Generic Assignment routine -***************************************************************************/ - -// This class generalize the assignment of a coefficient (or packet) from one dense evaluator -// to another dense writable evaluator. -// It is parametrized by the two evaluators, and the actual assignment functor. -// This abstraction level permits to keep the evaluation loops as simple and as generic as possible. -// One can customize the assignment using this generic dense_assignment_kernel with different -// functors, or by completely overloading it, by-passing a functor. -template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor> -class generic_dense_assignment_kernel -{ -protected: - typedef typename DstEvaluatorTypeT::XprType DstXprType; - typedef typename SrcEvaluatorTypeT::XprType SrcXprType; -public: - - typedef DstEvaluatorTypeT DstEvaluatorType; - typedef SrcEvaluatorTypeT SrcEvaluatorType; - typedef typename DstEvaluatorType::Scalar Scalar; - typedef typename DstEvaluatorType::Index Index; - typedef copy_using_evaluator_traits<DstXprType, SrcXprType> AssignmentTraits; - - - generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr) - : m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr) - {} - - Index size() const { return m_dstExpr.size(); } - Index innerSize() const { return m_dstExpr.innerSize(); } - Index outerSize() const { return m_dstExpr.outerSize(); } - Index outerStride() const { return m_dstExpr.outerStride(); } - - // TODO get rid of this one: - DstXprType& dstExpression() const { return m_dstExpr; } - - DstEvaluatorType& dstEvaluator() { return m_dst; } - const SrcEvaluatorType& srcEvaluator() const { return m_src; } - - void assignCoeff(Index row, Index col) - { - m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col)); - } - - void assignCoeff(Index index) - { - m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index)); - } - - void assignCoeffByOuterInner(Index outer, Index inner) - { - Index row = rowIndexByOuterInner(outer, inner); - Index col = colIndexByOuterInner(outer, inner); - assignCoeff(row, col); - } - - - template<int StoreMode, int LoadMode> - void assignPacket(Index row, Index col) - { - m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode>(row,col)); - } - - template<int StoreMode, int LoadMode> - void assignPacket(Index index) - { - m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode>(index)); - } - - template<int StoreMode, int LoadMode> - void assignPacketByOuterInner(Index outer, Index inner) - { - Index row = rowIndexByOuterInner(outer, inner); - Index col = colIndexByOuterInner(outer, inner); - assignPacket<StoreMode,LoadMode>(row, col); - } - - static Index rowIndexByOuterInner(Index outer, Index inner) - { - typedef typename DstEvaluatorType::ExpressionTraits Traits; - return int(Traits::RowsAtCompileTime) == 1 ? 0 - : int(Traits::ColsAtCompileTime) == 1 ? inner - : int(Traits::Flags)&RowMajorBit ? outer - : inner; - } - - static Index colIndexByOuterInner(Index outer, Index inner) - { - typedef typename DstEvaluatorType::ExpressionTraits Traits; - return int(Traits::ColsAtCompileTime) == 1 ? 0 - : int(Traits::RowsAtCompileTime) == 1 ? inner - : int(Traits::Flags)&RowMajorBit ? inner - : outer; - } - -protected: - DstEvaluatorType& m_dst; - const SrcEvaluatorType& m_src; - const Functor &m_functor; - // TODO find a way to avoid the needs of the original expression - DstXprType& m_dstExpr; -}; - -template<typename DstXprType, typename SrcXprType, typename Functor> -void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src, const Functor &func) -{ -#ifdef EIGEN_DEBUG_ASSIGN - // TODO these traits should be computed from information provided by the evaluators - internal::copy_using_evaluator_traits<DstXprType, SrcXprType>::debug(); -#endif - eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); - - typedef typename evaluator<DstXprType>::type DstEvaluatorType; - typedef typename evaluator<SrcXprType>::type SrcEvaluatorType; - - DstEvaluatorType dstEvaluator(dst); - SrcEvaluatorType srcEvaluator(src); - - typedef generic_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Functor> Kernel; - Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived()); - - dense_assignment_loop<Kernel>::run(kernel); -} - -template<typename DstXprType, typename SrcXprType> -void call_dense_assignment_loop(const DstXprType& dst, const SrcXprType& src) -{ - call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar>()); -} - -/*************************************************************************** -* Part 5 : Entry points -***************************************************************************/ - -// Based on DenseBase::LazyAssign() -// The following functions are just for testing and they are meant to be moved to operator= and the likes. - -template<typename DstXprType, template <typename> class StorageBase, typename SrcXprType> -EIGEN_STRONG_INLINE -const DstXprType& copy_using_evaluator(const NoAlias<DstXprType, StorageBase>& dst, - const EigenBase<SrcXprType>& src) -{ - return noalias_copy_using_evaluator(dst.expression(), src.derived(), internal::assign_op<typename DstXprType::Scalar>()); -} - -template<typename XprType, int AssumeAliasing = evaluator_traits<XprType>::AssumeAliasing> -struct AddEvalIfAssumingAliasing; - -template<typename XprType> -struct AddEvalIfAssumingAliasing<XprType, 0> -{ - static const XprType& run(const XprType& xpr) - { - return xpr; - } -}; - -template<typename XprType> -struct AddEvalIfAssumingAliasing<XprType, 1> -{ - static const EvalToTemp<XprType> run(const XprType& xpr) - { - return EvalToTemp<XprType>(xpr); - } -}; - -template<typename DstXprType, typename SrcXprType, typename Functor> -EIGEN_STRONG_INLINE -const DstXprType& copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func) -{ - return noalias_copy_using_evaluator(dst.const_cast_derived(), - AddEvalIfAssumingAliasing<SrcXprType>::run(src.derived()), - func - ); -} - -// this mimics operator= -template<typename DstXprType, typename SrcXprType> -EIGEN_STRONG_INLINE -const DstXprType& copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src) -{ - return copy_using_evaluator(dst.const_cast_derived(), src.derived(), internal::assign_op<typename DstXprType::Scalar>()); -} - -template<typename DstXprType, typename SrcXprType, typename Functor> -EIGEN_STRONG_INLINE -const DstXprType& noalias_copy_using_evaluator(const PlainObjectBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func) -{ -#ifdef EIGEN_DEBUG_ASSIGN - internal::copy_using_evaluator_traits<DstXprType, SrcXprType>::debug(); -#endif -#ifdef EIGEN_NO_AUTOMATIC_RESIZING - eigen_assert((dst.size()==0 || (IsVectorAtCompileTime ? (dst.size() == src.size()) - : (dst.rows() == src.rows() && dst.cols() == src.cols()))) - && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined"); -#else - dst.const_cast_derived().resizeLike(src.derived()); -#endif - call_dense_assignment_loop(dst.const_cast_derived(), src.derived(), func); - return dst.derived(); -} - -template<typename DstXprType, typename SrcXprType, typename Functor> -EIGEN_STRONG_INLINE -const DstXprType& noalias_copy_using_evaluator(const EigenBase<DstXprType>& dst, const EigenBase<SrcXprType>& src, const Functor &func) -{ - call_dense_assignment_loop(dst.const_cast_derived(), src.derived(), func); - return dst.derived(); -} - -// Based on DenseBase::swap() -// TODO: Check whether we need to do something special for swapping two -// Arrays or Matrices. (Jitse) - -// Overload default assignPacket behavior for swapping them -template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT> -class swap_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar> > -{ - typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar> > Base; - typedef typename DstEvaluatorTypeT::PacketScalar PacketScalar; - using Base::m_dst; - using Base::m_src; - using Base::m_functor; - -public: - typedef typename Base::Scalar Scalar; - typedef typename Base::Index Index; - typedef typename Base::DstXprType DstXprType; - - swap_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, DstXprType& dstExpr) - : Base(dst, src, swap_assign_op<Scalar>(), dstExpr) - {} - - template<int StoreMode, int LoadMode> - void assignPacket(Index row, Index col) - { - m_functor.template swapPacket<StoreMode,LoadMode,PacketScalar>(&m_dst.coeffRef(row,col), &const_cast<SrcEvaluatorTypeT&>(m_src).coeffRef(row,col)); - } - - template<int StoreMode, int LoadMode> - void assignPacket(Index index) - { - m_functor.template swapPacket<StoreMode,LoadMode,PacketScalar>(&m_dst.coeffRef(index), &const_cast<SrcEvaluatorTypeT&>(m_src).coeffRef(index)); - } - - // TODO find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I mean no CRTP (Gael) - template<int StoreMode, int LoadMode> - void assignPacketByOuterInner(Index outer, Index inner) - { - Index row = Base::rowIndexByOuterInner(outer, inner); - Index col = Base::colIndexByOuterInner(outer, inner); - assignPacket<StoreMode,LoadMode>(row, col); - } -}; - -template<typename DstXprType, typename SrcXprType> -void swap_using_evaluator(const DstXprType& dst, const SrcXprType& src) -{ - // TODO there is too much redundancy with call_dense_assignment_loop - - eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols()); - - typedef typename evaluator<DstXprType>::type DstEvaluatorType; - typedef typename evaluator<SrcXprType>::type SrcEvaluatorType; - - DstEvaluatorType dstEvaluator(dst); - SrcEvaluatorType srcEvaluator(src); - - typedef swap_kernel<DstEvaluatorType,SrcEvaluatorType> Kernel; - Kernel kernel(dstEvaluator, srcEvaluator, dst.const_cast_derived()); - - dense_assignment_loop<Kernel>::run(kernel); -} - -// Based on MatrixBase::operator+= (in CwiseBinaryOp.h) -template<typename DstXprType, typename SrcXprType> -void add_assign_using_evaluator(const MatrixBase<DstXprType>& dst, const MatrixBase<SrcXprType>& src) -{ - typedef typename DstXprType::Scalar Scalar; - copy_using_evaluator(dst.derived(), src.derived(), add_assign_op<Scalar>()); -} - -// Based on ArrayBase::operator+= -template<typename DstXprType, typename SrcXprType> -void add_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src) -{ - typedef typename DstXprType::Scalar Scalar; - copy_using_evaluator(dst.derived(), src.derived(), add_assign_op<Scalar>()); -} - -// TODO: Add add_assign_using_evaluator for EigenBase ? (Jitse) - -template<typename DstXprType, typename SrcXprType> -void subtract_assign_using_evaluator(const MatrixBase<DstXprType>& dst, const MatrixBase<SrcXprType>& src) -{ - typedef typename DstXprType::Scalar Scalar; - copy_using_evaluator(dst.derived(), src.derived(), sub_assign_op<Scalar>()); -} - -template<typename DstXprType, typename SrcXprType> -void subtract_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src) -{ - typedef typename DstXprType::Scalar Scalar; - copy_using_evaluator(dst.derived(), src.derived(), sub_assign_op<Scalar>()); -} - -template<typename DstXprType, typename SrcXprType> -void multiply_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src) -{ - typedef typename DstXprType::Scalar Scalar; - copy_using_evaluator(dst.derived(), src.derived(), mul_assign_op<Scalar>()); -} - -template<typename DstXprType, typename SrcXprType> -void divide_assign_using_evaluator(const ArrayBase<DstXprType>& dst, const ArrayBase<SrcXprType>& src) -{ - typedef typename DstXprType::Scalar Scalar; - copy_using_evaluator(dst.derived(), src.derived(), div_assign_op<Scalar>()); -} - - -} // namespace internal - -} // end namespace Eigen - -#endif // EIGEN_ASSIGN_EVALUATOR_H diff --git a/third_party/eigen3/Eigen/src/Core/Assign_MKL.h b/third_party/eigen3/Eigen/src/Core/Assign_MKL.h deleted file mode 100644 index 97134ffd72..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Assign_MKL.h +++ /dev/null @@ -1,225 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin() - ******************************************************************************** -*/ - -#ifndef EIGEN_ASSIGN_VML_H -#define EIGEN_ASSIGN_VML_H - -namespace Eigen { - -namespace internal { - -template<typename Op> struct vml_call -{ enum { IsSupported = 0 }; }; - -template<typename Dst, typename Src, typename UnaryOp> -class vml_assign_traits -{ - private: - enum { - DstHasDirectAccess = Dst::Flags & DirectAccessBit, - SrcHasDirectAccess = Src::Flags & DirectAccessBit, - - StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)), - InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime) - : int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime) - : int(Dst::RowsAtCompileTime), - InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime) - : int(Dst::Flags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime) - : int(Dst::MaxRowsAtCompileTime), - MaxSizeAtCompileTime = Dst::SizeAtCompileTime, - - MightEnableVml = vml_call<UnaryOp>::IsSupported && StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess - && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1, - MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit), - VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize, - LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD, - MayEnableVml = MightEnableVml && LargeEnough, - MayLinearize = MayEnableVml && MightLinearize - }; - public: - enum { - Traversal = MayLinearize ? LinearVectorizedTraversal - : MayEnableVml ? InnerVectorizedTraversal - : DefaultTraversal - }; -}; - -template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling, - int VmlTraversal = vml_assign_traits<Derived1, Derived2, UnaryOp>::Traversal > -struct vml_assign_impl - : assign_impl<Derived1, Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn> -{ -}; - -template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling> -struct vml_assign_impl<Derived1, Derived2, UnaryOp, Traversal, Unrolling, InnerVectorizedTraversal> -{ - typedef typename Derived1::Scalar Scalar; - typedef typename Derived1::Index Index; - static inline void run(Derived1& dst, const CwiseUnaryOp<UnaryOp, Derived2>& src) - { - // in case we want to (or have to) skip VML at runtime we can call: - // assign_impl<Derived1,Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>::run(dst,src); - const Index innerSize = dst.innerSize(); - const Index outerSize = dst.outerSize(); - for(Index outer = 0; outer < outerSize; ++outer) { - const Scalar *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) : - &(src.nestedExpression().coeffRef(0, outer)); - Scalar *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer)); - vml_call<UnaryOp>::run(src.functor(), innerSize, src_ptr, dst_ptr ); - } - } -}; - -template<typename Derived1, typename Derived2, typename UnaryOp, int Traversal, int Unrolling> -struct vml_assign_impl<Derived1, Derived2, UnaryOp, Traversal, Unrolling, LinearVectorizedTraversal> -{ - static inline void run(Derived1& dst, const CwiseUnaryOp<UnaryOp, Derived2>& src) - { - // in case we want to (or have to) skip VML at runtime we can call: - // assign_impl<Derived1,Eigen::CwiseUnaryOp<UnaryOp, Derived2>,Traversal,Unrolling,BuiltIn>::run(dst,src); - vml_call<UnaryOp>::run(src.functor(), dst.size(), src.nestedExpression().data(), dst.data() ); - } -}; - -// Macroses - -#define EIGEN_MKL_VML_SPECIALIZE_ASSIGN(TRAVERSAL,UNROLLING) \ - template<typename Derived1, typename Derived2, typename UnaryOp> \ - struct assign_impl<Derived1, Eigen::CwiseUnaryOp<UnaryOp, Derived2>, TRAVERSAL, UNROLLING, Specialized> { \ - static inline void run(Derived1 &dst, const Eigen::CwiseUnaryOp<UnaryOp, Derived2> &src) { \ - vml_assign_impl<Derived1,Derived2,UnaryOp,TRAVERSAL,UNROLLING>::run(dst, src); \ - } \ - }; - -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,NoUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,CompleteUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(DefaultTraversal,InnerUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,NoUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearTraversal,CompleteUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,NoUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,CompleteUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(InnerVectorizedTraversal,InnerUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,CompleteUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(LinearVectorizedTraversal,NoUnrolling) -EIGEN_MKL_VML_SPECIALIZE_ASSIGN(SliceVectorizedTraversal,NoUnrolling) - - -#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1) -#define EIGEN_MKL_VML_MODE VML_HA -#else -#define EIGEN_MKL_VML_MODE VML_LA -#endif - -#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \ - template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \ - enum { IsSupported = 1 }; \ - static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/, \ - int size, const EIGENTYPE* src, EIGENTYPE* dst) { \ - VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst); \ - } \ - }; - -#define EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \ - template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \ - enum { IsSupported = 1 }; \ - static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& /*func*/, \ - int size, const EIGENTYPE* src, EIGENTYPE* dst) { \ - MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \ - VMLOP(size, (const VMLTYPE*)src, (VMLTYPE*)dst, vmlMode); \ - } \ - }; - -#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE) \ - template<> struct vml_call< scalar_##EIGENOP##_op<EIGENTYPE> > { \ - enum { IsSupported = 1 }; \ - static inline void run( const scalar_##EIGENOP##_op<EIGENTYPE>& func, \ - int size, const EIGENTYPE* src, EIGENTYPE* dst) { \ - EIGENTYPE exponent = func.m_exponent; \ - MKL_INT64 vmlMode = EIGEN_MKL_VML_MODE; \ - VMLOP(&size, (const VMLTYPE*)src, (const VMLTYPE*)&exponent, \ - (VMLTYPE*)dst, &vmlMode); \ - } \ - }; - -#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vs##VMLOP, float, float) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vd##VMLOP, double, double) - -#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vc##VMLOP, scomplex, MKL_Complex8) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, vz##VMLOP, dcomplex, MKL_Complex16) - -#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX(EIGENOP, VMLOP) - - -#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vms##VMLOP, float, float) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmd##VMLOP, double, double) - -#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmc##VMLOP, scomplex, MKL_Complex8) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALL_LA(EIGENOP, vmz##VMLOP, dcomplex, MKL_Complex16) - -#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(EIGENOP, VMLOP) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL_LA(EIGENOP, VMLOP) \ - EIGEN_MKL_VML_DECLARE_UNARY_CALLS_COMPLEX_LA(EIGENOP, VMLOP) - - -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sin, Sin) -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(asin, Asin) -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(cos, Cos) -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(acos, Acos) -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(tan, Tan) -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(atan, Atan) -//EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs, Abs) -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(exp, Exp) -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(log, Ln) -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_LA(sqrt, Sqrt) - -EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr) - -// The vm*powx functions are not avaibale in the windows version of MKL. -#ifndef _WIN32 -EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmspowx_, float, float) -EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdpowx_, double, double) -EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcpowx_, scomplex, MKL_Complex8) -EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzpowx_, dcomplex, MKL_Complex16) -#endif - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_ASSIGN_VML_H diff --git a/third_party/eigen3/Eigen/src/Core/BandMatrix.h b/third_party/eigen3/Eigen/src/Core/BandMatrix.h deleted file mode 100644 index ffd7fe8b30..0000000000 --- a/third_party/eigen3/Eigen/src/Core/BandMatrix.h +++ /dev/null @@ -1,334 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BANDMATRIX_H -#define EIGEN_BANDMATRIX_H - -namespace Eigen { - -namespace internal { - -template<typename Derived> -class BandMatrixBase : public EigenBase<Derived> -{ - public: - - enum { - Flags = internal::traits<Derived>::Flags, - CoeffReadCost = internal::traits<Derived>::CoeffReadCost, - RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime, - ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime, - MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime, - MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime, - Supers = internal::traits<Derived>::Supers, - Subs = internal::traits<Derived>::Subs, - Options = internal::traits<Derived>::Options - }; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType; - typedef typename DenseMatrixType::Index Index; - typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType; - typedef EigenBase<Derived> Base; - - protected: - enum { - DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) - ? 1 + Supers + Subs - : Dynamic, - SizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime) - }; - - public: - - using Base::derived; - using Base::rows; - using Base::cols; - - /** \returns the number of super diagonals */ - inline Index supers() const { return derived().supers(); } - - /** \returns the number of sub diagonals */ - inline Index subs() const { return derived().subs(); } - - /** \returns an expression of the underlying coefficient matrix */ - inline const CoefficientsType& coeffs() const { return derived().coeffs(); } - - /** \returns an expression of the underlying coefficient matrix */ - inline CoefficientsType& coeffs() { return derived().coeffs(); } - - /** \returns a vector expression of the \a i -th column, - * only the meaningful part is returned. - * \warning the internal storage must be column major. */ - inline Block<CoefficientsType,Dynamic,1> col(Index i) - { - EIGEN_STATIC_ASSERT((Options&RowMajor)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); - Index start = 0; - Index len = coeffs().rows(); - if (i<=supers()) - { - start = supers()-i; - len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i))); - } - else if (i>=rows()-subs()) - len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs())); - return Block<CoefficientsType,Dynamic,1>(coeffs(), start, i, len, 1); - } - - /** \returns a vector expression of the main diagonal */ - inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal() - { return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); } - - /** \returns a vector expression of the main diagonal (const version) */ - inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const - { return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); } - - template<int Index> struct DiagonalIntReturnType { - enum { - ReturnOpposite = (Options&SelfAdjoint) && (((Index)>0 && Supers==0) || ((Index)<0 && Subs==0)), - Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex, - ActualIndex = ReturnOpposite ? -Index : Index, - DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic) - ? Dynamic - : (ActualIndex<0 - ? EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime, RowsAtCompileTime + ActualIndex) - : EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime, ColsAtCompileTime - ActualIndex)) - }; - typedef Block<CoefficientsType,1, DiagonalSize> BuildType; - typedef typename internal::conditional<Conjugate, - CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >, - BuildType>::type Type; - }; - - /** \returns a vector expression of the \a N -th sub or super diagonal */ - template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal() - { - return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N)); - } - - /** \returns a vector expression of the \a N -th sub or super diagonal */ - template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const - { - return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N)); - } - - /** \returns a vector expression of the \a i -th sub or super diagonal */ - inline Block<CoefficientsType,1,Dynamic> diagonal(Index i) - { - eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers())); - return Block<CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i)); - } - - /** \returns a vector expression of the \a i -th sub or super diagonal */ - inline const Block<const CoefficientsType,1,Dynamic> diagonal(Index i) const - { - eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers())); - return Block<const CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i)); - } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - dst.resize(rows(),cols()); - dst.setZero(); - dst.diagonal() = diagonal(); - for (Index i=1; i<=supers();++i) - dst.diagonal(i) = diagonal(i); - for (Index i=1; i<=subs();++i) - dst.diagonal(-i) = diagonal(-i); - } - - DenseMatrixType toDenseMatrix() const - { - DenseMatrixType res(rows(),cols()); - evalTo(res); - return res; - } - - protected: - - inline Index diagonalLength(Index i) const - { return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); } -}; - -/** - * \class BandMatrix - * \ingroup Core_Module - * - * \brief Represents a rectangular matrix with a banded storage - * - * \param _Scalar Numeric type, i.e. float, double, int - * \param Rows Number of rows, or \b Dynamic - * \param Cols Number of columns, or \b Dynamic - * \param Supers Number of super diagonal - * \param Subs Number of sub diagonal - * \param _Options A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint - * The former controls \ref TopicStorageOrders "storage order", and defaults to - * column-major. The latter controls whether the matrix represents a selfadjoint - * matrix in which case either Supers of Subs have to be null. - * - * \sa class TridiagonalMatrix - */ - -template<typename _Scalar, int _Rows, int _Cols, int _Supers, int _Subs, int _Options> -struct traits<BandMatrix<_Scalar,_Rows,_Cols,_Supers,_Subs,_Options> > -{ - typedef _Scalar Scalar; - typedef Dense StorageKind; - typedef DenseIndex Index; - enum { - CoeffReadCost = NumTraits<Scalar>::ReadCost, - RowsAtCompileTime = _Rows, - ColsAtCompileTime = _Cols, - MaxRowsAtCompileTime = _Rows, - MaxColsAtCompileTime = _Cols, - Flags = LvalueBit, - Supers = _Supers, - Subs = _Subs, - Options = _Options, - DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic - }; - typedef Matrix<Scalar,DataRowsAtCompileTime,ColsAtCompileTime,Options&RowMajor?RowMajor:ColMajor> CoefficientsType; -}; - -template<typename _Scalar, int Rows, int Cols, int Supers, int Subs, int Options> -class BandMatrix : public BandMatrixBase<BandMatrix<_Scalar,Rows,Cols,Supers,Subs,Options> > -{ - public: - - typedef typename internal::traits<BandMatrix>::Scalar Scalar; - typedef typename internal::traits<BandMatrix>::Index Index; - typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType; - - inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs) - : m_coeffs(1+supers+subs,cols), - m_rows(rows), m_supers(supers), m_subs(subs) - { - } - - /** \returns the number of columns */ - inline Index rows() const { return m_rows.value(); } - - /** \returns the number of rows */ - inline Index cols() const { return m_coeffs.cols(); } - - /** \returns the number of super diagonals */ - inline Index supers() const { return m_supers.value(); } - - /** \returns the number of sub diagonals */ - inline Index subs() const { return m_subs.value(); } - - inline const CoefficientsType& coeffs() const { return m_coeffs; } - inline CoefficientsType& coeffs() { return m_coeffs; } - - protected: - - CoefficientsType m_coeffs; - internal::variable_if_dynamic<Index, Rows> m_rows; - internal::variable_if_dynamic<Index, Supers> m_supers; - internal::variable_if_dynamic<Index, Subs> m_subs; -}; - -template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options> -class BandMatrixWrapper; - -template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options> -struct traits<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> > -{ - typedef typename _CoefficientsType::Scalar Scalar; - typedef typename _CoefficientsType::StorageKind StorageKind; - typedef typename _CoefficientsType::Index Index; - enum { - CoeffReadCost = internal::traits<_CoefficientsType>::CoeffReadCost, - RowsAtCompileTime = _Rows, - ColsAtCompileTime = _Cols, - MaxRowsAtCompileTime = _Rows, - MaxColsAtCompileTime = _Cols, - Flags = LvalueBit, - Supers = _Supers, - Subs = _Subs, - Options = _Options, - DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic - }; - typedef _CoefficientsType CoefficientsType; -}; - -template<typename _CoefficientsType,int _Rows, int _Cols, int _Supers, int _Subs,int _Options> -class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<_CoefficientsType,_Rows,_Cols,_Supers,_Subs,_Options> > -{ - public: - - typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar; - typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType; - typedef typename internal::traits<BandMatrixWrapper>::Index Index; - - inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=_Rows, Index cols=_Cols, Index supers=_Supers, Index subs=_Subs) - : m_coeffs(coeffs), - m_rows(rows), m_supers(supers), m_subs(subs) - { - EIGEN_UNUSED_VARIABLE(cols); - //internal::assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows()); - } - - /** \returns the number of columns */ - inline Index rows() const { return m_rows.value(); } - - /** \returns the number of rows */ - inline Index cols() const { return m_coeffs.cols(); } - - /** \returns the number of super diagonals */ - inline Index supers() const { return m_supers.value(); } - - /** \returns the number of sub diagonals */ - inline Index subs() const { return m_subs.value(); } - - inline const CoefficientsType& coeffs() const { return m_coeffs; } - - protected: - - const CoefficientsType& m_coeffs; - internal::variable_if_dynamic<Index, _Rows> m_rows; - internal::variable_if_dynamic<Index, _Supers> m_supers; - internal::variable_if_dynamic<Index, _Subs> m_subs; -}; - -/** - * \class TridiagonalMatrix - * \ingroup Core_Module - * - * \brief Represents a tridiagonal matrix with a compact banded storage - * - * \param _Scalar Numeric type, i.e. float, double, int - * \param Size Number of rows and cols, or \b Dynamic - * \param _Options Can be 0 or \b SelfAdjoint - * - * \sa class BandMatrix - */ -template<typename Scalar, int Size, int Options> -class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> -{ - typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base; - typedef typename Base::Index Index; - public: - TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {} - - inline typename Base::template DiagonalIntReturnType<1>::Type super() - { return Base::template diagonal<1>(); } - inline const typename Base::template DiagonalIntReturnType<1>::Type super() const - { return Base::template diagonal<1>(); } - inline typename Base::template DiagonalIntReturnType<-1>::Type sub() - { return Base::template diagonal<-1>(); } - inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const - { return Base::template diagonal<-1>(); } - protected: -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_BANDMATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/Block.h b/third_party/eigen3/Eigen/src/Core/Block.h deleted file mode 100644 index da193d1a22..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Block.h +++ /dev/null @@ -1,432 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BLOCK_H -#define EIGEN_BLOCK_H - -namespace Eigen { - -/** \class Block - * \ingroup Core_Module - * - * \brief Expression of a fixed-size or dynamic-size block - * - * \param XprType the type of the expression in which we are taking a block - * \param BlockRows the number of rows of the block we are taking at compile time (optional) - * \param BlockCols the number of columns of the block we are taking at compile time (optional) - * \param InnerPanel is true, if the block maps to a set of rows of a row major matrix or - * to set of columns of a column major matrix (optional). The parameter allows to determine - * at compile time whether aligned access is possible on the block expression. - * - * This class represents an expression of either a fixed-size or dynamic-size block. It is the return - * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and - * most of the time this is the only way it is used. - * - * However, if you want to directly maniputate block expressions, - * for instance if you want to write a function returning such an expression, you - * will need to use this class. - * - * Here is an example illustrating the dynamic case: - * \include class_Block.cpp - * Output: \verbinclude class_Block.out - * - * \note Even though this expression has dynamic size, in the case where \a XprType - * has fixed size, this expression inherits a fixed maximal size which means that evaluating - * it does not cause a dynamic memory allocation. - * - * Here is an example illustrating the fixed-size case: - * \include class_FixedBlock.cpp - * Output: \verbinclude class_FixedBlock.out - * - * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock - */ - -namespace internal { -template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> -struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprType> -{ - typedef typename traits<XprType>::Scalar Scalar; - typedef typename traits<XprType>::StorageKind StorageKind; - typedef typename traits<XprType>::XprKind XprKind; - typedef typename nested<XprType>::type XprTypeNested; - typedef typename remove_reference<XprTypeNested>::type _XprTypeNested; - enum{ - MatrixRows = traits<XprType>::RowsAtCompileTime, - MatrixCols = traits<XprType>::ColsAtCompileTime, - RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows, - ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols, - MaxRowsAtCompileTime = BlockRows==0 ? 0 - : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime) - : int(traits<XprType>::MaxRowsAtCompileTime), - MaxColsAtCompileTime = BlockCols==0 ? 0 - : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime) - : int(traits<XprType>::MaxColsAtCompileTime), - XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0, - IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1 - : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0 - : XprTypeIsRowMajor, - HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor), - InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime), - InnerStrideAtCompileTime = HasSameStorageOrderAsXprType - ? int(inner_stride_at_compile_time<XprType>::ret) - : int(outer_stride_at_compile_time<XprType>::ret), - OuterStrideAtCompileTime = HasSameStorageOrderAsXprType - ? int(outer_stride_at_compile_time<XprType>::ret) - : int(inner_stride_at_compile_time<XprType>::ret), - MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0) - && (InnerStrideAtCompileTime == 1) - ? PacketAccessBit : 0, - MaskAlignedBit = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % EIGEN_ALIGN_BYTES) == 0)) ? AlignedBit : 0, - FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (traits<XprType>::Flags&LinearAccessBit))) ? LinearAccessBit : 0, - FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0, - FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0, - Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) | - DirectAccessBit | - MaskPacketAccessBit | - MaskAlignedBit), - Flags = Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit - }; -}; - -template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false, - bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class BlockImpl_dense; - -} // end namespace internal - -template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind> class BlockImpl; - -template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class Block - : public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> -{ - typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl; - public: - //typedef typename Impl::Base Base; - typedef Impl Base; - EIGEN_GENERIC_PUBLIC_INTERFACE(Block) - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block) - - /** Column or Row constructor - */ - EIGEN_DEVICE_FUNC - inline Block(XprType& xpr, Index i) : Impl(xpr,i) - { - eigen_assert( (i>=0) && ( - ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows()) - ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols()))); - } - - /** Fixed-size constructor - */ - EIGEN_DEVICE_FUNC - inline Block(XprType& xpr, Index a_startRow, Index a_startCol) - : Impl(xpr, a_startRow, a_startCol) - { - EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE) - eigen_assert(a_startRow >= 0 && BlockRows >= 1 && a_startRow + BlockRows <= xpr.rows() - && a_startCol >= 0 && BlockCols >= 1 && a_startCol + BlockCols <= xpr.cols()); - } - - /** Dynamic-size constructor - */ - EIGEN_DEVICE_FUNC - inline Block(XprType& xpr, - Index a_startRow, Index a_startCol, - Index blockRows, Index blockCols) - : Impl(xpr, a_startRow, a_startCol, blockRows, blockCols) - { - eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows) - && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols)); - eigen_assert(a_startRow >= 0 && blockRows >= 0 && a_startRow <= xpr.rows() - blockRows - && a_startCol >= 0 && blockCols >= 0 && a_startCol <= xpr.cols() - blockCols); - } -}; - -// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense -// that must be specialized for direct and non-direct access... -template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> -class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense> - : public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> -{ - typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl; - typedef typename XprType::Index Index; - public: - typedef Impl Base; - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl) - EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {} - EIGEN_DEVICE_FUNC inline BlockImpl(XprType& xpr, Index a_startRow, Index a_startCol) : Impl(xpr, a_startRow, a_startCol) {} - EIGEN_DEVICE_FUNC - inline BlockImpl(XprType& xpr, Index a_startRow, Index a_startCol, Index blockRows, Index blockCols) - : Impl(xpr, a_startRow, a_startCol, blockRows, blockCols) {} -}; - -namespace internal { - -/** \internal Internal implementation of dense Blocks in the general case. */ -template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class BlockImpl_dense - : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type -{ - typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType; - public: - - typedef typename internal::dense_xpr_base<BlockType>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(BlockType) - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense) - - class InnerIterator; - - /** Column or Row constructor - */ - EIGEN_DEVICE_FUNC - inline BlockImpl_dense(XprType& xpr, Index i) - : m_xpr(xpr), - // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime, - // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1, - // all other cases are invalid. - // The case a 1x1 matrix seems ambiguous, but the result is the same anyway. - m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0), - m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0), - m_blockRows(BlockRows==1 ? 1 : xpr.rows()), - m_blockCols(BlockCols==1 ? 1 : xpr.cols()) - {} - - /** Fixed-size constructor - */ - EIGEN_DEVICE_FUNC - inline BlockImpl_dense(XprType& xpr, Index a_startRow, Index a_startCol) - : m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol), - m_blockRows(BlockRows), m_blockCols(BlockCols) - {} - - /** Dynamic-size constructor - */ - EIGEN_DEVICE_FUNC - inline BlockImpl_dense(XprType& xpr, - Index a_startRow, Index a_startCol, - Index blockRows, Index blockCols) - : m_xpr(xpr), m_startRow(a_startRow), m_startCol(a_startCol), - m_blockRows(blockRows), m_blockCols(blockCols) - {} - - EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); } - EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index rowId, Index colId) - { - EIGEN_STATIC_ASSERT_LVALUE(XprType) - return m_xpr.const_cast_derived() - .coeffRef(rowId + m_startRow.value(), colId + m_startCol.value()); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index rowId, Index colId) const - { - return m_xpr.derived() - .coeffRef(rowId + m_startRow.value(), colId + m_startCol.value()); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const - { - return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value()); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index index) - { - EIGEN_STATIC_ASSERT_LVALUE(XprType) - return m_xpr.const_cast_derived() - .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), - m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index index) const - { - return m_xpr.const_cast_derived() - .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), - m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); - } - - EIGEN_DEVICE_FUNC - inline const CoeffReturnType coeff(Index index) const - { - return m_xpr - .coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), - m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); - } - - template<int LoadMode> - inline PacketScalar packet(Index rowId, Index colId) const - { - return m_xpr.template packet<Unaligned> - (rowId + m_startRow.value(), colId + m_startCol.value()); - } - - template<int LoadMode> - inline void writePacket(Index rowId, Index colId, const PacketScalar& val) - { - m_xpr.const_cast_derived().template writePacket<Unaligned> - (rowId + m_startRow.value(), colId + m_startCol.value(), val); - } - - template<int LoadMode> - inline PacketScalar packet(Index index) const - { - return m_xpr.template packet<Unaligned> - (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), - m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); - } - - template<int LoadMode> - inline void writePacket(Index index, const PacketScalar& val) - { - m_xpr.const_cast_derived().template writePacket<Unaligned> - (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), - m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val); - } - - #ifdef EIGEN_PARSED_BY_DOXYGEN - /** \sa MapBase::data() */ - EIGEN_DEVICE_FUNC inline const Scalar* data() const; - EIGEN_DEVICE_FUNC inline Index innerStride() const; - EIGEN_DEVICE_FUNC inline Index outerStride() const; - #endif - - EIGEN_DEVICE_FUNC - const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const - { - return m_xpr; - } - - EIGEN_DEVICE_FUNC - Index startRow() const - { - return m_startRow.value(); - } - - EIGEN_DEVICE_FUNC - Index startCol() const - { - return m_startCol.value(); - } - - protected: - - const typename XprType::Nested m_xpr; - const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow; - const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol; - const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows; - const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols; -}; - -/** \internal Internal implementation of dense Blocks in the direct access case.*/ -template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> -class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true> - : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> > -{ - typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType; - public: - - typedef MapBase<BlockType> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(BlockType) - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense) - - /** Column or Row constructor - */ - EIGEN_DEVICE_FUNC - inline BlockImpl_dense(XprType& xpr, Index i) - : Base(internal::const_cast_ptr(&xpr.coeffRef( - (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0, - (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)), - BlockRows==1 ? 1 : xpr.rows(), - BlockCols==1 ? 1 : xpr.cols()), - m_xpr(xpr) - { - init(); - } - - /** Fixed-size constructor - */ - EIGEN_DEVICE_FUNC - inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol) - : Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol))), m_xpr(xpr) - { - init(); - } - - /** Dynamic-size constructor - */ - EIGEN_DEVICE_FUNC - inline BlockImpl_dense(XprType& xpr, - Index startRow, Index startCol, - Index blockRows, Index blockCols) - : Base(internal::const_cast_ptr(&xpr.coeffRef(startRow,startCol)), blockRows, blockCols), - m_xpr(xpr) - { - init(); - } - - EIGEN_DEVICE_FUNC - const typename internal::remove_all<typename XprType::Nested>::type& nestedExpression() const - { - return m_xpr; - } - - /** \sa MapBase::innerStride() */ - EIGEN_DEVICE_FUNC - inline Index innerStride() const - { - return internal::traits<BlockType>::HasSameStorageOrderAsXprType - ? m_xpr.innerStride() - : m_xpr.outerStride(); - } - - /** \sa MapBase::outerStride() */ - EIGEN_DEVICE_FUNC - inline Index outerStride() const - { - return m_outerStride; - } - - #ifndef __SUNPRO_CC - // FIXME sunstudio is not friendly with the above friend... - // META-FIXME there is no 'friend' keyword around here. Is this obsolete? - protected: - #endif - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** \internal used by allowAligned() */ - EIGEN_DEVICE_FUNC - inline BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols) - : Base(data, blockRows, blockCols), m_xpr(xpr) - { - init(); - } - #endif - - protected: - EIGEN_DEVICE_FUNC - void init() - { - m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType - ? m_xpr.outerStride() - : m_xpr.innerStride(); - } - - typename XprType::Nested m_xpr; - Index m_outerStride; -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_BLOCK_H diff --git a/third_party/eigen3/Eigen/src/Core/BooleanRedux.h b/third_party/eigen3/Eigen/src/Core/BooleanRedux.h deleted file mode 100644 index be9f48a8c7..0000000000 --- a/third_party/eigen3/Eigen/src/Core/BooleanRedux.h +++ /dev/null @@ -1,154 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ALLANDANY_H -#define EIGEN_ALLANDANY_H - -namespace Eigen { - -namespace internal { - -template<typename Derived, int UnrollCount> -struct all_unroller -{ - enum { - col = (UnrollCount-1) / Derived::RowsAtCompileTime, - row = (UnrollCount-1) % Derived::RowsAtCompileTime - }; - - static inline bool run(const Derived &mat) - { - return all_unroller<Derived, UnrollCount-1>::run(mat) && mat.coeff(row, col); - } -}; - -template<typename Derived> -struct all_unroller<Derived, 0> -{ - static inline bool run(const Derived &/*mat*/) { return true; } -}; - -template<typename Derived> -struct all_unroller<Derived, Dynamic> -{ - static inline bool run(const Derived &) { return false; } -}; - -template<typename Derived, int UnrollCount> -struct any_unroller -{ - enum { - col = (UnrollCount-1) / Derived::RowsAtCompileTime, - row = (UnrollCount-1) % Derived::RowsAtCompileTime - }; - - static inline bool run(const Derived &mat) - { - return any_unroller<Derived, UnrollCount-1>::run(mat) || mat.coeff(row, col); - } -}; - -template<typename Derived> -struct any_unroller<Derived, 0> -{ - static inline bool run(const Derived & /*mat*/) { return false; } -}; - -template<typename Derived> -struct any_unroller<Derived, Dynamic> -{ - static inline bool run(const Derived &) { return false; } -}; - -} // end namespace internal - -/** \returns true if all coefficients are true - * - * Example: \include MatrixBase_all.cpp - * Output: \verbinclude MatrixBase_all.out - * - * \sa any(), Cwise::operator<() - */ -template<typename Derived> -inline bool DenseBase<Derived>::all() const -{ - enum { - unroll = SizeAtCompileTime != Dynamic - && CoeffReadCost != Dynamic - && NumTraits<Scalar>::AddCost != Dynamic - && SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT - }; - if(unroll) - return internal::all_unroller<Derived, unroll ? int(SizeAtCompileTime) : Dynamic>::run(derived()); - else - { - for(Index j = 0; j < cols(); ++j) - for(Index i = 0; i < rows(); ++i) - if (!coeff(i, j)) return false; - return true; - } -} - -/** \returns true if at least one coefficient is true - * - * \sa all() - */ -template<typename Derived> -inline bool DenseBase<Derived>::any() const -{ - enum { - unroll = SizeAtCompileTime != Dynamic - && CoeffReadCost != Dynamic - && NumTraits<Scalar>::AddCost != Dynamic - && SizeAtCompileTime * (CoeffReadCost + NumTraits<Scalar>::AddCost) <= EIGEN_UNROLLING_LIMIT - }; - if(unroll) - return internal::any_unroller<Derived, unroll ? int(SizeAtCompileTime) : Dynamic>::run(derived()); - else - { - for(Index j = 0; j < cols(); ++j) - for(Index i = 0; i < rows(); ++i) - if (coeff(i, j)) return true; - return false; - } -} - -/** \returns the number of coefficients which evaluate to true - * - * \sa all(), any() - */ -template<typename Derived> -inline typename DenseBase<Derived>::Index DenseBase<Derived>::count() const -{ - return derived().template cast<bool>().template cast<Index>().sum(); -} - -/** \returns true is \c *this contains at least one Not A Number (NaN). - * - * \sa allFinite() - */ -template<typename Derived> -inline bool DenseBase<Derived>::hasNaN() const -{ - return !((derived().array()==derived().array()).all()); -} - -/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values. - * - * \sa hasNaN() - */ -template<typename Derived> -inline bool DenseBase<Derived>::allFinite() const -{ - return !((derived()-derived()).hasNaN()); -} - -} // end namespace Eigen - -#endif // EIGEN_ALLANDANY_H diff --git a/third_party/eigen3/Eigen/src/Core/CommaInitializer.h b/third_party/eigen3/Eigen/src/Core/CommaInitializer.h deleted file mode 100644 index 70cbfeff55..0000000000 --- a/third_party/eigen3/Eigen/src/Core/CommaInitializer.h +++ /dev/null @@ -1,161 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COMMAINITIALIZER_H -#define EIGEN_COMMAINITIALIZER_H - -namespace Eigen { - -/** \class CommaInitializer - * \ingroup Core_Module - * - * \brief Helper class used by the comma initializer operator - * - * This class is internally used to implement the comma initializer feature. It is - * the return type of MatrixBase::operator<<, and most of the time this is the only - * way it is used. - * - * \sa \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished() - */ -template<typename XprType> -struct CommaInitializer -{ - typedef typename XprType::Scalar Scalar; - typedef typename XprType::Index Index; - - EIGEN_DEVICE_FUNC - inline CommaInitializer(XprType& xpr, const Scalar& s) - : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1) - { - m_xpr.coeffRef(0,0) = s; - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other) - : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows()) - { - m_xpr.block(0, 0, other.rows(), other.cols()) = other; - } - - /* Copy/Move constructor which transfers ownership. This is crucial in - * absence of return value optimization to avoid assertions during destruction. */ - // FIXME in C++11 mode this could be replaced by a proper RValue constructor - EIGEN_DEVICE_FUNC - inline CommaInitializer(const CommaInitializer& o) - : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) { - // Mark original object as finished. In absence of R-value references we need to const_cast: - const_cast<CommaInitializer&>(o).m_row = m_xpr.rows(); - const_cast<CommaInitializer&>(o).m_col = m_xpr.cols(); - const_cast<CommaInitializer&>(o).m_currentBlockRows = 0; - } - - /* inserts a scalar value in the target matrix */ - EIGEN_DEVICE_FUNC - CommaInitializer& operator,(const Scalar& s) - { - if (m_col==m_xpr.cols()) - { - m_row+=m_currentBlockRows; - m_col = 0; - m_currentBlockRows = 1; - eigen_assert(m_row<m_xpr.rows() - && "Too many rows passed to comma initializer (operator<<)"); - } - eigen_assert(m_col<m_xpr.cols() - && "Too many coefficients passed to comma initializer (operator<<)"); - eigen_assert(m_currentBlockRows==1); - m_xpr.coeffRef(m_row, m_col++) = s; - return *this; - } - - /* inserts a matrix expression in the target matrix */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - CommaInitializer& operator,(const DenseBase<OtherDerived>& other) - { - if(other.cols()==0 || other.rows()==0) - return *this; - if (m_col==m_xpr.cols()) - { - m_row+=m_currentBlockRows; - m_col = 0; - m_currentBlockRows = other.rows(); - eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows() - && "Too many rows passed to comma initializer (operator<<)"); - } - eigen_assert(m_col<m_xpr.cols() - && "Too many coefficients passed to comma initializer (operator<<)"); - eigen_assert(m_currentBlockRows==other.rows()); - if (OtherDerived::SizeAtCompileTime != Dynamic) - m_xpr.template block<OtherDerived::RowsAtCompileTime != Dynamic ? OtherDerived::RowsAtCompileTime : 1, - OtherDerived::ColsAtCompileTime != Dynamic ? OtherDerived::ColsAtCompileTime : 1> - (m_row, m_col) = other; - else - m_xpr.block(m_row, m_col, other.rows(), other.cols()) = other; - m_col += other.cols(); - return *this; - } - - EIGEN_DEVICE_FUNC - inline ~CommaInitializer() - { - eigen_assert((m_row+m_currentBlockRows) == m_xpr.rows() - && m_col == m_xpr.cols() - && "Too few coefficients passed to comma initializer (operator<<)"); - } - - /** \returns the built matrix once all its coefficients have been set. - * Calling finished is 100% optional. Its purpose is to write expressions - * like this: - * \code - * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished()); - * \endcode - */ - EIGEN_DEVICE_FUNC - inline XprType& finished() { return m_xpr; } - - XprType& m_xpr; // target expression - Index m_row; // current row id - Index m_col; // current col id - Index m_currentBlockRows; // current block height -}; - -/** \anchor MatrixBaseCommaInitRef - * Convenient operator to set the coefficients of a matrix. - * - * The coefficients must be provided in a row major order and exactly match - * the size of the matrix. Otherwise an assertion is raised. - * - * Example: \include MatrixBase_set.cpp - * Output: \verbinclude MatrixBase_set.out - * - * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order. - * - * \sa CommaInitializer::finished(), class CommaInitializer - */ -template<typename Derived> -inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s) -{ - return CommaInitializer<Derived>(*static_cast<Derived*>(this), s); -} - -/** \sa operator<<(const Scalar&) */ -template<typename Derived> -template<typename OtherDerived> -inline CommaInitializer<Derived> -DenseBase<Derived>::operator<<(const DenseBase<OtherDerived>& other) -{ - return CommaInitializer<Derived>(*static_cast<Derived *>(this), other); -} - -} // end namespace Eigen - -#endif // EIGEN_COMMAINITIALIZER_H diff --git a/third_party/eigen3/Eigen/src/Core/CoreEvaluators.h b/third_party/eigen3/Eigen/src/Core/CoreEvaluators.h deleted file mode 100644 index 3568cb85f9..0000000000 --- a/third_party/eigen3/Eigen/src/Core/CoreEvaluators.h +++ /dev/null @@ -1,1121 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -#ifndef EIGEN_COREEVALUATORS_H -#define EIGEN_COREEVALUATORS_H - -namespace Eigen { - -namespace internal { - -// evaluator_traits<T> contains traits for evaluator_impl<T> - -template<typename T> -struct evaluator_traits -{ - // 1 if evaluator_impl<T>::evalTo() exists - // 0 if evaluator_impl<T> allows coefficient-based access - static const int HasEvalTo = 0; - - // 1 if assignment A = B assumes aliasing when B is of type T and thus B needs to be evaluated into a - // temporary; 0 if not. - static const int AssumeAliasing = 0; -}; - -// expression class for evaluating nested expression to a temporary - -template<typename ArgType> -class EvalToTemp; - -// evaluator<T>::type is type of evaluator for T -// evaluator<T>::nestedType is type of evaluator if T is nested inside another evaluator - -template<typename T> -struct evaluator_impl -{ }; - -template<typename T, int Nested = evaluator_traits<T>::HasEvalTo> -struct evaluator_nested_type; - -template<typename T> -struct evaluator_nested_type<T, 0> -{ - typedef evaluator_impl<T> type; -}; - -template<typename T> -struct evaluator_nested_type<T, 1> -{ - typedef evaluator_impl<EvalToTemp<T> > type; -}; - -template<typename T> -struct evaluator -{ - typedef evaluator_impl<T> type; - typedef typename evaluator_nested_type<T>::type nestedType; -}; - -// TODO: Think about const-correctness - -template<typename T> -struct evaluator<const T> - : evaluator<T> -{ }; - -// ---------- base class for all writable evaluators ---------- - -// TODO this class does not seem to be necessary anymore -template<typename ExpressionType> -struct evaluator_impl_base -{ - typedef typename ExpressionType::Index Index; - // TODO that's not very nice to have to propagate all these traits. They are currently only needed to handle outer,inner indices. - typedef traits<ExpressionType> ExpressionTraits; - - evaluator_impl<ExpressionType>& derived() - { - return *static_cast<evaluator_impl<ExpressionType>*>(this); - } -}; - -// -------------------- Matrix and Array -------------------- -// -// evaluator_impl<PlainObjectBase> is a common base class for the -// Matrix and Array evaluators. - -template<typename Derived> -struct evaluator_impl<PlainObjectBase<Derived> > - : evaluator_impl_base<Derived> -{ - typedef PlainObjectBase<Derived> PlainObjectType; - - enum { - IsRowMajor = PlainObjectType::IsRowMajor, - IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime, - RowsAtCompileTime = PlainObjectType::RowsAtCompileTime, - ColsAtCompileTime = PlainObjectType::ColsAtCompileTime - }; - - evaluator_impl(const PlainObjectType& m) - : m_data(m.data()), m_outerStride(IsVectorAtCompileTime ? 0 : m.outerStride()) - { } - - typedef typename PlainObjectType::Index Index; - typedef typename PlainObjectType::Scalar Scalar; - typedef typename PlainObjectType::CoeffReturnType CoeffReturnType; - typedef typename PlainObjectType::PacketScalar PacketScalar; - typedef typename PlainObjectType::PacketReturnType PacketReturnType; - - CoeffReturnType coeff(Index row, Index col) const - { - if (IsRowMajor) - return m_data[row * m_outerStride.value() + col]; - else - return m_data[row + col * m_outerStride.value()]; - } - - CoeffReturnType coeff(Index index) const - { - return m_data[index]; - } - - Scalar& coeffRef(Index row, Index col) - { - if (IsRowMajor) - return const_cast<Scalar*>(m_data)[row * m_outerStride.value() + col]; - else - return const_cast<Scalar*>(m_data)[row + col * m_outerStride.value()]; - } - - Scalar& coeffRef(Index index) - { - return const_cast<Scalar*>(m_data)[index]; - } - - template<int LoadMode> - PacketReturnType packet(Index row, Index col) const - { - if (IsRowMajor) - return ploadt<PacketScalar, LoadMode>(m_data + row * m_outerStride.value() + col); - else - return ploadt<PacketScalar, LoadMode>(m_data + row + col * m_outerStride.value()); - } - - template<int LoadMode> - PacketReturnType packet(Index index) const - { - return ploadt<PacketScalar, LoadMode>(m_data + index); - } - - template<int StoreMode> - void writePacket(Index row, Index col, const PacketScalar& x) - { - if (IsRowMajor) - return pstoret<Scalar, PacketScalar, StoreMode> - (const_cast<Scalar*>(m_data) + row * m_outerStride.value() + col, x); - else - return pstoret<Scalar, PacketScalar, StoreMode> - (const_cast<Scalar*>(m_data) + row + col * m_outerStride.value(), x); - } - - template<int StoreMode> - void writePacket(Index index, const PacketScalar& x) - { - return pstoret<Scalar, PacketScalar, StoreMode>(const_cast<Scalar*>(m_data) + index, x); - } - -protected: - const Scalar *m_data; - - // We do not need to know the outer stride for vectors - variable_if_dynamic<Index, IsVectorAtCompileTime ? 0 - : int(IsRowMajor) ? ColsAtCompileTime - : RowsAtCompileTime> m_outerStride; -}; - -template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols> -struct evaluator_impl<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > - : evaluator_impl<PlainObjectBase<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > > -{ - typedef Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType; - - evaluator_impl(const XprType& m) - : evaluator_impl<PlainObjectBase<XprType> >(m) - { } -}; - -template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols> -struct evaluator_impl<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > - : evaluator_impl<PlainObjectBase<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > > -{ - typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType; - - evaluator_impl(const XprType& m) - : evaluator_impl<PlainObjectBase<XprType> >(m) - { } -}; - -// -------------------- EvalToTemp -------------------- - -template<typename ArgType> -struct traits<EvalToTemp<ArgType> > - : public traits<ArgType> -{ }; - -template<typename ArgType> -class EvalToTemp - : public dense_xpr_base<EvalToTemp<ArgType> >::type -{ - public: - - typedef typename dense_xpr_base<EvalToTemp>::type Base; - EIGEN_GENERIC_PUBLIC_INTERFACE(EvalToTemp) - - EvalToTemp(const ArgType& arg) - : m_arg(arg) - { } - - const ArgType& arg() const - { - return m_arg; - } - - Index rows() const - { - return m_arg.rows(); - } - - Index cols() const - { - return m_arg.cols(); - } - - private: - const ArgType& m_arg; -}; - -template<typename ArgType> -struct evaluator_impl<EvalToTemp<ArgType> > -{ - typedef EvalToTemp<ArgType> XprType; - typedef typename ArgType::PlainObject PlainObject; - - evaluator_impl(const XprType& xpr) - : m_result(xpr.rows(), xpr.cols()), m_resultImpl(m_result) - { - // TODO we should simply do m_result(xpr.arg()); - call_dense_assignment_loop(m_result, xpr.arg()); - } - - // This constructor is used when nesting an EvalTo evaluator in another evaluator - evaluator_impl(const ArgType& arg) - : m_result(arg.rows(), arg.cols()), m_resultImpl(m_result) - { - // TODO we should simply do m_result(xpr.arg()); - call_dense_assignment_loop(m_result, arg); - } - - typedef typename PlainObject::Index Index; - typedef typename PlainObject::Scalar Scalar; - typedef typename PlainObject::CoeffReturnType CoeffReturnType; - typedef typename PlainObject::PacketScalar PacketScalar; - typedef typename PlainObject::PacketReturnType PacketReturnType; - - // All other functions are forwarded to m_resultImpl - - CoeffReturnType coeff(Index row, Index col) const - { - return m_resultImpl.coeff(row, col); - } - - CoeffReturnType coeff(Index index) const - { - return m_resultImpl.coeff(index); - } - - Scalar& coeffRef(Index row, Index col) - { - return m_resultImpl.coeffRef(row, col); - } - - Scalar& coeffRef(Index index) - { - return m_resultImpl.coeffRef(index); - } - - template<int LoadMode> - PacketReturnType packet(Index row, Index col) const - { - return m_resultImpl.template packet<LoadMode>(row, col); - } - - template<int LoadMode> - PacketReturnType packet(Index index) const - { - return m_resultImpl.packet<LoadMode>(index); - } - - template<int StoreMode> - void writePacket(Index row, Index col, const PacketScalar& x) - { - m_resultImpl.template writePacket<StoreMode>(row, col, x); - } - - template<int StoreMode> - void writePacket(Index index, const PacketScalar& x) - { - m_resultImpl.template writePacket<StoreMode>(index, x); - } - -protected: - PlainObject m_result; - typename evaluator<PlainObject>::nestedType m_resultImpl; -}; - -// -------------------- Transpose -------------------- - -template<typename ArgType> -struct evaluator_impl<Transpose<ArgType> > - : evaluator_impl_base<Transpose<ArgType> > -{ - typedef Transpose<ArgType> XprType; - - evaluator_impl(const XprType& t) : m_argImpl(t.nestedExpression()) {} - - typedef typename XprType::Index Index; - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - typedef typename XprType::PacketReturnType PacketReturnType; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_argImpl.coeff(col, row); - } - - CoeffReturnType coeff(Index index) const - { - return m_argImpl.coeff(index); - } - - Scalar& coeffRef(Index row, Index col) - { - return m_argImpl.coeffRef(col, row); - } - - typename XprType::Scalar& coeffRef(Index index) - { - return m_argImpl.coeffRef(index); - } - - template<int LoadMode> - PacketReturnType packet(Index row, Index col) const - { - return m_argImpl.template packet<LoadMode>(col, row); - } - - template<int LoadMode> - PacketReturnType packet(Index index) const - { - return m_argImpl.template packet<LoadMode>(index); - } - - template<int StoreMode> - void writePacket(Index row, Index col, const PacketScalar& x) - { - m_argImpl.template writePacket<StoreMode>(col, row, x); - } - - template<int StoreMode> - void writePacket(Index index, const PacketScalar& x) - { - m_argImpl.template writePacket<StoreMode>(index, x); - } - -protected: - typename evaluator<ArgType>::nestedType m_argImpl; -}; - -// -------------------- CwiseNullaryOp -------------------- - -template<typename NullaryOp, typename PlainObjectType> -struct evaluator_impl<CwiseNullaryOp<NullaryOp,PlainObjectType> > -{ - typedef CwiseNullaryOp<NullaryOp,PlainObjectType> XprType; - - evaluator_impl(const XprType& n) - : m_functor(n.functor()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_functor(row, col); - } - - CoeffReturnType coeff(Index index) const - { - return m_functor(index); - } - - template<int LoadMode> - PacketScalar packet(Index row, Index col) const - { - return m_functor.packetOp(row, col); - } - - template<int LoadMode> - PacketScalar packet(Index index) const - { - return m_functor.packetOp(index); - } - -protected: - const NullaryOp m_functor; -}; - -// -------------------- CwiseUnaryOp -------------------- - -template<typename UnaryOp, typename ArgType> -struct evaluator_impl<CwiseUnaryOp<UnaryOp, ArgType> > -{ - typedef CwiseUnaryOp<UnaryOp, ArgType> XprType; - - evaluator_impl(const XprType& op) - : m_functor(op.functor()), - m_argImpl(op.nestedExpression()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_functor(m_argImpl.coeff(row, col)); - } - - CoeffReturnType coeff(Index index) const - { - return m_functor(m_argImpl.coeff(index)); - } - - template<int LoadMode> - PacketScalar packet(Index row, Index col) const - { - return m_functor.packetOp(m_argImpl.template packet<LoadMode>(row, col)); - } - - template<int LoadMode> - PacketScalar packet(Index index) const - { - return m_functor.packetOp(m_argImpl.template packet<LoadMode>(index)); - } - -protected: - const UnaryOp m_functor; - typename evaluator<ArgType>::nestedType m_argImpl; -}; - -// -------------------- CwiseBinaryOp -------------------- - -template<typename BinaryOp, typename Lhs, typename Rhs> -struct evaluator_impl<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > -{ - typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType; - - evaluator_impl(const XprType& xpr) - : m_functor(xpr.functor()), - m_lhsImpl(xpr.lhs()), - m_rhsImpl(xpr.rhs()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_functor(m_lhsImpl.coeff(row, col), m_rhsImpl.coeff(row, col)); - } - - CoeffReturnType coeff(Index index) const - { - return m_functor(m_lhsImpl.coeff(index), m_rhsImpl.coeff(index)); - } - - template<int LoadMode> - PacketScalar packet(Index row, Index col) const - { - return m_functor.packetOp(m_lhsImpl.template packet<LoadMode>(row, col), - m_rhsImpl.template packet<LoadMode>(row, col)); - } - - template<int LoadMode> - PacketScalar packet(Index index) const - { - return m_functor.packetOp(m_lhsImpl.template packet<LoadMode>(index), - m_rhsImpl.template packet<LoadMode>(index)); - } - -protected: - const BinaryOp m_functor; - typename evaluator<Lhs>::nestedType m_lhsImpl; - typename evaluator<Rhs>::nestedType m_rhsImpl; -}; - -// -------------------- CwiseUnaryView -------------------- - -template<typename UnaryOp, typename ArgType> -struct evaluator_impl<CwiseUnaryView<UnaryOp, ArgType> > - : evaluator_impl_base<CwiseUnaryView<UnaryOp, ArgType> > -{ - typedef CwiseUnaryView<UnaryOp, ArgType> XprType; - - evaluator_impl(const XprType& op) - : m_unaryOp(op.functor()), - m_argImpl(op.nestedExpression()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_unaryOp(m_argImpl.coeff(row, col)); - } - - CoeffReturnType coeff(Index index) const - { - return m_unaryOp(m_argImpl.coeff(index)); - } - - Scalar& coeffRef(Index row, Index col) - { - return m_unaryOp(m_argImpl.coeffRef(row, col)); - } - - Scalar& coeffRef(Index index) - { - return m_unaryOp(m_argImpl.coeffRef(index)); - } - -protected: - const UnaryOp m_unaryOp; - typename evaluator<ArgType>::nestedType m_argImpl; -}; - -// -------------------- Map -------------------- - -template<typename Derived, int AccessorsType> -struct evaluator_impl<MapBase<Derived, AccessorsType> > - : evaluator_impl_base<Derived> -{ - typedef MapBase<Derived, AccessorsType> MapType; - typedef Derived XprType; - - typedef typename XprType::PointerType PointerType; - typedef typename XprType::Index Index; - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - typedef typename XprType::PacketReturnType PacketReturnType; - - evaluator_impl(const XprType& map) - : m_data(const_cast<PointerType>(map.data())), - m_rowStride(map.rowStride()), - m_colStride(map.colStride()) - { } - - enum { - RowsAtCompileTime = XprType::RowsAtCompileTime - }; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_data[col * m_colStride + row * m_rowStride]; - } - - CoeffReturnType coeff(Index index) const - { - return coeff(RowsAtCompileTime == 1 ? 0 : index, - RowsAtCompileTime == 1 ? index : 0); - } - - Scalar& coeffRef(Index row, Index col) - { - return m_data[col * m_colStride + row * m_rowStride]; - } - - Scalar& coeffRef(Index index) - { - return coeffRef(RowsAtCompileTime == 1 ? 0 : index, - RowsAtCompileTime == 1 ? index : 0); - } - - template<int LoadMode> - PacketReturnType packet(Index row, Index col) const - { - PointerType ptr = m_data + row * m_rowStride + col * m_colStride; - return internal::ploadt<PacketScalar, LoadMode>(ptr); - } - - template<int LoadMode> - PacketReturnType packet(Index index) const - { - return packet<LoadMode>(RowsAtCompileTime == 1 ? 0 : index, - RowsAtCompileTime == 1 ? index : 0); - } - - template<int StoreMode> - void writePacket(Index row, Index col, const PacketScalar& x) - { - PointerType ptr = m_data + row * m_rowStride + col * m_colStride; - return internal::pstoret<Scalar, PacketScalar, StoreMode>(ptr, x); - } - - template<int StoreMode> - void writePacket(Index index, const PacketScalar& x) - { - return writePacket<StoreMode>(RowsAtCompileTime == 1 ? 0 : index, - RowsAtCompileTime == 1 ? index : 0, - x); - } - -protected: - PointerType m_data; - int m_rowStride; - int m_colStride; -}; - -template<typename PlainObjectType, int MapOptions, typename StrideType> -struct evaluator_impl<Map<PlainObjectType, MapOptions, StrideType> > - : public evaluator_impl<MapBase<Map<PlainObjectType, MapOptions, StrideType> > > -{ - typedef Map<PlainObjectType, MapOptions, StrideType> XprType; - - evaluator_impl(const XprType& map) - : evaluator_impl<MapBase<XprType> >(map) - { } -}; - -// -------------------- Block -------------------- - -template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel, - bool HasDirectAccess = internal::has_direct_access<ArgType>::ret> struct block_evaluator; - -template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel> -struct evaluator_impl<Block<ArgType, BlockRows, BlockCols, InnerPanel> > - : block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> -{ - typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType; - typedef block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> block_evaluator_type; - evaluator_impl(const XprType& block) : block_evaluator_type(block) {} -}; - -template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel> -struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /*HasDirectAccess*/ false> - : evaluator_impl_base<Block<ArgType, BlockRows, BlockCols, InnerPanel> > -{ - typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType; - - block_evaluator(const XprType& block) - : m_argImpl(block.nestedExpression()), - m_startRow(block.startRow()), - m_startCol(block.startCol()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - typedef typename XprType::PacketReturnType PacketReturnType; - - enum { - RowsAtCompileTime = XprType::RowsAtCompileTime - }; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_argImpl.coeff(m_startRow.value() + row, m_startCol.value() + col); - } - - CoeffReturnType coeff(Index index) const - { - return coeff(RowsAtCompileTime == 1 ? 0 : index, - RowsAtCompileTime == 1 ? index : 0); - } - - Scalar& coeffRef(Index row, Index col) - { - return m_argImpl.coeffRef(m_startRow.value() + row, m_startCol.value() + col); - } - - Scalar& coeffRef(Index index) - { - return coeffRef(RowsAtCompileTime == 1 ? 0 : index, - RowsAtCompileTime == 1 ? index : 0); - } - - template<int LoadMode> - PacketReturnType packet(Index row, Index col) const - { - return m_argImpl.template packet<LoadMode>(m_startRow.value() + row, m_startCol.value() + col); - } - - template<int LoadMode> - PacketReturnType packet(Index index) const - { - return packet<LoadMode>(RowsAtCompileTime == 1 ? 0 : index, - RowsAtCompileTime == 1 ? index : 0); - } - - template<int StoreMode> - void writePacket(Index row, Index col, const PacketScalar& x) - { - return m_argImpl.template writePacket<StoreMode>(m_startRow.value() + row, m_startCol.value() + col, x); - } - - template<int StoreMode> - void writePacket(Index index, const PacketScalar& x) - { - return writePacket<StoreMode>(RowsAtCompileTime == 1 ? 0 : index, - RowsAtCompileTime == 1 ? index : 0, - x); - } - -protected: - typename evaluator<ArgType>::nestedType m_argImpl; - const variable_if_dynamic<Index, ArgType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow; - const variable_if_dynamic<Index, ArgType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol; -}; - -// TODO: This evaluator does not actually use the child evaluator; -// all action is via the data() as returned by the Block expression. - -template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel> -struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /* HasDirectAccess */ true> - : evaluator_impl<MapBase<Block<ArgType, BlockRows, BlockCols, InnerPanel> > > -{ - typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType; - - block_evaluator(const XprType& block) - : evaluator_impl<MapBase<XprType> >(block) - { } -}; - - -// -------------------- Select -------------------- - -template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType> -struct evaluator_impl<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> > -{ - typedef Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> XprType; - - evaluator_impl(const XprType& select) - : m_conditionImpl(select.conditionMatrix()), - m_thenImpl(select.thenMatrix()), - m_elseImpl(select.elseMatrix()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::CoeffReturnType CoeffReturnType; - - CoeffReturnType coeff(Index row, Index col) const - { - if (m_conditionImpl.coeff(row, col)) - return m_thenImpl.coeff(row, col); - else - return m_elseImpl.coeff(row, col); - } - - CoeffReturnType coeff(Index index) const - { - if (m_conditionImpl.coeff(index)) - return m_thenImpl.coeff(index); - else - return m_elseImpl.coeff(index); - } - -protected: - typename evaluator<ConditionMatrixType>::nestedType m_conditionImpl; - typename evaluator<ThenMatrixType>::nestedType m_thenImpl; - typename evaluator<ElseMatrixType>::nestedType m_elseImpl; -}; - - -// -------------------- Replicate -------------------- - -template<typename ArgType, int RowFactor, int ColFactor> -struct evaluator_impl<Replicate<ArgType, RowFactor, ColFactor> > -{ - typedef Replicate<ArgType, RowFactor, ColFactor> XprType; - - evaluator_impl(const XprType& replicate) - : m_argImpl(replicate.nestedExpression()), - m_rows(replicate.nestedExpression().rows()), - m_cols(replicate.nestedExpression().cols()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketReturnType PacketReturnType; - - CoeffReturnType coeff(Index row, Index col) const - { - // try to avoid using modulo; this is a pure optimization strategy - const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0 - : RowFactor==1 ? row - : row % m_rows.value(); - const Index actual_col = internal::traits<XprType>::ColsAtCompileTime==1 ? 0 - : ColFactor==1 ? col - : col % m_cols.value(); - - return m_argImpl.coeff(actual_row, actual_col); - } - - template<int LoadMode> - PacketReturnType packet(Index row, Index col) const - { - const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0 - : RowFactor==1 ? row - : row % m_rows.value(); - const Index actual_col = internal::traits<XprType>::ColsAtCompileTime==1 ? 0 - : ColFactor==1 ? col - : col % m_cols.value(); - - return m_argImpl.template packet<LoadMode>(actual_row, actual_col); - } - -protected: - typename evaluator<ArgType>::nestedType m_argImpl; - const variable_if_dynamic<Index, XprType::RowsAtCompileTime> m_rows; - const variable_if_dynamic<Index, XprType::ColsAtCompileTime> m_cols; -}; - - -// -------------------- PartialReduxExpr -------------------- -// -// This is a wrapper around the expression object. -// TODO: Find out how to write a proper evaluator without duplicating -// the row() and col() member functions. - -template< typename ArgType, typename MemberOp, int Direction> -struct evaluator_impl<PartialReduxExpr<ArgType, MemberOp, Direction> > -{ - typedef PartialReduxExpr<ArgType, MemberOp, Direction> XprType; - - evaluator_impl(const XprType expr) - : m_expr(expr) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::CoeffReturnType CoeffReturnType; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_expr.coeff(row, col); - } - - CoeffReturnType coeff(Index index) const - { - return m_expr.coeff(index); - } - -protected: - const XprType m_expr; -}; - - -// -------------------- MatrixWrapper and ArrayWrapper -------------------- -// -// evaluator_impl_wrapper_base<T> is a common base class for the -// MatrixWrapper and ArrayWrapper evaluators. - -template<typename XprType> -struct evaluator_impl_wrapper_base - : evaluator_impl_base<XprType> -{ - typedef typename remove_all<typename XprType::NestedExpressionType>::type ArgType; - - evaluator_impl_wrapper_base(const ArgType& arg) : m_argImpl(arg) {} - - typedef typename ArgType::Index Index; - typedef typename ArgType::Scalar Scalar; - typedef typename ArgType::CoeffReturnType CoeffReturnType; - typedef typename ArgType::PacketScalar PacketScalar; - typedef typename ArgType::PacketReturnType PacketReturnType; - - CoeffReturnType coeff(Index row, Index col) const - { - return m_argImpl.coeff(row, col); - } - - CoeffReturnType coeff(Index index) const - { - return m_argImpl.coeff(index); - } - - Scalar& coeffRef(Index row, Index col) - { - return m_argImpl.coeffRef(row, col); - } - - Scalar& coeffRef(Index index) - { - return m_argImpl.coeffRef(index); - } - - template<int LoadMode> - PacketReturnType packet(Index row, Index col) const - { - return m_argImpl.template packet<LoadMode>(row, col); - } - - template<int LoadMode> - PacketReturnType packet(Index index) const - { - return m_argImpl.template packet<LoadMode>(index); - } - - template<int StoreMode> - void writePacket(Index row, Index col, const PacketScalar& x) - { - m_argImpl.template writePacket<StoreMode>(row, col, x); - } - - template<int StoreMode> - void writePacket(Index index, const PacketScalar& x) - { - m_argImpl.template writePacket<StoreMode>(index, x); - } - -protected: - typename evaluator<ArgType>::nestedType m_argImpl; -}; - -template<typename TArgType> -struct evaluator_impl<MatrixWrapper<TArgType> > - : evaluator_impl_wrapper_base<MatrixWrapper<TArgType> > -{ - typedef MatrixWrapper<TArgType> XprType; - - evaluator_impl(const XprType& wrapper) - : evaluator_impl_wrapper_base<MatrixWrapper<TArgType> >(wrapper.nestedExpression()) - { } -}; - -template<typename TArgType> -struct evaluator_impl<ArrayWrapper<TArgType> > - : evaluator_impl_wrapper_base<ArrayWrapper<TArgType> > -{ - typedef ArrayWrapper<TArgType> XprType; - - evaluator_impl(const XprType& wrapper) - : evaluator_impl_wrapper_base<ArrayWrapper<TArgType> >(wrapper.nestedExpression()) - { } -}; - - -// -------------------- Reverse -------------------- - -// defined in Reverse.h: -template<typename PacketScalar, bool ReversePacket> struct reverse_packet_cond; - -template<typename ArgType, int Direction> -struct evaluator_impl<Reverse<ArgType, Direction> > - : evaluator_impl_base<Reverse<ArgType, Direction> > -{ - typedef Reverse<ArgType, Direction> XprType; - typedef typename XprType::Index Index; - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - typedef typename XprType::PacketReturnType PacketReturnType; - - enum { - PacketSize = internal::packet_traits<Scalar>::size, - IsRowMajor = XprType::IsRowMajor, - IsColMajor = !IsRowMajor, - ReverseRow = (Direction == Vertical) || (Direction == BothDirections), - ReverseCol = (Direction == Horizontal) || (Direction == BothDirections), - OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1, - OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1, - ReversePacket = (Direction == BothDirections) - || ((Direction == Vertical) && IsColMajor) - || ((Direction == Horizontal) && IsRowMajor) - }; - typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet; - - evaluator_impl(const XprType& reverse) - : m_argImpl(reverse.nestedExpression()), - m_rows(ReverseRow ? reverse.nestedExpression().rows() : 0), - m_cols(ReverseCol ? reverse.nestedExpression().cols() : 0) - { } - - CoeffReturnType coeff(Index row, Index col) const - { - return m_argImpl.coeff(ReverseRow ? m_rows.value() - row - 1 : row, - ReverseCol ? m_cols.value() - col - 1 : col); - } - - CoeffReturnType coeff(Index index) const - { - return m_argImpl.coeff(m_rows.value() * m_cols.value() - index - 1); - } - - Scalar& coeffRef(Index row, Index col) - { - return m_argImpl.coeffRef(ReverseRow ? m_rows.value() - row - 1 : row, - ReverseCol ? m_cols.value() - col - 1 : col); - } - - Scalar& coeffRef(Index index) - { - return m_argImpl.coeffRef(m_rows.value() * m_cols.value() - index - 1); - } - - template<int LoadMode> - PacketScalar packet(Index row, Index col) const - { - return reverse_packet::run(m_argImpl.template packet<LoadMode>( - ReverseRow ? m_rows.value() - row - OffsetRow : row, - ReverseCol ? m_cols.value() - col - OffsetCol : col)); - } - - template<int LoadMode> - PacketScalar packet(Index index) const - { - return preverse(m_argImpl.template packet<LoadMode>(m_rows.value() * m_cols.value() - index - PacketSize)); - } - - template<int LoadMode> - void writePacket(Index row, Index col, const PacketScalar& x) - { - m_argImpl.template writePacket<LoadMode>( - ReverseRow ? m_rows.value() - row - OffsetRow : row, - ReverseCol ? m_cols.value() - col - OffsetCol : col, - reverse_packet::run(x)); - } - - template<int LoadMode> - void writePacket(Index index, const PacketScalar& x) - { - m_argImpl.template writePacket<LoadMode> - (m_rows.value() * m_cols.value() - index - PacketSize, preverse(x)); - } - -protected: - typename evaluator<ArgType>::nestedType m_argImpl; - - // If we do not reverse rows, then we do not need to know the number of rows; same for columns - const variable_if_dynamic<Index, ReverseRow ? ArgType::RowsAtCompileTime : 0> m_rows; - const variable_if_dynamic<Index, ReverseCol ? ArgType::ColsAtCompileTime : 0> m_cols; -}; - - -// -------------------- Diagonal -------------------- - -template<typename ArgType, int DiagIndex> -struct evaluator_impl<Diagonal<ArgType, DiagIndex> > - : evaluator_impl_base<Diagonal<ArgType, DiagIndex> > -{ - typedef Diagonal<ArgType, DiagIndex> XprType; - - evaluator_impl(const XprType& diagonal) - : m_argImpl(diagonal.nestedExpression()), - m_index(diagonal.index()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - - CoeffReturnType coeff(Index row, Index) const - { - return m_argImpl.coeff(row + rowOffset(), row + colOffset()); - } - - CoeffReturnType coeff(Index index) const - { - return m_argImpl.coeff(index + rowOffset(), index + colOffset()); - } - - Scalar& coeffRef(Index row, Index) - { - return m_argImpl.coeffRef(row + rowOffset(), row + colOffset()); - } - - Scalar& coeffRef(Index index) - { - return m_argImpl.coeffRef(index + rowOffset(), index + colOffset()); - } - -protected: - typename evaluator<ArgType>::nestedType m_argImpl; - const internal::variable_if_dynamicindex<Index, XprType::DiagIndex> m_index; - -private: - EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value() > 0 ? 0 : -m_index.value(); } - EIGEN_STRONG_INLINE Index colOffset() const { return m_index.value() > 0 ? m_index.value() : 0; } -}; - -} // namespace internal - -} // end namespace Eigen - -#endif // EIGEN_COREEVALUATORS_H diff --git a/third_party/eigen3/Eigen/src/Core/CoreIterators.h b/third_party/eigen3/Eigen/src/Core/CoreIterators.h deleted file mode 100644 index 6da4683d2c..0000000000 --- a/third_party/eigen3/Eigen/src/Core/CoreIterators.h +++ /dev/null @@ -1,61 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COREITERATORS_H -#define EIGEN_COREITERATORS_H - -namespace Eigen { - -/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core - */ - -/** \ingroup SparseCore_Module - * \class InnerIterator - * \brief An InnerIterator allows to loop over the element of a sparse (or dense) matrix or expression - * - * todo - */ - -// generic version for dense matrix and expressions -template<typename Derived> class DenseBase<Derived>::InnerIterator -{ - protected: - typedef typename Derived::Scalar Scalar; - typedef typename Derived::Index Index; - - enum { IsRowMajor = (Derived::Flags&RowMajorBit)==RowMajorBit }; - public: - EIGEN_STRONG_INLINE InnerIterator(const Derived& expr, Index outer) - : m_expression(expr), m_inner(0), m_outer(outer), m_end(expr.innerSize()) - {} - - EIGEN_STRONG_INLINE Scalar value() const - { - return (IsRowMajor) ? m_expression.coeff(m_outer, m_inner) - : m_expression.coeff(m_inner, m_outer); - } - - EIGEN_STRONG_INLINE InnerIterator& operator++() { m_inner++; return *this; } - - EIGEN_STRONG_INLINE Index index() const { return m_inner; } - inline Index row() const { return IsRowMajor ? m_outer : index(); } - inline Index col() const { return IsRowMajor ? index() : m_outer; } - - EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; } - - protected: - const Derived& m_expression; - Index m_inner; - const Index m_outer; - const Index m_end; -}; - -} // end namespace Eigen - -#endif // EIGEN_COREITERATORS_H diff --git a/third_party/eigen3/Eigen/src/Core/CwiseBinaryOp.h b/third_party/eigen3/Eigen/src/Core/CwiseBinaryOp.h deleted file mode 100644 index e20daacc8c..0000000000 --- a/third_party/eigen3/Eigen/src/Core/CwiseBinaryOp.h +++ /dev/null @@ -1,238 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CWISE_BINARY_OP_H -#define EIGEN_CWISE_BINARY_OP_H - -namespace Eigen { - -/** \class CwiseBinaryOp - * \ingroup Core_Module - * - * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions - * - * \param BinaryOp template functor implementing the operator - * \param Lhs the type of the left-hand side - * \param Rhs the type of the right-hand side - * - * This class represents an expression where a coefficient-wise binary operator is applied to two expressions. - * It is the return type of binary operators, by which we mean only those binary operators where - * both the left-hand side and the right-hand side are Eigen expressions. - * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp. - * - * Most of the time, this is the only way that it is used, so you typically don't have to name - * CwiseBinaryOp types explicitly. - * - * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp - */ - -namespace internal { -template<typename BinaryOp, typename Lhs, typename Rhs> -struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > -{ - // we must not inherit from traits<Lhs> since it has - // the potential to cause problems with MSVC - typedef typename remove_all<Lhs>::type Ancestor; - typedef typename traits<Ancestor>::XprKind XprKind; - enum { - RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime, - ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime, - MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime, - MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime - }; - - // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor), - // we still want to handle the case when the result type is different. - typedef typename result_of< - BinaryOp( - typename Lhs::Scalar, - typename Rhs::Scalar - ) - >::type Scalar; - typedef typename promote_storage_type<typename traits<Lhs>::StorageKind, - typename traits<Rhs>::StorageKind>::ret StorageKind; - typedef typename promote_index_type<typename traits<Lhs>::Index, - typename traits<Rhs>::Index>::type Index; - typedef typename Lhs::Nested LhsNested; - typedef typename Rhs::Nested RhsNested; - typedef typename remove_reference<LhsNested>::type _LhsNested; - typedef typename remove_reference<RhsNested>::type _RhsNested; - enum { - LhsCoeffReadCost = _LhsNested::CoeffReadCost, - RhsCoeffReadCost = _RhsNested::CoeffReadCost, - LhsFlags = _LhsNested::Flags, - RhsFlags = _RhsNested::Flags, - SameType = is_same<typename _LhsNested::Scalar,typename _RhsNested::Scalar>::value, - StorageOrdersAgree = (int(Lhs::Flags)&RowMajorBit)==(int(Rhs::Flags)&RowMajorBit), - Flags0 = (int(LhsFlags) | int(RhsFlags)) & ( - HereditaryBits - | (int(LhsFlags) & int(RhsFlags) & - ( AlignedBit - | (StorageOrdersAgree ? LinearAccessBit : 0) - | (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0) - ) - ) - ), - Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit), - CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + functor_traits<BinaryOp>::Cost - }; -}; -} // end namespace internal - -// we require Lhs and Rhs to have the same scalar type. Currently there is no example of a binary functor -// that would take two operands of different types. If there were such an example, then this check should be -// moved to the BinaryOp functors, on a per-case basis. This would however require a change in the BinaryOp functors, as -// currently they take only one typename Scalar template parameter. -// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths. -// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to -// add together a float matrix and a double matrix. -#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \ - EIGEN_STATIC_ASSERT((internal::functor_is_product_like<BINOP>::ret \ - ? int(internal::scalar_product_traits<LHS, RHS>::Defined) \ - : int(internal::is_same<LHS, RHS>::value)), \ - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - -template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind> -class CwiseBinaryOpImpl; - -template<typename BinaryOp, typename Lhs, typename Rhs> -class CwiseBinaryOp : internal::no_assignment_operator, - public CwiseBinaryOpImpl< - BinaryOp, Lhs, Rhs, - typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind, - typename internal::traits<Rhs>::StorageKind>::ret> -{ - public: - - typedef typename CwiseBinaryOpImpl< - BinaryOp, Lhs, Rhs, - typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind, - typename internal::traits<Rhs>::StorageKind>::ret>::Base Base; - EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp) - - typedef typename internal::nested<Lhs>::type LhsNested; - typedef typename internal::nested<Rhs>::type RhsNested; - typedef typename internal::remove_reference<LhsNested>::type _LhsNested; - typedef typename internal::remove_reference<RhsNested>::type _RhsNested; - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp()) - : m_lhs(aLhs), m_rhs(aRhs), m_functor(func) - { - EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar); - // require the sizes to match - EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs) - eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols()); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index rows() const { - // return the fixed size type if available to enable compile time optimizations - if (internal::traits<typename internal::remove_all<LhsNested>::type>::RowsAtCompileTime==Dynamic) - return m_rhs.rows(); - else - return m_lhs.rows(); - } - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index cols() const { - // return the fixed size type if available to enable compile time optimizations - if (internal::traits<typename internal::remove_all<LhsNested>::type>::ColsAtCompileTime==Dynamic) - return m_rhs.cols(); - else - return m_lhs.cols(); - } - - /** \returns the left hand side nested expression */ - EIGEN_DEVICE_FUNC - const _LhsNested& lhs() const { return m_lhs; } - /** \returns the right hand side nested expression */ - EIGEN_DEVICE_FUNC - const _RhsNested& rhs() const { return m_rhs; } - /** \returns the functor representing the binary operation */ - EIGEN_DEVICE_FUNC - const BinaryOp& functor() const { return m_functor; } - - protected: - LhsNested m_lhs; - RhsNested m_rhs; - const BinaryOp m_functor; -}; - -template<typename BinaryOp, typename Lhs, typename Rhs> -class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Dense> - : public internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type -{ - typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived; - public: - - typedef typename internal::dense_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE( Derived ) - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const - { - return derived().functor()(derived().lhs().coeff(rowId, colId), - derived().rhs().coeff(rowId, colId)); - } - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const - { - return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(rowId, colId), - derived().rhs().template packet<LoadMode>(rowId, colId)); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar coeff(Index index) const - { - return derived().functor()(derived().lhs().coeff(index), - derived().rhs().coeff(index)); - } - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index index) const - { - return derived().functor().packetOp(derived().lhs().template packet<LoadMode>(index), - derived().rhs().template packet<LoadMode>(index)); - } -}; - -/** replaces \c *this by \c *this - \a other. - * - * \returns a reference to \c *this - */ -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived & -MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other) -{ - SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, OtherDerived> tmp(derived()); - tmp = other.derived(); - return derived(); -} - -/** replaces \c *this by \c *this + \a other. - * - * \returns a reference to \c *this - */ -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived & -MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other) -{ - SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, OtherDerived> tmp(derived()); - tmp = other.derived(); - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_CWISE_BINARY_OP_H - diff --git a/third_party/eigen3/Eigen/src/Core/CwiseNullaryOp.h b/third_party/eigen3/Eigen/src/Core/CwiseNullaryOp.h deleted file mode 100644 index 1243831142..0000000000 --- a/third_party/eigen3/Eigen/src/Core/CwiseNullaryOp.h +++ /dev/null @@ -1,875 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CWISE_NULLARY_OP_H -#define EIGEN_CWISE_NULLARY_OP_H - -namespace Eigen { - -/** \class CwiseNullaryOp - * \ingroup Core_Module - * - * \brief Generic expression of a matrix where all coefficients are defined by a functor - * - * \param NullaryOp template functor implementing the operator - * \param PlainObjectType the underlying plain matrix/array type - * - * This class represents an expression of a generic nullary operator. - * It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods, - * and most of the time this is the only way it is used. - * - * However, if you want to write a function returning such an expression, you - * will need to use this class. - * - * \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr() - */ - -namespace internal { -template<typename NullaryOp, typename PlainObjectType> -struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType> -{ - enum { - Flags = (traits<PlainObjectType>::Flags - & ( HereditaryBits - | (functor_has_linear_access<NullaryOp>::ret ? LinearAccessBit : 0) - | (functor_traits<NullaryOp>::PacketAccess ? PacketAccessBit : 0))) - | (functor_traits<NullaryOp>::IsRepeatable ? 0 : EvalBeforeNestingBit), - CoeffReadCost = functor_traits<NullaryOp>::Cost - }; -}; -} - -template<typename NullaryOp, typename PlainObjectType> -class CwiseNullaryOp : internal::no_assignment_operator, - public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type -{ - public: - - typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp) - - EIGEN_DEVICE_FUNC - CwiseNullaryOp(Index nbRows, Index nbCols, const NullaryOp& func = NullaryOp()) - : m_rows(nbRows), m_cols(nbCols), m_functor(func) - { - eigen_assert(nbRows >= 0 - && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows) - && nbCols >= 0 - && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols)); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index rows() const { return m_rows.value(); } - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index cols() const { return m_cols.value(); } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const - { - return m_functor(rowId, colId); - } - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const - { - return m_functor.packetOp(rowId, colId); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar coeff(Index index) const - { - return m_functor(index); - } - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index index) const - { - return m_functor.packetOp(index); - } - - /** \returns the functor representing the nullary operation */ - EIGEN_DEVICE_FUNC - const NullaryOp& functor() const { return m_functor; } - - protected: - const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows; - const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols; - const NullaryOp m_functor; -}; - - -/** \returns an expression of a matrix defined by a custom functor \a func - * - * The parameters \a rows and \a cols are the number of rows and of columns of - * the returned matrix. Must be compatible with this MatrixBase type. - * - * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, - * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used - * instead. - * - * The template parameter \a CustomNullaryOp is the type of the functor. - * - * \sa class CwiseNullaryOp - */ -template<typename Derived> -template<typename CustomNullaryOp> -EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived> -DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func) -{ - return CwiseNullaryOp<CustomNullaryOp, Derived>(rows, cols, func); -} - -/** \returns an expression of a matrix defined by a custom functor \a func - * - * The parameter \a size is the size of the returned vector. - * Must be compatible with this MatrixBase type. - * - * \only_for_vectors - * - * This variant is meant to be used for dynamic-size vector types. For fixed-size types, - * it is redundant to pass \a size as argument, so Zero() should be used - * instead. - * - * The template parameter \a CustomNullaryOp is the type of the functor. - * - * Here is an example with C++11 random generators: \include random_cpp11.cpp - * Output: \verbinclude random_cpp11.out - * - * \sa class CwiseNullaryOp - */ -template<typename Derived> -template<typename CustomNullaryOp> -EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived> -DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, Derived>(1, size, func); - else return CwiseNullaryOp<CustomNullaryOp, Derived>(size, 1, func); -} - -/** \returns an expression of a matrix defined by a custom functor \a func - * - * This variant is only for fixed-size DenseBase types. For dynamic-size types, you - * need to use the variants taking size arguments. - * - * The template parameter \a CustomNullaryOp is the type of the functor. - * - * \sa class CwiseNullaryOp - */ -template<typename Derived> -template<typename CustomNullaryOp> -EIGEN_STRONG_INLINE const CwiseNullaryOp<CustomNullaryOp, Derived> -DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func) -{ - return CwiseNullaryOp<CustomNullaryOp, Derived>(RowsAtCompileTime, ColsAtCompileTime, func); -} - -/** \returns an expression of a constant matrix of value \a value - * - * The parameters \a nbRows and \a nbCols are the number of rows and of columns of - * the returned matrix. Must be compatible with this DenseBase type. - * - * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, - * it is redundant to pass \a nbRows and \a nbCols as arguments, so Zero() should be used - * instead. - * - * The template parameter \a CustomNullaryOp is the type of the functor. - * - * \sa class CwiseNullaryOp - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Constant(Index nbRows, Index nbCols, const Scalar& value) -{ - return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_constant_op<Scalar>(value)); -} - -/** \returns an expression of a constant matrix of value \a value - * - * The parameter \a size is the size of the returned vector. - * Must be compatible with this DenseBase type. - * - * \only_for_vectors - * - * This variant is meant to be used for dynamic-size vector types. For fixed-size types, - * it is redundant to pass \a size as argument, so Zero() should be used - * instead. - * - * The template parameter \a CustomNullaryOp is the type of the functor. - * - * \sa class CwiseNullaryOp - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Constant(Index size, const Scalar& value) -{ - return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value)); -} - -/** \returns an expression of a constant matrix of value \a value - * - * This variant is only for fixed-size DenseBase types. For dynamic-size types, you - * need to use the variants taking size arguments. - * - * The template parameter \a CustomNullaryOp is the type of the functor. - * - * \sa class CwiseNullaryOp - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Constant(const Scalar& value) -{ - EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) - return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op<Scalar>(value)); -} - -/** - * \brief Sets a linearly space vector. - * - * The function generates 'size' equally spaced values in the closed interval [low,high]. - * This particular version of LinSpaced() uses sequential access, i.e. vector access is - * assumed to be a(0), a(1), ..., a(size). This assumption allows for better vectorization - * and yields faster code than the random access version. - * - * When size is set to 1, a vector of length 1 containing 'high' is returned. - * - * \only_for_vectors - * - * Example: \include DenseBase_LinSpaced_seq.cpp - * Output: \verbinclude DenseBase_LinSpaced_seq.out - * - * \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Index,Scalar,Scalar), CwiseNullaryOp - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturnType -DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,false>(low,high,size)); -} - -/** - * \copydoc DenseBase::LinSpaced(Sequential_t, Index, const Scalar&, const Scalar&) - * Special version for fixed size types which does not require the size parameter. - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::SequentialLinSpacedReturnType -DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) - return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,false>(low,high,Derived::SizeAtCompileTime)); -} - -/** - * \brief Sets a linearly space vector. - * - * The function generates 'size' equally spaced values in the closed interval [low,high]. - * When size is set to 1, a vector of length 1 containing 'high' is returned. - * - * \only_for_vectors - * - * Example: \include DenseBase_LinSpaced.cpp - * Output: \verbinclude DenseBase_LinSpaced.out - * - * \sa setLinSpaced(Index,const Scalar&,const Scalar&), LinSpaced(Sequential_t,Index,const Scalar&,const Scalar&,Index), CwiseNullaryOp - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType -DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar,true>(low,high,size)); -} - -/** - * \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&) - * Special version for fixed size types which does not require the size parameter. - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType -DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) - return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,true>(low,high,Derived::SizeAtCompileTime)); -} - -/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */ -template<typename Derived> -bool DenseBase<Derived>::isApproxToConstant -(const Scalar& val, const RealScalar& prec) const -{ - for(Index j = 0; j < cols(); ++j) - for(Index i = 0; i < rows(); ++i) - if(!internal::isApprox(this->coeff(i, j), val, prec)) - return false; - return true; -} - -/** This is just an alias for isApproxToConstant(). - * - * \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */ -template<typename Derived> -bool DenseBase<Derived>::isConstant -(const Scalar& val, const RealScalar& prec) const -{ - return isApproxToConstant(val, prec); -} - -/** Alias for setConstant(): sets all coefficients in this expression to \a val. - * - * \sa setConstant(), Constant(), class CwiseNullaryOp - */ -template<typename Derived> -EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val) -{ - setConstant(val); -} - -/** Sets all coefficients in this expression to \a value. - * - * \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val) -{ - return derived() = Constant(rows(), cols(), val); -} - -/** Resizes to the given \a size, and sets all coefficients in this expression to the given \a value. - * - * \only_for_vectors - * - * Example: \include Matrix_setConstant_int.cpp - * Output: \verbinclude Matrix_setConstant_int.out - * - * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&) - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val) -{ - resize(size); - return setConstant(val); -} - -/** Resizes to the given size, and sets all coefficients in this expression to the given \a value. - * - * \param nbRows the new number of rows - * \param nbCols the new number of columns - * \param val the value to which all coefficients are set - * - * Example: \include Matrix_setConstant_int_int.cpp - * Output: \verbinclude Matrix_setConstant_int_int.out - * - * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&) - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -PlainObjectBase<Derived>::setConstant(Index nbRows, Index nbCols, const Scalar& val) -{ - resize(nbRows, nbCols); - return setConstant(val); -} - -/** - * \brief Sets a linearly space vector. - * - * The function generates 'size' equally spaced values in the closed interval [low,high]. - * When size is set to 1, a vector of length 1 containing 'high' is returned. - * - * \only_for_vectors - * - * Example: \include DenseBase_setLinSpaced.cpp - * Output: \verbinclude DenseBase_setLinSpaced.out - * - * \sa CwiseNullaryOp - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar,false>(low,high,newSize)); -} - -/** - * \brief Sets a linearly space vector. - * - * The function fill *this with equally spaced values in the closed interval [low,high]. - * When size is set to 1, a vector of length 1 containing 'high' is returned. - * - * \only_for_vectors - * - * \sa setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return setLinSpaced(size(), low, high); -} - -// zero: - -/** \returns an expression of a zero matrix. - * - * The parameters \a rows and \a cols are the number of rows and of columns of - * the returned matrix. Must be compatible with this MatrixBase type. - * - * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, - * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used - * instead. - * - * Example: \include MatrixBase_zero_int_int.cpp - * Output: \verbinclude MatrixBase_zero_int_int.out - * - * \sa Zero(), Zero(Index) - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Zero(Index nbRows, Index nbCols) -{ - return Constant(nbRows, nbCols, Scalar(0)); -} - -/** \returns an expression of a zero vector. - * - * The parameter \a size is the size of the returned vector. - * Must be compatible with this MatrixBase type. - * - * \only_for_vectors - * - * This variant is meant to be used for dynamic-size vector types. For fixed-size types, - * it is redundant to pass \a size as argument, so Zero() should be used - * instead. - * - * Example: \include MatrixBase_zero_int.cpp - * Output: \verbinclude MatrixBase_zero_int.out - * - * \sa Zero(), Zero(Index,Index) - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Zero(Index size) -{ - return Constant(size, Scalar(0)); -} - -/** \returns an expression of a fixed-size zero matrix or vector. - * - * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you - * need to use the variants taking size arguments. - * - * Example: \include MatrixBase_zero.cpp - * Output: \verbinclude MatrixBase_zero.out - * - * \sa Zero(Index), Zero(Index,Index) - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Zero() -{ - return Constant(Scalar(0)); -} - -/** \returns true if *this is approximately equal to the zero matrix, - * within the precision given by \a prec. - * - * Example: \include MatrixBase_isZero.cpp - * Output: \verbinclude MatrixBase_isZero.out - * - * \sa class CwiseNullaryOp, Zero() - */ -template<typename Derived> -bool DenseBase<Derived>::isZero(const RealScalar& prec) const -{ - for(Index j = 0; j < cols(); ++j) - for(Index i = 0; i < rows(); ++i) - if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<Scalar>(1), prec)) - return false; - return true; -} - -/** Sets all coefficients in this expression to zero. - * - * Example: \include MatrixBase_setZero.cpp - * Output: \verbinclude MatrixBase_setZero.out - * - * \sa class CwiseNullaryOp, Zero() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero() -{ - return setConstant(Scalar(0)); -} - -/** Resizes to the given \a size, and sets all coefficients in this expression to zero. - * - * \only_for_vectors - * - * Example: \include Matrix_setZero_int.cpp - * Output: \verbinclude Matrix_setZero_int.out - * - * \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -PlainObjectBase<Derived>::setZero(Index newSize) -{ - resize(newSize); - return setConstant(Scalar(0)); -} - -/** Resizes to the given size, and sets all coefficients in this expression to zero. - * - * \param nbRows the new number of rows - * \param nbCols the new number of columns - * - * Example: \include Matrix_setZero_int_int.cpp - * Output: \verbinclude Matrix_setZero_int_int.out - * - * \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols) -{ - resize(nbRows, nbCols); - return setConstant(Scalar(0)); -} - -// ones: - -/** \returns an expression of a matrix where all coefficients equal one. - * - * The parameters \a nbRows and \a nbCols are the number of rows and of columns of - * the returned matrix. Must be compatible with this MatrixBase type. - * - * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, - * it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used - * instead. - * - * Example: \include MatrixBase_ones_int_int.cpp - * Output: \verbinclude MatrixBase_ones_int_int.out - * - * \sa Ones(), Ones(Index), isOnes(), class Ones - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Ones(Index nbRows, Index nbCols) -{ - return Constant(nbRows, nbCols, Scalar(1)); -} - -/** \returns an expression of a vector where all coefficients equal one. - * - * The parameter \a newSize is the size of the returned vector. - * Must be compatible with this MatrixBase type. - * - * \only_for_vectors - * - * This variant is meant to be used for dynamic-size vector types. For fixed-size types, - * it is redundant to pass \a size as argument, so Ones() should be used - * instead. - * - * Example: \include MatrixBase_ones_int.cpp - * Output: \verbinclude MatrixBase_ones_int.out - * - * \sa Ones(), Ones(Index,Index), isOnes(), class Ones - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Ones(Index newSize) -{ - return Constant(newSize, Scalar(1)); -} - -/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one. - * - * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you - * need to use the variants taking size arguments. - * - * Example: \include MatrixBase_ones.cpp - * Output: \verbinclude MatrixBase_ones.out - * - * \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType -DenseBase<Derived>::Ones() -{ - return Constant(Scalar(1)); -} - -/** \returns true if *this is approximately equal to the matrix where all coefficients - * are equal to 1, within the precision given by \a prec. - * - * Example: \include MatrixBase_isOnes.cpp - * Output: \verbinclude MatrixBase_isOnes.out - * - * \sa class CwiseNullaryOp, Ones() - */ -template<typename Derived> -bool DenseBase<Derived>::isOnes -(const RealScalar& prec) const -{ - return isApproxToConstant(Scalar(1), prec); -} - -/** Sets all coefficients in this expression to one. - * - * Example: \include MatrixBase_setOnes.cpp - * Output: \verbinclude MatrixBase_setOnes.out - * - * \sa class CwiseNullaryOp, Ones() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes() -{ - return setConstant(Scalar(1)); -} - -/** Resizes to the given \a newSize, and sets all coefficients in this expression to one. - * - * \only_for_vectors - * - * Example: \include Matrix_setOnes_int.cpp - * Output: \verbinclude Matrix_setOnes_int.out - * - * \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -PlainObjectBase<Derived>::setOnes(Index newSize) -{ - resize(newSize); - return setConstant(Scalar(1)); -} - -/** Resizes to the given size, and sets all coefficients in this expression to one. - * - * \param nbRows the new number of rows - * \param nbCols the new number of columns - * - * Example: \include Matrix_setOnes_int_int.cpp - * Output: \verbinclude Matrix_setOnes_int_int.out - * - * \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols) -{ - resize(nbRows, nbCols); - return setConstant(Scalar(1)); -} - -// Identity: - -/** \returns an expression of the identity matrix (not necessarily square). - * - * The parameters \a nbRows and \a nbCols are the number of rows and of columns of - * the returned matrix. Must be compatible with this MatrixBase type. - * - * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, - * it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used - * instead. - * - * Example: \include MatrixBase_identity_int_int.cpp - * Output: \verbinclude MatrixBase_identity_int_int.out - * - * \sa Identity(), setIdentity(), isIdentity() - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType -MatrixBase<Derived>::Identity(Index nbRows, Index nbCols) -{ - return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_identity_op<Scalar>()); -} - -/** \returns an expression of the identity matrix (not necessarily square). - * - * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you - * need to use the variant taking size arguments. - * - * Example: \include MatrixBase_identity.cpp - * Output: \verbinclude MatrixBase_identity.out - * - * \sa Identity(Index,Index), setIdentity(), isIdentity() - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType -MatrixBase<Derived>::Identity() -{ - EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) - return MatrixBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op<Scalar>()); -} - -/** \returns true if *this is approximately equal to the identity matrix - * (not necessarily square), - * within the precision given by \a prec. - * - * Example: \include MatrixBase_isIdentity.cpp - * Output: \verbinclude MatrixBase_isIdentity.out - * - * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity() - */ -template<typename Derived> -bool MatrixBase<Derived>::isIdentity -(const RealScalar& prec) const -{ - for(Index j = 0; j < cols(); ++j) - { - for(Index i = 0; i < rows(); ++i) - { - if(i == j) - { - if(!internal::isApprox(this->coeff(i, j), static_cast<Scalar>(1), prec)) - return false; - } - else - { - if(!internal::isMuchSmallerThan(this->coeff(i, j), static_cast<RealScalar>(1), prec)) - return false; - } - } - } - return true; -} - -namespace internal { - -template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)> -struct setIdentity_impl -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Derived& run(Derived& m) - { - return m = Derived::Identity(m.rows(), m.cols()); - } -}; - -template<typename Derived> -struct setIdentity_impl<Derived, true> -{ - typedef typename Derived::Index Index; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Derived& run(Derived& m) - { - m.setZero(); - const Index size = (std::min)(m.rows(), m.cols()); - for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1); - return m; - } -}; - -} // end namespace internal - -/** Writes the identity expression (not necessarily square) into *this. - * - * Example: \include MatrixBase_setIdentity.cpp - * Output: \verbinclude MatrixBase_setIdentity.out - * - * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity() -{ - return internal::setIdentity_impl<Derived>::run(derived()); -} - -/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this. - * - * \param nbRows the new number of rows - * \param nbCols the new number of columns - * - * Example: \include Matrix_setIdentity_int_int.cpp - * Output: \verbinclude Matrix_setIdentity_int_int.out - * - * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index nbRows, Index nbCols) -{ - derived().resize(nbRows, nbCols); - return setIdentity(); -} - -/** \returns an expression of the i-th unit (basis) vector. - * - * \only_for_vectors - * - * \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i); -} - -/** \returns an expression of the i-th unit (basis) vector. - * - * \only_for_vectors - * - * This variant is for fixed-size vector only. - * - * \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return BasisReturnType(SquareMatrixType::Identity(),i); -} - -/** \returns an expression of the X axis unit vector (1{,0}^*) - * - * \only_for_vectors - * - * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX() -{ return Derived::Unit(0); } - -/** \returns an expression of the Y axis unit vector (0,1{,0}^*) - * - * \only_for_vectors - * - * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY() -{ return Derived::Unit(1); } - -/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*) - * - * \only_for_vectors - * - * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ() -{ return Derived::Unit(2); } - -/** \returns an expression of the W axis unit vector (0,0,0,1) - * - * \only_for_vectors - * - * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW() - */ -template<typename Derived> -EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW() -{ return Derived::Unit(3); } - -} // end namespace Eigen - -#endif // EIGEN_CWISE_NULLARY_OP_H diff --git a/third_party/eigen3/Eigen/src/Core/CwiseUnaryOp.h b/third_party/eigen3/Eigen/src/Core/CwiseUnaryOp.h deleted file mode 100644 index aa7df197f9..0000000000 --- a/third_party/eigen3/Eigen/src/Core/CwiseUnaryOp.h +++ /dev/null @@ -1,135 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CWISE_UNARY_OP_H -#define EIGEN_CWISE_UNARY_OP_H - -namespace Eigen { - -/** \class CwiseUnaryOp - * \ingroup Core_Module - * - * \brief Generic expression where a coefficient-wise unary operator is applied to an expression - * - * \param UnaryOp template functor implementing the operator - * \param XprType the type of the expression to which we are applying the unary operator - * - * This class represents an expression where a unary operator is applied to an expression. - * It is the return type of all operations taking exactly 1 input expression, regardless of the - * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix - * is considered unary, because only the right-hand side is an expression, and its - * return type is a specialization of CwiseUnaryOp. - * - * Most of the time, this is the only way that it is used, so you typically don't have to name - * CwiseUnaryOp types explicitly. - * - * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp - */ - -namespace internal { -template<typename UnaryOp, typename XprType> -struct traits<CwiseUnaryOp<UnaryOp, XprType> > - : traits<XprType> -{ - typedef typename result_of< - UnaryOp(typename XprType::Scalar) - >::type Scalar; - typedef typename XprType::Nested XprTypeNested; - typedef typename remove_reference<XprTypeNested>::type _XprTypeNested; - enum { - Flags = _XprTypeNested::Flags & ( - HereditaryBits | LinearAccessBit | AlignedBit - | (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)), - CoeffReadCost = _XprTypeNested::CoeffReadCost + functor_traits<UnaryOp>::Cost - }; -}; -} - -template<typename UnaryOp, typename XprType, typename StorageKind> -class CwiseUnaryOpImpl; - -template<typename UnaryOp, typename XprType> -class CwiseUnaryOp : internal::no_assignment_operator, - public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind> -{ - public: - - typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base; - EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp) - - EIGEN_DEVICE_FUNC - inline CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp()) - : m_xpr(xpr), m_functor(func) {} - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index rows() const { return m_xpr.rows(); } - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index cols() const { return m_xpr.cols(); } - - /** \returns the functor representing the unary operation */ - EIGEN_DEVICE_FUNC - const UnaryOp& functor() const { return m_functor; } - - /** \returns the nested expression */ - EIGEN_DEVICE_FUNC - const typename internal::remove_all<typename XprType::Nested>::type& - nestedExpression() const { return m_xpr; } - - /** \returns the nested expression */ - EIGEN_DEVICE_FUNC - typename internal::remove_all<typename XprType::Nested>::type& - nestedExpression() { return m_xpr.const_cast_derived(); } - - protected: - typename XprType::Nested m_xpr; - const UnaryOp m_functor; -}; - -// This is the generic implementation for dense storage. -// It can be used for any expression types implementing the dense concept. -template<typename UnaryOp, typename XprType> -class CwiseUnaryOpImpl<UnaryOp,XprType,Dense> - : public internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type -{ - public: - - typedef CwiseUnaryOp<UnaryOp, XprType> Derived; - typedef typename internal::dense_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Derived) - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar coeff(Index rowId, Index colId) const - { - return derived().functor()(derived().nestedExpression().coeff(rowId, colId)); - } - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const - { - return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(rowId, colId)); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar coeff(Index index) const - { - return derived().functor()(derived().nestedExpression().coeff(index)); - } - - template<int LoadMode> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE PacketScalar packet(Index index) const - { - return derived().functor().packetOp(derived().nestedExpression().template packet<LoadMode>(index)); - } -}; - -} // end namespace Eigen - -#endif // EIGEN_CWISE_UNARY_OP_H diff --git a/third_party/eigen3/Eigen/src/Core/CwiseUnaryView.h b/third_party/eigen3/Eigen/src/Core/CwiseUnaryView.h deleted file mode 100644 index b2638d3265..0000000000 --- a/third_party/eigen3/Eigen/src/Core/CwiseUnaryView.h +++ /dev/null @@ -1,139 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CWISE_UNARY_VIEW_H -#define EIGEN_CWISE_UNARY_VIEW_H - -namespace Eigen { - -/** \class CwiseUnaryView - * \ingroup Core_Module - * - * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector - * - * \param ViewOp template functor implementing the view - * \param MatrixType the type of the matrix we are applying the unary operator - * - * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector. - * It is the return type of real() and imag(), and most of the time this is the only way it is used. - * - * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp - */ - -namespace internal { -template<typename ViewOp, typename MatrixType> -struct traits<CwiseUnaryView<ViewOp, MatrixType> > - : traits<MatrixType> -{ - typedef typename result_of< - ViewOp(typename traits<MatrixType>::Scalar) - >::type Scalar; - typedef typename MatrixType::Nested MatrixTypeNested; - typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested; - enum { - Flags = (traits<_MatrixTypeNested>::Flags & (HereditaryBits | LvalueBit | LinearAccessBit | DirectAccessBit)), - CoeffReadCost = traits<_MatrixTypeNested>::CoeffReadCost + functor_traits<ViewOp>::Cost, - MatrixTypeInnerStride = inner_stride_at_compile_time<MatrixType>::ret, - // need to cast the sizeof's from size_t to int explicitly, otherwise: - // "error: no integral type can represent all of the enumerator values - InnerStrideAtCompileTime = MatrixTypeInnerStride == Dynamic - ? int(Dynamic) - : int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)), - OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret == Dynamic - ? int(Dynamic) - : outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)) - }; -}; -} - -template<typename ViewOp, typename MatrixType, typename StorageKind> -class CwiseUnaryViewImpl; - -template<typename ViewOp, typename MatrixType> -class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind> -{ - public: - - typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base; - EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView) - - inline CwiseUnaryView(const MatrixType& mat, const ViewOp& func = ViewOp()) - : m_matrix(mat), m_functor(func) {} - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView) - - EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); } - EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); } - - /** \returns the functor representing unary operation */ - const ViewOp& functor() const { return m_functor; } - - /** \returns the nested expression */ - const typename internal::remove_all<typename MatrixType::Nested>::type& - nestedExpression() const { return m_matrix; } - - /** \returns the nested expression */ - typename internal::remove_all<typename MatrixType::Nested>::type& - nestedExpression() { return m_matrix.const_cast_derived(); } - - protected: - // FIXME changed from MatrixType::Nested because of a weird compilation error with sun CC - typename internal::nested<MatrixType>::type m_matrix; - ViewOp m_functor; -}; - -template<typename ViewOp, typename MatrixType> -class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense> - : public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type -{ - public: - - typedef CwiseUnaryView<ViewOp, MatrixType> Derived; - typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base; - - EIGEN_DENSE_PUBLIC_INTERFACE(Derived) - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl) - - inline Scalar* data() { return &coeffRef(0); } - inline const Scalar* data() const { return &coeff(0); } - - inline Index innerStride() const - { - return derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar); - } - - inline Index outerStride() const - { - return derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar); - } - - EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const - { - return derived().functor()(derived().nestedExpression().coeff(row, col)); - } - - EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const - { - return derived().functor()(derived().nestedExpression().coeff(index)); - } - - EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) - { - return derived().functor()(const_cast_derived().nestedExpression().coeffRef(row, col)); - } - - EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) - { - return derived().functor()(const_cast_derived().nestedExpression().coeffRef(index)); - } -}; - -} // end namespace Eigen - -#endif // EIGEN_CWISE_UNARY_VIEW_H diff --git a/third_party/eigen3/Eigen/src/Core/DenseBase.h b/third_party/eigen3/Eigen/src/Core/DenseBase.h deleted file mode 100644 index 55cec0bc26..0000000000 --- a/third_party/eigen3/Eigen/src/Core/DenseBase.h +++ /dev/null @@ -1,561 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_DENSEBASE_H -#define EIGEN_DENSEBASE_H - -namespace Eigen { - -namespace internal { - -// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type. -// This dummy function simply aims at checking that at compile time. -static inline void check_DenseIndex_is_signed() { - EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE); -} - -} // end namespace internal - -/** \class DenseBase - * \ingroup Core_Module - * - * \brief Base class for all dense matrices, vectors, and arrays - * - * This class is the base that is inherited by all dense objects (matrix, vector, arrays, - * and related expression types). The common Eigen API for dense objects is contained in this class. - * - * \tparam Derived is the derived type, e.g., a matrix type or an expression. - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN. - * - * \sa \ref TopicClassHierarchy - */ -template<typename Derived> class DenseBase -#ifndef EIGEN_PARSED_BY_DOXYGEN - : public internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar, - typename NumTraits<typename internal::traits<Derived>::Scalar>::Real> -#else - : public DenseCoeffsBase<Derived> -#endif // not EIGEN_PARSED_BY_DOXYGEN -{ - public: - using internal::special_scalar_op_base<Derived,typename internal::traits<Derived>::Scalar, - typename NumTraits<typename internal::traits<Derived>::Scalar>::Real>::operator*; - - class InnerIterator; - - typedef typename internal::traits<Derived>::StorageKind StorageKind; - - /** \brief The type of indices - * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE. - * \sa \ref TopicPreprocessorDirectives. - */ - typedef typename internal::traits<Derived>::Index Index; - - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::packet_traits<Scalar>::type PacketScalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - typedef DenseCoeffsBase<Derived> Base; - using Base::derived; - using Base::const_cast_derived; - using Base::rows; - using Base::cols; - using Base::size; - using Base::rowIndexByOuterInner; - using Base::colIndexByOuterInner; - using Base::coeff; - using Base::coeffByOuterInner; - using Base::packet; - using Base::packetByOuterInner; - using Base::writePacket; - using Base::writePacketByOuterInner; - using Base::coeffRef; - using Base::coeffRefByOuterInner; - using Base::copyCoeff; - using Base::copyCoeffByOuterInner; - using Base::copyPacket; - using Base::copyPacketByOuterInner; - using Base::operator(); - using Base::operator[]; - using Base::x; - using Base::y; - using Base::z; - using Base::w; - using Base::stride; - using Base::innerStride; - using Base::outerStride; - using Base::rowStride; - using Base::colStride; - typedef typename Base::CoeffReturnType CoeffReturnType; - - enum { - - RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime, - /**< The number of rows at compile-time. This is just a copy of the value provided - * by the \a Derived type. If a value is not known at compile-time, - * it is set to the \a Dynamic constant. - * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */ - - ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime, - /**< The number of columns at compile-time. This is just a copy of the value provided - * by the \a Derived type. If a value is not known at compile-time, - * it is set to the \a Dynamic constant. - * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */ - - - SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime, - internal::traits<Derived>::ColsAtCompileTime>::ret), - /**< This is equal to the number of coefficients, i.e. the number of - * rows times the number of columns, or to \a Dynamic if this is not - * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */ - - MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime, - /**< This value is equal to the maximum possible number of rows that this expression - * might have. If this expression might have an arbitrarily high number of rows, - * this value is set to \a Dynamic. - * - * This value is useful to know when evaluating an expression, in order to determine - * whether it is possible to avoid doing a dynamic memory allocation. - * - * \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime - */ - - MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime, - /**< This value is equal to the maximum possible number of columns that this expression - * might have. If this expression might have an arbitrarily high number of columns, - * this value is set to \a Dynamic. - * - * This value is useful to know when evaluating an expression, in order to determine - * whether it is possible to avoid doing a dynamic memory allocation. - * - * \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime - */ - - MaxSizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::MaxRowsAtCompileTime, - internal::traits<Derived>::MaxColsAtCompileTime>::ret), - /**< This value is equal to the maximum possible number of coefficients that this expression - * might have. If this expression might have an arbitrarily high number of coefficients, - * this value is set to \a Dynamic. - * - * This value is useful to know when evaluating an expression, in order to determine - * whether it is possible to avoid doing a dynamic memory allocation. - * - * \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime - */ - - IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1 - || internal::traits<Derived>::MaxColsAtCompileTime == 1, - /**< This is set to true if either the number of rows or the number of - * columns is known at compile-time to be equal to 1. Indeed, in that case, - * we are dealing with a column-vector (if there is only one column) or with - * a row-vector (if there is only one row). */ - - Flags = internal::traits<Derived>::Flags, - /**< This stores expression \ref flags flags which may or may not be inherited by new expressions - * constructed from this one. See the \ref flags "list of flags". - */ - - IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */ - - InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime) - : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime), - - CoeffReadCost = internal::traits<Derived>::CoeffReadCost, - /**< This is a rough measure of how expensive it is to read one coefficient from - * this expression. - */ - - InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret, - OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret - }; - - enum { ThisConstantIsPrivateInPlainObjectBase }; - - /** \returns the number of nonzero coefficients which is in practice the number - * of stored coefficients. */ - EIGEN_DEVICE_FUNC - inline Index nonZeros() const { return size(); } - /** \returns true if either the number of rows or the number of columns is equal to 1. - * In other words, this function returns - * \code rows()==1 || cols()==1 \endcode - * \sa rows(), cols(), IsVectorAtCompileTime. */ - - /** \returns the outer size. - * - * \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension - * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a - * column-major matrix, and the number of rows for a row-major matrix. */ - EIGEN_DEVICE_FUNC - Index outerSize() const - { - return IsVectorAtCompileTime ? 1 - : int(IsRowMajor) ? this->rows() : this->cols(); - } - - /** \returns the inner size. - * - * \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension - * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a - * column-major matrix, and the number of columns for a row-major matrix. */ - EIGEN_DEVICE_FUNC - Index innerSize() const - { - return IsVectorAtCompileTime ? this->size() - : int(IsRowMajor) ? this->cols() : this->rows(); - } - - /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are - * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does - * nothing else. - */ - EIGEN_DEVICE_FUNC - void resize(Index newSize) - { - EIGEN_ONLY_USED_FOR_DEBUG(newSize); - eigen_assert(newSize == this->size() - && "DenseBase::resize() does not actually allow to resize."); - } - /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are - * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does - * nothing else. - */ - EIGEN_DEVICE_FUNC - void resize(Index nbRows, Index nbCols) - { - EIGEN_ONLY_USED_FOR_DEBUG(nbRows); - EIGEN_ONLY_USED_FOR_DEBUG(nbCols); - eigen_assert(nbRows == this->rows() && nbCols == this->cols() - && "DenseBase::resize() does not actually allow to resize."); - } - -#ifndef EIGEN_PARSED_BY_DOXYGEN - - /** \internal Represents a matrix with all coefficients equal to one another*/ - typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType; - /** \internal Represents a vector with linearly spaced coefficients that allows sequential access only. */ - typedef CwiseNullaryOp<internal::linspaced_op<Scalar,false>,Derived> SequentialLinSpacedReturnType; - /** \internal Represents a vector with linearly spaced coefficients that allows random access. */ - typedef CwiseNullaryOp<internal::linspaced_op<Scalar,true>,Derived> RandomAccessLinSpacedReturnType; - /** \internal the return type of MatrixBase::eigenvalues() */ - typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType; - -#endif // not EIGEN_PARSED_BY_DOXYGEN - - /** Copies \a other into *this. \returns a reference to *this. */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator=(const DenseBase<OtherDerived>& other); - - /** Special case of the template operator=, in order to prevent the compiler - * from generating a default operator= (issue hit with g++ 4.1) - */ - EIGEN_DEVICE_FUNC - Derived& operator=(const DenseBase& other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator=(const EigenBase<OtherDerived> &other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator+=(const EigenBase<OtherDerived> &other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator-=(const EigenBase<OtherDerived> &other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator=(const ReturnByValue<OtherDerived>& func); - -#ifndef EIGEN_PARSED_BY_DOXYGEN - /** Copies \a other into *this without evaluating other. \returns a reference to *this. */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& lazyAssign(const DenseBase<OtherDerived>& other); -#endif // not EIGEN_PARSED_BY_DOXYGEN - - EIGEN_DEVICE_FUNC - CommaInitializer<Derived> operator<< (const Scalar& s); - - template<unsigned int Added,unsigned int Removed> - const Flagged<Derived, Added, Removed> flagged() const; - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other); - - EIGEN_DEVICE_FUNC - Eigen::Transpose<Derived> transpose(); - typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType; - EIGEN_DEVICE_FUNC - ConstTransposeReturnType transpose() const; - EIGEN_DEVICE_FUNC - void transposeInPlace(); -#ifndef EIGEN_NO_DEBUG - protected: - template<typename OtherDerived> - void checkTransposeAliasing(const OtherDerived& other) const; - public: -#endif - - - EIGEN_DEVICE_FUNC static const ConstantReturnType - Constant(Index rows, Index cols, const Scalar& value); - EIGEN_DEVICE_FUNC static const ConstantReturnType - Constant(Index size, const Scalar& value); - EIGEN_DEVICE_FUNC static const ConstantReturnType - Constant(const Scalar& value); - - EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType - LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high); - EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType - LinSpaced(Index size, const Scalar& low, const Scalar& high); - EIGEN_DEVICE_FUNC static const SequentialLinSpacedReturnType - LinSpaced(Sequential_t, const Scalar& low, const Scalar& high); - EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType - LinSpaced(const Scalar& low, const Scalar& high); - - template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC - static const CwiseNullaryOp<CustomNullaryOp, Derived> - NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func); - template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC - static const CwiseNullaryOp<CustomNullaryOp, Derived> - NullaryExpr(Index size, const CustomNullaryOp& func); - template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC - static const CwiseNullaryOp<CustomNullaryOp, Derived> - NullaryExpr(const CustomNullaryOp& func); - - EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols); - EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size); - EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(); - EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols); - EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size); - EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(); - - EIGEN_DEVICE_FUNC void fill(const Scalar& value); - EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value); - EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high); - EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high); - EIGEN_DEVICE_FUNC Derived& setZero(); - EIGEN_DEVICE_FUNC Derived& setOnes(); - EIGEN_DEVICE_FUNC Derived& setRandom(); - - template<typename OtherDerived> EIGEN_DEVICE_FUNC - bool isApprox(const DenseBase<OtherDerived>& other, - const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - EIGEN_DEVICE_FUNC - bool isMuchSmallerThan(const RealScalar& other, - const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - template<typename OtherDerived> EIGEN_DEVICE_FUNC - bool isMuchSmallerThan(const DenseBase<OtherDerived>& other, - const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - - EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - - inline bool hasNaN() const; - inline bool allFinite() const; - - EIGEN_DEVICE_FUNC - inline Derived& operator*=(const Scalar& other); - EIGEN_DEVICE_FUNC - inline Derived& operator/=(const Scalar& other); - - typedef typename internal::add_const_on_value_type<typename internal::eval<Derived>::type>::type EvalReturnType; - /** \returns the matrix or vector obtained by evaluating this expression. - * - * Notice that in the case of a plain matrix or vector (not an expression) this function just returns - * a const reference, in order to avoid a useless copy. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE EvalReturnType eval() const - { - // Even though MSVC does not honor strong inlining when the return type - // is a dynamic matrix, we desperately need strong inlining for fixed - // size types on MSVC. - return typename internal::eval<Derived>::type(derived()); - } - - /** swaps *this with the expression \a other. - * - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void swap(const DenseBase<OtherDerived>& other, - int = OtherDerived::ThisConstantIsPrivateInPlainObjectBase) - { - SwapWrapper<Derived>(derived()).lazyAssign(other.derived()); - } - - /** swaps *this with the matrix or array \a other. - * - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void swap(PlainObjectBase<OtherDerived>& other) - { - SwapWrapper<Derived>(derived()).lazyAssign(other.derived()); - } - - - EIGEN_DEVICE_FUNC inline const NestByValue<Derived> nestByValue() const; - EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const; - EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess(); - template<bool Enable> EIGEN_DEVICE_FUNC - inline const typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf() const; - template<bool Enable> EIGEN_DEVICE_FUNC - inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf(); - - EIGEN_DEVICE_FUNC Scalar sum() const; - EIGEN_DEVICE_FUNC Scalar mean() const; - EIGEN_DEVICE_FUNC Scalar trace() const; - - EIGEN_DEVICE_FUNC Scalar prod() const; - - EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const; - EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const; - - template<typename IndexType> EIGEN_DEVICE_FUNC - typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const; - template<typename IndexType> EIGEN_DEVICE_FUNC - typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const; - template<typename IndexType> EIGEN_DEVICE_FUNC - typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const; - template<typename IndexType> EIGEN_DEVICE_FUNC - typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const; - - template<typename BinaryOp> - EIGEN_DEVICE_FUNC - typename internal::result_of<BinaryOp(typename internal::traits<Derived>::Scalar)>::type - redux(const BinaryOp& func) const; - - template<typename Visitor> - EIGEN_DEVICE_FUNC - void visit(Visitor& func) const; - - inline const WithFormat<Derived> format(const IOFormat& fmt) const; - - /** \returns the unique coefficient of a 1x1 expression */ - EIGEN_DEVICE_FUNC - CoeffReturnType value() const - { - EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) - eigen_assert(this->rows() == 1 && this->cols() == 1); - return derived().coeff(0,0); - } - - bool all() const; - bool any() const; - Index count() const; - - typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType; - typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType; - typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType; - typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType; - - ConstRowwiseReturnType rowwise() const; - RowwiseReturnType rowwise(); - ConstColwiseReturnType colwise() const; - ColwiseReturnType colwise(); - - static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(Index rows, Index cols); - static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(Index size); - static const CwiseNullaryOp<internal::scalar_random_op<Scalar>,Derived> Random(); - - template<typename ThenDerived,typename ElseDerived> - const Select<Derived,ThenDerived,ElseDerived> - select(const DenseBase<ThenDerived>& thenMatrix, - const DenseBase<ElseDerived>& elseMatrix) const; - - template<typename ThenDerived> - inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType> - select(const DenseBase<ThenDerived>& thenMatrix, const typename ThenDerived::Scalar& elseScalar) const; - - template<typename ElseDerived> - inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived > - select(const typename ElseDerived::Scalar& thenScalar, const DenseBase<ElseDerived>& elseMatrix) const; - - template<int p> RealScalar lpNorm() const; - - template<int RowFactor, int ColFactor> - const Replicate<Derived,RowFactor,ColFactor> replicate() const; - const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const; - - typedef Reverse<Derived, BothDirections> ReverseReturnType; - typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType; - ReverseReturnType reverse(); - ConstReverseReturnType reverse() const; - void reverseInPlace(); - -#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase -# include "../plugins/BlockMethods.h" -# ifdef EIGEN_DENSEBASE_PLUGIN -# include EIGEN_DENSEBASE_PLUGIN -# endif -// Because of an intra-Google include scanner limitation, -// third_party/stan cannot define the EIGEN_DENSEBASE_PLUGIN -// macro -// as "stan/math/matrix/EigenDenseBaseAddons.hpp". According to -// ambrose@google.com, this is a known limitation: the include -// scanner doesn't maintain any preprocessor state about macros, -// previously visited files, etc. See also //base/stacktrace.cc. -# ifdef STAN_MATH_MATRIX_EIGEN_DENSEBASE_PLUGIN -# include "stan/math/matrix/EigenDenseBaseAddons.hpp" -# endif -#undef EIGEN_CURRENT_STORAGE_BASE_CLASS - -#ifdef EIGEN2_SUPPORT - - Block<Derived> corner(CornerType type, Index cRows, Index cCols); - const Block<Derived> corner(CornerType type, Index cRows, Index cCols) const; - template<int CRows, int CCols> - Block<Derived, CRows, CCols> corner(CornerType type); - template<int CRows, int CCols> - const Block<Derived, CRows, CCols> corner(CornerType type) const; - -#endif // EIGEN2_SUPPORT - - - // disable the use of evalTo for dense objects with a nice compilation error - template<typename Dest> - EIGEN_DEVICE_FUNC - inline void evalTo(Dest& ) const - { - EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS); - } - - protected: - /** Default constructor. Do nothing. */ - EIGEN_DEVICE_FUNC DenseBase() - { - /* Just checks for self-consistency of the flags. - * Only do it when debugging Eigen, as this borders on paranoiac and could slow compilation down - */ -#ifdef EIGEN_INTERNAL_DEBUGGING - EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor)) - && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))), - INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION) -#endif - } - - private: - EIGEN_DEVICE_FUNC explicit DenseBase(int); - EIGEN_DEVICE_FUNC DenseBase(int,int); - template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&); -}; - -} // end namespace Eigen - -#endif // EIGEN_DENSEBASE_H diff --git a/third_party/eigen3/Eigen/src/Core/DenseCoeffsBase.h b/third_party/eigen3/Eigen/src/Core/DenseCoeffsBase.h deleted file mode 100644 index efabb5e675..0000000000 --- a/third_party/eigen3/Eigen/src/Core/DenseCoeffsBase.h +++ /dev/null @@ -1,787 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_DENSECOEFFSBASE_H -#define EIGEN_DENSECOEFFSBASE_H - -namespace Eigen { - -namespace internal { -template<typename T> struct add_const_on_value_type_if_arithmetic -{ - typedef typename conditional<is_arithmetic<T>::value, T, typename add_const_on_value_type<T>::type>::type type; -}; -} - -/** \brief Base class providing read-only coefficient access to matrices and arrays. - * \ingroup Core_Module - * \tparam Derived Type of the derived class - * \tparam #ReadOnlyAccessors Constant indicating read-only access - * - * This class defines the \c operator() \c const function and friends, which can be used to read specific - * entries of a matrix or array. - * - * \sa DenseCoeffsBase<Derived, WriteAccessors>, DenseCoeffsBase<Derived, DirectAccessors>, - * \ref TopicClassHierarchy - */ -template<typename Derived> -class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived> -{ - public: - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::packet_traits<Scalar>::type PacketScalar; - - // Explanation for this CoeffReturnType typedef. - // - This is the return type of the coeff() method. - // - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references - // to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value). - // - The is_artihmetic check is required since "const int", "const double", etc. will cause warnings on some systems - // while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is - // not possible, since the underlying expressions might not offer a valid address the reference could be referring to. - typedef typename internal::conditional<bool(internal::traits<Derived>::Flags&LvalueBit), - const Scalar&, - typename internal::conditional<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>::type - >::type CoeffReturnType; - - typedef typename internal::add_const_on_value_type_if_arithmetic< - typename internal::packet_traits<Scalar>::type - >::type PacketReturnType; - - typedef EigenBase<Derived> Base; - using Base::rows; - using Base::cols; - using Base::size; - using Base::derived; - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const - { - return int(Derived::RowsAtCompileTime) == 1 ? 0 - : int(Derived::ColsAtCompileTime) == 1 ? inner - : int(Derived::Flags)&RowMajorBit ? outer - : inner; - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const - { - return int(Derived::ColsAtCompileTime) == 1 ? 0 - : int(Derived::RowsAtCompileTime) == 1 ? inner - : int(Derived::Flags)&RowMajorBit ? inner - : outer; - } - - /** Short version: don't use this function, use - * \link operator()(Index,Index) const \endlink instead. - * - * Long version: this function is similar to - * \link operator()(Index,Index) const \endlink, but without the assertion. - * Use this for limiting the performance cost of debugging code when doing - * repeated coefficient access. Only use this when it is guaranteed that the - * parameters \a row and \a col are in range. - * - * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this - * function equivalent to \link operator()(Index,Index) const \endlink. - * - * \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const - { - eigen_internal_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - return derived().coeff(row, col); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const - { - return coeff(rowIndexByOuterInner(outer, inner), - colIndexByOuterInner(outer, inner)); - } - - /** \returns the coefficient at given the given row and column. - * - * \sa operator()(Index,Index), operator[](Index) - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const - { - eigen_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - return derived().coeff(row, col); - } - - /** Short version: don't use this function, use - * \link operator[](Index) const \endlink instead. - * - * Long version: this function is similar to - * \link operator[](Index) const \endlink, but without the assertion. - * Use this for limiting the performance cost of debugging code when doing - * repeated coefficient access. Only use this when it is guaranteed that the - * parameter \a index is in range. - * - * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this - * function equivalent to \link operator[](Index) const \endlink. - * - * \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const - */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType - coeff(Index index) const - { - eigen_internal_assert(index >= 0 && index < size()); - return derived().coeff(index); - } - - - /** \returns the coefficient at given index. - * - * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. - * - * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const, - * z() const, w() const - */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType - operator[](Index index) const - { - #ifndef EIGEN2_SUPPORT - EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime, - THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD) - #endif - eigen_assert(index >= 0 && index < size()); - return derived().coeff(index); - } - - /** \returns the coefficient at given index. - * - * This is synonymous to operator[](Index) const. - * - * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. - * - * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const, - * z() const, w() const - */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType - operator()(Index index) const - { - eigen_assert(index >= 0 && index < size()); - return derived().coeff(index); - } - - /** equivalent to operator[](0). */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType - x() const { return (*this)[0]; } - - /** equivalent to operator[](1). */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType - y() const { return (*this)[1]; } - - /** equivalent to operator[](2). */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType - z() const { return (*this)[2]; } - - /** equivalent to operator[](3). */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE CoeffReturnType - w() const { return (*this)[3]; } - - /** \internal - * \returns the packet of coefficients starting at the given row and column. It is your responsibility - * to ensure that a packet really starts there. This method is only available on expressions having the - * PacketAccessBit. - * - * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select - * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets - * starting at an address which is a multiple of the packet size. - */ - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const - { - eigen_internal_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - return derived().template packet<LoadMode>(row,col); - } - - - /** \internal */ - template<int LoadMode> - EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const - { - return packet<LoadMode>(rowIndexByOuterInner(outer, inner), - colIndexByOuterInner(outer, inner)); - } - - /** \internal - * \returns the packet of coefficients starting at the given index. It is your responsibility - * to ensure that a packet really starts there. This method is only available on expressions having the - * PacketAccessBit and the LinearAccessBit. - * - * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select - * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets - * starting at an address which is a multiple of the packet size. - */ - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const - { - eigen_internal_assert(index >= 0 && index < size()); - return derived().template packet<LoadMode>(index); - } - - protected: - // explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase. - // But some methods are only available in the DirectAccess case. - // So we add dummy methods here with these names, so that "using... " doesn't fail. - // It's not private so that the child class DenseBase can access them, and it's not public - // either since it's an implementation detail, so has to be protected. - void coeffRef(); - void coeffRefByOuterInner(); - void writePacket(); - void writePacketByOuterInner(); - void copyCoeff(); - void copyCoeffByOuterInner(); - void copyPacket(); - void copyPacketByOuterInner(); - void stride(); - void innerStride(); - void outerStride(); - void rowStride(); - void colStride(); -}; - -/** \brief Base class providing read/write coefficient access to matrices and arrays. - * \ingroup Core_Module - * \tparam Derived Type of the derived class - * \tparam #WriteAccessors Constant indicating read/write access - * - * This class defines the non-const \c operator() function and friends, which can be used to write specific - * entries of a matrix or array. This class inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which - * defines the const variant for reading specific entries. - * - * \sa DenseCoeffsBase<Derived, DirectAccessors>, \ref TopicClassHierarchy - */ -template<typename Derived> -class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors> -{ - public: - - typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base; - - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::packet_traits<Scalar>::type PacketScalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - using Base::coeff; - using Base::rows; - using Base::cols; - using Base::size; - using Base::derived; - using Base::rowIndexByOuterInner; - using Base::colIndexByOuterInner; - using Base::operator[]; - using Base::operator(); - using Base::x; - using Base::y; - using Base::z; - using Base::w; - - /** Short version: don't use this function, use - * \link operator()(Index,Index) \endlink instead. - * - * Long version: this function is similar to - * \link operator()(Index,Index) \endlink, but without the assertion. - * Use this for limiting the performance cost of debugging code when doing - * repeated coefficient access. Only use this when it is guaranteed that the - * parameters \a row and \a col are in range. - * - * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this - * function equivalent to \link operator()(Index,Index) \endlink. - * - * \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index) - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col) - { - eigen_internal_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - return derived().coeffRef(row, col); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - coeffRefByOuterInner(Index outer, Index inner) - { - return coeffRef(rowIndexByOuterInner(outer, inner), - colIndexByOuterInner(outer, inner)); - } - - /** \returns a reference to the coefficient at given the given row and column. - * - * \sa operator[](Index) - */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - operator()(Index row, Index col) - { - eigen_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - return derived().coeffRef(row, col); - } - - - /** Short version: don't use this function, use - * \link operator[](Index) \endlink instead. - * - * Long version: this function is similar to - * \link operator[](Index) \endlink, but without the assertion. - * Use this for limiting the performance cost of debugging code when doing - * repeated coefficient access. Only use this when it is guaranteed that the - * parameters \a row and \a col are in range. - * - * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this - * function equivalent to \link operator[](Index) \endlink. - * - * \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index) - */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - coeffRef(Index index) - { - eigen_internal_assert(index >= 0 && index < size()); - return derived().coeffRef(index); - } - - /** \returns a reference to the coefficient at given index. - * - * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. - * - * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w() - */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - operator[](Index index) - { - #ifndef EIGEN2_SUPPORT - EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime, - THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD) - #endif - eigen_assert(index >= 0 && index < size()); - return derived().coeffRef(index); - } - - /** \returns a reference to the coefficient at given index. - * - * This is synonymous to operator[](Index). - * - * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit. - * - * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w() - */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - operator()(Index index) - { - eigen_assert(index >= 0 && index < size()); - return derived().coeffRef(index); - } - - /** equivalent to operator[](0). */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - x() { return (*this)[0]; } - - /** equivalent to operator[](1). */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - y() { return (*this)[1]; } - - /** equivalent to operator[](2). */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - z() { return (*this)[2]; } - - /** equivalent to operator[](3). */ - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& - w() { return (*this)[3]; } - - /** \internal - * Stores the given packet of coefficients, at the given row and column of this expression. It is your responsibility - * to ensure that a packet really starts there. This method is only available on expressions having the - * PacketAccessBit. - * - * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select - * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets - * starting at an address which is a multiple of the packet size. - */ - - template<int StoreMode> - EIGEN_STRONG_INLINE void writePacket - (Index row, Index col, const typename internal::packet_traits<Scalar>::type& val) - { - eigen_internal_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - derived().template writePacket<StoreMode>(row,col,val); - } - - - /** \internal */ - template<int StoreMode> - EIGEN_STRONG_INLINE void writePacketByOuterInner - (Index outer, Index inner, const typename internal::packet_traits<Scalar>::type& val) - { - writePacket<StoreMode>(rowIndexByOuterInner(outer, inner), - colIndexByOuterInner(outer, inner), - val); - } - - /** \internal - * Stores the given packet of coefficients, at the given index in this expression. It is your responsibility - * to ensure that a packet really starts there. This method is only available on expressions having the - * PacketAccessBit and the LinearAccessBit. - * - * The \a LoadMode parameter may have the value \a Aligned or \a Unaligned. Its effect is to select - * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets - * starting at an address which is a multiple of the packet size. - */ - template<int StoreMode> - EIGEN_STRONG_INLINE void writePacket - (Index index, const typename internal::packet_traits<Scalar>::type& val) - { - eigen_internal_assert(index >= 0 && index < size()); - derived().template writePacket<StoreMode>(index,val); - } - -#ifndef EIGEN_PARSED_BY_DOXYGEN - - /** \internal Copies the coefficient at position (row,col) of other into *this. - * - * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code - * with usual assignments. - * - * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox. - */ - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other) - { - eigen_internal_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - derived().coeffRef(row, col) = other.derived().coeff(row, col); - } - - /** \internal Copies the coefficient at the given index of other into *this. - * - * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code - * with usual assignments. - * - * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox. - */ - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void copyCoeff(Index index, const DenseBase<OtherDerived>& other) - { - eigen_internal_assert(index >= 0 && index < size()); - derived().coeffRef(index) = other.derived().coeff(index); - } - - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void copyCoeffByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other) - { - const Index row = rowIndexByOuterInner(outer,inner); - const Index col = colIndexByOuterInner(outer,inner); - // derived() is important here: copyCoeff() may be reimplemented in Derived! - derived().copyCoeff(row, col, other); - } - - /** \internal Copies the packet at position (row,col) of other into *this. - * - * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code - * with usual assignments. - * - * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox. - */ - - template<typename OtherDerived, int StoreMode, int LoadMode> - EIGEN_STRONG_INLINE void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other) - { - eigen_internal_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - derived().template writePacket<StoreMode>(row, col, - other.derived().template packet<LoadMode>(row, col)); - } - - /** \internal Copies the packet at the given index of other into *this. - * - * This method is overridden in SwapWrapper, allowing swap() assignments to share 99% of their code - * with usual assignments. - * - * Outside of this internal usage, this method has probably no usefulness. It is hidden in the public API dox. - */ - - template<typename OtherDerived, int StoreMode, int LoadMode> - EIGEN_STRONG_INLINE void copyPacket(Index index, const DenseBase<OtherDerived>& other) - { - eigen_internal_assert(index >= 0 && index < size()); - derived().template writePacket<StoreMode>(index, - other.derived().template packet<LoadMode>(index)); - } - - /** \internal */ - template<typename OtherDerived, int StoreMode, int LoadMode> - EIGEN_STRONG_INLINE void copyPacketByOuterInner(Index outer, Index inner, const DenseBase<OtherDerived>& other) - { - const Index row = rowIndexByOuterInner(outer,inner); - const Index col = colIndexByOuterInner(outer,inner); - // derived() is important here: copyCoeff() may be reimplemented in Derived! - derived().template copyPacket< OtherDerived, StoreMode, LoadMode>(row, col, other); - } -#endif - -}; - -/** \brief Base class providing direct read-only coefficient access to matrices and arrays. - * \ingroup Core_Module - * \tparam Derived Type of the derived class - * \tparam #DirectAccessors Constant indicating direct access - * - * This class defines functions to work with strides which can be used to access entries directly. This class - * inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using - * \c operator() . - * - * \sa \ref TopicClassHierarchy - */ -template<typename Derived> -class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors> -{ - public: - - typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - using Base::rows; - using Base::cols; - using Base::size; - using Base::derived; - - /** \returns the pointer increment between two consecutive elements within a slice in the inner direction. - * - * \sa outerStride(), rowStride(), colStride() - */ - EIGEN_DEVICE_FUNC - inline Index innerStride() const - { - return derived().innerStride(); - } - - /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns - * in a column-major matrix). - * - * \sa innerStride(), rowStride(), colStride() - */ - EIGEN_DEVICE_FUNC - inline Index outerStride() const - { - return derived().outerStride(); - } - - // FIXME shall we remove it ? - inline Index stride() const - { - return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); - } - - /** \returns the pointer increment between two consecutive rows. - * - * \sa innerStride(), outerStride(), colStride() - */ - EIGEN_DEVICE_FUNC - inline Index rowStride() const - { - return Derived::IsRowMajor ? outerStride() : innerStride(); - } - - /** \returns the pointer increment between two consecutive columns. - * - * \sa innerStride(), outerStride(), rowStride() - */ - EIGEN_DEVICE_FUNC - inline Index colStride() const - { - return Derived::IsRowMajor ? innerStride() : outerStride(); - } -}; - -/** \brief Base class providing direct read/write coefficient access to matrices and arrays. - * \ingroup Core_Module - * \tparam Derived Type of the derived class - * \tparam #DirectWriteAccessors Constant indicating direct access - * - * This class defines functions to work with strides which can be used to access entries directly. This class - * inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using - * \c operator(). - * - * \sa \ref TopicClassHierarchy - */ -template<typename Derived> -class DenseCoeffsBase<Derived, DirectWriteAccessors> - : public DenseCoeffsBase<Derived, WriteAccessors> -{ - public: - - typedef DenseCoeffsBase<Derived, WriteAccessors> Base; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - using Base::rows; - using Base::cols; - using Base::size; - using Base::derived; - - /** \returns the pointer increment between two consecutive elements within a slice in the inner direction. - * - * \sa outerStride(), rowStride(), colStride() - */ - EIGEN_DEVICE_FUNC - inline Index innerStride() const - { - return derived().innerStride(); - } - - /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns - * in a column-major matrix). - * - * \sa innerStride(), rowStride(), colStride() - */ - EIGEN_DEVICE_FUNC - inline Index outerStride() const - { - return derived().outerStride(); - } - - // FIXME shall we remove it ? - inline Index stride() const - { - return Derived::IsVectorAtCompileTime ? innerStride() : outerStride(); - } - - /** \returns the pointer increment between two consecutive rows. - * - * \sa innerStride(), outerStride(), colStride() - */ - EIGEN_DEVICE_FUNC - inline Index rowStride() const - { - return Derived::IsRowMajor ? outerStride() : innerStride(); - } - - /** \returns the pointer increment between two consecutive columns. - * - * \sa innerStride(), outerStride(), rowStride() - */ - EIGEN_DEVICE_FUNC - inline Index colStride() const - { - return Derived::IsRowMajor ? innerStride() : outerStride(); - } -}; - -namespace internal { - -template<typename Derived, bool JustReturnZero> -struct first_aligned_impl -{ - static inline typename Derived::Index run(const Derived&) - { return 0; } -}; - -template<typename Derived> -struct first_aligned_impl<Derived, false> -{ - static inline typename Derived::Index run(const Derived& m) - { - return internal::first_aligned(&m.const_cast_derived().coeffRef(0,0), m.size()); - } -}; - -/** \internal \returns the index of the first element of the array that is well aligned for vectorization. - * - * There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more - * documentation. - */ -template<typename Derived> -static inline typename Derived::Index first_aligned(const Derived& m) -{ - return first_aligned_impl - <Derived, (Derived::Flags & AlignedBit) || !(Derived::Flags & DirectAccessBit)> - ::run(m); -} - -template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret> -struct inner_stride_at_compile_time -{ - enum { ret = traits<Derived>::InnerStrideAtCompileTime }; -}; - -template<typename Derived> -struct inner_stride_at_compile_time<Derived, false> -{ - enum { ret = 0 }; -}; - -template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret> -struct outer_stride_at_compile_time -{ - enum { ret = traits<Derived>::OuterStrideAtCompileTime }; -}; - -template<typename Derived> -struct outer_stride_at_compile_time<Derived, false> -{ - enum { ret = 0 }; -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_DENSECOEFFSBASE_H diff --git a/third_party/eigen3/Eigen/src/Core/DenseStorage.h b/third_party/eigen3/Eigen/src/Core/DenseStorage.h deleted file mode 100644 index 59f5154956..0000000000 --- a/third_party/eigen3/Eigen/src/Core/DenseStorage.h +++ /dev/null @@ -1,480 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MATRIXSTORAGE_H -#define EIGEN_MATRIXSTORAGE_H - -#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN - #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN EIGEN_DENSE_STORAGE_CTOR_PLUGIN; -#else - #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN -#endif - -namespace Eigen { - -namespace internal { - -struct constructor_without_unaligned_array_assert {}; - -template<typename T, int Size> -EIGEN_DEVICE_FUNC -void check_static_allocation_size() -{ - // if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit - #if EIGEN_STACK_ALLOCATION_LIMIT - EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG); - #endif -} - -/** \internal - * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned: - * to 16 bytes boundary if the total size is a multiple of 16 bytes. - */ -template <typename T, int Size, int MatrixOrArrayOptions, - int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0 - : (((Size*sizeof(T))%EIGEN_ALIGN_BYTES)==0) ? EIGEN_ALIGN_BYTES - : 0 > -struct plain_array -{ - T array[Size]; - - EIGEN_DEVICE_FUNC - plain_array() - { - check_static_allocation_size<T,Size>(); - } - - EIGEN_DEVICE_FUNC - plain_array(constructor_without_unaligned_array_assert) - { - check_static_allocation_size<T,Size>(); - } -}; - -#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT) - #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) -#elif EIGEN_GNUC_AT_LEAST(4,7) - // GCC 4.7 is too aggressive in its optimizations and remove the alignement test based on the fact the array is declared to be aligned. - // See this bug report: http://gcc.gnu.org/bugzilla/show_bug.cgi?id=53900 - // Hiding the origin of the array pointer behind a function argument seems to do the trick even if the function is inlined: - template<typename PtrType> - EIGEN_ALWAYS_INLINE PtrType eigen_unaligned_array_assert_workaround_gcc47(PtrType array) { return array; } - #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \ - eigen_assert((reinterpret_cast<size_t>(eigen_unaligned_array_assert_workaround_gcc47(array)) & (sizemask)) == 0 \ - && "this assertion is explained here: " \ - "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \ - " **** READ THIS WEB PAGE !!! ****"); -#else - #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \ - eigen_assert((reinterpret_cast<size_t>(array) & (sizemask)) == 0 \ - && "this assertion is explained here: " \ - "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \ - " **** READ THIS WEB PAGE !!! ****"); -#endif - -template <typename T, int Size, int MatrixOrArrayOptions> -struct plain_array<T, Size, MatrixOrArrayOptions, EIGEN_ALIGN_BYTES> -{ - EIGEN_USER_ALIGN_DEFAULT T array[Size]; - - EIGEN_DEVICE_FUNC - plain_array() - { - EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(EIGEN_ALIGN_BYTES-1); - check_static_allocation_size<T,Size>(); - } - - EIGEN_DEVICE_FUNC - plain_array(constructor_without_unaligned_array_assert) - { - check_static_allocation_size<T,Size>(); - } -}; - -template <typename T, int MatrixOrArrayOptions, int Alignment> -struct plain_array<T, 0, MatrixOrArrayOptions, Alignment> -{ - EIGEN_USER_ALIGN_DEFAULT T array[1]; - EIGEN_DEVICE_FUNC plain_array() {} - EIGEN_DEVICE_FUNC plain_array(constructor_without_unaligned_array_assert) {} -}; - -} // end namespace internal - -/** \internal - * - * \class DenseStorage - * \ingroup Core_Module - * - * \brief Stores the data of a matrix - * - * This class stores the data of fixed-size, dynamic-size or mixed matrices - * in a way as compact as possible. - * - * \sa Matrix - */ -template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage; - -// purely fixed-size matrix -template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseStorage -{ - internal::plain_array<T,Size,_Options> m_data; - public: - EIGEN_DEVICE_FUNC DenseStorage() {} - EIGEN_DEVICE_FUNC - DenseStorage(internal::constructor_without_unaligned_array_assert) - : m_data(internal::constructor_without_unaligned_array_assert()) {} - EIGEN_DEVICE_FUNC - DenseStorage(const DenseStorage& other) : m_data(other.m_data) {} - EIGEN_DEVICE_FUNC - DenseStorage& operator=(const DenseStorage& other) - { - if (this != &other) m_data = other.m_data; - return *this; - } - EIGEN_DEVICE_FUNC DenseStorage(DenseIndex,DenseIndex,DenseIndex) {} - EIGEN_DEVICE_FUNC void swap(DenseStorage& other) { std::swap(m_data,other.m_data); } - EIGEN_DEVICE_FUNC static DenseIndex rows(void) {return _Rows;} - EIGEN_DEVICE_FUNC static DenseIndex cols(void) {return _Cols;} - EIGEN_DEVICE_FUNC void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {} - EIGEN_DEVICE_FUNC void resize(DenseIndex,DenseIndex,DenseIndex) {} - EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; } - EIGEN_DEVICE_FUNC T *data() { return m_data.array; } -}; - -// null matrix -template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options> -{ - public: - EIGEN_DEVICE_FUNC DenseStorage() {} - EIGEN_DEVICE_FUNC DenseStorage(internal::constructor_without_unaligned_array_assert) {} - EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage&) {} - EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage&) { return *this; } - EIGEN_DEVICE_FUNC DenseStorage(DenseIndex,DenseIndex,DenseIndex) {} - EIGEN_DEVICE_FUNC void swap(DenseStorage& ) {} - EIGEN_DEVICE_FUNC static DenseIndex rows(void) {return _Rows;} - EIGEN_DEVICE_FUNC static DenseIndex cols(void) {return _Cols;} - EIGEN_DEVICE_FUNC void conservativeResize(DenseIndex,DenseIndex,DenseIndex) {} - EIGEN_DEVICE_FUNC void resize(DenseIndex,DenseIndex,DenseIndex) {} - EIGEN_DEVICE_FUNC const T *data() const { return 0; } - EIGEN_DEVICE_FUNC T *data() { return 0; } -}; - -// more specializations for null matrices; these are necessary to resolve ambiguities -template<typename T, int _Options> class DenseStorage<T, 0, Dynamic, Dynamic, _Options> -: public DenseStorage<T, 0, 0, 0, _Options> { }; - -template<typename T, int _Rows, int _Options> class DenseStorage<T, 0, _Rows, Dynamic, _Options> -: public DenseStorage<T, 0, 0, 0, _Options> { }; - -template<typename T, int _Cols, int _Options> class DenseStorage<T, 0, Dynamic, _Cols, _Options> -: public DenseStorage<T, 0, 0, 0, _Options> { }; - -// dynamic-size matrix with fixed-size storage -template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic, Dynamic, _Options> -{ - internal::plain_array<T,Size,_Options> m_data; - DenseIndex m_rows; - DenseIndex m_cols; - public: - EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {} - DenseStorage(internal::constructor_without_unaligned_array_assert) - : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {} - DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows), m_cols(other.m_cols) {} - DenseStorage& operator=(const DenseStorage& other) - { - if (this != &other) - { - m_data = other.m_data; - m_rows = other.m_rows; - m_cols = other.m_cols; - } - return *this; - } - DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) : m_rows(nbRows), m_cols(nbCols) {} - void swap(DenseStorage& other) - { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); } - EIGEN_DEVICE_FUNC DenseIndex rows() const {return m_rows;} - EIGEN_DEVICE_FUNC DenseIndex cols() const {return m_cols;} - void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; } - void resize(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) { m_rows = nbRows; m_cols = nbCols; } - EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; } - EIGEN_DEVICE_FUNC T *data() { return m_data.array; } -}; - -// dynamic-size matrix with fixed-size storage and fixed width -template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Size, Dynamic, _Cols, _Options> -{ - internal::plain_array<T,Size,_Options> m_data; - DenseIndex m_rows; - public: - EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {} - DenseStorage(internal::constructor_without_unaligned_array_assert) - : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {} - DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_rows(other.m_rows) {} - DenseStorage& operator=(const DenseStorage& other) - { - if (this != &other) - { - m_data = other.m_data; - m_rows = other.m_rows; - } - return *this; - } - DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex) : m_rows(nbRows) {} - void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); } - EIGEN_DEVICE_FUNC DenseIndex rows(void) const {return m_rows;} - EIGEN_DEVICE_FUNC DenseIndex cols(void) const {return _Cols;} - void conservativeResize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; } - void resize(DenseIndex, DenseIndex nbRows, DenseIndex) { m_rows = nbRows; } - EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; } - EIGEN_DEVICE_FUNC T *data() { return m_data.array; } -}; - -// dynamic-size matrix with fixed-size storage and fixed height -template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Size, _Rows, Dynamic, _Options> -{ - internal::plain_array<T,Size,_Options> m_data; - DenseIndex m_cols; - public: - EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {} - DenseStorage(internal::constructor_without_unaligned_array_assert) - : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {} - DenseStorage(const DenseStorage& other) : m_data(other.m_data), m_cols(other.m_cols) {} - DenseStorage& operator=(const DenseStorage& other) - { - if (this != &other) - { - m_data = other.m_data; - m_cols = other.m_cols; - } - return *this; - } - DenseStorage(DenseIndex, DenseIndex, DenseIndex nbCols) : m_cols(nbCols) {} - void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); } - EIGEN_DEVICE_FUNC DenseIndex rows(void) const {return _Rows;} - EIGEN_DEVICE_FUNC DenseIndex cols(void) const {return m_cols;} - void conservativeResize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; } - void resize(DenseIndex, DenseIndex, DenseIndex nbCols) { m_cols = nbCols; } - EIGEN_DEVICE_FUNC const T *data() const { return m_data.array; } - EIGEN_DEVICE_FUNC T *data() { return m_data.array; } -}; - -// purely dynamic matrix. -template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynamic, _Options> -{ - T *m_data; - DenseIndex m_rows; - DenseIndex m_cols; - public: - EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0), m_cols(0) {} - DenseStorage(internal::constructor_without_unaligned_array_assert) - : m_data(0), m_rows(0), m_cols(0) {} - DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols) - : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows), m_cols(nbCols) - { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } - DenseStorage(const DenseStorage& other) - : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*other.m_cols)) - , m_rows(other.m_rows) - , m_cols(other.m_cols) - { - internal::smart_copy(other.m_data, other.m_data+other.m_rows*other.m_cols, m_data); - } - DenseStorage& operator=(const DenseStorage& other) - { - if (this != &other) - { - DenseStorage tmp(other); - this->swap(tmp); - } - return *this; - } -#ifdef EIGEN_HAVE_RVALUE_REFERENCES - DenseStorage(DenseStorage&& other) - : m_data(std::move(other.m_data)) - , m_rows(std::move(other.m_rows)) - , m_cols(std::move(other.m_cols)) - { - other.m_data = nullptr; - } - DenseStorage& operator=(DenseStorage&& other) - { - using std::swap; - swap(m_data, other.m_data); - swap(m_rows, other.m_rows); - swap(m_cols, other.m_cols); - return *this; - } -#endif - ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); } - void swap(DenseStorage& other) - { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); std::swap(m_cols,other.m_cols); } - EIGEN_DEVICE_FUNC DenseIndex rows(void) const {return m_rows;} - EIGEN_DEVICE_FUNC DenseIndex cols(void) const {return m_cols;} - void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols) - { - m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*m_cols); - m_rows = nbRows; - m_cols = nbCols; - } - void resize(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols) - { - if(size != m_rows*m_cols) - { - internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, m_rows*m_cols); - if (size) - m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size); - else - m_data = 0; - EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN - } - m_rows = nbRows; - m_cols = nbCols; - } - EIGEN_DEVICE_FUNC const T *data() const { return m_data; } - EIGEN_DEVICE_FUNC T *data() { return m_data; } -}; - -// matrix with dynamic width and fixed height (so that matrix has dynamic size). -template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Rows, Dynamic, _Options> -{ - T *m_data; - DenseIndex m_cols; - public: - EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_cols(0) {} - DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {} - DenseStorage(DenseIndex size, DenseIndex, DenseIndex nbCols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(nbCols) - { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } - DenseStorage(const DenseStorage& other) - : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(_Rows*other.m_cols)) - , m_cols(other.m_cols) - { - internal::smart_copy(other.m_data, other.m_data+_Rows*m_cols, m_data); - } - DenseStorage& operator=(const DenseStorage& other) - { - if (this != &other) - { - DenseStorage tmp(other); - this->swap(tmp); - } - return *this; - } -#ifdef EIGEN_HAVE_RVALUE_REFERENCES - DenseStorage(DenseStorage&& other) - : m_data(std::move(other.m_data)) - , m_cols(std::move(other.m_cols)) - { - other.m_data = nullptr; - } - DenseStorage& operator=(DenseStorage&& other) - { - using std::swap; - swap(m_data, other.m_data); - swap(m_cols, other.m_cols); - return *this; - } -#endif - ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); } - void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_cols,other.m_cols); } - EIGEN_DEVICE_FUNC static DenseIndex rows(void) {return _Rows;} - EIGEN_DEVICE_FUNC DenseIndex cols(void) const {return m_cols;} - void conservativeResize(DenseIndex size, DenseIndex, DenseIndex nbCols) - { - m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, _Rows*m_cols); - m_cols = nbCols; - } - EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex, DenseIndex nbCols) - { - if(size != _Rows*m_cols) - { - internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Rows*m_cols); - if (size) - m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size); - else - m_data = 0; - EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN - } - m_cols = nbCols; - } - EIGEN_DEVICE_FUNC const T *data() const { return m_data; } - EIGEN_DEVICE_FUNC T *data() { return m_data; } -}; - -// matrix with dynamic height and fixed width (so that matrix has dynamic size). -template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dynamic, _Cols, _Options> -{ - T *m_data; - DenseIndex m_rows; - public: - EIGEN_DEVICE_FUNC DenseStorage() : m_data(0), m_rows(0) {} - DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {} - DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows) - { EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN } - DenseStorage(const DenseStorage& other) - : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(other.m_rows*_Cols)) - , m_rows(other.m_rows) - { - internal::smart_copy(other.m_data, other.m_data+other.m_rows*_Cols, m_data); - } - DenseStorage& operator=(const DenseStorage& other) - { - if (this != &other) - { - DenseStorage tmp(other); - this->swap(tmp); - } - return *this; - } -#ifdef EIGEN_HAVE_RVALUE_REFERENCES - DenseStorage(DenseStorage&& other) - : m_data(std::move(other.m_data)) - , m_rows(std::move(other.m_rows)) - { - other.m_data = nullptr; - } - DenseStorage& operator=(DenseStorage&& other) - { - using std::swap; - swap(m_data, other.m_data); - swap(m_rows, other.m_rows); - return *this; - } -#endif - ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); } - void swap(DenseStorage& other) { std::swap(m_data,other.m_data); std::swap(m_rows,other.m_rows); } - EIGEN_DEVICE_FUNC DenseIndex rows(void) const {return m_rows;} - EIGEN_DEVICE_FUNC static DenseIndex cols(void) {return _Cols;} - void conservativeResize(DenseIndex size, DenseIndex nbRows, DenseIndex) - { - m_data = internal::conditional_aligned_realloc_new_auto<T,(_Options&DontAlign)==0>(m_data, size, m_rows*_Cols); - m_rows = nbRows; - } - EIGEN_STRONG_INLINE void resize(DenseIndex size, DenseIndex nbRows, DenseIndex) - { - if(size != m_rows*_Cols) - { - internal::conditional_aligned_delete_auto<T,(_Options&DontAlign)==0>(m_data, _Cols*m_rows); - if (size) - m_data = internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size); - else - m_data = 0; - EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN - } - m_rows = nbRows; - } - EIGEN_DEVICE_FUNC const T *data() const { return m_data; } - EIGEN_DEVICE_FUNC T *data() { return m_data; } -}; - -} // end namespace Eigen - -#endif // EIGEN_MATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/Diagonal.h b/third_party/eigen3/Eigen/src/Core/Diagonal.h deleted file mode 100644 index d760762cc2..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Diagonal.h +++ /dev/null @@ -1,258 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_DIAGONAL_H -#define EIGEN_DIAGONAL_H - -namespace Eigen { - -/** \class Diagonal - * \ingroup Core_Module - * - * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix - * - * \param MatrixType the type of the object in which we are taking a sub/main/super diagonal - * \param DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal. - * A positive value means a superdiagonal, a negative value means a subdiagonal. - * You can also use Dynamic so the index can be set at runtime. - * - * The matrix is not required to be square. - * - * This class represents an expression of the main diagonal, or any sub/super diagonal - * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the - * time this is the only way it is used. - * - * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index) - */ - -namespace internal { -template<typename MatrixType, int DiagIndex> -struct traits<Diagonal<MatrixType,DiagIndex> > - : traits<MatrixType> -{ - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested; - typedef typename MatrixType::StorageKind StorageKind; - enum { - RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic - : (EIGEN_PLAIN_ENUM_MIN(MatrixType::RowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0), - MatrixType::ColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))), - ColsAtCompileTime = 1, - MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic - : DiagIndex == DynamicIndex ? EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime, - MatrixType::MaxColsAtCompileTime) - : (EIGEN_PLAIN_ENUM_MIN(MatrixType::MaxRowsAtCompileTime - EIGEN_PLAIN_ENUM_MAX(-DiagIndex, 0), - MatrixType::MaxColsAtCompileTime - EIGEN_PLAIN_ENUM_MAX( DiagIndex, 0))), - MaxColsAtCompileTime = 1, - MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0, - Flags = (unsigned int)_MatrixTypeNested::Flags & (HereditaryBits | LinearAccessBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, - CoeffReadCost = _MatrixTypeNested::CoeffReadCost, - MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret, - InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1, - OuterStrideAtCompileTime = 0 - }; -}; -} - -template<typename MatrixType, int _DiagIndex> class Diagonal - : public internal::dense_xpr_base< Diagonal<MatrixType,_DiagIndex> >::type -{ - public: - - enum { DiagIndex = _DiagIndex }; - typedef typename internal::dense_xpr_base<Diagonal>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal) - - EIGEN_DEVICE_FUNC - inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index) {} - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal) - - EIGEN_DEVICE_FUNC - inline Index rows() const - { - return m_index.value()<0 ? numext::mini(Index(m_matrix.cols()),Index(m_matrix.rows()+m_index.value())) - : numext::mini(Index(m_matrix.rows()),Index(m_matrix.cols()-m_index.value())); - } - - EIGEN_DEVICE_FUNC - inline Index cols() const { return 1; } - - EIGEN_DEVICE_FUNC - inline Index innerStride() const - { - return m_matrix.outerStride() + 1; - } - - EIGEN_DEVICE_FUNC - inline Index outerStride() const - { - return 0; - } - - typedef typename internal::conditional< - internal::is_lvalue<MatrixType>::value, - Scalar, - const Scalar - >::type ScalarWithConstIfNotLvalue; - - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); } - EIGEN_DEVICE_FUNC - inline const Scalar* data() const { return &(m_matrix.const_cast_derived().coeffRef(rowOffset(), colOffset())); } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index row, Index) - { - EIGEN_STATIC_ASSERT_LVALUE(MatrixType) - return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset()); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index row, Index) const - { - return m_matrix.const_cast_derived().coeffRef(row+rowOffset(), row+colOffset()); - } - - EIGEN_DEVICE_FUNC - inline CoeffReturnType coeff(Index row, Index) const - { - return m_matrix.coeff(row+rowOffset(), row+colOffset()); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index idx) - { - EIGEN_STATIC_ASSERT_LVALUE(MatrixType) - return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset()); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index idx) const - { - return m_matrix.const_cast_derived().coeffRef(idx+rowOffset(), idx+colOffset()); - } - - EIGEN_DEVICE_FUNC - inline CoeffReturnType coeff(Index idx) const - { - return m_matrix.coeff(idx+rowOffset(), idx+colOffset()); - } - - EIGEN_DEVICE_FUNC - const typename internal::remove_all<typename MatrixType::Nested>::type& - nestedExpression() const - { - return m_matrix; - } - - EIGEN_DEVICE_FUNC - int index() const - { - return m_index.value(); - } - - protected: - typename MatrixType::Nested m_matrix; - const internal::variable_if_dynamicindex<Index, DiagIndex> m_index; - - private: - // some compilers may fail to optimize std::max etc in case of compile-time constants... - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index absDiagIndex() const { return m_index.value()>0 ? m_index.value() : -m_index.value(); } - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index rowOffset() const { return m_index.value()>0 ? 0 : -m_index.value(); } - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index colOffset() const { return m_index.value()>0 ? m_index.value() : 0; } - // triger a compile time error is someone try to call packet - template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const; - template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const; -}; - -/** \returns an expression of the main diagonal of the matrix \c *this - * - * \c *this is not required to be square. - * - * Example: \include MatrixBase_diagonal.cpp - * Output: \verbinclude MatrixBase_diagonal.out - * - * \sa class Diagonal */ -template<typename Derived> -inline typename MatrixBase<Derived>::DiagonalReturnType -MatrixBase<Derived>::diagonal() -{ - return derived(); -} - -/** This is the const version of diagonal(). */ -template<typename Derived> -inline typename MatrixBase<Derived>::ConstDiagonalReturnType -MatrixBase<Derived>::diagonal() const -{ - return ConstDiagonalReturnType(derived()); -} - -/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this - * - * \c *this is not required to be square. - * - * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0 - * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal. - * - * Example: \include MatrixBase_diagonal_int.cpp - * Output: \verbinclude MatrixBase_diagonal_int.out - * - * \sa MatrixBase::diagonal(), class Diagonal */ -template<typename Derived> -inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<DynamicIndex>::Type -MatrixBase<Derived>::diagonal(Index index) -{ - return typename DiagonalIndexReturnType<DynamicIndex>::Type(derived(), index); -} - -/** This is the const version of diagonal(Index). */ -template<typename Derived> -inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<DynamicIndex>::Type -MatrixBase<Derived>::diagonal(Index index) const -{ - return typename ConstDiagonalIndexReturnType<DynamicIndex>::Type(derived(), index); -} - -/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this - * - * \c *this is not required to be square. - * - * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0 - * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal. - * - * Example: \include MatrixBase_diagonal_template_int.cpp - * Output: \verbinclude MatrixBase_diagonal_template_int.out - * - * \sa MatrixBase::diagonal(), class Diagonal */ -template<typename Derived> -template<int Index> -inline typename MatrixBase<Derived>::template DiagonalIndexReturnType<Index>::Type -MatrixBase<Derived>::diagonal() -{ - return derived(); -} - -/** This is the const version of diagonal<int>(). */ -template<typename Derived> -template<int Index> -inline typename MatrixBase<Derived>::template ConstDiagonalIndexReturnType<Index>::Type -MatrixBase<Derived>::diagonal() const -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_DIAGONAL_H diff --git a/third_party/eigen3/Eigen/src/Core/DiagonalMatrix.h b/third_party/eigen3/Eigen/src/Core/DiagonalMatrix.h deleted file mode 100644 index f7ac22f8b0..0000000000 --- a/third_party/eigen3/Eigen/src/Core/DiagonalMatrix.h +++ /dev/null @@ -1,346 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_DIAGONALMATRIX_H -#define EIGEN_DIAGONALMATRIX_H - -namespace Eigen { - -#ifndef EIGEN_PARSED_BY_DOXYGEN -template<typename Derived> -class DiagonalBase : public EigenBase<Derived> -{ - public: - typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType; - typedef typename DiagonalVectorType::Scalar Scalar; - typedef typename DiagonalVectorType::RealScalar RealScalar; - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - - enum { - RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, - ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, - MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime, - MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime, - IsVectorAtCompileTime = 0, - Flags = 0 - }; - - typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType; - typedef DenseMatrixType DenseType; - typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject; - - EIGEN_DEVICE_FUNC - inline const Derived& derived() const { return *static_cast<const Derived*>(this); } - EIGEN_DEVICE_FUNC - inline Derived& derived() { return *static_cast<Derived*>(this); } - - EIGEN_DEVICE_FUNC - DenseMatrixType toDenseMatrix() const { return derived(); } - template<typename DenseDerived> - EIGEN_DEVICE_FUNC - void evalTo(MatrixBase<DenseDerived> &other) const; - template<typename DenseDerived> - EIGEN_DEVICE_FUNC - void addTo(MatrixBase<DenseDerived> &other) const - { other.diagonal() += diagonal(); } - template<typename DenseDerived> - EIGEN_DEVICE_FUNC - void subTo(MatrixBase<DenseDerived> &other) const - { other.diagonal() -= diagonal(); } - - EIGEN_DEVICE_FUNC - inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); } - EIGEN_DEVICE_FUNC - inline DiagonalVectorType& diagonal() { return derived().diagonal(); } - - EIGEN_DEVICE_FUNC - inline Index rows() const { return diagonal().size(); } - EIGEN_DEVICE_FUNC - inline Index cols() const { return diagonal().size(); } - - /** \returns the diagonal matrix product of \c *this by the matrix \a matrix. - */ - template<typename MatrixDerived> - EIGEN_DEVICE_FUNC - const DiagonalProduct<MatrixDerived, Derived, OnTheLeft> - operator*(const MatrixBase<MatrixDerived> &matrix) const - { - return DiagonalProduct<MatrixDerived, Derived, OnTheLeft>(matrix.derived(), derived()); - } - - EIGEN_DEVICE_FUNC - inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> > - inverse() const - { - return diagonal().cwiseInverse(); - } - - EIGEN_DEVICE_FUNC - inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> > - operator*(const Scalar& scalar) const - { - return diagonal() * scalar; - } - EIGEN_DEVICE_FUNC - friend inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> > - operator*(const Scalar& scalar, const DiagonalBase& other) - { - return other.diagonal() * scalar; - } - - #ifdef EIGEN2_SUPPORT - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const - { - return diagonal().isApprox(other.diagonal(), precision); - } - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const - { - return toDenseMatrix().isApprox(other, precision); - } - #endif -}; - -template<typename Derived> -template<typename DenseDerived> -void DiagonalBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const -{ - other.setZero(); - other.diagonal() = diagonal(); -} -#endif - -/** \class DiagonalMatrix - * \ingroup Core_Module - * - * \brief Represents a diagonal matrix with its storage - * - * \param _Scalar the type of coefficients - * \param SizeAtCompileTime the dimension of the matrix, or Dynamic - * \param MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults - * to SizeAtCompileTime. Most of the time, you do not need to specify it. - * - * \sa class DiagonalWrapper - */ - -namespace internal { -template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime> -struct traits<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> > - : traits<Matrix<_Scalar,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> > -{ - typedef Matrix<_Scalar,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType; - typedef Dense StorageKind; - typedef DenseIndex Index; - enum { - Flags = LvalueBit - }; -}; -} -template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime> -class DiagonalMatrix - : public DiagonalBase<DiagonalMatrix<_Scalar,SizeAtCompileTime,MaxSizeAtCompileTime> > -{ - public: - #ifndef EIGEN_PARSED_BY_DOXYGEN - typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType; - typedef const DiagonalMatrix& Nested; - typedef _Scalar Scalar; - typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind; - typedef typename internal::traits<DiagonalMatrix>::Index Index; - #endif - - protected: - - DiagonalVectorType m_diagonal; - - public: - - /** const version of diagonal(). */ - EIGEN_DEVICE_FUNC - inline const DiagonalVectorType& diagonal() const { return m_diagonal; } - /** \returns a reference to the stored vector of diagonal coefficients. */ - EIGEN_DEVICE_FUNC - inline DiagonalVectorType& diagonal() { return m_diagonal; } - - /** Default constructor without initialization */ - EIGEN_DEVICE_FUNC - inline DiagonalMatrix() {} - - /** Constructs a diagonal matrix with given dimension */ - EIGEN_DEVICE_FUNC - inline DiagonalMatrix(Index dim) : m_diagonal(dim) {} - - /** 2D constructor. */ - EIGEN_DEVICE_FUNC - inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {} - - /** 3D constructor. */ - EIGEN_DEVICE_FUNC - inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {} - - /** Copy constructor. */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {} - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */ - inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {} - #endif - - /** generic constructor from expression of the diagonal coefficients */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other) - {} - - /** Copy operator. */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other) - { - m_diagonal = other.diagonal(); - return *this; - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - EIGEN_DEVICE_FUNC - DiagonalMatrix& operator=(const DiagonalMatrix& other) - { - m_diagonal = other.diagonal(); - return *this; - } - #endif - - /** Resizes to given size. */ - EIGEN_DEVICE_FUNC - inline void resize(Index size) { m_diagonal.resize(size); } - /** Sets all coefficients to zero. */ - EIGEN_DEVICE_FUNC - inline void setZero() { m_diagonal.setZero(); } - /** Resizes and sets all coefficients to zero. */ - EIGEN_DEVICE_FUNC - inline void setZero(Index size) { m_diagonal.setZero(size); } - /** Sets this matrix to be the identity matrix of the current size. */ - EIGEN_DEVICE_FUNC - inline void setIdentity() { m_diagonal.setOnes(); } - /** Sets this matrix to be the identity matrix of the given size. */ - EIGEN_DEVICE_FUNC - inline void setIdentity(Index size) { m_diagonal.setOnes(size); } -}; - -/** \class DiagonalWrapper - * \ingroup Core_Module - * - * \brief Expression of a diagonal matrix - * - * \param _DiagonalVectorType the type of the vector of diagonal coefficients - * - * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients, - * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal() - * and most of the time this is the only way that it is used. - * - * \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal() - */ - -namespace internal { -template<typename _DiagonalVectorType> -struct traits<DiagonalWrapper<_DiagonalVectorType> > -{ - typedef _DiagonalVectorType DiagonalVectorType; - typedef typename DiagonalVectorType::Scalar Scalar; - typedef typename DiagonalVectorType::Index Index; - typedef typename DiagonalVectorType::StorageKind StorageKind; - enum { - RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, - ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, - MaxRowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, - MaxColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime, - Flags = traits<DiagonalVectorType>::Flags & LvalueBit - }; -}; -} - -template<typename _DiagonalVectorType> -class DiagonalWrapper - : public DiagonalBase<DiagonalWrapper<_DiagonalVectorType> >, internal::no_assignment_operator -{ - public: - #ifndef EIGEN_PARSED_BY_DOXYGEN - typedef _DiagonalVectorType DiagonalVectorType; - typedef DiagonalWrapper Nested; - #endif - - /** Constructor from expression of diagonal coefficients to wrap. */ - EIGEN_DEVICE_FUNC - inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {} - - /** \returns a const reference to the wrapped expression of diagonal coefficients. */ - EIGEN_DEVICE_FUNC - const DiagonalVectorType& diagonal() const { return m_diagonal; } - - protected: - typename DiagonalVectorType::Nested m_diagonal; -}; - -/** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients - * - * \only_for_vectors - * - * Example: \include MatrixBase_asDiagonal.cpp - * Output: \verbinclude MatrixBase_asDiagonal.out - * - * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal() - **/ -template<typename Derived> -inline const DiagonalWrapper<const Derived> -MatrixBase<Derived>::asDiagonal() const -{ - return derived(); -} - -/** \returns true if *this is approximately equal to a diagonal matrix, - * within the precision given by \a prec. - * - * Example: \include MatrixBase_isDiagonal.cpp - * Output: \verbinclude MatrixBase_isDiagonal.out - * - * \sa asDiagonal() - */ -template<typename Derived> -bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const -{ - using std::abs; - if(cols() != rows()) return false; - RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1); - for(Index j = 0; j < cols(); ++j) - { - RealScalar absOnDiagonal = abs(coeff(j,j)); - if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal; - } - for(Index j = 0; j < cols(); ++j) - for(Index i = 0; i < j; ++i) - { - if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false; - if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false; - } - return true; -} - -} // end namespace Eigen - -#endif // EIGEN_DIAGONALMATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/DiagonalProduct.h b/third_party/eigen3/Eigen/src/Core/DiagonalProduct.h deleted file mode 100644 index c03a0c2e12..0000000000 --- a/third_party/eigen3/Eigen/src/Core/DiagonalProduct.h +++ /dev/null @@ -1,130 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_DIAGONALPRODUCT_H -#define EIGEN_DIAGONALPRODUCT_H - -namespace Eigen { - -namespace internal { -template<typename MatrixType, typename DiagonalType, int ProductOrder> -struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> > - : traits<MatrixType> -{ - typedef typename scalar_product_traits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, - - _StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor, - _ScalarAccessOnDiag = !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft) - ||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)), - _SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value, - // FIXME currently we need same types, but in the future the next rule should be the one - //_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))), - _Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && (_ScalarAccessOnDiag || (bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))), - _LinearAccessMask = (RowsAtCompileTime==1 || ColsAtCompileTime==1) ? LinearAccessBit : 0, - - Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0) | AlignedBit,//(int(MatrixType::Flags)&int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit), - CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost - }; -}; -} - -template<typename MatrixType, typename DiagonalType, int ProductOrder> -class DiagonalProduct : internal::no_assignment_operator, - public MatrixBase<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> > -{ - public: - - typedef MatrixBase<DiagonalProduct> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(DiagonalProduct) - - inline DiagonalProduct(const MatrixType& matrix, const DiagonalType& diagonal) - : m_matrix(matrix), m_diagonal(diagonal) - { - eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols())); - } - - EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); } - EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); } - - EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const - { - return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col); - } - - EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const - { - enum { - StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor - }; - return coeff(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx); - } - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const - { - enum { - StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor - }; - const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col; - return packet_impl<LoadMode>(row,col,indexInDiagonalVector,typename internal::conditional< - ((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft) - ||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type()); - } - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index idx) const - { - enum { - StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor - }; - return packet<LoadMode>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx); - } - - protected: - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::true_type) const - { - return internal::pmul(m_matrix.template packet<LoadMode>(row, col), - internal::pset1<PacketScalar>(m_diagonal.diagonal().coeff(id))); - } - - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet_impl(Index row, Index col, Index id, internal::false_type) const - { - enum { - InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime, - DiagonalVectorPacketLoadMode = (LoadMode == Aligned && (((InnerSize%16) == 0) || (int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit)==AlignedBit) ? Aligned : Unaligned) - }; - return internal::pmul(m_matrix.template packet<LoadMode>(row, col), - m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id)); - } - - typename MatrixType::Nested m_matrix; - typename DiagonalType::Nested m_diagonal; -}; - -/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal. - */ -template<typename Derived> -template<typename DiagonalDerived> -inline const DiagonalProduct<Derived, DiagonalDerived, OnTheRight> -MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const -{ - return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), a_diagonal.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_DIAGONALPRODUCT_H diff --git a/third_party/eigen3/Eigen/src/Core/Dot.h b/third_party/eigen3/Eigen/src/Core/Dot.h deleted file mode 100644 index 718de5d1af..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Dot.h +++ /dev/null @@ -1,270 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_DOT_H -#define EIGEN_DOT_H - -namespace Eigen { - -namespace internal { - -// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot -// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE -// looking at the static assertions. Thus this is a trick to get better compile errors. -template<typename T, typename U, -// the NeedToTranspose condition here is taken straight from Assign.h - bool NeedToTranspose = T::IsVectorAtCompileTime - && U::IsVectorAtCompileTime - && ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1) - | // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&". - // revert to || as soon as not needed anymore. - (int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1)) -> -struct dot_nocheck -{ - typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar; - EIGEN_DEVICE_FUNC - static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b) - { - return a.template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum(); - } -}; - -template<typename T, typename U> -struct dot_nocheck<T, U, true> -{ - typedef typename scalar_product_traits<typename traits<T>::Scalar,typename traits<U>::Scalar>::ReturnType ResScalar; - EIGEN_DEVICE_FUNC - static inline ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b) - { - return a.transpose().template binaryExpr<scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> >(b).sum(); - } -}; - -} // end namespace internal - -/** \returns the dot product of *this with other. - * - * \only_for_vectors - * - * \note If the scalar type is complex numbers, then this function returns the hermitian - * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the - * second variable. - * - * \sa squaredNorm(), norm() - */ -template<typename Derived> -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType -MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived) - typedef internal::scalar_conj_product_op<Scalar,typename OtherDerived::Scalar> func; - EIGEN_CHECK_BINARY_COMPATIBILIY(func,Scalar,typename OtherDerived::Scalar); - - eigen_assert(size() == other.size()); - - return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other); -} - -#ifdef EIGEN2_SUPPORT -/** \returns the dot product of *this with other, with the Eigen2 convention that the dot product is linear in the first variable - * (conjugating the second variable). Of course this only makes a difference in the complex case. - * - * This method is only available in EIGEN2_SUPPORT mode. - * - * \only_for_vectors - * - * \sa dot() - */ -template<typename Derived> -template<typename OtherDerived> -typename internal::traits<Derived>::Scalar -MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived) - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - - eigen_assert(size() == other.size()); - - return internal::dot_nocheck<OtherDerived,Derived>::run(other,*this); -} -#endif - - -//---------- implementation of L2 norm and related functions ---------- - -/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the Frobenius norm. - * In both cases, it consists in the sum of the square of all the matrix entries. - * For vectors, this is also equals to the dot product of \c *this with itself. - * - * \sa dot(), norm() - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const -{ - return numext::real((*this).cwiseAbs2().sum()); -} - -/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm. - * In both cases, it consists in the square root of the sum of the square of all the matrix entries. - * For vectors, this is also equals to the square root of the dot product of \c *this with itself. - * - * \sa dot(), squaredNorm() - */ -template<typename Derived> -inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const -{ - using std::sqrt; - return sqrt(squaredNorm()); -} - -/** \returns an expression of the quotient of *this by its own norm. - * - * \only_for_vectors - * - * \sa norm(), normalize() - */ -template<typename Derived> -inline const typename MatrixBase<Derived>::PlainObject -MatrixBase<Derived>::normalized() const -{ - typedef typename internal::nested<Derived>::type Nested; - typedef typename internal::remove_reference<Nested>::type _Nested; - _Nested n(derived()); - return n / n.norm(); -} - -/** Normalizes the vector, i.e. divides it by its own norm. - * - * \only_for_vectors - * - * \sa norm(), normalized() - */ -template<typename Derived> -inline void MatrixBase<Derived>::normalize() -{ - *this /= norm(); -} - -//---------- implementation of other norms ---------- - -namespace internal { - -template<typename Derived, int p> -struct lpNorm_selector -{ - typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const MatrixBase<Derived>& m) - { - using std::pow; - return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p); - } -}; - -template<typename Derived> -struct lpNorm_selector<Derived, 1> -{ - EIGEN_DEVICE_FUNC - static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m) - { - return m.cwiseAbs().sum(); - } -}; - -template<typename Derived> -struct lpNorm_selector<Derived, 2> -{ - EIGEN_DEVICE_FUNC - static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m) - { - return m.norm(); - } -}; - -template<typename Derived> -struct lpNorm_selector<Derived, Infinity> -{ - EIGEN_DEVICE_FUNC - static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m) - { - return m.cwiseAbs().maxCoeff(); - } -}; - -} // end namespace internal - -/** \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values - * of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$ - * norm, that is the maximum of the absolute values of the coefficients of *this. - * - * \sa norm() - */ -template<typename Derived> -template<int p> -inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real -MatrixBase<Derived>::lpNorm() const -{ - return internal::lpNorm_selector<Derived, p>::run(*this); -} - -//---------- implementation of isOrthogonal / isUnitary ---------- - -/** \returns true if *this is approximately orthogonal to \a other, - * within the precision given by \a prec. - * - * Example: \include MatrixBase_isOrthogonal.cpp - * Output: \verbinclude MatrixBase_isOrthogonal.out - */ -template<typename Derived> -template<typename OtherDerived> -bool MatrixBase<Derived>::isOrthogonal -(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const -{ - typename internal::nested<Derived,2>::type nested(derived()); - typename internal::nested<OtherDerived,2>::type otherNested(other.derived()); - return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm(); -} - -/** \returns true if *this is approximately an unitary matrix, - * within the precision given by \a prec. In the case where the \a Scalar - * type is real numbers, a unitary matrix is an orthogonal matrix, whence the name. - * - * \note This can be used to check whether a family of vectors forms an orthonormal basis. - * Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an - * orthonormal basis. - * - * Example: \include MatrixBase_isUnitary.cpp - * Output: \verbinclude MatrixBase_isUnitary.out - */ -template<typename Derived> -bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const -{ - typename Derived::Nested nested(derived()); - for(Index i = 0; i < cols(); ++i) - { - if(!internal::isApprox(nested.col(i).squaredNorm(), static_cast<RealScalar>(1), prec)) - return false; - for(Index j = 0; j < i; ++j) - if(!internal::isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast<Scalar>(1), prec)) - return false; - } - return true; -} - -} // end namespace Eigen - -#endif // EIGEN_DOT_H diff --git a/third_party/eigen3/Eigen/src/Core/EigenBase.h b/third_party/eigen3/Eigen/src/Core/EigenBase.h deleted file mode 100644 index 1a577c2dce..0000000000 --- a/third_party/eigen3/Eigen/src/Core/EigenBase.h +++ /dev/null @@ -1,146 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_EIGENBASE_H -#define EIGEN_EIGENBASE_H - -namespace Eigen { - -/** Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T). - * - * In other words, an EigenBase object is an object that can be copied into a MatrixBase. - * - * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc. - * - * Notice that this class is trivial, it is only used to disambiguate overloaded functions. - * - * \sa \ref TopicClassHierarchy - */ -template<typename Derived> struct EigenBase -{ -// typedef typename internal::plain_matrix_type<Derived>::type PlainObject; - - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - - /** \returns a reference to the derived object */ - EIGEN_DEVICE_FUNC - Derived& derived() { return *static_cast<Derived*>(this); } - /** \returns a const reference to the derived object */ - EIGEN_DEVICE_FUNC - const Derived& derived() const { return *static_cast<const Derived*>(this); } - - EIGEN_DEVICE_FUNC - inline Derived& const_cast_derived() const - { return *static_cast<Derived*>(const_cast<EigenBase*>(this)); } - EIGEN_DEVICE_FUNC - inline const Derived& const_derived() const - { return *static_cast<const Derived*>(this); } - - /** \returns the number of rows. \sa cols(), RowsAtCompileTime */ - EIGEN_DEVICE_FUNC - inline Index rows() const { return derived().rows(); } - /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/ - EIGEN_DEVICE_FUNC - inline Index cols() const { return derived().cols(); } - /** \returns the number of coefficients, which is rows()*cols(). - * \sa rows(), cols(), SizeAtCompileTime. */ - EIGEN_DEVICE_FUNC - inline Index size() const { return rows() * cols(); } - - /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */ - template<typename Dest> - EIGEN_DEVICE_FUNC - inline void evalTo(Dest& dst) const - { derived().evalTo(dst); } - - /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */ - template<typename Dest> - EIGEN_DEVICE_FUNC - inline void addTo(Dest& dst) const - { - // This is the default implementation, - // derived class can reimplement it in a more optimized way. - typename Dest::PlainObject res(rows(),cols()); - evalTo(res); - dst += res; - } - - /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */ - template<typename Dest> - EIGEN_DEVICE_FUNC - inline void subTo(Dest& dst) const - { - // This is the default implementation, - // derived class can reimplement it in a more optimized way. - typename Dest::PlainObject res(rows(),cols()); - evalTo(res); - dst -= res; - } - - /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */ - template<typename Dest> - EIGEN_DEVICE_FUNC inline void applyThisOnTheRight(Dest& dst) const - { - // This is the default implementation, - // derived class can reimplement it in a more optimized way. - dst = dst * this->derived(); - } - - /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */ - template<typename Dest> - EIGEN_DEVICE_FUNC inline void applyThisOnTheLeft(Dest& dst) const - { - // This is the default implementation, - // derived class can reimplement it in a more optimized way. - dst = this->derived() * dst; - } - -}; - -/*************************************************************************** -* Implementation of matrix base methods -***************************************************************************/ - -/** \brief Copies the generic expression \a other into *this. - * - * \details The expression must provide a (templated) evalTo(Derived& dst) const - * function which does the actual job. In practice, this allows any user to write - * its own special matrix without having to modify MatrixBase - * - * \returns a reference to *this. - */ -template<typename Derived> -template<typename OtherDerived> -Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other) -{ - other.derived().evalTo(derived()); - return derived(); -} - -template<typename Derived> -template<typename OtherDerived> -Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other) -{ - other.derived().addTo(derived()); - return derived(); -} - -template<typename Derived> -template<typename OtherDerived> -Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other) -{ - other.derived().subTo(derived()); - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_EIGENBASE_H diff --git a/third_party/eigen3/Eigen/src/Core/Flagged.h b/third_party/eigen3/Eigen/src/Core/Flagged.h deleted file mode 100644 index 1f2955fc1d..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Flagged.h +++ /dev/null @@ -1,140 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_FLAGGED_H -#define EIGEN_FLAGGED_H - -namespace Eigen { - -/** \class Flagged - * \ingroup Core_Module - * - * \brief Expression with modified flags - * - * \param ExpressionType the type of the object of which we are modifying the flags - * \param Added the flags added to the expression - * \param Removed the flags removed from the expression (has priority over Added). - * - * This class represents an expression whose flags have been modified. - * It is the return type of MatrixBase::flagged() - * and most of the time this is the only way it is used. - * - * \sa MatrixBase::flagged() - */ - -namespace internal { -template<typename ExpressionType, unsigned int Added, unsigned int Removed> -struct traits<Flagged<ExpressionType, Added, Removed> > : traits<ExpressionType> -{ - enum { Flags = (ExpressionType::Flags | Added) & ~Removed }; -}; -} - -template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged - : public MatrixBase<Flagged<ExpressionType, Added, Removed> > -{ - public: - - typedef MatrixBase<Flagged> Base; - - EIGEN_DENSE_PUBLIC_INTERFACE(Flagged) - typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret, - ExpressionType, const ExpressionType&>::type ExpressionTypeNested; - typedef typename ExpressionType::InnerIterator InnerIterator; - - inline Flagged(const ExpressionType& matrix) : m_matrix(matrix) {} - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - inline Index outerStride() const { return m_matrix.outerStride(); } - inline Index innerStride() const { return m_matrix.innerStride(); } - - inline CoeffReturnType coeff(Index row, Index col) const - { - return m_matrix.coeff(row, col); - } - - inline CoeffReturnType coeff(Index index) const - { - return m_matrix.coeff(index); - } - - inline const Scalar& coeffRef(Index row, Index col) const - { - return m_matrix.const_cast_derived().coeffRef(row, col); - } - - inline const Scalar& coeffRef(Index index) const - { - return m_matrix.const_cast_derived().coeffRef(index); - } - - inline Scalar& coeffRef(Index row, Index col) - { - return m_matrix.const_cast_derived().coeffRef(row, col); - } - - inline Scalar& coeffRef(Index index) - { - return m_matrix.const_cast_derived().coeffRef(index); - } - - template<int LoadMode> - inline const PacketScalar packet(Index row, Index col) const - { - return m_matrix.template packet<LoadMode>(row, col); - } - - template<int LoadMode> - inline void writePacket(Index row, Index col, const PacketScalar& x) - { - m_matrix.const_cast_derived().template writePacket<LoadMode>(row, col, x); - } - - template<int LoadMode> - inline const PacketScalar packet(Index index) const - { - return m_matrix.template packet<LoadMode>(index); - } - - template<int LoadMode> - inline void writePacket(Index index, const PacketScalar& x) - { - m_matrix.const_cast_derived().template writePacket<LoadMode>(index, x); - } - - const ExpressionType& _expression() const { return m_matrix; } - - template<typename OtherDerived> - typename ExpressionType::PlainObject solveTriangular(const MatrixBase<OtherDerived>& other) const; - - template<typename OtherDerived> - void solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const; - - protected: - ExpressionTypeNested m_matrix; -}; - -/** \returns an expression of *this with added and removed flags - * - * This is mostly for internal use. - * - * \sa class Flagged - */ -template<typename Derived> -template<unsigned int Added,unsigned int Removed> -inline const Flagged<Derived, Added, Removed> -DenseBase<Derived>::flagged() const -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_FLAGGED_H diff --git a/third_party/eigen3/Eigen/src/Core/ForceAlignedAccess.h b/third_party/eigen3/Eigen/src/Core/ForceAlignedAccess.h deleted file mode 100644 index 807c7a2934..0000000000 --- a/third_party/eigen3/Eigen/src/Core/ForceAlignedAccess.h +++ /dev/null @@ -1,146 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_FORCEALIGNEDACCESS_H -#define EIGEN_FORCEALIGNEDACCESS_H - -namespace Eigen { - -/** \class ForceAlignedAccess - * \ingroup Core_Module - * - * \brief Enforce aligned packet loads and stores regardless of what is requested - * - * \param ExpressionType the type of the object of which we are forcing aligned packet access - * - * This class is the return type of MatrixBase::forceAlignedAccess() - * and most of the time this is the only way it is used. - * - * \sa MatrixBase::forceAlignedAccess() - */ - -namespace internal { -template<typename ExpressionType> -struct traits<ForceAlignedAccess<ExpressionType> > : public traits<ExpressionType> -{}; -} - -template<typename ExpressionType> class ForceAlignedAccess - : public internal::dense_xpr_base< ForceAlignedAccess<ExpressionType> >::type -{ - public: - - typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess) - - inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {} - - inline Index rows() const { return m_expression.rows(); } - inline Index cols() const { return m_expression.cols(); } - inline Index outerStride() const { return m_expression.outerStride(); } - inline Index innerStride() const { return m_expression.innerStride(); } - - inline const CoeffReturnType coeff(Index row, Index col) const - { - return m_expression.coeff(row, col); - } - - inline Scalar& coeffRef(Index row, Index col) - { - return m_expression.const_cast_derived().coeffRef(row, col); - } - - inline const CoeffReturnType coeff(Index index) const - { - return m_expression.coeff(index); - } - - inline Scalar& coeffRef(Index index) - { - return m_expression.const_cast_derived().coeffRef(index); - } - - template<int LoadMode> - inline const PacketScalar packet(Index row, Index col) const - { - return m_expression.template packet<Aligned>(row, col); - } - - template<int LoadMode> - inline void writePacket(Index row, Index col, const PacketScalar& x) - { - m_expression.const_cast_derived().template writePacket<Aligned>(row, col, x); - } - - template<int LoadMode> - inline const PacketScalar packet(Index index) const - { - return m_expression.template packet<Aligned>(index); - } - - template<int LoadMode> - inline void writePacket(Index index, const PacketScalar& x) - { - m_expression.const_cast_derived().template writePacket<Aligned>(index, x); - } - - operator const ExpressionType&() const { return m_expression; } - - protected: - const ExpressionType& m_expression; - - private: - ForceAlignedAccess& operator=(const ForceAlignedAccess&); -}; - -/** \returns an expression of *this with forced aligned access - * \sa forceAlignedAccessIf(),class ForceAlignedAccess - */ -template<typename Derived> -inline const ForceAlignedAccess<Derived> -MatrixBase<Derived>::forceAlignedAccess() const -{ - return ForceAlignedAccess<Derived>(derived()); -} - -/** \returns an expression of *this with forced aligned access - * \sa forceAlignedAccessIf(), class ForceAlignedAccess - */ -template<typename Derived> -inline ForceAlignedAccess<Derived> -MatrixBase<Derived>::forceAlignedAccess() -{ - return ForceAlignedAccess<Derived>(derived()); -} - -/** \returns an expression of *this with forced aligned access if \a Enable is true. - * \sa forceAlignedAccess(), class ForceAlignedAccess - */ -template<typename Derived> -template<bool Enable> -inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type -MatrixBase<Derived>::forceAlignedAccessIf() const -{ - return derived(); -} - -/** \returns an expression of *this with forced aligned access if \a Enable is true. - * \sa forceAlignedAccess(), class ForceAlignedAccess - */ -template<typename Derived> -template<bool Enable> -inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type -MatrixBase<Derived>::forceAlignedAccessIf() -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_FORCEALIGNEDACCESS_H diff --git a/third_party/eigen3/Eigen/src/Core/Functors.h b/third_party/eigen3/Eigen/src/Core/Functors.h deleted file mode 100644 index 39088995bb..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Functors.h +++ /dev/null @@ -1,1095 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_FUNCTORS_H -#define EIGEN_FUNCTORS_H - -namespace Eigen { - -namespace internal { - -// associative functors: - -/** \internal - * \brief Template functor to compute the sum of two scalars - * - * \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, MatrixBase::sum() - */ -template<typename Scalar> struct scalar_sum_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op) - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::padd(a,b); } - template<typename Packet> - EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const - { return internal::predux(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_sum_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasAdd - }; -}; - -/** \internal - * \brief Template functor to compute the product of two scalars - * - * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux() - */ -template<typename LhsScalar,typename RhsScalar> struct scalar_product_op { - enum { - // TODO vectorize mixed product - Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul - }; - typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op) - EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::pmul(a,b); } - template<typename Packet> - EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const - { return internal::predux_mul(a); } -}; -template<typename LhsScalar,typename RhsScalar> -struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > { - enum { - Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost)/2, // rough estimate! - PacketAccess = scalar_product_op<LhsScalar,RhsScalar>::Vectorizable - }; -}; - -/** \internal - * \brief Template functor to compute the conjugate product of two scalars - * - * This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y) - */ -template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op { - - enum { - Conj = NumTraits<LhsScalar>::IsComplex - }; - - typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type; - - EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op) - EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const - { return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); } - - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); } -}; -template<typename LhsScalar,typename RhsScalar> -struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > { - enum { - Cost = NumTraits<LhsScalar>::MulCost, - PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul - }; -}; - -/** \internal - * \brief Template functor to compute the min of two scalars - * - * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff() - */ -template<typename Scalar> struct scalar_min_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op) - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::min; return (min)(a, b); } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::pmin(a,b); } - template<typename Packet> - EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const - { return internal::predux_min(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_min_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasMin - }; -}; - -/** \internal - * \brief Template functor to compute the max of two scalars - * - * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff() - */ -template<typename Scalar> struct scalar_max_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op) - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::max; return (max)(a, b); } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::pmax(a,b); } - template<typename Packet> - EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const - { return internal::predux_max(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_max_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasMax - }; -}; - -/** \internal - * \brief Template functor to compute the hypot of two scalars - * - * \sa MatrixBase::stableNorm(), class Redux - */ -template<typename Scalar> struct scalar_hypot_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op) -// typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const - { - using std::max; - using std::min; - using std::sqrt; - Scalar p = (max)(_x, _y); - Scalar q = (min)(_x, _y); - Scalar qp = q/p; - return p * sqrt(Scalar(1) + qp*qp); - } -}; -template<typename Scalar> -struct functor_traits<scalar_hypot_op<Scalar> > { - enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess=0 }; -}; - -/** \internal - * \brief Template functor to compute the pow of two scalars - */ -template<typename Scalar, typename OtherScalar> struct scalar_binary_pow_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_binary_pow_op) - inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return numext::pow(a, b); } -}; -template<typename Scalar, typename OtherScalar> -struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > { - enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; -}; - -// other binary functors: - -/** \internal - * \brief Template functor to compute the difference of two scalars - * - * \sa class CwiseBinaryOp, MatrixBase::operator- - */ -template<typename Scalar> struct scalar_difference_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op) - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::psub(a,b); } -}; -template<typename Scalar> -struct functor_traits<scalar_difference_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasSub - }; -}; - -/** \internal - * \brief Template functor to compute the quotient of two scalars - * - * \sa class CwiseBinaryOp, Cwise::operator/() - */ -template<typename LhsScalar,typename RhsScalar> struct scalar_quotient_op { - enum { - // TODO vectorize mixed product - Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv - }; - typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op) - EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::pdiv(a,b); } -}; -template<typename LhsScalar,typename RhsScalar> -struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > { - enum { - Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost), // rough estimate! - PacketAccess = scalar_quotient_op<LhsScalar,RhsScalar>::Vectorizable - }; -}; - - - -/** \internal - * \brief Template functor to compute the and of two booleans - * - * \sa class CwiseBinaryOp, ArrayBase::operator&& - */ -struct scalar_boolean_and_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_and_op) - EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a && b; } -}; -template<> struct functor_traits<scalar_boolean_and_op> { - enum { - Cost = NumTraits<bool>::AddCost, - PacketAccess = false - }; -}; - -/** \internal - * \brief Template functor to compute the or of two booleans - * - * \sa class CwiseBinaryOp, ArrayBase::operator|| - */ -struct scalar_boolean_or_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_or_op) - EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a || b; } -}; -template<> struct functor_traits<scalar_boolean_or_op> { - enum { - Cost = NumTraits<bool>::AddCost, - PacketAccess = false - }; -}; - -/** \internal - * \brief Template functor to compute the xor of two booleans - * - * \sa class CwiseBinaryOp, ArrayBase::operator^ - */ -struct scalar_boolean_xor_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_xor_op) - EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a ^ b; } -}; -template<> struct functor_traits<scalar_boolean_xor_op> { - enum { - Cost = NumTraits<bool>::AddCost, - PacketAccess = false - }; -}; - -// unary functors: - -/** \internal - * \brief Template functor to compute the opposite of a scalar - * - * \sa class CwiseUnaryOp, MatrixBase::operator- - */ -template<typename Scalar> struct scalar_opposite_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_opposite_op) - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pnegate(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_opposite_op<Scalar> > -{ enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasNegate }; -}; - -/** \internal - * \brief Template functor to compute the absolute value of a scalar - * - * \sa class CwiseUnaryOp, Cwise::abs - */ -template<typename Scalar> struct scalar_abs_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { using std::abs; return abs(a); } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pabs(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_abs_op<Scalar> > -{ - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasAbs - }; -}; - -/** \internal - * \brief Template functor to compute the squared absolute value of a scalar - * - * \sa class CwiseUnaryOp, Cwise::abs2 - */ -template<typename Scalar> struct scalar_abs2_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs2(a); } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pmul(a,a); } -}; -template<typename Scalar> -struct functor_traits<scalar_abs2_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasAbs2 }; }; - -/** \internal - * \brief Template functor to compute the conjugate of a complex value - * - * \sa class CwiseUnaryOp, MatrixBase::conjugate() - */ -template<typename Scalar> struct scalar_conjugate_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op) - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { using numext::conj; return conj(a); } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_conjugate_op<Scalar> > -{ - enum { - Cost = NumTraits<Scalar>::IsComplex ? NumTraits<Scalar>::AddCost : 0, - PacketAccess = packet_traits<Scalar>::HasConj - }; -}; - -/** \internal - * \brief Template functor to cast a scalar to another type - * - * \sa class CwiseUnaryOp, MatrixBase::cast() - */ -template<typename Scalar, typename NewType> -struct scalar_cast_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op) - typedef NewType result_type; - EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast<Scalar, NewType>(a); } -}; -template<typename Scalar, typename NewType> -struct functor_traits<scalar_cast_op<Scalar,NewType> > -{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to convert a scalar to another type using a custom functor. - * - * \sa class CwiseUnaryOp, MatrixBase::convert() - */ -template<typename Scalar, typename NewType, typename ConvertOp> -struct scalar_convert_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_convert_op) - typedef NewType result_type; - EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return ConvertOp()(a); } -}; -template<typename Scalar, typename NewType, typename ConvertOp> -struct functor_traits<scalar_convert_op<Scalar,NewType,ConvertOp> > -{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to extract the real part of a complex - * - * \sa class CwiseUnaryOp, MatrixBase::real() - */ -template<typename Scalar> -struct scalar_real_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::real(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_real_op<Scalar> > -{ enum { Cost = 0, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to extract the imaginary part of a complex - * - * \sa class CwiseUnaryOp, MatrixBase::imag() - */ -template<typename Scalar> -struct scalar_imag_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::imag(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_imag_op<Scalar> > -{ enum { Cost = 0, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to extract the real part of a complex as a reference - * - * \sa class CwiseUnaryOp, MatrixBase::real() - */ -template<typename Scalar> -struct scalar_real_ref_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::real_ref(*const_cast<Scalar*>(&a)); } -}; -template<typename Scalar> -struct functor_traits<scalar_real_ref_op<Scalar> > -{ enum { Cost = 0, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to extract the imaginary part of a complex as a reference - * - * \sa class CwiseUnaryOp, MatrixBase::imag() - */ -template<typename Scalar> -struct scalar_imag_ref_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::imag_ref(*const_cast<Scalar*>(&a)); } -}; -template<typename Scalar> -struct functor_traits<scalar_imag_ref_op<Scalar> > -{ enum { Cost = 0, PacketAccess = false }; }; - -/** \internal - * - * \brief Template functor to compute the exponential of a scalar - * - * \sa class CwiseUnaryOp, Cwise::exp() - */ -template<typename Scalar> struct scalar_exp_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op) - inline const Scalar operator() (const Scalar& a) const { using std::exp; return exp(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::pexp(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_exp_op<Scalar> > -{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasExp }; }; - -/** \internal - * - * \brief Template functor to compute the logarithm of a scalar - * - * \sa class CwiseUnaryOp, Cwise::log() - */ -template<typename Scalar> struct scalar_log_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op) - inline const Scalar operator() (const Scalar& a) const { using std::log; return log(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::plog(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_log_op<Scalar> > -{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; }; - -/** \internal - * \brief Template functor to multiply a scalar by a fixed other one - * - * \sa class CwiseUnaryOp, MatrixBase::operator*, MatrixBase::operator/ - */ -/* NOTE why doing the pset1() in packetOp *is* an optimization ? - * indeed it seems better to declare m_other as a Packet and do the pset1() once - * in the constructor. However, in practice: - * - GCC does not like m_other as a Packet and generate a load every time it needs it - * - on the other hand GCC is able to moves the pset1() outside the loop :) - * - simpler code ;) - * (ICC and gcc 4.4 seems to perform well in both cases, the issue is visible with y = a*x + b*y) - */ -template<typename Scalar> -struct scalar_multiple_op { - typedef typename packet_traits<Scalar>::type Packet; - // FIXME default copy constructors seems bugged with std::complex<> - EIGEN_STRONG_INLINE scalar_multiple_op(const scalar_multiple_op& other) : m_other(other.m_other) { } - EIGEN_STRONG_INLINE scalar_multiple_op(const Scalar& other) : m_other(other) { } - EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; } - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pmul(a, pset1<Packet>(m_other)); } - typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_multiple_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; }; - -template<typename Scalar1, typename Scalar2> -struct scalar_multiple2_op { - typedef typename packet_traits<Scalar1>::type Packet1; - typedef typename scalar_product_traits<Scalar1,Scalar2>::ReturnType result_type; - typedef typename packet_traits<result_type>::type packet_result_type; - EIGEN_STRONG_INLINE scalar_multiple2_op(const scalar_multiple2_op& other) : m_other(other.m_other) { } - EIGEN_STRONG_INLINE scalar_multiple2_op(const Scalar2& other) : m_other(other) { } - EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a * m_other; } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const packet_result_type packetOp(const Packet1& a) const - { eigen_assert("packetOp is not defined"); } - typename add_const_on_value_type<typename NumTraits<Scalar2>::Nested>::type m_other; -}; -template<typename Scalar1,typename Scalar2> -struct functor_traits<scalar_multiple2_op<Scalar1,Scalar2> > -{ enum { Cost = NumTraits<Scalar1>::MulCost, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to divide a scalar by a fixed other one - * - * This functor is used to implement the quotient of a matrix by - * a scalar where the scalar type is not necessarily a floating point type. - * - * \sa class CwiseUnaryOp, MatrixBase::operator/ - */ -template<typename Scalar> -struct scalar_quotient1_op { - typedef typename packet_traits<Scalar>::type Packet; - // FIXME default copy constructors seems bugged with std::complex<> - EIGEN_STRONG_INLINE scalar_quotient1_op(const scalar_quotient1_op& other) : m_other(other.m_other) { } - EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other) : m_other(other) {} - EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; } - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pdiv(a, pset1<Packet>(m_other)); } - typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_quotient1_op<Scalar> > -{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; }; - -// nullary functors - -template<typename Scalar> -struct scalar_constant_op { - typedef typename packet_traits<Scalar>::type Packet; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { } - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index, Index = 0) const { return m_other; } - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(Index, Index = 0) const { return internal::pset1<Packet>(m_other); } - const Scalar m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_constant_op<Scalar> > -// FIXME replace this packet test by a safe one -{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; }; - -template<typename Scalar> struct scalar_identity_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op) - template<typename Index> - EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const { return row==col ? Scalar(1) : Scalar(0); } -}; -template<typename Scalar> -struct functor_traits<scalar_identity_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; }; - -template <typename Scalar, bool RandomAccess> struct linspaced_op_impl; - -// linear access for packet ops: -// 1) initialization -// base = [low, ..., low] + ([step, ..., step] * [-size, ..., 0]) -// 2) each step (where size is 1 for coeff access or PacketSize for packet access) -// base += [size*step, ..., size*step] -// -// TODO: Perhaps it's better to initialize lazily (so not in the constructor but in packetOp) -// in order to avoid the padd() in operator() ? -template <typename Scalar> -struct linspaced_op_impl<Scalar,false> -{ - typedef typename packet_traits<Scalar>::type Packet; - - linspaced_op_impl(const Scalar& low, const Scalar& step) : - m_low(low), m_step(step), - m_packetStep(pset1<Packet>(packet_traits<Scalar>::size*step)), - m_base(padd(pset1<Packet>(low), pmul(pset1<Packet>(step),plset<Scalar>(-packet_traits<Scalar>::size)))) {} - - template<typename Index> - EIGEN_STRONG_INLINE const Scalar operator() (Index i) const - { - m_base = padd(m_base, pset1<Packet>(m_step)); - return m_low+Scalar(i)*m_step; - } - - template<typename Index> - EIGEN_STRONG_INLINE const Packet packetOp(Index) const { return m_base = padd(m_base,m_packetStep); } - - const Scalar m_low; - const Scalar m_step; - const Packet m_packetStep; - mutable Packet m_base; -}; - -// random access for packet ops: -// 1) each step -// [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) ) -template <typename Scalar> -struct linspaced_op_impl<Scalar,true> -{ - typedef typename packet_traits<Scalar>::type Packet; - - linspaced_op_impl(const Scalar& low, const Scalar& step) : - m_low(low), m_step(step), - m_lowPacket(pset1<Packet>(m_low)), m_stepPacket(pset1<Packet>(m_step)), m_interPacket(plset<Scalar>(0)) {} - - template<typename Index> - EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; } - - template<typename Index> - EIGEN_STRONG_INLINE const Packet packetOp(Index i) const - { return internal::padd(m_lowPacket, pmul(m_stepPacket, padd(pset1<Packet>(i),m_interPacket))); } - - const Scalar m_low; - const Scalar m_step; - const Packet m_lowPacket; - const Packet m_stepPacket; - const Packet m_interPacket; -}; - -// ----- Linspace functor ---------------------------------------------------------------- - -// Forward declaration (we default to random access which does not really give -// us a speed gain when using packet access but it allows to use the functor in -// nested expressions). -template <typename Scalar, bool RandomAccess = true> struct linspaced_op; -template <typename Scalar, bool RandomAccess> struct functor_traits< linspaced_op<Scalar,RandomAccess> > -{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::HasSetLinear, IsRepeatable = true }; }; -template <typename Scalar, bool RandomAccess> struct linspaced_op -{ - typedef typename packet_traits<Scalar>::type Packet; - linspaced_op(const Scalar& low, const Scalar& high, DenseIndex num_steps) : impl((num_steps==1 ? high : low), (num_steps==1 ? Scalar() : (high-low)/(num_steps-1))) {} - - template<typename Index> - EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return impl(i); } - - // We need this function when assigning e.g. a RowVectorXd to a MatrixXd since - // there row==0 and col is used for the actual iteration. - template<typename Index> - EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const - { - eigen_assert(col==0 || row==0); - return impl(col + row); - } - - template<typename Index> - EIGEN_STRONG_INLINE const Packet packetOp(Index i) const { return impl.packetOp(i); } - - // We need this function when assigning e.g. a RowVectorXd to a MatrixXd since - // there row==0 and col is used for the actual iteration. - template<typename Index> - EIGEN_STRONG_INLINE const Packet packetOp(Index row, Index col) const - { - eigen_assert(col==0 || row==0); - return impl.packetOp(col + row); - } - - // This proxy object handles the actual required temporaries, the different - // implementations (random vs. sequential access) as well as the - // correct piping to size 2/4 packet operations. - const linspaced_op_impl<Scalar,RandomAccess> impl; -}; - -// all functors allow linear access, except scalar_identity_op. So we fix here a quick meta -// to indicate whether a functor allows linear access, just always answering 'yes' except for -// scalar_identity_op. -// FIXME move this to functor_traits adding a functor_default -template<typename Functor> struct functor_has_linear_access { enum { ret = 1 }; }; -template<typename Scalar> struct functor_has_linear_access<scalar_identity_op<Scalar> > { enum { ret = 0 }; }; - -// In Eigen, any binary op (Product, CwiseBinaryOp) require the Lhs and Rhs to have the same scalar type, except for multiplication -// where the mixing of different types is handled by scalar_product_traits -// In particular, real * complex<real> is allowed. -// FIXME move this to functor_traits adding a functor_default -template<typename Functor> struct functor_is_product_like { enum { ret = 0 }; }; -template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; }; -template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_conj_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; }; -template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_quotient_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; }; - - -/** \internal - * \brief Template functor to add a scalar to a fixed other one - * \sa class CwiseUnaryOp, Array::operator+ - */ -/* If you wonder why doing the pset1() in packetOp() is an optimization check scalar_multiple_op */ -template<typename Scalar> -struct scalar_add_op { - typedef typename packet_traits<Scalar>::type Packet; - // FIXME default copy constructors seems bugged with std::complex<> - inline scalar_add_op(const scalar_add_op& other) : m_other(other.m_other) { } - inline scalar_add_op(const Scalar& other) : m_other(other) { } - inline Scalar operator() (const Scalar& a) const { return a + m_other; } - inline const Packet packetOp(const Packet& a) const - { return internal::padd(a, pset1<Packet>(m_other)); } - const Scalar m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_add_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; }; - -/** \internal - * \brief Template functor to compute the square root of a scalar - * \sa class CwiseUnaryOp, Cwise::sqrt() - */ -template<typename Scalar> struct scalar_sqrt_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op) - inline const Scalar operator() (const Scalar& a) const { using std::sqrt; return sqrt(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_sqrt_op<Scalar> > -{ enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasSqrt - }; -}; - -/** \internal - * \brief Template functor to compute the cosine of a scalar - * \sa class CwiseUnaryOp, ArrayBase::cos() - */ -template<typename Scalar> struct scalar_cos_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op) - inline Scalar operator() (const Scalar& a) const { using std::cos; return cos(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::pcos(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_cos_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasCos - }; -}; - -/** \internal - * \brief Template functor to compute the sine of a scalar - * \sa class CwiseUnaryOp, ArrayBase::sin() - */ -template<typename Scalar> struct scalar_sin_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op) - inline const Scalar operator() (const Scalar& a) const { using std::sin; return sin(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::psin(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_sin_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasSin - }; -}; - -/** \internal - * \brief Template functor to compute the tan of a scalar - * \sa class CwiseUnaryOp, ArrayBase::tan() - */ -template<typename Scalar> struct scalar_tan_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op) - inline const Scalar operator() (const Scalar& a) const { using std::tan; return tan(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::ptan(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_tan_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasTan - }; -}; - -/** \internal - * \brief Template functor to compute the arc cosine of a scalar - * \sa class CwiseUnaryOp, ArrayBase::acos() - */ -template<typename Scalar> struct scalar_acos_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op) - inline const Scalar operator() (const Scalar& a) const { using std::acos; return acos(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::pacos(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_acos_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasACos - }; -}; - -/** \internal - * \brief Template functor to compute the arc sine of a scalar - * \sa class CwiseUnaryOp, ArrayBase::asin() - */ -template<typename Scalar> struct scalar_asin_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op) - inline const Scalar operator() (const Scalar& a) const { using std::asin; return asin(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::pasin(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_asin_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasASin - }; -}; - -/** \internal - * \brief Template functor to compute the lgamma of a scalar - * \sa class CwiseUnaryOp, ArrayBase::lgamma() - */ -template<typename Scalar> struct scalar_lgamma_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_lgamma_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { - using numext::lgamma; return lgamma(a); - } - typedef typename packet_traits<Scalar>::type Packet; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { - return internal::plgamma(a); - } -}; - -template<typename Scalar> -struct functor_traits<scalar_lgamma_op<Scalar> > -{ - enum { - // Guesstimate - Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasLGamma - }; -}; - -/** \internal - * \brief Template functor to compute the erf of a scalar - * \sa class CwiseUnaryOp, ArrayBase::erf() - */ -template<typename Scalar> struct scalar_erf_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_erf_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { - using numext::erf; return erf(a); - } - typedef typename packet_traits<Scalar>::type Packet; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { - return internal::perf(a); - } -}; - -template<typename Scalar> -struct functor_traits<scalar_erf_op<Scalar> > -{ - enum { - // Guesstimate - Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasErf - }; -}; - -/** \internal - * \brief Template functor to compute the erfc of a scalar - * \sa class CwiseUnaryOp, ArrayBase::erfc() - */ -template<typename Scalar> struct scalar_erfc_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_erfc_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { - using numext::erfc; return erfc(a); - } - typedef typename packet_traits<Scalar>::type Packet; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const { - return internal::perfc(a); - } -}; - -template<typename Scalar> -struct functor_traits<scalar_erfc_op<Scalar> > -{ - enum { - // Guesstimate - Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasErfc - }; -}; - - -/** \internal - * \brief Template functor to raise a scalar to a power - * \sa class CwiseUnaryOp, Cwise::pow - */ -template<typename Scalar> -struct scalar_pow_op { - // FIXME default copy constructors seems bugged with std::complex<> - inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { } - inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {} - inline Scalar operator() (const Scalar& a) const { return numext::pow(a, m_exponent); } - const Scalar m_exponent; -}; -template<typename Scalar> -struct functor_traits<scalar_pow_op<Scalar> > -{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to compute the quotient between a scalar and array entries. - * \sa class CwiseUnaryOp, Cwise::inverse() - */ -template<typename Scalar> -struct scalar_inverse_mult_op { - scalar_inverse_mult_op(const Scalar& other) : m_other(other) {} - inline Scalar operator() (const Scalar& a) const { return m_other / a; } - template<typename Packet> - inline const Packet packetOp(const Packet& a) const - { return internal::pdiv(pset1<Packet>(m_other),a); } - Scalar m_other; -}; - -/** \internal - * \brief Template functor to compute the inverse of a scalar - * \sa class CwiseUnaryOp, Cwise::inverse() - */ -template<typename Scalar> -struct scalar_inverse_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_op) - inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; } - template<typename Packet> - inline const Packet packetOp(const Packet& a) const - { return internal::pdiv(pset1<Packet>(Scalar(1)),a); } -}; -template<typename Scalar> -struct functor_traits<scalar_inverse_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; }; - -/** \internal - * \brief Template functor to compute the square of a scalar - * \sa class CwiseUnaryOp, Cwise::square() - */ -template<typename Scalar> -struct scalar_square_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op) - inline Scalar operator() (const Scalar& a) const { return a*a; } - template<typename Packet> - inline const Packet packetOp(const Packet& a) const - { return internal::pmul(a,a); } -}; -template<typename Scalar> -struct functor_traits<scalar_square_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; }; - -/** \internal - * \brief Template functor to compute the cube of a scalar - * \sa class CwiseUnaryOp, Cwise::cube() - */ -template<typename Scalar> -struct scalar_cube_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op) - inline Scalar operator() (const Scalar& a) const { return a*a*a; } - template<typename Packet> - inline const Packet packetOp(const Packet& a) const - { return internal::pmul(a,pmul(a,a)); } -}; -template<typename Scalar> -struct functor_traits<scalar_cube_op<Scalar> > -{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; }; - -// default functor traits for STL functors: - -template<typename T> -struct functor_traits<std::multiplies<T> > -{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::divides<T> > -{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::plus<T> > -{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::minus<T> > -{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::negate<T> > -{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::logical_or<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::logical_and<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::logical_not<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::greater<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::less<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::greater_equal<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::less_equal<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::equal_to<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::not_equal_to<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::binder2nd<T> > -{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::binder1st<T> > -{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::unary_negate<T> > -{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::binary_negate<T> > -{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; }; - -#ifdef EIGEN_STDEXT_SUPPORT - -template<typename T0,typename T1> -struct functor_traits<std::project1st<T0,T1> > -{ enum { Cost = 0, PacketAccess = false }; }; - -template<typename T0,typename T1> -struct functor_traits<std::project2nd<T0,T1> > -{ enum { Cost = 0, PacketAccess = false }; }; - -template<typename T0,typename T1> -struct functor_traits<std::select2nd<std::pair<T0,T1> > > -{ enum { Cost = 0, PacketAccess = false }; }; - -template<typename T0,typename T1> -struct functor_traits<std::select1st<std::pair<T0,T1> > > -{ enum { Cost = 0, PacketAccess = false }; }; - -template<typename T0,typename T1> -struct functor_traits<std::unary_compose<T0,T1> > -{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false }; }; - -template<typename T0,typename T1,typename T2> -struct functor_traits<std::binary_compose<T0,T1,T2> > -{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false }; }; - -#endif // EIGEN_STDEXT_SUPPORT - -// allow to add new functors and specializations of functor_traits from outside Eigen. -// this macro is really needed because functor_traits must be specialized after it is declared but before it is used... -#ifdef EIGEN_FUNCTORS_PLUGIN -#include EIGEN_FUNCTORS_PLUGIN -#endif - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_FUNCTORS_H diff --git a/third_party/eigen3/Eigen/src/Core/Fuzzy.h b/third_party/eigen3/Eigen/src/Core/Fuzzy.h deleted file mode 100644 index 0ff1b96f56..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Fuzzy.h +++ /dev/null @@ -1,155 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_FUZZY_H -#define EIGEN_FUZZY_H - -namespace Eigen { - -namespace internal -{ - -template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger> -struct isApprox_selector -{ - EIGEN_DEVICE_FUNC - static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec) - { - typename internal::nested<Derived,2>::type nested(x); - typename internal::nested<OtherDerived,2>::type otherNested(y); - return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum()); - } -}; - -template<typename Derived, typename OtherDerived> -struct isApprox_selector<Derived, OtherDerived, true> -{ - EIGEN_DEVICE_FUNC - static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&) - { - return x.matrix() == y.matrix(); - } -}; - -template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger> -struct isMuchSmallerThan_object_selector -{ - EIGEN_DEVICE_FUNC - static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec) - { - return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum(); - } -}; - -template<typename Derived, typename OtherDerived> -struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true> -{ - EIGEN_DEVICE_FUNC - static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&) - { - return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix(); - } -}; - -template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger> -struct isMuchSmallerThan_scalar_selector -{ - EIGEN_DEVICE_FUNC - static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec) - { - return x.cwiseAbs2().sum() <= numext::abs2(prec * y); - } -}; - -template<typename Derived> -struct isMuchSmallerThan_scalar_selector<Derived, true> -{ - EIGEN_DEVICE_FUNC - static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&) - { - return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix(); - } -}; - -} // end namespace internal - - -/** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$ - * are considered to be approximately equal within precision \f$ p \f$ if - * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f] - * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm - * L2 norm). - * - * \note Because of the multiplicativeness of this comparison, one can't use this function - * to check whether \c *this is approximately equal to the zero matrix or vector. - * Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix - * or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const - * RealScalar&, RealScalar) instead. - * - * \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const - */ -template<typename Derived> -template<typename OtherDerived> -bool DenseBase<Derived>::isApprox( - const DenseBase<OtherDerived>& other, - const RealScalar& prec -) const -{ - return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec); -} - -/** \returns \c true if the norm of \c *this is much smaller than \a other, - * within the precision determined by \a prec. - * - * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is - * considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if - * \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f] - * - * For matrices, the comparison is done using the Hilbert-Schmidt norm. For this reason, - * the value of the reference scalar \a other should come from the Hilbert-Schmidt norm - * of a reference matrix of same dimensions. - * - * \sa isApprox(), isMuchSmallerThan(const DenseBase<OtherDerived>&, RealScalar) const - */ -template<typename Derived> -bool DenseBase<Derived>::isMuchSmallerThan( - const typename NumTraits<Scalar>::Real& other, - const RealScalar& prec -) const -{ - return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec); -} - -/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other, - * within the precision determined by \a prec. - * - * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is - * considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if - * \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f] - * For matrices, the comparison is done using the Hilbert-Schmidt norm. - * - * \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const - */ -template<typename Derived> -template<typename OtherDerived> -bool DenseBase<Derived>::isMuchSmallerThan( - const DenseBase<OtherDerived>& other, - const RealScalar& prec -) const -{ - return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec); -} - -} // end namespace Eigen - -#endif // EIGEN_FUZZY_H diff --git a/third_party/eigen3/Eigen/src/Core/GeneralProduct.h b/third_party/eigen3/Eigen/src/Core/GeneralProduct.h deleted file mode 100644 index d2618ba25b..0000000000 --- a/third_party/eigen3/Eigen/src/Core/GeneralProduct.h +++ /dev/null @@ -1,674 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERAL_PRODUCT_H -#define EIGEN_GENERAL_PRODUCT_H - -namespace Eigen { - -/** \class GeneralProduct - * \ingroup Core_Module - * - * \brief Expression of the product of two general matrices or vectors - * - * \param LhsNested the type used to store the left-hand side - * \param RhsNested the type used to store the right-hand side - * \param ProductMode the type of the product - * - * This class represents an expression of the product of two general matrices. - * We call a general matrix, a dense matrix with full storage. For instance, - * This excludes triangular, selfadjoint, and sparse matrices. - * It is the return type of the operator* between general matrices. Its template - * arguments are determined automatically by ProductReturnType. Therefore, - * GeneralProduct should never be used direclty. To determine the result type of a - * function which involves a matrix product, use ProductReturnType::Type. - * - * \sa ProductReturnType, MatrixBase::operator*(const MatrixBase<OtherDerived>&) - */ -template<typename Lhs, typename Rhs, int ProductType = internal::product_type<Lhs,Rhs>::value> -class GeneralProduct; - -enum { - Large = 2, - Small = 3 -}; - -namespace internal { - -template<int Rows, int Cols, int Depth> struct product_type_selector; - -template<int Size, int MaxSize> struct product_size_category -{ - enum { is_large = MaxSize == Dynamic || - Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD, - value = is_large ? Large - : Size == 1 ? 1 - : Small - }; -}; - -template<typename Lhs, typename Rhs> struct product_type -{ - typedef typename remove_all<Lhs>::type _Lhs; - typedef typename remove_all<Rhs>::type _Rhs; - enum { - MaxRows = _Lhs::MaxRowsAtCompileTime, - Rows = _Lhs::RowsAtCompileTime, - MaxCols = _Rhs::MaxColsAtCompileTime, - Cols = _Rhs::ColsAtCompileTime, - MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::MaxColsAtCompileTime, - _Rhs::MaxRowsAtCompileTime), - Depth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::ColsAtCompileTime, - _Rhs::RowsAtCompileTime) - }; - - // the splitting into different lines of code here, introducing the _select enums and the typedef below, - // is to work around an internal compiler error with gcc 4.1 and 4.2. -private: - enum { - rows_select = product_size_category<Rows,MaxRows>::value, - cols_select = product_size_category<Cols,MaxCols>::value, - depth_select = product_size_category<Depth,MaxDepth>::value - }; - typedef product_type_selector<rows_select, cols_select, depth_select> selector; - -public: - enum { - value = selector::ret - }; -#ifdef EIGEN_DEBUG_PRODUCT - static void debug() - { - EIGEN_DEBUG_VAR(Rows); - EIGEN_DEBUG_VAR(Cols); - EIGEN_DEBUG_VAR(Depth); - EIGEN_DEBUG_VAR(rows_select); - EIGEN_DEBUG_VAR(cols_select); - EIGEN_DEBUG_VAR(depth_select); - EIGEN_DEBUG_VAR(value); - } -#endif -}; - - -/* The following allows to select the kind of product at compile time - * based on the three dimensions of the product. - * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */ -// FIXME I'm not sure the current mapping is the ideal one. -template<int M, int N> struct product_type_selector<M,N,1> { enum { ret = OuterProduct }; }; -template<int Depth> struct product_type_selector<1, 1, Depth> { enum { ret = InnerProduct }; }; -template<> struct product_type_selector<1, 1, 1> { enum { ret = InnerProduct }; }; -template<> struct product_type_selector<Small,1, Small> { enum { ret = CoeffBasedProductMode }; }; -template<> struct product_type_selector<1, Small,Small> { enum { ret = CoeffBasedProductMode }; }; -template<> struct product_type_selector<Small,Small,Small> { enum { ret = CoeffBasedProductMode }; }; -template<> struct product_type_selector<Small, Small, 1> { enum { ret = LazyCoeffBasedProductMode }; }; -template<> struct product_type_selector<Small, Large, 1> { enum { ret = LazyCoeffBasedProductMode }; }; -template<> struct product_type_selector<Large, Small, 1> { enum { ret = LazyCoeffBasedProductMode }; }; -template<> struct product_type_selector<1, Large,Small> { enum { ret = CoeffBasedProductMode }; }; -template<> struct product_type_selector<1, Large,Large> { enum { ret = GemvProduct }; }; -template<> struct product_type_selector<1, Small,Large> { enum { ret = CoeffBasedProductMode }; }; -template<> struct product_type_selector<Large,1, Small> { enum { ret = CoeffBasedProductMode }; }; -template<> struct product_type_selector<Large,1, Large> { enum { ret = GemvProduct }; }; -template<> struct product_type_selector<Small,1, Large> { enum { ret = CoeffBasedProductMode }; }; -template<> struct product_type_selector<Small,Small,Large> { enum { ret = GemmProduct }; }; -template<> struct product_type_selector<Large,Small,Large> { enum { ret = GemmProduct }; }; -template<> struct product_type_selector<Small,Large,Large> { enum { ret = GemmProduct }; }; -template<> struct product_type_selector<Large,Large,Large> { enum { ret = GemmProduct }; }; -template<> struct product_type_selector<Large,Small,Small> { enum { ret = GemmProduct }; }; -template<> struct product_type_selector<Small,Large,Small> { enum { ret = GemmProduct }; }; -template<> struct product_type_selector<Large,Large,Small> { enum { ret = GemmProduct }; }; - -} // end namespace internal - -/** \class ProductReturnType - * \ingroup Core_Module - * - * \brief Helper class to get the correct and optimized returned type of operator* - * - * \param Lhs the type of the left-hand side - * \param Rhs the type of the right-hand side - * \param ProductMode the type of the product (determined automatically by internal::product_mode) - * - * This class defines the typename Type representing the optimized product expression - * between two matrix expressions. In practice, using ProductReturnType<Lhs,Rhs>::Type - * is the recommended way to define the result type of a function returning an expression - * which involve a matrix product. The class Product should never be - * used directly. - * - * \sa class Product, MatrixBase::operator*(const MatrixBase<OtherDerived>&) - */ -template<typename Lhs, typename Rhs, int ProductType> -struct ProductReturnType -{ - // TODO use the nested type to reduce instanciations ???? -// typedef typename internal::nested<Lhs,Rhs::ColsAtCompileTime>::type LhsNested; -// typedef typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type RhsNested; - - typedef GeneralProduct<Lhs/*Nested*/, Rhs/*Nested*/, ProductType> Type; -}; - -template<typename Lhs, typename Rhs> -struct ProductReturnType<Lhs,Rhs,CoeffBasedProductMode> -{ - typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested; - typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested; - typedef CoeffBasedProduct<LhsNested, RhsNested, EvalBeforeAssigningBit | EvalBeforeNestingBit> Type; -}; - -template<typename Lhs, typename Rhs> -struct ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode> -{ - typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested; - typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested; - typedef CoeffBasedProduct<LhsNested, RhsNested, NestByRefBit> Type; -}; - -// this is a workaround for sun CC -template<typename Lhs, typename Rhs> -struct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode> -{}; - -/*********************************************************************** -* Implementation of Inner Vector Vector Product -***********************************************************************/ - -// FIXME : maybe the "inner product" could return a Scalar -// instead of a 1x1 matrix ?? -// Pro: more natural for the user -// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix -// product ends up to a row-vector times col-vector product... To tackle this use -// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x); - -namespace internal { - -template<typename Lhs, typename Rhs> -struct traits<GeneralProduct<Lhs,Rhs,InnerProduct> > - : traits<Matrix<typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> > -{}; - -} - -template<typename Lhs, typename Rhs> -class GeneralProduct<Lhs, Rhs, InnerProduct> - : internal::no_assignment_operator, - public Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> -{ - typedef Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> Base; - public: - GeneralProduct(const Lhs& lhs, const Rhs& rhs) - { - EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - - Base::coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum(); - } - - /** Convertion to scalar */ - operator const typename Base::Scalar() const { - return Base::coeff(0,0); - } -}; - -/*********************************************************************** -* Implementation of Outer Vector Vector Product -***********************************************************************/ - -namespace internal { - -// Column major -template<typename ProductType, typename Dest, typename Func> -EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const false_type&) -{ - typedef typename Dest::Index Index; - // FIXME make sure lhs is sequentially stored - // FIXME not very good if rhs is real and lhs complex while alpha is real too - const Index cols = dest.cols(); - for (Index j=0; j<cols; ++j) - func(dest.col(j), prod.rhs().coeff(j) * prod.lhs()); -} - -// Row major -template<typename ProductType, typename Dest, typename Func> -EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const true_type&) { - typedef typename Dest::Index Index; - // FIXME make sure rhs is sequentially stored - // FIXME not very good if lhs is real and rhs complex while alpha is real too - const Index rows = dest.rows(); - for (Index i=0; i<rows; ++i) - func(dest.row(i), prod.lhs().coeff(i) * prod.rhs()); -} - -template<typename Lhs, typename Rhs> -struct traits<GeneralProduct<Lhs,Rhs,OuterProduct> > - : traits<ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> > -{}; - -} - -template<typename Lhs, typename Rhs> -class GeneralProduct<Lhs, Rhs, OuterProduct> - : public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> -{ - template<typename T> struct IsRowMajor : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {}; - - public: - EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct) - - GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) - { - EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - } - - struct set { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } }; - struct add { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } }; - struct sub { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } }; - struct adds { - Scalar m_scale; - adds(const Scalar& s) : m_scale(s) {} - template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { - dst.const_cast_derived() += m_scale * src; - } - }; - - template<typename Dest> - inline void evalTo(Dest& dest) const { - internal::outer_product_selector_run(*this, dest, set(), IsRowMajor<Dest>()); - } - - template<typename Dest> - inline void addTo(Dest& dest) const { - internal::outer_product_selector_run(*this, dest, add(), IsRowMajor<Dest>()); - } - - template<typename Dest> - inline void subTo(Dest& dest) const { - internal::outer_product_selector_run(*this, dest, sub(), IsRowMajor<Dest>()); - } - - template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const - { - internal::outer_product_selector_run(*this, dest, adds(alpha), IsRowMajor<Dest>()); - } -}; - -/*********************************************************************** -* Implementation of General Matrix Vector Product -***********************************************************************/ - -/* According to the shape/flags of the matrix we have to distinghish 3 different cases: - * 1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine - * 2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine - * 3 - all other cases are handled using a simple loop along the outer-storage direction. - * Therefore we need a lower level meta selector. - * Furthermore, if the matrix is the rhs, then the product has to be transposed. - */ -namespace internal { - -template<typename Lhs, typename Rhs> -struct traits<GeneralProduct<Lhs,Rhs,GemvProduct> > - : traits<ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> > -{}; - -template<int Side, int StorageOrder, bool BlasCompatible> -struct gemv_selector; - -} // end namespace internal - -template<typename Lhs, typename Rhs> -class GeneralProduct<Lhs, Rhs, GemvProduct> - : public ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> -{ - public: - EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct) - - typedef typename Lhs::Scalar LhsScalar; - typedef typename Rhs::Scalar RhsScalar; - - GeneralProduct(const Lhs& a_lhs, const Rhs& a_rhs) : Base(a_lhs,a_rhs) - { -// EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value), -// YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - } - - enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight }; - typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType; - - template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const - { - eigen_assert(m_lhs.rows() == dst.rows() && m_rhs.cols() == dst.cols()); - internal::gemv_selector<Side,(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor, - bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(*this, dst, alpha); - } -}; - -namespace internal { - -// The vector is on the left => transposition -template<int StorageOrder, bool BlasCompatible> -struct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible> -{ - template<typename ProductType, typename Dest> - static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) - { - Transpose<Dest> destT(dest); - enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor }; - gemv_selector<OnTheRight,OtherStorageOrder,BlasCompatible> - ::run(GeneralProduct<Transpose<const typename ProductType::_RhsNested>,Transpose<const typename ProductType::_LhsNested>, GemvProduct> - (prod.rhs().transpose(), prod.lhs().transpose()), destT, alpha); - } -}; - -template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if; - -template<typename Scalar,int Size,int MaxSize> -struct gemv_static_vector_if<Scalar,Size,MaxSize,false> -{ - EIGEN_STRONG_INLINE Scalar* data() { eigen_internal_assert(false && "should never be called"); return 0; } -}; - -template<typename Scalar,int Size> -struct gemv_static_vector_if<Scalar,Size,Dynamic,true> -{ - EIGEN_STRONG_INLINE Scalar* data() { return 0; } -}; - -template<typename Scalar,int Size,int MaxSize> -struct gemv_static_vector_if<Scalar,Size,MaxSize,true> -{ - #if EIGEN_ALIGN_STATICALLY - internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0> m_data; - EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; } - #else - // Some architectures cannot align on the stack, - // => let's manually enforce alignment by allocating more data and return the address of the first aligned element. - enum { - ForceAlignment = internal::packet_traits<Scalar>::Vectorizable, - PacketSize = internal::packet_traits<Scalar>::size - }; - internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?PacketSize:0),0> m_data; - EIGEN_STRONG_INLINE Scalar* data() { - return ForceAlignment - ? reinterpret_cast<Scalar*>((reinterpret_cast<size_t>(m_data.array) & ~(size_t(EIGEN_ALIGN_BYTES-1))) + EIGEN_ALIGN_BYTES) - : m_data.array; - } - #endif -}; - -template<> struct gemv_selector<OnTheRight,ColMajor,true> -{ - template<typename ProductType, typename Dest> - static inline void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) - { - typedef typename ProductType::Index Index; - typedef typename ProductType::LhsScalar LhsScalar; - typedef typename ProductType::RhsScalar RhsScalar; - typedef typename ProductType::Scalar ResScalar; - typedef typename ProductType::RealScalar RealScalar; - typedef typename ProductType::ActualLhsType ActualLhsType; - typedef typename ProductType::ActualRhsType ActualRhsType; - typedef typename ProductType::LhsBlasTraits LhsBlasTraits; - typedef typename ProductType::RhsBlasTraits RhsBlasTraits; - typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest; - - ActualLhsType actualLhs = LhsBlasTraits::extract(prod.lhs()); - ActualRhsType actualRhs = RhsBlasTraits::extract(prod.rhs()); - - ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) - * RhsBlasTraits::extractScalarFactor(prod.rhs()); - - enum { - // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1 - // on, the other hand it is good for the cache to pack the vector anyways... - EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1, - ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex), - MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal - }; - - gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest; - - bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0)); - bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible; - - RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha); - - ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), - evalToDest ? dest.data() : static_dest.data()); - - if(!evalToDest) - { - #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN - int size = dest.size(); - EIGEN_DENSE_STORAGE_CTOR_PLUGIN - #endif - if(!alphaIsCompatible) - { - MappedDest(actualDestPtr, dest.size()).setZero(); - compatibleAlpha = RhsScalar(1); - } - else - MappedDest(actualDestPtr, dest.size()) = dest; - } - - typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper; - typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper; - general_matrix_vector_product - <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run( - actualLhs.rows(), actualLhs.cols(), - LhsMapper(actualLhs.data(), actualLhs.outerStride()), - RhsMapper(actualRhs.data(), actualRhs.innerStride()), - actualDestPtr, 1, - compatibleAlpha); - - if (!evalToDest) - { - if(!alphaIsCompatible) - dest += actualAlpha * MappedDest(actualDestPtr, dest.size()); - else - dest = MappedDest(actualDestPtr, dest.size()); - } - } -}; - -template<> struct gemv_selector<OnTheRight,RowMajor,true> -{ - template<typename ProductType, typename Dest> - static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) - { - typedef typename ProductType::LhsScalar LhsScalar; - typedef typename ProductType::RhsScalar RhsScalar; - typedef typename ProductType::Scalar ResScalar; - typedef typename ProductType::Index Index; - typedef typename ProductType::ActualLhsType ActualLhsType; - typedef typename ProductType::ActualRhsType ActualRhsType; - typedef typename ProductType::_ActualRhsType _ActualRhsType; - typedef typename ProductType::LhsBlasTraits LhsBlasTraits; - typedef typename ProductType::RhsBlasTraits RhsBlasTraits; - - typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs()); - typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs()); - - ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) - * RhsBlasTraits::extractScalarFactor(prod.rhs()); - - enum { - // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1 - // on, the other hand it is good for the cache to pack the vector anyways... - DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1 - }; - - gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs; - - ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(), - DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data()); - - if(!DirectlyUseRhs) - { - #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN - int size = actualRhs.size(); - EIGEN_DENSE_STORAGE_CTOR_PLUGIN - #endif - Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs; - } - - typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper; - typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper; - general_matrix_vector_product - <Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run( - actualLhs.rows(), actualLhs.cols(), - LhsMapper(actualLhs.data(), actualLhs.outerStride()), - RhsMapper(actualRhsPtr, 1), - dest.data(), dest.innerStride(), - actualAlpha); - } -}; - -template<> struct gemv_selector<OnTheRight,ColMajor,false> -{ - template<typename ProductType, typename Dest> - static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) - { - typedef typename Dest::Index Index; - // TODO makes sure dest is sequentially stored in memory, otherwise use a temp - const Index size = prod.rhs().rows(); - for(Index k=0; k<size; ++k) - dest += (alpha*prod.rhs().coeff(k)) * prod.lhs().col(k); - } -}; - -template<> struct gemv_selector<OnTheRight,RowMajor,false> -{ - template<typename ProductType, typename Dest> - static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) - { - typedef typename Dest::Index Index; - // TODO makes sure rhs is sequentially stored in memory, otherwise use a temp - const Index rows = prod.rows(); - for(Index i=0; i<rows; ++i) - dest.coeffRef(i) += alpha * (prod.lhs().row(i).cwiseProduct(prod.rhs().transpose())).sum(); - } -}; - -} // end namespace internal - -/*************************************************************************** -* Implementation of matrix base methods -***************************************************************************/ - -/** \returns the matrix product of \c *this and \a other. - * - * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*(). - * - * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*() - */ -#ifndef __CUDACC__ - -#ifdef EIGEN_TEST_EVALUATORS -template<typename Derived> -template<typename OtherDerived> -inline const Product<Derived, OtherDerived> -MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const -{ - // A note regarding the function declaration: In MSVC, this function will sometimes - // not be inlined since DenseStorage is an unwindable object for dynamic - // matrices and product types are holding a member to store the result. - // Thus it does not help tagging this function with EIGEN_STRONG_INLINE. - enum { - ProductIsValid = Derived::ColsAtCompileTime==Dynamic - || OtherDerived::RowsAtCompileTime==Dynamic - || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), - AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, - SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) - }; - // note to the lost user: - // * for a dot product use: v1.dot(v2) - // * for a coeff-wise product use: v1.cwiseProduct(v2) - EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), - INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) - EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), - INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) - EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) -#ifdef EIGEN_DEBUG_PRODUCT - internal::product_type<Derived,OtherDerived>::debug(); -#endif - - return Product<Derived, OtherDerived>(derived(), other.derived()); -} -#else -template<typename Derived> -template<typename OtherDerived> -inline const typename ProductReturnType<Derived, OtherDerived>::Type -MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const -{ - // A note regarding the function declaration: In MSVC, this function will sometimes - // not be inlined since DenseStorage is an unwindable object for dynamic - // matrices and product types are holding a member to store the result. - // Thus it does not help tagging this function with EIGEN_STRONG_INLINE. - enum { - ProductIsValid = Derived::ColsAtCompileTime==Dynamic - || OtherDerived::RowsAtCompileTime==Dynamic - || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), - AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, - SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) - }; - // note to the lost user: - // * for a dot product use: v1.dot(v2) - // * for a coeff-wise product use: v1.cwiseProduct(v2) - EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), - INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) - EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), - INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) - EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) -#ifdef EIGEN_DEBUG_PRODUCT - internal::product_type<Derived,OtherDerived>::debug(); -#endif - return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); -} -#endif - -#endif -/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation. - * - * The returned product will behave like any other expressions: the coefficients of the product will be - * computed once at a time as requested. This might be useful in some extremely rare cases when only - * a small and no coherent fraction of the result's coefficients have to be computed. - * - * \warning This version of the matrix product can be much much slower. So use it only if you know - * what you are doing and that you measured a true speed improvement. - * - * \sa operator*(const MatrixBase&) - */ -template<typename Derived> -template<typename OtherDerived> -const typename LazyProductReturnType<Derived,OtherDerived>::Type -MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const -{ - enum { - ProductIsValid = Derived::ColsAtCompileTime==Dynamic - || OtherDerived::RowsAtCompileTime==Dynamic - || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), - AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, - SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) - }; - // note to the lost user: - // * for a dot product use: v1.dot(v2) - // * for a coeff-wise product use: v1.cwiseProduct(v2) - EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), - INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) - EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), - INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) - EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) - - return typename LazyProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_PRODUCT_H diff --git a/third_party/eigen3/Eigen/src/Core/GenericPacketMath.h b/third_party/eigen3/Eigen/src/Core/GenericPacketMath.h deleted file mode 100644 index 8417a5458a..0000000000 --- a/third_party/eigen3/Eigen/src/Core/GenericPacketMath.h +++ /dev/null @@ -1,599 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERIC_PACKET_MATH_H -#define EIGEN_GENERIC_PACKET_MATH_H - -namespace Eigen { - -namespace internal { - -/** \internal - * \file GenericPacketMath.h - * - * Default implementation for types not supported by the vectorization. - * In practice these functions are provided to make easier the writing - * of generic vectorized code. - */ - -#ifndef EIGEN_DEBUG_ALIGNED_LOAD -#define EIGEN_DEBUG_ALIGNED_LOAD -#endif - -#ifndef EIGEN_DEBUG_UNALIGNED_LOAD -#define EIGEN_DEBUG_UNALIGNED_LOAD -#endif - -#ifndef EIGEN_DEBUG_ALIGNED_STORE -#define EIGEN_DEBUG_ALIGNED_STORE -#endif - -#ifndef EIGEN_DEBUG_UNALIGNED_STORE -#define EIGEN_DEBUG_UNALIGNED_STORE -#endif - -struct default_packet_traits -{ - enum { - HasHalfPacket = 0, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasNegate = 1, - HasAbs = 1, - HasAbs2 = 1, - HasMin = 1, - HasMax = 1, - HasConj = 1, - HasSetLinear = 1, - HasBlend = 0, - - HasDiv = 0, - HasSqrt = 0, - HasRsqrt = 0, - HasExp = 0, - HasLog = 0, - HasPow = 0, - - HasSin = 0, - HasCos = 0, - HasTan = 0, - HasASin = 0, - HasACos = 0, - HasATan = 0, - HasTanH = 0, - HasLGamma = 0, - HasErf = 0, - HasErfc = 0 - }; -}; - -template<typename T> struct packet_traits : default_packet_traits -{ - typedef T type; - typedef T half; - enum { - Vectorizable = 0, - size = 1, - AlignedOnScalar = 0, - HasHalfPacket = 0 - }; - enum { - HasAdd = 0, - HasSub = 0, - HasMul = 0, - HasNegate = 0, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasConj = 0, - HasSetLinear = 0 - }; -}; - -template<typename T> struct packet_traits<const T> : packet_traits<T> { }; - - -template <typename Src, typename Tgt> struct type_casting_traits { - enum { - VectorizedCast = 0, - SrcCoeffRatio = 1, - TgtCoeffRatio = 1 - }; -}; - -template <typename T> struct type_casting_traits<T, T> { - enum { - VectorizedCast = 1, - SrcCoeffRatio = 1, - TgtCoeffRatio = 1 - }; -}; - - -/** \internal \returns static_cast<TgtType>(a) (coeff-wise) */ -template <typename SrcPacket, typename TgtPacket> -EIGEN_DEVICE_FUNC inline TgtPacket -pcast(const SrcPacket& a) { - return static_cast<TgtPacket>(a); -} -template <typename SrcPacket, typename TgtPacket> -EIGEN_DEVICE_FUNC inline TgtPacket -pcast(const SrcPacket& a, const SrcPacket& /*b*/) { - return static_cast<TgtPacket>(a); -} - -template <typename SrcPacket, typename TgtPacket> -EIGEN_DEVICE_FUNC inline TgtPacket -pcast(const SrcPacket& a, const SrcPacket& /*b*/, const SrcPacket& /*c*/, const SrcPacket& /*d*/) { - return static_cast<TgtPacket>(a); -} - -/** \internal \returns a + b (coeff-wise) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -padd(const Packet& a, - const Packet& b) { return a+b; } - -/** \internal \returns a - b (coeff-wise) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -psub(const Packet& a, - const Packet& b) { return a-b; } - -/** \internal \returns true for if a == b */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -peq(const Packet& a, const Packet& b) { return a == b; } - -/** \internal \returns true for if a < b */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -plt(const Packet& a, const Packet& b) { return a < b; } - -/** \internal \returns true for if a <= b */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -ple(const Packet& a, const Packet& b) { return a <= b; } - -/** \internal \returns b if false_mask is set, else a */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pselect(const Packet& a, - const Packet& b, - const Packet& false_mask) { - return false_mask ? b : a; -} - -/** \internal \returns -a (coeff-wise) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pnegate(const Packet& a) { return -a; } - -/** \internal \returns conj(a) (coeff-wise) */ - -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pconj(const Packet& a) { return numext::conj(a); } - -/** \internal \returns a * b (coeff-wise) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pmul(const Packet& a, - const Packet& b) { return a*b; } - -/** \internal \returns a / b (coeff-wise) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pdiv(const Packet& a, - const Packet& b) { return a/b; } - -/** \internal \returns the min of \a a and \a b (coeff-wise) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pmin(const Packet& a, - const Packet& b) { return numext::mini(a, b); } - -/** \internal \returns the max of \a a and \a b (coeff-wise) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pmax(const Packet& a, - const Packet& b) { return numext::maxi(a, b); } - -/** \internal \returns the absolute value of \a a */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pabs(const Packet& a) { using std::abs; return abs(a); } - -/** \internal \returns the bitwise and of \a a and \a b */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pand(const Packet& a, const Packet& b) { return a & b; } - -/** \internal \returns the bitwise or of \a a and \a b */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -por(const Packet& a, const Packet& b) { return a | b; } - -/** \internal \returns the bitwise xor of \a a and \a b */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pxor(const Packet& a, const Packet& b) { return a ^ b; } - -/** \internal \returns the bitwise andnot of \a a and \a b */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pandnot(const Packet& a, const Packet& b) { return a & (!b); } - -/** \internal \returns a packet version of \a *from, from must be 16 bytes aligned */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pload(const typename unpacket_traits<Packet>::type* from) { return *from; } - -/** \internal \returns a packet version of \a *from, (un-aligned load) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; } - -/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pset1(const typename unpacket_traits<Packet>::type& a) { return a; } - -/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pload1(const typename unpacket_traits<Packet>::type *a) { return pset1<Packet>(*a); } - -/** \internal \returns a packet with elements of \a *from duplicated. - * For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and - * duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]} - * Currently, this function is only used for scalar * complex products. - */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; } - -/** \internal \returns a packet with elements of \a *from quadrupled. - * For instance, for a packet of 8 elements, 2 scalars will be read from \a *from and - * replicated to form: {from[0],from[0],from[0],from[0],from[1],from[1],from[1],from[1]} - * Currently, this function is only used in matrix products. - * For packet-size smaller or equal to 4, this function is equivalent to pload1 - */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -ploadquad(const typename unpacket_traits<Packet>::type* from) -{ return pload1<Packet>(from); } - -/** \internal equivalent to - * \code - * a0 = pload1(a+0); - * a1 = pload1(a+1); - * a2 = pload1(a+2); - * a3 = pload1(a+3); - * \endcode - * \sa pset1, pload1, ploaddup, pbroadcast2 - */ -template<typename Packet> EIGEN_DEVICE_FUNC -inline void pbroadcast4(const typename unpacket_traits<Packet>::type *a, - Packet& a0, Packet& a1, Packet& a2, Packet& a3) -{ - a0 = pload1<Packet>(a+0); - a1 = pload1<Packet>(a+1); - a2 = pload1<Packet>(a+2); - a3 = pload1<Packet>(a+3); -} - -/** \internal equivalent to - * \code - * a0 = pload1(a+0); - * a1 = pload1(a+1); - * \endcode - * \sa pset1, pload1, ploaddup, pbroadcast4 - */ -template<typename Packet> EIGEN_DEVICE_FUNC -inline void pbroadcast2(const typename unpacket_traits<Packet>::type *a, - Packet& a0, Packet& a1) -{ - a0 = pload1<Packet>(a+0); - a1 = pload1<Packet>(a+1); -} - -/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */ -template<typename Scalar> inline typename packet_traits<Scalar>::type -plset(const Scalar& a) { return a; } - -/** \internal copy the packet \a from to \a *to, \a to must be 16 bytes aligned */ -template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from) -{ (*to) = from; } - -/** \internal copy the packet \a from to \a *to, (un-aligned store) */ -template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from) -{ (*to) = from; } - - template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, int /*stride*/) - { return ploadu<Packet>(from); } - - template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, int /*stride*/) - { pstore(to, from); } - -/** \internal tries to do cache prefetching of \a addr */ -template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr) -{ -#ifdef __CUDA_ARCH__ -#if defined(__LP64__) - // 64-bit pointer operand constraint for inlined asm - asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr)); -#else - // 32-bit pointer operand constraint for inlined asm - asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr)); -#endif -#elif !defined(_MSC_VER) - __builtin_prefetch(addr); -#endif -} - -/** \internal \returns the first element of a packet */ -template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type pfirst(const Packet& a) -{ return a; } - -/** \internal \returns a packet where the element i contains the sum of the packet of \a vec[i] */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -preduxp(const Packet* vecs) { return vecs[0]; } - -/** \internal \returns the sum of the elements of \a a*/ -template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux(const Packet& a) -{ return a; } - -/** \internal \returns the sum of the elements of \a a by block of 4 elements. - * For a packet {a0, a1, a2, a3, a4, a5, a6, a7}, it returns a half packet {a0+a4, a1+a5, a2+a6, a3+a7} - * For packet-size smaller or equal to 4, this boils down to a noop. - */ -template<typename Packet> EIGEN_DEVICE_FUNC inline -typename conditional<(unpacket_traits<Packet>::size%8)==0,typename unpacket_traits<Packet>::half,Packet>::type -predux4(const Packet& a) -{ return a; } - -/** \internal \returns the product of the elements of \a a*/ -template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(const Packet& a) -{ return a; } - -/** \internal \returns the min of the elements of \a a*/ -template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(const Packet& a) -{ return a; } - -/** \internal \returns the max of the elements of \a a*/ -template<typename Packet> EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(const Packet& a) -{ return a; } - -/** \internal \returns the reversed elements of \a a*/ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a) -{ return a; } - -template<size_t offset, typename Packet> -struct protate_impl -{ - // Empty so attempts to use this unimplemented path will fail to compile. - // Only specializations of this template should be used. -}; - -/** \internal \returns a packet with the coefficients rotated to the right in little-endian convention, - * by the given offset, e.g. for offset == 1: - * (packet[3], packet[2], packet[1], packet[0]) becomes (packet[0], packet[3], packet[2], packet[1]) - */ -template<size_t offset, typename Packet> EIGEN_DEVICE_FUNC inline Packet protate(const Packet& a) -{ - return offset ? protate_impl<offset, Packet>::run(a) : a; -} - -/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a) -{ - // FIXME: uncomment the following in case we drop the internal imag and real functions. -// using std::imag; -// using std::real; - return Packet(imag(a),real(a)); -} - -/************************** -* Special math functions -***************************/ - -/** \internal \returns the sine of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet psin(const Packet& a) { using std::sin; return sin(a); } - -/** \internal \returns the cosine of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet pcos(const Packet& a) { using std::cos; return cos(a); } - -/** \internal \returns the tan of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet ptan(const Packet& a) { using std::tan; return tan(a); } - -/** \internal \returns the arc sine of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet pasin(const Packet& a) { using std::asin; return asin(a); } - -/** \internal \returns the arc cosine of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet pacos(const Packet& a) { using std::acos; return acos(a); } - -/** \internal \returns the atan of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet patan(const Packet& a) { using std::atan; return atan(a); } - -/** \internal \returns the exp of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet pexp(const Packet& a) { using std::exp; return exp(a); } - -/** \internal \returns the log of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet plog(const Packet& a) { using std::log; return log(a); } - -/** \internal \returns the square-root of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet psqrt(const Packet& a) { using std::sqrt; return sqrt(a); } - -/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet prsqrt(const Packet& a) { - using std::sqrt; - const Packet one(1); - return one/sqrt(a); -} - -// Default ptanh approximation threshold, assumes single precision -// floating point. -template<typename Packet> Packet ptanh_approx_threshold() { - return pset1<Packet>(0.01); -} - -/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet ptanh(const Packet& x) -{ - const Packet one = pset1<Packet>(1); - const Packet two = pset1<Packet>(2); - const Packet three = pset1<Packet>(3); - const Packet thresh = ptanh_approx_threshold<Packet>(); - const Packet x2 = pmul(x, x); - const Packet small_approx = pmul(x, psub(one, pdiv(x2, three))); - const Packet med_approx = psub(one, pdiv(two, padd(pexp(pmul(two, x)), one))); - - // If |x| > thresh, tanh(x) = 1-2/(exp(2*x) + 1) - // tanh(x) can be written: x(1 - x^2/3 + ...) for |x| < pi/2 - // Select a thresh s.t. |tanh(x) - x| = O(eps), where for floats, - // If |x| < thresh, tanh(x) = x*(1-x^2/3) - // Use theresh = 0.01 as this matches the float32 approximation - // threshold on my system! - return pselect(med_approx, small_approx, ple(pabs(x), thresh)); -} - -/** \internal \returns the ln(|gamma(\a a)|) (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet plgamma(const Packet& a) { return numext::lgamma(a); } - -/** \internal \returns the erf(\a a) (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet perf(const Packet& a) { return numext::erf(a); } - -/** \internal \returns the erfc(\a a) (coeff-wise) */ -template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -Packet perfc(const Packet& a) { return numext::erfc(a); } - -/*************************************************************************** -* The following functions might not have to be overwritten for vectorized types -***************************************************************************/ - -/** \internal copy a packet with constant coeficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */ -// NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type) -template<typename Packet> -inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename unpacket_traits<Packet>::type& a) -{ - pstore(to, pset1<Packet>(a)); -} - -/** \internal \returns a * b + c (coeff-wise) */ -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pmadd(const Packet& a, - const Packet& b, - const Packet& c) -{ return padd(pmul(a, b),c); } - -/** \internal \returns a packet version of \a *from. - * If LoadMode equals #Aligned, \a from must be 16 bytes aligned */ -template<typename Packet, int LoadMode> -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_traits<Packet>::type* from) -{ - if(LoadMode == Aligned) - return pload<Packet>(from); - else - return ploadu<Packet>(from); -} - -/** \internal copy the packet \a from to \a *to. - * If StoreMode equals #Aligned, \a to must be 16 bytes aligned */ -template<typename Scalar, typename Packet, int LoadMode> -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& from) -{ - if(LoadMode == Aligned) - pstore(to, from); - else - pstoreu(to, from); -} - -/** \internal \returns a packet version of \a *from. - * Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the - * hardware if available to speedup the loading of data that won't be modified - * by the current computation. - */ -template<typename Packet, int LoadMode> -inline Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from) -{ - return ploadt<Packet, LoadMode>(from); -} - -/** \internal default implementation of palign() allowing partial specialization */ -template<int Offset,typename PacketType> -struct palign_impl -{ - // by default data are aligned, so there is nothing to be done :) - static inline void run(PacketType&, const PacketType&) {} -}; - -/** \internal update \a first using the concatenation of the packet_size minus \a Offset last elements - * of \a first and \a Offset first elements of \a second. - * - * This function is currently only used to optimize matrix-vector products on unligned matrices. - * It takes 2 packets that represent a contiguous memory array, and returns a packet starting - * at the position \a Offset. For instance, for packets of 4 elements, we have: - * Input: - * - first = {f0,f1,f2,f3} - * - second = {s0,s1,s2,s3} - * Output: - * - if Offset==0 then {f0,f1,f2,f3} - * - if Offset==1 then {f1,f2,f3,s0} - * - if Offset==2 then {f2,f3,s0,s1} - * - if Offset==3 then {f3,s0,s1,s3} - */ -template<int Offset,typename PacketType> -inline void palign(PacketType& first, const PacketType& second) -{ - palign_impl<Offset,PacketType>::run(first,second); -} - -/*************************************************************************** -* Fast complex products (GCC generates a function call which is very slow) -***************************************************************************/ - -// Eigen+CUDA does not support complexes. -#ifndef __CUDACC__ - -template<> inline std::complex<float> pmul(const std::complex<float>& a, const std::complex<float>& b) -{ return std::complex<float>(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); } - -template<> inline std::complex<double> pmul(const std::complex<double>& a, const std::complex<double>& b) -{ return std::complex<double>(real(a)*real(b) - imag(a)*imag(b), imag(a)*real(b) + real(a)*imag(b)); } - -#endif - - -/*************************************************************************** - * PacketBlock, that is a collection of N packets where the number of words - * in the packet is a multiple of N. -***************************************************************************/ -template <typename Packet,int N=unpacket_traits<Packet>::size> struct PacketBlock { - Packet packet[N]; -}; - -template<typename SquarePacketBlock> EIGEN_DEVICE_FUNC inline void -ptranspose(SquarePacketBlock& /*kernel*/) { - // Nothing to do in the scalar case, i.e. a 1x1 matrix. -} - - -/*************************************************************************** - * Selector, i.e. vector of N boolean values used to select (i.e. blend) - * words from 2 packets. -***************************************************************************/ -template <size_t N> struct Selector { - bool select[N]; -}; - -template<typename Packet> EIGEN_DEVICE_FUNC inline Packet -pblend(const Selector<unpacket_traits<Packet>::size>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) { - return ifPacket.select[0] ? thenPacket : elsePacket; -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_GENERIC_PACKET_MATH_H diff --git a/third_party/eigen3/Eigen/src/Core/GlobalFunctions.h b/third_party/eigen3/Eigen/src/Core/GlobalFunctions.h deleted file mode 100644 index d78978dec2..0000000000 --- a/third_party/eigen3/Eigen/src/Core/GlobalFunctions.h +++ /dev/null @@ -1,97 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010-2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GLOBAL_FUNCTIONS_H -#define EIGEN_GLOBAL_FUNCTIONS_H - -#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR) \ - template<typename Derived> \ - inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \ - NAME(const Eigen::ArrayBase<Derived>& x) { \ - return x.derived(); \ - } - -#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \ - \ - template<typename Derived> \ - struct NAME##_retval<ArrayBase<Derived> > \ - { \ - typedef const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> type; \ - }; \ - template<typename Derived> \ - struct NAME##_impl<ArrayBase<Derived> > \ - { \ - static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \ - { \ - return x.derived(); \ - } \ - }; - - -namespace Eigen -{ - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op) - EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op) - - template<typename Derived> - inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar>, const Derived> - pow(const Eigen::ArrayBase<Derived>& x, const typename Derived::Scalar& exponent) { - return x.derived().pow(exponent); - } - - template<typename Derived> - inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const Derived, const Derived> - pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<Derived>& exponents) - { - return Eigen::CwiseBinaryOp<Eigen::internal::scalar_binary_pow_op<typename Derived::Scalar, typename Derived::Scalar>, const Derived, const Derived>( - x.derived(), - exponents.derived() - ); - } - - /** - * \brief Component-wise division of a scalar by array elements. - **/ - template <typename Derived> - inline const Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived> - operator/(const typename Derived::Scalar& s, const Eigen::ArrayBase<Derived>& a) - { - return Eigen::CwiseUnaryOp<Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>, const Derived>( - a.derived(), - Eigen::internal::scalar_inverse_mult_op<typename Derived::Scalar>(s) - ); - } - - namespace internal - { - EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op) - EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op) - EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op) - } -} - -// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...) - -#endif // EIGEN_GLOBAL_FUNCTIONS_H diff --git a/third_party/eigen3/Eigen/src/Core/IO.h b/third_party/eigen3/Eigen/src/Core/IO.h deleted file mode 100644 index a1a90c119d..0000000000 --- a/third_party/eigen3/Eigen/src/Core/IO.h +++ /dev/null @@ -1,257 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_IO_H -#define EIGEN_IO_H - -namespace Eigen { - -enum { DontAlignCols = 1 }; -enum { StreamPrecision = -1, - FullPrecision = -2 }; - -namespace internal { -template<typename Derived> -std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt); -} - -/** \class IOFormat - * \ingroup Core_Module - * - * \brief Stores a set of parameters controlling the way matrices are printed - * - * List of available parameters: - * - \b precision number of digits for floating point values, or one of the special constants \c StreamPrecision and \c FullPrecision. - * The default is the special value \c StreamPrecision which means to use the - * stream's own precision setting, as set for instance using \c cout.precision(3). The other special value - * \c FullPrecision means that the number of digits will be computed to match the full precision of each floating-point - * type. - * - \b flags an OR-ed combination of flags, the default value is 0, the only currently available flag is \c DontAlignCols which - * allows to disable the alignment of columns, resulting in faster code. - * - \b coeffSeparator string printed between two coefficients of the same row - * - \b rowSeparator string printed between two rows - * - \b rowPrefix string printed at the beginning of each row - * - \b rowSuffix string printed at the end of each row - * - \b matPrefix string printed at the beginning of the matrix - * - \b matSuffix string printed at the end of the matrix - * - * Example: \include IOFormat.cpp - * Output: \verbinclude IOFormat.out - * - * \sa DenseBase::format(), class WithFormat - */ -struct IOFormat -{ - /** Default constructor, see class IOFormat for the meaning of the parameters */ - IOFormat(int _precision = StreamPrecision, int _flags = 0, - const std::string& _coeffSeparator = " ", - const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="", - const std::string& _matPrefix="", const std::string& _matSuffix="") - : matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator), - rowSpacer(""), coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags) - { - // TODO check if rowPrefix, rowSuffix or rowSeparator contains a newline - // don't add rowSpacer if columns are not to be aligned - if((flags & DontAlignCols)) - return; - int i = int(matSuffix.length())-1; - while (i>=0 && matSuffix[i]!='\n') - { - rowSpacer += ' '; - i--; - } - } - std::string matPrefix, matSuffix; - std::string rowPrefix, rowSuffix, rowSeparator, rowSpacer; - std::string coeffSeparator; - int precision; - int flags; -}; - -/** \class WithFormat - * \ingroup Core_Module - * - * \brief Pseudo expression providing matrix output with given format - * - * \param ExpressionType the type of the object on which IO stream operations are performed - * - * This class represents an expression with stream operators controlled by a given IOFormat. - * It is the return type of DenseBase::format() - * and most of the time this is the only way it is used. - * - * See class IOFormat for some examples. - * - * \sa DenseBase::format(), class IOFormat - */ -template<typename ExpressionType> -class WithFormat -{ - public: - - WithFormat(const ExpressionType& matrix, const IOFormat& format) - : m_matrix(matrix), m_format(format) - {} - - friend std::ostream & operator << (std::ostream & s, const WithFormat& wf) - { - return internal::print_matrix(s, wf.m_matrix.eval(), wf.m_format); - } - - protected: - const typename ExpressionType::Nested m_matrix; - IOFormat m_format; -}; - -/** \returns a WithFormat proxy object allowing to print a matrix the with given - * format \a fmt. - * - * See class IOFormat for some examples. - * - * \sa class IOFormat, class WithFormat - */ -template<typename Derived> -inline const WithFormat<Derived> -DenseBase<Derived>::format(const IOFormat& fmt) const -{ - return WithFormat<Derived>(derived(), fmt); -} - -namespace internal { - -template<typename Scalar, bool IsInteger> -struct significant_decimals_default_impl -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - static inline int run() - { - using std::ceil; - using std::log; - return cast<RealScalar,int>(ceil(-log(NumTraits<RealScalar>::epsilon())/log(RealScalar(10)))); - } -}; - -template<typename Scalar> -struct significant_decimals_default_impl<Scalar, true> -{ - static inline int run() - { - return 0; - } -}; - -template<typename Scalar> -struct significant_decimals_impl - : significant_decimals_default_impl<Scalar, NumTraits<Scalar>::IsInteger> -{}; - -/** \internal - * print the matrix \a _m to the output stream \a s using the output format \a fmt */ -template<typename Derived> -std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt) -{ - if(_m.size() == 0) - { - s << fmt.matPrefix << fmt.matSuffix; - return s; - } - - typename Derived::Nested m = _m; - typedef typename Derived::Scalar Scalar; - typedef typename Derived::Index Index; - - Index width = 0; - - std::streamsize explicit_precision; - if(fmt.precision == StreamPrecision) - { - explicit_precision = 0; - } - else if(fmt.precision == FullPrecision) - { - if (NumTraits<Scalar>::IsInteger) - { - explicit_precision = 0; - } - else - { - explicit_precision = significant_decimals_impl<Scalar>::run(); - } - } - else - { - explicit_precision = fmt.precision; - } - - std::streamsize old_precision = 0; - if(explicit_precision) old_precision = s.precision(explicit_precision); - - bool align_cols = !(fmt.flags & DontAlignCols); - if(align_cols) - { - // compute the largest width - for(Index j = 0; j < m.cols(); ++j) - for(Index i = 0; i < m.rows(); ++i) - { - std::stringstream sstr; - sstr.copyfmt(s); - sstr << m.coeff(i,j); - width = std::max<Index>(width, Index(sstr.str().length())); - } - } - s << fmt.matPrefix; - const char old_fill = s.fill(); - s.fill(' '); - for(Index i = 0; i < m.rows(); ++i) - { - if (i) - s << fmt.rowSpacer; - s << fmt.rowPrefix; - if(width) s.width(width); - s << m.coeff(i, 0); - for(Index j = 1; j < m.cols(); ++j) - { - s << fmt.coeffSeparator; - if (width) s.width(width); - s << m.coeff(i, j); - } - s << fmt.rowSuffix; - if( i < m.rows() - 1) - s << fmt.rowSeparator; - } - s.fill(old_fill); - s << fmt.matSuffix; - if(explicit_precision) s.precision(old_precision); - return s; -} - -} // end namespace internal - -/** \relates DenseBase - * - * Outputs the matrix, to the given stream. - * - * If you wish to print the matrix with a format different than the default, use DenseBase::format(). - * - * It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers. - * If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default parameters. - * - * \sa DenseBase::format() - */ -template<typename Derived> -std::ostream & operator << -(std::ostream & s, - const DenseBase<Derived> & m) -{ - return internal::print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT); -} - -} // end namespace Eigen - -#endif // EIGEN_IO_H diff --git a/third_party/eigen3/Eigen/src/Core/Map.h b/third_party/eigen3/Eigen/src/Core/Map.h deleted file mode 100644 index 0838d69e37..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Map.h +++ /dev/null @@ -1,185 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MAP_H -#define EIGEN_MAP_H - -namespace Eigen { - -/** \class Map - * \ingroup Core_Module - * - * \brief A matrix or vector expression mapping an existing array of data. - * - * \tparam PlainObjectType the equivalent matrix type of the mapped data - * \tparam MapOptions specifies whether the pointer is \c #Aligned, or \c #Unaligned. - * The default is \c #Unaligned. - * \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout - * of an ordinary, contiguous array. This can be overridden by specifying strides. - * The type passed here must be a specialization of the Stride template, see examples below. - * - * This class represents a matrix or vector expression mapping an existing array of data. - * It can be used to let Eigen interface without any overhead with non-Eigen data structures, - * such as plain C arrays or structures from other libraries. By default, it assumes that the - * data is laid out contiguously in memory. You can however override this by explicitly specifying - * inner and outer strides. - * - * Here's an example of simply mapping a contiguous array as a \ref TopicStorageOrders "column-major" matrix: - * \include Map_simple.cpp - * Output: \verbinclude Map_simple.out - * - * If you need to map non-contiguous arrays, you can do so by specifying strides: - * - * Here's an example of mapping an array as a vector, specifying an inner stride, that is, the pointer - * increment between two consecutive coefficients. Here, we're specifying the inner stride as a compile-time - * fixed value. - * \include Map_inner_stride.cpp - * Output: \verbinclude Map_inner_stride.out - * - * Here's an example of mapping an array while specifying an outer stride. Here, since we're mapping - * as a column-major matrix, 'outer stride' means the pointer increment between two consecutive columns. - * Here, we're specifying the outer stride as a runtime parameter. Note that here \c OuterStride<> is - * a short version of \c OuterStride<Dynamic> because the default template parameter of OuterStride - * is \c Dynamic - * \include Map_outer_stride.cpp - * Output: \verbinclude Map_outer_stride.out - * - * For more details and for an example of specifying both an inner and an outer stride, see class Stride. - * - * \b Tip: to change the array of data mapped by a Map object, you can use the C++ - * placement new syntax: - * - * Example: \include Map_placement_new.cpp - * Output: \verbinclude Map_placement_new.out - * - * This class is the return type of PlainObjectBase::Map() but can also be used directly. - * - * \sa PlainObjectBase::Map(), \ref TopicStorageOrders - */ - -namespace internal { -template<typename PlainObjectType, int MapOptions, typename StrideType> -struct traits<Map<PlainObjectType, MapOptions, StrideType> > - : public traits<PlainObjectType> -{ - typedef traits<PlainObjectType> TraitsBase; - typedef typename PlainObjectType::Index Index; - typedef typename PlainObjectType::Scalar Scalar; - enum { - InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0 - ? int(PlainObjectType::InnerStrideAtCompileTime) - : int(StrideType::InnerStrideAtCompileTime), - OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0 - ? int(PlainObjectType::OuterStrideAtCompileTime) - : int(StrideType::OuterStrideAtCompileTime), - HasNoInnerStride = InnerStrideAtCompileTime == 1, - HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0, - HasNoStride = HasNoInnerStride && HasNoOuterStride, - IsAligned = bool(EIGEN_ALIGN) && ((int(MapOptions)&Aligned)==Aligned), - IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic, - KeepsPacketAccess = bool(HasNoInnerStride) - && ( bool(IsDynamicSize) - || HasNoOuterStride - || ( OuterStrideAtCompileTime!=Dynamic - && ((static_cast<int>(sizeof(Scalar))*OuterStrideAtCompileTime)%EIGEN_ALIGN_BYTES)==0 ) ), - Flags0 = TraitsBase::Flags & (~NestByRefBit), - Flags1 = IsAligned ? (int(Flags0) | AlignedBit) : (int(Flags0) & ~AlignedBit), - Flags2 = (bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime)) - ? int(Flags1) : int(Flags1 & ~LinearAccessBit), - Flags3 = is_lvalue<PlainObjectType>::value ? int(Flags2) : (int(Flags2) & ~LvalueBit), - Flags = KeepsPacketAccess ? int(Flags3) : (int(Flags3) & ~PacketAccessBit) - }; -private: - enum { Options }; // Expressions don't have Options -}; -} - -template<typename PlainObjectType, int MapOptions, typename StrideType> class Map - : public MapBase<Map<PlainObjectType, MapOptions, StrideType> > -{ - public: - - typedef MapBase<Map> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Map) - - typedef typename Base::PointerType PointerType; -#if EIGEN2_SUPPORT_STAGE <= STAGE30_FULL_EIGEN3_API - typedef const Scalar* PointerArgType; - inline PointerType cast_to_pointer_type(PointerArgType ptr) { return const_cast<PointerType>(ptr); } -#else - typedef PointerType PointerArgType; - EIGEN_DEVICE_FUNC - inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; } -#endif - - EIGEN_DEVICE_FUNC - inline Index innerStride() const - { - return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1; - } - - EIGEN_DEVICE_FUNC - inline Index outerStride() const - { - return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer() - : IsVectorAtCompileTime ? this->size() - : int(Flags)&RowMajorBit ? this->cols() - : this->rows(); - } - - /** Constructor in the fixed-size case. - * - * \param dataPtr pointer to the array to map - * \param a_stride optional Stride object, passing the strides. - */ - EIGEN_DEVICE_FUNC - inline Map(PointerArgType dataPtr, const StrideType& a_stride = StrideType()) - : Base(cast_to_pointer_type(dataPtr)), m_stride(a_stride) - { - PlainObjectType::Base::_check_template_params(); - } - - /** Constructor in the dynamic-size vector case. - * - * \param dataPtr pointer to the array to map - * \param a_size the size of the vector expression - * \param a_stride optional Stride object, passing the strides. - */ - EIGEN_DEVICE_FUNC - inline Map(PointerArgType dataPtr, Index a_size, const StrideType& a_stride = StrideType()) - : Base(cast_to_pointer_type(dataPtr), a_size), m_stride(a_stride) - { - PlainObjectType::Base::_check_template_params(); - } - - /** Constructor in the dynamic-size matrix case. - * - * \param dataPtr pointer to the array to map - * \param nbRows the number of rows of the matrix expression - * \param nbCols the number of columns of the matrix expression - * \param a_stride optional Stride object, passing the strides. - */ - EIGEN_DEVICE_FUNC - inline Map(PointerArgType dataPtr, Index nbRows, Index nbCols, const StrideType& a_stride = StrideType()) - : Base(cast_to_pointer_type(dataPtr), nbRows, nbCols), m_stride(a_stride) - { - PlainObjectType::Base::_check_template_params(); - } - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map) - - protected: - StrideType m_stride; -}; - - -} // end namespace Eigen - -#endif // EIGEN_MAP_H diff --git a/third_party/eigen3/Eigen/src/Core/MapBase.h b/third_party/eigen3/Eigen/src/Core/MapBase.h deleted file mode 100644 index e8ecb175bf..0000000000 --- a/third_party/eigen3/Eigen/src/Core/MapBase.h +++ /dev/null @@ -1,257 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MAPBASE_H -#define EIGEN_MAPBASE_H - -#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \ - EIGEN_STATIC_ASSERT((int(internal::traits<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \ - YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT) - -namespace Eigen { - -/** \class MapBase - * \ingroup Core_Module - * - * \brief Base class for Map and Block expression with direct access - * - * \sa class Map, class Block - */ -template<typename Derived> class MapBase<Derived, ReadOnlyAccessors> - : public internal::dense_xpr_base<Derived>::type -{ - public: - - typedef typename internal::dense_xpr_base<Derived>::type Base; - enum { - RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime, - ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime, - SizeAtCompileTime = Base::SizeAtCompileTime - }; - - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::packet_traits<Scalar>::type PacketScalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename internal::conditional< - bool(internal::is_lvalue<Derived>::value), - Scalar *, - const Scalar *>::type - PointerType; - - using Base::derived; -// using Base::RowsAtCompileTime; -// using Base::ColsAtCompileTime; -// using Base::SizeAtCompileTime; - using Base::MaxRowsAtCompileTime; - using Base::MaxColsAtCompileTime; - using Base::MaxSizeAtCompileTime; - using Base::IsVectorAtCompileTime; - using Base::Flags; - using Base::IsRowMajor; - - using Base::rows; - using Base::cols; - using Base::size; - using Base::coeff; - using Base::coeffRef; - using Base::lazyAssign; - using Base::eval; - - using Base::innerStride; - using Base::outerStride; - using Base::rowStride; - using Base::colStride; - - // bug 217 - compile error on ICC 11.1 - using Base::operator=; - - typedef typename Base::CoeffReturnType CoeffReturnType; - - EIGEN_DEVICE_FUNC inline Index rows() const { return m_rows.value(); } - EIGEN_DEVICE_FUNC inline Index cols() const { return m_cols.value(); } - - /** Returns a pointer to the first coefficient of the matrix or vector. - * - * \note When addressing this data, make sure to honor the strides returned by innerStride() and outerStride(). - * - * \sa innerStride(), outerStride() - */ - inline const Scalar* data() const { return m_data; } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeff(Index rowId, Index colId) const - { - return m_data[colId * colStride() + rowId * rowStride()]; - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeff(Index index) const - { - EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) - return m_data[index * innerStride()]; - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index rowId, Index colId) const - { - return this->m_data[colId * colStride() + rowId * rowStride()]; - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index index) const - { - EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) - return this->m_data[index * innerStride()]; - } - - template<int LoadMode> - inline PacketScalar packet(Index rowId, Index colId) const - { - return internal::ploadt<PacketScalar, LoadMode> - (m_data + (colId * colStride() + rowId * rowStride())); - } - - template<int LoadMode> - inline PacketScalar packet(Index index) const - { - EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) - return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride()); - } - - EIGEN_DEVICE_FUNC - inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime) - { - EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived) - checkSanity(); - } - - EIGEN_DEVICE_FUNC - inline MapBase(PointerType dataPtr, Index vecSize) - : m_data(dataPtr), - m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)), - m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime)) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - eigen_assert(vecSize >= 0); - eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize); - checkSanity(); - } - - EIGEN_DEVICE_FUNC - inline MapBase(PointerType dataPtr, Index nbRows, Index nbCols) - : m_data(dataPtr), m_rows(nbRows), m_cols(nbCols) - { - eigen_assert( (dataPtr == 0) - || ( nbRows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == nbRows) - && nbCols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == nbCols))); - checkSanity(); - } - - protected: - - EIGEN_DEVICE_FUNC - void checkSanity() const - { - EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(internal::traits<Derived>::Flags&PacketAccessBit, - internal::inner_stride_at_compile_time<Derived>::ret==1), - PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1); - eigen_assert(EIGEN_IMPLIES(internal::traits<Derived>::Flags&AlignedBit, (size_t(m_data) % EIGEN_ALIGN_BYTES) == 0) - && "data is not aligned"); - } - - PointerType m_data; - const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows; - const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols; -}; - -template<typename Derived> class MapBase<Derived, WriteAccessors> - : public MapBase<Derived, ReadOnlyAccessors> -{ - public: - - typedef MapBase<Derived, ReadOnlyAccessors> Base; - - typedef typename Base::Scalar Scalar; - typedef typename Base::PacketScalar PacketScalar; - typedef typename Base::Index Index; - typedef typename Base::PointerType PointerType; - - using Base::derived; - using Base::rows; - using Base::cols; - using Base::size; - using Base::coeff; - using Base::coeffRef; - - using Base::innerStride; - using Base::outerStride; - using Base::rowStride; - using Base::colStride; - - typedef typename internal::conditional< - internal::is_lvalue<Derived>::value, - Scalar, - const Scalar - >::type ScalarWithConstIfNotLvalue; - - EIGEN_DEVICE_FUNC - inline const Scalar* data() const { return this->m_data; } - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error - - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col) - { - return this->m_data[col * colStride() + row * rowStride()]; - } - - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue& coeffRef(Index index) - { - EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) - return this->m_data[index * innerStride()]; - } - - template<int StoreMode> - inline void writePacket(Index row, Index col, const PacketScalar& val) - { - internal::pstoret<Scalar, PacketScalar, StoreMode> - (this->m_data + (col * colStride() + row * rowStride()), val); - } - - template<int StoreMode> - inline void writePacket(Index index, const PacketScalar& val) - { - EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) - internal::pstoret<Scalar, PacketScalar, StoreMode> - (this->m_data + index * innerStride(), val); - } - - EIGEN_DEVICE_FUNC explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {} - EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {} - EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index nbRows, Index nbCols) : Base(dataPtr, nbRows, nbCols) {} - - EIGEN_DEVICE_FUNC - Derived& operator=(const MapBase& other) - { - Base::Base::operator=(other); - return derived(); - } - - using Base::Base::operator=; -}; - -#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS - -} // end namespace Eigen - -#endif // EIGEN_MAPBASE_H diff --git a/third_party/eigen3/Eigen/src/Core/MathFunctions.h b/third_party/eigen3/Eigen/src/Core/MathFunctions.h deleted file mode 100644 index 941f72d224..0000000000 --- a/third_party/eigen3/Eigen/src/Core/MathFunctions.h +++ /dev/null @@ -1,1089 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MATHFUNCTIONS_H -#define EIGEN_MATHFUNCTIONS_H - -// source: http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html -#define EIGEN_PI 3.141592653589793238462643383279502884197169399375105820974944592307816406 - -namespace Eigen { - -// On WINCE, std::abs is defined for int only, so let's defined our own overloads: -// This issue has been confirmed with MSVC 2008 only, but the issue might exist for more recent versions too. -#if EIGEN_OS_WINCE && EIGEN_COMP_MSVC && EIGEN_COMP_MSVC<=1500 -long abs(long x) { return (labs(x)); } -double abs(double x) { return (fabs(x)); } -float abs(float x) { return (fabsf(x)); } -long double abs(long double x) { return (fabsl(x)); } -#endif - -namespace internal { - -/** \internal \struct global_math_functions_filtering_base - * - * What it does: - * Defines a typedef 'type' as follows: - * - if type T has a member typedef Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl, then - * global_math_functions_filtering_base<T>::type is a typedef for it. - * - otherwise, global_math_functions_filtering_base<T>::type is a typedef for T. - * - * How it's used: - * To allow to defined the global math functions (like sin...) in certain cases, like the Array expressions. - * When you do sin(array1+array2), the object array1+array2 has a complicated expression type, all what you want to know - * is that it inherits ArrayBase. So we implement a partial specialization of sin_impl for ArrayBase<Derived>. - * So we must make sure to use sin_impl<ArrayBase<Derived> > and not sin_impl<Derived>, otherwise our partial specialization - * won't be used. How does sin know that? That's exactly what global_math_functions_filtering_base tells it. - * - * How it's implemented: - * SFINAE in the style of enable_if. Highly susceptible of breaking compilers. With GCC, it sure does work, but if you replace - * the typename dummy by an integer template parameter, it doesn't work anymore! - */ - -template<typename T, typename dummy = void> -struct global_math_functions_filtering_base -{ - typedef T type; -}; - -template<typename T> struct always_void { typedef void type; }; - -template<typename T> -struct global_math_functions_filtering_base - <T, - typename always_void<typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl>::type - > -{ - typedef typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl type; -}; - -#define EIGEN_MATHFUNC_IMPL(func, scalar) Eigen::internal::func##_impl<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type> -#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename Eigen::internal::func##_retval<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>::type - -/**************************************************************************** -* Implementation of real * -****************************************************************************/ - -template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex> -struct real_default_impl -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const Scalar& x) - { - return x; - } -}; - -template<typename Scalar> -struct real_default_impl<Scalar,true> -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const Scalar& x) - { - using std::real; - return real(x); - } -}; - -template<typename Scalar> struct real_impl : real_default_impl<Scalar> {}; - -template<typename Scalar> -struct real_retval -{ - typedef typename NumTraits<Scalar>::Real type; -}; - -/**************************************************************************** -* Implementation of imag * -****************************************************************************/ - -template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex> -struct imag_default_impl -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const Scalar&) - { - return RealScalar(0); - } -}; - -template<typename Scalar> -struct imag_default_impl<Scalar,true> -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const Scalar& x) - { - using std::imag; - return imag(x); - } -}; - -template<typename Scalar> struct imag_impl : imag_default_impl<Scalar> {}; - -template<typename Scalar> -struct imag_retval -{ - typedef typename NumTraits<Scalar>::Real type; -}; - -/**************************************************************************** -* Implementation of real_ref * -****************************************************************************/ - -template<typename Scalar> -struct real_ref_impl -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar& run(Scalar& x) - { - return reinterpret_cast<RealScalar*>(&x)[0]; - } - EIGEN_DEVICE_FUNC - static inline const RealScalar& run(const Scalar& x) - { - return reinterpret_cast<const RealScalar*>(&x)[0]; - } -}; - -template<typename Scalar> -struct real_ref_retval -{ - typedef typename NumTraits<Scalar>::Real & type; -}; - -/**************************************************************************** -* Implementation of imag_ref * -****************************************************************************/ - -template<typename Scalar, bool IsComplex> -struct imag_ref_default_impl -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar& run(Scalar& x) - { - return reinterpret_cast<RealScalar*>(&x)[1]; - } - EIGEN_DEVICE_FUNC - static inline const RealScalar& run(const Scalar& x) - { - return reinterpret_cast<RealScalar*>(&x)[1]; - } -}; - -template<typename Scalar> -struct imag_ref_default_impl<Scalar, false> -{ - EIGEN_DEVICE_FUNC - static inline Scalar run(Scalar&) - { - return Scalar(0); - } - EIGEN_DEVICE_FUNC - static inline const Scalar run(const Scalar&) - { - return Scalar(0); - } -}; - -template<typename Scalar> -struct imag_ref_impl : imag_ref_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {}; - -template<typename Scalar> -struct imag_ref_retval -{ - typedef typename NumTraits<Scalar>::Real & type; -}; - -/**************************************************************************** -* Implementation of conj * -****************************************************************************/ - -template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex> -struct conj_impl -{ - EIGEN_DEVICE_FUNC - static inline Scalar run(const Scalar& x) - { - return x; - } -}; - -template<typename Scalar> -struct conj_impl<Scalar,true> -{ - EIGEN_DEVICE_FUNC - static inline Scalar run(const Scalar& x) - { - using std::conj; - return conj(x); - } -}; - -template<typename Scalar> -struct conj_retval -{ - typedef Scalar type; -}; - -/**************************************************************************** -* Implementation of abs2 * -****************************************************************************/ - -template<typename Scalar> -struct abs2_impl -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const Scalar& x) - { - return x*x; - } -}; - -template<typename RealScalar> -struct abs2_impl<std::complex<RealScalar> > -{ - EIGEN_DEVICE_FUNC - static inline RealScalar run(const std::complex<RealScalar>& x) - { - return real(x)*real(x) + imag(x)*imag(x); - } -}; - -template<typename Scalar> -struct abs2_retval -{ - typedef typename NumTraits<Scalar>::Real type; -}; - -/**************************************************************************** -* Implementation of norm1 * -****************************************************************************/ - -template<typename Scalar, bool IsComplex> -struct norm1_default_impl -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const Scalar& x) - { - using std::abs; - return abs(real(x)) + abs(imag(x)); - } -}; - -template<typename Scalar> -struct norm1_default_impl<Scalar, false> -{ - EIGEN_DEVICE_FUNC - static inline Scalar run(const Scalar& x) - { - using std::abs; - return abs(x); - } -}; - -template<typename Scalar> -struct norm1_impl : norm1_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {}; - -template<typename Scalar> -struct norm1_retval -{ - typedef typename NumTraits<Scalar>::Real type; -}; - -/**************************************************************************** -* Implementation of hypot * -****************************************************************************/ - -template<typename Scalar> -struct hypot_impl -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - static inline RealScalar run(const Scalar& x, const Scalar& y) - { - using std::abs; - using std::sqrt; - RealScalar _x = abs(x); - RealScalar _y = abs(y); - Scalar p, qp; - if(_x>_y) - { - p = _x; - qp = _y / p; - } - else - { - p = _y; - qp = _x / p; - } - if(p==RealScalar(0)) return RealScalar(0); - return p * sqrt(RealScalar(1) + qp*qp); - } -}; - -template<typename Scalar> -struct hypot_retval -{ - typedef typename NumTraits<Scalar>::Real type; -}; - -/**************************************************************************** -* Implementation of cast * -****************************************************************************/ - -template<typename OldType, typename NewType> -struct cast_impl -{ - EIGEN_DEVICE_FUNC static inline NewType run(const OldType& x) - { - return static_cast<NewType>(x); - } -}; - -// here, for once, we're plainly returning NewType: we don't want cast to do weird things. - -template<typename OldType, typename NewType> -EIGEN_DEVICE_FUNC inline NewType cast(const OldType& x) -{ - return cast_impl<OldType, NewType>::run(x); -} - -/**************************************************************************** -* Implementation of atanh2 * -****************************************************************************/ - -template<typename Scalar> -struct atanh2_impl -{ - static inline Scalar run(const Scalar& x, const Scalar& r) - { - EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) - using std::abs; - using std::log; - using std::sqrt; - Scalar z = x / r; - if (r == 0 || abs(z) > sqrt(NumTraits<Scalar>::epsilon())) - return log((r + x) / (r - x)) / 2; - else - return z + z*z*z / 3; - } -}; - -template<typename RealScalar> -struct atanh2_impl<std::complex<RealScalar> > -{ - typedef std::complex<RealScalar> Scalar; - static inline Scalar run(const Scalar& x, const Scalar& r) - { - using std::log; - using std::norm; - using std::sqrt; - Scalar z = x / r; - if (r == Scalar(0) || norm(z) > NumTraits<RealScalar>::epsilon()) - return RealScalar(0.5) * log((r + x) / (r - x)); - else - return z + z*z*z / RealScalar(3); - } -}; - -template<typename Scalar> -struct atanh2_retval -{ - typedef Scalar type; -}; - -/**************************************************************************** -* Implementation of round * -****************************************************************************/ - -#if EIGEN_HAS_CXX11_MATH - template<typename Scalar> - struct round_impl { - static inline Scalar run(const Scalar& x) - { - EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL) - using std::round; - return round(x); - } - }; -#else - template<typename Scalar> - struct round_impl - { - static inline Scalar run(const Scalar& x) - { - EIGEN_STATIC_ASSERT((!NumTraits<Scalar>::IsComplex), NUMERIC_TYPE_MUST_BE_REAL) - using std::floor; - using std::ceil; - return (x > 0.0) ? floor(x + 0.5) : ceil(x - 0.5); - } - }; -#endif - -template<typename Scalar> -struct round_retval -{ - typedef Scalar type; -}; - -/**************************************************************************** -* Implementation of arg * -****************************************************************************/ - -#if EIGEN_HAS_CXX11_MATH - template<typename Scalar> - struct arg_impl { - static inline Scalar run(const Scalar& x) - { - using std::arg; - return arg(x); - } - }; -#else - template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex> - struct arg_default_impl - { - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const Scalar& x) - { - return (x < 0.0) ? EIGEN_PI : 0.0; } - }; - - template<typename Scalar> - struct arg_default_impl<Scalar,true> - { - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline RealScalar run(const Scalar& x) - { - using std::arg; - return arg(x); - } - }; - - template<typename Scalar> struct arg_impl : arg_default_impl<Scalar> {}; -#endif - -template<typename Scalar> -struct arg_retval -{ - typedef typename NumTraits<Scalar>::Real type; -}; - -/**************************************************************************** -* Implementation of log1p * -****************************************************************************/ -template<typename Scalar, bool isComplex = NumTraits<Scalar>::IsComplex > -struct log1p_impl -{ - static inline Scalar run(const Scalar& x) - { - EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) - typedef typename NumTraits<Scalar>::Real RealScalar; - using std::log; - Scalar x1p = RealScalar(1) + x; - return ( x1p == Scalar(1) ) ? x : x * ( log(x1p) / (x1p - RealScalar(1)) ); - } -}; - -#if EIGEN_HAS_CXX11_MATH -template<typename Scalar> -struct log1p_impl<Scalar, false> { - static inline Scalar run(const Scalar& x) - { - EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar) - using std::log1p; - return log1p(x); - } -}; -#endif - -template<typename Scalar> -struct log1p_retval -{ - typedef Scalar type; -}; - -/**************************************************************************** -* Implementation of pow * -****************************************************************************/ - -template<typename Scalar, bool IsInteger> -struct pow_default_impl -{ - typedef Scalar retval; - static inline Scalar run(const Scalar& x, const Scalar& y) - { - using std::pow; - return pow(x, y); - } -}; - -template<typename Scalar> -struct pow_default_impl<Scalar, true> -{ - static inline Scalar run(Scalar x, Scalar y) - { - Scalar res(1); - eigen_assert(!NumTraits<Scalar>::IsSigned || y >= 0); - if(y & 1) res *= x; - y >>= 1; - while(y) - { - x *= x; - if(y&1) res *= x; - y >>= 1; - } - return res; - } -}; - -template<typename Scalar> -struct pow_impl : pow_default_impl<Scalar, NumTraits<Scalar>::IsInteger> {}; - -template<typename Scalar> -struct pow_retval -{ - typedef Scalar type; -}; - -/**************************************************************************** -* Implementation of random * -****************************************************************************/ - -template<typename Scalar, - bool IsComplex, - bool IsInteger> -struct random_default_impl {}; - -template<typename Scalar> -struct random_impl : random_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {}; - -template<typename Scalar> -struct random_retval -{ - typedef Scalar type; -}; - -template<typename Scalar> inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y); -template<typename Scalar> inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(); - -template<typename Scalar> -struct random_default_impl<Scalar, false, false> -{ - static inline Scalar run(const Scalar& x, const Scalar& y) - { - return x + (y-x) * Scalar(std::rand()) / Scalar(RAND_MAX); - } - static inline Scalar run() - { - return run(Scalar(NumTraits<Scalar>::IsSigned ? -1 : 0), Scalar(1)); - } -}; - -enum { - meta_floor_log2_terminate, - meta_floor_log2_move_up, - meta_floor_log2_move_down, - meta_floor_log2_bogus -}; - -template<unsigned int n, int lower, int upper> struct meta_floor_log2_selector -{ - enum { middle = (lower + upper) / 2, - value = (upper <= lower + 1) ? int(meta_floor_log2_terminate) - : (n < (1 << middle)) ? int(meta_floor_log2_move_down) - : (n==0) ? int(meta_floor_log2_bogus) - : int(meta_floor_log2_move_up) - }; -}; - -template<unsigned int n, - int lower = 0, - int upper = sizeof(unsigned int) * CHAR_BIT - 1, - int selector = meta_floor_log2_selector<n, lower, upper>::value> -struct meta_floor_log2 {}; - -template<unsigned int n, int lower, int upper> -struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_down> -{ - enum { value = meta_floor_log2<n, lower, meta_floor_log2_selector<n, lower, upper>::middle>::value }; -}; - -template<unsigned int n, int lower, int upper> -struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_up> -{ - enum { value = meta_floor_log2<n, meta_floor_log2_selector<n, lower, upper>::middle, upper>::value }; -}; - -template<unsigned int n, int lower, int upper> -struct meta_floor_log2<n, lower, upper, meta_floor_log2_terminate> -{ - enum { value = (n >= ((unsigned int)(1) << (lower+1))) ? lower+1 : lower }; -}; - -template<unsigned int n, int lower, int upper> -struct meta_floor_log2<n, lower, upper, meta_floor_log2_bogus> -{ - // no value, error at compile time -}; - -template<typename Scalar> -struct random_default_impl<Scalar, false, true> -{ - static inline Scalar run(const Scalar& x, const Scalar& y) - { - typedef typename conditional<NumTraits<Scalar>::IsSigned,std::ptrdiff_t,std::size_t>::type ScalarX; - if(y<x) - return x; - std::size_t range = ScalarX(y)-ScalarX(x); - std::size_t offset = 0; - // rejection sampling - std::size_t divisor = (range+RAND_MAX-1)/(range+1); - std::size_t multiplier = (range+RAND_MAX-1)/std::size_t(RAND_MAX); - - do { - offset = ( (std::size_t(std::rand()) * multiplier) / divisor ); - } while (offset > range); - - return Scalar(ScalarX(x) + offset); - } - - static inline Scalar run() - { -#ifdef EIGEN_MAKING_DOCS - return run(Scalar(NumTraits<Scalar>::IsSigned ? -10 : 0), Scalar(10)); -#else - enum { rand_bits = meta_floor_log2<(unsigned int)(RAND_MAX)+1>::value, - scalar_bits = sizeof(Scalar) * CHAR_BIT, - shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits)), - offset = NumTraits<Scalar>::IsSigned ? (1 << (EIGEN_PLAIN_ENUM_MIN(rand_bits,scalar_bits)-1)) : 0 - }; - return Scalar((std::rand() >> shift) - offset); -#endif - } -}; - -template<typename Scalar> -struct random_default_impl<Scalar, true, false> -{ - static inline Scalar run(const Scalar& x, const Scalar& y) - { - return Scalar(random(real(x), real(y)), - random(imag(x), imag(y))); - } - static inline Scalar run() - { - typedef typename NumTraits<Scalar>::Real RealScalar; - return Scalar(random<RealScalar>(), random<RealScalar>()); - } -}; - -template<typename Scalar> -inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y) -{ - return EIGEN_MATHFUNC_IMPL(random, Scalar)::run(x, y); -} - -template<typename Scalar> -inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random() -{ - return EIGEN_MATHFUNC_IMPL(random, Scalar)::run(); -} - -} // end namespace internal - -/**************************************************************************** -* Generic math functions * -****************************************************************************/ - -namespace numext { - -#ifndef __CUDA_ARCH__ -template<typename T> -EIGEN_DEVICE_FUNC -EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y) -{ - EIGEN_USING_STD_MATH(min); - return min EIGEN_NOT_A_MACRO (x,y); -} - -template<typename T> -EIGEN_DEVICE_FUNC -EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y) -{ - EIGEN_USING_STD_MATH(max); - return max EIGEN_NOT_A_MACRO (x,y); -} -#else -template<typename T> -EIGEN_DEVICE_FUNC -EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y) -{ - return y < x ? y : x; -} -template<> -EIGEN_DEVICE_FUNC -EIGEN_ALWAYS_INLINE float mini(const float& x, const float& y) -{ - return fmin(x, y); -} -template<typename T> -EIGEN_DEVICE_FUNC -EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y) -{ - return x < y ? y : x; -} -template<> -EIGEN_DEVICE_FUNC -EIGEN_ALWAYS_INLINE float maxi(const float& x, const float& y) -{ - return fmax(x, y); -} -#endif - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x) -{ - return internal::real_ref_impl<Scalar>::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(arg, Scalar) arg(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(arg, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x) -{ - return internal::imag_ref_impl<Scalar>::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y) -{ - return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(atanh2, Scalar) atanh2(const Scalar& x, const Scalar& y) -{ - return EIGEN_MATHFUNC_IMPL(atanh2, Scalar)::run(x, y); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(pow, Scalar) pow(const Scalar& x, const Scalar& y) -{ - return EIGEN_MATHFUNC_IMPL(pow, Scalar)::run(x, y); -} - -template<typename T> -EIGEN_DEVICE_FUNC -bool (isfinite)(const T& x) -{ - #if EIGEN_HAS_CXX11_MATH - using std::isfinite; - return isfinite(x); - #else - return x<NumTraits<T>::highest() && x>NumTraits<T>::lowest(); - #endif -} - -template<typename T> -EIGEN_DEVICE_FUNC -bool (isfinite)(const std::complex<T>& x) -{ - return numext::isfinite(numext::real(x)) && numext::isfinite(numext::imag(x)); -} - -template<typename T> -EIGEN_DEVICE_FUNC -bool (isnan)(const T& x) -{ - #if EIGEN_HAS_CXX11_MATH - using std::isnan; - return isnan(x); - #else - return x != x; - #endif -} - -template<typename T> -EIGEN_DEVICE_FUNC -bool (isnan)(const std::complex<T>& x) -{ - return numext::isnan(numext::real(x)) || numext::isnan(numext::imag(x)); -} - -template<typename T> -EIGEN_DEVICE_FUNC -bool (isinf)(const T& x) -{ - #if EIGEN_HAS_CXX11_MATH - using std::isinf; - return isinf(x); - #else - return x>NumTraits<T>::highest() || x<NumTraits<T>::lowest(); - #endif -} - -template<typename T> -EIGEN_DEVICE_FUNC -bool (isinf)(const std::complex<T>& x) -{ - return (numext::isinf(numext::real(x)) || numext::isinf(numext::imag(x))) && (!numext::isnan(x)); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(round, Scalar) round(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(round, Scalar)::run(x); -} - -template<typename T> -EIGEN_DEVICE_FUNC -T (floor)(const T& x) -{ - using std::floor; - return floor(x); -} - -template<typename T> -EIGEN_DEVICE_FUNC -T (ceil)(const T& x) -{ - using std::ceil; - return ceil(x); -} - -// Log base 2 for 32 bits positive integers. -// Conveniently returns 0 for x==0. -inline int log2(int x) -{ - eigen_assert(x>=0); - unsigned int v(x); - static const int table[32] = { 0, 9, 1, 10, 13, 21, 2, 29, 11, 14, 16, 18, 22, 25, 3, 30, 8, 12, 20, 28, 15, 17, 24, 7, 19, 27, 23, 6, 26, 5, 4, 31 }; - v |= v >> 1; - v |= v >> 2; - v |= v >> 4; - v |= v >> 8; - v |= v >> 16; - return table[(v * 0x07C4ACDDU) >> 27]; -} - -} // end namespace numext - -namespace internal { - -/**************************************************************************** -* Implementation of fuzzy comparisons * -****************************************************************************/ - -template<typename Scalar, - bool IsComplex, - bool IsInteger> -struct scalar_fuzzy_default_impl {}; - -template<typename Scalar> -struct scalar_fuzzy_default_impl<Scalar, false, false> -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - template<typename OtherScalar> EIGEN_DEVICE_FUNC - static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec) - { - using std::abs; - return abs(x) <= abs(y) * prec; - } - EIGEN_DEVICE_FUNC - static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec) - { - using std::abs; - return abs(x - y) <= numext::mini(abs(x), abs(y)) * prec; - } - EIGEN_DEVICE_FUNC - static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec) - { - return x <= y || isApprox(x, y, prec); - } -}; - -template<typename Scalar> -struct scalar_fuzzy_default_impl<Scalar, false, true> -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - template<typename OtherScalar> EIGEN_DEVICE_FUNC - static inline bool isMuchSmallerThan(const Scalar& x, const Scalar&, const RealScalar&) - { - return x == Scalar(0); - } - EIGEN_DEVICE_FUNC - static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar&) - { - return x == y; - } - EIGEN_DEVICE_FUNC - static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar&) - { - return x <= y; - } -}; - -template<typename Scalar> -struct scalar_fuzzy_default_impl<Scalar, true, false> -{ - typedef typename NumTraits<Scalar>::Real RealScalar; - template<typename OtherScalar> - static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec) - { - return numext::abs2(x) <= numext::abs2(y) * prec * prec; - } - static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec) - { - return numext::abs2(x - y) <= numext::mini(numext::abs2(x), numext::abs2(y)) * prec * prec; - } -}; - -template<typename Scalar> -struct scalar_fuzzy_impl : scalar_fuzzy_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {}; - -template<typename Scalar, typename OtherScalar> EIGEN_DEVICE_FUNC -inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, - typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) -{ - return scalar_fuzzy_impl<Scalar>::template isMuchSmallerThan<OtherScalar>(x, y, precision); -} - -template<typename Scalar> EIGEN_DEVICE_FUNC -inline bool isApprox(const Scalar& x, const Scalar& y, - typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) -{ - return scalar_fuzzy_impl<Scalar>::isApprox(x, y, precision); -} - -template<typename Scalar> EIGEN_DEVICE_FUNC -inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, - typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) -{ - return scalar_fuzzy_impl<Scalar>::isApproxOrLessThan(x, y, precision); -} - -/****************************************** -*** The special case of the bool type *** -******************************************/ - -template<> struct random_impl<bool> -{ - static inline bool run() - { - return random<int>(0,1)==0 ? false : true; - } -}; - -template<> struct scalar_fuzzy_impl<bool> -{ - typedef bool RealScalar; - - template<typename OtherScalar> EIGEN_DEVICE_FUNC - static inline bool isMuchSmallerThan(const bool& x, const bool&, const bool&) - { - return !x; - } - - EIGEN_DEVICE_FUNC - static inline bool isApprox(bool x, bool y, bool) - { - return x == y; - } - - EIGEN_DEVICE_FUNC - static inline bool isApproxOrLessThan(const bool& x, const bool& y, const bool&) - { - return (!x) || y; - } - -}; - - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_MATHFUNCTIONS_H diff --git a/third_party/eigen3/Eigen/src/Core/Matrix.h b/third_party/eigen3/Eigen/src/Core/Matrix.h deleted file mode 100644 index 782d67f54f..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Matrix.h +++ /dev/null @@ -1,443 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MATRIX_H -#define EIGEN_MATRIX_H - -namespace Eigen { - -/** \class Matrix - * \ingroup Core_Module - * - * \brief The matrix class, also used for vectors and row-vectors - * - * The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors within Eigen. - * Vectors are matrices with one column, and row-vectors are matrices with one row. - * - * The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note"). - * - * The first three template parameters are required: - * \tparam _Scalar \anchor matrix_tparam_scalar Numeric type, e.g. float, double, int or std::complex<float>. - * User defined sclar types are supported as well (see \ref user_defined_scalars "here"). - * \tparam _Rows Number of rows, or \b Dynamic - * \tparam _Cols Number of columns, or \b Dynamic - * - * The remaining template parameters are optional -- in most cases you don't have to worry about them. - * \tparam _Options \anchor matrix_tparam_options A combination of either \b #RowMajor or \b #ColMajor, and of either - * \b #AutoAlign or \b #DontAlign. - * The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required - * for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size. - * \tparam _MaxRows Maximum number of rows. Defaults to \a _Rows (\ref maxrows "note"). - * \tparam _MaxCols Maximum number of columns. Defaults to \a _Cols (\ref maxrows "note"). - * - * Eigen provides a number of typedefs covering the usual cases. Here are some examples: - * - * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix<double, 2, 2>) - * \li \c Vector4f is a vector of 4 floats (\c Matrix<float, 4, 1>) - * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix<int, 1, 3>) - * - * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix<float, Dynamic, Dynamic>) - * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix<float, Dynamic, 1>) - * - * \li \c Matrix2Xf is a partially fixed-size (dynamic-size) matrix of floats (\c Matrix<float, 2, Dynamic>) - * \li \c MatrixX3d is a partially dynamic-size (fixed-size) matrix of double (\c Matrix<double, Dynamic, 3>) - * - * See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs. - * - * You can access elements of vectors and matrices using normal subscripting: - * - * \code - * Eigen::VectorXd v(10); - * v[0] = 0.1; - * v[1] = 0.2; - * v(0) = 0.3; - * v(1) = 0.4; - * - * Eigen::MatrixXi m(10, 10); - * m(0, 1) = 1; - * m(0, 2) = 2; - * m(0, 3) = 3; - * \endcode - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN. - * - * <i><b>Some notes:</b></i> - * - * <dl> - * <dt><b>\anchor dense Dense versus sparse:</b></dt> - * <dd>This %Matrix class handles dense, not sparse matrices and vectors. For sparse matrices and vectors, see the Sparse module. - * - * Dense matrices and vectors are plain usual arrays of coefficients. All the coefficients are stored, in an ordinary contiguous array. - * This is unlike Sparse matrices and vectors where the coefficients are stored as a list of nonzero coefficients.</dd> - * - * <dt><b>\anchor fixedsize Fixed-size versus dynamic-size:</b></dt> - * <dd>Fixed-size means that the numbers of rows and columns are known are compile-time. In this case, Eigen allocates the array - * of coefficients as a fixed-size array, as a class member. This makes sense for very small matrices, typically up to 4x4, sometimes up - * to 16x16. Larger matrices should be declared as dynamic-size even if one happens to know their size at compile-time. - * - * Dynamic-size means that the numbers of rows or columns are not necessarily known at compile-time. In this case they are runtime - * variables, and the array of coefficients is allocated dynamically on the heap. - * - * Note that \em dense matrices, be they Fixed-size or Dynamic-size, <em>do not</em> expand dynamically in the sense of a std::map. - * If you want this behavior, see the Sparse module.</dd> - * - * <dt><b>\anchor maxrows _MaxRows and _MaxCols:</b></dt> - * <dd>In most cases, one just leaves these parameters to the default values. - * These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases - * when the exact numbers of rows and columns are not known are compile-time, but it is known at compile-time that they cannot - * exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case _MaxRows and _MaxCols - * are the dimensions of the original matrix, while _Rows and _Cols are Dynamic.</dd> - * </dl> - * - * \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy, - * \ref TopicStorageOrders - */ - -namespace internal { -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -struct traits<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > -{ - typedef _Scalar Scalar; - typedef Dense StorageKind; - typedef DenseIndex Index; - typedef MatrixXpr XprKind; - enum { - RowsAtCompileTime = _Rows, - ColsAtCompileTime = _Cols, - MaxRowsAtCompileTime = _MaxRows, - MaxColsAtCompileTime = _MaxCols, - Flags = compute_matrix_flags<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>::ret, - CoeffReadCost = NumTraits<Scalar>::ReadCost, - Options = _Options, - InnerStrideAtCompileTime = 1, - OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime - }; -}; -} - -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -class Matrix - : public PlainObjectBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > -{ - public: - - /** \brief Base class typedef. - * \sa PlainObjectBase - */ - typedef PlainObjectBase<Matrix> Base; - - enum { Options = _Options }; - - EIGEN_DENSE_PUBLIC_INTERFACE(Matrix) - - typedef typename Base::PlainObject PlainObject; - - using Base::base; - using Base::coeffRef; - - /** - * \brief Assigns matrices to each other. - * - * \note This is a special case of the templated operator=. Its purpose is - * to prevent a default operator= from hiding the templated operator=. - * - * \callgraph - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other) - { - return Base::_set(other); - } - - /** \internal - * \brief Copies the value of the expression \a other into \c *this with automatic resizing. - * - * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), - * it will be initialized. - * - * Note that copying a row-vector into a vector (and conversely) is allowed. - * The resizing, if any, is then done in the appropriate way so that row-vectors - * remain row-vectors and vectors remain vectors. - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix& operator=(const MatrixBase<OtherDerived>& other) - { - return Base::_set(other); - } - - /* Here, doxygen failed to copy the brief information when using \copydoc */ - - /** - * \brief Copies the generic expression \a other into *this. - * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other) - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase<OtherDerived> &other) - { - return Base::operator=(other); - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue<OtherDerived>& func) - { - return Base::operator=(func); - } - - /** \brief Default constructor. - * - * For fixed-size matrices, does nothing. - * - * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix - * is called a null matrix. This constructor is the unique way to create null matrices: resizing - * a matrix to 0 is not supported. - * - * \sa resize(Index,Index) - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix() : Base() - { - Base::_check_template_params(); - EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED - } - - // FIXME is it still needed - EIGEN_DEVICE_FUNC - Matrix(internal::constructor_without_unaligned_array_assert) - : Base(internal::constructor_without_unaligned_array_assert()) - { Base::_check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED } - -#ifdef EIGEN_HAVE_RVALUE_REFERENCES - Matrix(Matrix&& other) - : Base(std::move(other)) - { - Base::_check_template_params(); - if (RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic) - Base::_set_noalias(other); - } - Matrix& operator=(Matrix&& other) - { - other.swap(*this); - return *this; - } -#endif - - #ifndef EIGEN_PARSED_BY_DOXYGEN - - // This constructor is for both 1x1 matrices and dynamic vectors - template<typename T> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE explicit Matrix(const T& x) - { - Base::_check_template_params(); - Base::template _init1<T>(x); - } - - template<typename T0, typename T1> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix(const T0& x, const T1& y) - { - Base::_check_template_params(); - Base::template _init2<T0,T1>(x, y); - } - #else - /** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */ - EIGEN_DEVICE_FUNC - explicit Matrix(const Scalar *data); - - /** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors - * - * Note that this is only useful for dynamic-size vectors. For fixed-size vectors, - * it is redundant to pass the dimension here, so it makes more sense to use the default - * constructor Matrix() instead. - */ - EIGEN_STRONG_INLINE explicit Matrix(Index dim); - /** \brief Constructs an initialized 1x1 matrix with the given coefficient */ - Matrix(const Scalar& x); - /** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns. - * - * This is useful for dynamic-size matrices. For fixed-size matrices, - * it is redundant to pass these parameters, so one should use the default constructor - * Matrix() instead. */ - EIGEN_DEVICE_FUNC - Matrix(Index rows, Index cols); - /** \brief Constructs an initialized 2D vector with given coefficients */ - Matrix(const Scalar& x, const Scalar& y); - #endif - - /** \brief Constructs an initialized 3D vector with given coefficients */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z) - { - Base::_check_template_params(); - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3) - m_storage.data()[0] = x; - m_storage.data()[1] = y; - m_storage.data()[2] = z; - } - /** \brief Constructs an initialized 4D vector with given coefficients */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w) - { - Base::_check_template_params(); - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4) - m_storage.data()[0] = x; - m_storage.data()[1] = y; - m_storage.data()[2] = z; - m_storage.data()[3] = w; - } - - - /** \brief Constructor copying the value of the expression \a other */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix(const MatrixBase<OtherDerived>& other) - : Base(other.rows() * other.cols(), other.rows(), other.cols()) - { - // This test resides here, to bring the error messages closer to the user. Normally, these checks - // are performed deeply within the library, thus causing long and scary error traces. - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - - Base::_check_template_params(); - Base::_set_noalias(other); - } - /** \brief Copy constructor */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix(const Matrix& other) - : Base(other.rows() * other.cols(), other.rows(), other.cols()) - { - Base::_check_template_params(); - Base::_set_noalias(other); - } - /** \brief Copy constructor with in-place evaluation */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix(const ReturnByValue<OtherDerived>& other) - { - Base::_check_template_params(); - Base::resize(other.rows(), other.cols()); - other.evalTo(*this); - } - - /** \brief Copy constructor for generic expressions. - * \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other) - : Base(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols()) - { - Base::_check_template_params(); - Base::_resize_to_match(other); - // FIXME/CHECK: isn't *this = other.derived() more efficient. it allows to - // go for pure _set() implementations, right? - *this = other; - } - - /** \internal - * \brief Override MatrixBase::swap() since for dynamic-sized matrices - * of same type it is enough to swap the data pointers. - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void swap(MatrixBase<OtherDerived> const & other) - { this->_swap(other.derived()); } - - EIGEN_DEVICE_FUNC inline Index innerStride() const { return 1; } - EIGEN_DEVICE_FUNC inline Index outerStride() const { return this->innerSize(); } - - /////////// Geometry module /////////// - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r); - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r); - - #ifdef EIGEN2_SUPPORT - template<typename OtherDerived> - explicit Matrix(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r); - template<typename OtherDerived> - Matrix& operator=(const eigen2_RotationBase<OtherDerived,ColsAtCompileTime>& r); - #endif - - // allow to extend Matrix outside Eigen - #ifdef EIGEN_MATRIX_PLUGIN - #include EIGEN_MATRIX_PLUGIN - #endif - - protected: - template <typename Derived, typename OtherDerived, bool IsVector> - friend struct internal::conservative_resize_like_impl; - - using Base::m_storage; -}; - -/** \defgroup matrixtypedefs Global matrix typedefs - * - * \ingroup Core_Module - * - * Eigen defines several typedef shortcuts for most common matrix and vector types. - * - * The general patterns are the following: - * - * \c MatrixSizeType where \c Size can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size, - * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd - * for complex double. - * - * For example, \c Matrix3d is a fixed-size 3x3 matrix type of doubles, and \c MatrixXf is a dynamic-size matrix of floats. - * - * There are also \c VectorSizeType and \c RowVectorSizeType which are self-explanatory. For example, \c Vector4cf is - * a fixed-size vector of 4 complex floats. - * - * \sa class Matrix - */ - -#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \ -/** \ingroup matrixtypedefs */ \ -typedef Matrix<Type, Size, Size> Matrix##SizeSuffix##TypeSuffix; \ -/** \ingroup matrixtypedefs */ \ -typedef Matrix<Type, Size, 1> Vector##SizeSuffix##TypeSuffix; \ -/** \ingroup matrixtypedefs */ \ -typedef Matrix<Type, 1, Size> RowVector##SizeSuffix##TypeSuffix; - -#define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size) \ -/** \ingroup matrixtypedefs */ \ -typedef Matrix<Type, Size, Dynamic> Matrix##Size##X##TypeSuffix; \ -/** \ingroup matrixtypedefs */ \ -typedef Matrix<Type, Dynamic, Size> Matrix##X##Size##TypeSuffix; - -#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \ -EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \ -EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \ -EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 4) - -EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int, i) -EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float, f) -EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double, d) -EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<float>, cf) -EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<double>, cd) - -#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES -#undef EIGEN_MAKE_TYPEDEFS -#undef EIGEN_MAKE_FIXED_TYPEDEFS - -} // end namespace Eigen - -#endif // EIGEN_MATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/MatrixBase.h b/third_party/eigen3/Eigen/src/Core/MatrixBase.h deleted file mode 100644 index 598b38ed47..0000000000 --- a/third_party/eigen3/Eigen/src/Core/MatrixBase.h +++ /dev/null @@ -1,614 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MATRIXBASE_H -#define EIGEN_MATRIXBASE_H - -namespace Eigen { - -/** \class MatrixBase - * \ingroup Core_Module - * - * \brief Base class for all dense matrices, vectors, and expressions - * - * This class is the base that is inherited by all matrix, vector, and related expression - * types. Most of the Eigen API is contained in this class, and its base classes. Other important - * classes for the Eigen API are Matrix, and VectorwiseOp. - * - * Note that some methods are defined in other modules such as the \ref LU_Module LU module - * for all functions related to matrix inversions. - * - * \tparam Derived is the derived type, e.g. a matrix type, or an expression, etc. - * - * When writing a function taking Eigen objects as argument, if you want your function - * to take as argument any matrix, vector, or expression, just let it take a - * MatrixBase argument. As an example, here is a function printFirstRow which, given - * a matrix, vector, or expression \a x, prints the first row of \a x. - * - * \code - template<typename Derived> - void printFirstRow(const Eigen::MatrixBase<Derived>& x) - { - cout << x.row(0) << endl; - } - * \endcode - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN. - * - * \sa \ref TopicClassHierarchy - */ -template<typename Derived> class MatrixBase - : public DenseBase<Derived> -{ - public: -#ifndef EIGEN_PARSED_BY_DOXYGEN - typedef MatrixBase StorageBaseType; - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::packet_traits<Scalar>::type PacketScalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - typedef DenseBase<Derived> Base; - using Base::RowsAtCompileTime; - using Base::ColsAtCompileTime; - using Base::SizeAtCompileTime; - using Base::MaxRowsAtCompileTime; - using Base::MaxColsAtCompileTime; - using Base::MaxSizeAtCompileTime; - using Base::IsVectorAtCompileTime; - using Base::Flags; - using Base::CoeffReadCost; - - using Base::derived; - using Base::const_cast_derived; - using Base::rows; - using Base::cols; - using Base::size; - using Base::coeff; - using Base::coeffRef; - using Base::lazyAssign; - using Base::eval; - using Base::operator+=; - using Base::operator-=; - using Base::operator*=; - using Base::operator/=; - - typedef typename Base::CoeffReturnType CoeffReturnType; - typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType; - typedef typename Base::RowXpr RowXpr; - typedef typename Base::ColXpr ColXpr; -#endif // not EIGEN_PARSED_BY_DOXYGEN - - - -#ifndef EIGEN_PARSED_BY_DOXYGEN - /** type of the equivalent square matrix */ - typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime), - EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType; -#endif // not EIGEN_PARSED_BY_DOXYGEN - - /** \returns the size of the main diagonal, which is min(rows(),cols()). - * \sa rows(), cols(), SizeAtCompileTime. */ - EIGEN_DEVICE_FUNC - inline Index diagonalSize() const { return (std::min)(rows(),cols()); } - - /** \brief The plain matrix type corresponding to this expression. - * - * This is not necessarily exactly the return type of eval(). In the case of plain matrices, - * the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed - * that the return type of eval() is either PlainObject or const PlainObject&. - */ - typedef Matrix<typename internal::traits<Derived>::Scalar, - internal::traits<Derived>::RowsAtCompileTime, - internal::traits<Derived>::ColsAtCompileTime, - AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor), - internal::traits<Derived>::MaxRowsAtCompileTime, - internal::traits<Derived>::MaxColsAtCompileTime - > PlainObject; - -#ifndef EIGEN_PARSED_BY_DOXYGEN - /** \internal Represents a matrix with all coefficients equal to one another*/ - typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Derived> ConstantReturnType; - /** \internal the return type of MatrixBase::adjoint() */ - typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, - CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>, - ConstTransposeReturnType - >::type AdjointReturnType; - /** \internal Return type of eigenvalues() */ - typedef Matrix<std::complex<RealScalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType; - /** \internal the return type of identity */ - typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,Derived> IdentityReturnType; - /** \internal the return type of unit vectors */ - typedef Block<const CwiseNullaryOp<internal::scalar_identity_op<Scalar>, SquareMatrixType>, - internal::traits<Derived>::RowsAtCompileTime, - internal::traits<Derived>::ColsAtCompileTime> BasisReturnType; -#endif // not EIGEN_PARSED_BY_DOXYGEN - -#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase -# include "../plugins/CommonCwiseUnaryOps.h" -# include "../plugins/CommonCwiseBinaryOps.h" -# include "../plugins/MatrixCwiseUnaryOps.h" -# include "../plugins/MatrixCwiseBinaryOps.h" -# ifdef EIGEN_MATRIXBASE_PLUGIN -# include EIGEN_MATRIXBASE_PLUGIN -# endif -#undef EIGEN_CURRENT_STORAGE_BASE_CLASS - - /** Special case of the template operator=, in order to prevent the compiler - * from generating a default operator= (issue hit with g++ 4.1) - */ - EIGEN_DEVICE_FUNC - Derived& operator=(const MatrixBase& other); - - // We cannot inherit here via Base::operator= since it is causing - // trouble with MSVC. - - template <typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator=(const DenseBase<OtherDerived>& other); - - template <typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator=(const EigenBase<OtherDerived>& other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator=(const ReturnByValue<OtherDerived>& other); - -#ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename ProductDerived, typename Lhs, typename Rhs> - EIGEN_DEVICE_FUNC - Derived& lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other); -#endif // not EIGEN_PARSED_BY_DOXYGEN - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator+=(const MatrixBase<OtherDerived>& other); - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - Derived& operator-=(const MatrixBase<OtherDerived>& other); - -#ifdef __CUDACC__ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - const typename LazyProductReturnType<Derived,OtherDerived>::Type - operator*(const MatrixBase<OtherDerived> &other) const - { return this->lazyProduct(other); } -#else - -#ifdef EIGEN_TEST_EVALUATORS - template<typename OtherDerived> - const Product<Derived,OtherDerived> - operator*(const MatrixBase<OtherDerived> &other) const; -#else - template<typename OtherDerived> - const typename ProductReturnType<Derived,OtherDerived>::Type - operator*(const MatrixBase<OtherDerived> &other) const; -#endif - -#endif - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - const typename LazyProductReturnType<Derived,OtherDerived>::Type - lazyProduct(const MatrixBase<OtherDerived> &other) const; - - template<typename OtherDerived> - Derived& operator*=(const EigenBase<OtherDerived>& other); - - template<typename OtherDerived> - void applyOnTheLeft(const EigenBase<OtherDerived>& other); - - template<typename OtherDerived> - void applyOnTheRight(const EigenBase<OtherDerived>& other); - - template<typename DiagonalDerived> - EIGEN_DEVICE_FUNC - const DiagonalProduct<Derived, DiagonalDerived, OnTheRight> - operator*(const DiagonalBase<DiagonalDerived> &diagonal) const; - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType - dot(const MatrixBase<OtherDerived>& other) const; - - #ifdef EIGEN2_SUPPORT - template<typename OtherDerived> - Scalar eigen2_dot(const MatrixBase<OtherDerived>& other) const; - #endif - - EIGEN_DEVICE_FUNC RealScalar squaredNorm() const; - EIGEN_DEVICE_FUNC RealScalar norm() const; - RealScalar stableNorm() const; - RealScalar blueNorm() const; - RealScalar hypotNorm() const; - EIGEN_DEVICE_FUNC const PlainObject normalized() const; - EIGEN_DEVICE_FUNC void normalize(); - - EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const; - EIGEN_DEVICE_FUNC void adjointInPlace(); - - typedef Diagonal<Derived> DiagonalReturnType; - EIGEN_DEVICE_FUNC - DiagonalReturnType diagonal(); - - typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType; - EIGEN_DEVICE_FUNC - ConstDiagonalReturnType diagonal() const; - - template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; }; - template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; }; - - template<int Index> - EIGEN_DEVICE_FUNC - typename DiagonalIndexReturnType<Index>::Type diagonal(); - - template<int Index> - EIGEN_DEVICE_FUNC - typename ConstDiagonalIndexReturnType<Index>::Type diagonal() const; - - // Note: The "MatrixBase::" prefixes are added to help MSVC9 to match these declarations with the later implementations. - // On the other hand they confuse MSVC8... - #if EIGEN_COMP_MSVC >= 1500 // 2008 or later - typename MatrixBase::template DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index); - typename MatrixBase::template ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const; - #else - EIGEN_DEVICE_FUNC - typename DiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index); - - EIGEN_DEVICE_FUNC - typename ConstDiagonalIndexReturnType<DynamicIndex>::Type diagonal(Index index) const; - #endif - - #ifdef EIGEN2_SUPPORT - template<unsigned int Mode> typename internal::eigen2_part_return_type<Derived, Mode>::type part(); - template<unsigned int Mode> const typename internal::eigen2_part_return_type<Derived, Mode>::type part() const; - - // huuuge hack. make Eigen2's matrix.part<Diagonal>() work in eigen3. Problem: Diagonal is now a class template instead - // of an integer constant. Solution: overload the part() method template wrt template parameters list. - template<template<typename T, int N> class U> - const DiagonalWrapper<ConstDiagonalReturnType> part() const - { return diagonal().asDiagonal(); } - #endif // EIGEN2_SUPPORT - - template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; }; - template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; }; - - template<unsigned int Mode> - EIGEN_DEVICE_FUNC - typename TriangularViewReturnType<Mode>::Type triangularView(); - template<unsigned int Mode> - EIGEN_DEVICE_FUNC - typename ConstTriangularViewReturnType<Mode>::Type triangularView() const; - - template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SelfAdjointView<Derived, UpLo> Type; }; - template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView<const Derived, UpLo> Type; }; - - template<unsigned int UpLo> - EIGEN_DEVICE_FUNC - typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView(); - template<unsigned int UpLo> - EIGEN_DEVICE_FUNC - typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const; - - const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0), - const typename NumTraits<Scalar>::Real& m_epsilon = NumTraits<Scalar>::dummy_precision()) const; - EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(); - EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols); - EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i); - EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i); - EIGEN_DEVICE_FUNC static const BasisReturnType UnitX(); - EIGEN_DEVICE_FUNC static const BasisReturnType UnitY(); - EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ(); - EIGEN_DEVICE_FUNC static const BasisReturnType UnitW(); - - EIGEN_DEVICE_FUNC - const DiagonalWrapper<const Derived> asDiagonal() const; - const PermutationWrapper<const Derived> asPermutation() const; - - EIGEN_DEVICE_FUNC - Derived& setIdentity(); - EIGEN_DEVICE_FUNC - Derived& setIdentity(Index rows, Index cols); - - bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - bool isDiagonal(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - - bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - - template<typename OtherDerived> - bool isOrthogonal(const MatrixBase<OtherDerived>& other, - const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - bool isUnitary(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const; - - /** \returns true if each coefficients of \c *this and \a other are all exactly equal. - * \warning When using floating point scalar values you probably should rather use a - * fuzzy comparison such as isApprox() - * \sa isApprox(), operator!= */ - template<typename OtherDerived> - inline bool operator==(const MatrixBase<OtherDerived>& other) const - { return cwiseEqual(other).all(); } - - /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other. - * \warning When using floating point scalar values you probably should rather use a - * fuzzy comparison such as isApprox() - * \sa isApprox(), operator== */ - template<typename OtherDerived> - inline bool operator!=(const MatrixBase<OtherDerived>& other) const - { return cwiseNotEqual(other).any(); } - - NoAlias<Derived,Eigen::MatrixBase > noalias(); - - inline const ForceAlignedAccess<Derived> forceAlignedAccess() const; - inline ForceAlignedAccess<Derived> forceAlignedAccess(); - template<bool Enable> inline typename internal::add_const_on_value_type<typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type>::type forceAlignedAccessIf() const; - template<bool Enable> inline typename internal::conditional<Enable,ForceAlignedAccess<Derived>,Derived&>::type forceAlignedAccessIf(); - - Scalar trace() const; - - template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const; - - EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; } - EIGEN_DEVICE_FUNC const MatrixBase<Derived>& matrix() const { return *this; } - - /** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix - * \sa ArrayBase::matrix() */ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return derived(); } - /** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix - * \sa ArrayBase::matrix() */ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return derived(); } - -/////////// LU module /////////// - - EIGEN_DEVICE_FUNC const FullPivLU<PlainObject> fullPivLu() const; - EIGEN_DEVICE_FUNC const PartialPivLU<PlainObject> partialPivLu() const; - - #if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS - const LU<PlainObject> lu() const; - #endif - - #ifdef EIGEN2_SUPPORT - const LU<PlainObject> eigen2_lu() const; - #endif - - #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS - const PartialPivLU<PlainObject> lu() const; - #endif - - #ifdef EIGEN2_SUPPORT - template<typename ResultType> - void computeInverse(MatrixBase<ResultType> *result) const { - *result = this->inverse(); - } - #endif - - EIGEN_DEVICE_FUNC - const internal::inverse_impl<Derived> inverse() const; - template<typename ResultType> - void computeInverseAndDetWithCheck( - ResultType& inverse, - typename ResultType::Scalar& determinant, - bool& invertible, - const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision() - ) const; - template<typename ResultType> - void computeInverseWithCheck( - ResultType& inverse, - bool& invertible, - const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision() - ) const; - Scalar determinant() const; - -/////////// Cholesky module /////////// - - const LLT<PlainObject> llt() const; - const LDLT<PlainObject> ldlt() const; - -/////////// QR module /////////// - - const HouseholderQR<PlainObject> householderQr() const; - const ColPivHouseholderQR<PlainObject> colPivHouseholderQr() const; - const FullPivHouseholderQR<PlainObject> fullPivHouseholderQr() const; - - #ifdef EIGEN2_SUPPORT - const QR<PlainObject> qr() const; - #endif - - EigenvaluesReturnType eigenvalues() const; - RealScalar operatorNorm() const; - -/////////// SVD module /////////// - - JacobiSVD<PlainObject> jacobiSvd(unsigned int computationOptions = 0) const; - - #ifdef EIGEN2_SUPPORT - SVD<PlainObject> svd() const; - #endif - -/////////// Geometry module /////////// - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /// \internal helper struct to form the return type of the cross product - template<typename OtherDerived> struct cross_product_return_type { - typedef typename internal::scalar_product_traits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar; - typedef Matrix<Scalar,MatrixBase::RowsAtCompileTime,MatrixBase::ColsAtCompileTime> type; - }; - #endif // EIGEN_PARSED_BY_DOXYGEN - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - typename cross_product_return_type<OtherDerived>::type - cross(const MatrixBase<OtherDerived>& other) const; - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - PlainObject cross3(const MatrixBase<OtherDerived>& other) const; - - EIGEN_DEVICE_FUNC - PlainObject unitOrthogonal(void) const; - - Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const; - - #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS - ScalarMultipleReturnType operator*(const UniformScaling<Scalar>& s) const; - // put this as separate enum value to work around possible GCC 4.3 bug (?) - enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1?Vertical:Horizontal }; - typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType; - HomogeneousReturnType homogeneous() const; - #endif - - enum { - SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1 - }; - typedef Block<const Derived, - internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1, - internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne; - typedef CwiseUnaryOp<internal::scalar_quotient1_op<typename internal::traits<Derived>::Scalar>, - const ConstStartMinusOne > HNormalizedReturnType; - - const HNormalizedReturnType hnormalized() const; - -////////// Householder module /////////// - - void makeHouseholderInPlace(Scalar& tau, RealScalar& beta); - template<typename EssentialPart> - void makeHouseholder(EssentialPart& essential, - Scalar& tau, RealScalar& beta) const; - template<typename EssentialPart> - void applyHouseholderOnTheLeft(const EssentialPart& essential, - const Scalar& tau, - Scalar* workspace); - template<typename EssentialPart> - void applyHouseholderOnTheRight(const EssentialPart& essential, - const Scalar& tau, - Scalar* workspace); - -///////// Jacobi module ///////// - - template<typename OtherScalar> - void applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j); - template<typename OtherScalar> - void applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j); - -///////// MatrixFunctions module ///////// - - typedef typename internal::stem_function<Scalar>::type StemFunction; - const MatrixExponentialReturnValue<Derived> exp() const; - const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const; - const MatrixFunctionReturnValue<Derived> cosh() const; - const MatrixFunctionReturnValue<Derived> sinh() const; - const MatrixFunctionReturnValue<Derived> cos() const; - const MatrixFunctionReturnValue<Derived> sin() const; - const MatrixSquareRootReturnValue<Derived> sqrt() const; - const MatrixLogarithmReturnValue<Derived> log() const; - const MatrixPowerReturnValue<Derived> pow(const RealScalar& p) const; - const MatrixComplexPowerReturnValue<Derived> pow(const std::complex<RealScalar>& p) const; - -#ifdef EIGEN2_SUPPORT - template<typename ProductDerived, typename Lhs, typename Rhs> - Derived& operator+=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0, - EvalBeforeAssigningBit>& other); - - template<typename ProductDerived, typename Lhs, typename Rhs> - Derived& operator-=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0, - EvalBeforeAssigningBit>& other); - - /** \deprecated because .lazy() is deprecated - * Overloaded for cache friendly product evaluation */ - template<typename OtherDerived> - Derived& lazyAssign(const Flagged<OtherDerived, 0, EvalBeforeAssigningBit>& other) - { return lazyAssign(other._expression()); } - - template<unsigned int Added> - const Flagged<Derived, Added, 0> marked() const; - const Flagged<Derived, 0, EvalBeforeAssigningBit> lazy() const; - - inline const Cwise<Derived> cwise() const; - inline Cwise<Derived> cwise(); - - VectorBlock<Derived> start(Index size); - const VectorBlock<const Derived> start(Index size) const; - VectorBlock<Derived> end(Index size); - const VectorBlock<const Derived> end(Index size) const; - template<int Size> VectorBlock<Derived,Size> start(); - template<int Size> const VectorBlock<const Derived,Size> start() const; - template<int Size> VectorBlock<Derived,Size> end(); - template<int Size> const VectorBlock<const Derived,Size> end() const; - - Minor<Derived> minor(Index row, Index col); - const Minor<Derived> minor(Index row, Index col) const; -#endif - - protected: - EIGEN_DEVICE_FUNC MatrixBase() : Base() {} - - private: - EIGEN_DEVICE_FUNC explicit MatrixBase(int); - EIGEN_DEVICE_FUNC MatrixBase(int,int); - template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase<OtherDerived>&); - protected: - // mixing arrays and matrices is not legal - template<typename OtherDerived> Derived& operator+=(const ArrayBase<OtherDerived>& ) - {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} - // mixing arrays and matrices is not legal - template<typename OtherDerived> Derived& operator-=(const ArrayBase<OtherDerived>& ) - {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;} -}; - - -/*************************************************************************** -* Implementation of matrix base methods -***************************************************************************/ - -/** replaces \c *this by \c *this * \a other. - * - * \returns a reference to \c *this - * - * Example: \include MatrixBase_applyOnTheRight.cpp - * Output: \verbinclude MatrixBase_applyOnTheRight.out - */ -template<typename Derived> -template<typename OtherDerived> -inline Derived& -MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other) -{ - other.derived().applyThisOnTheRight(derived()); - return derived(); -} - -/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=(). - * - * Example: \include MatrixBase_applyOnTheRight.cpp - * Output: \verbinclude MatrixBase_applyOnTheRight.out - */ -template<typename Derived> -template<typename OtherDerived> -inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other) -{ - other.derived().applyThisOnTheRight(derived()); -} - -/** replaces \c *this by \a other * \c *this. - * - * Example: \include MatrixBase_applyOnTheLeft.cpp - * Output: \verbinclude MatrixBase_applyOnTheLeft.out - */ -template<typename Derived> -template<typename OtherDerived> -inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other) -{ - other.derived().applyThisOnTheLeft(derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_MATRIXBASE_H diff --git a/third_party/eigen3/Eigen/src/Core/NestByValue.h b/third_party/eigen3/Eigen/src/Core/NestByValue.h deleted file mode 100644 index 1944bd7858..0000000000 --- a/third_party/eigen3/Eigen/src/Core/NestByValue.h +++ /dev/null @@ -1,112 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_NESTBYVALUE_H -#define EIGEN_NESTBYVALUE_H - -namespace Eigen { - -/** \class NestByValue - * \ingroup Core_Module - * - * \brief Expression which must be nested by value - * - * \param ExpressionType the type of the object of which we are requiring nesting-by-value - * - * This class is the return type of MatrixBase::nestByValue() - * and most of the time this is the only way it is used. - * - * \sa MatrixBase::nestByValue() - */ - -namespace internal { -template <typename ExpressionType> -struct traits<NestByValue<ExpressionType> > : public traits<ExpressionType> { - enum { Flags = traits<ExpressionType>::Flags & ~NestByRefBit }; -}; -} - -template<typename ExpressionType> class NestByValue - : public internal::dense_xpr_base< NestByValue<ExpressionType> >::type -{ - public: - - typedef typename internal::dense_xpr_base<NestByValue>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue) - - inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {} - - inline Index rows() const { return m_expression.rows(); } - inline Index cols() const { return m_expression.cols(); } - inline Index outerStride() const { return m_expression.outerStride(); } - inline Index innerStride() const { return m_expression.innerStride(); } - - inline const CoeffReturnType coeff(Index row, Index col) const - { - return m_expression.coeff(row, col); - } - - inline Scalar& coeffRef(Index row, Index col) - { - return m_expression.const_cast_derived().coeffRef(row, col); - } - - inline const CoeffReturnType coeff(Index index) const - { - return m_expression.coeff(index); - } - - inline Scalar& coeffRef(Index index) - { - return m_expression.const_cast_derived().coeffRef(index); - } - - template<int LoadMode> - inline const PacketScalar packet(Index row, Index col) const - { - return m_expression.template packet<LoadMode>(row, col); - } - - template<int LoadMode> - inline void writePacket(Index row, Index col, const PacketScalar& x) - { - m_expression.const_cast_derived().template writePacket<LoadMode>(row, col, x); - } - - template<int LoadMode> - inline const PacketScalar packet(Index index) const - { - return m_expression.template packet<LoadMode>(index); - } - - template<int LoadMode> - inline void writePacket(Index index, const PacketScalar& x) - { - m_expression.const_cast_derived().template writePacket<LoadMode>(index, x); - } - - operator const ExpressionType&() const { return m_expression; } - - protected: - const ExpressionType m_expression; -}; - -/** \returns an expression of the temporary version of *this. - */ -template<typename Derived> -inline const NestByValue<Derived> -DenseBase<Derived>::nestByValue() const -{ - return NestByValue<Derived>(derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_NESTBYVALUE_H diff --git a/third_party/eigen3/Eigen/src/Core/NoAlias.h b/third_party/eigen3/Eigen/src/Core/NoAlias.h deleted file mode 100644 index 0a1c327433..0000000000 --- a/third_party/eigen3/Eigen/src/Core/NoAlias.h +++ /dev/null @@ -1,141 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_NOALIAS_H -#define EIGEN_NOALIAS_H - -namespace Eigen { - -/** \class NoAlias - * \ingroup Core_Module - * - * \brief Pseudo expression providing an operator = assuming no aliasing - * - * \param ExpressionType the type of the object on which to do the lazy assignment - * - * This class represents an expression with special assignment operators - * assuming no aliasing between the target expression and the source expression. - * More precisely it alloas to bypass the EvalBeforeAssignBit flag of the source expression. - * It is the return type of MatrixBase::noalias() - * and most of the time this is the only way it is used. - * - * \sa MatrixBase::noalias() - */ -template<typename ExpressionType, template <typename> class StorageBase> -class NoAlias -{ - typedef typename ExpressionType::Scalar Scalar; - public: - NoAlias(ExpressionType& expression) : m_expression(expression) {} - - /** Behaves like MatrixBase::lazyAssign(other) - * \sa MatrixBase::lazyAssign() */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other) - { return internal::assign_selector<ExpressionType,OtherDerived,false>::run(m_expression,other.derived()); } - - /** \sa MatrixBase::operator+= */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other) - { - typedef SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, ExpressionType, OtherDerived> SelfAdder; - SelfAdder tmp(m_expression); - typedef typename internal::nested<OtherDerived>::type OtherDerivedNested; - typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested; - internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived())); - return m_expression; - } - - /** \sa MatrixBase::operator-= */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other) - { - typedef SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, ExpressionType, OtherDerived> SelfAdder; - SelfAdder tmp(m_expression); - typedef typename internal::nested<OtherDerived>::type OtherDerivedNested; - typedef typename internal::remove_all<OtherDerivedNested>::type _OtherDerivedNested; - internal::assign_selector<SelfAdder,_OtherDerivedNested,false>::run(tmp,OtherDerivedNested(other.derived())); - return m_expression; - } - -#ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename ProductDerived, typename Lhs, typename Rhs> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE ExpressionType& operator+=(const ProductBase<ProductDerived, Lhs,Rhs>& other) - { other.derived().addTo(m_expression); return m_expression; } - - template<typename ProductDerived, typename Lhs, typename Rhs> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE ExpressionType& operator-=(const ProductBase<ProductDerived, Lhs,Rhs>& other) - { other.derived().subTo(m_expression); return m_expression; } - - template<typename Lhs, typename Rhs, int NestingFlags> - EIGEN_STRONG_INLINE ExpressionType& operator+=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other) - { return m_expression.derived() += CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); } - - template<typename Lhs, typename Rhs, int NestingFlags> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE ExpressionType& operator-=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other) - { return m_expression.derived() -= CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); } - - template<typename OtherDerived> - ExpressionType& operator=(const ReturnByValue<OtherDerived>& func) - { return m_expression = func; } -#endif - - EIGEN_DEVICE_FUNC - ExpressionType& expression() const - { - return m_expression; - } - - protected: - ExpressionType& m_expression; -}; - -/** \returns a pseudo expression of \c *this with an operator= assuming - * no aliasing between \c *this and the source expression. - * - * More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag. - * Currently, even though several expressions may alias, only product - * expressions have this flag. Therefore, noalias() is only usefull when - * the source expression contains a matrix product. - * - * Here are some examples where noalias is usefull: - * \code - * D.noalias() = A * B; - * D.noalias() += A.transpose() * B; - * D.noalias() -= 2 * A * B.adjoint(); - * \endcode - * - * On the other hand the following example will lead to a \b wrong result: - * \code - * A.noalias() = A * B; - * \endcode - * because the result matrix A is also an operand of the matrix product. Therefore, - * there is no alternative than evaluating A * B in a temporary, that is the default - * behavior when you write: - * \code - * A = A * B; - * \endcode - * - * \sa class NoAlias - */ -template<typename Derived> -NoAlias<Derived,MatrixBase> MatrixBase<Derived>::noalias() -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_NOALIAS_H diff --git a/third_party/eigen3/Eigen/src/Core/NumTraits.h b/third_party/eigen3/Eigen/src/Core/NumTraits.h deleted file mode 100644 index dee9159517..0000000000 --- a/third_party/eigen3/Eigen/src/Core/NumTraits.h +++ /dev/null @@ -1,177 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_NUMTRAITS_H -#define EIGEN_NUMTRAITS_H - -namespace Eigen { - -/** \class NumTraits - * \ingroup Core_Module - * - * \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen. - * - * \param T the numeric type at hand - * - * This class stores enums, typedefs and static methods giving information about a numeric type. - * - * The provided data consists of: - * \li A typedef \a Real, giving the "real part" type of \a T. If \a T is already real, - * then \a Real is just a typedef to \a T. If \a T is \c std::complex<U> then \a Real - * is a typedef to \a U. - * \li A typedef \a NonInteger, giving the type that should be used for operations producing non-integral values, - * such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives - * \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to - * take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is - * only intended as a helper for code that needs to explicitly promote types. - * \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what - * this means, just use \a T here. - * \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex - * type, and to 0 otherwise. - * \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int, - * and to \c 0 otherwise. - * \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed - * to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers. - * Stay vague here. No need to do architecture-specific stuff. - * \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned. - * \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must - * be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise. - * \li An epsilon() function which, unlike std::numeric_limits::epsilon(), returns a \a Real instead of a \a T. - * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default - * value by the fuzzy comparison operators. - * \li highest() and lowest() functions returning the highest and lowest possible values respectively. - */ - -template<typename T> struct GenericNumTraits -{ - enum { - IsInteger = std::numeric_limits<T>::is_integer, - IsSigned = std::numeric_limits<T>::is_signed, - IsComplex = 0, - RequireInitialization = internal::is_arithmetic<T>::value ? 0 : 1, - ReadCost = 1, - AddCost = 1, - MulCost = 1 - }; - - typedef T Real; - typedef typename internal::conditional< - IsInteger, - typename internal::conditional<sizeof(T)<=2, float, double>::type, - T - >::type NonInteger; - typedef T Nested; - - EIGEN_DEVICE_FUNC - static inline Real epsilon() - { -#if defined(__CUDA_ARCH__) && !defined(__GCUDACC__) - return internal::device::numeric_limits<T>::epsilon(); -#else - return std::numeric_limits<T>::epsilon(); -#endif - } - EIGEN_DEVICE_FUNC - static inline Real dummy_precision() - { - // make sure to override this for floating-point types - return Real(0); - } - - EIGEN_DEVICE_FUNC - static inline T highest() { -#if defined(__CUDA_ARCH__) && !defined(__GCUDACC__) - return internal::device::numeric_limits<T>::max(); -#else - return (std::numeric_limits<T>::max)(); -#endif - } - - EIGEN_DEVICE_FUNC - static inline T lowest() { -#if defined(__CUDA_ARCH__) && !defined(__GCUDACC__) - return internal::device::numeric_limits<T>::lowest(); -#else - return IsInteger ? (std::numeric_limits<T>::min)() : (-(std::numeric_limits<T>::max)()); -#endif - } - -#ifdef EIGEN2_SUPPORT - enum { - HasFloatingPoint = !IsInteger - }; - typedef NonInteger FloatingPoint; -#endif -}; - -template<typename T> struct NumTraits : GenericNumTraits<T> -{}; - -template<> struct NumTraits<float> - : GenericNumTraits<float> -{ - EIGEN_DEVICE_FUNC - static inline float dummy_precision() { return 1e-5f; } -}; - -template<> struct NumTraits<double> : GenericNumTraits<double> -{ - EIGEN_DEVICE_FUNC - static inline double dummy_precision() { return 1e-12; } -}; - -template<> struct NumTraits<long double> - : GenericNumTraits<long double> -{ - static inline long double dummy_precision() { return 1e-15l; } -}; - -template<typename _Real> struct NumTraits<std::complex<_Real> > - : GenericNumTraits<std::complex<_Real> > -{ - typedef _Real Real; - enum { - IsComplex = 1, - RequireInitialization = NumTraits<_Real>::RequireInitialization, - ReadCost = 2 * NumTraits<_Real>::ReadCost, - AddCost = 2 * NumTraits<Real>::AddCost, - MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost - }; - - static inline Real epsilon() { return NumTraits<Real>::epsilon(); } - static inline Real dummy_precision() { return NumTraits<Real>::dummy_precision(); } -}; - -template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols> -struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > -{ - typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> ArrayType; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Array<RealScalar, Rows, Cols, Options, MaxRows, MaxCols> Real; - typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar; - typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger; - typedef ArrayType & Nested; - - enum { - IsComplex = NumTraits<Scalar>::IsComplex, - IsInteger = NumTraits<Scalar>::IsInteger, - IsSigned = NumTraits<Scalar>::IsSigned, - RequireInitialization = 1, - ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::ReadCost, - AddCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::AddCost, - MulCost = ArrayType::SizeAtCompileTime==Dynamic ? Dynamic : ArrayType::SizeAtCompileTime * NumTraits<Scalar>::MulCost - }; - - static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); } - static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); } -}; - -} // end namespace Eigen - -#endif // EIGEN_NUMTRAITS_H diff --git a/third_party/eigen3/Eigen/src/Core/PermutationMatrix.h b/third_party/eigen3/Eigen/src/Core/PermutationMatrix.h deleted file mode 100644 index 1297b8413f..0000000000 --- a/third_party/eigen3/Eigen/src/Core/PermutationMatrix.h +++ /dev/null @@ -1,689 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PERMUTATIONMATRIX_H -#define EIGEN_PERMUTATIONMATRIX_H - -namespace Eigen { - -template<int RowCol,typename IndicesType,typename MatrixType, typename StorageKind> class PermutedImpl; - -/** \class PermutationBase - * \ingroup Core_Module - * - * \brief Base class for permutations - * - * \param Derived the derived class - * - * This class is the base class for all expressions representing a permutation matrix, - * internally stored as a vector of integers. - * The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix - * \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have: - * \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f] - * This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have: - * \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f] - * - * Permutation matrices are square and invertible. - * - * Notice that in addition to the member functions and operators listed here, there also are non-member - * operator* to multiply any kind of permutation object with any kind of matrix expression (MatrixBase) - * on either side. - * - * \sa class PermutationMatrix, class PermutationWrapper - */ - -namespace internal { - -template<typename PermutationType, typename MatrixType, int Side, bool Transposed=false> -struct permut_matrix_product_retval; -template<typename PermutationType, typename MatrixType, int Side, bool Transposed=false> -struct permut_sparsematrix_product_retval; -enum PermPermProduct_t {PermPermProduct}; - -} // end namespace internal - -template<typename Derived> -class PermutationBase : public EigenBase<Derived> -{ - typedef internal::traits<Derived> Traits; - typedef EigenBase<Derived> Base; - public: - - #ifndef EIGEN_PARSED_BY_DOXYGEN - typedef typename Traits::IndicesType IndicesType; - enum { - Flags = Traits::Flags, - CoeffReadCost = Traits::CoeffReadCost, - RowsAtCompileTime = Traits::RowsAtCompileTime, - ColsAtCompileTime = Traits::ColsAtCompileTime, - MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime, - MaxColsAtCompileTime = Traits::MaxColsAtCompileTime - }; - typedef typename Traits::Scalar Scalar; - typedef typename Traits::Index Index; - typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime> - DenseMatrixType; - typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,Index> - PlainPermutationType; - using Base::derived; - #endif - - /** Copies the other permutation into *this */ - template<typename OtherDerived> - Derived& operator=(const PermutationBase<OtherDerived>& other) - { - indices() = other.indices(); - return derived(); - } - - /** Assignment from the Transpositions \a tr */ - template<typename OtherDerived> - Derived& operator=(const TranspositionsBase<OtherDerived>& tr) - { - setIdentity(tr.size()); - for(Index k=size()-1; k>=0; --k) - applyTranspositionOnTheRight(k,tr.coeff(k)); - return derived(); - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - Derived& operator=(const PermutationBase& other) - { - indices() = other.indices(); - return derived(); - } - #endif - - /** \returns the number of rows */ - inline Index rows() const { return Index(indices().size()); } - - /** \returns the number of columns */ - inline Index cols() const { return Index(indices().size()); } - - /** \returns the size of a side of the respective square matrix, i.e., the number of indices */ - inline Index size() const { return Index(indices().size()); } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename DenseDerived> - void evalTo(MatrixBase<DenseDerived>& other) const - { - other.setZero(); - for (int i=0; i<rows();++i) - other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1); - } - #endif - - /** \returns a Matrix object initialized from this permutation matrix. Notice that it - * is inefficient to return this Matrix object by value. For efficiency, favor using - * the Matrix constructor taking EigenBase objects. - */ - DenseMatrixType toDenseMatrix() const - { - return derived(); - } - - /** const version of indices(). */ - const IndicesType& indices() const { return derived().indices(); } - /** \returns a reference to the stored array representing the permutation. */ - IndicesType& indices() { return derived().indices(); } - - /** Resizes to given size. - */ - inline void resize(Index newSize) - { - indices().resize(newSize); - } - - /** Sets *this to be the identity permutation matrix */ - void setIdentity() - { - for(Index i = 0; i < size(); ++i) - indices().coeffRef(i) = i; - } - - /** Sets *this to be the identity permutation matrix of given size. - */ - void setIdentity(Index newSize) - { - resize(newSize); - setIdentity(); - } - - /** Multiplies *this by the transposition \f$(ij)\f$ on the left. - * - * \returns a reference to *this. - * - * \warning This is much slower than applyTranspositionOnTheRight(int,int): - * this has linear complexity and requires a lot of branching. - * - * \sa applyTranspositionOnTheRight(int,int) - */ - Derived& applyTranspositionOnTheLeft(Index i, Index j) - { - eigen_assert(i>=0 && j>=0 && i<size() && j<size()); - for(Index k = 0; k < size(); ++k) - { - if(indices().coeff(k) == i) indices().coeffRef(k) = j; - else if(indices().coeff(k) == j) indices().coeffRef(k) = i; - } - return derived(); - } - - /** Multiplies *this by the transposition \f$(ij)\f$ on the right. - * - * \returns a reference to *this. - * - * This is a fast operation, it only consists in swapping two indices. - * - * \sa applyTranspositionOnTheLeft(int,int) - */ - Derived& applyTranspositionOnTheRight(Index i, Index j) - { - eigen_assert(i>=0 && j>=0 && i<size() && j<size()); - std::swap(indices().coeffRef(i), indices().coeffRef(j)); - return derived(); - } - - /** \returns the inverse permutation matrix. - * - * \note \note_try_to_help_rvo - */ - inline Transpose<PermutationBase> inverse() const - { return derived(); } - /** \returns the tranpose permutation matrix. - * - * \note \note_try_to_help_rvo - */ - inline Transpose<PermutationBase> transpose() const - { return derived(); } - - /**** multiplication helpers to hopefully get RVO ****/ - - -#ifndef EIGEN_PARSED_BY_DOXYGEN - protected: - template<typename OtherDerived> - void assignTranspose(const PermutationBase<OtherDerived>& other) - { - for (int i=0; i<rows();++i) indices().coeffRef(other.indices().coeff(i)) = i; - } - template<typename Lhs,typename Rhs> - void assignProduct(const Lhs& lhs, const Rhs& rhs) - { - eigen_assert(lhs.cols() == rhs.rows()); - for (int i=0; i<rows();++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i)); - } -#endif - - public: - - /** \returns the product permutation matrix. - * - * \note \note_try_to_help_rvo - */ - template<typename Other> - inline PlainPermutationType operator*(const PermutationBase<Other>& other) const - { return PlainPermutationType(internal::PermPermProduct, derived(), other.derived()); } - - /** \returns the product of a permutation with another inverse permutation. - * - * \note \note_try_to_help_rvo - */ - template<typename Other> - inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other) const - { return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); } - - /** \returns the product of an inverse permutation with another permutation. - * - * \note \note_try_to_help_rvo - */ - template<typename Other> friend - inline PlainPermutationType operator*(const Transpose<PermutationBase<Other> >& other, const PermutationBase& perm) - { return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); } - - protected: - -}; - -/** \class PermutationMatrix - * \ingroup Core_Module - * - * \brief Permutation matrix - * - * \param SizeAtCompileTime the number of rows/cols, or Dynamic - * \param MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it. - * \param IndexType the interger type of the indices - * - * This class represents a permutation matrix, internally stored as a vector of integers. - * - * \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix - */ - -namespace internal { -template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType> -struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> > - : traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> > -{ - typedef IndexType Index; - typedef Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType; -}; -} - -template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType> -class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType> > -{ - typedef PermutationBase<PermutationMatrix> Base; - typedef internal::traits<PermutationMatrix> Traits; - public: - - #ifndef EIGEN_PARSED_BY_DOXYGEN - typedef typename Traits::IndicesType IndicesType; - #endif - - inline PermutationMatrix() - {} - - /** Constructs an uninitialized permutation matrix of given size. - */ - inline PermutationMatrix(int size) : m_indices(size) - {} - - /** Copy constructor. */ - template<typename OtherDerived> - inline PermutationMatrix(const PermutationBase<OtherDerived>& other) - : m_indices(other.indices()) {} - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** Standard copy constructor. Defined only to prevent a default copy constructor - * from hiding the other templated constructor */ - inline PermutationMatrix(const PermutationMatrix& other) : m_indices(other.indices()) {} - #endif - - /** Generic constructor from expression of the indices. The indices - * array has the meaning that the permutations sends each integer i to indices[i]. - * - * \warning It is your responsibility to check that the indices array that you passes actually - * describes a permutation, i.e., each value between 0 and n-1 occurs exactly once, where n is the - * array's size. - */ - template<typename Other> - explicit inline PermutationMatrix(const MatrixBase<Other>& a_indices) : m_indices(a_indices) - {} - - /** Convert the Transpositions \a tr to a permutation matrix */ - template<typename Other> - explicit PermutationMatrix(const TranspositionsBase<Other>& tr) - : m_indices(tr.size()) - { - *this = tr; - } - - /** Copies the other permutation into *this */ - template<typename Other> - PermutationMatrix& operator=(const PermutationBase<Other>& other) - { - m_indices = other.indices(); - return *this; - } - - /** Assignment from the Transpositions \a tr */ - template<typename Other> - PermutationMatrix& operator=(const TranspositionsBase<Other>& tr) - { - return Base::operator=(tr.derived()); - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - PermutationMatrix& operator=(const PermutationMatrix& other) - { - m_indices = other.m_indices; - return *this; - } - #endif - - /** const version of indices(). */ - const IndicesType& indices() const { return m_indices; } - /** \returns a reference to the stored array representing the permutation. */ - IndicesType& indices() { return m_indices; } - - - /**** multiplication helpers to hopefully get RVO ****/ - -#ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename Other> - PermutationMatrix(const Transpose<PermutationBase<Other> >& other) - : m_indices(other.nestedPermutation().size()) - { - for (int i=0; i<m_indices.size();++i) m_indices.coeffRef(other.nestedPermutation().indices().coeff(i)) = i; - } - template<typename Lhs,typename Rhs> - PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs) - : m_indices(lhs.indices().size()) - { - Base::assignProduct(lhs,rhs); - } -#endif - - protected: - - IndicesType m_indices; -}; - - -namespace internal { -template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess> -struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> > - : traits<Matrix<IndexType,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> > -{ - typedef IndexType Index; - typedef Map<const Matrix<IndexType, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, _PacketAccess> IndicesType; -}; -} - -template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess> -class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> - : public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, IndexType>,_PacketAccess> > -{ - typedef PermutationBase<Map> Base; - typedef internal::traits<Map> Traits; - public: - - #ifndef EIGEN_PARSED_BY_DOXYGEN - typedef typename Traits::IndicesType IndicesType; - typedef typename IndicesType::Scalar Index; - #endif - - inline Map(const Index* indicesPtr) - : m_indices(indicesPtr) - {} - - inline Map(const Index* indicesPtr, Index size) - : m_indices(indicesPtr,size) - {} - - /** Copies the other permutation into *this */ - template<typename Other> - Map& operator=(const PermutationBase<Other>& other) - { return Base::operator=(other.derived()); } - - /** Assignment from the Transpositions \a tr */ - template<typename Other> - Map& operator=(const TranspositionsBase<Other>& tr) - { return Base::operator=(tr.derived()); } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - Map& operator=(const Map& other) - { - m_indices = other.m_indices; - return *this; - } - #endif - - /** const version of indices(). */ - const IndicesType& indices() const { return m_indices; } - /** \returns a reference to the stored array representing the permutation. */ - IndicesType& indices() { return m_indices; } - - protected: - - IndicesType m_indices; -}; - -/** \class PermutationWrapper - * \ingroup Core_Module - * - * \brief Class to view a vector of integers as a permutation matrix - * - * \param _IndicesType the type of the vector of integer (can be any compatible expression) - * - * This class allows to view any vector expression of integers as a permutation matrix. - * - * \sa class PermutationBase, class PermutationMatrix - */ - -struct PermutationStorage {}; - -template<typename _IndicesType> class TranspositionsWrapper; -namespace internal { -template<typename _IndicesType> -struct traits<PermutationWrapper<_IndicesType> > -{ - typedef PermutationStorage StorageKind; - typedef typename _IndicesType::Scalar Scalar; - typedef typename _IndicesType::Scalar Index; - typedef _IndicesType IndicesType; - enum { - RowsAtCompileTime = _IndicesType::SizeAtCompileTime, - ColsAtCompileTime = _IndicesType::SizeAtCompileTime, - MaxRowsAtCompileTime = IndicesType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = IndicesType::MaxColsAtCompileTime, - Flags = 0, - CoeffReadCost = _IndicesType::CoeffReadCost - }; -}; -} - -template<typename _IndicesType> -class PermutationWrapper : public PermutationBase<PermutationWrapper<_IndicesType> > -{ - typedef PermutationBase<PermutationWrapper> Base; - typedef internal::traits<PermutationWrapper> Traits; - public: - - #ifndef EIGEN_PARSED_BY_DOXYGEN - typedef typename Traits::IndicesType IndicesType; - #endif - - inline PermutationWrapper(const IndicesType& a_indices) - : m_indices(a_indices) - {} - - /** const version of indices(). */ - const typename internal::remove_all<typename IndicesType::Nested>::type& - indices() const { return m_indices; } - - protected: - - typename IndicesType::Nested m_indices; -}; - -/** \returns the matrix with the permutation applied to the columns. - */ -template<typename Derived, typename PermutationDerived> -inline const internal::permut_matrix_product_retval<PermutationDerived, Derived, OnTheRight> -operator*(const MatrixBase<Derived>& matrix, - const PermutationBase<PermutationDerived> &permutation) -{ - return internal::permut_matrix_product_retval - <PermutationDerived, Derived, OnTheRight> - (permutation.derived(), matrix.derived()); -} - -/** \returns the matrix with the permutation applied to the rows. - */ -template<typename Derived, typename PermutationDerived> -inline const internal::permut_matrix_product_retval - <PermutationDerived, Derived, OnTheLeft> -operator*(const PermutationBase<PermutationDerived> &permutation, - const MatrixBase<Derived>& matrix) -{ - return internal::permut_matrix_product_retval - <PermutationDerived, Derived, OnTheLeft> - (permutation.derived(), matrix.derived()); -} - -namespace internal { - -template<typename PermutationType, typename MatrixType, int Side, bool Transposed> -struct traits<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> > -{ - typedef typename MatrixType::PlainObject ReturnType; -}; - -template<typename PermutationType, typename MatrixType, int Side, bool Transposed> -struct permut_matrix_product_retval - : public ReturnByValue<permut_matrix_product_retval<PermutationType, MatrixType, Side, Transposed> > -{ - typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned; - typedef typename MatrixType::Index Index; - - permut_matrix_product_retval(const PermutationType& perm, const MatrixType& matrix) - : m_permutation(perm), m_matrix(matrix) - {} - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - const Index n = Side==OnTheLeft ? rows() : cols(); - // FIXME we need an is_same for expression that is not sensitive to constness. For instance - // is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true. - if(is_same<MatrixTypeNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_matrix)) - { - // apply the permutation inplace - Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(m_permutation.size()); - mask.fill(false); - Index r = 0; - while(r < m_permutation.size()) - { - // search for the next seed - while(r<m_permutation.size() && mask[r]) r++; - if(r>=m_permutation.size()) - break; - // we got one, let's follow it until we are back to the seed - Index k0 = r++; - Index kPrev = k0; - mask.coeffRef(k0) = true; - for(Index k=m_permutation.indices().coeff(k0); k!=k0; k=m_permutation.indices().coeff(k)) - { - Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k) - .swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime> - (dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev)); - - mask.coeffRef(k) = true; - kPrev = k; - } - } - } - else - { - for(int i = 0; i < n; ++i) - { - Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime> - (dst, ((Side==OnTheLeft) ^ Transposed) ? m_permutation.indices().coeff(i) : i) - - = - - Block<const MatrixTypeNestedCleaned,Side==OnTheLeft ? 1 : MatrixType::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixType::ColsAtCompileTime> - (m_matrix, ((Side==OnTheRight) ^ Transposed) ? m_permutation.indices().coeff(i) : i); - } - } - } - - protected: - const PermutationType& m_permutation; - typename MatrixType::Nested m_matrix; -}; - -/* Template partial specialization for transposed/inverse permutations */ - -template<typename Derived> -struct traits<Transpose<PermutationBase<Derived> > > - : traits<Derived> -{}; - -} // end namespace internal - -template<typename Derived> -class Transpose<PermutationBase<Derived> > - : public EigenBase<Transpose<PermutationBase<Derived> > > -{ - typedef Derived PermutationType; - typedef typename PermutationType::IndicesType IndicesType; - typedef typename PermutationType::PlainPermutationType PlainPermutationType; - public: - - #ifndef EIGEN_PARSED_BY_DOXYGEN - typedef internal::traits<PermutationType> Traits; - typedef typename Derived::DenseMatrixType DenseMatrixType; - enum { - Flags = Traits::Flags, - CoeffReadCost = Traits::CoeffReadCost, - RowsAtCompileTime = Traits::RowsAtCompileTime, - ColsAtCompileTime = Traits::ColsAtCompileTime, - MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime, - MaxColsAtCompileTime = Traits::MaxColsAtCompileTime - }; - typedef typename Traits::Scalar Scalar; - #endif - - Transpose(const PermutationType& p) : m_permutation(p) {} - - inline int rows() const { return m_permutation.rows(); } - inline int cols() const { return m_permutation.cols(); } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename DenseDerived> - void evalTo(MatrixBase<DenseDerived>& other) const - { - other.setZero(); - for (int i=0; i<rows();++i) - other.coeffRef(i, m_permutation.indices().coeff(i)) = typename DenseDerived::Scalar(1); - } - #endif - - /** \return the equivalent permutation matrix */ - PlainPermutationType eval() const { return *this; } - - DenseMatrixType toDenseMatrix() const { return *this; } - - /** \returns the matrix with the inverse permutation applied to the columns. - */ - template<typename OtherDerived> friend - inline const internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true> - operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trPerm) - { - return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheRight, true>(trPerm.m_permutation, matrix.derived()); - } - - /** \returns the matrix with the inverse permutation applied to the rows. - */ - template<typename OtherDerived> - inline const internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true> - operator*(const MatrixBase<OtherDerived>& matrix) const - { - return internal::permut_matrix_product_retval<PermutationType, OtherDerived, OnTheLeft, true>(m_permutation, matrix.derived()); - } - - const PermutationType& nestedPermutation() const { return m_permutation; } - - protected: - const PermutationType& m_permutation; -}; - -template<typename Derived> -const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() const -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_PERMUTATIONMATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/PlainObjectBase.h b/third_party/eigen3/Eigen/src/Core/PlainObjectBase.h deleted file mode 100644 index 50c3656a98..0000000000 --- a/third_party/eigen3/Eigen/src/Core/PlainObjectBase.h +++ /dev/null @@ -1,895 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_DENSESTORAGEBASE_H -#define EIGEN_DENSESTORAGEBASE_H - -#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO) -# define EIGEN_INITIALIZE_COEFFS -# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=Scalar(0); -#elif defined(EIGEN_INITIALIZE_MATRICES_BY_NAN) -# define EIGEN_INITIALIZE_COEFFS -# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(int i=0;i<base().size();++i) coeffRef(i)=std::numeric_limits<Scalar>::quiet_NaN(); -#else -# undef EIGEN_INITIALIZE_COEFFS -# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED -#endif - -namespace Eigen { - -namespace internal { - -template<int MaxSizeAtCompileTime> struct check_rows_cols_for_overflow { - template<typename Index> - EIGEN_DEVICE_FUNC - static EIGEN_ALWAYS_INLINE void run(Index, Index) - { - } -}; - -template<> struct check_rows_cols_for_overflow<Dynamic> { - template<typename Index> - EIGEN_DEVICE_FUNC - static EIGEN_ALWAYS_INLINE void run(Index rows, Index cols) - { - // http://hg.mozilla.org/mozilla-central/file/6c8a909977d3/xpcom/ds/CheckedInt.h#l242 - // we assume Index is signed - Index max_index = (size_t(1) << (8 * sizeof(Index) - 1)) - 1; // assume Index is signed - bool error = (rows == 0 || cols == 0) ? false - : (rows > max_index / cols); - if (error) - throw_std_bad_alloc(); - } -}; - -template <typename Derived, - typename OtherDerived = Derived, - bool IsVector = bool(Derived::IsVectorAtCompileTime) && bool(OtherDerived::IsVectorAtCompileTime)> -struct conservative_resize_like_impl; - -template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl; - -} // end namespace internal - -/** \class PlainObjectBase - * \brief %Dense storage base class for matrices and arrays. - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN. - * - * \sa \ref TopicClassHierarchy - */ -#ifdef EIGEN_PARSED_BY_DOXYGEN -namespace internal { - -// this is a warkaround to doxygen not being able to understand the inheritence logic -// when it is hidden by the dense_xpr_base helper struct. -template<typename Derived> struct dense_xpr_base_dispatcher_for_doxygen;// : public MatrixBase<Derived> {}; -/** This class is just a workaround for Doxygen and it does not not actually exist. */ -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -struct dense_xpr_base_dispatcher_for_doxygen<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > - : public MatrixBase<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {}; -/** This class is just a workaround for Doxygen and it does not not actually exist. */ -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -struct dense_xpr_base_dispatcher_for_doxygen<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > - : public ArrayBase<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> > {}; - -} // namespace internal - -template<typename Derived> -class PlainObjectBase : public internal::dense_xpr_base_dispatcher_for_doxygen<Derived> -#else -template<typename Derived> -class PlainObjectBase : public internal::dense_xpr_base<Derived>::type -#endif -{ - public: - enum { Options = internal::traits<Derived>::Options }; - typedef typename internal::dense_xpr_base<Derived>::type Base; - - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::packet_traits<Scalar>::type PacketScalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Derived DenseType; - - using Base::RowsAtCompileTime; - using Base::ColsAtCompileTime; - using Base::SizeAtCompileTime; - using Base::MaxRowsAtCompileTime; - using Base::MaxColsAtCompileTime; - using Base::MaxSizeAtCompileTime; - using Base::IsVectorAtCompileTime; - using Base::Flags; - - template<typename PlainObjectType, int MapOptions, typename StrideType> friend class Eigen::Map; - friend class Eigen::Map<Derived, Unaligned>; - typedef Eigen::Map<Derived, Unaligned> MapType; - friend class Eigen::Map<const Derived, Unaligned>; - typedef const Eigen::Map<const Derived, Unaligned> ConstMapType; - friend class Eigen::Map<Derived, Aligned>; - typedef Eigen::Map<Derived, Aligned> AlignedMapType; - friend class Eigen::Map<const Derived, Aligned>; - typedef const Eigen::Map<const Derived, Aligned> ConstAlignedMapType; - template<typename StrideType> struct StridedMapType { typedef Eigen::Map<Derived, Unaligned, StrideType> type; }; - template<typename StrideType> struct StridedConstMapType { typedef Eigen::Map<const Derived, Unaligned, StrideType> type; }; - template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, Aligned, StrideType> type; }; - template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, Aligned, StrideType> type; }; - - protected: - DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage; - - public: - enum { NeedsToAlign = SizeAtCompileTime != Dynamic && (internal::traits<Derived>::Flags & AlignedBit) != 0 }; - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) - - EIGEN_DEVICE_FUNC - Base& base() { return *static_cast<Base*>(this); } - EIGEN_DEVICE_FUNC - const Base& base() const { return *static_cast<const Base*>(this); } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index rows() const { return m_storage.rows(); } - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Index cols() const { return m_storage.cols(); } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar& coeff(Index rowId, Index colId) const - { - if(Flags & RowMajorBit) - return m_storage.data()[colId + rowId * m_storage.cols()]; - else // column-major - return m_storage.data()[rowId + colId * m_storage.rows()]; - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const - { - return m_storage.data()[index]; - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& coeffRef(Index rowId, Index colId) - { - if(Flags & RowMajorBit) - return m_storage.data()[colId + rowId * m_storage.cols()]; - else // column-major - return m_storage.data()[rowId + colId * m_storage.rows()]; - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar& coeffRef(Index index) - { - return m_storage.data()[index]; - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar& coeffRef(Index rowId, Index colId) const - { - if(Flags & RowMajorBit) - return m_storage.data()[colId + rowId * m_storage.cols()]; - else // column-major - return m_storage.data()[rowId + colId * m_storage.rows()]; - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar& coeffRef(Index index) const - { - return m_storage.data()[index]; - } - - /** \internal */ - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const - { - return internal::ploadt<PacketScalar, LoadMode> - (m_storage.data() + (Flags & RowMajorBit - ? colId + rowId * m_storage.cols() - : rowId + colId * m_storage.rows())); - } - - /** \internal */ - template<int LoadMode> - EIGEN_STRONG_INLINE PacketScalar packet(Index index) const - { - return internal::ploadt<PacketScalar, LoadMode>(m_storage.data() + index); - } - - /** \internal */ - template<int StoreMode> - EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val) - { - internal::pstoret<Scalar, PacketScalar, StoreMode> - (m_storage.data() + (Flags & RowMajorBit - ? colId + rowId * m_storage.cols() - : rowId + colId * m_storage.rows()), val); - } - - /** \internal */ - template<int StoreMode> - EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val) - { - internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, val); - } - - /** \returns a const pointer to the data array of this matrix */ - EIGEN_STRONG_INLINE const Scalar *data() const - { return m_storage.data(); } - - /** \returns a pointer to the data array of this matrix */ - EIGEN_STRONG_INLINE Scalar *data() - { return m_storage.data(); } - - /** Resizes \c *this to a \a rows x \a cols matrix. - * - * This method is intended for dynamic-size matrices, although it is legal to call it on any - * matrix as long as fixed dimensions are left unchanged. If you only want to change the number - * of rows and/or of columns, you can use resize(NoChange_t, Index), resize(Index, NoChange_t). - * - * If the current number of coefficients of \c *this exactly matches the - * product \a rows * \a cols, then no memory allocation is performed and - * the current values are left unchanged. In all other cases, including - * shrinking, the data is reallocated and all previous values are lost. - * - * Example: \include Matrix_resize_int_int.cpp - * Output: \verbinclude Matrix_resize_int_int.out - * - * \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t) - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void resize(Index nbRows, Index nbCols) - { - eigen_assert( EIGEN_IMPLIES(RowsAtCompileTime!=Dynamic,nbRows==RowsAtCompileTime) - && EIGEN_IMPLIES(ColsAtCompileTime!=Dynamic,nbCols==ColsAtCompileTime) - && EIGEN_IMPLIES(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic,nbRows<=MaxRowsAtCompileTime) - && EIGEN_IMPLIES(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic,nbCols<=MaxColsAtCompileTime) - && nbRows>=0 && nbCols>=0 && "Invalid sizes when resizing a matrix or array."); - internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(nbRows, nbCols); - #ifdef EIGEN_INITIALIZE_COEFFS - Index size = nbRows*nbCols; - bool size_changed = size != this->size(); - m_storage.resize(size, nbRows, nbCols); - if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED - #else - m_storage.resize(nbRows*nbCols, nbRows, nbCols); - #endif - } - - /** Resizes \c *this to a vector of length \a size - * - * \only_for_vectors. This method does not work for - * partially dynamic matrices when the static dimension is anything other - * than 1. For example it will not work with Matrix<double, 2, Dynamic>. - * - * Example: \include Matrix_resize_int.cpp - * Output: \verbinclude Matrix_resize_int.out - * - * \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t) - */ - EIGEN_DEVICE_FUNC - inline void resize(Index size) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase) - eigen_assert(((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime==Dynamic || size<=MaxSizeAtCompileTime)) || SizeAtCompileTime == size) && size>=0); - #ifdef EIGEN_INITIALIZE_COEFFS - bool size_changed = size != this->size(); - #endif - if(RowsAtCompileTime == 1) - m_storage.resize(size, 1, size); - else - m_storage.resize(size, size, 1); - #ifdef EIGEN_INITIALIZE_COEFFS - if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED - #endif - } - - /** Resizes the matrix, changing only the number of columns. For the parameter of type NoChange_t, just pass the special value \c NoChange - * as in the example below. - * - * Example: \include Matrix_resize_NoChange_int.cpp - * Output: \verbinclude Matrix_resize_NoChange_int.out - * - * \sa resize(Index,Index) - */ - EIGEN_DEVICE_FUNC - inline void resize(NoChange_t, Index nbCols) - { - resize(rows(), nbCols); - } - - /** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange - * as in the example below. - * - * Example: \include Matrix_resize_int_NoChange.cpp - * Output: \verbinclude Matrix_resize_int_NoChange.out - * - * \sa resize(Index,Index) - */ - EIGEN_DEVICE_FUNC - inline void resize(Index nbRows, NoChange_t) - { - resize(nbRows, cols()); - } - - /** Resizes \c *this to have the same dimensions as \a other. - * Takes care of doing all the checking that's needed. - * - * Note that copying a row-vector into a vector (and conversely) is allowed. - * The resizing, if any, is then done in the appropriate way so that row-vectors - * remain row-vectors and vectors remain vectors. - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other) - { - const OtherDerived& other = _other.derived(); - internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.rows(), other.cols()); - const Index othersize = other.rows()*other.cols(); - if(RowsAtCompileTime == 1) - { - eigen_assert(other.rows() == 1 || other.cols() == 1); - resize(1, othersize); - } - else if(ColsAtCompileTime == 1) - { - eigen_assert(other.rows() == 1 || other.cols() == 1); - resize(othersize, 1); - } - else resize(other.rows(), other.cols()); - } - - /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. - * - * The method is intended for matrices of dynamic size. If you only want to change the number - * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or - * conservativeResize(Index, NoChange_t). - * - * Matrices are resized relative to the top-left element. In case values need to be - * appended to the matrix they will be uninitialized. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void conservativeResize(Index nbRows, Index nbCols) - { - internal::conservative_resize_like_impl<Derived>::run(*this, nbRows, nbCols); - } - - /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. - * - * As opposed to conservativeResize(Index rows, Index cols), this version leaves - * the number of columns unchanged. - * - * In case the matrix is growing, new rows will be uninitialized. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void conservativeResize(Index nbRows, NoChange_t) - { - // Note: see the comment in conservativeResize(Index,Index) - conservativeResize(nbRows, cols()); - } - - /** Resizes the matrix to \a rows x \a cols while leaving old values untouched. - * - * As opposed to conservativeResize(Index rows, Index cols), this version leaves - * the number of rows unchanged. - * - * In case the matrix is growing, new columns will be uninitialized. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index nbCols) - { - // Note: see the comment in conservativeResize(Index,Index) - conservativeResize(rows(), nbCols); - } - - /** Resizes the vector to \a size while retaining old values. - * - * \only_for_vectors. This method does not work for - * partially dynamic matrices when the static dimension is anything other - * than 1. For example it will not work with Matrix<double, 2, Dynamic>. - * - * When values are appended, they will be uninitialized. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void conservativeResize(Index size) - { - internal::conservative_resize_like_impl<Derived>::run(*this, size); - } - - /** Resizes the matrix to \a rows x \a cols of \c other, while leaving old values untouched. - * - * The method is intended for matrices of dynamic size. If you only want to change the number - * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or - * conservativeResize(Index, NoChange_t). - * - * Matrices are resized relative to the top-left element. In case values need to be - * appended to the matrix they will copied from \c other. - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase<OtherDerived>& other) - { - internal::conservative_resize_like_impl<Derived,OtherDerived>::run(*this, other); - } - - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other) - { - return _set(other); - } - - /** \sa MatrixBase::lazyAssign() */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase<OtherDerived>& other) - { - _resize_to_match(other); - return Base::lazyAssign(other.derived()); - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func) - { - resize(func.rows(), func.cols()); - return Base::operator=(func); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE PlainObjectBase() : m_storage() - { -// _check_template_params(); -// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED - } - -#ifndef EIGEN_PARSED_BY_DOXYGEN - // FIXME is it still needed ? - /** \internal */ - EIGEN_DEVICE_FUNC - PlainObjectBase(internal::constructor_without_unaligned_array_assert) - : m_storage(internal::constructor_without_unaligned_array_assert()) - { -// _check_template_params(); EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED - } -#endif - -#ifdef EIGEN_HAVE_RVALUE_REFERENCES - EIGEN_DEVICE_FUNC - PlainObjectBase(PlainObjectBase&& other) - : m_storage( std::move(other.m_storage) ) - { - } - - EIGEN_DEVICE_FUNC - PlainObjectBase& operator=(PlainObjectBase&& other) - { - using std::swap; - swap(m_storage, other.m_storage); - return *this; - } -#endif - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE PlainObjectBase(Index a_size, Index nbRows, Index nbCols) - : m_storage(a_size, nbRows, nbCols) - { -// _check_template_params(); -// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED - } - - /** \copydoc MatrixBase::operator=(const EigenBase<OtherDerived>&) - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other) - { - _resize_to_match(other); - Base::operator=(other.derived()); - return this->derived(); - } - - /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other) - : m_storage(other.derived().rows() * other.derived().cols(), other.derived().rows(), other.derived().cols()) - { - _check_template_params(); - internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime>::run(other.derived().rows(), other.derived().cols()); - Base::operator=(other.derived()); - } - - /** \name Map - * These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects, - * while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned - * \a data pointers. - * - * \see class Map - */ - //@{ - static inline ConstMapType Map(const Scalar* data) - { return ConstMapType(data); } - static inline MapType Map(Scalar* data) - { return MapType(data); } - static inline ConstMapType Map(const Scalar* data, Index size) - { return ConstMapType(data, size); } - static inline MapType Map(Scalar* data, Index size) - { return MapType(data, size); } - static inline ConstMapType Map(const Scalar* data, Index rows, Index cols) - { return ConstMapType(data, rows, cols); } - static inline MapType Map(Scalar* data, Index rows, Index cols) - { return MapType(data, rows, cols); } - - static inline ConstAlignedMapType MapAligned(const Scalar* data) - { return ConstAlignedMapType(data); } - static inline AlignedMapType MapAligned(Scalar* data) - { return AlignedMapType(data); } - static inline ConstAlignedMapType MapAligned(const Scalar* data, Index size) - { return ConstAlignedMapType(data, size); } - static inline AlignedMapType MapAligned(Scalar* data, Index size) - { return AlignedMapType(data, size); } - static inline ConstAlignedMapType MapAligned(const Scalar* data, Index rows, Index cols) - { return ConstAlignedMapType(data, rows, cols); } - static inline AlignedMapType MapAligned(Scalar* data, Index rows, Index cols) - { return AlignedMapType(data, rows, cols); } - - template<int Outer, int Inner> - static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, const Stride<Outer, Inner>& stride) - { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, stride); } - template<int Outer, int Inner> - static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, const Stride<Outer, Inner>& stride) - { return typename StridedMapType<Stride<Outer, Inner> >::type(data, stride); } - template<int Outer, int Inner> - static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index size, const Stride<Outer, Inner>& stride) - { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, size, stride); } - template<int Outer, int Inner> - static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index size, const Stride<Outer, Inner>& stride) - { return typename StridedMapType<Stride<Outer, Inner> >::type(data, size, stride); } - template<int Outer, int Inner> - static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride) - { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); } - template<int Outer, int Inner> - static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride) - { return typename StridedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); } - - template<int Outer, int Inner> - static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, const Stride<Outer, Inner>& stride) - { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, stride); } - template<int Outer, int Inner> - static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, const Stride<Outer, Inner>& stride) - { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, stride); } - template<int Outer, int Inner> - static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index size, const Stride<Outer, Inner>& stride) - { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); } - template<int Outer, int Inner> - static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index size, const Stride<Outer, Inner>& stride) - { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); } - template<int Outer, int Inner> - static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride) - { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); } - template<int Outer, int Inner> - static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride) - { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); } - //@} - - using Base::setConstant; - EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& value); - EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& value); - - using Base::setZero; - EIGEN_DEVICE_FUNC Derived& setZero(Index size); - EIGEN_DEVICE_FUNC Derived& setZero(Index rows, Index cols); - - using Base::setOnes; - EIGEN_DEVICE_FUNC Derived& setOnes(Index size); - EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, Index cols); - - using Base::setRandom; - Derived& setRandom(Index size); - Derived& setRandom(Index rows, Index cols); - - #ifdef EIGEN_PLAINOBJECTBASE_PLUGIN - #include EIGEN_PLAINOBJECTBASE_PLUGIN - #endif - - protected: - /** \internal Resizes *this in preparation for assigning \a other to it. - * Takes care of doing all the checking that's needed. - * - * Note that copying a row-vector into a vector (and conversely) is allowed. - * The resizing, if any, is then done in the appropriate way so that row-vectors - * remain row-vectors and vectors remain vectors. - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other) - { - #ifdef EIGEN_NO_AUTOMATIC_RESIZING - eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size()) - : (rows() == other.rows() && cols() == other.cols()))) - && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined"); - EIGEN_ONLY_USED_FOR_DEBUG(other); - #else - resizeLike(other); - #endif - } - - /** - * \brief Copies the value of the expression \a other into \c *this with automatic resizing. - * - * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized), - * it will be initialized. - * - * Note that copying a row-vector into a vector (and conversely) is allowed. - * The resizing, if any, is then done in the appropriate way so that row-vectors - * remain row-vectors and vectors remain vectors. - * - * \sa operator=(const MatrixBase<OtherDerived>&), _set_noalias() - * - * \internal - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Derived& _set(const DenseBase<OtherDerived>& other) - { - _set_selector(other.derived(), typename internal::conditional<static_cast<bool>(int(OtherDerived::Flags) & EvalBeforeAssigningBit), internal::true_type, internal::false_type>::type()); - return this->derived(); - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::true_type&) { _set_noalias(other.eval()); } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _set_selector(const OtherDerived& other, const internal::false_type&) { _set_noalias(other); } - - /** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which - * is the case when creating a new matrix) so one can enforce lazy evaluation. - * - * \sa operator=(const MatrixBase<OtherDerived>&), _set() - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase<OtherDerived>& other) - { - // I don't think we need this resize call since the lazyAssign will anyways resize - // and lazyAssign will be called by the assign selector. - //_resize_to_match(other); - // the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because - // it wouldn't allow to copy a row-vector into a column-vector. - return internal::assign_selector<Derived,OtherDerived,false>::run(this->derived(), other.derived()); - } - - template<typename T0, typename T1> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init2(Index nbRows, Index nbCols, typename internal::enable_if<Base::SizeAtCompileTime!=2,T0>::type* = 0) - { - EIGEN_STATIC_ASSERT(bool(NumTraits<T0>::IsInteger) && - bool(NumTraits<T1>::IsInteger), - FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED) - resize(nbRows,nbCols); - } - template<typename T0, typename T1> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init2(const Scalar& val0, const Scalar& val1, typename internal::enable_if<Base::SizeAtCompileTime==2,T0>::type* = 0) - { - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2) - m_storage.data()[0] = val0; - m_storage.data()[1] = val1; - } - - template<typename T> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init1(Index size, typename internal::enable_if<Base::SizeAtCompileTime!=1,T>::type* = 0) - { - EIGEN_STATIC_ASSERT(bool(NumTraits<T>::IsInteger), - FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED) - resize(size); - } - template<typename T> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init1(const Scalar& val0, typename internal::enable_if<Base::SizeAtCompileTime==1,T>::type* = 0) - { - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1) - m_storage.data()[0] = val0; - } - - template<typename T> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init1(const Scalar* data){ - this->_set_noalias(ConstMapType(data)); - } - - template<typename T, typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init1(const DenseBase<OtherDerived>& other){ - this->_set_noalias(other); - } - - template<typename T, typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init1(const EigenBase<OtherDerived>& other){ - this->derived() = other; - } - - template<typename T, typename OtherDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init1(const ReturnByValue<OtherDerived>& other) - { - resize(other.rows(), other.cols()); - other.evalTo(this->derived()); - } - - template<typename T, typename OtherDerived, int ColsAtCompileTime> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void _init1(const RotationBase<OtherDerived,ColsAtCompileTime>& r) - { - this->derived() = r; - } - - template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> - friend struct internal::matrix_swap_impl; - - /** \internal generic implementation of swap for dense storage since for dynamic-sized matrices of same type it is enough to swap the - * data pointers. - */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void _swap(DenseBase<OtherDerived> const & other) - { - enum { SwapPointers = internal::is_same<Derived, OtherDerived>::value && Base::SizeAtCompileTime==Dynamic }; - internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.const_cast_derived()); - } - - public: -#ifndef EIGEN_PARSED_BY_DOXYGEN - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void _check_template_params() - { - EIGEN_STATIC_ASSERT((EIGEN_IMPLIES(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (Options&RowMajor)==RowMajor) - && EIGEN_IMPLIES(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (Options&RowMajor)==0) - && ((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0)) - && ((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0)) - && ((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0)) - && ((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0)) - && (MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic) - && (MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic) - && (Options & (DontAlign|RowMajor)) == Options), - INVALID_MATRIX_TEMPLATE_PARAMETERS) - } -#endif - -private: - enum { ThisConstantIsPrivateInPlainObjectBase }; -}; - -namespace internal { - -template <typename Derived, typename OtherDerived, bool IsVector> -struct conservative_resize_like_impl -{ - typedef typename Derived::Index Index; - static void run(DenseBase<Derived>& _this, Index rows, Index cols) - { - if (_this.rows() == rows && _this.cols() == cols) return; - EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived) - - if ( ( Derived::IsRowMajor && _this.cols() == cols) || // row-major and we change only the number of rows - (!Derived::IsRowMajor && _this.rows() == rows) ) // column-major and we change only the number of columns - { - internal::check_rows_cols_for_overflow<Derived::MaxSizeAtCompileTime>::run(rows, cols); - _this.derived().m_storage.conservativeResize(rows*cols,rows,cols); - } - else - { - // The storage order does not allow us to use reallocation. - typename Derived::PlainObject tmp(rows,cols); - const Index common_rows = (std::min)(rows, _this.rows()); - const Index common_cols = (std::min)(cols, _this.cols()); - tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols); - _this.derived().swap(tmp); - } - } - - static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other) - { - if (_this.rows() == other.rows() && _this.cols() == other.cols()) return; - - // Note: Here is space for improvement. Basically, for conservativeResize(Index,Index), - // neither RowsAtCompileTime or ColsAtCompileTime must be Dynamic. If only one of the - // dimensions is dynamic, one could use either conservativeResize(Index rows, NoChange_t) or - // conservativeResize(NoChange_t, Index cols). For these methods new static asserts like - // EIGEN_STATIC_ASSERT_DYNAMIC_ROWS and EIGEN_STATIC_ASSERT_DYNAMIC_COLS would be good. - EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived) - EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(OtherDerived) - - if ( ( Derived::IsRowMajor && _this.cols() == other.cols()) || // row-major and we change only the number of rows - (!Derived::IsRowMajor && _this.rows() == other.rows()) ) // column-major and we change only the number of columns - { - const Index new_rows = other.rows() - _this.rows(); - const Index new_cols = other.cols() - _this.cols(); - _this.derived().m_storage.conservativeResize(other.size(),other.rows(),other.cols()); - if (new_rows>0) - _this.bottomRightCorner(new_rows, other.cols()) = other.bottomRows(new_rows); - else if (new_cols>0) - _this.bottomRightCorner(other.rows(), new_cols) = other.rightCols(new_cols); - } - else - { - // The storage order does not allow us to use reallocation. - typename Derived::PlainObject tmp(other); - const Index common_rows = (std::min)(tmp.rows(), _this.rows()); - const Index common_cols = (std::min)(tmp.cols(), _this.cols()); - tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols); - _this.derived().swap(tmp); - } - } -}; - -// Here, the specialization for vectors inherits from the general matrix case -// to allow calling .conservativeResize(rows,cols) on vectors. -template <typename Derived, typename OtherDerived> -struct conservative_resize_like_impl<Derived,OtherDerived,true> - : conservative_resize_like_impl<Derived,OtherDerived,false> -{ - using conservative_resize_like_impl<Derived,OtherDerived,false>::run; - - typedef typename Derived::Index Index; - static void run(DenseBase<Derived>& _this, Index size) - { - const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size; - const Index new_cols = Derived::RowsAtCompileTime==1 ? size : 1; - _this.derived().m_storage.conservativeResize(size,new_rows,new_cols); - } - - static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other) - { - if (_this.rows() == other.rows() && _this.cols() == other.cols()) return; - - const Index num_new_elements = other.size() - _this.size(); - - const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : other.rows(); - const Index new_cols = Derived::RowsAtCompileTime==1 ? other.cols() : 1; - _this.derived().m_storage.conservativeResize(other.size(),new_rows,new_cols); - - if (num_new_elements > 0) - _this.tail(num_new_elements) = other.tail(num_new_elements); - } -}; - -template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> -struct matrix_swap_impl -{ - EIGEN_DEVICE_FUNC - static inline void run(MatrixTypeA& a, MatrixTypeB& b) - { - a.base().swap(b); - } -}; - -template<typename MatrixTypeA, typename MatrixTypeB> -struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true> -{ - EIGEN_DEVICE_FUNC - static inline void run(MatrixTypeA& a, MatrixTypeB& b) - { - static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_DENSESTORAGEBASE_H diff --git a/third_party/eigen3/Eigen/src/Core/Product.h b/third_party/eigen3/Eigen/src/Core/Product.h deleted file mode 100644 index 5d3789be74..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Product.h +++ /dev/null @@ -1,107 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PRODUCT_H -#define EIGEN_PRODUCT_H - -namespace Eigen { - -template<typename Lhs, typename Rhs> class Product; -template<typename Lhs, typename Rhs, typename StorageKind> class ProductImpl; - -/** \class Product - * \ingroup Core_Module - * - * \brief Expression of the product of two arbitrary matrices or vectors - * - * \param Lhs the type of the left-hand side expression - * \param Rhs the type of the right-hand side expression - * - * This class represents an expression of the product of two arbitrary matrices. - * - */ - -// Use ProductReturnType to get correct traits, in particular vectorization flags -namespace internal { -template<typename Lhs, typename Rhs> -struct traits<Product<Lhs, Rhs> > - : traits<typename ProductReturnType<Lhs, Rhs>::Type> -{ - // We want A+B*C to be of type Product<Matrix, Sum> and not Product<Matrix, Matrix> - // TODO: This flag should eventually go in a separate evaluator traits class - enum { - Flags = traits<typename ProductReturnType<Lhs, Rhs>::Type>::Flags & ~(EvalBeforeNestingBit | DirectAccessBit) - }; -}; -} // end namespace internal - - -template<typename Lhs, typename Rhs> -class Product : public ProductImpl<Lhs,Rhs,typename internal::promote_storage_type<typename internal::traits<Lhs>::StorageKind, - typename internal::traits<Rhs>::StorageKind>::ret> -{ - public: - - typedef typename ProductImpl< - Lhs, Rhs, - typename internal::promote_storage_type<typename Lhs::StorageKind, - typename Rhs::StorageKind>::ret>::Base Base; - EIGEN_GENERIC_PUBLIC_INTERFACE(Product) - - typedef typename Lhs::Nested LhsNested; - typedef typename Rhs::Nested RhsNested; - typedef typename internal::remove_all<LhsNested>::type LhsNestedCleaned; - typedef typename internal::remove_all<RhsNested>::type RhsNestedCleaned; - - Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs) - { - eigen_assert(lhs.cols() == rhs.rows() - && "invalid matrix product" - && "if you wanted a coeff-wise or a dot product use the respective explicit functions"); - } - - inline Index rows() const { return m_lhs.rows(); } - inline Index cols() const { return m_rhs.cols(); } - - const LhsNestedCleaned& lhs() const { return m_lhs; } - const RhsNestedCleaned& rhs() const { return m_rhs; } - - protected: - - LhsNested m_lhs; - RhsNested m_rhs; -}; - -template<typename Lhs, typename Rhs> -class ProductImpl<Lhs,Rhs,Dense> : public internal::dense_xpr_base<Product<Lhs,Rhs> >::type -{ - typedef Product<Lhs, Rhs> Derived; - public: - - typedef typename internal::dense_xpr_base<Product<Lhs, Rhs> >::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Derived) -}; - -/*************************************************************************** -* Implementation of matrix base methods -***************************************************************************/ - - -/** \internal used to test the evaluator only - */ -template<typename Lhs,typename Rhs> -const Product<Lhs,Rhs> -prod(const Lhs& lhs, const Rhs& rhs) -{ - return Product<Lhs,Rhs>(lhs,rhs); -} - -} // end namespace Eigen - -#endif // EIGEN_PRODUCT_H diff --git a/third_party/eigen3/Eigen/src/Core/ProductBase.h b/third_party/eigen3/Eigen/src/Core/ProductBase.h deleted file mode 100644 index b6152cb8ca..0000000000 --- a/third_party/eigen3/Eigen/src/Core/ProductBase.h +++ /dev/null @@ -1,280 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PRODUCTBASE_H -#define EIGEN_PRODUCTBASE_H - -namespace Eigen { - -/** \class ProductBase - * \ingroup Core_Module - * - */ - -namespace internal { -template<typename Derived, typename _Lhs, typename _Rhs> -struct traits<ProductBase<Derived,_Lhs,_Rhs> > -{ - typedef MatrixXpr XprKind; - typedef typename remove_all<_Lhs>::type Lhs; - typedef typename remove_all<_Rhs>::type Rhs; - typedef typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType Scalar; - typedef typename promote_storage_type<typename traits<Lhs>::StorageKind, - typename traits<Rhs>::StorageKind>::ret StorageKind; - typedef typename promote_index_type<typename traits<Lhs>::Index, - typename traits<Rhs>::Index>::type Index; - enum { - RowsAtCompileTime = traits<Lhs>::RowsAtCompileTime, - ColsAtCompileTime = traits<Rhs>::ColsAtCompileTime, - MaxRowsAtCompileTime = traits<Lhs>::MaxRowsAtCompileTime, - MaxColsAtCompileTime = traits<Rhs>::MaxColsAtCompileTime, - Flags = (MaxRowsAtCompileTime==1 ? RowMajorBit : 0) - | EvalBeforeNestingBit | EvalBeforeAssigningBit | NestByRefBit, - // Note that EvalBeforeNestingBit and NestByRefBit - // are not used in practice because nested is overloaded for products - CoeffReadCost = 0 // FIXME why is it needed ? - }; -}; -} - -#define EIGEN_PRODUCT_PUBLIC_INTERFACE(Derived) \ - typedef ProductBase<Derived, Lhs, Rhs > Base; \ - EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \ - typedef typename Base::LhsNested LhsNested; \ - typedef typename Base::_LhsNested _LhsNested; \ - typedef typename Base::LhsBlasTraits LhsBlasTraits; \ - typedef typename Base::ActualLhsType ActualLhsType; \ - typedef typename Base::_ActualLhsType _ActualLhsType; \ - typedef typename Base::RhsNested RhsNested; \ - typedef typename Base::_RhsNested _RhsNested; \ - typedef typename Base::RhsBlasTraits RhsBlasTraits; \ - typedef typename Base::ActualRhsType ActualRhsType; \ - typedef typename Base::_ActualRhsType _ActualRhsType; \ - using Base::m_lhs; \ - using Base::m_rhs; - -template<typename Derived, typename Lhs, typename Rhs> -class ProductBase : public MatrixBase<Derived> -{ - public: - typedef MatrixBase<Derived> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(ProductBase) - - typedef typename Lhs::Nested LhsNested; - typedef typename internal::remove_all<LhsNested>::type _LhsNested; - typedef internal::blas_traits<_LhsNested> LhsBlasTraits; - typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType; - typedef typename internal::remove_all<ActualLhsType>::type _ActualLhsType; - typedef typename internal::traits<Lhs>::Scalar LhsScalar; - - typedef typename Rhs::Nested RhsNested; - typedef typename internal::remove_all<RhsNested>::type _RhsNested; - typedef internal::blas_traits<_RhsNested> RhsBlasTraits; - typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType; - typedef typename internal::remove_all<ActualRhsType>::type _ActualRhsType; - typedef typename internal::traits<Rhs>::Scalar RhsScalar; - - // Diagonal of a product: no need to evaluate the arguments because they are going to be evaluated only once - typedef CoeffBasedProduct<LhsNested, RhsNested, 0> FullyLazyCoeffBaseProductType; - - public: - - typedef typename Base::PlainObject PlainObject; - - ProductBase(const Lhs& a_lhs, const Rhs& a_rhs) - : m_lhs(a_lhs), m_rhs(a_rhs) - { - eigen_assert(a_lhs.cols() == a_rhs.rows() - && "invalid matrix product" - && "if you wanted a coeff-wise or a dot product use the respective explicit functions"); - } - - inline Index rows() const { return m_lhs.rows(); } - inline Index cols() const { return m_rhs.cols(); } - - template<typename Dest> - inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst,Scalar(1)); } - - template<typename Dest> - inline void addTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(1)); } - - template<typename Dest> - inline void subTo(Dest& dst) const { scaleAndAddTo(dst,Scalar(-1)); } - - template<typename Dest> - inline void scaleAndAddTo(Dest& dst, const Scalar& alpha) const { derived().scaleAndAddTo(dst,alpha); } - - const _LhsNested& lhs() const { return m_lhs; } - const _RhsNested& rhs() const { return m_rhs; } - - // Implicit conversion to the nested type (trigger the evaluation of the product) - operator const PlainObject& () const - { - m_result.resize(m_lhs.rows(), m_rhs.cols()); - derived().evalTo(m_result); - return m_result; - } - - const Diagonal<const FullyLazyCoeffBaseProductType,0> diagonal() const - { return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); } - - template<int Index> - const Diagonal<const FullyLazyCoeffBaseProductType,Index> diagonal() const - { return FullyLazyCoeffBaseProductType(m_lhs, m_rhs); } - - const Diagonal<const FullyLazyCoeffBaseProductType, DynamicIndex> diagonal(Index index) const { - return Diagonal<const FullyLazyCoeffBaseProductType, DynamicIndex>( - FullyLazyCoeffBaseProductType(m_lhs, m_rhs), index); - } - - // restrict coeff accessors to 1x1 expressions. No need to care about mutators here since this isnt a Lvalue expression - typename Base::CoeffReturnType coeff(Index row, Index col) const - { -#ifdef EIGEN2_SUPPORT - return lhs().row(row).cwiseProduct(rhs().col(col).transpose()).sum(); -#else - EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) - eigen_assert(this->rows() == 1 && this->cols() == 1); - Matrix<Scalar,1,1> result = *this; - return result.coeff(row,col); -#endif - } - - typename Base::CoeffReturnType coeff(Index i) const - { - EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) - eigen_assert(this->rows() == 1 && this->cols() == 1); - Matrix<Scalar,1,1> result = *this; - return result.coeff(i); - } - - const Scalar& coeffRef(Index row, Index col) const - { - EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) - eigen_assert(this->rows() == 1 && this->cols() == 1); - return derived().coeffRef(row,col); - } - - const Scalar& coeffRef(Index i) const - { - EIGEN_STATIC_ASSERT_SIZE_1x1(Derived) - eigen_assert(this->rows() == 1 && this->cols() == 1); - return derived().coeffRef(i); - } - - protected: - - LhsNested m_lhs; - RhsNested m_rhs; - - mutable PlainObject m_result; -}; - -// here we need to overload the nested rule for products -// such that the nested type is a const reference to a plain matrix -namespace internal { -template<typename Lhs, typename Rhs, int Mode, int N, typename PlainObject> -struct nested<GeneralProduct<Lhs,Rhs,Mode>, N, PlainObject> -{ - typedef PlainObject const& type; -}; -} - -template<typename NestedProduct> -class ScaledProduct; - -// Note that these two operator* functions are not defined as member -// functions of ProductBase, because, otherwise we would have to -// define all overloads defined in MatrixBase. Furthermore, Using -// "using Base::operator*" would not work with MSVC. -// -// Also note that here we accept any compatible scalar types -template<typename Derived,typename Lhs,typename Rhs> -const ScaledProduct<Derived> -operator*(const ProductBase<Derived,Lhs,Rhs>& prod, const typename Derived::Scalar& x) -{ return ScaledProduct<Derived>(prod.derived(), x); } - -template<typename Derived,typename Lhs,typename Rhs> -typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value, - const ScaledProduct<Derived> >::type -operator*(const ProductBase<Derived,Lhs,Rhs>& prod, const typename Derived::RealScalar& x) -{ return ScaledProduct<Derived>(prod.derived(), x); } - - -template<typename Derived,typename Lhs,typename Rhs> -const ScaledProduct<Derived> -operator*(const typename Derived::Scalar& x,const ProductBase<Derived,Lhs,Rhs>& prod) -{ return ScaledProduct<Derived>(prod.derived(), x); } - -template<typename Derived,typename Lhs,typename Rhs> -typename internal::enable_if<!internal::is_same<typename Derived::Scalar,typename Derived::RealScalar>::value, - const ScaledProduct<Derived> >::type -operator*(const typename Derived::RealScalar& x,const ProductBase<Derived,Lhs,Rhs>& prod) -{ return ScaledProduct<Derived>(prod.derived(), x); } - -namespace internal { -template<typename NestedProduct> -struct traits<ScaledProduct<NestedProduct> > - : traits<ProductBase<ScaledProduct<NestedProduct>, - typename NestedProduct::_LhsNested, - typename NestedProduct::_RhsNested> > -{ - typedef typename traits<NestedProduct>::StorageKind StorageKind; -}; -} - -template<typename NestedProduct> -class ScaledProduct - : public ProductBase<ScaledProduct<NestedProduct>, - typename NestedProduct::_LhsNested, - typename NestedProduct::_RhsNested> -{ - public: - typedef ProductBase<ScaledProduct<NestedProduct>, - typename NestedProduct::_LhsNested, - typename NestedProduct::_RhsNested> Base; - typedef typename Base::Scalar Scalar; - typedef typename Base::PlainObject PlainObject; -// EIGEN_PRODUCT_PUBLIC_INTERFACE(ScaledProduct) - - ScaledProduct(const NestedProduct& prod, const Scalar& x) - : Base(prod.lhs(),prod.rhs()), m_prod(prod), m_alpha(x) {} - - template<typename Dest> - inline void evalTo(Dest& dst) const { dst.setZero(); scaleAndAddTo(dst, Scalar(1)); } - - template<typename Dest> - inline void addTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(1)); } - - template<typename Dest> - inline void subTo(Dest& dst) const { scaleAndAddTo(dst, Scalar(-1)); } - - template<typename Dest> - inline void scaleAndAddTo(Dest& dst, const Scalar& a_alpha) const { m_prod.derived().scaleAndAddTo(dst,a_alpha * m_alpha); } - - const Scalar& alpha() const { return m_alpha; } - - protected: - const NestedProduct& m_prod; - Scalar m_alpha; -}; - -/** \internal - * Overloaded to perform an efficient C = (A*B).lazy() */ -template<typename Derived> -template<typename ProductDerived, typename Lhs, typename Rhs> -Derived& MatrixBase<Derived>::lazyAssign(const ProductBase<ProductDerived, Lhs,Rhs>& other) -{ - other.derived().evalTo(derived()); - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_PRODUCTBASE_H diff --git a/third_party/eigen3/Eigen/src/Core/ProductEvaluators.h b/third_party/eigen3/Eigen/src/Core/ProductEvaluators.h deleted file mode 100644 index 855914f2eb..0000000000 --- a/third_party/eigen3/Eigen/src/Core/ProductEvaluators.h +++ /dev/null @@ -1,411 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -#ifndef EIGEN_PRODUCTEVALUATORS_H -#define EIGEN_PRODUCTEVALUATORS_H - -namespace Eigen { - -namespace internal { - -// We can evaluate the product either all at once, like GeneralProduct and its evalTo() function, or -// traverse the matrix coefficient by coefficient, like CoeffBasedProduct. Use the existing logic -// in ProductReturnType to decide. - -template<typename XprType, typename ProductType> -struct product_evaluator_dispatcher; - -template<typename Lhs, typename Rhs> -struct evaluator_impl<Product<Lhs, Rhs> > - : product_evaluator_dispatcher<Product<Lhs, Rhs>, typename ProductReturnType<Lhs, Rhs>::Type> -{ - typedef Product<Lhs, Rhs> XprType; - typedef product_evaluator_dispatcher<XprType, typename ProductReturnType<Lhs, Rhs>::Type> Base; - - evaluator_impl(const XprType& xpr) : Base(xpr) - { } -}; - -template<typename XprType, typename ProductType> -struct product_evaluator_traits_dispatcher; - -template<typename Lhs, typename Rhs> -struct evaluator_traits<Product<Lhs, Rhs> > - : product_evaluator_traits_dispatcher<Product<Lhs, Rhs>, typename ProductReturnType<Lhs, Rhs>::Type> -{ - static const int AssumeAliasing = 1; -}; - -// Case 1: Evaluate all at once -// -// We can view the GeneralProduct class as a part of the product evaluator. -// Four sub-cases: InnerProduct, OuterProduct, GemmProduct and GemvProduct. -// InnerProduct is special because GeneralProduct does not have an evalTo() method in this case. - -template<typename Lhs, typename Rhs> -struct product_evaluator_traits_dispatcher<Product<Lhs, Rhs>, GeneralProduct<Lhs, Rhs, InnerProduct> > -{ - static const int HasEvalTo = 0; -}; - -template<typename Lhs, typename Rhs> -struct product_evaluator_dispatcher<Product<Lhs, Rhs>, GeneralProduct<Lhs, Rhs, InnerProduct> > - : public evaluator<typename Product<Lhs, Rhs>::PlainObject>::type -{ - typedef Product<Lhs, Rhs> XprType; - typedef typename XprType::PlainObject PlainObject; - typedef typename evaluator<PlainObject>::type evaluator_base; - - // TODO: Computation is too early (?) - product_evaluator_dispatcher(const XprType& xpr) : evaluator_base(m_result) - { - m_result.coeffRef(0,0) = (xpr.lhs().transpose().cwiseProduct(xpr.rhs())).sum(); - } - -protected: - PlainObject m_result; -}; - -// For the other three subcases, simply call the evalTo() method of GeneralProduct -// TODO: GeneralProduct should take evaluators, not expression objects. - -template<typename Lhs, typename Rhs, int ProductType> -struct product_evaluator_traits_dispatcher<Product<Lhs, Rhs>, GeneralProduct<Lhs, Rhs, ProductType> > -{ - static const int HasEvalTo = 1; -}; - -template<typename Lhs, typename Rhs, int ProductType> -struct product_evaluator_dispatcher<Product<Lhs, Rhs>, GeneralProduct<Lhs, Rhs, ProductType> > -{ - typedef Product<Lhs, Rhs> XprType; - typedef typename XprType::PlainObject PlainObject; - typedef typename evaluator<PlainObject>::type evaluator_base; - - product_evaluator_dispatcher(const XprType& xpr) : m_xpr(xpr) - { } - - template<typename DstEvaluatorType, typename DstXprType> - void evalTo(DstEvaluatorType /* not used */, DstXprType& dst) const - { - dst.resize(m_xpr.rows(), m_xpr.cols()); - GeneralProduct<Lhs, Rhs, ProductType>(m_xpr.lhs(), m_xpr.rhs()).evalTo(dst); - } - -protected: - const XprType& m_xpr; -}; - -// Case 2: Evaluate coeff by coeff -// -// This is mostly taken from CoeffBasedProduct.h -// The main difference is that we add an extra argument to the etor_product_*_impl::run() function -// for the inner dimension of the product, because evaluator object do not know their size. - -template<int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar> -struct etor_product_coeff_impl; - -template<int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct etor_product_packet_impl; - -template<typename Lhs, typename Rhs, typename LhsNested, typename RhsNested, int Flags> -struct product_evaluator_traits_dispatcher<Product<Lhs, Rhs>, CoeffBasedProduct<LhsNested, RhsNested, Flags> > -{ - static const int HasEvalTo = 0; -}; - -template<typename Lhs, typename Rhs, typename LhsNested, typename RhsNested, int Flags> -struct product_evaluator_dispatcher<Product<Lhs, Rhs>, CoeffBasedProduct<LhsNested, RhsNested, Flags> > - : evaluator_impl_base<Product<Lhs, Rhs> > -{ - typedef Product<Lhs, Rhs> XprType; - typedef CoeffBasedProduct<LhsNested, RhsNested, Flags> CoeffBasedProductType; - - product_evaluator_dispatcher(const XprType& xpr) - : m_lhsImpl(xpr.lhs()), - m_rhsImpl(xpr.rhs()), - m_innerDim(xpr.lhs().cols()) - { } - - typedef typename XprType::Index Index; - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketScalar PacketScalar; - typedef typename XprType::PacketReturnType PacketReturnType; - - // Everything below here is taken from CoeffBasedProduct.h - - enum { - RowsAtCompileTime = traits<CoeffBasedProductType>::RowsAtCompileTime, - PacketSize = packet_traits<Scalar>::size, - InnerSize = traits<CoeffBasedProductType>::InnerSize, - CoeffReadCost = traits<CoeffBasedProductType>::CoeffReadCost, - Unroll = CoeffReadCost != Dynamic && CoeffReadCost <= EIGEN_UNROLLING_LIMIT, - CanVectorizeInner = traits<CoeffBasedProductType>::CanVectorizeInner - }; - - typedef typename evaluator<Lhs>::type LhsEtorType; - typedef typename evaluator<Rhs>::type RhsEtorType; - typedef etor_product_coeff_impl<CanVectorizeInner ? InnerVectorizedTraversal : DefaultTraversal, - Unroll ? InnerSize-1 : Dynamic, - LhsEtorType, RhsEtorType, Scalar> CoeffImpl; - - const CoeffReturnType coeff(Index row, Index col) const - { - Scalar res; - CoeffImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res); - return res; - } - - /* Allow index-based non-packet access. It is impossible though to allow index-based packed access, - * which is why we don't set the LinearAccessBit. - */ - const CoeffReturnType coeff(Index index) const - { - Scalar res; - const Index row = RowsAtCompileTime == 1 ? 0 : index; - const Index col = RowsAtCompileTime == 1 ? index : 0; - CoeffImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res); - return res; - } - - template<int LoadMode> - const PacketReturnType packet(Index row, Index col) const - { - PacketScalar res; - typedef etor_product_packet_impl<Flags&RowMajorBit ? RowMajor : ColMajor, - Unroll ? InnerSize-1 : Dynamic, - LhsEtorType, RhsEtorType, PacketScalar, LoadMode> PacketImpl; - PacketImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res); - return res; - } - -protected: - typename evaluator<Lhs>::type m_lhsImpl; - typename evaluator<Rhs>::type m_rhsImpl; - - // TODO: Get rid of m_innerDim if known at compile time - Index m_innerDim; -}; - -/*************************************************************************** -* Normal product .coeff() implementation (with meta-unrolling) -***************************************************************************/ - -/************************************** -*** Scalar path - no vectorization *** -**************************************/ - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar> -struct etor_product_coeff_impl<DefaultTraversal, UnrollingIndex, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, RetScalar &res) - { - etor_product_coeff_impl<DefaultTraversal, UnrollingIndex-1, Lhs, Rhs, RetScalar>::run(row, col, lhs, rhs, innerDim, res); - res += lhs.coeff(row, UnrollingIndex) * rhs.coeff(UnrollingIndex, col); - } -}; - -template<typename Lhs, typename Rhs, typename RetScalar> -struct etor_product_coeff_impl<DefaultTraversal, 0, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, RetScalar &res) - { - res = lhs.coeff(row, 0) * rhs.coeff(0, col); - } -}; - -template<typename Lhs, typename Rhs, typename RetScalar> -struct etor_product_coeff_impl<DefaultTraversal, Dynamic, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, RetScalar& res) - { - eigen_assert(innerDim>0 && "you are using a non initialized matrix"); - res = lhs.coeff(row, 0) * rhs.coeff(0, col); - for(Index i = 1; i < innerDim; ++i) - res += lhs.coeff(row, i) * rhs.coeff(i, col); - } -}; - -/******************************************* -*** Scalar path with inner vectorization *** -*******************************************/ - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet> -struct etor_product_coeff_vectorized_unroller -{ - typedef typename Lhs::Index Index; - enum { PacketSize = packet_traits<typename Lhs::Scalar>::size }; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, typename Lhs::PacketScalar &pres) - { - etor_product_coeff_vectorized_unroller<UnrollingIndex-PacketSize, Lhs, Rhs, Packet>::run(row, col, lhs, rhs, innerDim, pres); - pres = padd(pres, pmul( lhs.template packet<Aligned>(row, UnrollingIndex) , rhs.template packet<Aligned>(UnrollingIndex, col) )); - } -}; - -template<typename Lhs, typename Rhs, typename Packet> -struct etor_product_coeff_vectorized_unroller<0, Lhs, Rhs, Packet> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, typename Lhs::PacketScalar &pres) - { - pres = pmul(lhs.template packet<Aligned>(row, 0) , rhs.template packet<Aligned>(0, col)); - } -}; - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar> -struct etor_product_coeff_impl<InnerVectorizedTraversal, UnrollingIndex, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::PacketScalar Packet; - typedef typename Lhs::Index Index; - enum { PacketSize = packet_traits<typename Lhs::Scalar>::size }; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, RetScalar &res) - { - Packet pres; - etor_product_coeff_vectorized_unroller<UnrollingIndex+1-PacketSize, Lhs, Rhs, Packet>::run(row, col, lhs, rhs, innerDim, pres); - etor_product_coeff_impl<DefaultTraversal,UnrollingIndex,Lhs,Rhs,RetScalar>::run(row, col, lhs, rhs, innerDim, res); - res = predux(pres); - } -}; - -template<typename Lhs, typename Rhs, int LhsRows = Lhs::RowsAtCompileTime, int RhsCols = Rhs::ColsAtCompileTime> -struct etor_product_coeff_vectorized_dyn_selector -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, typename Lhs::Scalar &res) - { - res = lhs.row(row).transpose().cwiseProduct(rhs.col(col)).sum(); - } -}; - -// NOTE the 3 following specializations are because taking .col(0) on a vector is a bit slower -// NOTE maybe they are now useless since we have a specialization for Block<Matrix> -template<typename Lhs, typename Rhs, int RhsCols> -struct etor_product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,RhsCols> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index /*row*/, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, typename Lhs::Scalar &res) - { - res = lhs.transpose().cwiseProduct(rhs.col(col)).sum(); - } -}; - -template<typename Lhs, typename Rhs, int LhsRows> -struct etor_product_coeff_vectorized_dyn_selector<Lhs,Rhs,LhsRows,1> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index /*col*/, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, typename Lhs::Scalar &res) - { - res = lhs.row(row).transpose().cwiseProduct(rhs).sum(); - } -}; - -template<typename Lhs, typename Rhs> -struct etor_product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,1> -{ - typedef typename Lhs::Index Index; - EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, typename Lhs::Scalar &res) - { - res = lhs.transpose().cwiseProduct(rhs).sum(); - } -}; - -template<typename Lhs, typename Rhs, typename RetScalar> -struct etor_product_coeff_impl<InnerVectorizedTraversal, Dynamic, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, typename Lhs::Scalar &res) - { - etor_product_coeff_vectorized_dyn_selector<Lhs,Rhs>::run(row, col, lhs, rhs, innerDim, res); - } -}; - -/******************* -*** Packet path *** -*******************/ - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct etor_product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res) - { - etor_product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res); - res = pmadd(pset1<Packet>(lhs.coeff(row, UnrollingIndex)), rhs.template packet<LoadMode>(UnrollingIndex, col), res); - } -}; - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct etor_product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res) - { - etor_product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res); - res = pmadd(lhs.template packet<LoadMode>(row, UnrollingIndex), pset1<Packet>(rhs.coeff(UnrollingIndex, col)), res); - } -}; - -template<typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct etor_product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res) - { - res = pmul(pset1<Packet>(lhs.coeff(row, 0)),rhs.template packet<LoadMode>(0, col)); - } -}; - -template<typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct etor_product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res) - { - res = pmul(lhs.template packet<LoadMode>(row, 0), pset1<Packet>(rhs.coeff(0, col))); - } -}; - -template<typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct etor_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res) - { - eigen_assert(innerDim>0 && "you are using a non initialized matrix"); - res = pmul(pset1<Packet>(lhs.coeff(row, 0)),rhs.template packet<LoadMode>(0, col)); - for(Index i = 1; i < innerDim; ++i) - res = pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode>(i, col), res); - } -}; - -template<typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct etor_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res) - { - eigen_assert(innerDim>0 && "you are using a non initialized matrix"); - res = pmul(lhs.template packet<LoadMode>(row, 0), pset1<Packet>(rhs.coeff(0, col))); - for(Index i = 1; i < innerDim; ++i) - res = pmadd(lhs.template packet<LoadMode>(row, i), pset1<Packet>(rhs.coeff(i, col)), res); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PRODUCT_EVALUATORS_H diff --git a/third_party/eigen3/Eigen/src/Core/Random.h b/third_party/eigen3/Eigen/src/Core/Random.h deleted file mode 100644 index 2d3a7243bc..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Random.h +++ /dev/null @@ -1,193 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_RANDOM_H -#define EIGEN_RANDOM_H - -namespace Eigen { - -namespace internal { - -template<typename Scalar> struct scalar_random_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_random_op) - - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index, Index = 0) const { -#ifndef __CUDA_ARCH__ - // We're not compiling a cuda kernel - return random<Scalar>(); -#else - // We're trying to generate a random number from a cuda kernel. - assert(false && "Generating random numbers on gpu isn't supported yet"); - return Scalar(0); -#endif - } -}; - -template<typename Scalar> -struct functor_traits<scalar_random_op<Scalar> > -{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false }; }; - -} // end namespace internal - -/** \returns a random matrix expression - * - * Numbers are uniformly spread through their whole definition range for integer types, - * and in the [-1:1] range for floating point scalar types. - * - * The parameters \a rows and \a cols are the number of rows and of columns of - * the returned matrix. Must be compatible with this MatrixBase type. - * - * \not_reentrant - * - * This variant is meant to be used for dynamic-size matrix types. For fixed-size types, - * it is redundant to pass \a rows and \a cols as arguments, so Random() should be used - * instead. - * - * - * Example: \include MatrixBase_random_int_int.cpp - * Output: \verbinclude MatrixBase_random_int_int.out - * - * This expression has the "evaluate before nesting" flag so that it will be evaluated into - * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected - * behavior with expressions involving random matrices. - * - * See DenseBase::NullaryExpr(Index, const CustomNullaryOp&) for an example using C++11 random generators. - * - * \sa DenseBase::setRandom(), DenseBase::Random(Index), DenseBase::Random() - */ -template<typename Derived> -inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived> -DenseBase<Derived>::Random(Index rows, Index cols) -{ - return NullaryExpr(rows, cols, internal::scalar_random_op<Scalar>()); -} - -/** \returns a random vector expression - * - * Numbers are uniformly spread through their whole definition range for integer types, - * and in the [-1:1] range for floating point scalar types. - * - * The parameter \a size is the size of the returned vector. - * Must be compatible with this MatrixBase type. - * - * \only_for_vectors - * \not_reentrant - * - * This variant is meant to be used for dynamic-size vector types. For fixed-size types, - * it is redundant to pass \a size as argument, so Random() should be used - * instead. - * - * Example: \include MatrixBase_random_int.cpp - * Output: \verbinclude MatrixBase_random_int.out - * - * This expression has the "evaluate before nesting" flag so that it will be evaluated into - * a temporary vector whenever it is nested in a larger expression. This prevents unexpected - * behavior with expressions involving random matrices. - * - * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random() - */ -template<typename Derived> -inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived> -DenseBase<Derived>::Random(Index size) -{ - return NullaryExpr(size, internal::scalar_random_op<Scalar>()); -} - -/** \returns a fixed-size random matrix or vector expression - * - * Numbers are uniformly spread through their whole definition range for integer types, - * and in the [-1:1] range for floating point scalar types. - * - * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you - * need to use the variants taking size arguments. - * - * Example: \include MatrixBase_random.cpp - * Output: \verbinclude MatrixBase_random.out - * - * This expression has the "evaluate before nesting" flag so that it will be evaluated into - * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected - * behavior with expressions involving random matrices. - * - * \not_reentrant - * - * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random(Index) - */ -template<typename Derived> -inline const CwiseNullaryOp<internal::scalar_random_op<typename internal::traits<Derived>::Scalar>, Derived> -DenseBase<Derived>::Random() -{ - return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op<Scalar>()); -} - -/** Sets all coefficients in this expression to random values. - * - * Numbers are uniformly spread through their whole definition range for integer types, - * and in the [-1:1] range for floating point scalar types. - * - * \not_reentrant - * - * Example: \include MatrixBase_setRandom.cpp - * Output: \verbinclude MatrixBase_setRandom.out - * - * \sa class CwiseNullaryOp, setRandom(Index), setRandom(Index,Index) - */ -template<typename Derived> -inline Derived& DenseBase<Derived>::setRandom() -{ - return *this = Random(rows(), cols()); -} - -/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values. - * - * Numbers are uniformly spread through their whole definition range for integer types, - * and in the [-1:1] range for floating point scalar types. - * - * \only_for_vectors - * \not_reentrant - * - * Example: \include Matrix_setRandom_int.cpp - * Output: \verbinclude Matrix_setRandom_int.out - * - * \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -PlainObjectBase<Derived>::setRandom(Index newSize) -{ - resize(newSize); - return setRandom(); -} - -/** Resizes to the given size, and sets all coefficients in this expression to random values. - * - * Numbers are uniformly spread through their whole definition range for integer types, - * and in the [-1:1] range for floating point scalar types. - * - * \not_reentrant - * - * \param nbRows the new number of rows - * \param nbCols the new number of columns - * - * Example: \include Matrix_setRandom_int_int.cpp - * Output: \verbinclude Matrix_setRandom_int_int.out - * - * \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random() - */ -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -PlainObjectBase<Derived>::setRandom(Index nbRows, Index nbCols) -{ - resize(nbRows, nbCols); - return setRandom(); -} - -} // end namespace Eigen - -#endif // EIGEN_RANDOM_H diff --git a/third_party/eigen3/Eigen/src/Core/Redux.h b/third_party/eigen3/Eigen/src/Core/Redux.h deleted file mode 100644 index 5b82c9a654..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Redux.h +++ /dev/null @@ -1,417 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_REDUX_H -#define EIGEN_REDUX_H - -namespace Eigen { - -namespace internal { - -// TODO -// * implement other kind of vectorization -// * factorize code - -/*************************************************************************** -* Part 1 : the logic deciding a strategy for vectorization and unrolling -***************************************************************************/ - -template<typename Func, typename Derived> -struct redux_traits -{ -public: - enum { - PacketSize = packet_traits<typename Derived::Scalar>::size, - InnerMaxSize = int(Derived::IsRowMajor) - ? Derived::MaxColsAtCompileTime - : Derived::MaxRowsAtCompileTime - }; - - enum { - MightVectorize = (int(Derived::Flags)&ActualPacketAccessBit) - && (functor_traits<Func>::PacketAccess), - MayLinearVectorize = MightVectorize && (int(Derived::Flags)&LinearAccessBit), - MaySliceVectorize = MightVectorize && int(InnerMaxSize)>=3*PacketSize - }; - -public: - enum { - Traversal = int(MayLinearVectorize) ? int(LinearVectorizedTraversal) - : int(MaySliceVectorize) ? int(SliceVectorizedTraversal) - : int(DefaultTraversal) - }; - -public: - enum { - Cost = ( Derived::SizeAtCompileTime == Dynamic - || Derived::CoeffReadCost == Dynamic - || (Derived::SizeAtCompileTime!=1 && functor_traits<Func>::Cost == Dynamic) - ) ? Dynamic - : Derived::SizeAtCompileTime * Derived::CoeffReadCost - + (Derived::SizeAtCompileTime-1) * functor_traits<Func>::Cost, - UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize)) - }; - -public: - enum { - Unrolling = Cost != Dynamic && Cost <= UnrollingLimit - ? CompleteUnrolling - : NoUnrolling - }; -}; - -/*************************************************************************** -* Part 2 : unrollers -***************************************************************************/ - -/*** no vectorization ***/ - -template<typename Func, typename Derived, int Start, int Length> -struct redux_novec_unroller -{ - enum { - HalfLength = Length/2 - }; - - typedef typename Derived::Scalar Scalar; - - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func& func) - { - return func(redux_novec_unroller<Func, Derived, Start, HalfLength>::run(mat,func), - redux_novec_unroller<Func, Derived, Start+HalfLength, Length-HalfLength>::run(mat,func)); - } -}; - -template<typename Func, typename Derived, int Start> -struct redux_novec_unroller<Func, Derived, Start, 1> -{ - enum { - outer = Start / Derived::InnerSizeAtCompileTime, - inner = Start % Derived::InnerSizeAtCompileTime - }; - - typedef typename Derived::Scalar Scalar; - - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Derived &mat, const Func&) - { - return mat.coeffByOuterInner(outer, inner); - } -}; - -// This is actually dead code and will never be called. It is required -// to prevent false warnings regarding failed inlining though -// for 0 length run() will never be called at all. -template<typename Func, typename Derived, int Start> -struct redux_novec_unroller<Func, Derived, Start, 0> -{ - typedef typename Derived::Scalar Scalar; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Derived&, const Func&) { return Scalar(); } -}; - -/*** vectorization ***/ - -template<typename Func, typename Derived, int Start, int Length> -struct redux_vec_unroller -{ - enum { - PacketSize = packet_traits<typename Derived::Scalar>::size, - HalfLength = Length/2 - }; - - typedef typename Derived::Scalar Scalar; - typedef typename packet_traits<Scalar>::type PacketScalar; - - static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func& func) - { - return func.packetOp( - redux_vec_unroller<Func, Derived, Start, HalfLength>::run(mat,func), - redux_vec_unroller<Func, Derived, Start+HalfLength, Length-HalfLength>::run(mat,func) ); - } -}; - -template<typename Func, typename Derived, int Start> -struct redux_vec_unroller<Func, Derived, Start, 1> -{ - enum { - index = Start * packet_traits<typename Derived::Scalar>::size, - outer = index / int(Derived::InnerSizeAtCompileTime), - inner = index % int(Derived::InnerSizeAtCompileTime), - alignment = (Derived::Flags & AlignedBit) ? Aligned : Unaligned - }; - - typedef typename Derived::Scalar Scalar; - typedef typename packet_traits<Scalar>::type PacketScalar; - - static EIGEN_STRONG_INLINE PacketScalar run(const Derived &mat, const Func&) - { - return mat.template packetByOuterInner<alignment>(outer, inner); - } -}; - -/*************************************************************************** -* Part 3 : implementation of all cases -***************************************************************************/ - -template<typename Func, typename Derived, - int Traversal = redux_traits<Func, Derived>::Traversal, - int Unrolling = redux_traits<Func, Derived>::Unrolling -> -struct redux_impl; - -template<typename Func, typename Derived> -struct redux_impl<Func, Derived, DefaultTraversal, NoUnrolling> -{ - typedef typename Derived::Scalar Scalar; - typedef typename Derived::Index Index; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func) - { - eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); - Scalar res; - res = mat.coeffByOuterInner(0, 0); - for(Index i = 1; i < mat.innerSize(); ++i) - res = func(res, mat.coeffByOuterInner(0, i)); - for(Index i = 1; i < mat.outerSize(); ++i) - for(Index j = 0; j < mat.innerSize(); ++j) - res = func(res, mat.coeffByOuterInner(i, j)); - return res; - } -}; - -template<typename Func, typename Derived> -struct redux_impl<Func,Derived, DefaultTraversal, CompleteUnrolling> - : public redux_novec_unroller<Func,Derived, 0, Derived::SizeAtCompileTime> -{}; - -template<typename Func, typename Derived> -struct redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling> -{ - typedef typename Derived::Scalar Scalar; - typedef typename packet_traits<Scalar>::type PacketScalar; - typedef typename Derived::Index Index; - - static Scalar run(const Derived& mat, const Func& func) - { - const Index size = mat.size(); - eigen_assert(size && "you are using an empty matrix"); - const Index packetSize = packet_traits<Scalar>::size; - const Index alignedStart = internal::first_aligned(mat); - enum { - alignment = bool(Derived::Flags & DirectAccessBit) || bool(Derived::Flags & AlignedBit) - ? Aligned : Unaligned - }; - const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize); - const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize); - const Index alignedEnd2 = alignedStart + alignedSize2; - const Index alignedEnd = alignedStart + alignedSize; - Scalar res; - if(alignedSize) - { - PacketScalar packet_res0 = mat.template packet<alignment>(alignedStart); - if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop - { - PacketScalar packet_res1 = mat.template packet<alignment>(alignedStart+packetSize); - for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize) - { - packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment>(index)); - packet_res1 = func.packetOp(packet_res1, mat.template packet<alignment>(index+packetSize)); - } - - packet_res0 = func.packetOp(packet_res0,packet_res1); - if(alignedEnd>alignedEnd2) - packet_res0 = func.packetOp(packet_res0, mat.template packet<alignment>(alignedEnd2)); - } - res = func.predux(packet_res0); - - for(Index index = 0; index < alignedStart; ++index) - res = func(res,mat.coeff(index)); - - for(Index index = alignedEnd; index < size; ++index) - res = func(res,mat.coeff(index)); - } - else // too small to vectorize anything. - // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize. - { - res = mat.coeff(0); - for(Index index = 1; index < size; ++index) - res = func(res,mat.coeff(index)); - } - - return res; - } -}; - -template<typename Func, typename Derived> -struct redux_impl<Func, Derived, SliceVectorizedTraversal, NoUnrolling> -{ - typedef typename Derived::Scalar Scalar; - typedef typename packet_traits<Scalar>::type PacketScalar; - typedef typename Derived::Index Index; - - static Scalar run(const Derived& mat, const Func& func) - { - eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); - const Index innerSize = mat.innerSize(); - const Index outerSize = mat.outerSize(); - enum { - packetSize = packet_traits<Scalar>::size - }; - const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize; - Scalar res; - if(packetedInnerSize) - { - PacketScalar packet_res = mat.template packet<Unaligned>(0,0); - for(Index j=0; j<outerSize; ++j) - for(Index i=(j==0?packetSize:0); i<packetedInnerSize; i+=Index(packetSize)) - packet_res = func.packetOp(packet_res, mat.template packetByOuterInner<Unaligned>(j,i)); - - res = func.predux(packet_res); - for(Index j=0; j<outerSize; ++j) - for(Index i=packetedInnerSize; i<innerSize; ++i) - res = func(res, mat.coeffByOuterInner(j,i)); - } - else // too small to vectorize anything. - // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize. - { - res = redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>::run(mat, func); - } - - return res; - } -}; - -template<typename Func, typename Derived> -struct redux_impl<Func, Derived, LinearVectorizedTraversal, CompleteUnrolling> -{ - typedef typename Derived::Scalar Scalar; - typedef typename packet_traits<Scalar>::type PacketScalar; - enum { - PacketSize = packet_traits<Scalar>::size, - Size = Derived::SizeAtCompileTime, - VectorizedSize = (Size / PacketSize) * PacketSize - }; - static EIGEN_STRONG_INLINE Scalar run(const Derived& mat, const Func& func) - { - eigen_assert(mat.rows()>0 && mat.cols()>0 && "you are using an empty matrix"); - if (VectorizedSize > 0) { - Scalar res = func.predux(redux_vec_unroller<Func, Derived, 0, Size / PacketSize>::run(mat,func)); - if (VectorizedSize != Size) - res = func(res,redux_novec_unroller<Func, Derived, VectorizedSize, Size-VectorizedSize>::run(mat,func)); - return res; - } - else { - return redux_novec_unroller<Func, Derived, 0, Size>::run(mat,func); - } - } -}; - -} // end namespace internal - -/*************************************************************************** -* Part 4 : public API -***************************************************************************/ - - -/** \returns the result of a full redux operation on the whole matrix or vector using \a func - * - * The template parameter \a BinaryOp is the type of the functor \a func which must be - * an associative operator. Both current STL and TR1 functor styles are handled. - * - * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise() - */ -template<typename Derived> -template<typename Func> -EIGEN_STRONG_INLINE typename internal::result_of<Func(typename internal::traits<Derived>::Scalar)>::type -DenseBase<Derived>::redux(const Func& func) const -{ - typedef typename internal::remove_all<typename Derived::Nested>::type ThisNested; - return internal::redux_impl<Func, ThisNested> - ::run(derived(), func); -} - -/** \returns the minimum of all coefficients of \c *this. - * \warning the result is undefined if \c *this contains NaN. - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::minCoeff() const -{ - return this->redux(Eigen::internal::scalar_min_op<Scalar>()); -} - -/** \returns the maximum of all coefficients of \c *this. - * \warning the result is undefined if \c *this contains NaN. - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::maxCoeff() const -{ - return this->redux(Eigen::internal::scalar_max_op<Scalar>()); -} - -/** \returns the sum of all coefficients of *this - * - * \sa trace(), prod(), mean() - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::sum() const -{ - if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0)) - return Scalar(0); - return this->redux(Eigen::internal::scalar_sum_op<Scalar>()); -} - -/** \returns the mean of all coefficients of *this -* -* \sa trace(), prod(), sum() -*/ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::mean() const -{ - return Scalar(this->redux(Eigen::internal::scalar_sum_op<Scalar>())) / Scalar(this->size()); -} - -/** \returns the product of all coefficients of *this - * - * Example: \include MatrixBase_prod.cpp - * Output: \verbinclude MatrixBase_prod.out - * - * \sa sum(), mean(), trace() - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -DenseBase<Derived>::prod() const -{ - if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0)) - return Scalar(1); - return this->redux(Eigen::internal::scalar_product_op<Scalar>()); -} - -/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal. - * - * \c *this can be any matrix, not necessarily square. - * - * \sa diagonal(), sum() - */ -template<typename Derived> -EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar -MatrixBase<Derived>::trace() const -{ - return derived().diagonal().sum(); -} - -} // end namespace Eigen - -#endif // EIGEN_REDUX_H diff --git a/third_party/eigen3/Eigen/src/Core/Ref.h b/third_party/eigen3/Eigen/src/Core/Ref.h deleted file mode 100644 index cd6d949c4c..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Ref.h +++ /dev/null @@ -1,260 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_REF_H -#define EIGEN_REF_H - -namespace Eigen { - -template<typename Derived> class RefBase; -template<typename PlainObjectType, int Options = 0, - typename StrideType = typename internal::conditional<PlainObjectType::IsVectorAtCompileTime,InnerStride<1>,OuterStride<> >::type > class Ref; - -/** \class Ref - * \ingroup Core_Module - * - * \brief A matrix or vector expression mapping an existing expressions - * - * \tparam PlainObjectType the equivalent matrix type of the mapped data - * \tparam Options specifies whether the pointer is \c #Aligned, or \c #Unaligned. - * The default is \c #Unaligned. - * \tparam StrideType optionally specifies strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1), - * but accept a variable outer stride (leading dimension). - * This can be overridden by specifying strides. - * The type passed here must be a specialization of the Stride template, see examples below. - * - * This class permits to write non template functions taking Eigen's object as parameters while limiting the number of copies. - * A Ref<> object can represent either a const expression or a l-value: - * \code - * // in-out argument: - * void foo1(Ref<VectorXf> x); - * - * // read-only const argument: - * void foo2(const Ref<const VectorXf>& x); - * \endcode - * - * In the in-out case, the input argument must satisfies the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered. - * By default, a Ref<VectorXf> can reference any dense vector expression of float having a contiguous memory layout. - * Likewise, a Ref<MatrixXf> can reference any column major dense matrix expression of float whose column's elements are contiguously stored with - * the possibility to have a constant space inbetween each column, i.e.: the inner stride mmust be equal to 1, but the outer-stride (or leading dimension), - * can be greater than the number of rows. - * - * In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function. - * Here are some examples: - * \code - * MatrixXf A; - * VectorXf a; - * foo1(a.head()); // OK - * foo1(A.col()); // OK - * foo1(A.row()); // compilation error because here innerstride!=1 - * foo2(A.row()); // The row is copied into a contiguous temporary - * foo2(2*a); // The expression is evaluated into a temporary - * foo2(A.col().segment(2,4)); // No temporary - * \endcode - * - * The range of inputs that can be referenced without temporary can be enlarged using the last two template parameter. - * Here is an example accepting an innerstride!=1: - * \code - * // in-out argument: - * void foo3(Ref<VectorXf,0,InnerStride<> > x); - * foo3(A.row()); // OK - * \endcode - * The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involved more - * expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overloads internally calling a - * template function, e.g.: - * \code - * // in the .h: - * void foo(const Ref<MatrixXf>& A); - * void foo(const Ref<MatrixXf,0,Stride<> >& A); - * - * // in the .cpp: - * template<typename TypeOfA> void foo_impl(const TypeOfA& A) { - * ... // crazy code goes here - * } - * void foo(const Ref<MatrixXf>& A) { foo_impl(A); } - * void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); } - * \endcode - * - * - * \sa PlainObjectBase::Map(), \ref TopicStorageOrders - */ - -namespace internal { - -template<typename _PlainObjectType, int _Options, typename _StrideType> -struct traits<Ref<_PlainObjectType, _Options, _StrideType> > - : public traits<Map<_PlainObjectType, _Options, _StrideType> > -{ - typedef _PlainObjectType PlainObjectType; - typedef _StrideType StrideType; - enum { - Options = _Options, - Flags = traits<Map<_PlainObjectType, _Options, _StrideType> >::Flags | NestByRefBit - }; - - template<typename Derived> struct match { - enum { - HasDirectAccess = internal::has_direct_access<Derived>::ret, - StorageOrderMatch = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)), - InnerStrideMatch = int(StrideType::InnerStrideAtCompileTime)==int(Dynamic) - || int(StrideType::InnerStrideAtCompileTime)==int(Derived::InnerStrideAtCompileTime) - || (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1), - OuterStrideMatch = Derived::IsVectorAtCompileTime - || int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime), - AlignmentMatch = (_Options!=Aligned) || ((PlainObjectType::Flags&AlignedBit)==0) || ((traits<Derived>::Flags&AlignedBit)==AlignedBit), - MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch - }; - typedef typename internal::conditional<MatchAtCompileTime,internal::true_type,internal::false_type>::type type; - }; - -}; - -template<typename Derived> -struct traits<RefBase<Derived> > : public traits<Derived> {}; - -} - -template<typename Derived> class RefBase - : public MapBase<Derived> -{ - typedef typename internal::traits<Derived>::PlainObjectType PlainObjectType; - typedef typename internal::traits<Derived>::StrideType StrideType; - -public: - - typedef MapBase<Derived> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(RefBase) - - inline Index innerStride() const - { - return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1; - } - - inline Index outerStride() const - { - return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer() - : IsVectorAtCompileTime ? this->size() - : int(Flags)&RowMajorBit ? this->cols() - : this->rows(); - } - - RefBase() - : Base(0,RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime), - // Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values: - m_stride(StrideType::OuterStrideAtCompileTime==Dynamic?0:StrideType::OuterStrideAtCompileTime, - StrideType::InnerStrideAtCompileTime==Dynamic?0:StrideType::InnerStrideAtCompileTime) - {} - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase) - -protected: - - typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase; - - template<typename Expression> - void construct(Expression& expr) - { - if(PlainObjectType::RowsAtCompileTime==1) - { - eigen_assert(expr.rows()==1 || expr.cols()==1); - ::new (static_cast<Base*>(this)) Base(expr.data(), 1, expr.size()); - } - else if(PlainObjectType::ColsAtCompileTime==1) - { - eigen_assert(expr.rows()==1 || expr.cols()==1); - ::new (static_cast<Base*>(this)) Base(expr.data(), expr.size(), 1); - } - else - ::new (static_cast<Base*>(this)) Base(expr.data(), expr.rows(), expr.cols()); - - if(Expression::IsVectorAtCompileTime && (!PlainObjectType::IsVectorAtCompileTime) && ((Expression::Flags&RowMajorBit)!=(PlainObjectType::Flags&RowMajorBit))) - ::new (&m_stride) StrideBase(expr.innerStride(), StrideType::InnerStrideAtCompileTime==0?0:1); - else - ::new (&m_stride) StrideBase(StrideType::OuterStrideAtCompileTime==0?0:expr.outerStride(), - StrideType::InnerStrideAtCompileTime==0?0:expr.innerStride()); - } - - StrideBase m_stride; -}; - - -template<typename PlainObjectType, int Options, typename StrideType> class Ref - : public RefBase<Ref<PlainObjectType, Options, StrideType> > -{ - typedef internal::traits<Ref> Traits; - public: - - typedef RefBase<Ref> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Ref) - - - #ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename Derived> - inline Ref(PlainObjectBase<Derived>& expr, - typename internal::enable_if<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>::type* = 0) - { - Base::construct(expr); - } - template<typename Derived> - inline Ref(const DenseBase<Derived>& expr, - typename internal::enable_if<bool(internal::is_lvalue<Derived>::value&&bool(Traits::template match<Derived>::MatchAtCompileTime)),Derived>::type* = 0, - int = Derived::ThisConstantIsPrivateInPlainObjectBase) - #else - template<typename Derived> - inline Ref(DenseBase<Derived>& expr) - #endif - { - Base::construct(expr.const_cast_derived()); - } - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref) - -}; - -// this is the const ref version -template<typename TPlainObjectType, int Options, typename StrideType> class Ref<const TPlainObjectType, Options, StrideType> - : public RefBase<Ref<const TPlainObjectType, Options, StrideType> > -{ - typedef internal::traits<Ref> Traits; - public: - - typedef RefBase<Ref> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Ref) - - template<typename Derived> - inline Ref(const DenseBase<Derived>& expr) - { -// std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n"; -// std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n"; -// std::cout << int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n"; - construct(expr.derived(), typename Traits::template match<Derived>::type()); - } - - protected: - - template<typename Expression> - void construct(const Expression& expr,internal::true_type) - { - Base::construct(expr); - } - - template<typename Expression> - void construct(const Expression& expr, internal::false_type) - { - m_object.lazyAssign(expr); - Base::construct(m_object); - } - - protected: - TPlainObjectType m_object; -}; - -} // end namespace Eigen - -#endif // EIGEN_REF_H diff --git a/third_party/eigen3/Eigen/src/Core/Replicate.h b/third_party/eigen3/Eigen/src/Core/Replicate.h deleted file mode 100644 index dde86a8349..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Replicate.h +++ /dev/null @@ -1,177 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_REPLICATE_H -#define EIGEN_REPLICATE_H - -namespace Eigen { - -/** - * \class Replicate - * \ingroup Core_Module - * - * \brief Expression of the multiple replication of a matrix or vector - * - * \param MatrixType the type of the object we are replicating - * - * This class represents an expression of the multiple replication of a matrix or vector. - * It is the return type of DenseBase::replicate() and most of the time - * this is the only way it is used. - * - * \sa DenseBase::replicate() - */ - -namespace internal { -template<typename MatrixType,int RowFactor,int ColFactor> -struct traits<Replicate<MatrixType,RowFactor,ColFactor> > - : traits<MatrixType> -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename traits<MatrixType>::StorageKind StorageKind; - typedef typename traits<MatrixType>::XprKind XprKind; - enum { - Factor = (RowFactor==Dynamic || ColFactor==Dynamic) ? Dynamic : RowFactor*ColFactor - }; - typedef typename nested<MatrixType,Factor>::type MatrixTypeNested; - typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested; - enum { - RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic - ? Dynamic - : RowFactor * MatrixType::RowsAtCompileTime, - ColsAtCompileTime = ColFactor==Dynamic || int(MatrixType::ColsAtCompileTime)==Dynamic - ? Dynamic - : ColFactor * MatrixType::ColsAtCompileTime, - //FIXME we don't propagate the max sizes !!! - MaxRowsAtCompileTime = RowsAtCompileTime, - MaxColsAtCompileTime = ColsAtCompileTime, - IsRowMajor = MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1 ? 1 - : MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1 ? 0 - : (MatrixType::Flags & RowMajorBit) ? 1 : 0, - Flags = (_MatrixTypeNested::Flags & HereditaryBits & ~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0), - CoeffReadCost = _MatrixTypeNested::CoeffReadCost - }; -}; -} - -template<typename MatrixType,int RowFactor,int ColFactor> class Replicate - : public internal::dense_xpr_base< Replicate<MatrixType,RowFactor,ColFactor> >::type -{ - typedef typename internal::traits<Replicate>::MatrixTypeNested MatrixTypeNested; - typedef typename internal::traits<Replicate>::_MatrixTypeNested _MatrixTypeNested; - public: - - typedef typename internal::dense_xpr_base<Replicate>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Replicate) - - template<typename OriginalMatrixType> - inline explicit Replicate(const OriginalMatrixType& a_matrix) - : m_matrix(a_matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor) - { - EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value), - THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE) - eigen_assert(RowFactor!=Dynamic && ColFactor!=Dynamic); - } - - template<typename OriginalMatrixType> - inline Replicate(const OriginalMatrixType& a_matrix, Index rowFactor, Index colFactor) - : m_matrix(a_matrix), m_rowFactor(rowFactor), m_colFactor(colFactor) - { - EIGEN_STATIC_ASSERT((internal::is_same<typename internal::remove_const<MatrixType>::type,OriginalMatrixType>::value), - THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE) - } - - inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); } - inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); } - - inline Scalar coeff(Index rowId, Index colId) const - { - // try to avoid using modulo; this is a pure optimization strategy - const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0 - : RowFactor==1 ? rowId - : rowId%m_matrix.rows(); - const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0 - : ColFactor==1 ? colId - : colId%m_matrix.cols(); - - return m_matrix.coeff(actual_row, actual_col); - } - template<int LoadMode> - inline PacketScalar packet(Index rowId, Index colId) const - { - const Index actual_row = internal::traits<MatrixType>::RowsAtCompileTime==1 ? 0 - : RowFactor==1 ? rowId - : rowId%m_matrix.rows(); - const Index actual_col = internal::traits<MatrixType>::ColsAtCompileTime==1 ? 0 - : ColFactor==1 ? colId - : colId%m_matrix.cols(); - - return m_matrix.template packet<LoadMode>(actual_row, actual_col); - } - - const _MatrixTypeNested& nestedExpression() const - { - return m_matrix; - } - - protected: - MatrixTypeNested m_matrix; - const internal::variable_if_dynamic<Index, RowFactor> m_rowFactor; - const internal::variable_if_dynamic<Index, ColFactor> m_colFactor; -}; - -/** - * \return an expression of the replication of \c *this - * - * Example: \include MatrixBase_replicate.cpp - * Output: \verbinclude MatrixBase_replicate.out - * - * \sa VectorwiseOp::replicate(), DenseBase::replicate(Index,Index), class Replicate - */ -template<typename Derived> -template<int RowFactor, int ColFactor> -inline const Replicate<Derived,RowFactor,ColFactor> -DenseBase<Derived>::replicate() const -{ - return Replicate<Derived,RowFactor,ColFactor>(derived()); -} - -/** - * \return an expression of the replication of \c *this - * - * Example: \include MatrixBase_replicate_int_int.cpp - * Output: \verbinclude MatrixBase_replicate_int_int.out - * - * \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate - */ -template<typename Derived> -inline const Replicate<Derived,Dynamic,Dynamic> -DenseBase<Derived>::replicate(Index rowFactor,Index colFactor) const -{ - return Replicate<Derived,Dynamic,Dynamic>(derived(),rowFactor,colFactor); -} - -/** - * \return an expression of the replication of each column (or row) of \c *this - * - * Example: \include DirectionWise_replicate_int.cpp - * Output: \verbinclude DirectionWise_replicate_int.out - * - * \sa VectorwiseOp::replicate(), DenseBase::replicate(), class Replicate - */ -template<typename ExpressionType, int Direction> -const typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType -VectorwiseOp<ExpressionType,Direction>::replicate(Index factor) const -{ - return typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType - (_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1); -} - -} // end namespace Eigen - -#endif // EIGEN_REPLICATE_H diff --git a/third_party/eigen3/Eigen/src/Core/ReturnByValue.h b/third_party/eigen3/Eigen/src/Core/ReturnByValue.h deleted file mode 100644 index 7834f6cbcd..0000000000 --- a/third_party/eigen3/Eigen/src/Core/ReturnByValue.h +++ /dev/null @@ -1,89 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_RETURNBYVALUE_H -#define EIGEN_RETURNBYVALUE_H - -namespace Eigen { - -/** \class ReturnByValue - * \ingroup Core_Module - * - */ - -namespace internal { - -template<typename Derived> -struct traits<ReturnByValue<Derived> > - : public traits<typename traits<Derived>::ReturnType> -{ - enum { - // We're disabling the DirectAccess because e.g. the constructor of - // the Block-with-DirectAccess expression requires to have a coeffRef method. - // Also, we don't want to have to implement the stride stuff. - Flags = (traits<typename traits<Derived>::ReturnType>::Flags - | EvalBeforeNestingBit) & ~DirectAccessBit - }; -}; - -/* The ReturnByValue object doesn't even have a coeff() method. - * So the only way that nesting it in an expression can work, is by evaluating it into a plain matrix. - * So internal::nested always gives the plain return matrix type. - * - * FIXME: I don't understand why we need this specialization: isn't this taken care of by the EvalBeforeNestingBit ?? - */ -template<typename Derived,int n,typename PlainObject> -struct nested<ReturnByValue<Derived>, n, PlainObject> -{ - typedef typename traits<Derived>::ReturnType type; -}; - -} // end namespace internal - -template<typename Derived> class ReturnByValue - : internal::no_assignment_operator, public internal::dense_xpr_base< ReturnByValue<Derived> >::type -{ - public: - typedef typename internal::traits<Derived>::ReturnType ReturnType; - - typedef typename internal::dense_xpr_base<ReturnByValue>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue) - - template<typename Dest> - EIGEN_DEVICE_FUNC - inline void evalTo(Dest& dst) const - { static_cast<const Derived*>(this)->evalTo(dst); } - EIGEN_DEVICE_FUNC inline Index rows() const { return static_cast<const Derived*>(this)->rows(); } - EIGEN_DEVICE_FUNC inline Index cols() const { return static_cast<const Derived*>(this)->cols(); } - -#ifndef EIGEN_PARSED_BY_DOXYGEN -#define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT - class Unusable{ - Unusable(const Unusable&) {} - Unusable& operator=(const Unusable&) {return *this;} - }; - const Unusable& coeff(Index) const { return *reinterpret_cast<const Unusable*>(this); } - const Unusable& coeff(Index,Index) const { return *reinterpret_cast<const Unusable*>(this); } - Unusable& coeffRef(Index) { return *reinterpret_cast<Unusable*>(this); } - Unusable& coeffRef(Index,Index) { return *reinterpret_cast<Unusable*>(this); } -#endif -}; - -template<typename Derived> -template<typename OtherDerived> -Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other) -{ - other.evalTo(derived()); - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_RETURNBYVALUE_H diff --git a/third_party/eigen3/Eigen/src/Core/Reverse.h b/third_party/eigen3/Eigen/src/Core/Reverse.h deleted file mode 100644 index e30ae3d281..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Reverse.h +++ /dev/null @@ -1,224 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009 Ricard Marxer <email@ricardmarxer.com> -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_REVERSE_H -#define EIGEN_REVERSE_H - -namespace Eigen { - -/** \class Reverse - * \ingroup Core_Module - * - * \brief Expression of the reverse of a vector or matrix - * - * \param MatrixType the type of the object of which we are taking the reverse - * - * This class represents an expression of the reverse of a vector. - * It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse() - * and most of the time this is the only way it is used. - * - * \sa MatrixBase::reverse(), VectorwiseOp::reverse() - */ - -namespace internal { - -template<typename MatrixType, int Direction> -struct traits<Reverse<MatrixType, Direction> > - : traits<MatrixType> -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename traits<MatrixType>::StorageKind StorageKind; - typedef typename traits<MatrixType>::XprKind XprKind; - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, - - // let's enable LinearAccess only with vectorization because of the product overhead - LinearAccess = ( (Direction==BothDirections) && (int(_MatrixTypeNested::Flags)&PacketAccessBit) ) - ? LinearAccessBit : 0, - - Flags = int(_MatrixTypeNested::Flags) & (HereditaryBits | LvalueBit | PacketAccessBit | LinearAccess), - - CoeffReadCost = _MatrixTypeNested::CoeffReadCost - }; -}; - -template<typename PacketScalar, bool ReversePacket> struct reverse_packet_cond -{ - static inline PacketScalar run(const PacketScalar& x) { return preverse(x); } -}; - -template<typename PacketScalar> struct reverse_packet_cond<PacketScalar,false> -{ - static inline PacketScalar run(const PacketScalar& x) { return x; } -}; - -} // end namespace internal - -template<typename MatrixType, int Direction> class Reverse - : public internal::dense_xpr_base< Reverse<MatrixType, Direction> >::type -{ - public: - - typedef typename internal::dense_xpr_base<Reverse>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Reverse) - using Base::IsRowMajor; - - // next line is necessary because otherwise const version of operator() - // is hidden by non-const version defined in this file - using Base::operator(); - - protected: - enum { - PacketSize = internal::packet_traits<Scalar>::size, - IsColMajor = !IsRowMajor, - ReverseRow = (Direction == Vertical) || (Direction == BothDirections), - ReverseCol = (Direction == Horizontal) || (Direction == BothDirections), - OffsetRow = ReverseRow && IsColMajor ? PacketSize : 1, - OffsetCol = ReverseCol && IsRowMajor ? PacketSize : 1, - ReversePacket = (Direction == BothDirections) - || ((Direction == Vertical) && IsColMajor) - || ((Direction == Horizontal) && IsRowMajor) - }; - typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet; - public: - - inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { } - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse) - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - inline Index innerStride() const - { - return -m_matrix.innerStride(); - } - - inline Scalar& operator()(Index row, Index col) - { - eigen_assert(row >= 0 && row < rows() && col >= 0 && col < cols()); - return coeffRef(row, col); - } - - inline Scalar& coeffRef(Index row, Index col) - { - return m_matrix.const_cast_derived().coeffRef(ReverseRow ? m_matrix.rows() - row - 1 : row, - ReverseCol ? m_matrix.cols() - col - 1 : col); - } - - inline CoeffReturnType coeff(Index row, Index col) const - { - return m_matrix.coeff(ReverseRow ? m_matrix.rows() - row - 1 : row, - ReverseCol ? m_matrix.cols() - col - 1 : col); - } - - inline CoeffReturnType coeff(Index index) const - { - return m_matrix.coeff(m_matrix.size() - index - 1); - } - - inline Scalar& coeffRef(Index index) - { - return m_matrix.const_cast_derived().coeffRef(m_matrix.size() - index - 1); - } - - inline Scalar& operator()(Index index) - { - eigen_assert(index >= 0 && index < m_matrix.size()); - return coeffRef(index); - } - - template<int LoadMode> - inline const PacketScalar packet(Index row, Index col) const - { - return reverse_packet::run(m_matrix.template packet<LoadMode>( - ReverseRow ? m_matrix.rows() - row - OffsetRow : row, - ReverseCol ? m_matrix.cols() - col - OffsetCol : col)); - } - - template<int LoadMode> - inline void writePacket(Index row, Index col, const PacketScalar& x) - { - m_matrix.const_cast_derived().template writePacket<LoadMode>( - ReverseRow ? m_matrix.rows() - row - OffsetRow : row, - ReverseCol ? m_matrix.cols() - col - OffsetCol : col, - reverse_packet::run(x)); - } - - template<int LoadMode> - inline const PacketScalar packet(Index index) const - { - return internal::preverse(m_matrix.template packet<LoadMode>( m_matrix.size() - index - PacketSize )); - } - - template<int LoadMode> - inline void writePacket(Index index, const PacketScalar& x) - { - m_matrix.const_cast_derived().template writePacket<LoadMode>(m_matrix.size() - index - PacketSize, internal::preverse(x)); - } - - const typename internal::remove_all<typename MatrixType::Nested>::type& - nestedExpression() const - { - return m_matrix; - } - - protected: - typename MatrixType::Nested m_matrix; -}; - -/** \returns an expression of the reverse of *this. - * - * Example: \include MatrixBase_reverse.cpp - * Output: \verbinclude MatrixBase_reverse.out - * - */ -template<typename Derived> -inline typename DenseBase<Derived>::ReverseReturnType -DenseBase<Derived>::reverse() -{ - return derived(); -} - -/** This is the const version of reverse(). */ -template<typename Derived> -inline const typename DenseBase<Derived>::ConstReverseReturnType -DenseBase<Derived>::reverse() const -{ - return derived(); -} - -/** This is the "in place" version of reverse: it reverses \c *this. - * - * In most cases it is probably better to simply use the reversed expression - * of a matrix. However, when reversing the matrix data itself is really needed, - * then this "in-place" version is probably the right choice because it provides - * the following additional features: - * - less error prone: doing the same operation with .reverse() requires special care: - * \code m = m.reverse().eval(); \endcode - * - this API allows to avoid creating a temporary (the current implementation creates a temporary, but that could be avoided using swap) - * - it allows future optimizations (cache friendliness, etc.) - * - * \sa reverse() */ -template<typename Derived> -inline void DenseBase<Derived>::reverseInPlace() -{ - derived() = derived().reverse().eval(); -} - -} // end namespace Eigen - -#endif // EIGEN_REVERSE_H diff --git a/third_party/eigen3/Eigen/src/Core/Select.h b/third_party/eigen3/Eigen/src/Core/Select.h deleted file mode 100644 index 87993bbb55..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Select.h +++ /dev/null @@ -1,162 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SELECT_H -#define EIGEN_SELECT_H - -namespace Eigen { - -/** \class Select - * \ingroup Core_Module - * - * \brief Expression of a coefficient wise version of the C++ ternary operator ?: - * - * \param ConditionMatrixType the type of the \em condition expression which must be a boolean matrix - * \param ThenMatrixType the type of the \em then expression - * \param ElseMatrixType the type of the \em else expression - * - * This class represents an expression of a coefficient wise version of the C++ ternary operator ?:. - * It is the return type of DenseBase::select() and most of the time this is the only way it is used. - * - * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const - */ - -namespace internal { -template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType> -struct traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> > - : traits<ThenMatrixType> -{ - typedef typename traits<ThenMatrixType>::Scalar Scalar; - typedef Dense StorageKind; - typedef typename traits<ThenMatrixType>::XprKind XprKind; - typedef typename ConditionMatrixType::Nested ConditionMatrixNested; - typedef typename ThenMatrixType::Nested ThenMatrixNested; - typedef typename ElseMatrixType::Nested ElseMatrixNested; - enum { - RowsAtCompileTime = ConditionMatrixType::RowsAtCompileTime, - ColsAtCompileTime = ConditionMatrixType::ColsAtCompileTime, - MaxRowsAtCompileTime = ConditionMatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = ConditionMatrixType::MaxColsAtCompileTime, - Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & HereditaryBits, - CoeffReadCost = traits<typename remove_all<ConditionMatrixNested>::type>::CoeffReadCost - + EIGEN_SIZE_MAX(traits<typename remove_all<ThenMatrixNested>::type>::CoeffReadCost, - traits<typename remove_all<ElseMatrixNested>::type>::CoeffReadCost) - }; -}; -} - -template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType> -class Select : internal::no_assignment_operator, - public internal::dense_xpr_base< Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >::type -{ - public: - - typedef typename internal::dense_xpr_base<Select>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Select) - - Select(const ConditionMatrixType& a_conditionMatrix, - const ThenMatrixType& a_thenMatrix, - const ElseMatrixType& a_elseMatrix) - : m_condition(a_conditionMatrix), m_then(a_thenMatrix), m_else(a_elseMatrix) - { - eigen_assert(m_condition.rows() == m_then.rows() && m_condition.rows() == m_else.rows()); - eigen_assert(m_condition.cols() == m_then.cols() && m_condition.cols() == m_else.cols()); - } - - Index rows() const { return m_condition.rows(); } - Index cols() const { return m_condition.cols(); } - - const Scalar coeff(Index i, Index j) const - { - if (m_condition.coeff(i,j)) - return m_then.coeff(i,j); - else - return m_else.coeff(i,j); - } - - const Scalar coeff(Index i) const - { - if (m_condition.coeff(i)) - return m_then.coeff(i); - else - return m_else.coeff(i); - } - - const ConditionMatrixType& conditionMatrix() const - { - return m_condition; - } - - const ThenMatrixType& thenMatrix() const - { - return m_then; - } - - const ElseMatrixType& elseMatrix() const - { - return m_else; - } - - protected: - typename ConditionMatrixType::Nested m_condition; - typename ThenMatrixType::Nested m_then; - typename ElseMatrixType::Nested m_else; -}; - - -/** \returns a matrix where each coefficient (i,j) is equal to \a thenMatrix(i,j) - * if \c *this(i,j), and \a elseMatrix(i,j) otherwise. - * - * Example: \include MatrixBase_select.cpp - * Output: \verbinclude MatrixBase_select.out - * - * \sa class Select - */ -template<typename Derived> -template<typename ThenDerived,typename ElseDerived> -inline const Select<Derived,ThenDerived,ElseDerived> -DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix, - const DenseBase<ElseDerived>& elseMatrix) const -{ - return Select<Derived,ThenDerived,ElseDerived>(derived(), thenMatrix.derived(), elseMatrix.derived()); -} - -/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with - * the \em else expression being a scalar value. - * - * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select - */ -template<typename Derived> -template<typename ThenDerived> -inline const Select<Derived,ThenDerived, typename ThenDerived::ConstantReturnType> -DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix, - const typename ThenDerived::Scalar& elseScalar) const -{ - return Select<Derived,ThenDerived,typename ThenDerived::ConstantReturnType>( - derived(), thenMatrix.derived(), ThenDerived::Constant(rows(),cols(),elseScalar)); -} - -/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with - * the \em then expression being a scalar value. - * - * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select - */ -template<typename Derived> -template<typename ElseDerived> -inline const Select<Derived, typename ElseDerived::ConstantReturnType, ElseDerived > -DenseBase<Derived>::select(const typename ElseDerived::Scalar& thenScalar, - const DenseBase<ElseDerived>& elseMatrix) const -{ - return Select<Derived,typename ElseDerived::ConstantReturnType,ElseDerived>( - derived(), ElseDerived::Constant(rows(),cols(),thenScalar), elseMatrix.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_SELECT_H diff --git a/third_party/eigen3/Eigen/src/Core/SelfAdjointView.h b/third_party/eigen3/Eigen/src/Core/SelfAdjointView.h deleted file mode 100644 index 8231e3f5cd..0000000000 --- a/third_party/eigen3/Eigen/src/Core/SelfAdjointView.h +++ /dev/null @@ -1,338 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SELFADJOINTMATRIX_H -#define EIGEN_SELFADJOINTMATRIX_H - -namespace Eigen { - -/** \class SelfAdjointView - * \ingroup Core_Module - * - * - * \brief Expression of a selfadjoint matrix from a triangular part of a dense matrix - * - * \param MatrixType the type of the dense matrix storing the coefficients - * \param TriangularPart can be either \c #Lower or \c #Upper - * - * This class is an expression of a sefladjoint matrix from a triangular part of a matrix - * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView() - * and most of the time this is the only way that it is used. - * - * \sa class TriangularBase, MatrixBase::selfadjointView() - */ - -namespace internal { -template<typename MatrixType, unsigned int UpLo> -struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType> -{ - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned; - typedef MatrixType ExpressionType; - typedef typename MatrixType::PlainObject DenseMatrixType; - enum { - Mode = UpLo | SelfAdjoint, - Flags = MatrixTypeNestedCleaned::Flags & (HereditaryBits) - & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)), // FIXME these flags should be preserved - CoeffReadCost = MatrixTypeNestedCleaned::CoeffReadCost - }; -}; -} - -template <typename Lhs, int LhsMode, bool LhsIsVector, - typename Rhs, int RhsMode, bool RhsIsVector> -struct SelfadjointProductMatrix; - -// FIXME could also be called SelfAdjointWrapper to be consistent with DiagonalWrapper ?? -template<typename MatrixType, unsigned int UpLo> class SelfAdjointView - : public TriangularBase<SelfAdjointView<MatrixType, UpLo> > -{ - public: - - typedef TriangularBase<SelfAdjointView> Base; - typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested; - typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned; - - /** \brief The type of coefficients in this matrix */ - typedef typename internal::traits<SelfAdjointView>::Scalar Scalar; - - typedef typename MatrixType::Index Index; - - enum { - Mode = internal::traits<SelfAdjointView>::Mode - }; - typedef typename MatrixType::PlainObject PlainObject; - - EIGEN_DEVICE_FUNC - inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix) - {} - - EIGEN_DEVICE_FUNC - inline Index rows() const { return m_matrix.rows(); } - EIGEN_DEVICE_FUNC - inline Index cols() const { return m_matrix.cols(); } - EIGEN_DEVICE_FUNC - inline Index outerStride() const { return m_matrix.outerStride(); } - EIGEN_DEVICE_FUNC - inline Index innerStride() const { return m_matrix.innerStride(); } - - /** \sa MatrixBase::coeff() - * \warning the coordinates must fit into the referenced triangular part - */ - EIGEN_DEVICE_FUNC - inline Scalar coeff(Index row, Index col) const - { - Base::check_coordinates_internal(row, col); - return m_matrix.coeff(row, col); - } - - /** \sa MatrixBase::coeffRef() - * \warning the coordinates must fit into the referenced triangular part - */ - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index row, Index col) - { - Base::check_coordinates_internal(row, col); - return m_matrix.const_cast_derived().coeffRef(row, col); - } - - /** \internal */ - EIGEN_DEVICE_FUNC - const MatrixTypeNestedCleaned& _expression() const { return m_matrix; } - - EIGEN_DEVICE_FUNC - const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; } - EIGEN_DEVICE_FUNC - MatrixTypeNestedCleaned& nestedExpression() { return *const_cast<MatrixTypeNestedCleaned*>(&m_matrix); } - - /** Efficient self-adjoint matrix times vector/matrix product */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - SelfadjointProductMatrix<MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime> - operator*(const MatrixBase<OtherDerived>& rhs) const - { - return SelfadjointProductMatrix - <MatrixType,Mode,false,OtherDerived,0,OtherDerived::IsVectorAtCompileTime> - (m_matrix, rhs.derived()); - } - - /** Efficient vector/matrix times self-adjoint matrix product */ - template<typename OtherDerived> friend - EIGEN_DEVICE_FUNC - SelfadjointProductMatrix<OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false> - operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView& rhs) - { - return SelfadjointProductMatrix - <OtherDerived,0,OtherDerived::IsVectorAtCompileTime,MatrixType,Mode,false> - (lhs.derived(),rhs.m_matrix); - } - - /** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this: - * \f$ this = this + \alpha u v^* + conj(\alpha) v u^* \f$ - * \returns a reference to \c *this - * - * The vectors \a u and \c v \b must be column vectors, however they can be - * a adjoint expression without any overhead. Only the meaningful triangular - * part of the matrix is updated, the rest is left unchanged. - * - * \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar) - */ - template<typename DerivedU, typename DerivedV> - EIGEN_DEVICE_FUNC - SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha = Scalar(1)); - - /** Perform a symmetric rank K update of the selfadjoint matrix \c *this: - * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix. - * - * \returns a reference to \c *this - * - * Note that to perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply - * call this function with u.adjoint(). - * - * \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar) - */ - template<typename DerivedU> - EIGEN_DEVICE_FUNC - SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1)); - -/////////// Cholesky module /////////// - - const LLT<PlainObject, UpLo> llt() const; - const LDLT<PlainObject, UpLo> ldlt() const; - -/////////// Eigenvalue module /////////// - - /** Real part of #Scalar */ - typedef typename NumTraits<Scalar>::Real RealScalar; - /** Return type of eigenvalues() */ - typedef Matrix<RealScalar, internal::traits<MatrixType>::ColsAtCompileTime, 1> EigenvaluesReturnType; - - EIGEN_DEVICE_FUNC - EigenvaluesReturnType eigenvalues() const; - EIGEN_DEVICE_FUNC - RealScalar operatorNorm() const; - - #ifdef EIGEN2_SUPPORT - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - SelfAdjointView& operator=(const MatrixBase<OtherDerived>& other) - { - enum { - OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper - }; - m_matrix.const_cast_derived().template triangularView<UpLo>() = other; - m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.adjoint(); - return *this; - } - template<typename OtherMatrixType, unsigned int OtherMode> - EIGEN_DEVICE_FUNC - SelfAdjointView& operator=(const TriangularView<OtherMatrixType, OtherMode>& other) - { - enum { - OtherPart = UpLo == Upper ? StrictlyLower : StrictlyUpper - }; - m_matrix.const_cast_derived().template triangularView<UpLo>() = other.toDenseMatrix(); - m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.toDenseMatrix().adjoint(); - return *this; - } - #endif - - protected: - MatrixTypeNested m_matrix; -}; - - -// template<typename OtherDerived, typename MatrixType, unsigned int UpLo> -// internal::selfadjoint_matrix_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> > -// operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView<MatrixType,UpLo>& rhs) -// { -// return internal::matrix_selfadjoint_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >(lhs.derived(),rhs); -// } - -// selfadjoint to dense matrix - -namespace internal { - -template<typename Derived1, typename Derived2, int UnrollCount, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount, ClearOpposite> -{ - enum { - col = (UnrollCount-1) / Derived1::RowsAtCompileTime, - row = (UnrollCount-1) % Derived1::RowsAtCompileTime - }; - - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount-1, ClearOpposite>::run(dst, src); - - if(row == col) - dst.coeffRef(row, col) = numext::real(src.coeff(row, col)); - else if(row < col) - dst.coeffRef(col, row) = numext::conj(dst.coeffRef(row, col) = src.coeff(row, col)); - } -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, 0, ClearOpposite> -{ - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &, const Derived2 &) {} -}; - -template<typename Derived1, typename Derived2, int UnrollCount, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount, ClearOpposite> -{ - enum { - col = (UnrollCount-1) / Derived1::RowsAtCompileTime, - row = (UnrollCount-1) % Derived1::RowsAtCompileTime - }; - - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount-1, ClearOpposite>::run(dst, src); - - if(row == col) - dst.coeffRef(row, col) = numext::real(src.coeff(row, col)); - else if(row > col) - dst.coeffRef(col, row) = numext::conj(dst.coeffRef(row, col) = src.coeff(row, col)); - } -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, 0, ClearOpposite> -{ - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &, const Derived2 &) {} -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, Dynamic, ClearOpposite> -{ - typedef typename Derived1::Index Index; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - for(Index j = 0; j < dst.cols(); ++j) - { - for(Index i = 0; i < j; ++i) - { - dst.copyCoeff(i, j, src); - dst.coeffRef(j,i) = numext::conj(dst.coeff(i,j)); - } - dst.copyCoeff(j, j, src); - } - } -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, Dynamic, ClearOpposite> -{ - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - typedef typename Derived1::Index Index; - for(Index i = 0; i < dst.rows(); ++i) - { - for(Index j = 0; j < i; ++j) - { - dst.copyCoeff(i, j, src); - dst.coeffRef(j,i) = numext::conj(dst.coeff(i,j)); - } - dst.copyCoeff(i, i, src); - } - } -}; - -} // end namespace internal - -/*************************************************************************** -* Implementation of MatrixBase methods -***************************************************************************/ - -template<typename Derived> -template<unsigned int UpLo> -typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type -MatrixBase<Derived>::selfadjointView() const -{ - return derived(); -} - -template<typename Derived> -template<unsigned int UpLo> -typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type -MatrixBase<Derived>::selfadjointView() -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_SELFADJOINTMATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h b/third_party/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h deleted file mode 100644 index 65864adf84..0000000000 --- a/third_party/eigen3/Eigen/src/Core/SelfCwiseBinaryOp.h +++ /dev/null @@ -1,226 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SELFCWISEBINARYOP_H -#define EIGEN_SELFCWISEBINARYOP_H - -namespace Eigen { - -/** \class SelfCwiseBinaryOp - * \ingroup Core_Module - * - * \internal - * - * \brief Internal helper class for optimizing operators like +=, -= - * - * This is a pseudo expression class re-implementing the copyCoeff/copyPacket - * method to directly performs a +=/-= operations in an optimal way. In particular, - * this allows to make sure that the input/output data are loaded only once using - * aligned packet loads. - * - * \sa class SwapWrapper for a similar trick. - */ - -namespace internal { -template<typename BinaryOp, typename Lhs, typename Rhs> -struct traits<SelfCwiseBinaryOp<BinaryOp,Lhs,Rhs> > - : traits<CwiseBinaryOp<BinaryOp,Lhs,Rhs> > -{ - enum { - // Note that it is still a good idea to preserve the DirectAccessBit - // so that assign can correctly align the data. - Flags = traits<CwiseBinaryOp<BinaryOp,Lhs,Rhs> >::Flags | (Lhs::Flags&AlignedBit) | (Lhs::Flags&DirectAccessBit) | (Lhs::Flags&LvalueBit), - OuterStrideAtCompileTime = Lhs::OuterStrideAtCompileTime, - InnerStrideAtCompileTime = Lhs::InnerStrideAtCompileTime - }; -}; -} - -template<typename BinaryOp, typename Lhs, typename Rhs> class SelfCwiseBinaryOp - : public internal::dense_xpr_base< SelfCwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type -{ - public: - - typedef typename internal::dense_xpr_base<SelfCwiseBinaryOp>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(SelfCwiseBinaryOp) - - typedef typename internal::packet_traits<Scalar>::type Packet; - - EIGEN_DEVICE_FUNC - inline SelfCwiseBinaryOp(Lhs& xpr, const BinaryOp& func = BinaryOp()) : m_matrix(xpr), m_functor(func) {} - - EIGEN_DEVICE_FUNC inline Index rows() const { return m_matrix.rows(); } - EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.cols(); } - EIGEN_DEVICE_FUNC inline Index outerStride() const { return m_matrix.outerStride(); } - EIGEN_DEVICE_FUNC inline Index innerStride() const { return m_matrix.innerStride(); } - EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_matrix.data(); } - - // note that this function is needed by assign to correctly align loads/stores - // TODO make Assign use .data() - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index row, Index col) - { - EIGEN_STATIC_ASSERT_LVALUE(Lhs) - return m_matrix.const_cast_derived().coeffRef(row, col); - } - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index row, Index col) const - { - return m_matrix.coeffRef(row, col); - } - - // note that this function is needed by assign to correctly align loads/stores - // TODO make Assign use .data() - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index index) - { - EIGEN_STATIC_ASSERT_LVALUE(Lhs) - return m_matrix.const_cast_derived().coeffRef(index); - } - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index index) const - { - return m_matrix.const_cast_derived().coeffRef(index); - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void copyCoeff(Index row, Index col, const DenseBase<OtherDerived>& other) - { - OtherDerived& _other = other.const_cast_derived(); - eigen_internal_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - Scalar& tmp = m_matrix.coeffRef(row,col); - tmp = m_functor(tmp, _other.coeff(row,col)); - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void copyCoeff(Index index, const DenseBase<OtherDerived>& other) - { - OtherDerived& _other = other.const_cast_derived(); - eigen_internal_assert(index >= 0 && index < m_matrix.size()); - Scalar& tmp = m_matrix.coeffRef(index); - tmp = m_functor(tmp, _other.coeff(index)); - } - - template<typename OtherDerived, int StoreMode, int LoadMode> - void copyPacket(Index row, Index col, const DenseBase<OtherDerived>& other) - { - OtherDerived& _other = other.const_cast_derived(); - eigen_internal_assert(row >= 0 && row < rows() - && col >= 0 && col < cols()); - m_matrix.template writePacket<StoreMode>(row, col, - m_functor.packetOp(m_matrix.template packet<StoreMode>(row, col),_other.template packet<LoadMode>(row, col)) ); - } - - template<typename OtherDerived, int StoreMode, int LoadMode> - void copyPacket(Index index, const DenseBase<OtherDerived>& other) - { - OtherDerived& _other = other.const_cast_derived(); - eigen_internal_assert(index >= 0 && index < m_matrix.size()); - m_matrix.template writePacket<StoreMode>(index, - m_functor.packetOp(m_matrix.template packet<StoreMode>(index),_other.template packet<LoadMode>(index)) ); - } - - // reimplement lazyAssign to handle complex *= real - // see CwiseBinaryOp ctor for details - template<typename RhsDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE SelfCwiseBinaryOp& lazyAssign(const DenseBase<RhsDerived>& rhs) - { - EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs,RhsDerived) - EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename RhsDerived::Scalar); - - #ifdef EIGEN_DEBUG_ASSIGN - internal::assign_traits<SelfCwiseBinaryOp, RhsDerived>::debug(); - #endif - eigen_assert(rows() == rhs.rows() && cols() == rhs.cols()); - internal::assign_impl<SelfCwiseBinaryOp, RhsDerived>::run(*this,rhs.derived()); - #ifndef EIGEN_NO_DEBUG - this->checkTransposeAliasing(rhs.derived()); - #endif - return *this; - } - - // overloaded to honor evaluation of special matrices - // maybe another solution would be to not use SelfCwiseBinaryOp - // at first... - EIGEN_DEVICE_FUNC - SelfCwiseBinaryOp& operator=(const Rhs& _rhs) - { - typename internal::nested<Rhs>::type rhs(_rhs); - return Base::operator=(rhs); - } - - EIGEN_DEVICE_FUNC - Lhs& expression() const - { - return m_matrix; - } - - EIGEN_DEVICE_FUNC - const BinaryOp& functor() const - { - return m_functor; - } - - protected: - Lhs& m_matrix; - const BinaryOp& m_functor; - - private: - SelfCwiseBinaryOp& operator=(const SelfCwiseBinaryOp&); -}; - -template<typename Derived> -inline Derived& DenseBase<Derived>::operator*=(const Scalar& other) -{ - typedef typename Derived::PlainObject PlainObject; - SelfCwiseBinaryOp<internal::scalar_product_op<Scalar>, Derived, typename PlainObject::ConstantReturnType> tmp(derived()); - tmp = PlainObject::Constant(rows(),cols(),other); - return derived(); -} - -template<typename Derived> -inline Derived& ArrayBase<Derived>::operator+=(const Scalar& other) -{ - typedef typename Derived::PlainObject PlainObject; - SelfCwiseBinaryOp<internal::scalar_sum_op<Scalar>, Derived, typename PlainObject::ConstantReturnType> tmp(derived()); - tmp = PlainObject::Constant(rows(),cols(),other); - return derived(); -} - -template<typename Derived> -inline Derived& ArrayBase<Derived>::operator-=(const Scalar& other) -{ - typedef typename Derived::PlainObject PlainObject; - SelfCwiseBinaryOp<internal::scalar_difference_op<Scalar>, Derived, typename PlainObject::ConstantReturnType> tmp(derived()); - tmp = PlainObject::Constant(rows(),cols(),other); - return derived(); -} - -template<typename Derived> -inline Derived& DenseBase<Derived>::operator/=(const Scalar& other) -{ - typedef typename internal::conditional<NumTraits<Scalar>::IsInteger, - internal::scalar_quotient_op<Scalar>, - internal::scalar_product_op<Scalar> >::type BinOp; - typedef typename Derived::PlainObject PlainObject; - SelfCwiseBinaryOp<BinOp, Derived, typename PlainObject::ConstantReturnType> tmp(derived()); - Scalar actual_other; - if(NumTraits<Scalar>::IsInteger) actual_other = other; - else actual_other = Scalar(1)/other; - tmp = PlainObject::Constant(rows(),cols(), actual_other); - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_SELFCWISEBINARYOP_H diff --git a/third_party/eigen3/Eigen/src/Core/SolveTriangular.h b/third_party/eigen3/Eigen/src/Core/SolveTriangular.h deleted file mode 100644 index e158e31626..0000000000 --- a/third_party/eigen3/Eigen/src/Core/SolveTriangular.h +++ /dev/null @@ -1,260 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SOLVETRIANGULAR_H -#define EIGEN_SOLVETRIANGULAR_H - -namespace Eigen { - -namespace internal { - -// Forward declarations: -// The following two routines are implemented in the products/TriangularSolver*.h files -template<typename LhsScalar, typename RhsScalar, typename Index, int Side, int Mode, bool Conjugate, int StorageOrder> -struct triangular_solve_vector; - -template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder, int OtherStorageOrder> -struct triangular_solve_matrix; - -// small helper struct extracting some traits on the underlying solver operation -template<typename Lhs, typename Rhs, int Side> -class trsolve_traits -{ - private: - enum { - RhsIsVectorAtCompileTime = (Side==OnTheLeft ? Rhs::ColsAtCompileTime : Rhs::RowsAtCompileTime)==1 - }; - public: - enum { - Unrolling = (RhsIsVectorAtCompileTime && Rhs::SizeAtCompileTime != Dynamic && Rhs::SizeAtCompileTime <= 8) - ? CompleteUnrolling : NoUnrolling, - RhsVectors = RhsIsVectorAtCompileTime ? 1 : Dynamic - }; -}; - -template<typename Lhs, typename Rhs, - int Side, // can be OnTheLeft/OnTheRight - int Mode, // can be Upper/Lower | UnitDiag - int Unrolling = trsolve_traits<Lhs,Rhs,Side>::Unrolling, - int RhsVectors = trsolve_traits<Lhs,Rhs,Side>::RhsVectors - > -struct triangular_solver_selector; - -template<typename Lhs, typename Rhs, int Side, int Mode> -struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,1> -{ - typedef typename Lhs::Scalar LhsScalar; - typedef typename Rhs::Scalar RhsScalar; - typedef blas_traits<Lhs> LhsProductTraits; - typedef typename LhsProductTraits::ExtractType ActualLhsType; - typedef Map<Matrix<RhsScalar,Dynamic,1>, Aligned> MappedRhs; - static void run(const Lhs& lhs, Rhs& rhs) - { - ActualLhsType actualLhs = LhsProductTraits::extract(lhs); - - // FIXME find a way to allow an inner stride if packet_traits<Scalar>::size==1 - - bool useRhsDirectly = Rhs::InnerStrideAtCompileTime==1 || rhs.innerStride()==1; - - ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhs,rhs.size(), - (useRhsDirectly ? rhs.data() : 0)); - - if(!useRhsDirectly) - MappedRhs(actualRhs,rhs.size()) = rhs; - - triangular_solve_vector<LhsScalar, RhsScalar, typename Lhs::Index, Side, Mode, LhsProductTraits::NeedToConjugate, - (int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor> - ::run(actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhs); - - if(!useRhsDirectly) - rhs = MappedRhs(actualRhs, rhs.size()); - } -}; - -// the rhs is a matrix -template<typename Lhs, typename Rhs, int Side, int Mode> -struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic> -{ - typedef typename Rhs::Scalar Scalar; - typedef typename Rhs::Index Index; - typedef blas_traits<Lhs> LhsProductTraits; - typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType; - - static void run(const Lhs& lhs, Rhs& rhs) - { - typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsProductTraits::extract(lhs); - - const Index size = lhs.rows(); - const Index othersize = Side==OnTheLeft? rhs.cols() : rhs.rows(); - - typedef internal::gemm_blocking_space<(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar, - Rhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxRowsAtCompileTime,4> BlockingType; - - BlockingType blocking(rhs.rows(), rhs.cols(), size, 1, false); - - triangular_solve_matrix<Scalar,Index,Side,Mode,LhsProductTraits::NeedToConjugate,(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor, - (Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor> - ::run(size, othersize, &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &rhs.coeffRef(0,0), rhs.outerStride(), blocking); - } -}; - -/*************************************************************************** -* meta-unrolling implementation -***************************************************************************/ - -template<typename Lhs, typename Rhs, int Mode, int Index, int Size, - bool Stop = Index==Size> -struct triangular_solver_unroller; - -template<typename Lhs, typename Rhs, int Mode, int Index, int Size> -struct triangular_solver_unroller<Lhs,Rhs,Mode,Index,Size,false> { - enum { - IsLower = ((Mode&Lower)==Lower), - I = IsLower ? Index : Size - Index - 1, - S = IsLower ? 0 : I+1 - }; - static void run(const Lhs& lhs, Rhs& rhs) - { - if (Index>0) - rhs.coeffRef(I) -= lhs.row(I).template segment<Index>(S).transpose() - .cwiseProduct(rhs.template segment<Index>(S)).sum(); - - if(!(Mode & UnitDiag)) - rhs.coeffRef(I) /= lhs.coeff(I,I); - - triangular_solver_unroller<Lhs,Rhs,Mode,Index+1,Size>::run(lhs,rhs); - } -}; - -template<typename Lhs, typename Rhs, int Mode, int Index, int Size> -struct triangular_solver_unroller<Lhs,Rhs,Mode,Index,Size,true> { - static void run(const Lhs&, Rhs&) {} -}; - -template<typename Lhs, typename Rhs, int Mode> -struct triangular_solver_selector<Lhs,Rhs,OnTheLeft,Mode,CompleteUnrolling,1> { - static void run(const Lhs& lhs, Rhs& rhs) - { triangular_solver_unroller<Lhs,Rhs,Mode,0,Rhs::SizeAtCompileTime>::run(lhs,rhs); } -}; - -template<typename Lhs, typename Rhs, int Mode> -struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> { - static void run(const Lhs& lhs, Rhs& rhs) - { - Transpose<const Lhs> trLhs(lhs); - Transpose<Rhs> trRhs(rhs); - - triangular_solver_unroller<Transpose<const Lhs>,Transpose<Rhs>, - ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag), - 0,Rhs::SizeAtCompileTime>::run(trLhs,trRhs); - } -}; - -} // end namespace internal - -/*************************************************************************** -* TriangularView methods -***************************************************************************/ - -/** "in-place" version of TriangularView::solve() where the result is written in \a other - * - * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here. - * This function will const_cast it, so constness isn't honored here. - * - * See TriangularView:solve() for the details. - */ -template<typename MatrixType, unsigned int Mode> -template<int Side, typename OtherDerived> -void TriangularView<MatrixType,Mode>::solveInPlace(const MatrixBase<OtherDerived>& _other) const -{ - OtherDerived& other = _other.const_cast_derived(); - eigen_assert( cols() == rows() && ((Side==OnTheLeft && cols() == other.rows()) || (Side==OnTheRight && cols() == other.cols())) ); - eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower))); - - enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit && OtherDerived::IsVectorAtCompileTime }; - typedef typename internal::conditional<copy, - typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy; - OtherCopy otherCopy(other); - - internal::triangular_solver_selector<MatrixType, typename internal::remove_reference<OtherCopy>::type, - Side, Mode>::run(nestedExpression(), otherCopy); - - if (copy) - other = otherCopy; -} - -/** \returns the product of the inverse of \c *this with \a other, \a *this being triangular. - * - * This function computes the inverse-matrix matrix product inverse(\c *this) * \a other if - * \a Side==OnTheLeft (the default), or the right-inverse-multiply \a other * inverse(\c *this) if - * \a Side==OnTheRight. - * - * The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the - * diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this - * is an upper (resp. lower) triangular matrix. - * - * Example: \include MatrixBase_marked.cpp - * Output: \verbinclude MatrixBase_marked.out - * - * This function returns an expression of the inverse-multiply and can works in-place if it is assigned - * to the same matrix or vector \a other. - * - * For users coming from BLAS, this function (and more specifically solveInPlace()) offer - * all the operations supported by the \c *TRSV and \c *TRSM BLAS routines. - * - * \sa TriangularView::solveInPlace() - */ -template<typename Derived, unsigned int Mode> -template<int Side, typename Other> -const internal::triangular_solve_retval<Side,TriangularView<Derived,Mode>,Other> -TriangularView<Derived,Mode>::solve(const MatrixBase<Other>& other) const -{ - return internal::triangular_solve_retval<Side,TriangularView,Other>(*this, other.derived()); -} - -namespace internal { - - -template<int Side, typename TriangularType, typename Rhs> -struct traits<triangular_solve_retval<Side, TriangularType, Rhs> > -{ - typedef typename internal::plain_matrix_type_column_major<Rhs>::type ReturnType; -}; - -template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval - : public ReturnByValue<triangular_solve_retval<Side, TriangularType, Rhs> > -{ - typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned; - typedef ReturnByValue<triangular_solve_retval> Base; - typedef typename Base::Index Index; - - triangular_solve_retval(const TriangularType& tri, const Rhs& rhs) - : m_triangularMatrix(tri), m_rhs(rhs) - {} - - inline Index rows() const { return m_rhs.rows(); } - inline Index cols() const { return m_rhs.cols(); } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - if(!(is_same<RhsNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_rhs))) - dst = m_rhs; - m_triangularMatrix.template solveInPlace<Side>(dst); - } - - protected: - const TriangularType& m_triangularMatrix; - typename Rhs::Nested m_rhs; -}; - -} // namespace internal - -} // end namespace Eigen - -#endif // EIGEN_SOLVETRIANGULAR_H diff --git a/third_party/eigen3/Eigen/src/Core/SpecialFunctions.h b/third_party/eigen3/Eigen/src/Core/SpecialFunctions.h deleted file mode 100644 index 5fdcedb032..0000000000 --- a/third_party/eigen3/Eigen/src/Core/SpecialFunctions.h +++ /dev/null @@ -1,142 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2015 Eugene Brevdo <ebrevdo@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPECIALFUNCTIONS_H -#define EIGEN_SPECIALFUNCTIONS_H - -namespace Eigen { - -namespace internal { - -template <typename Scalar> -EIGEN_STRONG_INLINE Scalar __lgamma(Scalar x) { - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), - THIS_TYPE_IS_NOT_SUPPORTED); -} - -template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float __lgamma<float>(float x) { return lgammaf(x); } -template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double __lgamma<double>(double x) { return lgamma(x); } - -template <typename Scalar> -EIGEN_STRONG_INLINE Scalar __erf(Scalar x) { - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), - THIS_TYPE_IS_NOT_SUPPORTED); -} - -template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float __erf<float>(float x) { return erff(x); } -template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double __erf<double>(double x) { return erf(x); } - -template <typename Scalar> -EIGEN_STRONG_INLINE Scalar __erfc(Scalar x) { - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, Scalar>::value == false), - THIS_TYPE_IS_NOT_SUPPORTED); -} - -template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float __erfc<float>(float x) { return erfcf(x); } -template <> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double __erfc<double>(double x) { return erfc(x); } - -} // end namespace internal - -/**************************************************************************** -* Implementations * -****************************************************************************/ - -namespace internal { - -/**************************************************************************** -* Implementation of lgamma * -****************************************************************************/ - -template<typename Scalar> -struct lgamma_impl -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Scalar& x) - { - return __lgamma<Scalar>(x); - } -}; - -template<typename Scalar> -struct lgamma_retval -{ - typedef Scalar type; -}; - -/**************************************************************************** -* Implementation of erf * -****************************************************************************/ - -template<typename Scalar> -struct erf_impl -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Scalar& x) - { - return __erf<Scalar>(x); - } -}; - -template<typename Scalar> -struct erf_retval -{ - typedef Scalar type; -}; - -/**************************************************************************** -* Implementation of erfc * -****************************************************************************/ - -template<typename Scalar> -struct erfc_impl -{ - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE Scalar run(const Scalar& x) - { - return __erfc<Scalar>(x); - } -}; - -template<typename Scalar> -struct erfc_retval -{ - typedef Scalar type; -}; - -} // end namespace internal - -namespace numext { - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(lgamma, Scalar) lgamma(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(lgamma, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(erf, Scalar) erf(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(erf, Scalar)::run(x); -} - -template<typename Scalar> -EIGEN_DEVICE_FUNC -inline EIGEN_MATHFUNC_RETVAL(erfc, Scalar) erfc(const Scalar& x) -{ - return EIGEN_MATHFUNC_IMPL(erfc, Scalar)::run(x); -} - -} // end namespace numext - -} // end namespace Eigen - -#endif // EIGEN_SPECIALFUNCTIONS_H diff --git a/third_party/eigen3/Eigen/src/Core/StableNorm.h b/third_party/eigen3/Eigen/src/Core/StableNorm.h deleted file mode 100644 index c862c0b63e..0000000000 --- a/third_party/eigen3/Eigen/src/Core/StableNorm.h +++ /dev/null @@ -1,200 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_STABLENORM_H -#define EIGEN_STABLENORM_H - -namespace Eigen { - -namespace internal { - -template<typename ExpressionType, typename Scalar> -inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale) -{ - using std::max; - Scalar maxCoeff = bl.cwiseAbs().maxCoeff(); - - if (maxCoeff>scale) - { - ssq = ssq * numext::abs2(scale/maxCoeff); - Scalar tmp = Scalar(1)/maxCoeff; - if(tmp > NumTraits<Scalar>::highest()) - { - invScale = NumTraits<Scalar>::highest(); - scale = Scalar(1)/invScale; - } - else - { - scale = maxCoeff; - invScale = tmp; - } - } - - // TODO if the maxCoeff is much much smaller than the current scale, - // then we can neglect this sub vector - if(scale>Scalar(0)) // if scale==0, then bl is 0 - ssq += (bl*invScale).squaredNorm(); -} - -template<typename Derived> -inline typename NumTraits<typename traits<Derived>::Scalar>::Real -blueNorm_impl(const EigenBase<Derived>& _vec) -{ - typedef typename Derived::RealScalar RealScalar; - typedef typename Derived::Index Index; - using std::pow; - using std::sqrt; - using std::abs; - const Derived& vec(_vec.derived()); - static bool initialized = false; - static RealScalar b1, b2, s1m, s2m, overfl, rbig, relerr; - if(!initialized) - { - int ibeta, it, iemin, iemax, iexp; - RealScalar eps; - // This program calculates the machine-dependent constants - // bl, b2, slm, s2m, relerr overfl - // from the "basic" machine-dependent numbers - // nbig, ibeta, it, iemin, iemax, rbig. - // The following define the basic machine-dependent constants. - // For portability, the PORT subprograms "ilmaeh" and "rlmach" - // are used. For any specific computer, each of the assignment - // statements can be replaced - ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers - it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa - iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent - iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent - rbig = (std::numeric_limits<RealScalar>::max)(); // largest floating-point number - - iexp = -((1-iemin)/2); - b1 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // lower boundary of midrange - iexp = (iemax + 1 - it)/2; - b2 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // upper boundary of midrange - - iexp = (2-iemin)/2; - s1m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for lower range - iexp = - ((iemax+it)/2); - s2m = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // scaling factor for upper range - - overfl = rbig*s2m; // overflow boundary for abig - eps = RealScalar(pow(double(ibeta), 1-it)); - relerr = sqrt(eps); // tolerance for neglecting asml - initialized = true; - } - Index n = vec.size(); - RealScalar ab2 = b2 / RealScalar(n); - RealScalar asml = RealScalar(0); - RealScalar amed = RealScalar(0); - RealScalar abig = RealScalar(0); - for(typename Derived::InnerIterator it(vec, 0); it; ++it) - { - RealScalar ax = abs(it.value()); - if(ax > ab2) abig += numext::abs2(ax*s2m); - else if(ax < b1) asml += numext::abs2(ax*s1m); - else amed += numext::abs2(ax); - } - if(abig > RealScalar(0)) - { - abig = sqrt(abig); - if(abig > overfl) - { - return rbig; - } - if(amed > RealScalar(0)) - { - abig = abig/s2m; - amed = sqrt(amed); - } - else - return abig/s2m; - } - else if(asml > RealScalar(0)) - { - if (amed > RealScalar(0)) - { - abig = sqrt(amed); - amed = sqrt(asml) / s1m; - } - else - return sqrt(asml)/s1m; - } - else - return sqrt(amed); - asml = numext::mini(abig, amed); - abig = numext::maxi(abig, amed); - if(asml <= abig*relerr) - return abig; - else - return abig * sqrt(RealScalar(1) + numext::abs2(asml/abig)); -} - -} // end namespace internal - -/** \returns the \em l2 norm of \c *this avoiding underflow and overflow. - * This version use a blockwise two passes algorithm: - * 1 - find the absolute largest coefficient \c s - * 2 - compute \f$ s \Vert \frac{*this}{s} \Vert \f$ in a standard way - * - * For architecture/scalar types supporting vectorization, this version - * is faster than blueNorm(). Otherwise the blueNorm() is much faster. - * - * \sa norm(), blueNorm(), hypotNorm() - */ -template<typename Derived> -inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real -MatrixBase<Derived>::stableNorm() const -{ - using std::sqrt; - const Index blockSize = 4096; - RealScalar scale(0); - RealScalar invScale(1); - RealScalar ssq(0); // sum of square - enum { - Alignment = (int(Flags)&DirectAccessBit) || (int(Flags)&AlignedBit) ? 1 : 0 - }; - Index n = size(); - Index bi = internal::first_aligned(derived()); - if (bi>0) - internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale); - for (; bi<n; bi+=blockSize) - internal::stable_norm_kernel(this->segment(bi,numext::mini(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale); - return scale * sqrt(ssq); -} - -/** \returns the \em l2 norm of \c *this using the Blue's algorithm. - * A Portable Fortran Program to Find the Euclidean Norm of a Vector, - * ACM TOMS, Vol 4, Issue 1, 1978. - * - * For architecture/scalar types without vectorization, this version - * is much faster than stableNorm(). Otherwise the stableNorm() is faster. - * - * \sa norm(), stableNorm(), hypotNorm() - */ -template<typename Derived> -inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real -MatrixBase<Derived>::blueNorm() const -{ - return internal::blueNorm_impl(*this); -} - -/** \returns the \em l2 norm of \c *this avoiding undeflow and overflow. - * This version use a concatenation of hypot() calls, and it is very slow. - * - * \sa norm(), stableNorm() - */ -template<typename Derived> -inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real -MatrixBase<Derived>::hypotNorm() const -{ - return this->cwiseAbs().redux(internal::scalar_hypot_op<RealScalar>()); -} - -} // end namespace Eigen - -#endif // EIGEN_STABLENORM_H diff --git a/third_party/eigen3/Eigen/src/Core/Stride.h b/third_party/eigen3/Eigen/src/Core/Stride.h deleted file mode 100644 index d3d454e4e2..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Stride.h +++ /dev/null @@ -1,113 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_STRIDE_H -#define EIGEN_STRIDE_H - -namespace Eigen { - -/** \class Stride - * \ingroup Core_Module - * - * \brief Holds strides information for Map - * - * This class holds the strides information for mapping arrays with strides with class Map. - * - * It holds two values: the inner stride and the outer stride. - * - * The inner stride is the pointer increment between two consecutive entries within a given row of a - * row-major matrix or within a given column of a column-major matrix. - * - * The outer stride is the pointer increment between two consecutive rows of a row-major matrix or - * between two consecutive columns of a column-major matrix. - * - * These two values can be passed either at compile-time as template parameters, or at runtime as - * arguments to the constructor. - * - * Indeed, this class takes two template parameters: - * \param _OuterStrideAtCompileTime the outer stride, or Dynamic if you want to specify it at runtime. - * \param _InnerStrideAtCompileTime the inner stride, or Dynamic if you want to specify it at runtime. - * - * Here is an example: - * \include Map_general_stride.cpp - * Output: \verbinclude Map_general_stride.out - * - * \sa class InnerStride, class OuterStride, \ref TopicStorageOrders - */ -template<int _OuterStrideAtCompileTime, int _InnerStrideAtCompileTime> -class Stride -{ - public: - typedef DenseIndex Index; - enum { - InnerStrideAtCompileTime = _InnerStrideAtCompileTime, - OuterStrideAtCompileTime = _OuterStrideAtCompileTime - }; - - /** Default constructor, for use when strides are fixed at compile time */ - EIGEN_DEVICE_FUNC - Stride() - : m_outer(OuterStrideAtCompileTime), m_inner(InnerStrideAtCompileTime) - { - eigen_assert(InnerStrideAtCompileTime != Dynamic && OuterStrideAtCompileTime != Dynamic); - } - - /** Constructor allowing to pass the strides at runtime */ - EIGEN_DEVICE_FUNC - Stride(Index outerStride, Index innerStride) - : m_outer(outerStride), m_inner(innerStride) - { - eigen_assert(innerStride>=0 && outerStride>=0); - } - - /** Copy constructor */ - EIGEN_DEVICE_FUNC - Stride(const Stride& other) - : m_outer(other.outer()), m_inner(other.inner()) - {} - - /** \returns the outer stride */ - EIGEN_DEVICE_FUNC - inline Index outer() const { return m_outer.value(); } - /** \returns the inner stride */ - EIGEN_DEVICE_FUNC - inline Index inner() const { return m_inner.value(); } - - protected: - internal::variable_if_dynamic<Index, OuterStrideAtCompileTime> m_outer; - internal::variable_if_dynamic<Index, InnerStrideAtCompileTime> m_inner; -}; - -/** \brief Convenience specialization of Stride to specify only an inner stride - * See class Map for some examples */ -template<int Value = Dynamic> -class InnerStride : public Stride<0, Value> -{ - typedef Stride<0, Value> Base; - public: - typedef DenseIndex Index; - EIGEN_DEVICE_FUNC InnerStride() : Base() {} - EIGEN_DEVICE_FUNC InnerStride(Index v) : Base(0, v) {} -}; - -/** \brief Convenience specialization of Stride to specify only an outer stride - * See class Map for some examples */ -template<int Value = Dynamic> -class OuterStride : public Stride<Value, 0> -{ - typedef Stride<Value, 0> Base; - public: - typedef DenseIndex Index; - EIGEN_DEVICE_FUNC OuterStride() : Base() {} - EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {} -}; - -} // end namespace Eigen - -#endif // EIGEN_STRIDE_H diff --git a/third_party/eigen3/Eigen/src/Core/Swap.h b/third_party/eigen3/Eigen/src/Core/Swap.h deleted file mode 100644 index d602fba653..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Swap.h +++ /dev/null @@ -1,140 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SWAP_H -#define EIGEN_SWAP_H - -namespace Eigen { - -/** \class SwapWrapper - * \ingroup Core_Module - * - * \internal - * - * \brief Internal helper class for swapping two expressions - */ -namespace internal { -template<typename ExpressionType> -struct traits<SwapWrapper<ExpressionType> > : traits<ExpressionType> {}; -} - -template<typename ExpressionType> class SwapWrapper - : public internal::dense_xpr_base<SwapWrapper<ExpressionType> >::type -{ - public: - - typedef typename internal::dense_xpr_base<SwapWrapper>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(SwapWrapper) - typedef typename internal::packet_traits<Scalar>::type Packet; - - EIGEN_DEVICE_FUNC - inline SwapWrapper(ExpressionType& xpr) : m_expression(xpr) {} - - EIGEN_DEVICE_FUNC - inline Index rows() const { return m_expression.rows(); } - EIGEN_DEVICE_FUNC - inline Index cols() const { return m_expression.cols(); } - EIGEN_DEVICE_FUNC - inline Index outerStride() const { return m_expression.outerStride(); } - EIGEN_DEVICE_FUNC - inline Index innerStride() const { return m_expression.innerStride(); } - - typedef typename internal::conditional< - internal::is_lvalue<ExpressionType>::value, - Scalar, - const Scalar - >::type ScalarWithConstIfNotLvalue; - - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); } - EIGEN_DEVICE_FUNC - inline const Scalar* data() const { return m_expression.data(); } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index rowId, Index colId) - { - return m_expression.const_cast_derived().coeffRef(rowId, colId); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index index) - { - return m_expression.const_cast_derived().coeffRef(index); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index rowId, Index colId) const - { - return m_expression.coeffRef(rowId, colId); - } - - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index index) const - { - return m_expression.coeffRef(index); - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void copyCoeff(Index rowId, Index colId, const DenseBase<OtherDerived>& other) - { - OtherDerived& _other = other.const_cast_derived(); - eigen_internal_assert(rowId >= 0 && rowId < rows() - && colId >= 0 && colId < cols()); - Scalar tmp = m_expression.coeff(rowId, colId); - m_expression.coeffRef(rowId, colId) = _other.coeff(rowId, colId); - _other.coeffRef(rowId, colId) = tmp; - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void copyCoeff(Index index, const DenseBase<OtherDerived>& other) - { - OtherDerived& _other = other.const_cast_derived(); - eigen_internal_assert(index >= 0 && index < m_expression.size()); - Scalar tmp = m_expression.coeff(index); - m_expression.coeffRef(index) = _other.coeff(index); - _other.coeffRef(index) = tmp; - } - - template<typename OtherDerived, int StoreMode, int LoadMode> - void copyPacket(Index rowId, Index colId, const DenseBase<OtherDerived>& other) - { - OtherDerived& _other = other.const_cast_derived(); - eigen_internal_assert(rowId >= 0 && rowId < rows() - && colId >= 0 && colId < cols()); - Packet tmp = m_expression.template packet<StoreMode>(rowId, colId); - m_expression.template writePacket<StoreMode>(rowId, colId, - _other.template packet<LoadMode>(rowId, colId) - ); - _other.template writePacket<LoadMode>(rowId, colId, tmp); - } - - template<typename OtherDerived, int StoreMode, int LoadMode> - void copyPacket(Index index, const DenseBase<OtherDerived>& other) - { - OtherDerived& _other = other.const_cast_derived(); - eigen_internal_assert(index >= 0 && index < m_expression.size()); - Packet tmp = m_expression.template packet<StoreMode>(index); - m_expression.template writePacket<StoreMode>(index, - _other.template packet<LoadMode>(index) - ); - _other.template writePacket<LoadMode>(index, tmp); - } - - EIGEN_DEVICE_FUNC - ExpressionType& expression() const { return m_expression; } - - protected: - ExpressionType& m_expression; -}; - -} // end namespace Eigen - -#endif // EIGEN_SWAP_H diff --git a/third_party/eigen3/Eigen/src/Core/Transpose.h b/third_party/eigen3/Eigen/src/Core/Transpose.h deleted file mode 100644 index aba3f66704..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Transpose.h +++ /dev/null @@ -1,428 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRANSPOSE_H -#define EIGEN_TRANSPOSE_H - -namespace Eigen { - -/** \class Transpose - * \ingroup Core_Module - * - * \brief Expression of the transpose of a matrix - * - * \param MatrixType the type of the object of which we are taking the transpose - * - * This class represents an expression of the transpose of a matrix. - * It is the return type of MatrixBase::transpose() and MatrixBase::adjoint() - * and most of the time this is the only way it is used. - * - * \sa MatrixBase::transpose(), MatrixBase::adjoint() - */ - -namespace internal { -template<typename MatrixType> -struct traits<Transpose<MatrixType> > : traits<MatrixType> -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedPlain; - typedef typename traits<MatrixType>::StorageKind StorageKind; - typedef typename traits<MatrixType>::XprKind XprKind; - enum { - RowsAtCompileTime = MatrixType::ColsAtCompileTime, - ColsAtCompileTime = MatrixType::RowsAtCompileTime, - MaxRowsAtCompileTime = MatrixType::MaxColsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0, - Flags0 = MatrixTypeNestedPlain::Flags & ~(LvalueBit | NestByRefBit), - Flags1 = Flags0 | FlagsLvalueBit, - Flags = Flags1 ^ RowMajorBit, - CoeffReadCost = MatrixTypeNestedPlain::CoeffReadCost, - InnerStrideAtCompileTime = inner_stride_at_compile_time<MatrixType>::ret, - OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret - }; -}; -} - -template<typename MatrixType, typename StorageKind> class TransposeImpl; - -template<typename MatrixType> class Transpose - : public TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind> -{ - public: - - typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base; - EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose) - - EIGEN_DEVICE_FUNC - inline Transpose(MatrixType& a_matrix) : m_matrix(a_matrix) {} - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose) - - EIGEN_DEVICE_FUNC inline Index rows() const { return m_matrix.cols(); } - EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.rows(); } - - /** \returns the nested expression */ - EIGEN_DEVICE_FUNC - const typename internal::remove_all<typename MatrixType::Nested>::type& - nestedExpression() const { return m_matrix; } - - /** \returns the nested expression */ - EIGEN_DEVICE_FUNC - typename internal::remove_all<typename MatrixType::Nested>::type& - nestedExpression() { return m_matrix.const_cast_derived(); } - - protected: - typename MatrixType::Nested m_matrix; -}; - -namespace internal { - -template<typename MatrixType, bool HasDirectAccess = has_direct_access<MatrixType>::ret> -struct TransposeImpl_base -{ - typedef typename dense_xpr_base<Transpose<MatrixType> >::type type; -}; - -template<typename MatrixType> -struct TransposeImpl_base<MatrixType, false> -{ - typedef typename dense_xpr_base<Transpose<MatrixType> >::type type; -}; - -} // end namespace internal - -template<typename MatrixType> class TransposeImpl<MatrixType,Dense> - : public internal::TransposeImpl_base<MatrixType>::type -{ - public: - - typedef typename internal::TransposeImpl_base<MatrixType>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>) - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl) - - EIGEN_DEVICE_FUNC inline Index innerStride() const { return derived().nestedExpression().innerStride(); } - EIGEN_DEVICE_FUNC inline Index outerStride() const { return derived().nestedExpression().outerStride(); } - - typedef typename internal::conditional< - internal::is_lvalue<MatrixType>::value, - Scalar, - const Scalar - >::type ScalarWithConstIfNotLvalue; - - inline ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); } - inline const Scalar* data() const { return derived().nestedExpression().data(); } - - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue& coeffRef(Index rowId, Index colId) - { - EIGEN_STATIC_ASSERT_LVALUE(MatrixType) - return derived().nestedExpression().const_cast_derived().coeffRef(colId, rowId); - } - - EIGEN_DEVICE_FUNC - inline ScalarWithConstIfNotLvalue& coeffRef(Index index) - { - EIGEN_STATIC_ASSERT_LVALUE(MatrixType) - return derived().nestedExpression().const_cast_derived().coeffRef(index); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index rowId, Index colId) const - { - return derived().nestedExpression().coeffRef(colId, rowId); - } - - EIGEN_DEVICE_FUNC - inline const Scalar& coeffRef(Index index) const - { - return derived().nestedExpression().coeffRef(index); - } - - EIGEN_DEVICE_FUNC - inline CoeffReturnType coeff(Index rowId, Index colId) const - { - return derived().nestedExpression().coeff(colId, rowId); - } - - EIGEN_DEVICE_FUNC - inline CoeffReturnType coeff(Index index) const - { - return derived().nestedExpression().coeff(index); - } - - template<int LoadMode> - inline const PacketScalar packet(Index rowId, Index colId) const - { - return derived().nestedExpression().template packet<LoadMode>(colId, rowId); - } - - template<int LoadMode> - inline void writePacket(Index rowId, Index colId, const PacketScalar& x) - { - derived().nestedExpression().const_cast_derived().template writePacket<LoadMode>(colId, rowId, x); - } - - template<int LoadMode> - inline const PacketScalar packet(Index index) const - { - return derived().nestedExpression().template packet<LoadMode>(index); - } - - template<int LoadMode> - inline void writePacket(Index index, const PacketScalar& x) - { - derived().nestedExpression().const_cast_derived().template writePacket<LoadMode>(index, x); - } -}; - -/** \returns an expression of the transpose of *this. - * - * Example: \include MatrixBase_transpose.cpp - * Output: \verbinclude MatrixBase_transpose.out - * - * \warning If you want to replace a matrix by its own transpose, do \b NOT do this: - * \code - * m = m.transpose(); // bug!!! caused by aliasing effect - * \endcode - * Instead, use the transposeInPlace() method: - * \code - * m.transposeInPlace(); - * \endcode - * which gives Eigen good opportunities for optimization, or alternatively you can also do: - * \code - * m = m.transpose().eval(); - * \endcode - * - * \sa transposeInPlace(), adjoint() */ -template<typename Derived> -inline Transpose<Derived> -DenseBase<Derived>::transpose() -{ - return derived(); -} - -/** This is the const version of transpose(). - * - * Make sure you read the warning for transpose() ! - * - * \sa transposeInPlace(), adjoint() */ -template<typename Derived> -inline typename DenseBase<Derived>::ConstTransposeReturnType -DenseBase<Derived>::transpose() const -{ - return ConstTransposeReturnType(derived()); -} - -/** \returns an expression of the adjoint (i.e. conjugate transpose) of *this. - * - * Example: \include MatrixBase_adjoint.cpp - * Output: \verbinclude MatrixBase_adjoint.out - * - * \warning If you want to replace a matrix by its own adjoint, do \b NOT do this: - * \code - * m = m.adjoint(); // bug!!! caused by aliasing effect - * \endcode - * Instead, use the adjointInPlace() method: - * \code - * m.adjointInPlace(); - * \endcode - * which gives Eigen good opportunities for optimization, or alternatively you can also do: - * \code - * m = m.adjoint().eval(); - * \endcode - * - * \sa adjointInPlace(), transpose(), conjugate(), class Transpose, class internal::scalar_conjugate_op */ -template<typename Derived> -inline const typename MatrixBase<Derived>::AdjointReturnType -MatrixBase<Derived>::adjoint() const -{ - return this->transpose(); // in the complex case, the .conjugate() is be implicit here - // due to implicit conversion to return type -} - -/*************************************************************************** -* "in place" transpose implementation -***************************************************************************/ - -namespace internal { - -template<typename MatrixType, - bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic> -struct inplace_transpose_selector; - -template<typename MatrixType> -struct inplace_transpose_selector<MatrixType,true> { // square matrix - static void run(MatrixType& m) { - m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose()); - } -}; - -template<typename MatrixType> -struct inplace_transpose_selector<MatrixType,false> { // non square matrix - static void run(MatrixType& m) { - if (m.rows()==m.cols()) - m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose()); - else - m = m.transpose().eval(); - } -}; - -} // end namespace internal - -/** This is the "in place" version of transpose(): it replaces \c *this by its own transpose. - * Thus, doing - * \code - * m.transposeInPlace(); - * \endcode - * has the same effect on m as doing - * \code - * m = m.transpose().eval(); - * \endcode - * and is faster and also safer because in the latter line of code, forgetting the eval() results - * in a bug caused by \ref TopicAliasing "aliasing". - * - * Notice however that this method is only useful if you want to replace a matrix by its own transpose. - * If you just need the transpose of a matrix, use transpose(). - * - * \note if the matrix is not square, then \c *this must be a resizable matrix. - * This excludes (non-square) fixed-size matrices, block-expressions and maps. - * - * \sa transpose(), adjoint(), adjointInPlace() */ -template<typename Derived> -inline void DenseBase<Derived>::transposeInPlace() -{ - eigen_assert((rows() == cols() || (RowsAtCompileTime == Dynamic && ColsAtCompileTime == Dynamic)) - && "transposeInPlace() called on a non-square non-resizable matrix"); - internal::inplace_transpose_selector<Derived>::run(derived()); -} - -/*************************************************************************** -* "in place" adjoint implementation -***************************************************************************/ - -/** This is the "in place" version of adjoint(): it replaces \c *this by its own transpose. - * Thus, doing - * \code - * m.adjointInPlace(); - * \endcode - * has the same effect on m as doing - * \code - * m = m.adjoint().eval(); - * \endcode - * and is faster and also safer because in the latter line of code, forgetting the eval() results - * in a bug caused by aliasing. - * - * Notice however that this method is only useful if you want to replace a matrix by its own adjoint. - * If you just need the adjoint of a matrix, use adjoint(). - * - * \note if the matrix is not square, then \c *this must be a resizable matrix. - * This excludes (non-square) fixed-size matrices, block-expressions and maps. - * - * \sa transpose(), adjoint(), transposeInPlace() */ -template<typename Derived> -inline void MatrixBase<Derived>::adjointInPlace() -{ - derived() = adjoint().eval(); -} - -#ifndef EIGEN_NO_DEBUG - -// The following is to detect aliasing problems in most common cases. - -namespace internal { - -template<typename BinOp,typename NestedXpr,typename Rhs> -struct blas_traits<SelfCwiseBinaryOp<BinOp,NestedXpr,Rhs> > - : blas_traits<NestedXpr> -{ - typedef SelfCwiseBinaryOp<BinOp,NestedXpr,Rhs> XprType; - static inline const XprType extract(const XprType& x) { return x; } -}; - -template<bool DestIsTransposed, typename OtherDerived> -struct check_transpose_aliasing_compile_time_selector -{ - enum { ret = bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed }; -}; - -template<bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB> -struct check_transpose_aliasing_compile_time_selector<DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> > -{ - enum { ret = bool(blas_traits<DerivedA>::IsTransposed) != DestIsTransposed - || bool(blas_traits<DerivedB>::IsTransposed) != DestIsTransposed - }; -}; - -template<typename Scalar, bool DestIsTransposed, typename OtherDerived> -struct check_transpose_aliasing_run_time_selector -{ - static bool run(const Scalar* dest, const OtherDerived& src) - { - return (bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src)); - } -}; - -template<typename Scalar, bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB> -struct check_transpose_aliasing_run_time_selector<Scalar,DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> > -{ - static bool run(const Scalar* dest, const CwiseBinaryOp<BinOp,DerivedA,DerivedB>& src) - { - return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.lhs()))) - || ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.rhs()))); - } -}; - -// the following selector, checkTransposeAliasing_impl, based on MightHaveTransposeAliasing, -// is because when the condition controlling the assert is known at compile time, ICC emits a warning. -// This is actually a good warning: in expressions that don't have any transposing, the condition is -// known at compile time to be false, and using that, we can avoid generating the code of the assert again -// and again for all these expressions that don't need it. - -template<typename Derived, typename OtherDerived, - bool MightHaveTransposeAliasing - = check_transpose_aliasing_compile_time_selector - <blas_traits<Derived>::IsTransposed,OtherDerived>::ret - > -struct checkTransposeAliasing_impl -{ - static void run(const Derived& dst, const OtherDerived& other) - { - eigen_assert((!check_transpose_aliasing_run_time_selector - <typename Derived::Scalar,blas_traits<Derived>::IsTransposed,OtherDerived> - ::run(extract_data(dst), other)) - && "aliasing detected during transposition, use transposeInPlace() " - "or evaluate the rhs into a temporary using .eval()"); - - } -}; - -template<typename Derived, typename OtherDerived> -struct checkTransposeAliasing_impl<Derived, OtherDerived, false> -{ - static void run(const Derived&, const OtherDerived&) - { - } -}; - -} // end namespace internal - -template<typename Derived> -template<typename OtherDerived> -void DenseBase<Derived>::checkTransposeAliasing(const OtherDerived& other) const -{ - internal::checkTransposeAliasing_impl<Derived, OtherDerived>::run(derived(), other); -} -#endif - -} // end namespace Eigen - -#endif // EIGEN_TRANSPOSE_H diff --git a/third_party/eigen3/Eigen/src/Core/Transpositions.h b/third_party/eigen3/Eigen/src/Core/Transpositions.h deleted file mode 100644 index ac3aef5af5..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Transpositions.h +++ /dev/null @@ -1,436 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRANSPOSITIONS_H -#define EIGEN_TRANSPOSITIONS_H - -namespace Eigen { - -/** \class Transpositions - * \ingroup Core_Module - * - * \brief Represents a sequence of transpositions (row/column interchange) - * - * \param SizeAtCompileTime the number of transpositions, or Dynamic - * \param MaxSizeAtCompileTime the maximum number of transpositions, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it. - * - * This class represents a permutation transformation as a sequence of \em n transpositions - * \f$[T_{n-1} \ldots T_{i} \ldots T_{0}]\f$. It is internally stored as a vector of integers \c indices. - * Each transposition \f$ T_{i} \f$ applied on the left of a matrix (\f$ T_{i} M\f$) interchanges - * the rows \c i and \c indices[i] of the matrix \c M. - * A transposition applied on the right (e.g., \f$ M T_{i}\f$) yields a column interchange. - * - * Compared to the class PermutationMatrix, such a sequence of transpositions is what is - * computed during a decomposition with pivoting, and it is faster when applying the permutation in-place. - * - * To apply a sequence of transpositions to a matrix, simply use the operator * as in the following example: - * \code - * Transpositions tr; - * MatrixXf mat; - * mat = tr * mat; - * \endcode - * In this example, we detect that the matrix appears on both side, and so the transpositions - * are applied in-place without any temporary or extra copy. - * - * \sa class PermutationMatrix - */ - -namespace internal { -template<typename TranspositionType, typename MatrixType, int Side, bool Transposed=false> struct transposition_matrix_product_retval; -} - -template<typename Derived> -class TranspositionsBase -{ - typedef internal::traits<Derived> Traits; - - public: - - typedef typename Traits::IndicesType IndicesType; - typedef typename IndicesType::Scalar Index; - - Derived& derived() { return *static_cast<Derived*>(this); } - const Derived& derived() const { return *static_cast<const Derived*>(this); } - - /** Copies the \a other transpositions into \c *this */ - template<typename OtherDerived> - Derived& operator=(const TranspositionsBase<OtherDerived>& other) - { - indices() = other.indices(); - return derived(); - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - Derived& operator=(const TranspositionsBase& other) - { - indices() = other.indices(); - return derived(); - } - #endif - - /** \returns the number of transpositions */ - inline Index size() const { return indices().size(); } - - /** Direct access to the underlying index vector */ - inline const Index& coeff(Index i) const { return indices().coeff(i); } - /** Direct access to the underlying index vector */ - inline Index& coeffRef(Index i) { return indices().coeffRef(i); } - /** Direct access to the underlying index vector */ - inline const Index& operator()(Index i) const { return indices()(i); } - /** Direct access to the underlying index vector */ - inline Index& operator()(Index i) { return indices()(i); } - /** Direct access to the underlying index vector */ - inline const Index& operator[](Index i) const { return indices()(i); } - /** Direct access to the underlying index vector */ - inline Index& operator[](Index i) { return indices()(i); } - - /** const version of indices(). */ - const IndicesType& indices() const { return derived().indices(); } - /** \returns a reference to the stored array representing the transpositions. */ - IndicesType& indices() { return derived().indices(); } - - /** Resizes to given size. */ - inline void resize(Index newSize) - { - indices().resize(newSize); - } - - /** Sets \c *this to represents an identity transformation */ - void setIdentity() - { - for(int i = 0; i < indices().size(); ++i) - coeffRef(i) = i; - } - - // FIXME: do we want such methods ? - // might be usefull when the target matrix expression is complex, e.g.: - // object.matrix().block(..,..,..,..) = trans * object.matrix().block(..,..,..,..); - /* - template<typename MatrixType> - void applyForwardToRows(MatrixType& mat) const - { - for(Index k=0 ; k<size() ; ++k) - if(m_indices(k)!=k) - mat.row(k).swap(mat.row(m_indices(k))); - } - - template<typename MatrixType> - void applyBackwardToRows(MatrixType& mat) const - { - for(Index k=size()-1 ; k>=0 ; --k) - if(m_indices(k)!=k) - mat.row(k).swap(mat.row(m_indices(k))); - } - */ - - /** \returns the inverse transformation */ - inline Transpose<TranspositionsBase> inverse() const - { return Transpose<TranspositionsBase>(derived()); } - - /** \returns the tranpose transformation */ - inline Transpose<TranspositionsBase> transpose() const - { return Transpose<TranspositionsBase>(derived()); } - - protected: -}; - -namespace internal { -template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType> -struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType> > -{ - typedef IndexType Index; - typedef Matrix<Index, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType; -}; -} - -template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType> -class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType> > -{ - typedef internal::traits<Transpositions> Traits; - public: - - typedef TranspositionsBase<Transpositions> Base; - typedef typename Traits::IndicesType IndicesType; - typedef typename IndicesType::Scalar Index; - - inline Transpositions() {} - - /** Copy constructor. */ - template<typename OtherDerived> - inline Transpositions(const TranspositionsBase<OtherDerived>& other) - : m_indices(other.indices()) {} - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** Standard copy constructor. Defined only to prevent a default copy constructor - * from hiding the other templated constructor */ - inline Transpositions(const Transpositions& other) : m_indices(other.indices()) {} - #endif - - /** Generic constructor from expression of the transposition indices. */ - template<typename Other> - explicit inline Transpositions(const MatrixBase<Other>& a_indices) : m_indices(a_indices) - {} - - /** Copies the \a other transpositions into \c *this */ - template<typename OtherDerived> - Transpositions& operator=(const TranspositionsBase<OtherDerived>& other) - { - return Base::operator=(other); - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - Transpositions& operator=(const Transpositions& other) - { - m_indices = other.m_indices; - return *this; - } - #endif - - /** Constructs an uninitialized permutation matrix of given size. - */ - inline Transpositions(Index size) : m_indices(size) - {} - - /** const version of indices(). */ - const IndicesType& indices() const { return m_indices; } - /** \returns a reference to the stored array representing the transpositions. */ - IndicesType& indices() { return m_indices; } - - protected: - - IndicesType m_indices; -}; - - -namespace internal { -template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int _PacketAccess> -struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType>,_PacketAccess> > -{ - typedef IndexType Index; - typedef Map<const Matrix<Index,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, _PacketAccess> IndicesType; -}; -} - -template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename IndexType, int PacketAccess> -class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType>,PacketAccess> - : public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,IndexType>,PacketAccess> > -{ - typedef internal::traits<Map> Traits; - public: - - typedef TranspositionsBase<Map> Base; - typedef typename Traits::IndicesType IndicesType; - typedef typename IndicesType::Scalar Index; - - inline Map(const Index* indicesPtr) - : m_indices(indicesPtr) - {} - - inline Map(const Index* indicesPtr, Index size) - : m_indices(indicesPtr,size) - {} - - /** Copies the \a other transpositions into \c *this */ - template<typename OtherDerived> - Map& operator=(const TranspositionsBase<OtherDerived>& other) - { - return Base::operator=(other); - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - Map& operator=(const Map& other) - { - m_indices = other.m_indices; - return *this; - } - #endif - - /** const version of indices(). */ - const IndicesType& indices() const { return m_indices; } - - /** \returns a reference to the stored array representing the transpositions. */ - IndicesType& indices() { return m_indices; } - - protected: - - IndicesType m_indices; -}; - -namespace internal { -template<typename _IndicesType> -struct traits<TranspositionsWrapper<_IndicesType> > -{ - typedef typename _IndicesType::Scalar Index; - typedef _IndicesType IndicesType; -}; -} - -template<typename _IndicesType> -class TranspositionsWrapper - : public TranspositionsBase<TranspositionsWrapper<_IndicesType> > -{ - typedef internal::traits<TranspositionsWrapper> Traits; - public: - - typedef TranspositionsBase<TranspositionsWrapper> Base; - typedef typename Traits::IndicesType IndicesType; - typedef typename IndicesType::Scalar Index; - - inline TranspositionsWrapper(IndicesType& a_indices) - : m_indices(a_indices) - {} - - /** Copies the \a other transpositions into \c *this */ - template<typename OtherDerived> - TranspositionsWrapper& operator=(const TranspositionsBase<OtherDerived>& other) - { - return Base::operator=(other); - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is a special case of the templated operator=. Its purpose is to - * prevent a default operator= from hiding the templated operator=. - */ - TranspositionsWrapper& operator=(const TranspositionsWrapper& other) - { - m_indices = other.m_indices; - return *this; - } - #endif - - /** const version of indices(). */ - const IndicesType& indices() const { return m_indices; } - - /** \returns a reference to the stored array representing the transpositions. */ - IndicesType& indices() { return m_indices; } - - protected: - - const typename IndicesType::Nested m_indices; -}; - -/** \returns the \a matrix with the \a transpositions applied to the columns. - */ -template<typename Derived, typename TranspositionsDerived> -inline const internal::transposition_matrix_product_retval<TranspositionsDerived, Derived, OnTheRight> -operator*(const MatrixBase<Derived>& matrix, - const TranspositionsBase<TranspositionsDerived> &transpositions) -{ - return internal::transposition_matrix_product_retval - <TranspositionsDerived, Derived, OnTheRight> - (transpositions.derived(), matrix.derived()); -} - -/** \returns the \a matrix with the \a transpositions applied to the rows. - */ -template<typename Derived, typename TranspositionDerived> -inline const internal::transposition_matrix_product_retval - <TranspositionDerived, Derived, OnTheLeft> -operator*(const TranspositionsBase<TranspositionDerived> &transpositions, - const MatrixBase<Derived>& matrix) -{ - return internal::transposition_matrix_product_retval - <TranspositionDerived, Derived, OnTheLeft> - (transpositions.derived(), matrix.derived()); -} - -namespace internal { - -template<typename TranspositionType, typename MatrixType, int Side, bool Transposed> -struct traits<transposition_matrix_product_retval<TranspositionType, MatrixType, Side, Transposed> > -{ - typedef typename MatrixType::PlainObject ReturnType; -}; - -template<typename TranspositionType, typename MatrixType, int Side, bool Transposed> -struct transposition_matrix_product_retval - : public ReturnByValue<transposition_matrix_product_retval<TranspositionType, MatrixType, Side, Transposed> > -{ - typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned; - typedef typename TranspositionType::Index Index; - - transposition_matrix_product_retval(const TranspositionType& tr, const MatrixType& matrix) - : m_transpositions(tr), m_matrix(matrix) - {} - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - const Index size = m_transpositions.size(); - Index j = 0; - - if(!(is_same<MatrixTypeNestedCleaned,Dest>::value && extract_data(dst) == extract_data(m_matrix))) - dst = m_matrix; - - for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k) - if((j=m_transpositions.coeff(k))!=k) - { - if(Side==OnTheLeft) - dst.row(k).swap(dst.row(j)); - else if(Side==OnTheRight) - dst.col(k).swap(dst.col(j)); - } - } - - protected: - const TranspositionType& m_transpositions; - typename MatrixType::Nested m_matrix; -}; - -} // end namespace internal - -/* Template partial specialization for transposed/inverse transpositions */ - -template<typename TranspositionsDerived> -class Transpose<TranspositionsBase<TranspositionsDerived> > -{ - typedef TranspositionsDerived TranspositionType; - typedef typename TranspositionType::IndicesType IndicesType; - public: - - Transpose(const TranspositionType& t) : m_transpositions(t) {} - - inline int size() const { return m_transpositions.size(); } - - /** \returns the \a matrix with the inverse transpositions applied to the columns. - */ - template<typename Derived> friend - inline const internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheRight, true> - operator*(const MatrixBase<Derived>& matrix, const Transpose& trt) - { - return internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheRight, true>(trt.m_transpositions, matrix.derived()); - } - - /** \returns the \a matrix with the inverse transpositions applied to the rows. - */ - template<typename Derived> - inline const internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheLeft, true> - operator*(const MatrixBase<Derived>& matrix) const - { - return internal::transposition_matrix_product_retval<TranspositionType, Derived, OnTheLeft, true>(m_transpositions, matrix.derived()); - } - - protected: - const TranspositionType& m_transpositions; -}; - -} // end namespace Eigen - -#endif // EIGEN_TRANSPOSITIONS_H diff --git a/third_party/eigen3/Eigen/src/Core/TriangularMatrix.h b/third_party/eigen3/Eigen/src/Core/TriangularMatrix.h deleted file mode 100644 index 1d6e346506..0000000000 --- a/third_party/eigen3/Eigen/src/Core/TriangularMatrix.h +++ /dev/null @@ -1,900 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRIANGULARMATRIX_H -#define EIGEN_TRIANGULARMATRIX_H - -namespace Eigen { - -namespace internal { - -template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval; - -} - -/** \internal - * - * \class TriangularBase - * \ingroup Core_Module - * - * \brief Base class for triangular part in a matrix - */ -template<typename Derived> class TriangularBase : public EigenBase<Derived> -{ - public: - - enum { - Mode = internal::traits<Derived>::Mode, - CoeffReadCost = internal::traits<Derived>::CoeffReadCost, - RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime, - ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime, - MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime, - MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime - }; - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::traits<Derived>::DenseMatrixType DenseMatrixType; - typedef DenseMatrixType DenseType; - - EIGEN_DEVICE_FUNC - inline TriangularBase() { eigen_assert(!((Mode&UnitDiag) && (Mode&ZeroDiag))); } - - EIGEN_DEVICE_FUNC - inline Index rows() const { return derived().rows(); } - EIGEN_DEVICE_FUNC - inline Index cols() const { return derived().cols(); } - EIGEN_DEVICE_FUNC - inline Index outerStride() const { return derived().outerStride(); } - EIGEN_DEVICE_FUNC - inline Index innerStride() const { return derived().innerStride(); } - - EIGEN_DEVICE_FUNC - inline Scalar coeff(Index row, Index col) const { return derived().coeff(row,col); } - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index row, Index col) { return derived().coeffRef(row,col); } - - /** \see MatrixBase::copyCoeff(row,col) - */ - template<typename Other> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, Other& other) - { - derived().coeffRef(row, col) = other.coeff(row, col); - } - - EIGEN_DEVICE_FUNC - inline Scalar operator()(Index row, Index col) const - { - check_coordinates(row, col); - return coeff(row,col); - } - EIGEN_DEVICE_FUNC - inline Scalar& operator()(Index row, Index col) - { - check_coordinates(row, col); - return coeffRef(row,col); - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - EIGEN_DEVICE_FUNC - inline const Derived& derived() const { return *static_cast<const Derived*>(this); } - EIGEN_DEVICE_FUNC - inline Derived& derived() { return *static_cast<Derived*>(this); } - #endif // not EIGEN_PARSED_BY_DOXYGEN - - template<typename DenseDerived> - EIGEN_DEVICE_FUNC - void evalTo(MatrixBase<DenseDerived> &other) const; - template<typename DenseDerived> - EIGEN_DEVICE_FUNC - void evalToLazy(MatrixBase<DenseDerived> &other) const; - - EIGEN_DEVICE_FUNC - DenseMatrixType toDenseMatrix() const - { - DenseMatrixType res(rows(), cols()); - evalToLazy(res); - return res; - } - - protected: - - void check_coordinates(Index row, Index col) const - { - EIGEN_ONLY_USED_FOR_DEBUG(row); - EIGEN_ONLY_USED_FOR_DEBUG(col); - eigen_assert(col>=0 && col<cols() && row>=0 && row<rows()); - const int mode = int(Mode) & ~SelfAdjoint; - EIGEN_ONLY_USED_FOR_DEBUG(mode); - eigen_assert((mode==Upper && col>=row) - || (mode==Lower && col<=row) - || ((mode==StrictlyUpper || mode==UnitUpper) && col>row) - || ((mode==StrictlyLower || mode==UnitLower) && col<row)); - } - - #ifdef EIGEN_INTERNAL_DEBUGGING - void check_coordinates_internal(Index row, Index col) const - { - check_coordinates(row, col); - } - #else - void check_coordinates_internal(Index , Index ) const {} - #endif - -}; - -/** \class TriangularView - * \ingroup Core_Module - * - * \brief Base class for triangular part in a matrix - * - * \param MatrixType the type of the object in which we are taking the triangular part - * \param Mode the kind of triangular matrix expression to construct. Can be #Upper, - * #Lower, #UnitUpper, #UnitLower, #StrictlyUpper, or #StrictlyLower. - * This is in fact a bit field; it must have either #Upper or #Lower, - * and additionnaly it may have #UnitDiag or #ZeroDiag or neither. - * - * This class represents a triangular part of a matrix, not necessarily square. Strictly speaking, for rectangular - * matrices one should speak of "trapezoid" parts. This class is the return type - * of MatrixBase::triangularView() and most of the time this is the only way it is used. - * - * \sa MatrixBase::triangularView() - */ -namespace internal { -template<typename MatrixType, unsigned int _Mode> -struct traits<TriangularView<MatrixType, _Mode> > : traits<MatrixType> -{ - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef; - typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned; - typedef MatrixType ExpressionType; - typedef typename MatrixType::PlainObject DenseMatrixType; - enum { - Mode = _Mode, - Flags = (MatrixTypeNestedCleaned::Flags & (HereditaryBits) & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit))) | Mode, - CoeffReadCost = MatrixTypeNestedCleaned::CoeffReadCost - }; -}; -} - -template<int Mode, bool LhsIsTriangular, - typename Lhs, bool LhsIsVector, - typename Rhs, bool RhsIsVector> -struct TriangularProduct; - -template<typename _MatrixType, unsigned int _Mode> class TriangularView - : public TriangularBase<TriangularView<_MatrixType, _Mode> > -{ - public: - - typedef TriangularBase<TriangularView> Base; - typedef typename internal::traits<TriangularView>::Scalar Scalar; - - typedef _MatrixType MatrixType; - typedef typename internal::traits<TriangularView>::DenseMatrixType DenseMatrixType; - typedef DenseMatrixType PlainObject; - - protected: - typedef typename internal::traits<TriangularView>::MatrixTypeNested MatrixTypeNested; - typedef typename internal::traits<TriangularView>::MatrixTypeNestedNonRef MatrixTypeNestedNonRef; - typedef typename internal::traits<TriangularView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned; - - typedef typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type MatrixConjugateReturnType; - - public: - using Base::evalToLazy; - - - typedef typename internal::traits<TriangularView>::StorageKind StorageKind; - typedef typename internal::traits<TriangularView>::Index Index; - - enum { - Mode = _Mode, - TransposeMode = (Mode & Upper ? Lower : 0) - | (Mode & Lower ? Upper : 0) - | (Mode & (UnitDiag)) - | (Mode & (ZeroDiag)) - }; - - EIGEN_DEVICE_FUNC - inline TriangularView(const MatrixType& matrix) : m_matrix(matrix) - {} - - EIGEN_DEVICE_FUNC - inline Index rows() const { return m_matrix.rows(); } - EIGEN_DEVICE_FUNC - inline Index cols() const { return m_matrix.cols(); } - EIGEN_DEVICE_FUNC - inline Index outerStride() const { return m_matrix.outerStride(); } - EIGEN_DEVICE_FUNC - inline Index innerStride() const { return m_matrix.innerStride(); } - - /** \sa MatrixBase::operator+=() */ - template<typename Other> - EIGEN_DEVICE_FUNC - TriangularView& operator+=(const DenseBase<Other>& other) { return *this = m_matrix + other.derived(); } - /** \sa MatrixBase::operator-=() */ - template<typename Other> - EIGEN_DEVICE_FUNC - TriangularView& operator-=(const DenseBase<Other>& other) { return *this = m_matrix - other.derived(); } - /** \sa MatrixBase::operator*=() */ - EIGEN_DEVICE_FUNC - TriangularView& operator*=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = m_matrix * other; } - /** \sa MatrixBase::operator/=() */ - EIGEN_DEVICE_FUNC - TriangularView& operator/=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = m_matrix / other; } - - /** \sa MatrixBase::fill() */ - EIGEN_DEVICE_FUNC - void fill(const Scalar& value) { setConstant(value); } - /** \sa MatrixBase::setConstant() */ - EIGEN_DEVICE_FUNC - TriangularView& setConstant(const Scalar& value) - { return *this = MatrixType::Constant(rows(), cols(), value); } - /** \sa MatrixBase::setZero() */ - EIGEN_DEVICE_FUNC - TriangularView& setZero() { return setConstant(Scalar(0)); } - /** \sa MatrixBase::setOnes() */ - EIGEN_DEVICE_FUNC - TriangularView& setOnes() { return setConstant(Scalar(1)); } - - /** \sa MatrixBase::coeff() - * \warning the coordinates must fit into the referenced triangular part - */ - EIGEN_DEVICE_FUNC - inline Scalar coeff(Index row, Index col) const - { - Base::check_coordinates_internal(row, col); - return m_matrix.coeff(row, col); - } - - /** \sa MatrixBase::coeffRef() - * \warning the coordinates must fit into the referenced triangular part - */ - EIGEN_DEVICE_FUNC - inline Scalar& coeffRef(Index row, Index col) - { - Base::check_coordinates_internal(row, col); - return m_matrix.const_cast_derived().coeffRef(row, col); - } - - EIGEN_DEVICE_FUNC - const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; } - EIGEN_DEVICE_FUNC - MatrixTypeNestedCleaned& nestedExpression() { return *const_cast<MatrixTypeNestedCleaned*>(&m_matrix); } - - /** Assigns a triangular matrix to a triangular part of a dense matrix */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - TriangularView& operator=(const TriangularBase<OtherDerived>& other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - TriangularView& operator=(const MatrixBase<OtherDerived>& other); - - EIGEN_DEVICE_FUNC - TriangularView& operator=(const TriangularView& other) - { return *this = other.nestedExpression(); } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void lazyAssign(const TriangularBase<OtherDerived>& other); - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void lazyAssign(const MatrixBase<OtherDerived>& other); - - /** \sa MatrixBase::conjugate() */ - EIGEN_DEVICE_FUNC - inline TriangularView<MatrixConjugateReturnType,Mode> conjugate() - { return m_matrix.conjugate(); } - /** \sa MatrixBase::conjugate() const */ - EIGEN_DEVICE_FUNC - inline const TriangularView<MatrixConjugateReturnType,Mode> conjugate() const - { return m_matrix.conjugate(); } - - /** \sa MatrixBase::adjoint() const */ - EIGEN_DEVICE_FUNC - inline const TriangularView<const typename MatrixType::AdjointReturnType,TransposeMode> adjoint() const - { return m_matrix.adjoint(); } - - /** \sa MatrixBase::transpose() */ - EIGEN_DEVICE_FUNC - inline TriangularView<Transpose<MatrixType>,TransposeMode> transpose() - { - EIGEN_STATIC_ASSERT_LVALUE(MatrixType) - return m_matrix.const_cast_derived().transpose(); - } - /** \sa MatrixBase::transpose() const */ - EIGEN_DEVICE_FUNC - inline const TriangularView<Transpose<MatrixType>,TransposeMode> transpose() const - { - return m_matrix.transpose(); - } - - /** Efficient triangular matrix times vector/matrix product */ - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - TriangularProduct<Mode,true,MatrixType,false,OtherDerived, OtherDerived::IsVectorAtCompileTime> - operator*(const MatrixBase<OtherDerived>& rhs) const - { - return TriangularProduct - <Mode,true,MatrixType,false,OtherDerived,OtherDerived::IsVectorAtCompileTime> - (m_matrix, rhs.derived()); - } - - /** Efficient vector/matrix times triangular matrix product */ - template<typename OtherDerived> friend - EIGEN_DEVICE_FUNC - TriangularProduct<Mode,false,OtherDerived,OtherDerived::IsVectorAtCompileTime,MatrixType,false> - operator*(const MatrixBase<OtherDerived>& lhs, const TriangularView& rhs) - { - return TriangularProduct - <Mode,false,OtherDerived,OtherDerived::IsVectorAtCompileTime,MatrixType,false> - (lhs.derived(),rhs.m_matrix); - } - - #ifdef EIGEN2_SUPPORT - template<typename OtherDerived> - struct eigen2_product_return_type - { - typedef typename TriangularView<MatrixType,Mode>::DenseMatrixType DenseMatrixType; - typedef typename OtherDerived::PlainObject::DenseType OtherPlainObject; - typedef typename ProductReturnType<DenseMatrixType, OtherPlainObject>::Type ProdRetType; - typedef typename ProdRetType::PlainObject type; - }; - template<typename OtherDerived> - const typename eigen2_product_return_type<OtherDerived>::type - operator*(const EigenBase<OtherDerived>& rhs) const - { - typename OtherDerived::PlainObject::DenseType rhsPlainObject; - rhs.evalTo(rhsPlainObject); - return this->toDenseMatrix() * rhsPlainObject; - } - template<typename OtherMatrixType> - bool isApprox(const TriangularView<OtherMatrixType, Mode>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const - { - return this->toDenseMatrix().isApprox(other.toDenseMatrix(), precision); - } - template<typename OtherDerived> - bool isApprox(const MatrixBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const - { - return this->toDenseMatrix().isApprox(other, precision); - } - #endif // EIGEN2_SUPPORT - - template<int Side, typename Other> - EIGEN_DEVICE_FUNC - inline const internal::triangular_solve_retval<Side,TriangularView, Other> - solve(const MatrixBase<Other>& other) const; - - template<int Side, typename OtherDerived> - EIGEN_DEVICE_FUNC - void solveInPlace(const MatrixBase<OtherDerived>& other) const; - - template<typename Other> - EIGEN_DEVICE_FUNC - inline const internal::triangular_solve_retval<OnTheLeft,TriangularView, Other> - solve(const MatrixBase<Other>& other) const - { return solve<OnTheLeft>(other); } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void solveInPlace(const MatrixBase<OtherDerived>& other) const - { return solveInPlace<OnTheLeft>(other); } - - EIGEN_DEVICE_FUNC - const SelfAdjointView<MatrixTypeNestedNonRef,Mode> selfadjointView() const - { - EIGEN_STATIC_ASSERT((Mode&UnitDiag)==0,PROGRAMMING_ERROR); - return SelfAdjointView<MatrixTypeNestedNonRef,Mode>(m_matrix); - } - EIGEN_DEVICE_FUNC - SelfAdjointView<MatrixTypeNestedNonRef,Mode> selfadjointView() - { - EIGEN_STATIC_ASSERT((Mode&UnitDiag)==0,PROGRAMMING_ERROR); - return SelfAdjointView<MatrixTypeNestedNonRef,Mode>(m_matrix); - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void swap(TriangularBase<OtherDerived> const & other) - { - TriangularView<SwapWrapper<MatrixType>,Mode>(const_cast<MatrixType&>(m_matrix)).lazyAssign(other.derived()); - } - - template<typename OtherDerived> - EIGEN_DEVICE_FUNC - void swap(MatrixBase<OtherDerived> const & other) - { - SwapWrapper<MatrixType> swaper(const_cast<MatrixType&>(m_matrix)); - TriangularView<SwapWrapper<MatrixType>,Mode>(swaper).lazyAssign(other.derived()); - } - - EIGEN_DEVICE_FUNC - Scalar determinant() const - { - if (Mode & UnitDiag) - return 1; - else if (Mode & ZeroDiag) - return 0; - else - return m_matrix.diagonal().prod(); - } - - // TODO simplify the following: - template<typename ProductDerived, typename Lhs, typename Rhs> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TriangularView& operator=(const ProductBase<ProductDerived, Lhs,Rhs>& other) - { - setZero(); - return assignProduct(other,1); - } - - template<typename ProductDerived, typename Lhs, typename Rhs> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TriangularView& operator+=(const ProductBase<ProductDerived, Lhs,Rhs>& other) - { - return assignProduct(other,1); - } - - template<typename ProductDerived, typename Lhs, typename Rhs> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TriangularView& operator-=(const ProductBase<ProductDerived, Lhs,Rhs>& other) - { - return assignProduct(other,-1); - } - - - template<typename ProductDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TriangularView& operator=(const ScaledProduct<ProductDerived>& other) - { - setZero(); - return assignProduct(other,other.alpha()); - } - - template<typename ProductDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TriangularView& operator+=(const ScaledProduct<ProductDerived>& other) - { - return assignProduct(other,other.alpha()); - } - - template<typename ProductDerived> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TriangularView& operator-=(const ScaledProduct<ProductDerived>& other) - { - return assignProduct(other,-other.alpha()); - } - - protected: - - template<typename ProductDerived, typename Lhs, typename Rhs> - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE TriangularView& assignProduct(const ProductBase<ProductDerived, Lhs,Rhs>& prod, const Scalar& alpha); - - MatrixTypeNested m_matrix; -}; - -/*************************************************************************** -* Implementation of triangular evaluation/assignment -***************************************************************************/ - -namespace internal { - -template<typename Derived1, typename Derived2, unsigned int Mode, int UnrollCount, bool ClearOpposite> -struct triangular_assignment_selector -{ - enum { - col = (UnrollCount-1) / Derived1::RowsAtCompileTime, - row = (UnrollCount-1) % Derived1::RowsAtCompileTime - }; - - typedef typename Derived1::Scalar Scalar; - - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - triangular_assignment_selector<Derived1, Derived2, Mode, UnrollCount-1, ClearOpposite>::run(dst, src); - - eigen_assert( Mode == Upper || Mode == Lower - || Mode == StrictlyUpper || Mode == StrictlyLower - || Mode == UnitUpper || Mode == UnitLower); - if((Mode == Upper && row <= col) - || (Mode == Lower && row >= col) - || (Mode == StrictlyUpper && row < col) - || (Mode == StrictlyLower && row > col) - || (Mode == UnitUpper && row < col) - || (Mode == UnitLower && row > col)) - dst.copyCoeff(row, col, src); - else if(ClearOpposite) - { - if (Mode&UnitDiag && row==col) - dst.coeffRef(row, col) = Scalar(1); - else - dst.coeffRef(row, col) = Scalar(0); - } - } -}; - -// prevent buggy user code from causing an infinite recursion -template<typename Derived1, typename Derived2, unsigned int Mode, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, Mode, 0, ClearOpposite> -{ - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &, const Derived2 &) {} -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, Upper, Dynamic, ClearOpposite> -{ - typedef typename Derived1::Index Index; - typedef typename Derived1::Scalar Scalar; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - for(Index j = 0; j < dst.cols(); ++j) - { - Index maxi = (std::min)(j, dst.rows()-1); - for(Index i = 0; i <= maxi; ++i) - dst.copyCoeff(i, j, src); - if (ClearOpposite) - for(Index i = maxi+1; i < dst.rows(); ++i) - dst.coeffRef(i, j) = Scalar(0); - } - } -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, Lower, Dynamic, ClearOpposite> -{ - typedef typename Derived1::Index Index; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - for(Index j = 0; j < dst.cols(); ++j) - { - for(Index i = j; i < dst.rows(); ++i) - dst.copyCoeff(i, j, src); - Index maxi = (std::min)(j, dst.rows()); - if (ClearOpposite) - for(Index i = 0; i < maxi; ++i) - dst.coeffRef(i, j) = static_cast<typename Derived1::Scalar>(0); - } - } -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, StrictlyUpper, Dynamic, ClearOpposite> -{ - typedef typename Derived1::Index Index; - typedef typename Derived1::Scalar Scalar; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - for(Index j = 0; j < dst.cols(); ++j) - { - Index maxi = (std::min)(j, dst.rows()); - for(Index i = 0; i < maxi; ++i) - dst.copyCoeff(i, j, src); - if (ClearOpposite) - for(Index i = maxi; i < dst.rows(); ++i) - dst.coeffRef(i, j) = Scalar(0); - } - } -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, StrictlyLower, Dynamic, ClearOpposite> -{ - typedef typename Derived1::Index Index; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - for(Index j = 0; j < dst.cols(); ++j) - { - for(Index i = j+1; i < dst.rows(); ++i) - dst.copyCoeff(i, j, src); - Index maxi = (std::min)(j, dst.rows()-1); - if (ClearOpposite) - for(Index i = 0; i <= maxi; ++i) - dst.coeffRef(i, j) = static_cast<typename Derived1::Scalar>(0); - } - } -}; - -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, UnitUpper, Dynamic, ClearOpposite> -{ - typedef typename Derived1::Index Index; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - for(Index j = 0; j < dst.cols(); ++j) - { - Index maxi = (std::min)(j, dst.rows()); - for(Index i = 0; i < maxi; ++i) - dst.copyCoeff(i, j, src); - if (ClearOpposite) - { - for(Index i = maxi+1; i < dst.rows(); ++i) - dst.coeffRef(i, j) = 0; - } - } - dst.diagonal().setOnes(); - } -}; -template<typename Derived1, typename Derived2, bool ClearOpposite> -struct triangular_assignment_selector<Derived1, Derived2, UnitLower, Dynamic, ClearOpposite> -{ - typedef typename Derived1::Index Index; - EIGEN_DEVICE_FUNC - static inline void run(Derived1 &dst, const Derived2 &src) - { - for(Index j = 0; j < dst.cols(); ++j) - { - Index maxi = (std::min)(j, dst.rows()); - for(Index i = maxi+1; i < dst.rows(); ++i) - dst.copyCoeff(i, j, src); - if (ClearOpposite) - { - for(Index i = 0; i < maxi; ++i) - dst.coeffRef(i, j) = 0; - } - } - dst.diagonal().setOnes(); - } -}; - -} // end namespace internal - -// FIXME should we keep that possibility -template<typename MatrixType, unsigned int Mode> -template<typename OtherDerived> -inline TriangularView<MatrixType, Mode>& -TriangularView<MatrixType, Mode>::operator=(const MatrixBase<OtherDerived>& other) -{ - if(OtherDerived::Flags & EvalBeforeAssigningBit) - { - typename internal::plain_matrix_type<OtherDerived>::type other_evaluated(other.rows(), other.cols()); - other_evaluated.template triangularView<Mode>().lazyAssign(other.derived()); - lazyAssign(other_evaluated); - } - else - lazyAssign(other.derived()); - return *this; -} - -// FIXME should we keep that possibility -template<typename MatrixType, unsigned int Mode> -template<typename OtherDerived> -void TriangularView<MatrixType, Mode>::lazyAssign(const MatrixBase<OtherDerived>& other) -{ - enum { - unroll = MatrixType::SizeAtCompileTime != Dynamic - && internal::traits<OtherDerived>::CoeffReadCost != Dynamic - && MatrixType::SizeAtCompileTime*internal::traits<OtherDerived>::CoeffReadCost/2 <= EIGEN_UNROLLING_LIMIT - }; - eigen_assert(m_matrix.rows() == other.rows() && m_matrix.cols() == other.cols()); - - internal::triangular_assignment_selector - <MatrixType, OtherDerived, int(Mode), - unroll ? int(MatrixType::SizeAtCompileTime) : Dynamic, - false // do not change the opposite triangular part - >::run(m_matrix.const_cast_derived(), other.derived()); -} - - - -template<typename MatrixType, unsigned int Mode> -template<typename OtherDerived> -inline TriangularView<MatrixType, Mode>& -TriangularView<MatrixType, Mode>::operator=(const TriangularBase<OtherDerived>& other) -{ - eigen_assert(Mode == int(OtherDerived::Mode)); - if(internal::traits<OtherDerived>::Flags & EvalBeforeAssigningBit) - { - typename OtherDerived::DenseMatrixType other_evaluated(other.rows(), other.cols()); - other_evaluated.template triangularView<Mode>().lazyAssign(other.derived().nestedExpression()); - lazyAssign(other_evaluated); - } - else - lazyAssign(other.derived().nestedExpression()); - return *this; -} - -template<typename MatrixType, unsigned int Mode> -template<typename OtherDerived> -void TriangularView<MatrixType, Mode>::lazyAssign(const TriangularBase<OtherDerived>& other) -{ - enum { - unroll = MatrixType::SizeAtCompileTime != Dynamic - && internal::traits<OtherDerived>::CoeffReadCost != Dynamic - && MatrixType::SizeAtCompileTime * internal::traits<OtherDerived>::CoeffReadCost / 2 - <= EIGEN_UNROLLING_LIMIT - }; - eigen_assert(m_matrix.rows() == other.rows() && m_matrix.cols() == other.cols()); - - internal::triangular_assignment_selector - <MatrixType, OtherDerived, int(Mode), - unroll ? int(MatrixType::SizeAtCompileTime) : Dynamic, - false // preserve the opposite triangular part - >::run(m_matrix.const_cast_derived(), other.derived().nestedExpression()); -} - -/*************************************************************************** -* Implementation of TriangularBase methods -***************************************************************************/ - -/** Assigns a triangular or selfadjoint matrix to a dense matrix. - * If the matrix is triangular, the opposite part is set to zero. */ -template<typename Derived> -template<typename DenseDerived> -void TriangularBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const -{ - if(internal::traits<Derived>::Flags & EvalBeforeAssigningBit) - { - typename internal::plain_matrix_type<Derived>::type other_evaluated(rows(), cols()); - evalToLazy(other_evaluated); - other.derived().swap(other_evaluated); - } - else - evalToLazy(other.derived()); -} - -/** Assigns a triangular or selfadjoint matrix to a dense matrix. - * If the matrix is triangular, the opposite part is set to zero. */ -template<typename Derived> -template<typename DenseDerived> -void TriangularBase<Derived>::evalToLazy(MatrixBase<DenseDerived> &other) const -{ - enum { - unroll = DenseDerived::SizeAtCompileTime != Dynamic - && internal::traits<Derived>::CoeffReadCost != Dynamic - && DenseDerived::SizeAtCompileTime * internal::traits<Derived>::CoeffReadCost / 2 - <= EIGEN_UNROLLING_LIMIT - }; - other.derived().resize(this->rows(), this->cols()); - - internal::triangular_assignment_selector - <DenseDerived, typename internal::traits<Derived>::MatrixTypeNestedCleaned, Derived::Mode, - unroll ? int(DenseDerived::SizeAtCompileTime) : Dynamic, - true // clear the opposite triangular part - >::run(other.derived(), derived().nestedExpression()); -} - -/*************************************************************************** -* Implementation of TriangularView methods -***************************************************************************/ - -/*************************************************************************** -* Implementation of MatrixBase methods -***************************************************************************/ - -#ifdef EIGEN2_SUPPORT - -// implementation of part<>(), including the SelfAdjoint case. - -namespace internal { -template<typename MatrixType, unsigned int Mode> -struct eigen2_part_return_type -{ - typedef TriangularView<MatrixType, Mode> type; -}; - -template<typename MatrixType> -struct eigen2_part_return_type<MatrixType, SelfAdjoint> -{ - typedef SelfAdjointView<MatrixType, Upper> type; -}; -} - -/** \deprecated use MatrixBase::triangularView() */ -template<typename Derived> -template<unsigned int Mode> -const typename internal::eigen2_part_return_type<Derived, Mode>::type MatrixBase<Derived>::part() const -{ - return derived(); -} - -/** \deprecated use MatrixBase::triangularView() */ -template<typename Derived> -template<unsigned int Mode> -typename internal::eigen2_part_return_type<Derived, Mode>::type MatrixBase<Derived>::part() -{ - return derived(); -} -#endif - -/** - * \returns an expression of a triangular view extracted from the current matrix - * - * The parameter \a Mode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper, - * \c #Lower, \c #StrictlyLower, \c #UnitLower. - * - * Example: \include MatrixBase_extract.cpp - * Output: \verbinclude MatrixBase_extract.out - * - * \sa class TriangularView - */ -template<typename Derived> -template<unsigned int Mode> -typename MatrixBase<Derived>::template TriangularViewReturnType<Mode>::Type -MatrixBase<Derived>::triangularView() -{ - return derived(); -} - -/** This is the const version of MatrixBase::triangularView() */ -template<typename Derived> -template<unsigned int Mode> -typename MatrixBase<Derived>::template ConstTriangularViewReturnType<Mode>::Type -MatrixBase<Derived>::triangularView() const -{ - return derived(); -} - -/** \returns true if *this is approximately equal to an upper triangular matrix, - * within the precision given by \a prec. - * - * \sa isLowerTriangular() - */ -template<typename Derived> -bool MatrixBase<Derived>::isUpperTriangular(const RealScalar& prec) const -{ - using std::abs; - RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1); - for(Index j = 0; j < cols(); ++j) - { - Index maxi = (std::min)(j, rows()-1); - for(Index i = 0; i <= maxi; ++i) - { - RealScalar absValue = abs(coeff(i,j)); - if(absValue > maxAbsOnUpperPart) maxAbsOnUpperPart = absValue; - } - } - RealScalar threshold = maxAbsOnUpperPart * prec; - for(Index j = 0; j < cols(); ++j) - for(Index i = j+1; i < rows(); ++i) - if(abs(coeff(i, j)) > threshold) return false; - return true; -} - -/** \returns true if *this is approximately equal to a lower triangular matrix, - * within the precision given by \a prec. - * - * \sa isUpperTriangular() - */ -template<typename Derived> -bool MatrixBase<Derived>::isLowerTriangular(const RealScalar& prec) const -{ - using std::abs; - RealScalar maxAbsOnLowerPart = static_cast<RealScalar>(-1); - for(Index j = 0; j < cols(); ++j) - for(Index i = j; i < rows(); ++i) - { - RealScalar absValue = abs(coeff(i,j)); - if(absValue > maxAbsOnLowerPart) maxAbsOnLowerPart = absValue; - } - RealScalar threshold = maxAbsOnLowerPart * prec; - for(Index j = 1; j < cols(); ++j) - { - Index maxi = (std::min)(j, rows()-1); - for(Index i = 0; i < maxi; ++i) - if(abs(coeff(i, j)) > threshold) return false; - } - return true; -} - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULARMATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/VectorBlock.h b/third_party/eigen3/Eigen/src/Core/VectorBlock.h deleted file mode 100644 index 216c568c4f..0000000000 --- a/third_party/eigen3/Eigen/src/Core/VectorBlock.h +++ /dev/null @@ -1,97 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_VECTORBLOCK_H -#define EIGEN_VECTORBLOCK_H - -namespace Eigen { - -/** \class VectorBlock - * \ingroup Core_Module - * - * \brief Expression of a fixed-size or dynamic-size sub-vector - * - * \param VectorType the type of the object in which we are taking a sub-vector - * \param Size size of the sub-vector we are taking at compile time (optional) - * - * This class represents an expression of either a fixed-size or dynamic-size sub-vector. - * It is the return type of DenseBase::segment(Index,Index) and DenseBase::segment<int>(Index) and - * most of the time this is the only way it is used. - * - * However, if you want to directly maniputate sub-vector expressions, - * for instance if you want to write a function returning such an expression, you - * will need to use this class. - * - * Here is an example illustrating the dynamic case: - * \include class_VectorBlock.cpp - * Output: \verbinclude class_VectorBlock.out - * - * \note Even though this expression has dynamic size, in the case where \a VectorType - * has fixed size, this expression inherits a fixed maximal size which means that evaluating - * it does not cause a dynamic memory allocation. - * - * Here is an example illustrating the fixed-size case: - * \include class_FixedVectorBlock.cpp - * Output: \verbinclude class_FixedVectorBlock.out - * - * \sa class Block, DenseBase::segment(Index,Index,Index,Index), DenseBase::segment(Index,Index) - */ - -namespace internal { -template<typename VectorType, int Size> -struct traits<VectorBlock<VectorType, Size> > - : public traits<Block<VectorType, - traits<VectorType>::Flags & RowMajorBit ? 1 : Size, - traits<VectorType>::Flags & RowMajorBit ? Size : 1> > -{ -}; -} - -template<typename VectorType, int Size> class VectorBlock - : public Block<VectorType, - internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size, - internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1> -{ - typedef Block<VectorType, - internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size, - internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1> Base; - enum { - IsColVector = !(internal::traits<VectorType>::Flags & RowMajorBit) - }; - public: - EIGEN_DENSE_PUBLIC_INTERFACE(VectorBlock) - - using Base::operator=; - - /** Dynamic-size constructor - */ - EIGEN_DEVICE_FUNC - inline VectorBlock(VectorType& vector, Index start, Index size) - : Base(vector, - IsColVector ? start : 0, IsColVector ? 0 : start, - IsColVector ? size : 1, IsColVector ? 1 : size) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock); - } - - /** Fixed-size constructor - */ - EIGEN_DEVICE_FUNC - inline VectorBlock(VectorType& vector, Index start) - : Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock); - } -}; - - -} // end namespace Eigen - -#endif // EIGEN_VECTORBLOCK_H diff --git a/third_party/eigen3/Eigen/src/Core/VectorwiseOp.h b/third_party/eigen3/Eigen/src/Core/VectorwiseOp.h deleted file mode 100644 index f25ddca174..0000000000 --- a/third_party/eigen3/Eigen/src/Core/VectorwiseOp.h +++ /dev/null @@ -1,651 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PARTIAL_REDUX_H -#define EIGEN_PARTIAL_REDUX_H - -namespace Eigen { - -/** \class PartialReduxExpr - * \ingroup Core_Module - * - * \brief Generic expression of a partially reduxed matrix - * - * \tparam MatrixType the type of the matrix we are applying the redux operation - * \tparam MemberOp type of the member functor - * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal) - * - * This class represents an expression of a partial redux operator of a matrix. - * It is the return type of some VectorwiseOp functions, - * and most of the time this is the only way it is used. - * - * \sa class VectorwiseOp - */ - -template< typename MatrixType, typename MemberOp, int Direction> -class PartialReduxExpr; - -namespace internal { -template<typename MatrixType, typename MemberOp, int Direction> -struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> > - : traits<MatrixType> -{ - typedef typename MemberOp::result_type Scalar; - typedef typename traits<MatrixType>::StorageKind StorageKind; - typedef typename traits<MatrixType>::XprKind XprKind; - typedef typename MatrixType::Scalar InputScalar; - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested; - enum { - RowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::RowsAtCompileTime, - ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime, - MaxRowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime, - Flags0 = (unsigned int)_MatrixTypeNested::Flags & HereditaryBits, - Flags = (Flags0 & ~RowMajorBit) | (RowsAtCompileTime == 1 ? RowMajorBit : 0), - TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime : MatrixType::ColsAtCompileTime - }; - #if EIGEN_GNUC_AT_LEAST(3,4) - typedef typename MemberOp::template Cost<InputScalar,int(TraversalSize)> CostOpType; - #else - typedef typename MemberOp::template Cost<InputScalar,TraversalSize> CostOpType; - #endif - enum { - CoeffReadCost = TraversalSize==Dynamic ? Dynamic - : TraversalSize * traits<_MatrixTypeNested>::CoeffReadCost + int(CostOpType::value) - }; -}; -} - -template< typename MatrixType, typename MemberOp, int Direction> -class PartialReduxExpr : internal::no_assignment_operator, - public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type -{ - public: - - typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base; - EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr) - typedef typename internal::traits<PartialReduxExpr>::MatrixTypeNested MatrixTypeNested; - typedef typename internal::traits<PartialReduxExpr>::_MatrixTypeNested _MatrixTypeNested; - - PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp()) - : m_matrix(mat), m_functor(func) {} - - Index rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); } - Index cols() const { return (Direction==Horizontal ? 1 : m_matrix.cols()); } - - EIGEN_STRONG_INLINE const Scalar coeff(Index i, Index j) const - { - if (Direction==Vertical) - return m_functor(m_matrix.col(j)); - else - return m_functor(m_matrix.row(i)); - } - - const Scalar coeff(Index index) const - { - if (Direction==Vertical) - return m_functor(m_matrix.col(index)); - else - return m_functor(m_matrix.row(index)); - } - - protected: - MatrixTypeNested m_matrix; - const MemberOp m_functor; -}; - -#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \ - template <typename ResultType> \ - struct member_##MEMBER { \ - EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER) \ - typedef ResultType result_type; \ - template<typename Scalar, int Size> struct Cost \ - { enum { value = COST }; }; \ - template<typename XprType> \ - EIGEN_STRONG_INLINE ResultType operator()(const XprType& mat) const \ - { return mat.MEMBER(); } \ - } - -namespace internal { - -EIGEN_MEMBER_FUNCTOR(squaredNorm, Size * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits<scalar_hypot_op<Scalar> >::Cost ); -EIGEN_MEMBER_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(mean, (Size-1)*NumTraits<Scalar>::AddCost + NumTraits<Scalar>::MulCost); -EIGEN_MEMBER_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost); -EIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost); - - -template <typename BinaryOp, typename Scalar> -struct member_redux { - typedef typename result_of< - BinaryOp(Scalar) - >::type result_type; - template<typename _Scalar, int Size> struct Cost - { enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; }; - member_redux(const BinaryOp func) : m_functor(func) {} - template<typename Derived> - inline result_type operator()(const DenseBase<Derived>& mat) const - { return mat.redux(m_functor); } - const BinaryOp m_functor; -}; -} - -/** \class VectorwiseOp - * \ingroup Core_Module - * - * \brief Pseudo expression providing partial reduction operations - * - * \param ExpressionType the type of the object on which to do partial reductions - * \param Direction indicates the direction of the redux (#Vertical or #Horizontal) - * - * This class represents a pseudo expression with partial reduction features. - * It is the return type of DenseBase::colwise() and DenseBase::rowwise() - * and most of the time this is the only way it is used. - * - * Example: \include MatrixBase_colwise.cpp - * Output: \verbinclude MatrixBase_colwise.out - * - * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr - */ -template<typename ExpressionType, int Direction> class VectorwiseOp -{ - public: - - typedef typename ExpressionType::Scalar Scalar; - typedef typename ExpressionType::RealScalar RealScalar; - typedef typename ExpressionType::Index Index; - typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret, - ExpressionType, ExpressionType&>::type ExpressionTypeNested; - typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned; - - template<template<typename _Scalar> class Functor, - typename Scalar=typename internal::traits<ExpressionType>::Scalar> struct ReturnType - { - typedef PartialReduxExpr<ExpressionType, - Functor<Scalar>, - Direction - > Type; - }; - - template<typename BinaryOp> struct ReduxReturnType - { - typedef PartialReduxExpr<ExpressionType, - internal::member_redux<BinaryOp,typename internal::traits<ExpressionType>::Scalar>, - Direction - > Type; - }; - - enum { - IsVertical = (Direction==Vertical) ? 1 : 0, - IsHorizontal = (Direction==Horizontal) ? 1 : 0 - }; - - protected: - - /** \internal - * \returns the i-th subvector according to the \c Direction */ - typedef typename internal::conditional<Direction==Vertical, - typename ExpressionType::ColXpr, - typename ExpressionType::RowXpr>::type SubVector; - SubVector subVector(Index i) - { - return SubVector(m_matrix.derived(),i); - } - - /** \internal - * \returns the number of subvectors in the direction \c Direction */ - Index subVectors() const - { return Direction==Vertical?m_matrix.cols():m_matrix.rows(); } - - template<typename OtherDerived> struct ExtendedType { - typedef Replicate<OtherDerived, - Direction==Vertical ? 1 : ExpressionType::RowsAtCompileTime, - Direction==Horizontal ? 1 : ExpressionType::ColsAtCompileTime> Type; - }; - - /** \internal - * Replicates a vector to match the size of \c *this */ - template<typename OtherDerived> - typename ExtendedType<OtherDerived>::Type - extendedTo(const DenseBase<OtherDerived>& other) const - { - EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, OtherDerived::MaxColsAtCompileTime==1), - YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED) - EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxRowsAtCompileTime==1), - YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED) - return typename ExtendedType<OtherDerived>::Type - (other.derived(), - Direction==Vertical ? 1 : m_matrix.rows(), - Direction==Horizontal ? 1 : m_matrix.cols()); - } - - template<typename OtherDerived> struct OppositeExtendedType { - typedef Replicate<OtherDerived, - Direction==Horizontal ? 1 : ExpressionType::RowsAtCompileTime, - Direction==Vertical ? 1 : ExpressionType::ColsAtCompileTime> Type; - }; - - /** \internal - * Replicates a vector in the opposite direction to match the size of \c *this */ - template<typename OtherDerived> - typename OppositeExtendedType<OtherDerived>::Type - extendedToOpposite(const DenseBase<OtherDerived>& other) const - { - EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxColsAtCompileTime==1), - YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED) - EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, OtherDerived::MaxRowsAtCompileTime==1), - YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED) - return typename OppositeExtendedType<OtherDerived>::Type - (other.derived(), - Direction==Horizontal ? 1 : m_matrix.rows(), - Direction==Vertical ? 1 : m_matrix.cols()); - } - - public: - - inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {} - - /** \internal */ - inline const ExpressionType& _expression() const { return m_matrix; } - - /** \returns a row or column vector expression of \c *this reduxed by \a func - * - * The template parameter \a BinaryOp is the type of the functor - * of the custom redux operator. Note that func must be an associative operator. - * - * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise() - */ - template<typename BinaryOp> - const typename ReduxReturnType<BinaryOp>::Type - redux(const BinaryOp& func = BinaryOp()) const - { return typename ReduxReturnType<BinaryOp>::Type(_expression(), func); } - - /** \returns a row (or column) vector expression of the smallest coefficient - * of each column (or row) of the referenced expression. - * - * \warning the result is undefined if \c *this contains NaN. - * - * Example: \include PartialRedux_minCoeff.cpp - * Output: \verbinclude PartialRedux_minCoeff.out - * - * \sa DenseBase::minCoeff() */ - const typename ReturnType<internal::member_minCoeff>::Type minCoeff() const - { return _expression(); } - - /** \returns a row (or column) vector expression of the largest coefficient - * of each column (or row) of the referenced expression. - * - * \warning the result is undefined if \c *this contains NaN. - * - * Example: \include PartialRedux_maxCoeff.cpp - * Output: \verbinclude PartialRedux_maxCoeff.out - * - * \sa DenseBase::maxCoeff() */ - const typename ReturnType<internal::member_maxCoeff>::Type maxCoeff() const - { return _expression(); } - - /** \returns a row (or column) vector expression of the squared norm - * of each column (or row) of the referenced expression. - * This is a vector with real entries, even if the original matrix has complex entries. - * - * Example: \include PartialRedux_squaredNorm.cpp - * Output: \verbinclude PartialRedux_squaredNorm.out - * - * \sa DenseBase::squaredNorm() */ - const typename ReturnType<internal::member_squaredNorm,RealScalar>::Type squaredNorm() const - { return _expression(); } - - /** \returns a row (or column) vector expression of the norm - * of each column (or row) of the referenced expression. - * This is a vector with real entries, even if the original matrix has complex entries. - * - * Example: \include PartialRedux_norm.cpp - * Output: \verbinclude PartialRedux_norm.out - * - * \sa DenseBase::norm() */ - const typename ReturnType<internal::member_norm,RealScalar>::Type norm() const - { return _expression(); } - - - /** \returns a row (or column) vector expression of the norm - * of each column (or row) of the referenced expression, using - * Blue's algorithm. - * This is a vector with real entries, even if the original matrix has complex entries. - * - * \sa DenseBase::blueNorm() */ - const typename ReturnType<internal::member_blueNorm,RealScalar>::Type blueNorm() const - { return _expression(); } - - - /** \returns a row (or column) vector expression of the norm - * of each column (or row) of the referenced expression, avoiding - * underflow and overflow. - * This is a vector with real entries, even if the original matrix has complex entries. - * - * \sa DenseBase::stableNorm() */ - const typename ReturnType<internal::member_stableNorm,RealScalar>::Type stableNorm() const - { return _expression(); } - - - /** \returns a row (or column) vector expression of the norm - * of each column (or row) of the referenced expression, avoiding - * underflow and overflow using a concatenation of hypot() calls. - * This is a vector with real entries, even if the original matrix has complex entries. - * - * \sa DenseBase::hypotNorm() */ - const typename ReturnType<internal::member_hypotNorm,RealScalar>::Type hypotNorm() const - { return _expression(); } - - /** \returns a row (or column) vector expression of the sum - * of each column (or row) of the referenced expression. - * - * Example: \include PartialRedux_sum.cpp - * Output: \verbinclude PartialRedux_sum.out - * - * \sa DenseBase::sum() */ - const typename ReturnType<internal::member_sum>::Type sum() const - { return _expression(); } - - /** \returns a row (or column) vector expression of the mean - * of each column (or row) of the referenced expression. - * - * \sa DenseBase::mean() */ - const typename ReturnType<internal::member_mean>::Type mean() const - { return _expression(); } - - /** \returns a row (or column) vector expression representing - * whether \b all coefficients of each respective column (or row) are \c true. - * This expression can be assigned to a vector with entries of type \c bool. - * - * \sa DenseBase::all() */ - const typename ReturnType<internal::member_all>::Type all() const - { return _expression(); } - - /** \returns a row (or column) vector expression representing - * whether \b at \b least one coefficient of each respective column (or row) is \c true. - * This expression can be assigned to a vector with entries of type \c bool. - * - * \sa DenseBase::any() */ - const typename ReturnType<internal::member_any>::Type any() const - { return _expression(); } - - /** \returns a row (or column) vector expression representing - * the number of \c true coefficients of each respective column (or row). - * This expression can be assigned to a vector whose entries have the same type as is used to - * index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t . - * - * Example: \include PartialRedux_count.cpp - * Output: \verbinclude PartialRedux_count.out - * - * \sa DenseBase::count() */ - const PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> count() const - { return _expression(); } - - /** \returns a row (or column) vector expression of the product - * of each column (or row) of the referenced expression. - * - * Example: \include PartialRedux_prod.cpp - * Output: \verbinclude PartialRedux_prod.out - * - * \sa DenseBase::prod() */ - const typename ReturnType<internal::member_prod>::Type prod() const - { return _expression(); } - - - /** \returns a matrix expression - * where each column (or row) are reversed. - * - * Example: \include Vectorwise_reverse.cpp - * Output: \verbinclude Vectorwise_reverse.out - * - * \sa DenseBase::reverse() */ - const Reverse<ExpressionType, Direction> reverse() const - { return Reverse<ExpressionType, Direction>( _expression() ); } - - typedef Replicate<ExpressionType,Direction==Vertical?Dynamic:1,Direction==Horizontal?Dynamic:1> ReplicateReturnType; - const ReplicateReturnType replicate(Index factor) const; - - /** - * \return an expression of the replication of each column (or row) of \c *this - * - * Example: \include DirectionWise_replicate.cpp - * Output: \verbinclude DirectionWise_replicate.out - * - * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate - */ - // NOTE implemented here because of sunstudio's compilation errors - template<int Factor> const Replicate<ExpressionType,(IsVertical?Factor:1),(IsHorizontal?Factor:1)> - replicate(Index factor = Factor) const - { - return Replicate<ExpressionType,Direction==Vertical?Factor:1,Direction==Horizontal?Factor:1> - (_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1); - } - -/////////// Artithmetic operators /////////// - - /** Copies the vector \a other to each subvector of \c *this */ - template<typename OtherDerived> - ExpressionType& operator=(const DenseBase<OtherDerived>& other) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - //eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME - return const_cast<ExpressionType&>(m_matrix = extendedTo(other.derived())); - } - - /** Adds the vector \a other to each subvector of \c *this */ - template<typename OtherDerived> - ExpressionType& operator+=(const DenseBase<OtherDerived>& other) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - return const_cast<ExpressionType&>(m_matrix += extendedTo(other.derived())); - } - - /** Substracts the vector \a other to each subvector of \c *this */ - template<typename OtherDerived> - ExpressionType& operator-=(const DenseBase<OtherDerived>& other) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - return const_cast<ExpressionType&>(m_matrix -= extendedTo(other.derived())); - } - - /** Multiples each subvector of \c *this by the vector \a other */ - template<typename OtherDerived> - ExpressionType& operator*=(const DenseBase<OtherDerived>& other) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - m_matrix *= extendedTo(other.derived()); - return const_cast<ExpressionType&>(m_matrix); - } - - /** Divides each subvector of \c *this by the vector \a other */ - template<typename OtherDerived> - ExpressionType& operator/=(const DenseBase<OtherDerived>& other) - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - m_matrix /= extendedTo(other.derived()); - return const_cast<ExpressionType&>(m_matrix); - } - - /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */ - template<typename OtherDerived> EIGEN_STRONG_INLINE - CwiseBinaryOp<internal::scalar_sum_op<Scalar>, - const ExpressionTypeNestedCleaned, - const typename ExtendedType<OtherDerived>::Type> - operator+(const DenseBase<OtherDerived>& other) const - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - return m_matrix + extendedTo(other.derived()); - } - - /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */ - template<typename OtherDerived> - CwiseBinaryOp<internal::scalar_difference_op<Scalar>, - const ExpressionTypeNestedCleaned, - const typename ExtendedType<OtherDerived>::Type> - operator-(const DenseBase<OtherDerived>& other) const - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - return m_matrix - extendedTo(other.derived()); - } - - /** Returns the expression where each subvector is the product of the vector \a other - * by the corresponding subvector of \c *this */ - template<typename OtherDerived> EIGEN_STRONG_INLINE - CwiseBinaryOp<internal::scalar_product_op<Scalar>, - const ExpressionTypeNestedCleaned, - const typename ExtendedType<OtherDerived>::Type> - operator*(const DenseBase<OtherDerived>& other) const - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - return m_matrix * extendedTo(other.derived()); - } - - /** Returns the expression where each subvector is the quotient of the corresponding - * subvector of \c *this by the vector \a other */ - template<typename OtherDerived> - CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, - const ExpressionTypeNestedCleaned, - const typename ExtendedType<OtherDerived>::Type> - operator/(const DenseBase<OtherDerived>& other) const - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) - EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) - return m_matrix / extendedTo(other.derived()); - } - - /** \returns an expression where each column of row of the referenced matrix are normalized. - * The referenced matrix is \b not modified. - * \sa MatrixBase::normalized(), normalize() - */ - CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, - const ExpressionTypeNestedCleaned, - const typename OppositeExtendedType<typename ReturnType<internal::member_norm,RealScalar>::Type>::Type> - normalized() const { return m_matrix.cwiseQuotient(extendedToOpposite(this->norm())); } - - - /** Normalize in-place each row or columns of the referenced matrix. - * \sa MatrixBase::normalize(), normalized() - */ - void normalize() { - m_matrix = this->normalized(); - } - -/////////// Geometry module /////////// - - #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS - Homogeneous<ExpressionType,Direction> homogeneous() const; - #endif - - typedef typename ExpressionType::PlainObject CrossReturnType; - template<typename OtherDerived> - const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const; - - enum { - HNormalized_Size = Direction==Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime - : internal::traits<ExpressionType>::ColsAtCompileTime, - HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1 - }; - typedef Block<const ExpressionType, - Direction==Vertical ? int(HNormalized_SizeMinusOne) - : int(internal::traits<ExpressionType>::RowsAtCompileTime), - Direction==Horizontal ? int(HNormalized_SizeMinusOne) - : int(internal::traits<ExpressionType>::ColsAtCompileTime)> - HNormalized_Block; - typedef Block<const ExpressionType, - Direction==Vertical ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime), - Direction==Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)> - HNormalized_Factors; - typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>, - const HNormalized_Block, - const Replicate<HNormalized_Factors, - Direction==Vertical ? HNormalized_SizeMinusOne : 1, - Direction==Horizontal ? HNormalized_SizeMinusOne : 1> > - HNormalizedReturnType; - - const HNormalizedReturnType hnormalized() const; - - protected: - ExpressionTypeNested m_matrix; -}; - -/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations - * - * Example: \include MatrixBase_colwise.cpp - * Output: \verbinclude MatrixBase_colwise.out - * - * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting - */ -template<typename Derived> -inline const typename DenseBase<Derived>::ConstColwiseReturnType -DenseBase<Derived>::colwise() const -{ - return derived(); -} - -/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations - * - * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting - */ -template<typename Derived> -inline typename DenseBase<Derived>::ColwiseReturnType -DenseBase<Derived>::colwise() -{ - return derived(); -} - -/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations - * - * Example: \include MatrixBase_rowwise.cpp - * Output: \verbinclude MatrixBase_rowwise.out - * - * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting - */ -template<typename Derived> -inline const typename DenseBase<Derived>::ConstRowwiseReturnType -DenseBase<Derived>::rowwise() const -{ - return derived(); -} - -/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations - * - * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting - */ -template<typename Derived> -inline typename DenseBase<Derived>::RowwiseReturnType -DenseBase<Derived>::rowwise() -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_PARTIAL_REDUX_H diff --git a/third_party/eigen3/Eigen/src/Core/Visitor.h b/third_party/eigen3/Eigen/src/Core/Visitor.h deleted file mode 100644 index 64867b7a2c..0000000000 --- a/third_party/eigen3/Eigen/src/Core/Visitor.h +++ /dev/null @@ -1,237 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_VISITOR_H -#define EIGEN_VISITOR_H - -namespace Eigen { - -namespace internal { - -template<typename Visitor, typename Derived, int UnrollCount> -struct visitor_impl -{ - enum { - col = (UnrollCount-1) / Derived::RowsAtCompileTime, - row = (UnrollCount-1) % Derived::RowsAtCompileTime - }; - - static inline void run(const Derived &mat, Visitor& visitor) - { - visitor_impl<Visitor, Derived, UnrollCount-1>::run(mat, visitor); - visitor(mat.coeff(row, col), row, col); - } -}; - -template<typename Visitor, typename Derived> -struct visitor_impl<Visitor, Derived, 1> -{ - static inline void run(const Derived &mat, Visitor& visitor) - { - return visitor.init(mat.coeff(0, 0), 0, 0); - } -}; - -template<typename Visitor, typename Derived> -struct visitor_impl<Visitor, Derived, Dynamic> -{ - typedef typename Derived::Index Index; - static inline void run(const Derived& mat, Visitor& visitor) - { - visitor.init(mat.coeff(0,0), 0, 0); - for(Index i = 1; i < mat.rows(); ++i) - visitor(mat.coeff(i, 0), i, 0); - for(Index j = 1; j < mat.cols(); ++j) - for(Index i = 0; i < mat.rows(); ++i) - visitor(mat.coeff(i, j), i, j); - } -}; - -} // end namespace internal - -/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector. - * - * The template parameter \a Visitor is the type of the visitor and provides the following interface: - * \code - * struct MyVisitor { - * // called for the first coefficient - * void init(const Scalar& value, Index i, Index j); - * // called for all other coefficients - * void operator() (const Scalar& value, Index i, Index j); - * }; - * \endcode - * - * \note compared to one or two \em for \em loops, visitors offer automatic - * unrolling for small fixed size matrix. - * - * \sa minCoeff(Index*,Index*), maxCoeff(Index*,Index*), DenseBase::redux() - */ -template<typename Derived> -template<typename Visitor> -void DenseBase<Derived>::visit(Visitor& visitor) const -{ - enum { unroll = SizeAtCompileTime != Dynamic - && CoeffReadCost != Dynamic - && (SizeAtCompileTime == 1 || internal::functor_traits<Visitor>::Cost != Dynamic) - && SizeAtCompileTime * CoeffReadCost + (SizeAtCompileTime-1) * internal::functor_traits<Visitor>::Cost - <= EIGEN_UNROLLING_LIMIT }; - return internal::visitor_impl<Visitor, Derived, - unroll ? int(SizeAtCompileTime) : Dynamic - >::run(derived(), visitor); -} - -namespace internal { - -/** \internal - * \brief Base class to implement min and max visitors - */ -template <typename Derived> -struct coeff_visitor -{ - typedef typename Derived::Index Index; - typedef typename Derived::Scalar Scalar; - Index row, col; - Scalar res; - inline void init(const Scalar& value, Index i, Index j) - { - res = value; - row = i; - col = j; - } -}; - -/** \internal - * \brief Visitor computing the min coefficient with its value and coordinates - * - * \sa DenseBase::minCoeff(Index*, Index*) - */ -template <typename Derived> -struct min_coeff_visitor : coeff_visitor<Derived> -{ - typedef typename Derived::Index Index; - typedef typename Derived::Scalar Scalar; - void operator() (const Scalar& value, Index i, Index j) - { - if(value < this->res) - { - this->res = value; - this->row = i; - this->col = j; - } - } -}; - -template<typename Scalar> -struct functor_traits<min_coeff_visitor<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost - }; -}; - -/** \internal - * \brief Visitor computing the max coefficient with its value and coordinates - * - * \sa DenseBase::maxCoeff(Index*, Index*) - */ -template <typename Derived> -struct max_coeff_visitor : coeff_visitor<Derived> -{ - typedef typename Derived::Index Index; - typedef typename Derived::Scalar Scalar; - void operator() (const Scalar& value, Index i, Index j) - { - if(value > this->res) - { - this->res = value; - this->row = i; - this->col = j; - } - } -}; - -template<typename Scalar> -struct functor_traits<max_coeff_visitor<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost - }; -}; - -} // end namespace internal - -/** \returns the minimum of all coefficients of *this and puts in *row and *col its location. - * \warning the result is undefined if \c *this contains NaN. - * - * \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visitor(), DenseBase::minCoeff() - */ -template<typename Derived> -template<typename IndexType> -typename internal::traits<Derived>::Scalar -DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const -{ - internal::min_coeff_visitor<Derived> minVisitor; - this->visit(minVisitor); - *rowId = minVisitor.row; - if (colId) *colId = minVisitor.col; - return minVisitor.res; -} - -/** \returns the minimum of all coefficients of *this and puts in *index its location. - * \warning the result is undefined if \c *this contains NaN. - * - * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::minCoeff() - */ -template<typename Derived> -template<typename IndexType> -typename internal::traits<Derived>::Scalar -DenseBase<Derived>::minCoeff(IndexType* index) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - internal::min_coeff_visitor<Derived> minVisitor; - this->visit(minVisitor); - *index = (RowsAtCompileTime==1) ? minVisitor.col : minVisitor.row; - return minVisitor.res; -} - -/** \returns the maximum of all coefficients of *this and puts in *row and *col its location. - * \warning the result is undefined if \c *this contains NaN. - * - * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff() - */ -template<typename Derived> -template<typename IndexType> -typename internal::traits<Derived>::Scalar -DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const -{ - internal::max_coeff_visitor<Derived> maxVisitor; - this->visit(maxVisitor); - *rowPtr = maxVisitor.row; - if (colPtr) *colPtr = maxVisitor.col; - return maxVisitor.res; -} - -/** \returns the maximum of all coefficients of *this and puts in *index its location. - * \warning the result is undefined if \c *this contains NaN. - * - * \sa DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff() - */ -template<typename Derived> -template<typename IndexType> -typename internal::traits<Derived>::Scalar -DenseBase<Derived>::maxCoeff(IndexType* index) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - internal::max_coeff_visitor<Derived> maxVisitor; - this->visit(maxVisitor); - *index = (RowsAtCompileTime==1) ? maxVisitor.col : maxVisitor.row; - return maxVisitor.res; -} - -} // end namespace Eigen - -#endif // EIGEN_VISITOR_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/AVX/Complex.h b/third_party/eigen3/Eigen/src/Core/arch/AVX/Complex.h deleted file mode 100644 index e98c40e1f1..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/AVX/Complex.h +++ /dev/null @@ -1,463 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com) -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COMPLEX_AVX_H -#define EIGEN_COMPLEX_AVX_H - -namespace Eigen { - -namespace internal { - -//---------- float ---------- -struct Packet4cf -{ - EIGEN_STRONG_INLINE Packet4cf() {} - EIGEN_STRONG_INLINE explicit Packet4cf(const __m256& a) : v(a) {} - __m256 v; -}; - -template<> struct packet_traits<std::complex<float> > : default_packet_traits -{ - typedef Packet4cf type; - typedef Packet2cf half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size = 4, - HasHalfPacket = 1, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasDiv = 1, - HasNegate = 1, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasSetLinear = 0 - }; -}; - -template<> struct unpacket_traits<Packet4cf> { typedef std::complex<float> type; enum {size=4}; typedef Packet2cf half; }; - -template<> EIGEN_STRONG_INLINE Packet4cf padd<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_add_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet4cf psub<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_sub_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet4cf pnegate(const Packet4cf& a) -{ - return Packet4cf(pnegate(a.v)); -} -template<> EIGEN_STRONG_INLINE Packet4cf pconj(const Packet4cf& a) -{ - const __m256 mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000)); - return Packet4cf(_mm256_xor_ps(a.v,mask)); -} - -template<> EIGEN_STRONG_INLINE Packet4cf pmul<Packet4cf>(const Packet4cf& a, const Packet4cf& b) -{ - __m256 tmp1 = _mm256_mul_ps(_mm256_moveldup_ps(a.v), b.v); - __m256 tmp2 = _mm256_mul_ps(_mm256_movehdup_ps(a.v), _mm256_permute_ps(b.v, _MM_SHUFFLE(2,3,0,1))); - __m256 result = _mm256_addsub_ps(tmp1, tmp2); - return Packet4cf(result); -} - -template<> EIGEN_STRONG_INLINE Packet4cf pand <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_and_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet4cf por <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_or_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet4cf pxor <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_xor_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet4cf pandnot<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_andnot_ps(a.v,b.v)); } - -template<> EIGEN_STRONG_INLINE Packet4cf pload <Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet4cf(pload<Packet8f>(&numext::real_ref(*from))); } -template<> EIGEN_STRONG_INLINE Packet4cf ploadu<Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cf(ploadu<Packet8f>(&numext::real_ref(*from))); } - - -template<> EIGEN_STRONG_INLINE Packet4cf pset1<Packet4cf>(const std::complex<float>& from) -{ - return Packet4cf(_mm256_castpd_ps(_mm256_broadcast_sd((const double*)(const void*)&from))); -} - -template<> EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<float>* from) -{ - // FIXME The following might be optimized using _mm256_movedup_pd - Packet2cf a = ploaddup<Packet2cf>(from); - Packet2cf b = ploaddup<Packet2cf>(from+1); - return Packet4cf(_mm256_insertf128_ps(_mm256_castps128_ps256(a.v), b.v, 1)); -} - -template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); } -template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); } - -template<> EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packet4cf>(const std::complex<float>* from, int stride) -{ - return Packet4cf(_mm256_set_ps(std::imag(from[3*stride]), std::real(from[3*stride]), - std::imag(from[2*stride]), std::real(from[2*stride]), - std::imag(from[1*stride]), std::real(from[1*stride]), - std::imag(from[0*stride]), std::real(from[0*stride]))); -} - -template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from, int stride) -{ - __m128 low = _mm256_extractf128_ps(from.v, 0); - to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 0)), - _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1))); - to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 2)), - _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3))); - - __m128 high = _mm256_extractf128_ps(from.v, 1); - to[stride*2] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 0)), - _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1))); - to[stride*3] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 2)), - _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3))); - -} - -template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet4cf>(const Packet4cf& a) -{ - return pfirst(Packet2cf(_mm256_castps256_ps128(a.v))); -} - -template<> EIGEN_STRONG_INLINE Packet4cf preverse(const Packet4cf& a) { - __m128 low = _mm256_extractf128_ps(a.v, 0); - __m128 high = _mm256_extractf128_ps(a.v, 1); - __m128d lowd = _mm_castps_pd(low); - __m128d highd = _mm_castps_pd(high); - low = _mm_castpd_ps(_mm_shuffle_pd(lowd,lowd,0x1)); - high = _mm_castpd_ps(_mm_shuffle_pd(highd,highd,0x1)); - __m256 result = _mm256_setzero_ps(); - result = _mm256_insertf128_ps(result, low, 1); - result = _mm256_insertf128_ps(result, high, 0); - return Packet4cf(result); -} - -template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet4cf>(const Packet4cf& a) -{ - return predux(padd(Packet2cf(_mm256_extractf128_ps(a.v,0)), - Packet2cf(_mm256_extractf128_ps(a.v,1)))); -} - -template<> EIGEN_STRONG_INLINE Packet4cf preduxp<Packet4cf>(const Packet4cf* vecs) -{ - Packet8f t0 = _mm256_shuffle_ps(vecs[0].v, vecs[0].v, _MM_SHUFFLE(3, 1, 2 ,0)); - Packet8f t1 = _mm256_shuffle_ps(vecs[1].v, vecs[1].v, _MM_SHUFFLE(3, 1, 2 ,0)); - t0 = _mm256_hadd_ps(t0,t1); - Packet8f t2 = _mm256_shuffle_ps(vecs[2].v, vecs[2].v, _MM_SHUFFLE(3, 1, 2 ,0)); - Packet8f t3 = _mm256_shuffle_ps(vecs[3].v, vecs[3].v, _MM_SHUFFLE(3, 1, 2 ,0)); - t2 = _mm256_hadd_ps(t2,t3); - - t1 = _mm256_permute2f128_ps(t0,t2, 0 + (2<<4)); - t3 = _mm256_permute2f128_ps(t0,t2, 1 + (3<<4)); - - return Packet4cf(_mm256_add_ps(t1,t3)); -} - -template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet4cf>(const Packet4cf& a) -{ - return predux_mul(pmul(Packet2cf(_mm256_extractf128_ps(a.v, 0)), - Packet2cf(_mm256_extractf128_ps(a.v, 1)))); -} - -template<int Offset> -struct palign_impl<Offset,Packet4cf> -{ - static EIGEN_STRONG_INLINE void run(Packet4cf& first, const Packet4cf& second) - { - if (Offset==0) return; - palign_impl<Offset*2,Packet8f>::run(first.v, second.v); - } -}; - -template<> struct conj_helper<Packet4cf, Packet4cf, false,true> -{ - EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& x, const Packet4cf& y, const Packet4cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& a, const Packet4cf& b) const - { - return internal::pmul(a, pconj(b)); - } -}; - -template<> struct conj_helper<Packet4cf, Packet4cf, true,false> -{ - EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& x, const Packet4cf& y, const Packet4cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& a, const Packet4cf& b) const - { - return internal::pmul(pconj(a), b); - } -}; - -template<> struct conj_helper<Packet4cf, Packet4cf, true,true> -{ - EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& x, const Packet4cf& y, const Packet4cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& a, const Packet4cf& b) const - { - return pconj(internal::pmul(a, b)); - } -}; - -template<> struct conj_helper<Packet8f, Packet4cf, false,false> -{ - EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet8f& x, const Packet4cf& y, const Packet4cf& c) const - { return padd(c, pmul(x,y)); } - - EIGEN_STRONG_INLINE Packet4cf pmul(const Packet8f& x, const Packet4cf& y) const - { return Packet4cf(Eigen::internal::pmul(x, y.v)); } -}; - -template<> struct conj_helper<Packet4cf, Packet8f, false,false> -{ - EIGEN_STRONG_INLINE Packet4cf pmadd(const Packet4cf& x, const Packet8f& y, const Packet4cf& c) const - { return padd(c, pmul(x,y)); } - - EIGEN_STRONG_INLINE Packet4cf pmul(const Packet4cf& x, const Packet8f& y) const - { return Packet4cf(Eigen::internal::pmul(x.v, y)); } -}; - -template<> EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b) -{ - Packet4cf num = pmul(a, pconj(b)); - __m256 tmp = _mm256_mul_ps(b.v, b.v); - __m256 tmp2 = _mm256_shuffle_ps(tmp,tmp,0xB1); - __m256 denom = _mm256_add_ps(tmp, tmp2); - return Packet4cf(_mm256_div_ps(num.v, denom)); -} - -template<> EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& x) -{ - return Packet4cf(_mm256_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0 ,1))); -} - -//---------- double ---------- -struct Packet2cd -{ - EIGEN_STRONG_INLINE Packet2cd() {} - EIGEN_STRONG_INLINE explicit Packet2cd(const __m256d& a) : v(a) {} - __m256d v; -}; - -template<> struct packet_traits<std::complex<double> > : default_packet_traits -{ - typedef Packet2cd type; - typedef Packet1cd half; - enum { - Vectorizable = 1, - AlignedOnScalar = 0, - size = 2, - HasHalfPacket = 1, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasDiv = 1, - HasNegate = 1, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasSetLinear = 0 - }; -}; - -template<> struct unpacket_traits<Packet2cd> { typedef std::complex<double> type; enum {size=2}; typedef Packet1cd half; }; - -template<> EIGEN_STRONG_INLINE Packet2cd padd<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_add_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cd psub<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_sub_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cd pnegate(const Packet2cd& a) { return Packet2cd(pnegate(a.v)); } -template<> EIGEN_STRONG_INLINE Packet2cd pconj(const Packet2cd& a) -{ - const __m256d mask = _mm256_castsi256_pd(_mm256_set_epi32(0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0)); - return Packet2cd(_mm256_xor_pd(a.v,mask)); -} - -template<> EIGEN_STRONG_INLINE Packet2cd pmul<Packet2cd>(const Packet2cd& a, const Packet2cd& b) -{ - __m256d tmp1 = _mm256_shuffle_pd(a.v,a.v,0x0); - __m256d even = _mm256_mul_pd(tmp1, b.v); - __m256d tmp2 = _mm256_shuffle_pd(a.v,a.v,0xF); - __m256d tmp3 = _mm256_shuffle_pd(b.v,b.v,0x5); - __m256d odd = _mm256_mul_pd(tmp2, tmp3); - return Packet2cd(_mm256_addsub_pd(even, odd)); -} - -template<> EIGEN_STRONG_INLINE Packet2cd pand <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_and_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cd por <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_or_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cd pxor <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_xor_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cd pandnot<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_andnot_pd(a.v,b.v)); } - -template<> EIGEN_STRONG_INLINE Packet2cd pload <Packet2cd>(const std::complex<double>* from) -{ EIGEN_DEBUG_ALIGNED_LOAD return Packet2cd(pload<Packet4d>((const double*)from)); } -template<> EIGEN_STRONG_INLINE Packet2cd ploadu<Packet2cd>(const std::complex<double>* from) -{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cd(ploadu<Packet4d>((const double*)from)); } - -template<> EIGEN_STRONG_INLINE Packet2cd pset1<Packet2cd>(const std::complex<double>& from) -{ - // in case casting to a __m128d* is really not safe, then we can still fallback to this version: (much slower though) -// return Packet2cd(_mm256_loadu2_m128d((const double*)&from,(const double*)&from)); - return Packet2cd(_mm256_broadcast_pd((const __m128d*)(const void*)&from)); -} - -template<> EIGEN_STRONG_INLINE Packet2cd ploaddup<Packet2cd>(const std::complex<double>* from) { return pset1<Packet2cd>(*from); } - -template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet2cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); } -template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet2cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); } - -template<> EIGEN_DEVICE_FUNC inline Packet2cd pgather<std::complex<double>, Packet2cd>(const std::complex<double>* from, int stride) -{ - return Packet2cd(_mm256_set_pd(std::imag(from[1*stride]), std::real(from[1*stride]), - std::imag(from[0*stride]), std::real(from[0*stride]))); -} - -template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from, int stride) -{ - __m128d low = _mm256_extractf128_pd(from.v, 0); - to[stride*0] = std::complex<double>(_mm_cvtsd_f64(low), _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1))); - __m128d high = _mm256_extractf128_pd(from.v, 1); - to[stride*1] = std::complex<double>(_mm_cvtsd_f64(high), _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1))); -} - -template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet2cd>(const Packet2cd& a) -{ - __m128d low = _mm256_extractf128_pd(a.v, 0); - EIGEN_ALIGN16 double res[2]; - _mm_store_pd(res, low); - return std::complex<double>(res[0],res[1]); -} - -template<> EIGEN_STRONG_INLINE Packet2cd preverse(const Packet2cd& a) { - __m256d result = _mm256_permute2f128_pd(a.v, a.v, 1); - return Packet2cd(result); -} - -template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet2cd>(const Packet2cd& a) -{ - return predux(padd(Packet1cd(_mm256_extractf128_pd(a.v,0)), - Packet1cd(_mm256_extractf128_pd(a.v,1)))); -} - -template<> EIGEN_STRONG_INLINE Packet2cd preduxp<Packet2cd>(const Packet2cd* vecs) -{ - Packet4d t0 = _mm256_permute2f128_pd(vecs[0].v,vecs[1].v, 0 + (2<<4)); - Packet4d t1 = _mm256_permute2f128_pd(vecs[0].v,vecs[1].v, 1 + (3<<4)); - - return Packet2cd(_mm256_add_pd(t0,t1)); -} - -template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet2cd>(const Packet2cd& a) -{ - return predux(pmul(Packet1cd(_mm256_extractf128_pd(a.v,0)), - Packet1cd(_mm256_extractf128_pd(a.v,1)))); -} - -template<int Offset> -struct palign_impl<Offset,Packet2cd> -{ - static EIGEN_STRONG_INLINE void run(Packet2cd& first, const Packet2cd& second) - { - if (Offset==0) return; - palign_impl<Offset*2,Packet4d>::run(first.v, second.v); - } -}; - -template<> struct conj_helper<Packet2cd, Packet2cd, false,true> -{ - EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& x, const Packet2cd& y, const Packet2cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& a, const Packet2cd& b) const - { - return internal::pmul(a, pconj(b)); - } -}; - -template<> struct conj_helper<Packet2cd, Packet2cd, true,false> -{ - EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& x, const Packet2cd& y, const Packet2cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& a, const Packet2cd& b) const - { - return internal::pmul(pconj(a), b); - } -}; - -template<> struct conj_helper<Packet2cd, Packet2cd, true,true> -{ - EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& x, const Packet2cd& y, const Packet2cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& a, const Packet2cd& b) const - { - return pconj(internal::pmul(a, b)); - } -}; - -template<> struct conj_helper<Packet4d, Packet2cd, false,false> -{ - EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet4d& x, const Packet2cd& y, const Packet2cd& c) const - { return padd(c, pmul(x,y)); } - - EIGEN_STRONG_INLINE Packet2cd pmul(const Packet4d& x, const Packet2cd& y) const - { return Packet2cd(Eigen::internal::pmul(x, y.v)); } -}; - -template<> struct conj_helper<Packet2cd, Packet4d, false,false> -{ - EIGEN_STRONG_INLINE Packet2cd pmadd(const Packet2cd& x, const Packet4d& y, const Packet2cd& c) const - { return padd(c, pmul(x,y)); } - - EIGEN_STRONG_INLINE Packet2cd pmul(const Packet2cd& x, const Packet4d& y) const - { return Packet2cd(Eigen::internal::pmul(x.v, y)); } -}; - -template<> EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b) -{ - Packet2cd num = pmul(a, pconj(b)); - __m256d tmp = _mm256_mul_pd(b.v, b.v); - __m256d denom = _mm256_hadd_pd(tmp, tmp); - return Packet2cd(_mm256_div_pd(num.v, denom)); -} - -template<> EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& x) -{ - return Packet2cd(_mm256_shuffle_pd(x.v, x.v, 0x5)); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet4cf,4>& kernel) { - __m256d P0 = _mm256_castps_pd(kernel.packet[0].v); - __m256d P1 = _mm256_castps_pd(kernel.packet[1].v); - __m256d P2 = _mm256_castps_pd(kernel.packet[2].v); - __m256d P3 = _mm256_castps_pd(kernel.packet[3].v); - - __m256d T0 = _mm256_shuffle_pd(P0, P1, 15); - __m256d T1 = _mm256_shuffle_pd(P0, P1, 0); - __m256d T2 = _mm256_shuffle_pd(P2, P3, 15); - __m256d T3 = _mm256_shuffle_pd(P2, P3, 0); - - kernel.packet[1].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 32)); - kernel.packet[3].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 49)); - kernel.packet[0].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 32)); - kernel.packet[2].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 49)); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet2cd,2>& kernel) { - __m256d tmp = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 0+(2<<4)); - kernel.packet[1].v = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 1+(3<<4)); - kernel.packet[0].v = tmp; -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_COMPLEX_AVX_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h b/third_party/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h deleted file mode 100644 index faa5c79021..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/AVX/MathFunctions.h +++ /dev/null @@ -1,495 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com) -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MATH_FUNCTIONS_AVX_H -#define EIGEN_MATH_FUNCTIONS_AVX_H - -// For some reason, this function didn't make it into the avxintirn.h -// used by the compiler, so we'll just wrap it. -#define _mm256_setr_m128(lo, hi) \ - _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1) - -/* The sin, cos, exp, and log functions of this file are loosely derived from - * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/ - */ - -namespace Eigen { - -namespace internal { - -// Sine function -// Computes sin(x) by wrapping x to the interval [-Pi/4,3*Pi/4] and -// evaluating interpolants in [-Pi/4,Pi/4] or [Pi/4,3*Pi/4]. The interpolants -// are (anti-)symmetric and thus have only odd/even coefficients -template <> -EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f -psin<Packet8f>(const Packet8f& _x) { - Packet8f x = _x; - - // Some useful values. - _EIGEN_DECLARE_CONST_Packet8i(one, 1); - _EIGEN_DECLARE_CONST_Packet8f(one, 1.0f); - _EIGEN_DECLARE_CONST_Packet8f(two, 2.0f); - _EIGEN_DECLARE_CONST_Packet8f(one_over_four, 0.25f); - _EIGEN_DECLARE_CONST_Packet8f(one_over_pi, 3.183098861837907e-01f); - _EIGEN_DECLARE_CONST_Packet8f(neg_pi_first, -3.140625000000000e+00); - _EIGEN_DECLARE_CONST_Packet8f(neg_pi_second, -9.670257568359375e-04); - _EIGEN_DECLARE_CONST_Packet8f(neg_pi_third, -6.278329571784980e-07); - _EIGEN_DECLARE_CONST_Packet8f(four_over_pi, 1.273239544735163e+00); - - // Map x from [-Pi/4,3*Pi/4] to z in [-1,3] and subtract the shifted period. - Packet8f z = pmul(x, p8f_one_over_pi); - Packet8f shift = _mm256_floor_ps(padd(z, p8f_one_over_four)); - x = pmadd(shift, p8f_neg_pi_first, x); - x = pmadd(shift, p8f_neg_pi_second, x); - x = pmadd(shift, p8f_neg_pi_third, x); - z = pmul(x, p8f_four_over_pi); - - // Make a mask for the entries that need flipping, i.e. wherever the shift - // is odd. - Packet8i shift_ints = _mm256_cvtps_epi32(shift); - Packet8i shift_isodd = - (__m256i)_mm256_and_ps((__m256)shift_ints, (__m256)p8i_one); -#ifdef EIGEN_VECTORIZE_AVX2 - Packet8i sign_flip_mask = _mm256_slli_epi32(shift_isodd, 31); -#else - __m128i lo = - _mm_slli_epi32(_mm256_extractf128_si256((__m256i)shift_isodd, 0), 31); - __m128i hi = - _mm_slli_epi32(_mm256_extractf128_si256((__m256i)shift_isodd, 1), 31); - Packet8i sign_flip_mask = _mm256_setr_m128(lo, hi); -#endif - - // Create a mask for which interpolant to use, i.e. if z > 1, then the mask - // is set to ones for that entry. - Packet8f ival_mask = _mm256_cmp_ps(z, p8f_one, _CMP_GT_OQ); - - // Evaluate the polynomial for the interval [1,3] in z. - _EIGEN_DECLARE_CONST_Packet8f(coeff_right_0, 9.999999724233232e-01f); - _EIGEN_DECLARE_CONST_Packet8f(coeff_right_2, -3.084242535619928e-01); - _EIGEN_DECLARE_CONST_Packet8f(coeff_right_4, 1.584991525700324e-02); - _EIGEN_DECLARE_CONST_Packet8f(coeff_right_6, -3.188805084631342e-04); - Packet8f z_minus_two = psub(z, p8f_two); - Packet8f z_minus_two2 = pmul(z_minus_two, z_minus_two); - Packet8f right = pmadd(p8f_coeff_right_6, z_minus_two2, p8f_coeff_right_4); - right = pmadd(right, z_minus_two2, p8f_coeff_right_2); - right = pmadd(right, z_minus_two2, p8f_coeff_right_0); - - // Evaluate the polynomial for the interval [-1,1] in z. - _EIGEN_DECLARE_CONST_Packet8f(coeff_left_1, 7.853981525427295e-01); - _EIGEN_DECLARE_CONST_Packet8f(coeff_left_3, -8.074536727092352e-02); - _EIGEN_DECLARE_CONST_Packet8f(coeff_left_5, 2.489871967827018e-03); - _EIGEN_DECLARE_CONST_Packet8f(coeff_left_7, -3.587725841214251e-05); - Packet8f z2 = pmul(z, z); - Packet8f left = pmadd(p8f_coeff_left_7, z2, p8f_coeff_left_5); - left = pmadd(left, z2, p8f_coeff_left_3); - left = pmadd(left, z2, p8f_coeff_left_1); - left = pmul(left, z); - - // Assemble the results, i.e. select the left and right polynomials. - left = _mm256_andnot_ps(ival_mask, left); - right = _mm256_and_ps(ival_mask, right); - Packet8f res = _mm256_or_ps(left, right); - - // Flip the sign on the odd intervals and return the result. - res = _mm256_xor_ps(res, (__m256)sign_flip_mask); - return res; -} - -// Natural logarithm -// Computes log(x) as log(2^e * m) = C*e + log(m), where the constant C =log(2) -// and m is in the range [sqrt(1/2),sqrt(2)). In this range, the logarithm can -// be easily approximated by a polynomial centered on m=1 for stability. -// TODO(gonnet): Further reduce the interval allowing for lower-degree -// polynomial interpolants -> ... -> profit! -template <> -EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f -plog<Packet8f>(const Packet8f& _x) { - Packet8f x = _x; - _EIGEN_DECLARE_CONST_Packet8f(1, 1.0f); - _EIGEN_DECLARE_CONST_Packet8f(half, 0.5f); - _EIGEN_DECLARE_CONST_Packet8f(126f, 126.0f); - - _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(inv_mant_mask, ~0x7f800000); - - // The smallest non denormalized float number. - _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(min_norm_pos, 0x00800000); - _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(minus_inf, 0xff800000); - - // Polynomial coefficients. - _EIGEN_DECLARE_CONST_Packet8f(cephes_SQRTHF, 0.707106781186547524f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p0, 7.0376836292E-2f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p1, -1.1514610310E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p2, 1.1676998740E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p3, -1.2420140846E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p4, +1.4249322787E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p5, -1.6668057665E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p6, +2.0000714765E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p7, -2.4999993993E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_p8, +3.3333331174E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_q1, -2.12194440e-4f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_log_q2, 0.693359375f); - - // invalid_mask is set to true when x is NaN - Packet8f invalid_mask = _mm256_cmp_ps(x, _mm256_setzero_ps(), _CMP_NGE_UQ); - Packet8f iszero_mask = _mm256_cmp_ps(x, _mm256_setzero_ps(), _CMP_EQ_OQ); - - // Truncate input values to the minimum positive normal. - x = pmax(x, p8f_min_norm_pos); - -// Extract the shifted exponents (No bitwise shifting in regular AVX, so -// convert to SSE and do it there). -#ifdef EIGEN_VECTORIZE_AVX2 - Packet8f emm0 = _mm256_cvtepi32_ps(_mm256_srli_epi32((__m256i)x, 23)); -#else - __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256((__m256i)x, 0), 23); - __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256((__m256i)x, 1), 23); - Packet8f emm0 = _mm256_cvtepi32_ps(_mm256_setr_m128(lo, hi)); -#endif - Packet8f e = _mm256_sub_ps(emm0, p8f_126f); - - // Set the exponents to -1, i.e. x are in the range [0.5,1). - x = _mm256_and_ps(x, p8f_inv_mant_mask); - x = _mm256_or_ps(x, p8f_half); - - // part2: Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2)) - // and shift by -1. The values are then centered around 0, which improves - // the stability of the polynomial evaluation. - // if( x < SQRTHF ) { - // e -= 1; - // x = x + x - 1.0; - // } else { x = x - 1.0; } - Packet8f mask = _mm256_cmp_ps(x, p8f_cephes_SQRTHF, _CMP_LT_OQ); - Packet8f tmp = _mm256_and_ps(x, mask); - x = psub(x, p8f_1); - e = psub(e, _mm256_and_ps(p8f_1, mask)); - x = padd(x, tmp); - - Packet8f x2 = pmul(x, x); - Packet8f x3 = pmul(x2, x); - - // Evaluate the polynomial approximant of degree 8 in three parts, probably - // to improve instruction-level parallelism. - Packet8f y, y1, y2; - y = pmadd(p8f_cephes_log_p0, x, p8f_cephes_log_p1); - y1 = pmadd(p8f_cephes_log_p3, x, p8f_cephes_log_p4); - y2 = pmadd(p8f_cephes_log_p6, x, p8f_cephes_log_p7); - y = pmadd(y, x, p8f_cephes_log_p2); - y1 = pmadd(y1, x, p8f_cephes_log_p5); - y2 = pmadd(y2, x, p8f_cephes_log_p8); - y = pmadd(y, x3, y1); - y = pmadd(y, x3, y2); - y = pmul(y, x3); - - // Add the logarithm of the exponent back to the result of the interpolation. - y1 = pmul(e, p8f_cephes_log_q1); - tmp = pmul(x2, p8f_half); - y = padd(y, y1); - x = psub(x, tmp); - y2 = pmul(e, p8f_cephes_log_q2); - x = padd(x, y); - x = padd(x, y2); - - // Filter out invalid inputs, i.e. negative arg will be NAN, 0 will be -INF. - return _mm256_or_ps( - _mm256_andnot_ps(iszero_mask, _mm256_or_ps(x, invalid_mask)), - _mm256_and_ps(iszero_mask, p8f_minus_inf)); -} - -// Exponential function. Works by writing "x = m*log(2) + r" where -// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then -// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1). -template <> -EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f -pexp<Packet8f>(const Packet8f& _x) { - _EIGEN_DECLARE_CONST_Packet8f(1, 1.0f); - _EIGEN_DECLARE_CONST_Packet8f(half, 0.5f); - _EIGEN_DECLARE_CONST_Packet8f(127, 127.0f); - - _EIGEN_DECLARE_CONST_Packet8f(exp_hi, 88.3762626647950f); - _EIGEN_DECLARE_CONST_Packet8f(exp_lo, -88.3762626647949f); - - _EIGEN_DECLARE_CONST_Packet8f(cephes_LOG2EF, 1.44269504088896341f); - - _EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p0, 1.9875691500E-4f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p1, 1.3981999507E-3f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p2, 8.3334519073E-3f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p3, 4.1665795894E-2f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p4, 1.6666665459E-1f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_exp_p5, 5.0000001201E-1f); - - // Clamp x. - Packet8f x = pmax(pmin(_x, p8f_exp_hi), p8f_exp_lo); - - // Express exp(x) as exp(m*ln(2) + r), start by extracting - // m = floor(x/ln(2) + 0.5). - Packet8f m = _mm256_floor_ps(pmadd(x, p8f_cephes_LOG2EF, p8f_half)); - -// Get r = x - m*ln(2). If no FMA instructions are available, m*ln(2) is -// subtracted out in two parts, m*C1+m*C2 = m*ln(2), to avoid accumulating -// truncation errors. Note that we don't use the "pmadd" function here to -// ensure that a precision-preserving FMA instruction is used. -#ifdef EIGEN_VECTORIZE_FMA - _EIGEN_DECLARE_CONST_Packet8f(nln2, -0.6931471805599453f); - Packet8f r = _mm256_fmadd_ps(m, p8f_nln2, x); -#else - _EIGEN_DECLARE_CONST_Packet8f(cephes_exp_C1, 0.693359375f); - _EIGEN_DECLARE_CONST_Packet8f(cephes_exp_C2, -2.12194440e-4f); - Packet8f r = psub(x, pmul(m, p8f_cephes_exp_C1)); - r = psub(r, pmul(m, p8f_cephes_exp_C2)); -#endif - - Packet8f r2 = pmul(r, r); - - // TODO(gonnet): Split into odd/even polynomials and try to exploit - // instruction-level parallelism. - Packet8f y = p8f_cephes_exp_p0; - y = pmadd(y, r, p8f_cephes_exp_p1); - y = pmadd(y, r, p8f_cephes_exp_p2); - y = pmadd(y, r, p8f_cephes_exp_p3); - y = pmadd(y, r, p8f_cephes_exp_p4); - y = pmadd(y, r, p8f_cephes_exp_p5); - y = pmadd(y, r2, r); - y = padd(y, p8f_1); - - // Build emm0 = 2^m. - Packet8i emm0 = _mm256_cvttps_epi32(padd(m, p8f_127)); -#ifdef EIGEN_VECTORIZE_AVX2 - emm0 = _mm256_slli_epi32(emm0, 23); -#else - __m128i lo = _mm_slli_epi32(_mm256_extractf128_si256(emm0, 0), 23); - __m128i hi = _mm_slli_epi32(_mm256_extractf128_si256(emm0, 1), 23); - emm0 = _mm256_setr_m128(lo, hi); -#endif - - // Return 2^m * exp(r). - return pmax(pmul(y, _mm256_castsi256_ps(emm0)), _x); -} -template <> -EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet4d -pexp<Packet4d>(const Packet4d& _x) { - Packet4d x = _x; - - _EIGEN_DECLARE_CONST_Packet4d(1, 1.0); - _EIGEN_DECLARE_CONST_Packet4d(2, 2.0); - _EIGEN_DECLARE_CONST_Packet4d(half, 0.5); - - _EIGEN_DECLARE_CONST_Packet4d(exp_hi, 709.437); - _EIGEN_DECLARE_CONST_Packet4d(exp_lo, -709.436139303); - - _EIGEN_DECLARE_CONST_Packet4d(cephes_LOG2EF, 1.4426950408889634073599); - - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_p0, 1.26177193074810590878e-4); - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_p1, 3.02994407707441961300e-2); - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_p2, 9.99999999999999999910e-1); - - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_q0, 3.00198505138664455042e-6); - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_q1, 2.52448340349684104192e-3); - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_q2, 2.27265548208155028766e-1); - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_q3, 2.00000000000000000009e0); - - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_C1, 0.693145751953125); - _EIGEN_DECLARE_CONST_Packet4d(cephes_exp_C2, 1.42860682030941723212e-6); - _EIGEN_DECLARE_CONST_Packet4i(1023, 1023); - - Packet4d tmp, fx; - - // clamp x - x = pmax(pmin(x, p4d_exp_hi), p4d_exp_lo); - // Express exp(x) as exp(g + n*log(2)). - fx = pmadd(p4d_cephes_LOG2EF, x, p4d_half); - - // Get the integer modulus of log(2), i.e. the "n" described above. - fx = _mm256_floor_pd(fx); - - // Get the remainder modulo log(2), i.e. the "g" described above. Subtract - // n*log(2) out in two steps, i.e. n*C1 + n*C2, C1+C2=log2 to get the last - // digits right. - tmp = pmul(fx, p4d_cephes_exp_C1); - Packet4d z = pmul(fx, p4d_cephes_exp_C2); - x = psub(x, tmp); - x = psub(x, z); - - Packet4d x2 = pmul(x, x); - - // Evaluate the numerator polynomial of the rational interpolant. - Packet4d px = p4d_cephes_exp_p0; - px = pmadd(px, x2, p4d_cephes_exp_p1); - px = pmadd(px, x2, p4d_cephes_exp_p2); - px = pmul(px, x); - - // Evaluate the denominator polynomial of the rational interpolant. - Packet4d qx = p4d_cephes_exp_q0; - qx = pmadd(qx, x2, p4d_cephes_exp_q1); - qx = pmadd(qx, x2, p4d_cephes_exp_q2); - qx = pmadd(qx, x2, p4d_cephes_exp_q3); - - // I don't really get this bit, copied from the SSE2 routines, so... - // TODO(gonnet): Figure out what is going on here, perhaps find a better - // rational interpolant? - x = _mm256_div_pd(px, psub(qx, px)); - x = pmadd(p4d_2, x, p4d_1); - - // Build e=2^n by constructing the exponents in a 128-bit vector and - // shifting them to where they belong in double-precision values. - __m128i emm0 = _mm256_cvtpd_epi32(fx); - emm0 = _mm_add_epi32(emm0, p4i_1023); - emm0 = _mm_shuffle_epi32(emm0, _MM_SHUFFLE(3, 1, 2, 0)); - __m128i lo = _mm_slli_epi64(emm0, 52); - __m128i hi = _mm_slli_epi64(_mm_srli_epi64(emm0, 32), 52); - __m256i e = _mm256_insertf128_si256(_mm256_setzero_si256(), lo, 0); - e = _mm256_insertf128_si256(e, hi, 1); - - // Construct the result 2^n * exp(g) = e * x. The max is used to catch - // non-finite values in the input. - return pmax(pmul(x, Packet4d(e)), _x); -} - -// Functions for sqrt. -// The EIGEN_FAST_MATH version uses the _mm_rsqrt_ps approximation and one step -// of Newton's method, at a cost of 1-2 bits of precision as opposed to the -// exact solution. The main advantage of this approach is not just speed, but -// also the fact that it can be inlined and pipelined with other computations, -// further reducing its effective latency. -#if EIGEN_FAST_MATH -template <> -EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f -psqrt<Packet8f>(const Packet8f& _x) { - _EIGEN_DECLARE_CONST_Packet8f(one_point_five, 1.5f); - _EIGEN_DECLARE_CONST_Packet8f(minus_half, -0.5f); - _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(flt_min, 0x00800000); - - Packet8f neg_half = pmul(_x, p8f_minus_half); - - // select only the inverse sqrt of positive normal inputs (denormals are - // flushed to zero and cause infs as well). - Packet8f non_zero_mask = _mm256_cmp_ps(_x, p8f_flt_min, _CMP_GE_OQ); - Packet8f x = _mm256_and_ps(non_zero_mask, _mm256_rsqrt_ps(_x)); - - // Do a single step of Newton's iteration. - x = pmul(x, pmadd(neg_half, pmul(x, x), p8f_one_point_five)); - - // Multiply the original _x by it's reciprocal square root to extract the - // square root. - return pmul(_x, x); -} -#else -template <> -EIGEN_STRONG_INLINE Packet8f psqrt<Packet8f>(const Packet8f& x) { - return _mm256_sqrt_ps(x); -} -#endif -template <> -EIGEN_STRONG_INLINE Packet4d psqrt<Packet4d>(const Packet4d& x) { - return _mm256_sqrt_pd(x); -} - -// Functions for rsqrt. -// Almost identical to the sqrt routine, just leave out the last multiplication -// and fill in NaN/Inf where needed. Note that this function only exists as an -// iterative version since there is no instruction for diretly computing the -// reciprocal square root in AVX/AVX2 (there will be one in AVX-512). -#ifdef EIGEN_FAST_MATH -template <> -EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f -prsqrt<Packet8f>(const Packet8f& _x) { - _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(inf, 0x7f800000); - _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(nan, 0x7fc00000); - _EIGEN_DECLARE_CONST_Packet8f(one_point_five, 1.5f); - _EIGEN_DECLARE_CONST_Packet8f(minus_half, -0.5f); - _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(flt_min, 0x00800000); - - Packet8f neg_half = pmul(_x, p8f_minus_half); - - // select only the inverse sqrt of positive normal inputs (denormals are - // flushed to zero and cause infs as well). - Packet8f le_zero_mask = _mm256_cmp_ps(_x, p8f_flt_min, _CMP_LT_OQ); - Packet8f x = _mm256_andnot_ps(le_zero_mask, _mm256_rsqrt_ps(_x)); - - // Fill in NaNs and Infs for the negative/zero entries. - Packet8f neg_mask = _mm256_cmp_ps(_x, _mm256_setzero_ps(), _CMP_LT_OQ); - Packet8f zero_mask = _mm256_andnot_ps(neg_mask, le_zero_mask); - Packet8f infs_and_nans = _mm256_or_ps(_mm256_and_ps(neg_mask, p8f_nan), - _mm256_and_ps(zero_mask, p8f_inf)); - - // Do a single step of Newton's iteration. - x = pmul(x, pmadd(neg_half, pmul(x, x), p8f_one_point_five)); - - // Insert NaNs and Infs in all the right places. - return _mm256_or_ps(x, infs_and_nans); -} -#else -template <> -EIGEN_STRONG_INLINE Packet8f prsqrt<Packet8f>(const Packet8f& x) { - _EIGEN_DECLARE_CONST_Packet8f(one, 1.0f); - return _mm256_div_ps(p8f_one, _mm256_sqrt_ps(x)); -} -#endif -template <> -EIGEN_STRONG_INLINE Packet4d prsqrt<Packet4d>(const Packet4d& x) { - _EIGEN_DECLARE_CONST_Packet4d(one, 1.0); - return _mm256_div_pd(p4d_one, _mm256_sqrt_pd(x)); -} - -// Functions for division. -// The EIGEN_FAST_MATH version uses the _mm_rcp_ps approximation and one step of -// Newton's method, at a cost of 1-2 bits of precision as opposed to the exact -// solution. The main advantage of this approach is not just speed, but also the -// fact that it can be inlined and pipelined with other computations, further -// reducing its effective latency. -#if EIGEN_FAST_DIV -template <> -EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED Packet8f -pdiv<Packet8f>(const Packet8f& a, const Packet8f& b) { - _EIGEN_DECLARE_CONST_Packet8f(two, 2.0f); - _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(inf, 0x7f800000); - - Packet8f neg_b = pnegate(b); - - /* select only the inverse of non-zero b */ - Packet8f non_zero_mask = _mm256_cmp_ps(b, _mm256_setzero_ps(), _CMP_NEQ_OQ); - Packet8f x = _mm256_and_ps(non_zero_mask, _mm256_rcp_ps(b)); - - /* One step of Newton's method on b - x^-1 == 0. */ - x = pmul(x, pmadd(neg_b, x, p8f_two)); - - /* Return Infs wherever there were zeros. */ - return pmul(a, _mm256_or_ps(_mm256_and_ps(non_zero_mask, x), - _mm256_andnot_ps(non_zero_mask, p8f_inf))); -} -#else -template <> -EIGEN_STRONG_INLINE Packet8f -pdiv<Packet8f>(const Packet8f& a, const Packet8f& b) { - return _mm256_div_ps(a, b); -} -#endif -template <> -EIGEN_STRONG_INLINE Packet4d -pdiv<Packet4d>(const Packet4d& a, const Packet4d& b) { - return _mm256_div_pd(a, b); -} -template <> -EIGEN_STRONG_INLINE Packet8i -pdiv<Packet8i>(const Packet8i& /*a*/, const Packet8i& /*b*/) { - eigen_assert(false && "packet integer division are not supported by AVX"); - return pset1<Packet8i>(0); -} - -// Identical to the ptanh in GenericPacketMath.h, but for doubles use -// a small/medium approximation threshold of 0.001. -template<> EIGEN_STRONG_INLINE Packet4d ptanh_approx_threshold() { - return pset1<Packet4d>(0.001); -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_MATH_FUNCTIONS_AVX_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h b/third_party/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h deleted file mode 100644 index 03a7d5127c..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/AVX/PacketMath.h +++ /dev/null @@ -1,650 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com) -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PACKET_MATH_AVX_H -#define EIGEN_PACKET_MATH_AVX_H - -namespace Eigen { - -namespace internal { - -#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD -#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8 -#endif - -#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS -#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2*sizeof(void*)) -#endif - -#ifdef __FMA__ -#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD -#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD -#endif -#endif - -typedef __m256 Packet8f; -typedef __m256i Packet8i; -typedef __m256d Packet4d; - -template<> struct is_arithmetic<__m256> { enum { value = true }; }; -template<> struct is_arithmetic<__m256i> { enum { value = true }; }; -template<> struct is_arithmetic<__m256d> { enum { value = true }; }; - -#define _EIGEN_DECLARE_CONST_Packet8f(NAME,X) \ - const Packet8f p8f_##NAME = pset1<Packet8f>(X) - -#define _EIGEN_DECLARE_CONST_Packet8f_FROM_INT(NAME,X) \ - const Packet8f p8f_##NAME = (__m256)pset1<Packet8i>(X) - -#define _EIGEN_DECLARE_CONST_Packet8i(NAME,X) \ - const Packet8i p8i_##NAME = pset1<Packet8i>(X) - -#define _EIGEN_DECLARE_CONST_Packet4d(NAME,X) \ - const Packet4d p4d_##NAME = pset1<Packet4d>(X) - - -template<> struct packet_traits<float> : default_packet_traits -{ - typedef Packet8f type; - typedef Packet4f half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=8, - HasHalfPacket = 1, - - HasDiv = 1, - HasSin = 1, - HasCos = 0, - HasTanH = 1, - HasBlend = 1, - HasLog = 1, - HasExp = 1, - HasSqrt = 1, - HasRsqrt = 1, - HasSelect = 1, - HasEq = 1 - }; - }; -template<> struct packet_traits<double> : default_packet_traits -{ - typedef Packet4d type; - typedef Packet2d half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size = 4, - HasHalfPacket = 1, - - HasDiv = 1, - HasBlend = 1, - HasExp = 1, - HasSqrt = 1, - HasRsqrt = 1, - HasSelect = 1, - HasEq = 1, - }; -}; - -/* Proper support for integers is only provided by AVX2. In the meantime, we'll - use SSE instructions and packets to deal with integers. -template<> struct packet_traits<int> : default_packet_traits -{ - typedef Packet8i type; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=8 - }; -}; -*/ - -template<> struct unpacket_traits<Packet8f> { typedef float type; typedef Packet4f half; enum {size=8}; }; -template<> struct unpacket_traits<Packet4d> { typedef double type; typedef Packet2d half; enum {size=4}; }; -template<> struct unpacket_traits<Packet8i> { typedef int type; typedef Packet4i half; enum {size=8}; }; - -template<> EIGEN_STRONG_INLINE Packet8f pset1<Packet8f>(const float& from) { return _mm256_set1_ps(from); } -template<> EIGEN_STRONG_INLINE Packet4d pset1<Packet4d>(const double& from) { return _mm256_set1_pd(from); } -template<> EIGEN_STRONG_INLINE Packet8i pset1<Packet8i>(const int& from) { return _mm256_set1_epi32(from); } - -template<> EIGEN_STRONG_INLINE Packet8f pload1<Packet8f>(const float* from) { return _mm256_broadcast_ss(from); } -template<> EIGEN_STRONG_INLINE Packet4d pload1<Packet4d>(const double* from) { return _mm256_broadcast_sd(from); } - -template<> EIGEN_STRONG_INLINE Packet8f plset<float>(const float& a) { return _mm256_add_ps(_mm256_set1_ps(a), _mm256_set_ps(7.0,6.0,5.0,4.0,3.0,2.0,1.0,0.0)); } -template<> EIGEN_STRONG_INLINE Packet4d plset<double>(const double& a) { return _mm256_add_pd(_mm256_set1_pd(a), _mm256_set_pd(3.0,2.0,1.0,0.0)); } - -template<> EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_add_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d padd<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_add_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet8f psub<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_sub_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d psub<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_sub_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet8f ple<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_NGT_UQ); } -template<> EIGEN_STRONG_INLINE Packet4d ple<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_NGT_UQ); } - -template<> EIGEN_STRONG_INLINE Packet8f plt<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_NGE_UQ); } -template<> EIGEN_STRONG_INLINE Packet4d plt<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_NGE_UQ); } - -template<> EIGEN_STRONG_INLINE Packet8f peq<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_EQ_UQ); } -template<> EIGEN_STRONG_INLINE Packet4d peq<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_EQ_UQ); } - -template<> EIGEN_STRONG_INLINE Packet8f pselect<Packet8f>(const Packet8f& a, const Packet8f& b, const Packet8f& false_mask) { return _mm256_blendv_ps(a,b,false_mask); } -template<> EIGEN_STRONG_INLINE Packet4d pselect<Packet4d>(const Packet4d& a, const Packet4d& b, const Packet4d& false_mask) { return _mm256_blendv_pd(a,b,false_mask); } - -template<> EIGEN_STRONG_INLINE Packet8f pnegate(const Packet8f& a) -{ - return _mm256_sub_ps(_mm256_set1_ps(0.0),a); -} -template<> EIGEN_STRONG_INLINE Packet4d pnegate(const Packet4d& a) -{ - return _mm256_sub_pd(_mm256_set1_pd(0.0),a); -} - -template<> EIGEN_STRONG_INLINE Packet8f pconj(const Packet8f& a) { return a; } -template<> EIGEN_STRONG_INLINE Packet4d pconj(const Packet4d& a) { return a; } -template<> EIGEN_STRONG_INLINE Packet8i pconj(const Packet8i& a) { return a; } - -template<> EIGEN_STRONG_INLINE Packet8f pmul<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_mul_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d pmul<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_mul_pd(a,b); } - -#ifdef __FMA__ -template<> EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) { -#if EIGEN_GNUC_AT_MOST(4, 8) || EIGEN_COMP_CLANG - // clang stupidly generates a vfmadd213ps instruction plus some vmovaps on registers, - // and gcc stupidly generates a vfmadd132ps instruction, - // so let's enforce it to generate a vfmadd231ps instruction since the most common use case is to accumulate - // the result of the product. the issue has been fixed in gcc 4.9 - Packet8f res = c; - asm("vfmadd231ps %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b)); - return res; -#else - return _mm256_fmadd_ps(a,b,c); -#endif -} -template<> EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) { -#if EIGEN_GNUC_AT_MOST(4, 8) || EIGEN_COMP_CLANG - // see above - Packet4d res = c; - asm("vfmadd231pd %[a], %[b], %[c]" : [c] "+x" (res) : [a] "x" (a), [b] "x" (b)); - return res; -#else - return _mm256_fmadd_pd(a,b,c); -#endif -} -#endif - -template<> EIGEN_STRONG_INLINE Packet8f pmin<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_min_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d pmin<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_min_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet8f pmax<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_max_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d pmax<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_max_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet8f pand<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_and_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d pand<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_and_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet8f por<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_or_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d por<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_or_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet8f pxor<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_xor_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d pxor<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_xor_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet8f pandnot<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_andnot_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet4d pandnot<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_andnot_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet8f pload<Packet8f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_ps(from); } -template<> EIGEN_STRONG_INLINE Packet4d pload<Packet4d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_pd(from); } -template<> EIGEN_STRONG_INLINE Packet8i pload<Packet8i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from)); } - -template<> EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_ps(from); } -template<> EIGEN_STRONG_INLINE Packet4d ploadu<Packet4d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_pd(from); } -template<> EIGEN_STRONG_INLINE Packet8i ploadu<Packet8i>(const int* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from)); } - -// Loads 4 floats from memory a returns the packet {a0, a0 a1, a1, a2, a2, a3, a3} -template<> EIGEN_STRONG_INLINE Packet8f ploaddup<Packet8f>(const float* from) -{ - // TODO try to find a way to avoid the need of a temporary register -// Packet8f tmp = _mm256_castps128_ps256(_mm_loadu_ps(from)); -// tmp = _mm256_insertf128_ps(tmp, _mm_movehl_ps(_mm256_castps256_ps128(tmp),_mm256_castps256_ps128(tmp)), 1); -// return _mm256_unpacklo_ps(tmp,tmp); - - // _mm256_insertf128_ps is very slow on Haswell, thus: - Packet8f tmp = _mm256_broadcast_ps((const __m128*)(const void*)from); - // mimic an "inplace" permutation of the lower 128bits using a blend - tmp = _mm256_blend_ps(tmp,_mm256_castps128_ps256(_mm_permute_ps( _mm256_castps256_ps128(tmp), _MM_SHUFFLE(1,0,1,0))), 15); - // then we can perform a consistent permutation on the global register to get everything in shape: - return _mm256_permute_ps(tmp, _MM_SHUFFLE(3,3,2,2)); -} -// Loads 2 doubles from memory a returns the packet {a0, a0 a1, a1} -template<> EIGEN_STRONG_INLINE Packet4d ploaddup<Packet4d>(const double* from) -{ - Packet4d tmp = _mm256_broadcast_pd((const __m128d*)(const void*)from); - return _mm256_permute_pd(tmp, 3<<2); -} - -// Loads 2 floats from memory a returns the packet {a0, a0 a0, a0, a1, a1, a1, a1} -template<> EIGEN_STRONG_INLINE Packet8f ploadquad<Packet8f>(const float* from) -{ - Packet8f tmp = _mm256_castps128_ps256(_mm_broadcast_ss(from)); - return _mm256_insertf128_ps(tmp, _mm_broadcast_ss(from+1), 1); -} - -template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet8f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_ps(to, from); } -template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_pd(to, from); } -template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet8i& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); } - -template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet8f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_ps(to, from); } -template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_pd(to, from); } -template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet8i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); } - -// NOTE: leverage _mm256_i32gather_ps and _mm256_i32gather_pd if AVX2 instructions are available -template<> EIGEN_DEVICE_FUNC inline Packet8f pgather<float, Packet8f>(const float* from, int stride) -{ -#ifdef EIGEN_VECTORIZE_AVX2 - return _mm256_i32gather_ps(from, _mm256_set1_epi32(stride), 4); -#else - return _mm256_set_ps(from[7*stride], from[6*stride], from[5*stride], from[4*stride], - from[3*stride], from[2*stride], from[1*stride], from[0*stride]); -#endif -} -template<> EIGEN_DEVICE_FUNC inline Packet4d pgather<double, Packet4d>(const double* from, int stride) -{ -#ifdef EIGEN_VECTORIZE_AVX2 - return _mm256_i32gather_pd(from, _mm_set1_epi32(stride), 8); -#else - return _mm256_set_pd(from[3*stride], from[2*stride], from[1*stride], from[0*stride]); -#endif -} - -template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, const Packet8f& from, int stride) -{ - __m128 low = _mm256_extractf128_ps(from, 0); - to[stride*0] = _mm_cvtss_f32(low); - to[stride*1] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1)); - to[stride*2] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 2)); - to[stride*3] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3)); - - __m128 high = _mm256_extractf128_ps(from, 1); - to[stride*4] = _mm_cvtss_f32(high); - to[stride*5] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1)); - to[stride*6] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 2)); - to[stride*7] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3)); -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4d>(double* to, const Packet4d& from, int stride) -{ - __m128d low = _mm256_extractf128_pd(from, 0); - to[stride*0] = _mm_cvtsd_f64(low); - to[stride*1] = _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1)); - __m128d high = _mm256_extractf128_pd(from, 1); - to[stride*2] = _mm_cvtsd_f64(high); - to[stride*3] = _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1)); -} - -template<> EIGEN_STRONG_INLINE void pstore1<Packet8f>(float* to, const float& a) -{ - Packet8f pa = pset1<Packet8f>(a); - pstore(to, pa); -} -template<> EIGEN_STRONG_INLINE void pstore1<Packet4d>(double* to, const double& a) -{ - Packet4d pa = pset1<Packet4d>(a); - pstore(to, pa); -} -template<> EIGEN_STRONG_INLINE void pstore1<Packet8i>(int* to, const int& a) -{ - Packet8i pa = pset1<Packet8i>(a); - pstore(to, pa); -} - -template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); } -template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); } -template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); } - -template<> EIGEN_STRONG_INLINE float pfirst<Packet8f>(const Packet8f& a) { - return _mm_cvtss_f32(_mm256_castps256_ps128(a)); -} -template<> EIGEN_STRONG_INLINE double pfirst<Packet4d>(const Packet4d& a) { - return _mm_cvtsd_f64(_mm256_castpd256_pd128(a)); -} -template<> EIGEN_STRONG_INLINE int pfirst<Packet8i>(const Packet8i& a) { - return _mm_cvtsi128_si32(_mm256_castsi256_si128(a)); -} - - -template<> EIGEN_STRONG_INLINE Packet8f preverse(const Packet8f& a) -{ - __m256 tmp = _mm256_shuffle_ps(a,a,0x1b); - return _mm256_permute2f128_ps(tmp, tmp, 1); -} -template<> EIGEN_STRONG_INLINE Packet4d preverse(const Packet4d& a) -{ - __m256d tmp = _mm256_shuffle_pd(a,a,5); - return _mm256_permute2f128_pd(tmp, tmp, 1); - - __m256d swap_halves = _mm256_permute2f128_pd(a,a,1); - return _mm256_permute_pd(swap_halves,5); -} - -// pabs should be ok -template<> EIGEN_STRONG_INLINE Packet8f pabs(const Packet8f& a) -{ - const Packet8f mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF)); - return _mm256_and_ps(a,mask); -} -template<> EIGEN_STRONG_INLINE Packet4d pabs(const Packet4d& a) -{ - const Packet4d mask = _mm256_castsi256_pd(_mm256_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF)); - return _mm256_and_pd(a,mask); -} - -// preduxp should be ok -// FIXME: why is this ok? why isn't the simply implementation working as expected? -template<> EIGEN_STRONG_INLINE Packet8f preduxp<Packet8f>(const Packet8f* vecs) -{ - __m256 hsum1 = _mm256_hadd_ps(vecs[0], vecs[1]); - __m256 hsum2 = _mm256_hadd_ps(vecs[2], vecs[3]); - __m256 hsum3 = _mm256_hadd_ps(vecs[4], vecs[5]); - __m256 hsum4 = _mm256_hadd_ps(vecs[6], vecs[7]); - - __m256 hsum5 = _mm256_hadd_ps(hsum1, hsum1); - __m256 hsum6 = _mm256_hadd_ps(hsum2, hsum2); - __m256 hsum7 = _mm256_hadd_ps(hsum3, hsum3); - __m256 hsum8 = _mm256_hadd_ps(hsum4, hsum4); - - __m256 perm1 = _mm256_permute2f128_ps(hsum5, hsum5, 0x23); - __m256 perm2 = _mm256_permute2f128_ps(hsum6, hsum6, 0x23); - __m256 perm3 = _mm256_permute2f128_ps(hsum7, hsum7, 0x23); - __m256 perm4 = _mm256_permute2f128_ps(hsum8, hsum8, 0x23); - - __m256 sum1 = _mm256_add_ps(perm1, hsum5); - __m256 sum2 = _mm256_add_ps(perm2, hsum6); - __m256 sum3 = _mm256_add_ps(perm3, hsum7); - __m256 sum4 = _mm256_add_ps(perm4, hsum8); - - __m256 blend1 = _mm256_blend_ps(sum1, sum2, 0xcc); - __m256 blend2 = _mm256_blend_ps(sum3, sum4, 0xcc); - - __m256 final = _mm256_blend_ps(blend1, blend2, 0xf0); - return final; -} -template<> EIGEN_STRONG_INLINE Packet4d preduxp<Packet4d>(const Packet4d* vecs) -{ - Packet4d tmp0, tmp1; - - tmp0 = _mm256_hadd_pd(vecs[0], vecs[1]); - tmp0 = _mm256_add_pd(tmp0, _mm256_permute2f128_pd(tmp0, tmp0, 1)); - - tmp1 = _mm256_hadd_pd(vecs[2], vecs[3]); - tmp1 = _mm256_add_pd(tmp1, _mm256_permute2f128_pd(tmp1, tmp1, 1)); - - return _mm256_blend_pd(tmp0, tmp1, 0xC); -} - -template<> EIGEN_STRONG_INLINE float predux<Packet8f>(const Packet8f& a) -{ - Packet8f tmp0 = _mm256_hadd_ps(a,_mm256_permute2f128_ps(a,a,1)); - tmp0 = _mm256_hadd_ps(tmp0,tmp0); - return pfirst(_mm256_hadd_ps(tmp0, tmp0)); -} -template<> EIGEN_STRONG_INLINE double predux<Packet4d>(const Packet4d& a) -{ - Packet4d tmp0 = _mm256_hadd_pd(a,_mm256_permute2f128_pd(a,a,1)); - return pfirst(_mm256_hadd_pd(tmp0,tmp0)); -} - -template<> EIGEN_STRONG_INLINE Packet4f predux4<Packet8f>(const Packet8f& a) -{ - return _mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1)); -} - -template<> EIGEN_STRONG_INLINE float predux_mul<Packet8f>(const Packet8f& a) -{ - Packet8f tmp; - tmp = _mm256_mul_ps(a, _mm256_permute2f128_ps(a,a,1)); - tmp = _mm256_mul_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2))); - return pfirst(_mm256_mul_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1))); -} -template<> EIGEN_STRONG_INLINE double predux_mul<Packet4d>(const Packet4d& a) -{ - Packet4d tmp; - tmp = _mm256_mul_pd(a, _mm256_permute2f128_pd(a,a,1)); - return pfirst(_mm256_mul_pd(tmp, _mm256_shuffle_pd(tmp,tmp,1))); -} - -template<> EIGEN_STRONG_INLINE float predux_min<Packet8f>(const Packet8f& a) -{ - Packet8f tmp = _mm256_min_ps(a, _mm256_permute2f128_ps(a,a,1)); - tmp = _mm256_min_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2))); - return pfirst(_mm256_min_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1))); -} -template<> EIGEN_STRONG_INLINE double predux_min<Packet4d>(const Packet4d& a) -{ - Packet4d tmp = _mm256_min_pd(a, _mm256_permute2f128_pd(a,a,1)); - return pfirst(_mm256_min_pd(tmp, _mm256_shuffle_pd(tmp, tmp, 1))); -} - -template<> EIGEN_STRONG_INLINE float predux_max<Packet8f>(const Packet8f& a) -{ - Packet8f tmp = _mm256_max_ps(a, _mm256_permute2f128_ps(a,a,1)); - tmp = _mm256_max_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2))); - return pfirst(_mm256_max_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1))); -} - -template<> EIGEN_STRONG_INLINE double predux_max<Packet4d>(const Packet4d& a) -{ - Packet4d tmp = _mm256_max_pd(a, _mm256_permute2f128_pd(a,a,1)); - return pfirst(_mm256_max_pd(tmp, _mm256_shuffle_pd(tmp, tmp, 1))); -} - - -template<int Offset> -struct palign_impl<Offset,Packet8f> -{ - static EIGEN_STRONG_INLINE void run(Packet8f& first, const Packet8f& second) - { - if (Offset==1) - { - first = _mm256_blend_ps(first, second, 1); - Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(0,3,2,1)); - first = _mm256_blend_ps(tmp, _mm256_permute2f128_ps (tmp, tmp, 1), 0x88); - } - else if (Offset==2) - { - first = _mm256_blend_ps(first, second, 3); - Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(1,0,3,2)); - first = _mm256_blend_ps(tmp, _mm256_permute2f128_ps (tmp, tmp, 1), 0xcc); - } - else if (Offset==3) - { - first = _mm256_blend_ps(first, second, 7); - Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(2,1,0,3)); - first = _mm256_blend_ps(tmp, _mm256_permute2f128_ps (tmp, tmp, 1), 0xee); - } - else if (Offset==4) - { - first = _mm256_blend_ps(first, second, 15); - Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(3,2,1,0)); - first = _mm256_permute_ps(_mm256_permute2f128_ps (tmp, tmp, 1), _MM_SHUFFLE(3,2,1,0)); - } - else if (Offset==5) - { - first = _mm256_blend_ps(first, second, 31); - first = _mm256_permute2f128_ps(first, first, 1); - Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(0,3,2,1)); - first = _mm256_permute2f128_ps(tmp, tmp, 1); - first = _mm256_blend_ps(tmp, first, 0x88); - } - else if (Offset==6) - { - first = _mm256_blend_ps(first, second, 63); - first = _mm256_permute2f128_ps(first, first, 1); - Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(1,0,3,2)); - first = _mm256_permute2f128_ps(tmp, tmp, 1); - first = _mm256_blend_ps(tmp, first, 0xcc); - } - else if (Offset==7) - { - first = _mm256_blend_ps(first, second, 127); - first = _mm256_permute2f128_ps(first, first, 1); - Packet8f tmp = _mm256_permute_ps (first, _MM_SHUFFLE(2,1,0,3)); - first = _mm256_permute2f128_ps(tmp, tmp, 1); - first = _mm256_blend_ps(tmp, first, 0xee); - } - } -}; - -template<int Offset> -struct palign_impl<Offset,Packet4d> -{ - static EIGEN_STRONG_INLINE void run(Packet4d& first, const Packet4d& second) - { - if (Offset==1) - { - first = _mm256_blend_pd(first, second, 1); - __m256d tmp = _mm256_permute_pd(first, 5); - first = _mm256_permute2f128_pd(tmp, tmp, 1); - first = _mm256_blend_pd(tmp, first, 0xA); - } - else if (Offset==2) - { - first = _mm256_blend_pd(first, second, 3); - first = _mm256_permute2f128_pd(first, first, 1); - } - else if (Offset==3) - { - first = _mm256_blend_pd(first, second, 7); - __m256d tmp = _mm256_permute_pd(first, 5); - first = _mm256_permute2f128_pd(tmp, tmp, 1); - first = _mm256_blend_pd(tmp, first, 5); - } - } -}; - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet8f,8>& kernel) { - __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]); - __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]); - __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]); - __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]); - __m256 T4 = _mm256_unpacklo_ps(kernel.packet[4], kernel.packet[5]); - __m256 T5 = _mm256_unpackhi_ps(kernel.packet[4], kernel.packet[5]); - __m256 T6 = _mm256_unpacklo_ps(kernel.packet[6], kernel.packet[7]); - __m256 T7 = _mm256_unpackhi_ps(kernel.packet[6], kernel.packet[7]); - __m256 S0 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(1,0,1,0)); - __m256 S1 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(3,2,3,2)); - __m256 S2 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(1,0,1,0)); - __m256 S3 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(3,2,3,2)); - __m256 S4 = _mm256_shuffle_ps(T4,T6,_MM_SHUFFLE(1,0,1,0)); - __m256 S5 = _mm256_shuffle_ps(T4,T6,_MM_SHUFFLE(3,2,3,2)); - __m256 S6 = _mm256_shuffle_ps(T5,T7,_MM_SHUFFLE(1,0,1,0)); - __m256 S7 = _mm256_shuffle_ps(T5,T7,_MM_SHUFFLE(3,2,3,2)); - kernel.packet[0] = _mm256_permute2f128_ps(S0, S4, 0x20); - kernel.packet[1] = _mm256_permute2f128_ps(S1, S5, 0x20); - kernel.packet[2] = _mm256_permute2f128_ps(S2, S6, 0x20); - kernel.packet[3] = _mm256_permute2f128_ps(S3, S7, 0x20); - kernel.packet[4] = _mm256_permute2f128_ps(S0, S4, 0x31); - kernel.packet[5] = _mm256_permute2f128_ps(S1, S5, 0x31); - kernel.packet[6] = _mm256_permute2f128_ps(S2, S6, 0x31); - kernel.packet[7] = _mm256_permute2f128_ps(S3, S7, 0x31); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet8f,4>& kernel) { - __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]); - __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]); - __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]); - __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]); - - __m256 S0 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(1,0,1,0)); - __m256 S1 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(3,2,3,2)); - __m256 S2 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(1,0,1,0)); - __m256 S3 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(3,2,3,2)); - - kernel.packet[0] = _mm256_permute2f128_ps(S0, S1, 0x20); - kernel.packet[1] = _mm256_permute2f128_ps(S2, S3, 0x20); - kernel.packet[2] = _mm256_permute2f128_ps(S0, S1, 0x31); - kernel.packet[3] = _mm256_permute2f128_ps(S2, S3, 0x31); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet4d,4>& kernel) { - __m256d T0 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 15); - __m256d T1 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 0); - __m256d T2 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 15); - __m256d T3 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 0); - - kernel.packet[1] = _mm256_permute2f128_pd(T0, T2, 32); - kernel.packet[3] = _mm256_permute2f128_pd(T0, T2, 49); - kernel.packet[0] = _mm256_permute2f128_pd(T1, T3, 32); - kernel.packet[2] = _mm256_permute2f128_pd(T1, T3, 49); -} - -template<> EIGEN_STRONG_INLINE Packet8f pblend(const Selector<8>& ifPacket, const Packet8f& thenPacket, const Packet8f& elsePacket) { - const __m256 zero = _mm256_setzero_ps(); - const __m256 select = _mm256_set_ps(ifPacket.select[7], ifPacket.select[6], ifPacket.select[5], ifPacket.select[4], ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]); - __m256 false_mask = _mm256_cmp_ps(select, zero, _CMP_EQ_UQ); - return _mm256_blendv_ps(thenPacket, elsePacket, false_mask); -} -template<> EIGEN_STRONG_INLINE Packet4d pblend(const Selector<4>& ifPacket, const Packet4d& thenPacket, const Packet4d& elsePacket) { - const __m256d zero = _mm256_setzero_pd(); - const __m256d select = _mm256_set_pd(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]); - __m256d false_mask = _mm256_cmp_pd(select, zero, _CMP_EQ_UQ); - return _mm256_blendv_pd(thenPacket, elsePacket, false_mask); -} - -// Functions to print vectors of different types, makes debugging much easier. -namespace{ -void print4f(char* name, __m128 val) { - float temp[4] __attribute__((aligned(32))); - _mm_store_ps(temp, val); - printf("%s: ", name); - for (int k = 0; k < 4; k++) printf("%.8e ", temp[k]); - printf("\n"); -} -void print8f(char* name, __m256 val) { - float temp[8] __attribute__((aligned(32))); - _mm256_store_ps(temp, val); - printf("%s: ", name); - for (int k = 0; k < 8; k++) printf("%.8e ", temp[k]); - printf("\n"); -} -void print4i(char* name, __m128i val) { - int temp[4] __attribute__((aligned(32))); - _mm_store_si128((__m128i*)temp, val); - printf("%s: ", name); - for (int k = 0; k < 4; k++) printf("%i ", temp[k]); - printf("\n"); -} -void print8i(char* name, __m256i val) { - int temp[8] __attribute__((aligned(32))); - _mm256_store_si256((__m256i*)temp, val); - printf("%s: ", name); - for (int k = 0; k < 8; k++) printf("%i ", temp[k]); - printf("\n"); -} -void print8b(char* name, __m256i val) { - int temp[8] __attribute__((aligned(32))); - _mm256_store_si256((__m256i*)temp, val); - printf("%s: ", name); - for (int k = 0; k < 8; k++) printf("0x%08x ", temp[k]); - printf("\n"); -} -void print4d(char* name, __m256d val) { - double temp[4] __attribute__((aligned(32))); - _mm256_store_pd(temp, val); - printf("%s: ", name); - for (int k = 0; k < 4; k++) printf("%.16e ", temp[k]); - printf("\n"); -} -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PACKET_MATH_AVX_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h b/third_party/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h deleted file mode 100644 index 83bfdc604b..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/AVX/TypeCasting.h +++ /dev/null @@ -1,51 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TYPE_CASTING_AVX_H -#define EIGEN_TYPE_CASTING_AVX_H - -namespace Eigen { - -namespace internal { - -// For now we use SSE to handle integers, so we can't use AVX instructions to cast -// from int to float -template <> -struct type_casting_traits<float, int> { - enum { - VectorizedCast = 0, - SrcCoeffRatio = 1, - TgtCoeffRatio = 1 - }; -}; - -template <> -struct type_casting_traits<int, float> { - enum { - VectorizedCast = 0, - SrcCoeffRatio = 1, - TgtCoeffRatio = 1 - }; -}; - - - -template<> EIGEN_STRONG_INLINE Packet8i pcast<Packet8f, Packet8i>(const Packet8f& a) { - return _mm256_cvtps_epi32(a); -} - -template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8i, Packet8f>(const Packet8i& a) { - return _mm256_cvtepi32_ps(a); -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TYPE_CASTING_AVX_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h b/third_party/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h deleted file mode 100644 index 57df9508b3..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/AltiVec/Complex.h +++ /dev/null @@ -1,439 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COMPLEX32_ALTIVEC_H -#define EIGEN_COMPLEX32_ALTIVEC_H - - -namespace Eigen { - -namespace internal { - -static Packet4ui p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_ZERO_);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 }; -#ifdef EIGEN_VECTORIZE_VSX -#ifdef _BIG_ENDIAN -static Packet2ul p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 }; -static Packet2ul p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO, (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 }; -#else -static Packet2ul p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO, (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 }; -static Packet2ul p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 }; -#endif -#endif // EIGEN_VECTORIZE_VSX - -//---------- float ---------- -struct Packet2cf -{ - EIGEN_STRONG_INLINE Packet2cf() {} - EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {} - Packet4f v; -}; - -template<> struct packet_traits<std::complex<float> > : default_packet_traits -{ - typedef Packet2cf type; - typedef Packet2cf half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size = 2, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasDiv = 1, - HasNegate = 1, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasSetLinear = 0 - }; -}; - -template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type; enum {size=2}; typedef Packet2cf half; }; - -template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) -{ - Packet2cf res; - /* On AltiVec we cannot load 64-bit registers, so wa have to take care of alignment */ - if((ptrdiff_t(&from) % 16) == 0) - res.v = pload<Packet4f>((const float *)&from); - else - res.v = ploadu<Packet4f>((const float *)&from); - res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI); - return res; -} - -template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, int stride) -{ - std::complex<float> EIGEN_ALIGN16 af[2]; - af[0] = from[0*stride]; - af[1] = from[1*stride]; - return Packet2cf(vec_ld(0, (const float*)af)); -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, int stride) -{ - std::complex<float> EIGEN_ALIGN16 af[2]; - vec_st(from.v, 0, (float*)af); - to[0*stride] = af[0]; - to[1*stride] = af[1]; -} - - -template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_add(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_sub(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(a.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf((Packet4f)vec_xor((Packet4ui)a.v, p4ui_CONJ_XOR)); } - -template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - Packet4f v1, v2; - - // Permute and multiply the real parts of a and b - v1 = vec_perm(a.v, a.v, p16uc_PSET32_WODD); - // Get the imaginary parts of a - v2 = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN); - // multiply a_re * b - v1 = vec_madd(v1, b.v, p4f_ZERO); - // multiply a_im * b and get the conjugate result - v2 = vec_madd(v2, b.v, p4f_ZERO); - v2 = (Packet4f) vec_xor((Packet4ui)v2, p4ui_CONJ_XOR); - // permute back to a proper order - v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV); - - return Packet2cf(vec_add(v1, v2)); -} - -template<> EIGEN_STRONG_INLINE Packet2cf pand <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_and(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf por <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_or(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pxor <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_xor(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(vec_and(a.v, vec_nor(b.v,b.v))); } - -template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from)); } -template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from)); } - -template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) -{ - return pset1<Packet2cf>(*from); -} - -template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); } -template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); } - -#ifndef __VSX__ -template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { vec_dstt((float *)addr, DST_CTRL(2,2,32), DST_CHAN); } -#endif - -template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) -{ - std::complex<float> EIGEN_ALIGN16 res[2]; - pstore((float *)&res, a.v); - - return res[0]; -} - -template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) -{ - Packet4f rev_a; - rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2); - return Packet2cf(rev_a); -} - -template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) -{ - Packet4f b; - b = (Packet4f) vec_sld(a.v, a.v, 8); - b = padd(a.v, b); - return pfirst(Packet2cf(b)); -} - -template<> EIGEN_STRONG_INLINE Packet2cf preduxp<Packet2cf>(const Packet2cf* vecs) -{ - Packet4f b1, b2; -#ifdef _BIG_ENDIAN - b1 = (Packet4f) vec_sld(vecs[0].v, vecs[1].v, 8); - b2 = (Packet4f) vec_sld(vecs[1].v, vecs[0].v, 8); -#else - b1 = (Packet4f) vec_sld(vecs[1].v, vecs[0].v, 8); - b2 = (Packet4f) vec_sld(vecs[0].v, vecs[1].v, 8); -#endif - b2 = (Packet4f) vec_sld(b2, b2, 8); - b2 = padd(b1, b2); - - return Packet2cf(b2); -} - -template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) -{ - Packet4f b; - Packet2cf prod; - b = (Packet4f) vec_sld(a.v, a.v, 8); - prod = pmul(a, Packet2cf(b)); - - return pfirst(prod); -} - -template<int Offset> -struct palign_impl<Offset,Packet2cf> -{ - static EIGEN_STRONG_INLINE void run(Packet2cf& first, const Packet2cf& second) - { - if (Offset==1) - { -#ifdef _BIG_ENDIAN - first.v = vec_sld(first.v, second.v, 8); -#else - first.v = vec_sld(second.v, first.v, 8); -#endif - } - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, false,true> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - return internal::pmul(a, pconj(b)); - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, true,false> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - return internal::pmul(pconj(a), b); - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, true,true> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - return pconj(internal::pmul(a, b)); - } -}; - -template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - // TODO optimize it for AltiVec - Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b); - Packet4f s = vec_madd(b.v, b.v, p4f_ZERO); - return Packet2cf(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_COMPLEX32_REV)))); -} - -template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x) -{ - return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV)); -} - -template<> EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel) -{ - Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI); - kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO); - kernel.packet[0].v = tmp; -} - -//---------- double ---------- -#if defined(EIGEN_VECTORIZE_VSX) -struct Packet1cd -{ - EIGEN_STRONG_INLINE Packet1cd() {} - EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {} - Packet2d v; -}; - -template<> struct packet_traits<std::complex<double> > : default_packet_traits -{ - typedef Packet1cd type; - typedef Packet1cd half; - enum { - Vectorizable = 1, - AlignedOnScalar = 0, - size = 1, - HasHalfPacket = 0, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasDiv = 1, - HasNegate = 1, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasSetLinear = 0 - }; -}; - -template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1}; typedef Packet1cd half; }; - -template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); } -template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); } -template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); } -template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); } - -template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from) -{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); } - -// Google-local: Change type from DenseIndex to int in patch. -template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, int/*DenseIndex*/ stride) -{ - std::complex<double> EIGEN_ALIGN16 af[2]; - af[0] = from[0*stride]; - af[1] = from[1*stride]; - return pload<Packet1cd>(af); -} -// Google-local: Change type from DenseIndex to int in patch. -template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, int/*DenseIndex*/ stride) -{ - std::complex<double> EIGEN_ALIGN16 af[2]; - pstore<std::complex<double> >(af, from); - to[0*stride] = af[0]; - to[1*stride] = af[1]; -} - -template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_add(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_sub(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); } -template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd((Packet2d)vec_xor((Packet2d)a.v, (Packet2d)p2ul_CONJ_XOR2)); } - -template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - Packet2d a_re, a_im, v1, v2; - - // Permute and multiply the real parts of a and b - a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI); - // Get the imaginary parts of a - a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO); - // multiply a_re * b - v1 = vec_madd(a_re, b.v, p2d_ZERO); - // multiply a_im * b and get the conjugate result - v2 = vec_madd(a_im, b.v, p2d_ZERO); - v2 = (Packet2d) vec_sld((Packet4ui)v2, (Packet4ui)v2, 8); - v2 = (Packet2d) vec_xor((Packet2d)v2, (Packet2d) p2ul_CONJ_XOR1); - - return Packet1cd(vec_add(v1, v2)); -} - -template<> EIGEN_STRONG_INLINE Packet1cd pand <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd por <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_or(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd pxor <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_xor(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v, vec_nor(b.v,b.v))); } - -template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) -{ - return pset1<Packet1cd>(*from); -} - -#ifndef __VSX__ -template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { vec_dstt((long *)addr, DST_CTRL(2,2,32), DST_CHAN); } -#endif - -template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) -{ - std::complex<double> EIGEN_ALIGN16 res[2]; - pstore<std::complex<double> >(res, a); - - return res[0]; -} - -template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; } - -template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) -{ - return pfirst(a); -} - -template<> EIGEN_STRONG_INLINE Packet1cd preduxp<Packet1cd>(const Packet1cd* vecs) -{ - return vecs[0]; -} - -template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) -{ - return pfirst(a); -} - -template<int Offset> -struct palign_impl<Offset,Packet1cd> -{ - static EIGEN_STRONG_INLINE void run(Packet1cd& /*first*/, const Packet1cd& /*second*/) - { - // FIXME is it sure we never have to align a Packet1cd? - // Even though a std::complex<double> has 16 bytes, it is not necessarily aligned on a 16 bytes boundary... - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, false,true> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - return internal::pmul(a, pconj(b)); - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, true,false> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - return internal::pmul(pconj(a), b); - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, true,true> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - return pconj(internal::pmul(a, b)); - } -}; - -template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - // TODO optimize it for AltiVec - Packet1cd res = conj_helper<Packet1cd,Packet1cd,false,true>().pmul(a,b); - Packet2d s = vec_madd(b.v, b.v, p2d_ZERO_); - return Packet1cd(pdiv(res.v, vec_add(s,vec_perm(s, s, p16uc_REVERSE64)))); -} - -EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x) -{ - return Packet1cd(preverse(Packet2d(x.v))); -} - -EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel) -{ - Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI); - kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO); - kernel.packet[0].v = tmp; -} -#endif // EIGEN_VECTORIZE_VSX -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_COMPLEX32_ALTIVEC_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h b/third_party/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h deleted file mode 100644 index e3545b4abc..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/AltiVec/MathFunctions.h +++ /dev/null @@ -1,299 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2007 Julien Pommier -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* The sin, cos, exp, and log functions of this file come from - * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/ - */ - -#ifndef EIGEN_MATH_FUNCTIONS_ALTIVEC_H -#define EIGEN_MATH_FUNCTIONS_ALTIVEC_H - -#include <iostream> - -#define DUMP(v) do { std::cout << #v " = " << (v) << std::endl; } while(0) - -namespace Eigen { - -namespace internal { - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f plog<Packet4f>(const Packet4f& _x) -{ - Packet4f x = _x; - _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f); - _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f); - _EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f); - _EIGEN_DECLARE_CONST_Packet4i(23, 23); - - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000); - - /* the smallest non denormalized float number */ - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos, 0x00800000); - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf, 0xff800000); // -1.f/0.f - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_nan, 0xffffffff); - - /* natural logarithm computed for 4 simultaneous float - return NaN for x <= 0 - */ - _EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, - 1.1514610310E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, - 1.2420140846E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, + 1.4249322787E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, - 1.6668057665E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, + 2.0000714765E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, - 2.4999993993E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, + 3.3333331174E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f); - - - Packet4i emm0; - - /* isvalid_mask is 0 if x < 0 or x is NaN. */ - Packet4ui isvalid_mask = reinterpret_cast<Packet4ui>(vec_cmpge(x, p4f_ZERO)); - Packet4ui iszero_mask = reinterpret_cast<Packet4ui>(vec_cmpeq(x, p4f_ZERO)); - - x = pmax(x, p4f_min_norm_pos); /* cut off denormalized stuff */ - emm0 = vec_sr(reinterpret_cast<Packet4i>(x), - reinterpret_cast<Packet4ui>(p4i_23)); - - /* keep only the fractional part */ - x = pand(x, p4f_inv_mant_mask); - x = por(x, p4f_half); - - emm0 = psub(emm0, p4i_0x7f); - Packet4f e = padd(vec_ctf(emm0, 0), p4f_1); - - /* part2: - if( x < SQRTHF ) { - e -= 1; - x = x + x - 1.0; - } else { x = x - 1.0; } - */ - Packet4f mask = reinterpret_cast<Packet4f>(vec_cmplt(x, p4f_cephes_SQRTHF)); - Packet4f tmp = pand(x, mask); - x = psub(x, p4f_1); - e = psub(e, pand(p4f_1, mask)); - x = padd(x, tmp); - - Packet4f x2 = pmul(x,x); - Packet4f x3 = pmul(x2,x); - - Packet4f y, y1, y2; - y = pmadd(p4f_cephes_log_p0, x, p4f_cephes_log_p1); - y1 = pmadd(p4f_cephes_log_p3, x, p4f_cephes_log_p4); - y2 = pmadd(p4f_cephes_log_p6, x, p4f_cephes_log_p7); - y = pmadd(y , x, p4f_cephes_log_p2); - y1 = pmadd(y1, x, p4f_cephes_log_p5); - y2 = pmadd(y2, x, p4f_cephes_log_p8); - y = pmadd(y, x3, y1); - y = pmadd(y, x3, y2); - y = pmul(y, x3); - - y1 = pmul(e, p4f_cephes_log_q1); - tmp = pmul(x2, p4f_half); - y = padd(y, y1); - x = psub(x, tmp); - y2 = pmul(e, p4f_cephes_log_q2); - x = padd(x, y); - x = padd(x, y2); - // negative arg will be NAN, 0 will be -INF - x = vec_sel(x, p4f_minus_inf, iszero_mask); - x = vec_sel(p4f_minus_nan, x, isvalid_mask); - return x; -} - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f pexp<Packet4f>(const Packet4f& _x) -{ - Packet4f x = _x; - _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f); - _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f); - _EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f); - _EIGEN_DECLARE_CONST_Packet4i(23, 23); - - - _EIGEN_DECLARE_CONST_Packet4f(exp_hi, 88.3762626647950f); - _EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f); - - _EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f); - - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f); - - Packet4f tmp, fx; - Packet4i emm0; - - // clamp x - x = vec_max(vec_min(x, p4f_exp_hi), p4f_exp_lo); - - /* express exp(x) as exp(g + n*log(2)) */ - fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half); - - fx = vec_floor(fx); - - tmp = pmul(fx, p4f_cephes_exp_C1); - Packet4f z = pmul(fx, p4f_cephes_exp_C2); - x = psub(x, tmp); - x = psub(x, z); - - z = pmul(x,x); - - Packet4f y = p4f_cephes_exp_p0; - y = pmadd(y, x, p4f_cephes_exp_p1); - y = pmadd(y, x, p4f_cephes_exp_p2); - y = pmadd(y, x, p4f_cephes_exp_p3); - y = pmadd(y, x, p4f_cephes_exp_p4); - y = pmadd(y, x, p4f_cephes_exp_p5); - y = pmadd(y, z, x); - y = padd(y, p4f_1); - - // build 2^n - emm0 = vec_cts(fx, 0); - emm0 = vec_add(emm0, p4i_0x7f); - emm0 = vec_sl(emm0, reinterpret_cast<Packet4ui>(p4i_23)); - - // Altivec's max & min operators just drop silent NaNs. Check NaNs in - // inputs and return them unmodified. - Packet4ui isnumber_mask = reinterpret_cast<Packet4ui>(vec_cmpeq(_x, _x)); - return vec_sel(_x, pmax(pmul(y, reinterpret_cast<Packet4f>(emm0)), _x), - isnumber_mask); -} - -#ifdef __VSX__ - -#undef GCC_VERSION -#define GCC_VERSION (__GNUC__ * 10000 \ - + __GNUC_MINOR__ * 100 \ - + __GNUC_PATCHLEVEL__) - -// VSX support varies between different compilers and even different -// versions of the same compiler. For gcc version >= 4.9.3, we can use -// vec_cts to efficiently convert Packet2d to Packet2l. Otherwise, use -// a slow version that works with older compilers. -static inline Packet2l ConvertToPacket2l(const Packet2d& x) { -#if GCC_VERSION >= 40903 || defined(__clang__) - return vec_cts(x, 0); -#else - double tmp[2]; - memcpy(tmp, &x, sizeof(tmp)); - Packet2l l = { static_cast<long long>(tmp[0]), - static_cast<long long>(tmp[1]) }; - return l; -#endif -} - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet2d pexp<Packet2d>(const Packet2d& _x) -{ - Packet2d x = _x; - - _EIGEN_DECLARE_CONST_Packet2d(1 , 1.0); - _EIGEN_DECLARE_CONST_Packet2d(2 , 2.0); - _EIGEN_DECLARE_CONST_Packet2d(half, 0.5); - - _EIGEN_DECLARE_CONST_Packet2d(exp_hi, 709.437); - _EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303); - - _EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599); - - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1); - - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0); - - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6); - - Packet2d tmp, fx; - Packet2l emm0; - - // clamp x - x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo); - /* express exp(x) as exp(g + n*log(2)) */ - fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half); - - fx = vec_floor(fx); - - tmp = pmul(fx, p2d_cephes_exp_C1); - Packet2d z = pmul(fx, p2d_cephes_exp_C2); - x = psub(x, tmp); - x = psub(x, z); - - Packet2d x2 = pmul(x,x); - - Packet2d px = p2d_cephes_exp_p0; - px = pmadd(px, x2, p2d_cephes_exp_p1); - px = pmadd(px, x2, p2d_cephes_exp_p2); - px = pmul (px, x); - - Packet2d qx = p2d_cephes_exp_q0; - qx = pmadd(qx, x2, p2d_cephes_exp_q1); - qx = pmadd(qx, x2, p2d_cephes_exp_q2); - qx = pmadd(qx, x2, p2d_cephes_exp_q3); - - x = pdiv(px,psub(qx,px)); - x = pmadd(p2d_2,x,p2d_1); - - // build 2^n - emm0 = ConvertToPacket2l(fx); - -#ifdef __POWER8_VECTOR__ - static const Packet2l p2l_1023 = { 1023, 1023 }; - static const Packet2ul p2ul_52 = { 52, 52 }; - - emm0 = vec_add(emm0, p2l_1023); - emm0 = vec_sl(emm0, p2ul_52); -#else - // Code is a bit complex for POWER7. There is actually a - // vec_xxsldi intrinsic but it is not supported by some gcc versions. - // So we shift (52-32) bits and do a word swap with zeros. - _EIGEN_DECLARE_CONST_Packet4i(1023, 1023); - _EIGEN_DECLARE_CONST_Packet4i(20, 20); // 52 - 32 - - Packet4i emm04i = reinterpret_cast<Packet4i>(emm0); - emm04i = vec_add(emm04i, p4i_1023); - emm04i = vec_sl(emm04i, reinterpret_cast<Packet4ui>(p4i_20)); - static const Packet16uc perm = { - 0x14, 0x15, 0x16, 0x17, 0x00, 0x01, 0x02, 0x03, - 0x1c, 0x1d, 0x1e, 0x1f, 0x08, 0x09, 0x0a, 0x0b }; -#ifdef _BIG_ENDIAN - emm0 = reinterpret_cast<Packet2l>(vec_perm(p4i_ZERO, emm04i, perm)); -#else - emm0 = reinterpret_cast<Packet2l>(vec_perm(emm04i, p4i_ZERO, perm)); -#endif - -#endif - - // Altivec's max & min operators just drop silent NaNs. Check NaNs in - // inputs and return them unmodified. - Packet2ul isnumber_mask = reinterpret_cast<Packet2ul>(vec_cmpeq(_x, _x)); - return vec_sel(_x, pmax(pmul(x, reinterpret_cast<Packet2d>(emm0)), _x), - isnumber_mask); -} -#endif - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_MATH_FUNCTIONS_ALTIVEC_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h b/third_party/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h deleted file mode 100644 index 640488e92b..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/AltiVec/PacketMath.h +++ /dev/null @@ -1,943 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Konstantinos Margaritis <markos@codex.gr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PACKET_MATH_ALTIVEC_H -#define EIGEN_PACKET_MATH_ALTIVEC_H - -namespace Eigen { - -namespace internal { - -#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD -#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 4 -#endif - -#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD -#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD -#endif - -#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD -#define EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD -#endif - -// NOTE Altivec has 32 registers, but Eigen only accepts a value of 8 or 16 -#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS -#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32 -#endif - -typedef __vector float Packet4f; -typedef __vector int Packet4i; -typedef __vector unsigned int Packet4ui; -typedef __vector __bool int Packet4bi; -typedef __vector short int Packet8i; -typedef __vector unsigned char Packet16uc; - -// We don't want to write the same code all the time, but we need to reuse the constants -// and it doesn't really work to declare them global, so we define macros instead - -#define _EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \ - Packet4f p4f_##NAME = (Packet4f) vec_splat_s32(X) - -#define _EIGEN_DECLARE_CONST_FAST_Packet4i(NAME,X) \ - Packet4i p4i_##NAME = vec_splat_s32(X) - -#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \ - Packet4f p4f_##NAME = pset1<Packet4f>(X) - -#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \ - Packet4i p4i_##NAME = pset1<Packet4i>(X) - -#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \ - Packet2d p2d_##NAME = pset1<Packet2d>(X) - -#define _EIGEN_DECLARE_CONST_Packet2l(NAME,X) \ - Packet2l p2l_##NAME = pset1<Packet2l>(X) - -#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \ - const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X)) - -#define DST_CHAN 1 -#define DST_CTRL(size, count, stride) (((size) << 24) | ((count) << 16) | (stride)) - -// These constants are endian-agnostic -static _EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); -static _EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); -#ifndef __VSX__ -static _EIGEN_DECLARE_CONST_FAST_Packet4i(ONE,1); -static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); -#endif -static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS16,-16); -static _EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); -static Packet4f p4f_ZERO_ = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); - -static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 }; -static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 }; - -static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 }; -static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 }; - -// Mask alignment -#ifdef __PPC64__ -#define _EIGEN_MASK_ALIGNMENT 0xfffffffffffffff0 -#else -#define _EIGEN_MASK_ALIGNMENT 0xfffffff0 -#endif - -#define _EIGEN_ALIGNED_PTR(x) ((ptrdiff_t)(x) & _EIGEN_MASK_ALIGNMENT) - -// Handle endianness properly while loading constants -// Define global static constants: -#ifdef _BIG_ENDIAN -static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0); -static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 }; -static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 }; -static Packet16uc p16uc_PSET32_WEVEN = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 }; -static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8); //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16}; -#else -static Packet16uc p16uc_FORWARD = p16uc_REVERSE32; -static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 }; -static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 1), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 }; -static Packet16uc p16uc_PSET32_WEVEN = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 }; -static Packet16uc p16uc_HALF64_0_16 = vec_sld(vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 0), (Packet16uc)p4i_ZERO, 8); //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16}; -#endif // _BIG_ENDIAN - -static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN); //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 }; -static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN); //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 }; -static Packet16uc p16uc_TRANSPOSE64_HI = vec_add(p16uc_PSET64_HI, p16uc_HALF64_0_16); //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23}; -static Packet16uc p16uc_TRANSPOSE64_LO = vec_add(p16uc_PSET64_LO, p16uc_HALF64_0_16); //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31}; - -static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8); //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 }; - -#ifdef _BIG_ENDIAN -static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8); //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 }; -#else -static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_PSET64_HI, p16uc_PSET64_LO, 8); //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 }; -#endif // _BIG_ENDIAN - -template<> struct packet_traits<float> : default_packet_traits -{ - typedef Packet4f type; - typedef Packet4f half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=4, - - // FIXME check the Has* -#if defined(__VSX__) - HasDiv = 1, -#endif - HasSin = 0, - HasCos = 0, - HasLog = 1, - HasExp = 1, - HasSqrt = 0 - }; -}; -template<> struct packet_traits<int> : default_packet_traits -{ - typedef Packet4i type; - typedef Packet4i half; - enum { - // FIXME check the Has* - Vectorizable = 1, - AlignedOnScalar = 1, - size=4 - }; -}; - - -template<> struct unpacket_traits<Packet4f> { typedef float type; enum {size=4}; typedef Packet4f half; }; -template<> struct unpacket_traits<Packet4i> { typedef int type; enum {size=4}; typedef Packet4i half; }; - -inline std::ostream & operator <<(std::ostream & s, const Packet16uc & v) -{ - union { - Packet16uc v; - unsigned char n[16]; - } vt; - vt.v = v; - for (int i=0; i< 16; i++) - s << (int)vt.n[i] << ", "; - return s; -} - -inline std::ostream & operator <<(std::ostream & s, const Packet4f & v) -{ - union { - Packet4f v; - float n[4]; - } vt; - vt.v = v; - s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3]; - return s; -} - -inline std::ostream & operator <<(std::ostream & s, const Packet4i & v) -{ - union { - Packet4i v; - int n[4]; - } vt; - vt.v = v; - s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3]; - return s; -} - -inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v) -{ - union { - Packet4ui v; - unsigned int n[4]; - } vt; - vt.v = v; - s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3]; - return s; -} -/* -inline std::ostream & operator <<(std::ostream & s, const Packetbi & v) -{ - union { - Packet4bi v; - unsigned int n[4]; - } vt; - vt.v = v; - s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3]; - return s; -}*/ - - -// Need to define them first or we get specialization after instantiation errors -template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); } -template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return vec_ld(0, from); } - -template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); } -template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st(from, 0, to); } - -template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) { - // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html - float EIGEN_ALIGN16 af[4]; - af[0] = from; - Packet4f vc = pload<Packet4f>(af); - vc = vec_splat(vc, 0); - return vc; -} - -template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) { - int EIGEN_ALIGN16 ai[4]; - ai[0] = from; - Packet4i vc = pload<Packet4i>(ai); - vc = vec_splat(vc, 0); - return vc; -} -template<> EIGEN_STRONG_INLINE void -pbroadcast4<Packet4f>(const float *a, - Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3) -{ - a3 = pload<Packet4f>(a); - a0 = vec_splat(a3, 0); - a1 = vec_splat(a3, 1); - a2 = vec_splat(a3, 2); - a3 = vec_splat(a3, 3); -} -template<> EIGEN_STRONG_INLINE void -pbroadcast4<Packet4i>(const int *a, - Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3) -{ - a3 = pload<Packet4i>(a); - a0 = vec_splat(a3, 0); - a1 = vec_splat(a3, 1); - a2 = vec_splat(a3, 2); - a3 = vec_splat(a3, 3); -} - -template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, int stride) -{ - float EIGEN_ALIGN16 af[4]; - af[0] = from[0*stride]; - af[1] = from[1*stride]; - af[2] = from[2*stride]; - af[3] = from[3*stride]; - return pload<Packet4f>(af); -} -template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, int stride) -{ - int EIGEN_ALIGN16 ai[4]; - ai[0] = from[0*stride]; - ai[1] = from[1*stride]; - ai[2] = from[2*stride]; - ai[3] = from[3*stride]; - return pload<Packet4i>(ai); -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, int stride) -{ - float EIGEN_ALIGN16 af[4]; - pstore<float>(af, from); - to[0*stride] = af[0]; - to[1*stride] = af[1]; - to[2*stride] = af[2]; - to[3*stride] = af[3]; -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, int stride) -{ - int EIGEN_ALIGN16 ai[4]; - pstore<int>((int *)ai, from); - to[0*stride] = ai[0]; - to[1*stride] = ai[1]; - to[2*stride] = ai[2]; - to[3*stride] = ai[3]; -} - -template<> EIGEN_STRONG_INLINE Packet4f plset<float>(const float& a) { return vec_add(pset1<Packet4f>(a), p4f_COUNTDOWN); } -template<> EIGEN_STRONG_INLINE Packet4i plset<int>(const int& a) { return vec_add(pset1<Packet4i>(a), p4i_COUNTDOWN); } - -template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_add(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_add(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_sub(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_sub(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return psub<Packet4f>(p4f_ZERO, a); } -template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return psub<Packet4i>(p4i_ZERO, a); } - -template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; } -template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; } - -template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_madd(a,b,p4f_ZERO); } -/* Commented out: it's actually slower than processing it scalar - * -template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) -{ - // Detailed in: http://freevec.org/content/32bit_signed_integer_multiplication_altivec - //Set up constants, variables - Packet4i a1, b1, bswap, low_prod, high_prod, prod, prod_, v1sel; - - // Get the absolute values - a1 = vec_abs(a); - b1 = vec_abs(b); - - // Get the signs using xor - Packet4bi sgn = (Packet4bi) vec_cmplt(vec_xor(a, b), p4i_ZERO); - - // Do the multiplication for the asbolute values. - bswap = (Packet4i) vec_rl((Packet4ui) b1, (Packet4ui) p4i_MINUS16 ); - low_prod = vec_mulo((Packet8i) a1, (Packet8i)b1); - high_prod = vec_msum((Packet8i) a1, (Packet8i) bswap, p4i_ZERO); - high_prod = (Packet4i) vec_sl((Packet4ui) high_prod, (Packet4ui) p4i_MINUS16); - prod = vec_add( low_prod, high_prod ); - - // NOR the product and select only the negative elements according to the sign mask - prod_ = vec_nor(prod, prod); - prod_ = vec_sel(p4i_ZERO, prod_, sgn); - - // Add 1 to the result to get the negative numbers - v1sel = vec_sel(p4i_ZERO, p4i_ONE, sgn); - prod_ = vec_add(prod_, v1sel); - - // Merge the results back to the final vector. - prod = vec_sel(prod, prod_, sgn); - - return prod; -} -*/ -template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) -{ -#if !defined(__VSX__) // VSX actually provides a div instruction - Packet4f t, y_0, y_1; - - // Altivec does not offer a divide instruction, we have to do a reciprocal approximation - y_0 = vec_re(b); - - // Do one Newton-Raphson iteration to get the needed accuracy - t = vec_nmsub(y_0, b, p4f_ONE); - y_1 = vec_madd(y_0, t, y_0); - - return vec_madd(a, y_1, p4f_ZERO); -#else - return vec_div(a, b); -#endif -} - -template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/) -{ eigen_assert(false && "packet integer division are not supported by AltiVec"); - return pset1<Packet4i>(0); -} - -// for some weird raisons, it has to be overloaded for packet of integers -template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a, b, c); } -template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd(pmul(a,b), c); } - -template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_min(a, b); } -template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); } - -template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_max(a, b); } -template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); } - -template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, b); } -template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); } - -template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_or(a, b); } -template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_or(a, b); } - -template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_xor(a, b); } -template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_xor(a, b); } - -template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, vec_nor(b, b)); } -template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, vec_nor(b, b)); } - -#ifdef _BIG_ENDIAN -template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) -{ - EIGEN_DEBUG_ALIGNED_LOAD - Packet16uc MSQ, LSQ; - Packet16uc mask; - MSQ = vec_ld(0, (unsigned char *)from); // most significant quadword - LSQ = vec_ld(15, (unsigned char *)from); // least significant quadword - mask = vec_lvsl(0, from); // create the permute mask - return (Packet4f) vec_perm(MSQ, LSQ, mask); // align the data - -} -template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) -{ - EIGEN_DEBUG_ALIGNED_LOAD - // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html - Packet16uc MSQ, LSQ; - Packet16uc mask; - MSQ = vec_ld(0, (unsigned char *)from); // most significant quadword - LSQ = vec_ld(15, (unsigned char *)from); // least significant quadword - mask = vec_lvsl(0, from); // create the permute mask - return (Packet4i) vec_perm(MSQ, LSQ, mask); // align the data -} -#else -// We also need ot redefine little endian loading of Packet4i/Packet4f using VSX -template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) -{ - EIGEN_DEBUG_ALIGNED_LOAD - return (Packet4i) vec_vsx_ld((long)from & 15, (const Packet4i*) _EIGEN_ALIGNED_PTR(from)); -} -template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) -{ - EIGEN_DEBUG_ALIGNED_LOAD - return (Packet4f) vec_vsx_ld((long)from & 15, (const Packet4f*) _EIGEN_ALIGNED_PTR(from)); -} -#endif - -template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) -{ - Packet4f p; - if((ptrdiff_t(from) % 16) == 0) p = pload<Packet4f>(from); - else p = ploadu<Packet4f>(from); - return vec_perm(p, p, p16uc_DUPLICATE32_HI); -} -template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from) -{ - Packet4i p; - if((ptrdiff_t(from) % 16) == 0) p = pload<Packet4i>(from); - else p = ploadu<Packet4i>(from); - return vec_perm(p, p, p16uc_DUPLICATE32_HI); -} - -#ifdef _BIG_ENDIAN -template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) -{ - EIGEN_DEBUG_UNALIGNED_STORE - // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html - // Warning: not thread safe! - Packet16uc MSQ, LSQ, edges; - Packet16uc edgeAlign, align; - - MSQ = vec_ld(0, (unsigned char *)to); // most significant quadword - LSQ = vec_ld(15, (unsigned char *)to); // least significant quadword - edgeAlign = vec_lvsl(0, to); // permute map to extract edges - edges=vec_perm(LSQ,MSQ,edgeAlign); // extract the edges - align = vec_lvsr( 0, to ); // permute map to misalign data - MSQ = vec_perm(edges,(Packet16uc)from,align); // misalign the data (MSQ) - LSQ = vec_perm((Packet16uc)from,edges,align); // misalign the data (LSQ) - vec_st( LSQ, 15, (unsigned char *)to ); // Store the LSQ part first - vec_st( MSQ, 0, (unsigned char *)to ); // Store the MSQ part -} -template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from) -{ - EIGEN_DEBUG_UNALIGNED_STORE - // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html - // Warning: not thread safe! - Packet16uc MSQ, LSQ, edges; - Packet16uc edgeAlign, align; - - MSQ = vec_ld(0, (unsigned char *)to); // most significant quadword - LSQ = vec_ld(15, (unsigned char *)to); // least significant quadword - edgeAlign = vec_lvsl(0, to); // permute map to extract edges - edges=vec_perm(LSQ, MSQ, edgeAlign); // extract the edges - align = vec_lvsr( 0, to ); // permute map to misalign data - MSQ = vec_perm(edges, (Packet16uc) from, align); // misalign the data (MSQ) - LSQ = vec_perm((Packet16uc) from, edges, align); // misalign the data (LSQ) - vec_st( LSQ, 15, (unsigned char *)to ); // Store the LSQ part first - vec_st( MSQ, 0, (unsigned char *)to ); // Store the MSQ part -} -#else -// We also need to redefine little endian loading of Packet4i/Packet4f using VSX -template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from) -{ - EIGEN_DEBUG_ALIGNED_STORE - vec_vsx_st(from, (long)to & 15, (Packet4i*) _EIGEN_ALIGNED_PTR(to)); -} -template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) -{ - EIGEN_DEBUG_ALIGNED_STORE - vec_vsx_st(from, (long)to & 15, (Packet4f*) _EIGEN_ALIGNED_PTR(to)); -} -#endif - -#ifndef __VSX__ -template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { vec_dstt(addr, DST_CTRL(2,2,32), DST_CHAN); } -template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { vec_dstt(addr, DST_CTRL(2,2,32), DST_CHAN); } -#endif - -template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; vec_st(a, 0, x); return x[0]; } -template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int EIGEN_ALIGN16 x[4]; vec_st(a, 0, x); return x[0]; } - -template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return (Packet4f)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE32); } -template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return (Packet4i)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE32); } - -template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vec_abs(a); } -template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vec_abs(a); } - -template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) -{ - Packet4f b, sum; - b = (Packet4f) vec_sld(a, a, 8); - sum = vec_add(a, b); - b = (Packet4f) vec_sld(sum, sum, 4); - sum = vec_add(sum, b); - return pfirst(sum); -} - -template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs) -{ - Packet4f v[4], sum[4]; - - // It's easier and faster to transpose then add as columns - // Check: http://www.freevec.org/function/matrix_4x4_transpose_floats for explanation - // Do the transpose, first set of moves - v[0] = vec_mergeh(vecs[0], vecs[2]); - v[1] = vec_mergel(vecs[0], vecs[2]); - v[2] = vec_mergeh(vecs[1], vecs[3]); - v[3] = vec_mergel(vecs[1], vecs[3]); - // Get the resulting vectors - sum[0] = vec_mergeh(v[0], v[2]); - sum[1] = vec_mergel(v[0], v[2]); - sum[2] = vec_mergeh(v[1], v[3]); - sum[3] = vec_mergel(v[1], v[3]); - - // Now do the summation: - // Lines 0+1 - sum[0] = vec_add(sum[0], sum[1]); - // Lines 2+3 - sum[1] = vec_add(sum[2], sum[3]); - // Add the results - sum[0] = vec_add(sum[0], sum[1]); - - return sum[0]; -} - -template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a) -{ - Packet4i sum; - sum = vec_sums(a, p4i_ZERO); -#ifdef _BIG_ENDIAN - sum = vec_sld(sum, p4i_ZERO, 12); -#else - sum = vec_sld(p4i_ZERO, sum, 4); -#endif - return pfirst(sum); -} - -template<> EIGEN_STRONG_INLINE Packet4i preduxp<Packet4i>(const Packet4i* vecs) -{ - Packet4i v[4], sum[4]; - - // It's easier and faster to transpose then add as columns - // Check: http://www.freevec.org/function/matrix_4x4_transpose_floats for explanation - // Do the transpose, first set of moves - v[0] = vec_mergeh(vecs[0], vecs[2]); - v[1] = vec_mergel(vecs[0], vecs[2]); - v[2] = vec_mergeh(vecs[1], vecs[3]); - v[3] = vec_mergel(vecs[1], vecs[3]); - // Get the resulting vectors - sum[0] = vec_mergeh(v[0], v[2]); - sum[1] = vec_mergel(v[0], v[2]); - sum[2] = vec_mergeh(v[1], v[3]); - sum[3] = vec_mergel(v[1], v[3]); - - // Now do the summation: - // Lines 0+1 - sum[0] = vec_add(sum[0], sum[1]); - // Lines 2+3 - sum[1] = vec_add(sum[2], sum[3]); - // Add the results - sum[0] = vec_add(sum[0], sum[1]); - - return sum[0]; -} - -// Other reduction functions: -// mul -template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) -{ - Packet4f prod; - prod = pmul(a, (Packet4f)vec_sld(a, a, 8)); - return pfirst(pmul(prod, (Packet4f)vec_sld(prod, prod, 4))); -} - -template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a) -{ - EIGEN_ALIGN16 int aux[4]; - pstore(aux, a); - return aux[0] * aux[1] * aux[2] * aux[3]; -} - -// min -template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) -{ - Packet4f b, res; - b = vec_min(a, vec_sld(a, a, 8)); - res = vec_min(b, vec_sld(b, b, 4)); - return pfirst(res); -} - -template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a) -{ - Packet4i b, res; - b = vec_min(a, vec_sld(a, a, 8)); - res = vec_min(b, vec_sld(b, b, 4)); - return pfirst(res); -} - -// max -template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) -{ - Packet4f b, res; - b = vec_max(a, vec_sld(a, a, 8)); - res = vec_max(b, vec_sld(b, b, 4)); - return pfirst(res); -} - -template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a) -{ - Packet4i b, res; - b = vec_max(a, vec_sld(a, a, 8)); - res = vec_max(b, vec_sld(b, b, 4)); - return pfirst(res); -} - -template<int Offset> -struct palign_impl<Offset,Packet4f> -{ - static EIGEN_STRONG_INLINE void run(Packet4f& first, const Packet4f& second) - { -#ifdef _BIG_ENDIAN - switch (Offset % 4) { - case 1: - first = vec_sld(first, second, 4); break; - case 2: - first = vec_sld(first, second, 8); break; - case 3: - first = vec_sld(first, second, 12); break; - } -#else - switch (Offset % 4) { - case 1: - first = vec_sld(second, first, 12); break; - case 2: - first = vec_sld(second, first, 8); break; - case 3: - first = vec_sld(second, first, 4); break; - } -#endif - } -}; - -template<int Offset> -struct palign_impl<Offset,Packet4i> -{ - static EIGEN_STRONG_INLINE void run(Packet4i& first, const Packet4i& second) - { -#ifdef _BIG_ENDIAN - switch (Offset % 4) { - case 1: - first = vec_sld(first, second, 4); break; - case 2: - first = vec_sld(first, second, 8); break; - case 3: - first = vec_sld(first, second, 12); break; - } -#else - switch (Offset % 4) { - case 1: - first = vec_sld(second, first, 12); break; - case 2: - first = vec_sld(second, first, 8); break; - case 3: - first = vec_sld(second, first, 4); break; - } -#endif - } -}; - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet4f,4>& kernel) { - Packet4f t0, t1, t2, t3; - t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]); - t1 = vec_mergel(kernel.packet[0], kernel.packet[2]); - t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]); - t3 = vec_mergel(kernel.packet[1], kernel.packet[3]); - kernel.packet[0] = vec_mergeh(t0, t2); - kernel.packet[1] = vec_mergel(t0, t2); - kernel.packet[2] = vec_mergeh(t1, t3); - kernel.packet[3] = vec_mergel(t1, t3); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet4i,4>& kernel) { - Packet4i t0, t1, t2, t3; - t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]); - t1 = vec_mergel(kernel.packet[0], kernel.packet[2]); - t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]); - t3 = vec_mergel(kernel.packet[1], kernel.packet[3]); - kernel.packet[0] = vec_mergeh(t0, t2); - kernel.packet[1] = vec_mergel(t0, t2); - kernel.packet[2] = vec_mergeh(t1, t3); - kernel.packet[3] = vec_mergel(t1, t3); -} - - -//---------- double ---------- -#if defined(__VSX__) -typedef __vector double Packet2d; -typedef __vector unsigned long long Packet2ul; -typedef __vector long long Packet2l; - -static Packet2l p2l_ZERO = (Packet2l) p4i_ZERO; -static Packet2d p2d_ONE = { 1.0, 1.0 }; -static Packet2d p2d_ZERO = (Packet2d) p4f_ZERO; -static Packet2d p2d_ZERO_ = { -0.0, -0.0 }; - -#ifdef _BIG_ENDIAN -static Packet2d p2d_COUNTDOWN = (Packet2d) vec_sld((Packet16uc) p2d_ZERO, (Packet16uc) p2d_ONE, 8); -#else -static Packet2d p2d_COUNTDOWN = (Packet2d) vec_sld((Packet16uc) p2d_ONE, (Packet16uc) p2d_ZERO, 8); -#endif - -static EIGEN_STRONG_INLINE Packet2d vec_splat_dbl(Packet2d& a, int index) -{ - switch (index) { - case 0: - return (Packet2d) vec_perm(a, a, p16uc_PSET64_HI); - case 1: - return (Packet2d) vec_perm(a, a, p16uc_PSET64_LO); - } - return a; -} - -template<> struct packet_traits<double> : default_packet_traits -{ - typedef Packet2d type; - typedef Packet2d half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=2, - HasHalfPacket = 0, - - HasDiv = 1, - HasExp = 1, - HasSqrt = 0 - }; -}; - -template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2}; typedef Packet2d half; }; - - -inline std::ostream & operator <<(std::ostream & s, const Packet2d & v) -{ - union { - Packet2d v; - double n[2]; - } vt; - vt.v = v; - s << vt.n[0] << ", " << vt.n[1]; - return s; -} - -// Need to define them first or we get specialization after instantiation errors -template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return (Packet2d) vec_ld(0, (const float *) from); } //FIXME - -template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE vec_st((Packet4f)from, 0, (float *)to); } - -template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { - double EIGEN_ALIGN16 af[2]; - af[0] = from; - Packet2d vc = pload<Packet2d>(af); - vc = vec_splat_dbl(vc, 0); - return vc; -} -template<> EIGEN_STRONG_INLINE void -pbroadcast4<Packet2d>(const double *a, - Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3) -{ - a1 = pload<Packet2d>(a); - a0 = vec_splat_dbl(a1, 0); - a1 = vec_splat_dbl(a1, 1); - a3 = pload<Packet2d>(a+2); - a2 = vec_splat_dbl(a3, 0); - a3 = vec_splat_dbl(a3, 1); -} -// Google-local: Change type from DenseIndex to int in patch. -template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, int/*DenseIndex*/ stride) -{ - double EIGEN_ALIGN16 af[2]; - af[0] = from[0*stride]; - af[1] = from[1*stride]; - return pload<Packet2d>(af); -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, /*DenseIndex*/int stride) -{ - double EIGEN_ALIGN16 af[2]; - pstore<double>(af, from); - to[0*stride] = af[0]; - to[1*stride] = af[1]; -} -template<> EIGEN_STRONG_INLINE Packet2d plset<double>(const double& a) { return vec_add(pset1<Packet2d>(a), p2d_COUNTDOWN); } - -template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_add(a,b); } - -template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_sub(a,b); } - -template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return psub<Packet2d>(p2d_ZERO, a); } - -template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; } - -template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_madd(a,b,p2d_ZERO); } -template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_div(a,b); } - -// for some weird raisons, it has to be overloaded for packet of integers -template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); } - -template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_min(a, b); } - -template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_max(a, b); } - -template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); } - -template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); } - -template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); } - -template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); } - -template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) -{ - EIGEN_DEBUG_ALIGNED_LOAD - return (Packet2d) vec_vsx_ld((long)from & 15, (const Packet2d*) _EIGEN_ALIGNED_PTR(from)); -} -template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) -{ - Packet2d p; - if((ptrdiff_t(from) % 16) == 0) p = pload<Packet2d>(from); - else p = ploadu<Packet2d>(from); - return vec_perm(p, p, p16uc_PSET64_HI); -} - -template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) -{ - EIGEN_DEBUG_ALIGNED_STORE - vec_vsx_st((Packet4f)from, (long)to & 15, (Packet4f*) _EIGEN_ALIGNED_PTR(to)); -} - -#ifndef __VSX__ -template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { vec_dstt((const float *) addr, DST_CTRL(2,2,32), DST_CHAN); } -#endif - -template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double EIGEN_ALIGN16 x[2]; pstore(x, a); return x[0]; } - -template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return (Packet2d)vec_perm((Packet16uc)a,(Packet16uc)a, p16uc_REVERSE64); } - -template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vec_abs(a); } - -template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) -{ - Packet2d b, sum; - b = (Packet2d) vec_sld((Packet4ui) a, (Packet4ui)a, 8); - sum = vec_add(a, b); - return pfirst(sum); -} - -template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs) -{ - Packet2d v[2], sum; - v[0] = vec_add(vecs[0], (Packet2d) vec_sld((Packet4ui) vecs[0], (Packet4ui) vecs[0], 8)); - v[1] = vec_add(vecs[1], (Packet2d) vec_sld((Packet4ui) vecs[1], (Packet4ui) vecs[1], 8)); - -#ifdef _BIG_ENDIAN - sum = (Packet2d) vec_sld((Packet4ui) v[0], (Packet4ui) v[1], 8); -#else - sum = (Packet2d) vec_sld((Packet4ui) v[1], (Packet4ui) v[0], 8); -#endif - - return sum; -} -// Other reduction functions: -// mul -template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) -{ - return pfirst(pmul(a, (Packet2d)vec_sld((Packet4ui) a, (Packet4ui) a, 8))); -} - -// min -template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) -{ - return pfirst(vec_min(a, (Packet2d) vec_sld((Packet4ui) a, (Packet4ui) a, 8))); -} - -// max -template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) -{ - return pfirst(vec_max(a, (Packet2d) vec_sld((Packet4ui) a, (Packet4ui) a, 8))); -} - -template<int Offset> -struct palign_impl<Offset,Packet2d> -{ - static EIGEN_STRONG_INLINE void run(Packet2d& first, const Packet2d& second) - { - if (Offset == 1) -#ifdef _BIG_ENDIAN - first = (Packet2d) vec_sld((Packet4ui) first, (Packet4ui) second, 8); -#else - first = (Packet2d) vec_sld((Packet4ui) second, (Packet4ui) first, 8); -#endif - } -}; - -EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet2d,2>& kernel) { - Packet2d t0, t1; - t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI); - t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO); - kernel.packet[0] = t0; - kernel.packet[1] = t1; -} - -#endif // defined(__VSX__) -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PACKET_MATH_ALTIVEC_H - diff --git a/third_party/eigen3/Eigen/src/Core/arch/CUDA/MathFunctions.h b/third_party/eigen3/Eigen/src/Core/arch/CUDA/MathFunctions.h deleted file mode 100644 index 7e2fb7e699..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/CUDA/MathFunctions.h +++ /dev/null @@ -1,112 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MATH_FUNCTIONS_CUDA_H -#define EIGEN_MATH_FUNCTIONS_CUDA_H - -namespace Eigen { - -namespace internal { - -// Make sure this is only available when targeting a GPU: we don't want to -// introduce conflicts between these packet_traits definitions and the ones -// we'll use on the host side (SSE, AVX, ...) -#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -float4 plog<float4>(const float4& a) -{ - return make_float4(logf(a.x), logf(a.y), logf(a.z), logf(a.w)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -double2 plog<double2>(const double2& a) -{ - return make_double2(log(a.x), log(a.y)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -float4 pexp<float4>(const float4& a) -{ - return make_float4(expf(a.x), expf(a.y), expf(a.z), expf(a.w)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -double2 pexp<double2>(const double2& a) -{ - return make_double2(exp(a.x), exp(a.y)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -float4 psqrt<float4>(const float4& a) -{ - return make_float4(sqrtf(a.x), sqrtf(a.y), sqrtf(a.z), sqrtf(a.w)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -double2 psqrt<double2>(const double2& a) -{ - return make_double2(sqrt(a.x), sqrt(a.y)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -float4 prsqrt<float4>(const float4& a) -{ - return make_float4(rsqrtf(a.x), rsqrtf(a.y), rsqrtf(a.z), rsqrtf(a.w)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -double2 prsqrt<double2>(const double2& a) -{ - return make_double2(rsqrt(a.x), rsqrt(a.y)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -float4 plgamma<float4>(const float4& a) -{ - return make_float4(lgammaf(a.x), lgammaf(a.y), lgammaf(a.z), lgammaf(a.w)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -double2 plgamma<double2>(const double2& a) -{ - return make_double2(lgamma(a.x), lgamma(a.y)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -float4 perf<float4>(const float4& a) -{ - return make_float4(erf(a.x), erf(a.y), erf(a.z), erf(a.w)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -double2 perf<double2>(const double2& a) -{ - return make_double2(erf(a.x), erf(a.y)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -float4 perfc<float4>(const float4& a) -{ - return make_float4(erfc(a.x), erfc(a.y), erfc(a.z), erfc(a.w)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE -double2 perfc<double2>(const double2& a) -{ - return make_double2(erfc(a.x), erfc(a.y)); -} - - -#endif - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_MATH_FUNCTIONS_CUDA_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/CUDA/PacketMath.h b/third_party/eigen3/Eigen/src/Core/arch/CUDA/PacketMath.h deleted file mode 100644 index 02aac06ed3..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/CUDA/PacketMath.h +++ /dev/null @@ -1,342 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PACKET_MATH_CUDA_H -#define EIGEN_PACKET_MATH_CUDA_H - -namespace Eigen { - -namespace internal { -// Make sure this is only available when targeting a GPU: we don't want to -// introduce conflicts between these packet_traits definitions and the ones -// we'll use on the host side (SSE, AVX, ...) -#if defined(EIGEN_USE_GPU) && defined(__CUDACC__) -template<> struct is_arithmetic<float4> { enum { value = true }; }; -template<> struct is_arithmetic<double2> { enum { value = true }; }; - - -template<> struct packet_traits<float> : default_packet_traits -{ - typedef float4 type; - typedef float4 half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=4, - HasHalfPacket = 0, - - HasDiv = 1, - HasSin = 0, - HasCos = 0, - HasLog = 1, - HasExp = 1, - HasSqrt = 1, - HasRsqrt = 1, - HasLGamma = 1, - HasErf = 1, - HasErfc = 1, - - HasBlend = 0, - HasSelect = 1, - HasEq = 1, - }; -}; - -template<> struct packet_traits<double> : default_packet_traits -{ - typedef double2 type; - typedef double2 half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=2, - HasHalfPacket = 0, - - HasDiv = 1, - HasLog = 1, - HasExp = 1, - HasSqrt = 1, - HasRsqrt = 1, - HasLGamma = 1, - HasErf = 1, - HasErfc = 1, - - HasBlend = 0, - HasSelect = 1, - HasEq = 1, - }; -}; - - -template<> struct unpacket_traits<float4> { typedef float type; enum {size=4}; typedef float4 half; }; -template<> struct unpacket_traits<double2> { typedef double type; enum {size=2}; typedef double2 half; }; - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pset1<float4>(const float& from) { - return make_float4(from, from, from, from); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pset1<double2>(const double& from) { - return make_double2(from, from); -} - - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plset<float>(const float& a) { - return make_float4(a, a+1, a+2, a+3); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plset<double>(const double& a) { - return make_double2(a, a+1); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 padd<float4>(const float4& a, const float4& b) { - return make_float4(a.x+b.x, a.y+b.y, a.z+b.z, a.w+b.w); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 padd<double2>(const double2& a, const double2& b) { - return make_double2(a.x+b.x, a.y+b.y); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 psub<float4>(const float4& a, const float4& b) { - return make_float4(a.x-b.x, a.y-b.y, a.z-b.z, a.w-b.w); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 psub<double2>(const double2& a, const double2& b) { - return make_double2(a.x-b.x, a.y-b.y); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 peq<float4>(const float4& a, const float4& b) { - return make_float4(a.x == b.x ? 1.f : 0, a.y == b.y ? 1.f : 0, a.z == b.z ? 1.f : 0, a.w == b.w ? 1.f : 0); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 peq<double2>(const double2& a, const double2& b) { - return make_double2(a.x == b.x ? 1. : 0, a.y == b.y ? 1. : 0); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ple<float4>(const float4& a, const float4& b) { - return make_float4(a.x <= b.x ? 1.f : 0, a.y <= b.y ? 1.f : 0, a.z <= b.z ? 1.f : 0, a.w <= b.w ? 1.f : 0); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ple<double2>(const double2& a, const double2& b) { - return make_double2(a.x <= b.x ? 1. : 0, a.y <= b.y ? 1. : 0); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plt<float4>(const float4& a, const float4& b) { - return make_float4(a.x < b.x ? 1.f : 0, a.y < b.y ? 1.f : 0, a.z < b.z ? 1.f : 0, a.w < b.w ? 1.f : 0); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plt<double2>(const double2& a, const double2& b) { - return make_double2(a.x < b.x ? 1. : 0, a.y < b.y ? 1. : 0); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pselect<float4>(const float4& a, const float4& b, const float4& c) { - return make_float4(c.x ? b.x : a.x, c.y ? b.y : a.y, c.z ? b.z : a.z, c.w ? b.w : a.w); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pselect<double2>(const double2& a, const double2& b, const double2& c) { - return make_double2(c.x ? b.x : a.x, c.y ? b.y : a.y); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pnegate(const float4& a) { - return make_float4(-a.x, -a.y, -a.z, -a.w); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pnegate(const double2& a) { - return make_double2(-a.x, -a.y); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pconj(const float4& a) { return a; } -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pconj(const double2& a) { return a; } - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmul<float4>(const float4& a, const float4& b) { - return make_float4(a.x*b.x, a.y*b.y, a.z*b.z, a.w*b.w); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmul<double2>(const double2& a, const double2& b) { - return make_double2(a.x*b.x, a.y*b.y); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pdiv<float4>(const float4& a, const float4& b) { - return make_float4(a.x/b.x, a.y/b.y, a.z/b.z, a.w/b.w); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pdiv<double2>(const double2& a, const double2& b) { - return make_double2(a.x/b.x, a.y/b.y); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmin<float4>(const float4& a, const float4& b) { - return make_float4(fminf(a.x, b.x), fminf(a.y, b.y), fminf(a.z, b.z), fminf(a.w, b.w)); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmin<double2>(const double2& a, const double2& b) { - return make_double2(fmin(a.x, b.x), fmin(a.y, b.y)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmax<float4>(const float4& a, const float4& b) { - return make_float4(fmaxf(a.x, b.x), fmaxf(a.y, b.y), fmaxf(a.z, b.z), fmaxf(a.w, b.w)); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmax<double2>(const double2& a, const double2& b) { - return make_double2(fmax(a.x, b.x), fmax(a.y, b.y)); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pload<float4>(const float* from) { - return *reinterpret_cast<const float4*>(from); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pload<double2>(const double* from) { - return *reinterpret_cast<const double2*>(from); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploadu<float4>(const float* from) { - return make_float4(from[0], from[1], from[2], from[3]); -} -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploadu<double2>(const double* from) { - return make_double2(from[0], from[1]); -} - -template<> EIGEN_STRONG_INLINE float4 ploaddup<float4>(const float* from) { - return make_float4(from[0], from[0], from[1], from[1]); -} -template<> EIGEN_STRONG_INLINE double2 ploaddup<double2>(const double* from) { - return make_double2(from[0], from[0]); -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<float>(float* to, const float4& from) { - *reinterpret_cast<float4*>(to) = from; -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<double>(double* to, const double2& from) { - *reinterpret_cast<double2*>(to) = from; -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const float4& from) { - to[0] = from.x; - to[1] = from.y; - to[2] = from.z; - to[3] = from.w; -} - -template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const double2& from) { - to[0] = from.x; - to[1] = from.y; -} - -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 350 -template<> -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Aligned>(const float* from) { - return __ldg((const float4*)from); -} -template<> -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Aligned>(const double* from) { - return __ldg((const double2*)from); -} - -template<> -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Unaligned>(const float* from) { - return make_float4(__ldg(from+0), __ldg(from+1), __ldg(from+2), __ldg(from+3)); -} -template<> -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Unaligned>(const double* from) { - return make_double2(__ldg(from+0), __ldg(from+1)); -} -#endif - -template<> EIGEN_DEVICE_FUNC inline float4 pgather<float, float4>(const float* from, int stride) { - return make_float4(from[0*stride], from[1*stride], from[2*stride], from[3*stride]); -} - -template<> EIGEN_DEVICE_FUNC inline double2 pgather<double, double2>(const double* from, int stride) { - return make_double2(from[0*stride], from[1*stride]); -} - -template<> EIGEN_DEVICE_FUNC inline void pscatter<float, float4>(float* to, const float4& from, int stride) { - to[stride*0] = from.x; - to[stride*1] = from.y; - to[stride*2] = from.z; - to[stride*3] = from.w; -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<double, double2>(double* to, const double2& from, int stride) { - to[stride*0] = from.x; - to[stride*1] = from.y; -} - -template<> EIGEN_DEVICE_FUNC inline float pfirst<float4>(const float4& a) { - return a.x; -} -template<> EIGEN_DEVICE_FUNC inline double pfirst<double2>(const double2& a) { - return a.x; -} - -template<> EIGEN_DEVICE_FUNC inline float predux<float4>(const float4& a) { - return a.x + a.y + a.z + a.w; -} -template<> EIGEN_DEVICE_FUNC inline double predux<double2>(const double2& a) { - return a.x + a.y; -} - -template<> EIGEN_DEVICE_FUNC inline float predux_max<float4>(const float4& a) { - return fmaxf(fmaxf(a.x, a.y), fmaxf(a.z, a.w)); -} -template<> EIGEN_DEVICE_FUNC inline double predux_max<double2>(const double2& a) { - return fmax(a.x, a.y); -} - -template<> EIGEN_DEVICE_FUNC inline float predux_min<float4>(const float4& a) { - return fminf(fminf(a.x, a.y), fminf(a.z, a.w)); -} -template<> EIGEN_DEVICE_FUNC inline double predux_min<double2>(const double2& a) { - return fmin(a.x, a.y); -} - -template <> -EIGEN_DEVICE_FUNC inline float predux_mul<float4>(const float4& a) { - return a.x * a.y * a.z * a.w; -} -template <> -EIGEN_DEVICE_FUNC inline double predux_mul<double2>(const double2& a) { - return a.x * a.y; -} - -template<> EIGEN_DEVICE_FUNC inline float4 pabs<float4>(const float4& a) { - return make_float4(fabsf(a.x), fabsf(a.y), fabsf(a.z), fabsf(a.w)); -} -template<> EIGEN_DEVICE_FUNC inline double2 pabs<double2>(const double2& a) { - return make_double2(fabs(a.x), fabs(a.y)); -} - - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<float4,4>& kernel) { - double tmp = kernel.packet[0].y; - kernel.packet[0].y = kernel.packet[1].x; - kernel.packet[1].x = tmp; - - tmp = kernel.packet[0].z; - kernel.packet[0].z = kernel.packet[2].x; - kernel.packet[2].x = tmp; - - tmp = kernel.packet[0].w; - kernel.packet[0].w = kernel.packet[3].x; - kernel.packet[3].x = tmp; - - tmp = kernel.packet[1].z; - kernel.packet[1].z = kernel.packet[2].y; - kernel.packet[2].y = tmp; - - tmp = kernel.packet[1].w; - kernel.packet[1].w = kernel.packet[3].y; - kernel.packet[3].y = tmp; - - tmp = kernel.packet[2].w; - kernel.packet[2].w = kernel.packet[3].z; - kernel.packet[3].z = tmp; -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<double2,2>& kernel) { - double tmp = kernel.packet[0].y; - kernel.packet[0].y = kernel.packet[1].x; - kernel.packet[1].x = tmp; -} - -#endif - -} // end namespace internal - -} // end namespace Eigen - - -#endif // EIGEN_PACKET_MATH_CUDA_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/Default/Settings.h b/third_party/eigen3/Eigen/src/Core/arch/Default/Settings.h deleted file mode 100644 index 097373c84d..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/Default/Settings.h +++ /dev/null @@ -1,49 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -/* All the parameters defined in this file can be specialized in the - * architecture specific files, and/or by the user. - * More to come... */ - -#ifndef EIGEN_DEFAULT_SETTINGS_H -#define EIGEN_DEFAULT_SETTINGS_H - -/** Defines the maximal loop size to enable meta unrolling of loops. - * Note that the value here is expressed in Eigen's own notion of "number of FLOPS", - * it does not correspond to the number of iterations or the number of instructions - */ -#ifndef EIGEN_UNROLLING_LIMIT -#define EIGEN_UNROLLING_LIMIT 100 -#endif - -/** Defines the threshold between a "small" and a "large" matrix. - * This threshold is mainly used to select the proper product implementation. - */ -#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD -#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8 -#endif - -/** Defines the maximal width of the blocks used in the triangular product and solver - * for vectors (level 2 blas xTRMV and xTRSV). The default is 8. - */ -#ifndef EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH -#define EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH 8 -#endif - - -/** Defines the default number of registers available for that architecture. - * Currently it must be 8 or 16. Other values will fail. - */ -#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS -#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 8 -#endif - -#endif // EIGEN_DEFAULT_SETTINGS_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/NEON/Complex.h b/third_party/eigen3/Eigen/src/Core/arch/NEON/Complex.h deleted file mode 100644 index 49e3fa1b02..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/NEON/Complex.h +++ /dev/null @@ -1,467 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COMPLEX_NEON_H -#define EIGEN_COMPLEX_NEON_H - -namespace Eigen { - -namespace internal { - -static uint32x4_t p4ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET4(0x00000000, 0x80000000, 0x00000000, 0x80000000); -static uint32x2_t p2ui_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x00000000, 0x80000000); - -//---------- float ---------- -struct Packet2cf -{ - EIGEN_STRONG_INLINE Packet2cf() {} - EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {} - Packet4f v; -}; - -template<> struct packet_traits<std::complex<float> > : default_packet_traits -{ - typedef Packet2cf type; - typedef Packet2cf half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size = 2, - HasHalfPacket = 0, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasDiv = 1, - HasNegate = 1, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasSetLinear = 0 - }; -}; - -template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type; enum {size=2}; typedef Packet2cf half; }; - -template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) -{ - float32x2_t r64; - r64 = vld1_f32((float *)&from); - - return Packet2cf(vcombine_f32(r64, r64)); -} - -template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(padd<Packet4f>(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(psub<Packet4f>(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate<Packet4f>(a.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) -{ - Packet4ui b = vreinterpretq_u32_f32(a.v); - return Packet2cf(vreinterpretq_f32_u32(veorq_u32(b, p4ui_CONJ_XOR))); -} - -template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - Packet4f v1, v2; - - // Get the real values of a | a1_re | a1_re | a2_re | a2_re | - v1 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 0), vdup_lane_f32(vget_high_f32(a.v), 0)); - // Get the real values of a | a1_im | a1_im | a2_im | a2_im | - v2 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 1), vdup_lane_f32(vget_high_f32(a.v), 1)); - // Multiply the real a with b - v1 = vmulq_f32(v1, b.v); - // Multiply the imag a with b - v2 = vmulq_f32(v2, b.v); - // Conjugate v2 - v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR)); - // Swap real/imag elements in v2. - v2 = vrev64q_f32(v2); - // Add and return the result - return Packet2cf(vaddq_f32(v1, v2)); -} - -template<> EIGEN_STRONG_INLINE Packet2cf pand <Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - return Packet2cf(vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a.v),vreinterpretq_u32_f32(b.v)))); -} -template<> EIGEN_STRONG_INLINE Packet2cf por <Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - return Packet2cf(vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a.v),vreinterpretq_u32_f32(b.v)))); -} -template<> EIGEN_STRONG_INLINE Packet2cf pxor <Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - return Packet2cf(vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a.v),vreinterpretq_u32_f32(b.v)))); -} -template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - return Packet2cf(vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a.v),vreinterpretq_u32_f32(b.v)))); -} - -template<> EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from)); } -template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from)); } - -template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); } - -template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); } -template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); } - -template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, int stride) -{ - Packet4f res = pset1<Packet4f>(0.f); - res = vsetq_lane_f32(std::real(from[0*stride]), res, 0); - res = vsetq_lane_f32(std::imag(from[0*stride]), res, 1); - res = vsetq_lane_f32(std::real(from[1*stride]), res, 2); - res = vsetq_lane_f32(std::imag(from[1*stride]), res, 3); - return Packet2cf(res); -} - -template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, int stride) -{ - to[stride*0] = std::complex<float>(vgetq_lane_f32(from.v, 0), vgetq_lane_f32(from.v, 1)); - to[stride*1] = std::complex<float>(vgetq_lane_f32(from.v, 2), vgetq_lane_f32(from.v, 3)); -} - -template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { EIGEN_ARM_PREFETCH((float *)addr); } - -template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) -{ - std::complex<float> EIGEN_ALIGN16 x[2]; - vst1q_f32((float *)x, a.v); - return x[0]; -} - -template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) -{ - float32x2_t a_lo, a_hi; - Packet4f a_r128; - - a_lo = vget_low_f32(a.v); - a_hi = vget_high_f32(a.v); - a_r128 = vcombine_f32(a_hi, a_lo); - - return Packet2cf(a_r128); -} - -template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a) -{ - return Packet2cf(vrev64q_f32(a.v)); -} - -template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) -{ - float32x2_t a1, a2; - std::complex<float> s; - - a1 = vget_low_f32(a.v); - a2 = vget_high_f32(a.v); - a2 = vadd_f32(a1, a2); - vst1_f32((float *)&s, a2); - - return s; -} - -template<> EIGEN_STRONG_INLINE Packet2cf preduxp<Packet2cf>(const Packet2cf* vecs) -{ - Packet4f sum1, sum2, sum; - - // Add the first two 64-bit float32x2_t of vecs[0] - sum1 = vcombine_f32(vget_low_f32(vecs[0].v), vget_low_f32(vecs[1].v)); - sum2 = vcombine_f32(vget_high_f32(vecs[0].v), vget_high_f32(vecs[1].v)); - sum = vaddq_f32(sum1, sum2); - - return Packet2cf(sum); -} - -template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) -{ - float32x2_t a1, a2, v1, v2, prod; - std::complex<float> s; - - a1 = vget_low_f32(a.v); - a2 = vget_high_f32(a.v); - // Get the real values of a | a1_re | a1_re | a2_re | a2_re | - v1 = vdup_lane_f32(a1, 0); - // Get the real values of a | a1_im | a1_im | a2_im | a2_im | - v2 = vdup_lane_f32(a1, 1); - // Multiply the real a with b - v1 = vmul_f32(v1, a2); - // Multiply the imag a with b - v2 = vmul_f32(v2, a2); - // Conjugate v2 - v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR)); - // Swap real/imag elements in v2. - v2 = vrev64_f32(v2); - // Add v1, v2 - prod = vadd_f32(v1, v2); - - vst1_f32((float *)&s, prod); - - return s; -} - -template<int Offset> -struct palign_impl<Offset,Packet2cf> -{ - EIGEN_STRONG_INLINE static void run(Packet2cf& first, const Packet2cf& second) - { - if (Offset==1) - { - first.v = vextq_f32(first.v, second.v, 2); - } - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, false,true> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - return internal::pmul(a, pconj(b)); - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, true,false> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - return internal::pmul(pconj(a), b); - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, true,true> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - return pconj(internal::pmul(a, b)); - } -}; - -template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - // TODO optimize it for NEON - Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b); - Packet4f s, rev_s; - - // this computes the norm - s = vmulq_f32(b.v, b.v); - rev_s = vrev64q_f32(s); - - return Packet2cf(pdiv(res.v, vaddq_f32(s,rev_s))); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet2cf,2>& kernel) { - Packet4f tmp = vcombine_f32(vget_high_f32(kernel.packet[0].v), vget_high_f32(kernel.packet[1].v)); - kernel.packet[0].v = vcombine_f32(vget_low_f32(kernel.packet[0].v), vget_low_f32(kernel.packet[1].v)); - kernel.packet[1].v = tmp; -} - -//---------- double ---------- -#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG - -static uint64x2_t p2ul_CONJ_XOR = EIGEN_INIT_NEON_PACKET2(0x0, 0x8000000000000000); - -struct Packet1cd -{ - EIGEN_STRONG_INLINE Packet1cd() {} - EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {} - Packet2d v; -}; - -template<> struct packet_traits<std::complex<double> > : default_packet_traits -{ - typedef Packet1cd type; - typedef Packet1cd half; - enum { - Vectorizable = 1, - AlignedOnScalar = 0, - size = 1, - HasHalfPacket = 0, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasDiv = 1, - HasNegate = 1, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasSetLinear = 0 - }; -}; - -template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1}; typedef Packet1cd half; }; - -template<> EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); } -template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); } - -template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from) -{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); } - -template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(padd<Packet2d>(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(psub<Packet2d>(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate<Packet2d>(a.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), p2ul_CONJ_XOR))); } - -template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - Packet2d v1, v2; - - // Get the real values of a - v1 = vdupq_lane_f64(vget_low_f64(a.v), 0); - // Get the real values of a - v2 = vdupq_lane_f64(vget_high_f64(a.v), 1); - // Multiply the real a with b - v1 = vmulq_f64(v1, b.v); - // Multiply the imag a with b - v2 = vmulq_f64(v2, b.v); - // Conjugate v2 - v2 = vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(v2), p2ul_CONJ_XOR)); - // Swap real/imag elements in v2. - v2 = preverse<Packet2d>(v2); - // Add and return the result - return Packet1cd(vaddq_f64(v1, v2)); -} - -template<> EIGEN_STRONG_INLINE Packet1cd pand <Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - return Packet1cd(vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); -} -template<> EIGEN_STRONG_INLINE Packet1cd por <Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - return Packet1cd(vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); -} -template<> EIGEN_STRONG_INLINE Packet1cd pxor <Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); -} -template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - return Packet1cd(vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); -} - -template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) { return pset1<Packet1cd>(*from); } - -template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); } -template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); } - -template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { EIGEN_ARM_PREFETCH((double *)addr); } - -template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, int stride) -{ - Packet2d res = pset1<Packet2d>(0.0); - res = vsetq_lane_f64(std::real(from[0*stride]), res, 0); - res = vsetq_lane_f64(std::imag(from[0*stride]), res, 1); - return Packet1cd(res); -} - -template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, int stride) -{ - to[stride*0] = std::complex<double>(vgetq_lane_f64(from.v, 0), vgetq_lane_f64(from.v, 1)); -} - - -template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) -{ - std::complex<double> EIGEN_ALIGN16 res; - pstore<std::complex<double> >(&res, a); - - return res; -} - -template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; } - -template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); } - -template<> EIGEN_STRONG_INLINE Packet1cd preduxp<Packet1cd>(const Packet1cd* vecs) { return vecs[0]; } - -template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); } - -template<int Offset> -struct palign_impl<Offset,Packet1cd> -{ - static EIGEN_STRONG_INLINE void run(Packet1cd& /*first*/, const Packet1cd& /*second*/) - { - // FIXME is it sure we never have to align a Packet1cd? - // Even though a std::complex<double> has 16 bytes, it is not necessarily aligned on a 16 bytes boundary... - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, false,true> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - return internal::pmul(a, pconj(b)); - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, true,false> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - return internal::pmul(pconj(a), b); - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, true,true> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - return pconj(internal::pmul(a, b)); - } -}; - -template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - // TODO optimize it for NEON - Packet1cd res = conj_helper<Packet1cd,Packet1cd,false,true>().pmul(a,b); - Packet2d s = pmul<Packet2d>(b.v, b.v); - Packet2d rev_s = preverse<Packet2d>(s); - - return Packet1cd(pdiv(res.v, padd<Packet2d>(s,rev_s))); -} - -EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x) -{ - return Packet1cd(preverse(Packet2d(x.v))); -} - -EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel) -{ - Packet2d tmp = vcombine_f64(vget_high_f64(kernel.packet[0].v), vget_high_f64(kernel.packet[1].v)); - kernel.packet[0].v = vcombine_f64(vget_low_f64(kernel.packet[0].v), vget_low_f64(kernel.packet[1].v)); - kernel.packet[1].v = tmp; -} -#endif // EIGEN_ARCH_ARM64 - - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_COMPLEX_NEON_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h b/third_party/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h deleted file mode 100644 index 6bb05bb922..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/NEON/MathFunctions.h +++ /dev/null @@ -1,91 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* The sin, cos, exp, and log functions of this file come from - * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/ - */ - -#ifndef EIGEN_MATH_FUNCTIONS_NEON_H -#define EIGEN_MATH_FUNCTIONS_NEON_H - -namespace Eigen { - -namespace internal { - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f pexp<Packet4f>(const Packet4f& _x) -{ - Packet4f x = _x; - Packet4f tmp, fx; - - _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f); - _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f); - _EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f); - _EIGEN_DECLARE_CONST_Packet4f(exp_hi, 88.3762626647950f); - _EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f); - - x = vminq_f32(x, p4f_exp_hi); - x = vmaxq_f32(x, p4f_exp_lo); - - /* express exp(x) as exp(g + n*log(2)) */ - fx = vmlaq_f32(p4f_half, x, p4f_cephes_LOG2EF); - - /* perform a floorf */ - tmp = vcvtq_f32_s32(vcvtq_s32_f32(fx)); - - /* if greater, substract 1 */ - Packet4ui mask = vcgtq_f32(tmp, fx); - mask = vandq_u32(mask, vreinterpretq_u32_f32(p4f_1)); - - fx = vsubq_f32(tmp, vreinterpretq_f32_u32(mask)); - - tmp = vmulq_f32(fx, p4f_cephes_exp_C1); - Packet4f z = vmulq_f32(fx, p4f_cephes_exp_C2); - x = vsubq_f32(x, tmp); - x = vsubq_f32(x, z); - - Packet4f y = vmulq_f32(p4f_cephes_exp_p0, x); - z = vmulq_f32(x, x); - y = vaddq_f32(y, p4f_cephes_exp_p1); - y = vmulq_f32(y, x); - y = vaddq_f32(y, p4f_cephes_exp_p2); - y = vmulq_f32(y, x); - y = vaddq_f32(y, p4f_cephes_exp_p3); - y = vmulq_f32(y, x); - y = vaddq_f32(y, p4f_cephes_exp_p4); - y = vmulq_f32(y, x); - y = vaddq_f32(y, p4f_cephes_exp_p5); - - y = vmulq_f32(y, z); - y = vaddq_f32(y, x); - y = vaddq_f32(y, p4f_1); - - /* build 2^n */ - int32x4_t mm; - mm = vcvtq_s32_f32(fx); - mm = vaddq_s32(mm, p4i_0x7f); - mm = vshlq_n_s32(mm, 23); - Packet4f pow2n = vreinterpretq_f32_s32(mm); - - y = vmulq_f32(y, pow2n); - return y; -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_MATH_FUNCTIONS_NEON_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h b/third_party/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h deleted file mode 100644 index 856a65ad7b..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/NEON/PacketMath.h +++ /dev/null @@ -1,745 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Konstantinos Margaritis <markos@codex.gr> -// Heavily based on Gael's SSE version. -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PACKET_MATH_NEON_H -#define EIGEN_PACKET_MATH_NEON_H - -namespace Eigen { - -namespace internal { - -#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD -#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 16 -#endif - -// FIXME NEON has 16 quad registers, but since the current register allocator -// is so bad, it is much better to reduce it to 8 -#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS -#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16 -#endif - -#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD -#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD -#endif - -#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD -#define EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD -#endif - -typedef float32x2_t Packet2f; -typedef float32x4_t Packet4f; -typedef int32x4_t Packet4i; -typedef int32x2_t Packet2i; -typedef uint32x4_t Packet4ui; - -#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \ - const Packet4f p4f_##NAME = pset1<Packet4f>(X) - -#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \ - const Packet4f p4f_##NAME = vreinterpretq_f32_u32(pset1<int>(X)) - -#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \ - const Packet4i p4i_##NAME = pset1<Packet4i>(X) - -#if EIGEN_COMP_LLVM && !EIGEN_COMP_CLANG - //Special treatment for Apple's llvm-gcc, its NEON packet types are unions - #define EIGEN_INIT_NEON_PACKET2(X, Y) {{X, Y}} - #define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {{X, Y, Z, W}} -#else - //Default initializer for packets - #define EIGEN_INIT_NEON_PACKET2(X, Y) {X, Y} - #define EIGEN_INIT_NEON_PACKET4(X, Y, Z, W) {X, Y, Z, W} -#endif - -// arm64 does have the pld instruction. If available, let's trust the __builtin_prefetch built-in function -// which available on LLVM and GCC (at least) -#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC - #define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR); -#elif defined __pld - #define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR) -#elif !EIGEN_ARCH_ARM64 - #define EIGEN_ARM_PREFETCH(ADDR) asm volatile ( " pld [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" ); -#else - // by default no explicit prefetching - #define EIGEN_ARM_PREFETCH(ADDR) -#endif - -template<> struct packet_traits<float> : default_packet_traits -{ - typedef Packet4f type; - typedef Packet4f half; // Packet2f intrinsics not implemented yet - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size = 4, - HasHalfPacket=0, // Packet2f intrinsics not implemented yet - - HasDiv = 1, - // FIXME check the Has* - HasSin = 0, - HasCos = 0, - HasTanH = 1, - HasLog = 0, - HasExp = 1, - HasSqrt = 0 - }; -}; -template<> struct packet_traits<int> : default_packet_traits -{ - typedef Packet4i type; - typedef Packet4i half; // Packet2i intrinsics not implemented yet - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=4, - HasHalfPacket=0 // Packet2i intrinsics not implemented yet - // FIXME check the Has* - }; -}; - -#if EIGEN_GNUC_AT_MOST(4,4) && !EIGEN_COMP_LLVM -// workaround gcc 4.2, 4.3 and 4.4 compilatin issue -EIGEN_STRONG_INLINE float32x4_t vld1q_f32(const float* x) { return ::vld1q_f32((const float32_t*)x); } -EIGEN_STRONG_INLINE float32x2_t vld1_f32 (const float* x) { return ::vld1_f32 ((const float32_t*)x); } -EIGEN_STRONG_INLINE float32x2_t vld1_dup_f32 (const float* x) { return ::vld1_dup_f32 ((const float32_t*)x); } -EIGEN_STRONG_INLINE void vst1q_f32(float* to, float32x4_t from) { ::vst1q_f32((float32_t*)to,from); } -EIGEN_STRONG_INLINE void vst1_f32 (float* to, float32x2_t from) { ::vst1_f32 ((float32_t*)to,from); } -#endif - -template<> struct unpacket_traits<Packet4f> { typedef float type; enum {size=4}; typedef Packet4f half; }; -template<> struct unpacket_traits<Packet4i> { typedef int type; enum {size=4}; typedef Packet4i half; }; - -template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) { return vdupq_n_f32(from); } -template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) { return vdupq_n_s32(from); } - -template<> EIGEN_STRONG_INLINE Packet4f plset<float>(const float& a) -{ - Packet4f countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3); - return vaddq_f32(pset1<Packet4f>(a), countdown); -} -template<> EIGEN_STRONG_INLINE Packet4i plset<int>(const int& a) -{ - Packet4i countdown = EIGEN_INIT_NEON_PACKET4(0, 1, 2, 3); - return vaddq_s32(pset1<Packet4i>(a), countdown); -} - -template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return vaddq_f32(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return vaddq_s32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return vsubq_f32(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return vsubq_s32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return vnegq_f32(a); } -template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return vnegq_s32(a); } - -template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; } -template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; } - -template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmulq_f32(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmulq_s32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pselect<Packet4f>(const Packet4f& a, const Packet4f& b, const Packet4f& false_mask) { - return vbslq_f32(vreinterpretq_u32_f32(false_mask), b, a); -} -template<> EIGEN_STRONG_INLINE Packet4i pselect<Packet4i>(const Packet4i& a, const Packet4i& b, const Packet4i& false_mask) { - return vbslq_s32(vreinterpretq_u32_s32(false_mask), b, a); -} - -template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) -{ -#if EIGEN_ARCH_ARM64 - return vdivq_f32(a,b); -#else - Packet4f inv, restep, div; - - // NEON does not offer a divide instruction, we have to do a reciprocal approximation - // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers - // a reciprocal estimate AND a reciprocal step -which saves a few instructions - // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with - // Newton-Raphson and vrecpsq_f32() - inv = vrecpeq_f32(b); - - // This returns a differential, by which we will have to multiply inv to get a better - // approximation of 1/b. - restep = vrecpsq_f32(b, inv); - inv = vmulq_f32(restep, inv); - - // Finally, multiply a by 1/b and get the wanted result of the division. - div = vmulq_f32(a, inv); - - return div; -#endif -} - -template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/) -{ eigen_assert(false && "packet integer division are not supported by NEON"); - return pset1<Packet4i>(0); -} - -#ifdef __ARM_FEATURE_FMA -// See bug 936. -// FMA is available on VFPv4 i.e. when compiling with -mfpu=neon-vfpv4. -// FMA is a true fused multiply-add i.e. only 1 rounding at the end, no intermediate rounding. -// MLA is not fused i.e. does 2 roundings. -// In addition to giving better accuracy, FMA also gives better performance here on a Krait (Nexus 4): -// MLA: 10 GFlop/s ; FMA: 12 GFlops/s. -template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vfmaq_f32(c,a,b); } -#else -template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vmlaq_f32(c,a,b); } -#endif - -// No FMA instruction for int, so use MLA unconditionally. -template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return vmlaq_s32(c,a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vminq_f32(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vminq_s32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmaxq_f32(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmaxq_s32(a,b); } - -// TODO(ebrevdo): add support for ple, plt, peq using vcle_f32/s32 or -// vcleq_f32/s32, and their ilk, respectively, once it's clear which condition code to use. - -// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics -template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) -{ - return vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); -} -template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vandq_s32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) -{ - return vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); -} -template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vorrq_s32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) -{ - return vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); -} -template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return veorq_s32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) -{ - return vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); -} -template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return vbicq_s32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f32(from); } -template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s32(from); } - -template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) { EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f32(from); } -template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) { EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s32(from); } - -template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) -{ - float32x2_t lo, hi; - lo = vld1_dup_f32(from); - hi = vld1_dup_f32(from+1); - return vcombine_f32(lo, hi); -} -template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from) -{ - int32x2_t lo, hi; - lo = vld1_dup_s32(from); - hi = vld1_dup_s32(from+1); - return vcombine_s32(lo, hi); -} - -template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE vst1q_f32(to, from); } -template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE vst1q_s32(to, from); } - -template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_UNALIGNED_STORE vst1q_f32(to, from); } -template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_UNALIGNED_STORE vst1q_s32(to, from); } - -template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, int stride) -{ - Packet4f res = pset1<Packet4f>(0); - res = vsetq_lane_f32(from[0*stride], res, 0); - res = vsetq_lane_f32(from[1*stride], res, 1); - res = vsetq_lane_f32(from[2*stride], res, 2); - res = vsetq_lane_f32(from[3*stride], res, 3); - return res; -} -template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, int stride) -{ - Packet4i res = pset1<Packet4i>(0); - res = vsetq_lane_s32(from[0*stride], res, 0); - res = vsetq_lane_s32(from[1*stride], res, 1); - res = vsetq_lane_s32(from[2*stride], res, 2); - res = vsetq_lane_s32(from[3*stride], res, 3); - return res; -} - -template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, int stride) -{ - to[stride*0] = vgetq_lane_f32(from, 0); - to[stride*1] = vgetq_lane_f32(from, 1); - to[stride*2] = vgetq_lane_f32(from, 2); - to[stride*3] = vgetq_lane_f32(from, 3); -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, int stride) -{ - to[stride*0] = vgetq_lane_s32(from, 0); - to[stride*1] = vgetq_lane_s32(from, 1); - to[stride*2] = vgetq_lane_s32(from, 2); - to[stride*3] = vgetq_lane_s32(from, 3); -} - -template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { EIGEN_ARM_PREFETCH(addr); } -template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { EIGEN_ARM_PREFETCH(addr); } - -// FIXME only store the 2 first elements ? -template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float EIGEN_ALIGN16 x[4]; vst1q_f32(x, a); return x[0]; } -template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int EIGEN_ALIGN16 x[4]; vst1q_s32(x, a); return x[0]; } - -template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { - float32x2_t a_lo, a_hi; - Packet4f a_r64; - - a_r64 = vrev64q_f32(a); - a_lo = vget_low_f32(a_r64); - a_hi = vget_high_f32(a_r64); - return vcombine_f32(a_hi, a_lo); -} -template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { - int32x2_t a_lo, a_hi; - Packet4i a_r64; - - a_r64 = vrev64q_s32(a); - a_lo = vget_low_s32(a_r64); - a_hi = vget_high_s32(a_r64); - return vcombine_s32(a_hi, a_lo); -} - -template<size_t offset> -struct protate_impl<offset, Packet4f> -{ - static Packet4f run(const Packet4f& a) { - return vextq_f32(a, a, offset); - } -}; - -template<size_t offset> -struct protate_impl<offset, Packet4i> -{ - static Packet4i run(const Packet4i& a) { - return vextq_s32(a, a, offset); - } -}; - -template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vabsq_f32(a); } -template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vabsq_s32(a); } - -template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) -{ - float32x2_t a_lo, a_hi, sum; - - a_lo = vget_low_f32(a); - a_hi = vget_high_f32(a); - sum = vpadd_f32(a_lo, a_hi); - sum = vpadd_f32(sum, sum); - return vget_lane_f32(sum, 0); -} - -template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs) -{ - float32x4x2_t vtrn1, vtrn2, res1, res2; - Packet4f sum1, sum2, sum; - - // NEON zip performs interleaving of the supplied vectors. - // We perform two interleaves in a row to acquire the transposed vector - vtrn1 = vzipq_f32(vecs[0], vecs[2]); - vtrn2 = vzipq_f32(vecs[1], vecs[3]); - res1 = vzipq_f32(vtrn1.val[0], vtrn2.val[0]); - res2 = vzipq_f32(vtrn1.val[1], vtrn2.val[1]); - - // Do the addition of the resulting vectors - sum1 = vaddq_f32(res1.val[0], res1.val[1]); - sum2 = vaddq_f32(res2.val[0], res2.val[1]); - sum = vaddq_f32(sum1, sum2); - - return sum; -} - -template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a) -{ - int32x2_t a_lo, a_hi, sum; - - a_lo = vget_low_s32(a); - a_hi = vget_high_s32(a); - sum = vpadd_s32(a_lo, a_hi); - sum = vpadd_s32(sum, sum); - return vget_lane_s32(sum, 0); -} - -template<> EIGEN_STRONG_INLINE Packet4i preduxp<Packet4i>(const Packet4i* vecs) -{ - int32x4x2_t vtrn1, vtrn2, res1, res2; - Packet4i sum1, sum2, sum; - - // NEON zip performs interleaving of the supplied vectors. - // We perform two interleaves in a row to acquire the transposed vector - vtrn1 = vzipq_s32(vecs[0], vecs[2]); - vtrn2 = vzipq_s32(vecs[1], vecs[3]); - res1 = vzipq_s32(vtrn1.val[0], vtrn2.val[0]); - res2 = vzipq_s32(vtrn1.val[1], vtrn2.val[1]); - - // Do the addition of the resulting vectors - sum1 = vaddq_s32(res1.val[0], res1.val[1]); - sum2 = vaddq_s32(res2.val[0], res2.val[1]); - sum = vaddq_s32(sum1, sum2); - - return sum; -} - -// Other reduction functions: -// mul -template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) -{ - float32x2_t a_lo, a_hi, prod; - - // Get a_lo = |a1|a2| and a_hi = |a3|a4| - a_lo = vget_low_f32(a); - a_hi = vget_high_f32(a); - // Get the product of a_lo * a_hi -> |a1*a3|a2*a4| - prod = vmul_f32(a_lo, a_hi); - // Multiply prod with its swapped value |a2*a4|a1*a3| - prod = vmul_f32(prod, vrev64_f32(prod)); - - return vget_lane_f32(prod, 0); -} -template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a) -{ - int32x2_t a_lo, a_hi, prod; - - // Get a_lo = |a1|a2| and a_hi = |a3|a4| - a_lo = vget_low_s32(a); - a_hi = vget_high_s32(a); - // Get the product of a_lo * a_hi -> |a1*a3|a2*a4| - prod = vmul_s32(a_lo, a_hi); - // Multiply prod with its swapped value |a2*a4|a1*a3| - prod = vmul_s32(prod, vrev64_s32(prod)); - - return vget_lane_s32(prod, 0); -} - -// min -template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) -{ - float32x2_t a_lo, a_hi, min; - - a_lo = vget_low_f32(a); - a_hi = vget_high_f32(a); - min = vpmin_f32(a_lo, a_hi); - min = vpmin_f32(min, min); - - return vget_lane_f32(min, 0); -} - -template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a) -{ - int32x2_t a_lo, a_hi, min; - - a_lo = vget_low_s32(a); - a_hi = vget_high_s32(a); - min = vpmin_s32(a_lo, a_hi); - min = vpmin_s32(min, min); - - return vget_lane_s32(min, 0); -} - -// max -template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) -{ - float32x2_t a_lo, a_hi, max; - - a_lo = vget_low_f32(a); - a_hi = vget_high_f32(a); - max = vpmax_f32(a_lo, a_hi); - max = vpmax_f32(max, max); - - return vget_lane_f32(max, 0); -} - -template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a) -{ - int32x2_t a_lo, a_hi, max; - - a_lo = vget_low_s32(a); - a_hi = vget_high_s32(a); - max = vpmax_s32(a_lo, a_hi); - max = vpmax_s32(max, max); - - return vget_lane_s32(max, 0); -} - -// this PALIGN_NEON business is to work around a bug in LLVM Clang 3.0 causing incorrect compilation errors, -// see bug 347 and this LLVM bug: http://llvm.org/bugs/show_bug.cgi?id=11074 -#define PALIGN_NEON(Offset,Type,Command) \ -template<>\ -struct palign_impl<Offset,Type>\ -{\ - EIGEN_STRONG_INLINE static void run(Type& first, const Type& second)\ - {\ - if (Offset!=0)\ - first = Command(first, second, Offset);\ - }\ -};\ - -PALIGN_NEON(0,Packet4f,vextq_f32) -PALIGN_NEON(1,Packet4f,vextq_f32) -PALIGN_NEON(2,Packet4f,vextq_f32) -PALIGN_NEON(3,Packet4f,vextq_f32) -PALIGN_NEON(0,Packet4i,vextq_s32) -PALIGN_NEON(1,Packet4i,vextq_s32) -PALIGN_NEON(2,Packet4i,vextq_s32) -PALIGN_NEON(3,Packet4i,vextq_s32) - -#undef PALIGN_NEON - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet4f,4>& kernel) { - float32x4x2_t tmp1 = vzipq_f32(kernel.packet[0], kernel.packet[1]); - float32x4x2_t tmp2 = vzipq_f32(kernel.packet[2], kernel.packet[3]); - - kernel.packet[0] = vcombine_f32(vget_low_f32(tmp1.val[0]), vget_low_f32(tmp2.val[0])); - kernel.packet[1] = vcombine_f32(vget_high_f32(tmp1.val[0]), vget_high_f32(tmp2.val[0])); - kernel.packet[2] = vcombine_f32(vget_low_f32(tmp1.val[1]), vget_low_f32(tmp2.val[1])); - kernel.packet[3] = vcombine_f32(vget_high_f32(tmp1.val[1]), vget_high_f32(tmp2.val[1])); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet4i,4>& kernel) { - int32x4x2_t tmp1 = vzipq_s32(kernel.packet[0], kernel.packet[1]); - int32x4x2_t tmp2 = vzipq_s32(kernel.packet[2], kernel.packet[3]); - kernel.packet[0] = vcombine_s32(vget_low_s32(tmp1.val[0]), vget_low_s32(tmp2.val[0])); - kernel.packet[1] = vcombine_s32(vget_high_s32(tmp1.val[0]), vget_high_s32(tmp2.val[0])); - kernel.packet[2] = vcombine_s32(vget_low_s32(tmp1.val[1]), vget_low_s32(tmp2.val[1])); - kernel.packet[3] = vcombine_s32(vget_high_s32(tmp1.val[1]), vget_high_s32(tmp2.val[1])); -} - -//---------- double ---------- - -// Clang 3.5 in the iOS toolchain has an ICE triggered by NEON intrisics for double. -// Confirmed at least with __apple_build_version__ = 6000054. -#ifdef __apple_build_version__ -// Let's hope that by the time __apple_build_version__ hits the 601* range, the bug will be fixed. -// https://gist.github.com/yamaya/2924292 suggests that the 3 first digits are only updated with -// major toolchain updates. -#define EIGEN_APPLE_DOUBLE_NEON_BUG (__apple_build_version__ < 6010000) -#else -#define EIGEN_APPLE_DOUBLE_NEON_BUG 0 -#endif - -#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG - -#if (EIGEN_COMP_GNUC_STRICT && defined(__ANDROID__)) || defined(__apple_build_version__) -// Bug 907: workaround missing declarations of the following two functions in the ADK -__extension__ static __inline uint64x2_t __attribute__ ((__always_inline__)) -vreinterpretq_u64_f64 (float64x2_t __a) -{ - return (uint64x2_t) __a; -} - -__extension__ static __inline float64x2_t __attribute__ ((__always_inline__)) -vreinterpretq_f64_u64 (uint64x2_t __a) -{ - return (float64x2_t) __a; -} -#endif - -typedef float64x2_t Packet2d; -typedef float64x1_t Packet1d; - -template<> struct packet_traits<double> : default_packet_traits -{ - typedef Packet2d type; - typedef Packet2d half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size = 2, - HasHalfPacket=0, - - HasDiv = 1, - // FIXME check the Has* - HasSin = 0, - HasCos = 0, - HasLog = 0, - HasExp = 0, - HasSqrt = 0 - }; -}; - -template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2}; typedef Packet2d half; }; - -template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return vdupq_n_f64(from); } - -template<> EIGEN_STRONG_INLINE Packet2d plset<double>(const double& a) -{ - Packet2d countdown = EIGEN_INIT_NEON_PACKET2(0, 1); - return vaddq_f64(pset1<Packet2d>(a), countdown); -} -template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vaddq_f64(a,b); } - -template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vsubq_f64(a,b); } - -template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return vnegq_f64(a); } - -template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; } - -template<> EIGEN_STRONG_INLINE Packet2d pselect<Packet2d>(const Packet2d& a, const Packet2d& b, const Packet2d& false_mask) { - return vbslq_f64(vreinterpretq_u64_f64(false_mask), b, a); -} - -template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmulq_f64(a,b); } - -template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vdivq_f64(a,b); } - -#ifdef __ARM_FEATURE_FMA -// See bug 936. See above comment about FMA for float. -template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vfmaq_f64(c,a,b); } -#else -template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vmlaq_f64(c,a,b); } -#endif - -template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vminq_f64(a,b); } - -template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmaxq_f64(a,b); } - -// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics -template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) -{ - return vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); -} - -template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) -{ - return vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); -} - -template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) -{ - return vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); -} - -template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) -{ - return vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); -} - -template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f64(from); } - -template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f64(from); } - -template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) -{ - return vld1q_dup_f64(from); -} -template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE vst1q_f64(to, from); } - -template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_UNALIGNED_STORE vst1q_f64(to, from); } - -template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, int stride) -{ - Packet2d res = pset1<Packet2d>(0.0); - res = vsetq_lane_f64(from[0*stride], res, 0); - res = vsetq_lane_f64(from[1*stride], res, 1); - return res; -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, int stride) -{ - to[stride*0] = vgetq_lane_f64(from, 0); - to[stride*1] = vgetq_lane_f64(from, 1); -} -template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ARM_PREFETCH(addr); } - -// FIXME only store the 2 first elements ? -template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(a, 0); } - -template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return vcombine_f64(vget_high_f64(a), vget_low_f64(a)); } - -template<size_t offset> -struct protate_impl<offset, Packet2d> -{ - static Packet2d run(const Packet2d& a) { - return vextq_f64(a, a, offset); - } -}; - -template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vabsq_f64(a); } - -#if EIGEN_COMP_CLANG && defined(__apple_build_version__) -// workaround ICE, see bug 907 -template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) { return (vget_low_f64(a) + vget_high_f64(a))[0]; } -#else -template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) { return vget_lane_f64(vget_low_f64(a) + vget_high_f64(a), 0); } -#endif - -template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs) -{ - float64x2_t trn1, trn2; - - // NEON zip performs interleaving of the supplied vectors. - // We perform two interleaves in a row to acquire the transposed vector - trn1 = vzip1q_f64(vecs[0], vecs[1]); - trn2 = vzip2q_f64(vecs[0], vecs[1]); - - // Do the addition of the resulting vectors - return vaddq_f64(trn1, trn2); -} -// Other reduction functions: -// mul -#if EIGEN_COMP_CLANG && defined(__apple_build_version__) -template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) { return (vget_low_f64(a) * vget_high_f64(a))[0]; } -#else -template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) { return vget_lane_f64(vget_low_f64(a) * vget_high_f64(a), 0); } -#endif - -// min -template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(vpminq_f64(a, a), 0); } - -// max -template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(vpmaxq_f64(a, a), 0); } - -// this PALIGN_NEON business is to work around a bug in LLVM Clang 3.0 causing incorrect compilation errors, -// see bug 347 and this LLVM bug: http://llvm.org/bugs/show_bug.cgi?id=11074 -#define PALIGN_NEON(Offset,Type,Command) \ -template<>\ -struct palign_impl<Offset,Type>\ -{\ - EIGEN_STRONG_INLINE static void run(Type& first, const Type& second)\ - {\ - if (Offset!=0)\ - first = Command(first, second, Offset);\ - }\ -};\ - -PALIGN_NEON(0,Packet2d,vextq_f64) -PALIGN_NEON(1,Packet2d,vextq_f64) -#undef PALIGN_NEON - -EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet2d,2>& kernel) { - float64x2_t trn1 = vzip1q_f64(kernel.packet[0], kernel.packet[1]); - float64x2_t trn2 = vzip2q_f64(kernel.packet[0], kernel.packet[1]); - - kernel.packet[0] = trn1; - kernel.packet[1] = trn2; -} -#endif // EIGEN_ARCH_ARM64 - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PACKET_MATH_NEON_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/SSE/Complex.h b/third_party/eigen3/Eigen/src/Core/arch/SSE/Complex.h deleted file mode 100644 index 2722893dcf..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/SSE/Complex.h +++ /dev/null @@ -1,486 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COMPLEX_SSE_H -#define EIGEN_COMPLEX_SSE_H - -namespace Eigen { - -namespace internal { - -//---------- float ---------- -struct Packet2cf -{ - EIGEN_STRONG_INLINE Packet2cf() {} - EIGEN_STRONG_INLINE explicit Packet2cf(const __m128& a) : v(a) {} - __m128 v; -}; - -// Use the packet_traits defined in AVX/PacketMath.h instead if we're going -// to leverage AVX instructions. -#ifndef EIGEN_VECTORIZE_AVX -template<> struct packet_traits<std::complex<float> > : default_packet_traits -{ - typedef Packet2cf type; - typedef Packet2cf half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size = 2, - HasHalfPacket = 0, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasDiv = 1, - HasNegate = 1, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasSetLinear = 0, - HasBlend = 1, - }; -}; -#endif - -template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type; enum {size=2}; typedef Packet2cf half; }; - -template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_add_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_sub_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) -{ - const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x80000000,0x80000000,0x80000000)); - return Packet2cf(_mm_xor_ps(a.v,mask)); -} -template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) -{ - const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000)); - return Packet2cf(_mm_xor_ps(a.v,mask)); -} - -template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - // TODO optimize it for SSE3 and 4 - #ifdef EIGEN_VECTORIZE_SSE3 - return Packet2cf(_mm_addsub_ps(_mm_mul_ps(_mm_moveldup_ps(a.v), b.v), - _mm_mul_ps(_mm_movehdup_ps(a.v), - vec4f_swizzle1(b.v, 1, 0, 3, 2)))); -// return Packet2cf(_mm_addsub_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v), -// _mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3), -// vec4f_swizzle1(b.v, 1, 0, 3, 2)))); - #else - const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x00000000,0x80000000,0x00000000)); - return Packet2cf(_mm_add_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v), - _mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3), - vec4f_swizzle1(b.v, 1, 0, 3, 2)), mask))); - #endif -} - -template<> EIGEN_STRONG_INLINE Packet2cf pand <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_and_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf por <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_or_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pxor <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_xor_ps(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_andnot_ps(a.v,b.v)); } - -template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&numext::real_ref(*from))); } -template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&numext::real_ref(*from))); } - -template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) -{ - Packet2cf res; -#if EIGEN_GNUC_AT_MOST(4,2) - // Workaround annoying "may be used uninitialized in this function" warning with gcc 4.2 - res.v = _mm_loadl_pi(_mm_set1_ps(0.0f), reinterpret_cast<const __m64*>(&from)); -#elif EIGEN_GNUC_AT_LEAST(4,6) - // Suppress annoying "may be used uninitialized in this function" warning with gcc >= 4.6 - #pragma GCC diagnostic push - #pragma GCC diagnostic ignored "-Wuninitialized" - res.v = _mm_loadl_pi(res.v, (const __m64*)&from); - #pragma GCC diagnostic pop -#else - res.v = _mm_loadl_pi(res.v, (const __m64*)&from); -#endif - return Packet2cf(_mm_movelh_ps(res.v,res.v)); -} - -template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); } - -template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), Packet4f(from.v)); } -template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), Packet4f(from.v)); } - - -template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, int stride) -{ - return Packet2cf(_mm_set_ps(std::imag(from[1*stride]), std::real(from[1*stride]), - std::imag(from[0*stride]), std::real(from[0*stride]))); -} - -template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, int stride) -{ - to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 0)), - _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 1))); - to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 2)), - _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 3))); -} - -template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); } - -template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) -{ - #if EIGEN_GNUC_AT_MOST(4,3) - // Workaround gcc 4.2 ICE - this is not performance wise ideal, but who cares... - // This workaround also fix invalid code generation with gcc 4.3 - EIGEN_ALIGN16 std::complex<float> res[2]; - _mm_store_ps((float*)res, a.v); - return res[0]; - #else - std::complex<float> res; - _mm_storel_pi((__m64*)&res, a.v); - return res; - #endif -} - -template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) { return Packet2cf(_mm_castpd_ps(preverse(Packet2d(_mm_castps_pd(a.v))))); } - -template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) -{ - return pfirst(Packet2cf(_mm_add_ps(a.v, _mm_movehl_ps(a.v,a.v)))); -} - -template<> EIGEN_STRONG_INLINE Packet2cf preduxp<Packet2cf>(const Packet2cf* vecs) -{ - return Packet2cf(_mm_add_ps(_mm_movelh_ps(vecs[0].v,vecs[1].v), _mm_movehl_ps(vecs[1].v,vecs[0].v))); -} - -template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) -{ - return pfirst(pmul(a, Packet2cf(_mm_movehl_ps(a.v,a.v)))); -} - -template<int Offset> -struct palign_impl<Offset,Packet2cf> -{ - static EIGEN_STRONG_INLINE void run(Packet2cf& first, const Packet2cf& second) - { - if (Offset==1) - { - first.v = _mm_movehl_ps(first.v, first.v); - first.v = _mm_movelh_ps(first.v, second.v); - } - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, false,true> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - #ifdef EIGEN_VECTORIZE_SSE3 - return internal::pmul(a, pconj(b)); - #else - const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000)); - return Packet2cf(_mm_add_ps(_mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v), mask), - _mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3), - vec4f_swizzle1(b.v, 1, 0, 3, 2)))); - #endif - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, true,false> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - #ifdef EIGEN_VECTORIZE_SSE3 - return internal::pmul(pconj(a), b); - #else - const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000)); - return Packet2cf(_mm_add_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v), - _mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3), - vec4f_swizzle1(b.v, 1, 0, 3, 2)), mask))); - #endif - } -}; - -template<> struct conj_helper<Packet2cf, Packet2cf, true,true> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b) const - { - #ifdef EIGEN_VECTORIZE_SSE3 - return pconj(internal::pmul(a, b)); - #else - const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000)); - return Packet2cf(_mm_sub_ps(_mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v), mask), - _mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3), - vec4f_swizzle1(b.v, 1, 0, 3, 2)))); - #endif - } -}; - -template<> struct conj_helper<Packet4f, Packet2cf, false,false> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet4f& x, const Packet2cf& y, const Packet2cf& c) const - { return padd(c, pmul(x,y)); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet4f& x, const Packet2cf& y) const - { return Packet2cf(Eigen::internal::pmul<Packet4f>(x, y.v)); } -}; - -template<> struct conj_helper<Packet2cf, Packet4f, false,false> -{ - EIGEN_STRONG_INLINE Packet2cf pmadd(const Packet2cf& x, const Packet4f& y, const Packet2cf& c) const - { return padd(c, pmul(x,y)); } - - EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& x, const Packet4f& y) const - { return Packet2cf(Eigen::internal::pmul<Packet4f>(x.v, y)); } -}; - -template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) -{ - // TODO optimize it for SSE3 and 4 - Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b); - __m128 s = _mm_mul_ps(b.v,b.v); - return Packet2cf(_mm_div_ps(res.v,_mm_add_ps(s,_mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(s), 0xb1))))); -} - -EIGEN_STRONG_INLINE Packet2cf pcplxflip/*<Packet2cf>*/(const Packet2cf& x) -{ - return Packet2cf(vec4f_swizzle1(x.v, 1, 0, 3, 2)); -} - - -//---------- double ---------- -struct Packet1cd -{ - EIGEN_STRONG_INLINE Packet1cd() {} - EIGEN_STRONG_INLINE explicit Packet1cd(const __m128d& a) : v(a) {} - __m128d v; -}; - -// Use the packet_traits defined in AVX/PacketMath.h instead if we're going -// to leverage AVX instructions. -#ifndef EIGEN_VECTORIZE_AVX -template<> struct packet_traits<std::complex<double> > : default_packet_traits -{ - typedef Packet1cd type; - typedef Packet1cd half; - enum { - Vectorizable = 1, - AlignedOnScalar = 0, - size = 1, - HasHalfPacket = 0, - - HasAdd = 1, - HasSub = 1, - HasMul = 1, - HasDiv = 1, - HasNegate = 1, - HasAbs = 0, - HasAbs2 = 0, - HasMin = 0, - HasMax = 0, - HasSetLinear = 0 - }; -}; -#endif - -template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1}; typedef Packet1cd half; }; - -template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_add_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_sub_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); } -template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) -{ - const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0)); - return Packet1cd(_mm_xor_pd(a.v,mask)); -} - -template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - // TODO optimize it for SSE3 and 4 - #ifdef EIGEN_VECTORIZE_SSE3 - return Packet1cd(_mm_addsub_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 0, 0), b.v), - _mm_mul_pd(vec2d_swizzle1(a.v, 1, 1), - vec2d_swizzle1(b.v, 1, 0)))); - #else - const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0)); - return Packet1cd(_mm_add_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 0, 0), b.v), - _mm_xor_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 1, 1), - vec2d_swizzle1(b.v, 1, 0)), mask))); - #endif -} - -template<> EIGEN_STRONG_INLINE Packet1cd pand <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_and_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd por <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_or_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd pxor <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_xor_pd(a.v,b.v)); } -template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_andnot_pd(a.v,b.v)); } - -// FIXME force unaligned load, this is a temporary fix -template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) -{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); } -template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) -{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); } -template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from) -{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); } - -template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) { return pset1<Packet1cd>(*from); } - -// FIXME force unaligned store, this is a temporary fix -template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, Packet2d(from.v)); } -template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> * to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, Packet2d(from.v)); } - -template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); } - -template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) -{ - EIGEN_ALIGN16 double res[2]; - _mm_store_pd(res, a.v); - return std::complex<double>(res[0],res[1]); -} - -template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; } - -template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) -{ - return pfirst(a); -} - -template<> EIGEN_STRONG_INLINE Packet1cd preduxp<Packet1cd>(const Packet1cd* vecs) -{ - return vecs[0]; -} - -template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) -{ - return pfirst(a); -} - -template<int Offset> -struct palign_impl<Offset,Packet1cd> -{ - static EIGEN_STRONG_INLINE void run(Packet1cd& /*first*/, const Packet1cd& /*second*/) - { - // FIXME is it sure we never have to align a Packet1cd? - // Even though a std::complex<double> has 16 bytes, it is not necessarily aligned on a 16 bytes boundary... - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, false,true> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - #ifdef EIGEN_VECTORIZE_SSE3 - return internal::pmul(a, pconj(b)); - #else - const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0)); - return Packet1cd(_mm_add_pd(_mm_xor_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 0, 0), b.v), mask), - _mm_mul_pd(vec2d_swizzle1(a.v, 1, 1), - vec2d_swizzle1(b.v, 1, 0)))); - #endif - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, true,false> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - #ifdef EIGEN_VECTORIZE_SSE3 - return internal::pmul(pconj(a), b); - #else - const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0)); - return Packet1cd(_mm_add_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 0, 0), b.v), - _mm_xor_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 1, 1), - vec2d_swizzle1(b.v, 1, 0)), mask))); - #endif - } -}; - -template<> struct conj_helper<Packet1cd, Packet1cd, true,true> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(pmul(x,y),c); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b) const - { - #ifdef EIGEN_VECTORIZE_SSE3 - return pconj(internal::pmul(a, b)); - #else - const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0)); - return Packet1cd(_mm_sub_pd(_mm_xor_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 0, 0), b.v), mask), - _mm_mul_pd(vec2d_swizzle1(a.v, 1, 1), - vec2d_swizzle1(b.v, 1, 0)))); - #endif - } -}; - -template<> struct conj_helper<Packet2d, Packet1cd, false,false> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet2d& x, const Packet1cd& y, const Packet1cd& c) const - { return padd(c, pmul(x,y)); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet2d& x, const Packet1cd& y) const - { return Packet1cd(Eigen::internal::pmul<Packet2d>(x, y.v)); } -}; - -template<> struct conj_helper<Packet1cd, Packet2d, false,false> -{ - EIGEN_STRONG_INLINE Packet1cd pmadd(const Packet1cd& x, const Packet2d& y, const Packet1cd& c) const - { return padd(c, pmul(x,y)); } - - EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& x, const Packet2d& y) const - { return Packet1cd(Eigen::internal::pmul<Packet2d>(x.v, y)); } -}; - -template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) -{ - // TODO optimize it for SSE3 and 4 - Packet1cd res = conj_helper<Packet1cd,Packet1cd,false,true>().pmul(a,b); - __m128d s = _mm_mul_pd(b.v,b.v); - return Packet1cd(_mm_div_pd(res.v, _mm_add_pd(s,_mm_shuffle_pd(s, s, 0x1)))); -} - -EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x) -{ - return Packet1cd(preverse(Packet2d(x.v))); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet2cf,2>& kernel) { - __m128d w1 = _mm_castps_pd(kernel.packet[0].v); - __m128d w2 = _mm_castps_pd(kernel.packet[1].v); - - __m128 tmp = _mm_castpd_ps(_mm_unpackhi_pd(w1, w2)); - kernel.packet[0].v = _mm_castpd_ps(_mm_unpacklo_pd(w1, w2)); - kernel.packet[1].v = tmp; -} - -template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) { - __m128d result = pblend(ifPacket, _mm_castps_pd(thenPacket.v), _mm_castps_pd(elsePacket.v)); - return Packet2cf(_mm_castpd_ps(result)); -} - - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_COMPLEX_SSE_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h b/third_party/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h deleted file mode 100644 index 0baa7b4b58..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/SSE/MathFunctions.h +++ /dev/null @@ -1,529 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2007 Julien Pommier -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* The sin, cos, exp, and log functions of this file come from - * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/ - */ - -#ifndef EIGEN_MATH_FUNCTIONS_SSE_H -#define EIGEN_MATH_FUNCTIONS_SSE_H - -namespace Eigen { - -namespace internal { - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f plog<Packet4f>(const Packet4f& _x) -{ - Packet4f x = _x; - _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f); - _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f); - _EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f); - - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000); - - /* the smallest non denormalized float number */ - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos, 0x00800000); - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf, 0xff800000);//-1.f/0.f); - - /* natural logarithm computed for 4 simultaneous float - return NaN for x <= 0 - */ - _EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, - 1.1514610310E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, - 1.2420140846E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, + 1.4249322787E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, - 1.6668057665E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, + 2.0000714765E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, - 2.4999993993E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, + 3.3333331174E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f); - - - Packet4i emm0; - - // invalid_mask is set to true when x is NaN - Packet4f invalid_mask = _mm_cmpnge_ps(x, _mm_setzero_ps()); - Packet4f iszero_mask = _mm_cmpeq_ps(x, _mm_setzero_ps()); - - x = pmax(x, p4f_min_norm_pos); /* cut off denormalized stuff */ - emm0 = _mm_srli_epi32(_mm_castps_si128(x), 23); - - /* keep only the fractional part */ - x = _mm_and_ps(x, p4f_inv_mant_mask); - x = _mm_or_ps(x, p4f_half); - - emm0 = _mm_sub_epi32(emm0, p4i_0x7f); - Packet4f e = padd(Packet4f(_mm_cvtepi32_ps(emm0)), p4f_1); - - /* part2: - if( x < SQRTHF ) { - e -= 1; - x = x + x - 1.0; - } else { x = x - 1.0; } - */ - Packet4f mask = _mm_cmplt_ps(x, p4f_cephes_SQRTHF); - Packet4f tmp = pand(x, mask); - x = psub(x, p4f_1); - e = psub(e, pand(p4f_1, mask)); - x = padd(x, tmp); - - Packet4f x2 = pmul(x,x); - Packet4f x3 = pmul(x2,x); - - Packet4f y, y1, y2; - y = pmadd(p4f_cephes_log_p0, x, p4f_cephes_log_p1); - y1 = pmadd(p4f_cephes_log_p3, x, p4f_cephes_log_p4); - y2 = pmadd(p4f_cephes_log_p6, x, p4f_cephes_log_p7); - y = pmadd(y , x, p4f_cephes_log_p2); - y1 = pmadd(y1, x, p4f_cephes_log_p5); - y2 = pmadd(y2, x, p4f_cephes_log_p8); - y = pmadd(y, x3, y1); - y = pmadd(y, x3, y2); - y = pmul(y, x3); - - y1 = pmul(e, p4f_cephes_log_q1); - tmp = pmul(x2, p4f_half); - y = padd(y, y1); - x = psub(x, tmp); - y2 = pmul(e, p4f_cephes_log_q2); - x = padd(x, y); - x = padd(x, y2); - // negative arg will be NAN, 0 will be -INF - return _mm_or_ps(_mm_andnot_ps(iszero_mask, _mm_or_ps(x, invalid_mask)), - _mm_and_ps(iszero_mask, p4f_minus_inf)); -} - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f pexp<Packet4f>(const Packet4f& _x) -{ - Packet4f x = _x; - _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f); - _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f); - _EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f); - - - _EIGEN_DECLARE_CONST_Packet4f(exp_hi, 88.3762626647950f); - _EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f); - - _EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f); - - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f); - - Packet4f tmp, fx; - Packet4i emm0; - - // clamp x - x = pmax(pmin(x, p4f_exp_hi), p4f_exp_lo); - - /* express exp(x) as exp(g + n*log(2)) */ - fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half); - -#ifdef EIGEN_VECTORIZE_SSE4_1 - fx = _mm_floor_ps(fx); -#else - emm0 = _mm_cvttps_epi32(fx); - tmp = _mm_cvtepi32_ps(emm0); - /* if greater, substract 1 */ - Packet4f mask = _mm_cmpgt_ps(tmp, fx); - mask = _mm_and_ps(mask, p4f_1); - fx = psub(tmp, mask); -#endif - - tmp = pmul(fx, p4f_cephes_exp_C1); - Packet4f z = pmul(fx, p4f_cephes_exp_C2); - x = psub(x, tmp); - x = psub(x, z); - - z = pmul(x,x); - - Packet4f y = p4f_cephes_exp_p0; - y = pmadd(y, x, p4f_cephes_exp_p1); - y = pmadd(y, x, p4f_cephes_exp_p2); - y = pmadd(y, x, p4f_cephes_exp_p3); - y = pmadd(y, x, p4f_cephes_exp_p4); - y = pmadd(y, x, p4f_cephes_exp_p5); - y = pmadd(y, z, x); - y = padd(y, p4f_1); - - // build 2^n - emm0 = _mm_cvttps_epi32(fx); - emm0 = _mm_add_epi32(emm0, p4i_0x7f); - emm0 = _mm_slli_epi32(emm0, 23); - return pmax(pmul(y, Packet4f(_mm_castsi128_ps(emm0))), _x); -} -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet2d pexp<Packet2d>(const Packet2d& _x) -{ - Packet2d x = _x; - - _EIGEN_DECLARE_CONST_Packet2d(1 , 1.0); - _EIGEN_DECLARE_CONST_Packet2d(2 , 2.0); - _EIGEN_DECLARE_CONST_Packet2d(half, 0.5); - - _EIGEN_DECLARE_CONST_Packet2d(exp_hi, 709.437); - _EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303); - - _EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599); - - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1); - - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0); - - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125); - _EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6); - static const __m128i p4i_1023_0 = _mm_setr_epi32(1023, 1023, 0, 0); - - Packet2d tmp, fx; - Packet4i emm0; - - // clamp x - x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo); - /* express exp(x) as exp(g + n*log(2)) */ - fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half); - -#ifdef EIGEN_VECTORIZE_SSE4_1 - fx = _mm_floor_pd(fx); -#else - emm0 = _mm_cvttpd_epi32(fx); - tmp = _mm_cvtepi32_pd(emm0); - /* if greater, substract 1 */ - Packet2d mask = _mm_cmpgt_pd(tmp, fx); - mask = _mm_and_pd(mask, p2d_1); - fx = psub(tmp, mask); -#endif - - tmp = pmul(fx, p2d_cephes_exp_C1); - Packet2d z = pmul(fx, p2d_cephes_exp_C2); - x = psub(x, tmp); - x = psub(x, z); - - Packet2d x2 = pmul(x,x); - - Packet2d px = p2d_cephes_exp_p0; - px = pmadd(px, x2, p2d_cephes_exp_p1); - px = pmadd(px, x2, p2d_cephes_exp_p2); - px = pmul (px, x); - - Packet2d qx = p2d_cephes_exp_q0; - qx = pmadd(qx, x2, p2d_cephes_exp_q1); - qx = pmadd(qx, x2, p2d_cephes_exp_q2); - qx = pmadd(qx, x2, p2d_cephes_exp_q3); - - x = pdiv(px,psub(qx,px)); - x = pmadd(p2d_2,x,p2d_1); - - // build 2^n - emm0 = _mm_cvttpd_epi32(fx); - emm0 = _mm_add_epi32(emm0, p4i_1023_0); - emm0 = _mm_slli_epi32(emm0, 20); - emm0 = _mm_shuffle_epi32(emm0, _MM_SHUFFLE(1,2,0,3)); - return pmax(pmul(x, Packet2d(_mm_castsi128_pd(emm0))), _x); -} - -/* evaluation of 4 sines at onces, using SSE2 intrinsics. - - The code is the exact rewriting of the cephes sinf function. - Precision is excellent as long as x < 8192 (I did not bother to - take into account the special handling they have for greater values - -- it does not return garbage for arguments over 8192, though, but - the extra precision is missing). - - Note that it is such that sinf((float)M_PI) = 8.74e-8, which is the - surprising but correct result. -*/ - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f psin<Packet4f>(const Packet4f& _x) -{ - Packet4f x = _x; - _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f); - _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f); - - _EIGEN_DECLARE_CONST_Packet4i(1, 1); - _EIGEN_DECLARE_CONST_Packet4i(not1, ~1); - _EIGEN_DECLARE_CONST_Packet4i(2, 2); - _EIGEN_DECLARE_CONST_Packet4i(4, 4); - - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(sign_mask, 0x80000000); - - _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP1,-0.78515625f); - _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP2, -2.4187564849853515625e-4f); - _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP3, -3.77489497744594108e-8f); - _EIGEN_DECLARE_CONST_Packet4f(sincof_p0, -1.9515295891E-4f); - _EIGEN_DECLARE_CONST_Packet4f(sincof_p1, 8.3321608736E-3f); - _EIGEN_DECLARE_CONST_Packet4f(sincof_p2, -1.6666654611E-1f); - _EIGEN_DECLARE_CONST_Packet4f(coscof_p0, 2.443315711809948E-005f); - _EIGEN_DECLARE_CONST_Packet4f(coscof_p1, -1.388731625493765E-003f); - _EIGEN_DECLARE_CONST_Packet4f(coscof_p2, 4.166664568298827E-002f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_FOPI, 1.27323954473516f); // 4 / M_PI - - Packet4f xmm1, xmm2, xmm3, sign_bit, y; - - Packet4i emm0, emm2; - sign_bit = x; - /* take the absolute value */ - x = pabs(x); - - /* take the modulo */ - - /* extract the sign bit (upper one) */ - sign_bit = _mm_and_ps(sign_bit, p4f_sign_mask); - - /* scale by 4/Pi */ - y = pmul(x, p4f_cephes_FOPI); - - /* store the integer part of y in mm0 */ - emm2 = _mm_cvttps_epi32(y); - /* j=(j+1) & (~1) (see the cephes sources) */ - emm2 = _mm_add_epi32(emm2, p4i_1); - emm2 = _mm_and_si128(emm2, p4i_not1); - y = _mm_cvtepi32_ps(emm2); - /* get the swap sign flag */ - emm0 = _mm_and_si128(emm2, p4i_4); - emm0 = _mm_slli_epi32(emm0, 29); - /* get the polynom selection mask - there is one polynom for 0 <= x <= Pi/4 - and another one for Pi/4<x<=Pi/2 - - Both branches will be computed. - */ - emm2 = _mm_and_si128(emm2, p4i_2); - emm2 = _mm_cmpeq_epi32(emm2, _mm_setzero_si128()); - - Packet4f swap_sign_bit = _mm_castsi128_ps(emm0); - Packet4f poly_mask = _mm_castsi128_ps(emm2); - sign_bit = _mm_xor_ps(sign_bit, swap_sign_bit); - - /* The magic pass: "Extended precision modular arithmetic" - x = ((x - y * DP1) - y * DP2) - y * DP3; */ - xmm1 = pmul(y, p4f_minus_cephes_DP1); - xmm2 = pmul(y, p4f_minus_cephes_DP2); - xmm3 = pmul(y, p4f_minus_cephes_DP3); - x = padd(x, xmm1); - x = padd(x, xmm2); - x = padd(x, xmm3); - - /* Evaluate the first polynom (0 <= x <= Pi/4) */ - y = p4f_coscof_p0; - Packet4f z = _mm_mul_ps(x,x); - - y = pmadd(y, z, p4f_coscof_p1); - y = pmadd(y, z, p4f_coscof_p2); - y = pmul(y, z); - y = pmul(y, z); - Packet4f tmp = pmul(z, p4f_half); - y = psub(y, tmp); - y = padd(y, p4f_1); - - /* Evaluate the second polynom (Pi/4 <= x <= 0) */ - - Packet4f y2 = p4f_sincof_p0; - y2 = pmadd(y2, z, p4f_sincof_p1); - y2 = pmadd(y2, z, p4f_sincof_p2); - y2 = pmul(y2, z); - y2 = pmul(y2, x); - y2 = padd(y2, x); - - /* select the correct result from the two polynoms */ - y2 = _mm_and_ps(poly_mask, y2); - y = _mm_andnot_ps(poly_mask, y); - y = _mm_or_ps(y,y2); - /* update the sign */ - return _mm_xor_ps(y, sign_bit); -} - -/* almost the same as psin */ -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f pcos<Packet4f>(const Packet4f& _x) -{ - Packet4f x = _x; - _EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f); - _EIGEN_DECLARE_CONST_Packet4f(half, 0.5f); - - _EIGEN_DECLARE_CONST_Packet4i(1, 1); - _EIGEN_DECLARE_CONST_Packet4i(not1, ~1); - _EIGEN_DECLARE_CONST_Packet4i(2, 2); - _EIGEN_DECLARE_CONST_Packet4i(4, 4); - - _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP1,-0.78515625f); - _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP2, -2.4187564849853515625e-4f); - _EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP3, -3.77489497744594108e-8f); - _EIGEN_DECLARE_CONST_Packet4f(sincof_p0, -1.9515295891E-4f); - _EIGEN_DECLARE_CONST_Packet4f(sincof_p1, 8.3321608736E-3f); - _EIGEN_DECLARE_CONST_Packet4f(sincof_p2, -1.6666654611E-1f); - _EIGEN_DECLARE_CONST_Packet4f(coscof_p0, 2.443315711809948E-005f); - _EIGEN_DECLARE_CONST_Packet4f(coscof_p1, -1.388731625493765E-003f); - _EIGEN_DECLARE_CONST_Packet4f(coscof_p2, 4.166664568298827E-002f); - _EIGEN_DECLARE_CONST_Packet4f(cephes_FOPI, 1.27323954473516f); // 4 / M_PI - - Packet4f xmm1, xmm2, xmm3, y; - Packet4i emm0, emm2; - - x = pabs(x); - - /* scale by 4/Pi */ - y = pmul(x, p4f_cephes_FOPI); - - /* get the integer part of y */ - emm2 = _mm_cvttps_epi32(y); - /* j=(j+1) & (~1) (see the cephes sources) */ - emm2 = _mm_add_epi32(emm2, p4i_1); - emm2 = _mm_and_si128(emm2, p4i_not1); - y = _mm_cvtepi32_ps(emm2); - - emm2 = _mm_sub_epi32(emm2, p4i_2); - - /* get the swap sign flag */ - emm0 = _mm_andnot_si128(emm2, p4i_4); - emm0 = _mm_slli_epi32(emm0, 29); - /* get the polynom selection mask */ - emm2 = _mm_and_si128(emm2, p4i_2); - emm2 = _mm_cmpeq_epi32(emm2, _mm_setzero_si128()); - - Packet4f sign_bit = _mm_castsi128_ps(emm0); - Packet4f poly_mask = _mm_castsi128_ps(emm2); - - /* The magic pass: "Extended precision modular arithmetic" - x = ((x - y * DP1) - y * DP2) - y * DP3; */ - xmm1 = pmul(y, p4f_minus_cephes_DP1); - xmm2 = pmul(y, p4f_minus_cephes_DP2); - xmm3 = pmul(y, p4f_minus_cephes_DP3); - x = padd(x, xmm1); - x = padd(x, xmm2); - x = padd(x, xmm3); - - /* Evaluate the first polynom (0 <= x <= Pi/4) */ - y = p4f_coscof_p0; - Packet4f z = pmul(x,x); - - y = pmadd(y,z,p4f_coscof_p1); - y = pmadd(y,z,p4f_coscof_p2); - y = pmul(y, z); - y = pmul(y, z); - Packet4f tmp = _mm_mul_ps(z, p4f_half); - y = psub(y, tmp); - y = padd(y, p4f_1); - - /* Evaluate the second polynom (Pi/4 <= x <= 0) */ - Packet4f y2 = p4f_sincof_p0; - y2 = pmadd(y2, z, p4f_sincof_p1); - y2 = pmadd(y2, z, p4f_sincof_p2); - y2 = pmul(y2, z); - y2 = pmadd(y2, x, x); - - /* select the correct result from the two polynoms */ - y2 = _mm_and_ps(poly_mask, y2); - y = _mm_andnot_ps(poly_mask, y); - y = _mm_or_ps(y,y2); - - /* update the sign */ - return _mm_xor_ps(y, sign_bit); -} - -#if EIGEN_FAST_MATH - -// This is based on Quake3's fast inverse square root. -// For detail see here: http://www.beyond3d.com/content/articles/8/ -// It lacks 1 (or 2 bits in some rare cases) of precision, and does not handle negative, +inf, or denormalized numbers correctly. -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f psqrt<Packet4f>(const Packet4f& _x) -{ - Packet4f half = pmul(_x, pset1<Packet4f>(.5f)); - - /* select only the inverse sqrt of non-zero inputs */ - Packet4f non_zero_mask = _mm_cmpge_ps(_x, pset1<Packet4f>((std::numeric_limits<float>::min)())); - Packet4f x = _mm_and_ps(non_zero_mask, _mm_rsqrt_ps(_x)); - - x = pmul(x, psub(pset1<Packet4f>(1.5f), pmul(half, pmul(x,x)))); - return pmul(_x,x); -} - -#else - -template<> EIGEN_STRONG_INLINE Packet4f psqrt<Packet4f>(const Packet4f& x) { return _mm_sqrt_ps(x); } - -#endif - -template<> EIGEN_STRONG_INLINE Packet2d psqrt<Packet2d>(const Packet2d& x) { return _mm_sqrt_pd(x); } - - -#if EIGEN_FAST_MATH - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f prsqrt<Packet4f>(const Packet4f& _x) { - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inf, 0x7f800000); - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(nan, 0x7fc00000); - _EIGEN_DECLARE_CONST_Packet4f(one_point_five, 1.5f); - _EIGEN_DECLARE_CONST_Packet4f(minus_half, -0.5f); - _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(flt_min, 0x00800000); - - Packet4f neg_half = pmul(_x, p4f_minus_half); - - // select only the inverse sqrt of positive normal inputs (denormals are - // flushed to zero and cause infs as well). - Packet4f le_zero_mask = _mm_cmple_ps(_x, p4f_flt_min); - Packet4f x = _mm_andnot_ps(le_zero_mask, _mm_rsqrt_ps(_x)); - - // Fill in NaNs and Infs for the negative/zero entries. - Packet4f neg_mask = _mm_cmplt_ps(_x, _mm_setzero_ps()); - Packet4f zero_mask = _mm_andnot_ps(neg_mask, le_zero_mask); - Packet4f infs_and_nans = _mm_or_ps(_mm_and_ps(neg_mask, p4f_nan), - _mm_and_ps(zero_mask, p4f_inf)); - - // Do a single step of Newton's iteration. - x = pmul(x, pmadd(neg_half, pmul(x, x), p4f_one_point_five)); - - // Insert NaNs and Infs in all the right places. - return _mm_or_ps(x, infs_and_nans); -} - -#else - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet4f prsqrt<Packet4f>(const Packet4f& x) { - // Unfortunately we can't use the much faster mm_rqsrt_ps since it only provides an approximation. - return _mm_div_ps(pset1<Packet4f>(1.0f), _mm_sqrt_ps(x)); -} - -#endif - -template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED -Packet2d prsqrt<Packet2d>(const Packet2d& x) { - // Unfortunately we can't use the much faster mm_rqsrt_pd since it only provides an approximation. - return _mm_div_pd(pset1<Packet2d>(1.0), _mm_sqrt_pd(x)); -} - -// Identical to the ptanh in GenericPacketMath.h, but for doubles use -// a small/medium approximation threshold of 0.001. -template<> EIGEN_STRONG_INLINE Packet2d ptanh_approx_threshold() { - return pset1<Packet2d>(0.001); -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_MATH_FUNCTIONS_SSE_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h b/third_party/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h deleted file mode 100644 index 7f4274fd99..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/SSE/PacketMath.h +++ /dev/null @@ -1,883 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PACKET_MATH_SSE_H -#define EIGEN_PACKET_MATH_SSE_H - -namespace Eigen { - -namespace internal { - -#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD -#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8 -#endif - -#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS -#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2*sizeof(void*)) -#endif - -#ifdef __FMA__ -#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD -#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD 1 -#endif -#endif - -typedef __m128 Packet4f; -typedef __m128i Packet4i; -typedef __m128d Packet2d; - -template<> struct is_arithmetic<__m128> { enum { value = true }; }; -template<> struct is_arithmetic<__m128i> { enum { value = true }; }; -template<> struct is_arithmetic<__m128d> { enum { value = true }; }; - -#define vec4f_swizzle1(v,p,q,r,s) \ - (_mm_castsi128_ps(_mm_shuffle_epi32( _mm_castps_si128(v), ((s)<<6|(r)<<4|(q)<<2|(p))))) - -#define vec4i_swizzle1(v,p,q,r,s) \ - (_mm_shuffle_epi32( v, ((s)<<6|(r)<<4|(q)<<2|(p)))) - -#define vec2d_swizzle1(v,p,q) \ - (_mm_castsi128_pd(_mm_shuffle_epi32( _mm_castpd_si128(v), ((q*2+1)<<6|(q*2)<<4|(p*2+1)<<2|(p*2))))) - -#define vec4f_swizzle2(a,b,p,q,r,s) \ - (_mm_shuffle_ps( (a), (b), ((s)<<6|(r)<<4|(q)<<2|(p)))) - -#define vec4i_swizzle2(a,b,p,q,r,s) \ - (_mm_castps_si128( (_mm_shuffle_ps( _mm_castsi128_ps(a), _mm_castsi128_ps(b), ((s)<<6|(r)<<4|(q)<<2|(p)))))) - -#define _EIGEN_DECLARE_CONST_Packet4f(NAME,X) \ - const Packet4f p4f_##NAME = pset1<Packet4f>(X) - -#define _EIGEN_DECLARE_CONST_Packet2d(NAME,X) \ - const Packet2d p2d_##NAME = pset1<Packet2d>(X) - -#define _EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \ - const Packet4f p4f_##NAME = _mm_castsi128_ps(pset1<Packet4i>(X)) - -#define _EIGEN_DECLARE_CONST_Packet4i(NAME,X) \ - const Packet4i p4i_##NAME = pset1<Packet4i>(X) - - -// Use the packet_traits defined in AVX/PacketMath.h instead if we're going -// to leverage AVX instructions. -#ifndef EIGEN_VECTORIZE_AVX -template<> struct packet_traits<float> : default_packet_traits -{ - typedef Packet4f type; - typedef Packet4f half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=4, - HasHalfPacket = 0, - - HasDiv = 1, - HasSin = EIGEN_FAST_MATH, - HasCos = EIGEN_FAST_MATH, - HasTanH = 1, - HasLog = 1, - HasExp = 1, - HasSqrt = 1, - HasRsqrt = 1, - - HasBlend = 1, - HasSelect = 1, - HasEq = 1, - }; -}; -template<> struct packet_traits<double> : default_packet_traits -{ - typedef Packet2d type; - typedef Packet2d half; - enum { - Vectorizable = 1, - AlignedOnScalar = 1, - size=2, - HasHalfPacket = 0, - - HasDiv = 1, - HasTanH = 1, - HasExp = 1, - HasSqrt = 1, - HasRsqrt = 1, - - HasBlend = 1, - HasSelect = 1, - HasEq = 1, - }; -}; -#endif -template<> struct packet_traits<int> : default_packet_traits -{ - typedef Packet4i type; - typedef Packet4i half; - enum { - // FIXME check the Has* - Vectorizable = 1, - AlignedOnScalar = 1, - size=4, - - HasBlend = 1, - }; -}; - -template<> struct unpacket_traits<Packet4f> { typedef float type; enum {size=4}; typedef Packet4f half; }; -template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2}; typedef Packet2d half; }; -template<> struct unpacket_traits<Packet4i> { typedef int type; enum {size=4}; typedef Packet4i half; }; - -#if EIGEN_COMP_MSVC==1500 -// Workaround MSVC 9 internal compiler error. -// TODO: It has been detected with win64 builds (amd64), so let's check whether it also happens in 32bits+SSE mode -// TODO: let's check whether there does not exist a better fix, like adding a pset0() function. (it crashed on pset1(0)). -template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) { return _mm_set_ps(from,from,from,from); } -template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return _mm_set_pd(from,from); } -template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) { return _mm_set_epi32(from,from,from,from); } -#else -template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) { return _mm_set_ps1(from); } -template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return _mm_set1_pd(from); } -template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int& from) { return _mm_set1_epi32(from); } -#endif - -// GCC generates a shufps instruction for _mm_set1_ps/_mm_load1_ps instead of the more efficient pshufd instruction. -// However, using inrinsics for pset1 makes gcc to generate crappy code in some cases (see bug 203) -// Using inline assembly is also not an option because then gcc fails to reorder properly the instructions. -// Therefore, we introduced the pload1 functions to be used in product kernels for which bug 203 does not apply. -// Also note that with AVX, we want it to generate a vbroadcastss. -#if EIGEN_COMP_GNUC_STRICT && (!defined __AVX__) -template<> EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float *from) { - return vec4f_swizzle1(_mm_load_ss(from),0,0,0,0); -} -#endif - -#ifndef EIGEN_VECTORIZE_AVX -template<> EIGEN_STRONG_INLINE Packet4f plset<float>(const float& a) { return _mm_add_ps(pset1<Packet4f>(a), _mm_set_ps(3,2,1,0)); } -template<> EIGEN_STRONG_INLINE Packet2d plset<double>(const double& a) { return _mm_add_pd(pset1<Packet2d>(a),_mm_set_pd(1,0)); } -#endif -template<> EIGEN_STRONG_INLINE Packet4i plset<int>(const int& a) { return _mm_add_epi32(pset1<Packet4i>(a),_mm_set_epi32(3,2,1,0)); } - -template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_add_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_add_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_add_epi32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_sub_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_sub_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_sub_epi32(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f ple<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_cmple_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d ple<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_cmple_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f plt<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_cmplt_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d plt<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_cmplt_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f peq<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_cmpeq_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d peq<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_cmpeq_pd(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pselect<Packet4f>(const Packet4f& a, const Packet4f& b, const Packet4f& false_mask) { -#if defined(EIGEN_VECTORIZE_SSE4_1) - return _mm_blendv_ps(a, b, false_mask); -#else - return _mm_or_ps(_mm_andnot_ps(false_mask, a), _mm_and_ps(false_mask, b)); -#endif -} -template<> EIGEN_STRONG_INLINE Packet2d pselect<Packet2d>(const Packet2d& a, const Packet2d& b, const Packet2d& false_mask) { -#if defined(EIGEN_VECTORIZE_SSE4_1) - return _mm_blendv_pd(a, b, false_mask); -#else - return _mm_or_pd(_mm_andnot_pd(false_mask, a), _mm_and_pd(false_mask, b)); -#endif -} - -template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) -{ - const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x80000000,0x80000000,0x80000000)); - return _mm_xor_ps(a,mask); -} -template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) -{ - const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0,0x80000000,0x0,0x80000000)); - return _mm_xor_pd(a,mask); -} -template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) -{ - return psub(Packet4i(_mm_setr_epi32(0,0,0,0)), a); -} - -template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; } -template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; } -template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; } - -template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_mul_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_mul_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) -{ -#ifdef EIGEN_VECTORIZE_SSE4_1 - return _mm_mullo_epi32(a,b); -#else - // this version is slightly faster than 4 scalar products - return vec4i_swizzle1( - vec4i_swizzle2( - _mm_mul_epu32(a,b), - _mm_mul_epu32(vec4i_swizzle1(a,1,0,3,2), - vec4i_swizzle1(b,1,0,3,2)), - 0,2,0,2), - 0,2,1,3); -#endif -} - -template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_div_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_div_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/) -{ eigen_assert(false && "packet integer division are not supported by SSE"); - return pset1<Packet4i>(0); -} - -// for some weird raisons, it has to be overloaded for packet of integers -template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd(pmul(a,b), c); } -#ifdef __FMA__ -template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return _mm_fmadd_ps(a,b,c); } -template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return _mm_fmadd_pd(a,b,c); } -#endif - -template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_min_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_min_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) -{ -#ifdef EIGEN_VECTORIZE_SSE4_1 - return _mm_min_epi32(a,b); -#else - // after some bench, this version *is* faster than a scalar implementation - Packet4i mask = _mm_cmplt_epi32(a,b); - return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b)); -#endif -} - -template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_max_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_max_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) -{ -#ifdef EIGEN_VECTORIZE_SSE4_1 - return _mm_max_epi32(a,b); -#else - // after some bench, this version *is* faster than a scalar implementation - Packet4i mask = _mm_cmpgt_epi32(a,b); - return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b)); -#endif -} - -template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_and_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_and_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_and_si128(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_or_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_or_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_or_si128(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_xor_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_xor_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_xor_si128(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_andnot_ps(a,b); } -template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_andnot_pd(a,b); } -template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_andnot_si128(a,b); } - -template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_ps(from); } -template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_pd(from); } -template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); } - -#if EIGEN_COMP_MSVC - template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) { - EIGEN_DEBUG_UNALIGNED_LOAD - #if (EIGEN_COMP_MSVC==1600) - // NOTE Some version of MSVC10 generates bad code when using _mm_loadu_ps - // (i.e., it does not generate an unaligned load!! - // TODO On most architectures this version should also be faster than a single _mm_loadu_ps - // so we could also enable it for MSVC08 but first we have to make this later does not generate crap when doing so... - __m128 res = _mm_loadl_pi(_mm_set1_ps(0.0f), (const __m64*)(from)); - res = _mm_loadh_pi(res, (const __m64*)(from+2)); - return res; - #else - return _mm_loadu_ps(from); - #endif - } - template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm_loadu_pd(from); } - template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from)); } -#else -// Fast unaligned loads. Note that here we cannot directly use intrinsics: this would -// require pointer casting to incompatible pointer types and leads to invalid code -// because of the strict aliasing rule. The "dummy" stuff are required to enforce -// a correct instruction dependency. -// TODO: do the same for MSVC (ICC is compatible) -// NOTE: with the code below, MSVC's compiler crashes! - -#if EIGEN_COMP_GNUC && (EIGEN_ARCH_i386 || (EIGEN_ARCH_x86_64 && EIGEN_GNUC_AT_LEAST(4, 8))) - // bug 195: gcc/i386 emits weird x87 fldl/fstpl instructions for _mm_load_sd - #define EIGEN_AVOID_CUSTOM_UNALIGNED_LOADS 1 - #define EIGEN_AVOID_CUSTOM_UNALIGNED_STORES 1 -#elif EIGEN_COMP_CLANG - // bug 201: Segfaults in __mm_loadh_pd with clang 2.8 - #define EIGEN_AVOID_CUSTOM_UNALIGNED_LOADS 1 - #define EIGEN_AVOID_CUSTOM_UNALIGNED_STORES 0 -#else - #define EIGEN_AVOID_CUSTOM_UNALIGNED_LOADS 0 - #define EIGEN_AVOID_CUSTOM_UNALIGNED_STORES 0 -#endif - -template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) -{ - EIGEN_DEBUG_UNALIGNED_LOAD -#if EIGEN_AVOID_CUSTOM_UNALIGNED_LOADS - return _mm_loadu_ps(from); -#else - __m128d res; - res = _mm_load_sd((const double*)(from)) ; - res = _mm_loadh_pd(res, (const double*)(from+2)) ; - return _mm_castpd_ps(res); -#endif -} -template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) -{ - EIGEN_DEBUG_UNALIGNED_LOAD -#if EIGEN_AVOID_CUSTOM_UNALIGNED_LOADS - return _mm_loadu_pd(from); -#else - __m128d res; - res = _mm_load_sd(from) ; - res = _mm_loadh_pd(res,from+1); - return res; -#endif -} -template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) -{ - EIGEN_DEBUG_UNALIGNED_LOAD -#if EIGEN_AVOID_CUSTOM_UNALIGNED_LOADS - return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from)); -#else - __m128d res; - res = _mm_load_sd((const double*)(from)) ; - res = _mm_loadh_pd(res, (const double*)(from+2)) ; - return _mm_castpd_si128(res); -#endif -} -#endif - -template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) -{ - return vec4f_swizzle1(_mm_castpd_ps(_mm_load_sd(reinterpret_cast<const double*>(from))), 0, 0, 1, 1); -} -template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) -{ return pset1<Packet2d>(from[0]); } -template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from) -{ - Packet4i tmp; - tmp = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(from)); - return vec4i_swizzle1(tmp, 0, 0, 1, 1); -} - -template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_ps(to, from); } -template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_pd(to, from); } -template<> EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from); } - -template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) { - EIGEN_DEBUG_UNALIGNED_STORE -#if EIGEN_AVOID_CUSTOM_UNALIGNED_STORES - _mm_storeu_pd(to, from); -#else - _mm_storel_pd((to), from); - _mm_storeh_pd((to+1), from); -#endif -} -template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<double*>(to), Packet2d(_mm_castps_pd(from))); } -template<> EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet4i& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<double*>(to), Packet2d(_mm_castsi128_pd(from))); } - -template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, int stride) -{ - return _mm_set_ps(from[3*stride], from[2*stride], from[1*stride], from[0*stride]); -} -template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, int stride) -{ - return _mm_set_pd(from[1*stride], from[0*stride]); -} -template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, int stride) -{ - return _mm_set_epi32(from[3*stride], from[2*stride], from[1*stride], from[0*stride]); - } - -template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, int stride) -{ - to[stride*0] = _mm_cvtss_f32(from); - to[stride*1] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 1)); - to[stride*2] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 2)); - to[stride*3] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 3)); -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, int stride) -{ - to[stride*0] = _mm_cvtsd_f64(from); - to[stride*1] = _mm_cvtsd_f64(_mm_shuffle_pd(from, from, 1)); -} -template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, int stride) -{ - to[stride*0] = _mm_cvtsi128_si32(from); - to[stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1)); - to[stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2)); - to[stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3)); -} - -// some compilers might be tempted to perform multiple moves instead of using a vector path. -template<> EIGEN_STRONG_INLINE void pstore1<Packet4f>(float* to, const float& a) -{ - Packet4f pa = _mm_set_ss(a); - pstore(to, Packet4f(vec4f_swizzle1(pa,0,0,0,0))); -} -// some compilers might be tempted to perform multiple moves instead of using a vector path. -template<> EIGEN_STRONG_INLINE void pstore1<Packet2d>(double* to, const double& a) -{ - Packet2d pa = _mm_set_sd(a); - pstore(to, Packet2d(vec2d_swizzle1(pa,0,0))); -} - -#ifndef EIGEN_VECTORIZE_AVX -template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); } -template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); } -template<> EIGEN_STRONG_INLINE void prefetch<int>(const int* addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); } -#endif - -#if EIGEN_COMP_MSVC_STRICT && EIGEN_OS_WIN64 -// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010 -// Direct of the struct members fixed bug #62. -template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { return a.m128_f32[0]; } -template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return a.m128d_f64[0]; } -template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; } -#elif EIGEN_COMP_MSVC_STRICT -// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010 -template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { float x = _mm_cvtss_f32(a); return x; } -template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double x = _mm_cvtsd_f64(a); return x; } -template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; } -#else -template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { return _mm_cvtss_f32(a); } -template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return _mm_cvtsd_f64(a); } -template<> EIGEN_STRONG_INLINE int pfirst<Packet4i>(const Packet4i& a) { return _mm_cvtsi128_si32(a); } -#endif - -template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) -{ return _mm_shuffle_ps(a,a,0x1B); } -template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) -{ return _mm_shuffle_pd(a,a,0x1); } -template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) -{ return _mm_shuffle_epi32(a,0x1B); } - -template<size_t offset> -struct protate_impl<offset, Packet4f> -{ - static Packet4f run(const Packet4f& a) { - return vec4f_swizzle1(a, offset, (offset + 1) % 4, (offset + 2) % 4, (offset + 3) % 4); - } -}; - -template<size_t offset> -struct protate_impl<offset, Packet4i> -{ - static Packet4i run(const Packet4i& a) { - return vec4i_swizzle1(a, offset, (offset + 1) % 4, (offset + 2) % 4, (offset + 3) % 4); - } -}; - -template<size_t offset> -struct protate_impl<offset, Packet2d> -{ - static Packet2d run(const Packet2d& a) { - return vec2d_swizzle1(a, offset, (offset + 1) % 2); - } -}; - -template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) -{ - const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF)); - return _mm_and_ps(a,mask); -} -template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) -{ - const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF)); - return _mm_and_pd(a,mask); -} -template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) -{ - #ifdef EIGEN_VECTORIZE_SSSE3 - return _mm_abs_epi32(a); - #else - Packet4i aux = _mm_srai_epi32(a,31); - return _mm_sub_epi32(_mm_xor_si128(a,aux),aux); - #endif -} - -// with AVX, the default implementations based on pload1 are faster -#ifndef __AVX__ -template<> EIGEN_STRONG_INLINE void -pbroadcast4<Packet4f>(const float *a, - Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3) -{ - a3 = pload<Packet4f>(a); - a0 = vec4f_swizzle1(a3, 0,0,0,0); - a1 = vec4f_swizzle1(a3, 1,1,1,1); - a2 = vec4f_swizzle1(a3, 2,2,2,2); - a3 = vec4f_swizzle1(a3, 3,3,3,3); -} -template<> EIGEN_STRONG_INLINE void -pbroadcast4<Packet2d>(const double *a, - Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3) -{ -#ifdef EIGEN_VECTORIZE_SSE3 - a0 = _mm_loaddup_pd(a+0); - a1 = _mm_loaddup_pd(a+1); - a2 = _mm_loaddup_pd(a+2); - a3 = _mm_loaddup_pd(a+3); -#else - a1 = pload<Packet2d>(a); - a0 = vec2d_swizzle1(a1, 0,0); - a1 = vec2d_swizzle1(a1, 1,1); - a3 = pload<Packet2d>(a+2); - a2 = vec2d_swizzle1(a3, 0,0); - a3 = vec2d_swizzle1(a3, 1,1); -#endif -} -#endif - -EIGEN_STRONG_INLINE void punpackp(Packet4f* vecs) -{ - vecs[1] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x55)); - vecs[2] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xAA)); - vecs[3] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xFF)); - vecs[0] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x00)); -} - -#ifdef EIGEN_VECTORIZE_SSE3 -// TODO implement SSE2 versions as well as integer versions -template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs) -{ - return _mm_hadd_ps(_mm_hadd_ps(vecs[0], vecs[1]),_mm_hadd_ps(vecs[2], vecs[3])); -} -template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs) -{ - return _mm_hadd_pd(vecs[0], vecs[1]); -} -// SSSE3 version: -// EIGEN_STRONG_INLINE Packet4i preduxp(const Packet4i* vecs) -// { -// return _mm_hadd_epi32(_mm_hadd_epi32(vecs[0], vecs[1]),_mm_hadd_epi32(vecs[2], vecs[3])); -// } - -template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) -{ - Packet4f tmp0 = _mm_hadd_ps(a,a); - return pfirst<Packet4f>(_mm_hadd_ps(tmp0, tmp0)); -} - -template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) { return pfirst<Packet2d>(_mm_hadd_pd(a, a)); } - -// SSSE3 version: -// EIGEN_STRONG_INLINE float predux(const Packet4i& a) -// { -// Packet4i tmp0 = _mm_hadd_epi32(a,a); -// return pfirst(_mm_hadd_epi32(tmp0, tmp0)); -// } -#else -// SSE2 versions -template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) -{ - Packet4f tmp = _mm_add_ps(a, _mm_movehl_ps(a,a)); - return pfirst(_mm_add_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1))); -} -template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) -{ - return pfirst(_mm_add_sd(a, _mm_unpackhi_pd(a,a))); -} - -template<> EIGEN_STRONG_INLINE Packet4f preduxp<Packet4f>(const Packet4f* vecs) -{ - Packet4f tmp0, tmp1, tmp2; - tmp0 = _mm_unpacklo_ps(vecs[0], vecs[1]); - tmp1 = _mm_unpackhi_ps(vecs[0], vecs[1]); - tmp2 = _mm_unpackhi_ps(vecs[2], vecs[3]); - tmp0 = _mm_add_ps(tmp0, tmp1); - tmp1 = _mm_unpacklo_ps(vecs[2], vecs[3]); - tmp1 = _mm_add_ps(tmp1, tmp2); - tmp2 = _mm_movehl_ps(tmp1, tmp0); - tmp0 = _mm_movelh_ps(tmp0, tmp1); - return _mm_add_ps(tmp0, tmp2); -} - -template<> EIGEN_STRONG_INLINE Packet2d preduxp<Packet2d>(const Packet2d* vecs) -{ - return _mm_add_pd(_mm_unpacklo_pd(vecs[0], vecs[1]), _mm_unpackhi_pd(vecs[0], vecs[1])); -} -#endif // SSE3 - -template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a) -{ - Packet4i tmp = _mm_add_epi32(a, _mm_unpackhi_epi64(a,a)); - return pfirst(tmp) + pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)); -} - -template<> EIGEN_STRONG_INLINE Packet4i preduxp<Packet4i>(const Packet4i* vecs) -{ - Packet4i tmp0, tmp1, tmp2; - tmp0 = _mm_unpacklo_epi32(vecs[0], vecs[1]); - tmp1 = _mm_unpackhi_epi32(vecs[0], vecs[1]); - tmp2 = _mm_unpackhi_epi32(vecs[2], vecs[3]); - tmp0 = _mm_add_epi32(tmp0, tmp1); - tmp1 = _mm_unpacklo_epi32(vecs[2], vecs[3]); - tmp1 = _mm_add_epi32(tmp1, tmp2); - tmp2 = _mm_unpacklo_epi64(tmp0, tmp1); - tmp0 = _mm_unpackhi_epi64(tmp0, tmp1); - return _mm_add_epi32(tmp0, tmp2); -} - -// Other reduction functions: - -// mul -template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) -{ - Packet4f tmp = _mm_mul_ps(a, _mm_movehl_ps(a,a)); - return pfirst<Packet4f>(_mm_mul_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1))); -} -template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) -{ - return pfirst<Packet2d>(_mm_mul_sd(a, _mm_unpackhi_pd(a,a))); -} -template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a) -{ - // after some experiments, it is seems this is the fastest way to implement it - // for GCC (eg., reusing pmul is very slow !) - // TODO try to call _mm_mul_epu32 directly - EIGEN_ALIGN16 int aux[4]; - pstore(aux, a); - return (aux[0] * aux[1]) * (aux[2] * aux[3]);; -} - -// min -template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) -{ - Packet4f tmp = _mm_min_ps(a, _mm_movehl_ps(a,a)); - return pfirst<Packet4f>(_mm_min_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1))); -} -template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) -{ - return pfirst<Packet2d>(_mm_min_sd(a, _mm_unpackhi_pd(a,a))); -} -template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a) -{ -#ifdef EIGEN_VECTORIZE_SSE4_1 - Packet4i tmp = _mm_min_epi32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2))); - return pfirst<Packet4i>(_mm_min_epi32(tmp,_mm_shuffle_epi32(tmp, 1))); -#else - // after some experiments, it is seems this is the fastest way to implement it - // for GCC (eg., it does not like using std::min after the pstore !!) - EIGEN_ALIGN16 int aux[4]; - pstore(aux, a); - int aux0 = aux[0]<aux[1] ? aux[0] : aux[1]; - int aux2 = aux[2]<aux[3] ? aux[2] : aux[3]; - return aux0<aux2 ? aux0 : aux2; -#endif // EIGEN_VECTORIZE_SSE4_1 -} - -// max -template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) -{ - Packet4f tmp = _mm_max_ps(a, _mm_movehl_ps(a,a)); - return pfirst<Packet4f>(_mm_max_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1))); -} -template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) -{ - return pfirst<Packet2d>(_mm_max_sd(a, _mm_unpackhi_pd(a,a))); -} -template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a) -{ -#ifdef EIGEN_VECTORIZE_SSE4_1 - Packet4i tmp = _mm_max_epi32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2))); - return pfirst<Packet4i>(_mm_max_epi32(tmp,_mm_shuffle_epi32(tmp, 1))); -#else - // after some experiments, it is seems this is the fastest way to implement it - // for GCC (eg., it does not like using std::min after the pstore !!) - EIGEN_ALIGN16 int aux[4]; - pstore(aux, a); - int aux0 = aux[0]>aux[1] ? aux[0] : aux[1]; - int aux2 = aux[2]>aux[3] ? aux[2] : aux[3]; - return aux0>aux2 ? aux0 : aux2; -#endif // EIGEN_VECTORIZE_SSE4_1 -} - -#if EIGEN_COMP_GNUC -// template <> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) -// { -// Packet4f res = b; -// asm("mulps %[a], %[b] \n\taddps %[c], %[b]" : [b] "+x" (res) : [a] "x" (a), [c] "x" (c)); -// return res; -// } -// EIGEN_STRONG_INLINE Packet4i _mm_alignr_epi8(const Packet4i& a, const Packet4i& b, const int i) -// { -// Packet4i res = a; -// asm("palignr %[i], %[a], %[b] " : [b] "+x" (res) : [a] "x" (a), [i] "i" (i)); -// return res; -// } -#endif - -#ifdef EIGEN_VECTORIZE_SSSE3 -// SSSE3 versions -template<int Offset> -struct palign_impl<Offset,Packet4f> -{ - static EIGEN_STRONG_INLINE void run(Packet4f& first, const Packet4f& second) - { - if (Offset!=0) - first = _mm_castsi128_ps(_mm_alignr_epi8(_mm_castps_si128(second), _mm_castps_si128(first), Offset*4)); - } -}; - -template<int Offset> -struct palign_impl<Offset,Packet4i> -{ - static EIGEN_STRONG_INLINE void run(Packet4i& first, const Packet4i& second) - { - if (Offset!=0) - first = _mm_alignr_epi8(second,first, Offset*4); - } -}; - -template<int Offset> -struct palign_impl<Offset,Packet2d> -{ - static EIGEN_STRONG_INLINE void run(Packet2d& first, const Packet2d& second) - { - if (Offset==1) - first = _mm_castsi128_pd(_mm_alignr_epi8(_mm_castpd_si128(second), _mm_castpd_si128(first), 8)); - } -}; -#else -// SSE2 versions -template<int Offset> -struct palign_impl<Offset,Packet4f> -{ - static EIGEN_STRONG_INLINE void run(Packet4f& first, const Packet4f& second) - { - if (Offset==1) - { - first = _mm_move_ss(first,second); - first = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(first),0x39)); - } - else if (Offset==2) - { - first = _mm_movehl_ps(first,first); - first = _mm_movelh_ps(first,second); - } - else if (Offset==3) - { - first = _mm_move_ss(first,second); - first = _mm_shuffle_ps(first,second,0x93); - } - } -}; - -template<int Offset> -struct palign_impl<Offset,Packet4i> -{ - static EIGEN_STRONG_INLINE void run(Packet4i& first, const Packet4i& second) - { - if (Offset==1) - { - first = _mm_castps_si128(_mm_move_ss(_mm_castsi128_ps(first),_mm_castsi128_ps(second))); - first = _mm_shuffle_epi32(first,0x39); - } - else if (Offset==2) - { - first = _mm_castps_si128(_mm_movehl_ps(_mm_castsi128_ps(first),_mm_castsi128_ps(first))); - first = _mm_castps_si128(_mm_movelh_ps(_mm_castsi128_ps(first),_mm_castsi128_ps(second))); - } - else if (Offset==3) - { - first = _mm_castps_si128(_mm_move_ss(_mm_castsi128_ps(first),_mm_castsi128_ps(second))); - first = _mm_castps_si128(_mm_shuffle_ps(_mm_castsi128_ps(first),_mm_castsi128_ps(second),0x93)); - } - } -}; - -template<int Offset> -struct palign_impl<Offset,Packet2d> -{ - static EIGEN_STRONG_INLINE void run(Packet2d& first, const Packet2d& second) - { - if (Offset==1) - { - first = _mm_castps_pd(_mm_movehl_ps(_mm_castpd_ps(first),_mm_castpd_ps(first))); - first = _mm_castps_pd(_mm_movelh_ps(_mm_castpd_ps(first),_mm_castpd_ps(second))); - } - } -}; -#endif - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet4f,4>& kernel) { - _MM_TRANSPOSE4_PS(kernel.packet[0], kernel.packet[1], kernel.packet[2], kernel.packet[3]); -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet2d,2>& kernel) { - __m128d tmp = _mm_unpackhi_pd(kernel.packet[0], kernel.packet[1]); - kernel.packet[0] = _mm_unpacklo_pd(kernel.packet[0], kernel.packet[1]); - kernel.packet[1] = tmp; -} - -template<> EIGEN_DEVICE_FUNC inline void -ptranspose(PacketBlock<Packet4i,4>& kernel) { - __m128i T0 = _mm_unpacklo_epi32(kernel.packet[0], kernel.packet[1]); - __m128i T1 = _mm_unpacklo_epi32(kernel.packet[2], kernel.packet[3]); - __m128i T2 = _mm_unpackhi_epi32(kernel.packet[0], kernel.packet[1]); - __m128i T3 = _mm_unpackhi_epi32(kernel.packet[2], kernel.packet[3]); - - kernel.packet[0] = _mm_unpacklo_epi64(T0, T1); - kernel.packet[1] = _mm_unpackhi_epi64(T0, T1); - kernel.packet[2] = _mm_unpacklo_epi64(T2, T3); - kernel.packet[3] = _mm_unpackhi_epi64(T2, T3); -} - -template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) { - const __m128i zero = _mm_setzero_si128(); - const __m128i select = _mm_set_epi32(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]); - __m128i false_mask = _mm_cmpeq_epi32(select, zero); -#ifdef EIGEN_VECTORIZE_SSE4_1 - return _mm_blendv_epi8(thenPacket, elsePacket, false_mask); -#else - return _mm_or_si128(_mm_andnot_si128(false_mask, thenPacket), _mm_and_si128(false_mask, elsePacket)); -#endif -} -template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) { - const __m128 zero = _mm_setzero_ps(); - const __m128 select = _mm_set_ps(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]); - __m128 false_mask = _mm_cmpeq_ps(select, zero); -#ifdef EIGEN_VECTORIZE_SSE4_1 - return _mm_blendv_ps(thenPacket, elsePacket, false_mask); -#else - return _mm_or_ps(_mm_andnot_ps(false_mask, thenPacket), _mm_and_ps(false_mask, elsePacket)); -#endif -} - -template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) { - const __m128d zero = _mm_setzero_pd(); - const __m128d select = _mm_set_pd(ifPacket.select[1], ifPacket.select[0]); - __m128d false_mask = _mm_cmpeq_pd(select, zero); -#ifdef EIGEN_VECTORIZE_SSE4_1 - return _mm_blendv_pd(thenPacket, elsePacket, false_mask); -#else - return _mm_or_pd(_mm_andnot_pd(false_mask, thenPacket), _mm_and_pd(false_mask, elsePacket)); -#endif -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PACKET_MATH_SSE_H diff --git a/third_party/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h b/third_party/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h deleted file mode 100644 index c848932306..0000000000 --- a/third_party/eigen3/Eigen/src/Core/arch/SSE/TypeCasting.h +++ /dev/null @@ -1,77 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TYPE_CASTING_SSE_H -#define EIGEN_TYPE_CASTING_SSE_H - -namespace Eigen { - -namespace internal { - -template <> -struct type_casting_traits<float, int> { - enum { - VectorizedCast = 1, - SrcCoeffRatio = 1, - TgtCoeffRatio = 1 - }; -}; - -template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) { - return _mm_cvttps_epi32(a); -} - - -template <> -struct type_casting_traits<int, float> { - enum { - VectorizedCast = 1, - SrcCoeffRatio = 1, - TgtCoeffRatio = 1 - }; -}; - -template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) { - return _mm_cvtepi32_ps(a); -} - - -template <> -struct type_casting_traits<double, float> { - enum { - VectorizedCast = 1, - SrcCoeffRatio = 2, - TgtCoeffRatio = 1 - }; -}; - -template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) { - return _mm_shuffle_ps(_mm_cvtpd_ps(a), _mm_cvtpd_ps(b), (1 << 2) | (1 << 6)); -} - -template <> -struct type_casting_traits<float, double> { - enum { - VectorizedCast = 1, - SrcCoeffRatio = 1, - TgtCoeffRatio = 2 - }; -}; - -template<> EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) { - // Simply discard the second half of the input - return _mm_cvtps_pd(a); -} - - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TYPE_CASTING_SSE_H diff --git a/third_party/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h b/third_party/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h deleted file mode 100644 index ae264aa640..0000000000 --- a/third_party/eigen3/Eigen/src/Core/functors/AssignmentFunctors.h +++ /dev/null @@ -1,167 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ASSIGNMENT_FUNCTORS_H -#define EIGEN_ASSIGNMENT_FUNCTORS_H - -namespace Eigen { - -namespace internal { - -/** \internal - * \brief Template functor for scalar/packet assignment - * - */ -template<typename Scalar> struct assign_op { - - EIGEN_EMPTY_STRUCT_CTOR(assign_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a = b; } - - template<int Alignment, typename Packet> - EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const - { internal::pstoret<Scalar,Packet,Alignment>(a,b); } -}; -template<typename Scalar> -struct functor_traits<assign_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::ReadCost, - PacketAccess = packet_traits<Scalar>::IsVectorized - }; -}; - -/** \internal - * \brief Template functor for scalar/packet assignment with addition - * - */ -template<typename Scalar> struct add_assign_op { - - EIGEN_EMPTY_STRUCT_CTOR(add_assign_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a += b; } - - template<int Alignment, typename Packet> - EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const - { internal::pstoret<Scalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); } -}; -template<typename Scalar> -struct functor_traits<add_assign_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasAdd - }; -}; - -/** \internal - * \brief Template functor for scalar/packet assignment with subtraction - * - */ -template<typename Scalar> struct sub_assign_op { - - EIGEN_EMPTY_STRUCT_CTOR(sub_assign_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a -= b; } - - template<int Alignment, typename Packet> - EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const - { internal::pstoret<Scalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); } -}; -template<typename Scalar> -struct functor_traits<sub_assign_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasAdd - }; -}; - -/** \internal - * \brief Template functor for scalar/packet assignment with multiplication - * - */ -template<typename Scalar> struct mul_assign_op { - - EIGEN_EMPTY_STRUCT_CTOR(mul_assign_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a *= b; } - - template<int Alignment, typename Packet> - EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const - { internal::pstoret<Scalar,Packet,Alignment>(a,internal::pmul(internal::ploadt<Packet,Alignment>(a),b)); } -}; -template<typename Scalar> -struct functor_traits<mul_assign_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasMul - }; -}; - -/** \internal - * \brief Template functor for scalar/packet assignment with diviving - * - */ -template<typename Scalar> struct div_assign_op { - - EIGEN_EMPTY_STRUCT_CTOR(div_assign_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const { a /= b; } - - template<int Alignment, typename Packet> - EIGEN_STRONG_INLINE void assignPacket(Scalar* a, const Packet& b) const - { internal::pstoret<Scalar,Packet,Alignment>(a,internal::pdiv(internal::ploadt<Packet,Alignment>(a),b)); } -}; -template<typename Scalar> -struct functor_traits<div_assign_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::ReadCost + NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasMul - }; -}; - - -/** \internal - * \brief Template functor for scalar/packet assignment with swaping - * - * It works as follow. For a non-vectorized evaluation loop, we have: - * for(i) func(A.coeffRef(i), B.coeff(i)); - * where B is a SwapWrapper expression. The trick is to make SwapWrapper::coeff behaves like a non-const coeffRef. - * Actually, SwapWrapper might not even be needed since even if B is a plain expression, since it has to be writable - * B.coeff already returns a const reference to the underlying scalar value. - * - * The case of a vectorized loop is more tricky: - * for(i,j) func.assignPacket<A_Align>(&A.coeffRef(i,j), B.packet<B_Align>(i,j)); - * Here, B must be a SwapWrapper whose packet function actually returns a proxy object holding a Scalar*, - * the actual alignment and Packet type. - * - */ -template<typename Scalar> struct swap_assign_op { - - EIGEN_EMPTY_STRUCT_CTOR(swap_assign_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const - { - using std::swap; - swap(a,const_cast<Scalar&>(b)); - } - - template<int LhsAlignment, int RhsAlignment, typename Packet> - EIGEN_STRONG_INLINE void swapPacket(Scalar* a, Scalar* b) const - { - Packet tmp = internal::ploadt<Packet,RhsAlignment>(b); - internal::pstoret<Scalar,Packet,RhsAlignment>(b, internal::ploadt<Packet,LhsAlignment>(a)); - internal::pstoret<Scalar,Packet,LhsAlignment>(a, tmp); - } -}; -template<typename Scalar> -struct functor_traits<swap_assign_op<Scalar> > { - enum { - Cost = 3 * NumTraits<Scalar>::ReadCost, - PacketAccess = packet_traits<Scalar>::IsVectorized - }; -}; - -} // namespace internal - -} // namespace Eigen - -#endif // EIGEN_ASSIGNMENT_FUNCTORS_H diff --git a/third_party/eigen3/Eigen/src/Core/functors/BinaryFunctors.h b/third_party/eigen3/Eigen/src/Core/functors/BinaryFunctors.h deleted file mode 100644 index bffc72151a..0000000000 --- a/third_party/eigen3/Eigen/src/Core/functors/BinaryFunctors.h +++ /dev/null @@ -1,556 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BINARY_FUNCTORS_H -#define EIGEN_BINARY_FUNCTORS_H - -// clang-format off - -namespace Eigen { - -namespace internal { - -//---------- associative binary functors ---------- - -/** \internal - * \brief Template functor to compute the sum of two scalars - * - * \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, DenseBase::sum() - */ -template<typename Scalar> struct scalar_sum_op { -// typedef Scalar result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_sum_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a + b; } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::padd(a,b); } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const - { return internal::predux(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_sum_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasAdd - }; -}; - -/** \internal - * \brief Template specialization to deprecate the summation of boolean expressions. - * This is required to solve Bug 426. - * \sa DenseBase::count(), DenseBase::any(), ArrayBase::cast(), MatrixBase::cast() - */ -template<> struct scalar_sum_op<bool> : scalar_sum_op<int> { - EIGEN_DEPRECATED - scalar_sum_op() {} -}; - - -/** \internal - * \brief Template functor to compute the product of two scalars - * - * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux() - */ -template<typename LhsScalar,typename RhsScalar> struct scalar_product_op { - enum { - // TODO vectorize mixed product - Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul - }; - typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_product_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::pmul(a,b); } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type predux(const Packet& a) const - { return internal::predux_mul(a); } -}; -template<typename LhsScalar,typename RhsScalar> -struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > { - enum { - Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost)/2, // rough estimate! - PacketAccess = scalar_product_op<LhsScalar,RhsScalar>::Vectorizable - }; -}; - -/** \internal - * \brief Template functor to compute the conjugate product of two scalars - * - * This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y) - */ -template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op { - - enum { - Conj = NumTraits<LhsScalar>::IsComplex - }; - - typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type; - - EIGEN_EMPTY_STRUCT_CTOR(scalar_conj_product_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const - { return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); } - - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); } -}; -template<typename LhsScalar,typename RhsScalar> -struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > { - enum { - Cost = NumTraits<LhsScalar>::MulCost, - PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul - }; -}; - -/** \internal - * \brief Template functor to compute the min of two scalars - * - * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff() - */ -template<typename Scalar> struct scalar_min_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return numext::mini(a, b); } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::pmin(a,b); } - template<typename Packet> - EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const - { return internal::predux_min(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_min_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasMin - }; -}; - -/** \internal - * \brief Template functor to compute the max of two scalars - * - * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff() - */ -template<typename Scalar> struct scalar_max_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return numext::maxi(a, b); } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::pmax(a,b); } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar predux(const Packet& a) const - { return internal::predux_max(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_max_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasMax - }; -}; - - -/** \internal - * \brief Template functors for comparison of two scalars - * \todo Implement packet-comparisons - */ -template<typename Scalar, ComparisonName cmp> struct scalar_cmp_op; - -template<typename Scalar, ComparisonName cmp> -struct functor_traits<scalar_cmp_op<Scalar, cmp> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = false - }; -}; - -template<ComparisonName Cmp, typename Scalar> -struct result_of<scalar_cmp_op<Scalar, Cmp>(Scalar,Scalar)> { - typedef bool type; -}; - - -template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_EQ> { - typedef bool result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a==b;} -}; -template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_LT> { - typedef bool result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a<b;} -}; -template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_LE> { - typedef bool result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a<=b;} -}; -template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_GT> { - typedef bool result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a>b;} -}; -template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_GE> { - typedef bool result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a>=b;} -}; -template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_UNORD> { - typedef bool result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return !(a<=b || b<=a);} -}; -template<typename Scalar> struct scalar_cmp_op<Scalar, cmp_NEQ> { - typedef bool result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_cmp_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator()(const Scalar& a, const Scalar& b) const {return a!=b;} -}; - - -/** \internal - * \brief Template functor to compute the hypot of two scalars - * - * \sa MatrixBase::stableNorm(), class Redux - */ -template<typename Scalar> struct scalar_hypot_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_hypot_op) -// typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& _x, const Scalar& _y) const - { - using std::sqrt; - Scalar p = numext::maxi(_x, _y); - Scalar q = numext::mini(_x, _y); - Scalar qp = q/p; - return p * sqrt(Scalar(1) + qp*qp); - } -}; -template<typename Scalar> -struct functor_traits<scalar_hypot_op<Scalar> > { - enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess=0 }; -}; - -/** \internal - * \brief Template functor to compute the pow of two scalars - */ -template<typename Scalar, typename OtherScalar> struct scalar_binary_pow_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_binary_pow_op) - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return numext::pow(a, b); } -}; -template<typename Scalar, typename OtherScalar> -struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > { - enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; -}; - - - -//---------- non associative binary functors ---------- - -/** \internal - * \brief Template functor to compute the difference of two scalars - * - * \sa class CwiseBinaryOp, MatrixBase::operator- - */ -template<typename Scalar> struct scalar_difference_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_difference_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { return a - b; } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::psub(a,b); } -}; -template<typename Scalar> -struct functor_traits<scalar_difference_op<Scalar> > { - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasSub - }; -}; - -/** \internal - * \brief Template functor to compute the quotient of two scalars - * - * \sa class CwiseBinaryOp, Cwise::operator/() - */ -template<typename LhsScalar,typename RhsScalar> struct scalar_quotient_op { - enum { - // TODO vectorize mixed product - Vectorizable = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv - }; - typedef typename scalar_product_traits<LhsScalar,RhsScalar>::ReturnType result_type; - EIGEN_EMPTY_STRUCT_CTOR(scalar_quotient_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; } - template<typename Packet> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const - { return internal::pdiv(a,b); } -}; -template<typename LhsScalar,typename RhsScalar> -struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > { - enum { - Cost = (NumTraits<LhsScalar>::MulCost + NumTraits<RhsScalar>::MulCost), // rough estimate! - PacketAccess = scalar_quotient_op<LhsScalar,RhsScalar>::Vectorizable - }; -}; - - - -/** \internal - * \brief Template functor to compute the and of two booleans - * - * \sa class CwiseBinaryOp, ArrayBase::operator&& - */ -struct scalar_boolean_and_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_and_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a && b; } -}; -template<> struct functor_traits<scalar_boolean_and_op> { - enum { - Cost = NumTraits<bool>::AddCost, - PacketAccess = false - }; -}; - -/** \internal - * \brief Template functor to compute the or of two booleans - * - * \sa class CwiseBinaryOp, ArrayBase::operator|| - */ -struct scalar_boolean_or_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_or_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a || b; } -}; -template<> struct functor_traits<scalar_boolean_or_op> { - enum { - Cost = NumTraits<bool>::AddCost, - PacketAccess = false - }; -}; - -/** \internal - * \brief Template functor to compute the xor of two booleans - * - * \sa class CwiseBinaryOp, ArrayBase::operator^ - */ -struct scalar_boolean_xor_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_boolean_xor_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const bool& a, const bool& b) const { return a ^ b; } -}; -template<> struct functor_traits<scalar_boolean_xor_op> { - enum { - Cost = NumTraits<bool>::AddCost, - PacketAccess = false - }; -}; - - - -//---------- binary functors bound to a constant, thus appearing as a unary functor ---------- - -/** \internal - * \brief Template functor to multiply a scalar by a fixed other one - * - * \sa class CwiseUnaryOp, MatrixBase::operator*, MatrixBase::operator/ - */ -/* NOTE why doing the pset1() in packetOp *is* an optimization ? - * indeed it seems better to declare m_other as a Packet and do the pset1() once - * in the constructor. However, in practice: - * - GCC does not like m_other as a Packet and generate a load every time it needs it - * - on the other hand GCC is able to moves the pset1() outside the loop :) - * - simpler code ;) - * (ICC and gcc 4.4 seems to perform well in both cases, the issue is visible with y = a*x + b*y) - */ -template<typename Scalar> -struct scalar_multiple_op { - typedef typename packet_traits<Scalar>::type Packet; - // FIXME default copy constructors seems bugged with std::complex<> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_multiple_op(const scalar_multiple_op& other) : m_other(other.m_other) { } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_multiple_op(const Scalar& other) : m_other(other) { } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pmul(a, pset1<Packet>(m_other)); } - typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_multiple_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; }; - -template<typename Scalar1, typename Scalar2> -struct scalar_multiple2_op { - typedef typename packet_traits<Scalar1>::type Packet1; - typedef typename scalar_product_traits<Scalar1,Scalar2>::ReturnType result_type; - typedef typename packet_traits<result_type>::type packet_result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_multiple2_op(const scalar_multiple2_op& other) : m_other(other.m_other) { } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_multiple2_op(const Scalar2& other) : m_other(other) { } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar1& a) const { return a * m_other; } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const packet_result_type packetOp(const Packet1& a) const - { eigen_assert("packetOp is not defined"); } - typename add_const_on_value_type<typename NumTraits<Scalar2>::Nested>::type m_other; -}; -template<typename Scalar1,typename Scalar2> -struct functor_traits<scalar_multiple2_op<Scalar1,Scalar2> > -{ enum { Cost = NumTraits<Scalar1>::MulCost, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to divide a scalar by a fixed other one - * - * This functor is used to implement the quotient of a matrix by - * a scalar where the scalar type is not necessarily a floating point type. - * - * \sa class CwiseUnaryOp, MatrixBase::operator/ - */ -template<typename Scalar> -struct scalar_quotient1_op { - typedef typename packet_traits<Scalar>::type Packet; - // FIXME default copy constructors seems bugged with std::complex<> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient1_op(const scalar_quotient1_op& other) : m_other(other.m_other) { } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other) : m_other(other) {} - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pdiv(a, pset1<Packet>(m_other)); } - typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_quotient1_op<Scalar> > -{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; }; - -// In Eigen, any binary op (Product, CwiseBinaryOp) require the Lhs and Rhs to have the same scalar type, except for multiplication -// where the mixing of different types is handled by scalar_product_traits -// In particular, real * complex<real> is allowed. -// FIXME move this to functor_traits adding a functor_default -template<typename Functor> struct functor_is_product_like { enum { ret = 0 }; }; -template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; }; -template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_conj_product_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; }; -template<typename LhsScalar,typename RhsScalar> struct functor_is_product_like<scalar_quotient_op<LhsScalar,RhsScalar> > { enum { ret = 1 }; }; - - -/** \internal - * \brief Template functor to add a scalar to a fixed other one - * \sa class CwiseUnaryOp, Array::operator+ - */ -/* If you wonder why doing the pset1() in packetOp() is an optimization check scalar_multiple_op */ -template<typename Scalar> -struct scalar_add_op { - typedef typename packet_traits<Scalar>::type Packet; - // FIXME default copy constructors seems bugged with std::complex<> - EIGEN_DEVICE_FUNC inline scalar_add_op(const scalar_add_op& other) : m_other(other.m_other) { } - EIGEN_DEVICE_FUNC inline scalar_add_op(const Scalar& other) : m_other(other) { } - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a + m_other; } - EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const - { return internal::padd(a, pset1<Packet>(m_other)); } - const Scalar m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_add_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; }; - -/** \internal - * \brief Template functor to subtract a fixed scalar to another one - * \sa class CwiseUnaryOp, Array::operator-, struct scalar_add_op, struct scalar_rsub_op - */ -template<typename Scalar> -struct scalar_sub_op { - typedef typename packet_traits<Scalar>::type Packet; - EIGEN_DEVICE_FUNC inline scalar_sub_op(const scalar_sub_op& other) : m_other(other.m_other) { } - EIGEN_DEVICE_FUNC inline scalar_sub_op(const Scalar& other) : m_other(other) { } - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a - m_other; } - EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const - { return internal::psub(a, pset1<Packet>(m_other)); } - const Scalar m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_sub_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; }; - -/** \internal - * \brief Template functor to subtract a scalar to fixed another one - * \sa class CwiseUnaryOp, Array::operator-, struct scalar_add_op, struct scalar_sub_op - */ -template<typename Scalar> -struct scalar_rsub_op { - typedef typename packet_traits<Scalar>::type Packet; - EIGEN_DEVICE_FUNC inline scalar_rsub_op(const scalar_rsub_op& other) : m_other(other.m_other) { } - EIGEN_DEVICE_FUNC inline scalar_rsub_op(const Scalar& other) : m_other(other) { } - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return m_other - a; } - EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const - { return internal::psub(pset1<Packet>(m_other), a); } - const Scalar m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_rsub_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasAdd }; }; - -/** \internal - * \brief Template functor to raise a scalar to a power - * \sa class CwiseUnaryOp, Cwise::pow - */ -template<typename Scalar> -struct scalar_pow_op { - // FIXME default copy constructors seems bugged with std::complex<> - EIGEN_DEVICE_FUNC inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { } - EIGEN_DEVICE_FUNC inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {} - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return numext::pow(a, m_exponent); } - const Scalar m_exponent; -}; -template<typename Scalar> -struct functor_traits<scalar_pow_op<Scalar> > -{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to compute the quotient between a scalar and array entries. - * \sa class CwiseUnaryOp, Cwise::inverse() - */ -template<typename Scalar> -struct scalar_inverse_mult_op { - EIGEN_DEVICE_FUNC scalar_inverse_mult_op(const Scalar& other) : m_other(other) {} - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return m_other / a; } - template<typename Packet> - EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const - { return internal::pdiv(pset1<Packet>(m_other),a); } - Scalar m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_inverse_mult_op<Scalar> > -{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; }; - -/** \internal - * \brief Template functor to compute the modulo between an array and a scalar. - */ -template <typename Scalar> -struct scalar_mod_op { - EIGEN_DEVICE_FUNC scalar_mod_op(const Scalar& divisor) : m_divisor(divisor) {} - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a % m_divisor; } - const Scalar m_divisor; -}; -template <typename Scalar> -struct functor_traits<scalar_mod_op<Scalar> > -{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to compute the float modulo between an array and a scalar. - */ -template <typename Scalar> -struct scalar_fmod_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_fmod_op); - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar - operator()(const Scalar& a, const Scalar& b) const { - EIGEN_USING_STD_MATH(fmod); - return (fmod)(a, b); - } -}; - -template <typename Scalar> -struct functor_traits<scalar_fmod_op<Scalar> > { - enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = false }; -}; - - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_BINARY_FUNCTORS_H diff --git a/third_party/eigen3/Eigen/src/Core/functors/NullaryFunctors.h b/third_party/eigen3/Eigen/src/Core/functors/NullaryFunctors.h deleted file mode 100644 index 6e464b2b8a..0000000000 --- a/third_party/eigen3/Eigen/src/Core/functors/NullaryFunctors.h +++ /dev/null @@ -1,158 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_NULLARY_FUNCTORS_H -#define EIGEN_NULLARY_FUNCTORS_H - -namespace Eigen { - -namespace internal { - -template<typename Scalar> -struct scalar_constant_op { - typedef typename packet_traits<Scalar>::type Packet; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { } - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index, Index = 0) const { return m_other; } - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(Index, Index = 0) const { return internal::pset1<Packet>(m_other); } - const Scalar m_other; -}; -template<typename Scalar> -struct functor_traits<scalar_constant_op<Scalar> > -// FIXME replace this packet test by a safe one -{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; }; - -template<typename Scalar> struct scalar_identity_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_identity_op) - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const { return row==col ? Scalar(1) : Scalar(0); } -}; -template<typename Scalar> -struct functor_traits<scalar_identity_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; }; - -template <typename Scalar, bool RandomAccess> struct linspaced_op_impl; - -// linear access for packet ops: -// 1) initialization -// base = [low, ..., low] + ([step, ..., step] * [-size, ..., 0]) -// 2) each step (where size is 1 for coeff access or PacketSize for packet access) -// base += [size*step, ..., size*step] -// -// TODO: Perhaps it's better to initialize lazily (so not in the constructor but in packetOp) -// in order to avoid the padd() in operator() ? -template <typename Scalar> -struct linspaced_op_impl<Scalar,false> -{ - typedef typename packet_traits<Scalar>::type Packet; - - linspaced_op_impl(const Scalar& low, const Scalar& step) : - m_low(low), m_step(step), - m_packetStep(pset1<Packet>(packet_traits<Scalar>::size*step)), - m_base(padd(pset1<Packet>(low), pmul(pset1<Packet>(step),plset<Scalar>(-packet_traits<Scalar>::size)))) {} - - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index i) const - { - m_base = padd(m_base, pset1<Packet>(m_step)); - return m_low+Scalar(i)*m_step; - } - - template<typename Index> - EIGEN_STRONG_INLINE const Packet packetOp(Index) const { return m_base = padd(m_base,m_packetStep); } - - const Scalar m_low; - const Scalar m_step; - const Packet m_packetStep; - mutable Packet m_base; -}; - -// random access for packet ops: -// 1) each step -// [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) ) -template <typename Scalar> -struct linspaced_op_impl<Scalar,true> -{ - typedef typename packet_traits<Scalar>::type Packet; - - linspaced_op_impl(const Scalar& low, const Scalar& step) : - m_low(low), m_step(step), - m_lowPacket(pset1<Packet>(m_low)), m_stepPacket(pset1<Packet>(m_step)), m_interPacket(plset<Scalar>(0)) {} - - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return m_low+i*m_step; } - - template<typename Index> - EIGEN_STRONG_INLINE const Packet packetOp(Index i) const - { return internal::padd(m_lowPacket, pmul(m_stepPacket, padd(pset1<Packet>(i),m_interPacket))); } - - const Scalar m_low; - const Scalar m_step; - const Packet m_lowPacket; - const Packet m_stepPacket; - const Packet m_interPacket; -}; - -// ----- Linspace functor ---------------------------------------------------------------- - -// Forward declaration (we default to random access which does not really give -// us a speed gain when using packet access but it allows to use the functor in -// nested expressions). -template <typename Scalar, bool RandomAccess = true> struct linspaced_op; -template <typename Scalar, bool RandomAccess> struct functor_traits< linspaced_op<Scalar,RandomAccess> > -{ enum { Cost = 1, PacketAccess = packet_traits<Scalar>::HasSetLinear, IsRepeatable = true }; }; -template <typename Scalar, bool RandomAccess> struct linspaced_op -{ - typedef typename packet_traits<Scalar>::type Packet; - linspaced_op(const Scalar& low, const Scalar& high, DenseIndex num_steps) : impl((num_steps==1 ? high : low), (num_steps==1 ? Scalar() : (high-low)/(num_steps-1))) {} - - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index i) const { return impl(i); } - - // We need this function when assigning e.g. a RowVectorXd to a MatrixXd since - // there row==0 and col is used for the actual iteration. - template<typename Index> - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (Index row, Index col) const - { - eigen_assert(col==0 || row==0); - return impl(col + row); - } - - template<typename Index> - EIGEN_STRONG_INLINE const Packet packetOp(Index i) const { return impl.packetOp(i); } - - // We need this function when assigning e.g. a RowVectorXd to a MatrixXd since - // there row==0 and col is used for the actual iteration. - template<typename Index> - EIGEN_STRONG_INLINE const Packet packetOp(Index row, Index col) const - { - eigen_assert(col==0 || row==0); - return impl.packetOp(col + row); - } - - // This proxy object handles the actual required temporaries, the different - // implementations (random vs. sequential access) as well as the - // correct piping to size 2/4 packet operations. - const linspaced_op_impl<Scalar,RandomAccess> impl; -}; - -// all functors allow linear access, except scalar_identity_op. So we fix here a quick meta -// to indicate whether a functor allows linear access, just always answering 'yes' except for -// scalar_identity_op. -// FIXME move this to functor_traits adding a functor_default -template<typename Functor> struct functor_has_linear_access { enum { ret = 1 }; }; -template<typename Scalar> struct functor_has_linear_access<scalar_identity_op<Scalar> > { enum { ret = 0 }; }; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_NULLARY_FUNCTORS_H diff --git a/third_party/eigen3/Eigen/src/Core/functors/StlFunctors.h b/third_party/eigen3/Eigen/src/Core/functors/StlFunctors.h deleted file mode 100644 index 863fd451d3..0000000000 --- a/third_party/eigen3/Eigen/src/Core/functors/StlFunctors.h +++ /dev/null @@ -1,129 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_STL_FUNCTORS_H -#define EIGEN_STL_FUNCTORS_H - -namespace Eigen { - -namespace internal { - -// default functor traits for STL functors: - -template<typename T> -struct functor_traits<std::multiplies<T> > -{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::divides<T> > -{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::plus<T> > -{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::minus<T> > -{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::negate<T> > -{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::logical_or<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::logical_and<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::logical_not<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::greater<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::less<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::greater_equal<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::less_equal<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::equal_to<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::not_equal_to<T> > -{ enum { Cost = 1, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::binder2nd<T> > -{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::binder1st<T> > -{ enum { Cost = functor_traits<T>::Cost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::unary_negate<T> > -{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; }; - -template<typename T> -struct functor_traits<std::binary_negate<T> > -{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; }; - -#ifdef EIGEN_STDEXT_SUPPORT - -template<typename T0,typename T1> -struct functor_traits<std::project1st<T0,T1> > -{ enum { Cost = 0, PacketAccess = false }; }; - -template<typename T0,typename T1> -struct functor_traits<std::project2nd<T0,T1> > -{ enum { Cost = 0, PacketAccess = false }; }; - -template<typename T0,typename T1> -struct functor_traits<std::select2nd<std::pair<T0,T1> > > -{ enum { Cost = 0, PacketAccess = false }; }; - -template<typename T0,typename T1> -struct functor_traits<std::select1st<std::pair<T0,T1> > > -{ enum { Cost = 0, PacketAccess = false }; }; - -template<typename T0,typename T1> -struct functor_traits<std::unary_compose<T0,T1> > -{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false }; }; - -template<typename T0,typename T1,typename T2> -struct functor_traits<std::binary_compose<T0,T1,T2> > -{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false }; }; - -#endif // EIGEN_STDEXT_SUPPORT - -// allow to add new functors and specializations of functor_traits from outside Eigen. -// this macro is really needed because functor_traits must be specialized after it is declared but before it is used... -#ifdef EIGEN_FUNCTORS_PLUGIN -#include EIGEN_FUNCTORS_PLUGIN -#endif - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_STL_FUNCTORS_H diff --git a/third_party/eigen3/Eigen/src/Core/functors/UnaryFunctors.h b/third_party/eigen3/Eigen/src/Core/functors/UnaryFunctors.h deleted file mode 100644 index 8e181b60ff..0000000000 --- a/third_party/eigen3/Eigen/src/Core/functors/UnaryFunctors.h +++ /dev/null @@ -1,611 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_UNARY_FUNCTORS_H -#define EIGEN_UNARY_FUNCTORS_H - -namespace Eigen { - -namespace internal { - -#if defined(__NVCC__) || !defined(__CUDA_ARCH__) -using std::abs; -using std::exp; -using std::log; -using std::min; -using std::sqrt; -using std::cos; -using std::sin; -using std::tan; -using std::acos; -using std::asin; -using std::atan; -#endif - -#if defined(__CUDA_ARCH__) -using std::lgamma; // Supported by all cuda compilers -using std::erf; // Supported by all cuda compilers -using std::erfc; // Supported by all cuda compilers -#endif - -/** \internal - * \brief Template functor to compute the opposite of a scalar - * - * \sa class CwiseUnaryOp, MatrixBase::operator- - */ -template<typename Scalar> struct scalar_opposite_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_opposite_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pnegate(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_opposite_op<Scalar> > -{ enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasNegate }; -}; - -/** \internal - * \brief Template functor to compute the absolute value of a scalar - * - * \sa class CwiseUnaryOp, Cwise::abs - */ -template<typename Scalar> struct scalar_abs_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_abs_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return abs(a); } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pabs(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_abs_op<Scalar> > -{ - enum { - Cost = NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasAbs - }; -}; - -/** \internal - * \brief Template functor to compute the squared absolute value of a scalar - * - * \sa class CwiseUnaryOp, Cwise::abs2 - */ -template<typename Scalar> struct scalar_abs2_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs2(a); } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const - { return internal::pmul(a,a); } -}; -template<typename Scalar> -struct functor_traits<scalar_abs2_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasAbs2 }; }; - -/** \internal - * \brief Template functor to compute the conjugate of a complex value - * - * \sa class CwiseUnaryOp, MatrixBase::conjugate() - */ -template<typename Scalar> struct scalar_conjugate_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op) - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { using numext::conj; return conj(a); } - template<typename Packet> - EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_conjugate_op<Scalar> > -{ - enum { - Cost = NumTraits<Scalar>::IsComplex ? NumTraits<Scalar>::AddCost : 0, - PacketAccess = packet_traits<Scalar>::HasConj - }; -}; - -/** \internal - * \brief Template functor to cast a scalar to another type - * - * \sa class CwiseUnaryOp, MatrixBase::cast() - */ -template<typename Scalar, typename NewType> -struct scalar_cast_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_cast_op) - typedef NewType result_type; - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast<Scalar, NewType>(a); } -}; -template<typename Scalar, typename NewType> -struct functor_traits<scalar_cast_op<Scalar,NewType> > -{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to convert a scalar to another type using a custom functor. - * - * \sa class CwiseUnaryOp, MatrixBase::convert() - */ -template<typename Scalar, typename NewType, typename ConvertOp> -struct scalar_convert_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_convert_op) - typedef NewType result_type; - EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return ConvertOp()(a); } -}; -template<typename Scalar, typename NewType, typename ConvertOp> -struct functor_traits<scalar_convert_op<Scalar,NewType,ConvertOp> > -{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; }; - - -/** \internal - * \brief Template functor to extract the real part of a complex - * - * \sa class CwiseUnaryOp, MatrixBase::real() - */ -template<typename Scalar> -struct scalar_real_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::real(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_real_op<Scalar> > -{ enum { Cost = 0, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to extract the imaginary part of a complex - * - * \sa class CwiseUnaryOp, MatrixBase::imag() - */ -template<typename Scalar> -struct scalar_imag_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::imag(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_imag_op<Scalar> > -{ enum { Cost = 0, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to extract the real part of a complex as a reference - * - * \sa class CwiseUnaryOp, MatrixBase::real() - */ -template<typename Scalar> -struct scalar_real_ref_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::real_ref(*const_cast<Scalar*>(&a)); } -}; -template<typename Scalar> -struct functor_traits<scalar_real_ref_op<Scalar> > -{ enum { Cost = 0, PacketAccess = false }; }; - -/** \internal - * \brief Template functor to extract the imaginary part of a complex as a reference - * - * \sa class CwiseUnaryOp, MatrixBase::imag() - */ -template<typename Scalar> -struct scalar_imag_ref_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op) - typedef typename NumTraits<Scalar>::Real result_type; - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::imag_ref(*const_cast<Scalar*>(&a)); } -}; -template<typename Scalar> -struct functor_traits<scalar_imag_ref_op<Scalar> > -{ enum { Cost = 0, PacketAccess = false }; }; - -/** \internal - * - * \brief Template functor to compute the exponential of a scalar - * - * \sa class CwiseUnaryOp, Cwise::exp() - */ -template<typename Scalar> struct scalar_exp_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_exp_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return exp(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::pexp(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_exp_op<Scalar> > -{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasExp }; }; - -/** \internal - * - * \brief Template functor to compute the logarithm of a scalar - * - * \sa class CwiseUnaryOp, Cwise::log() - */ -template<typename Scalar> struct scalar_log_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_log_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return log(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::plog(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_log_op<Scalar> > -{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; }; - - -/** \internal - * \brief Template functor to compute the square root of a scalar - * \sa class CwiseUnaryOp, Cwise::sqrt() - */ -template<typename Scalar> struct scalar_sqrt_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sqrt_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return sqrt(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_sqrt_op<Scalar> > -{ enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasSqrt - }; -}; - -/** \internal - * \brief Template functor to compute the reciprocal square root of a scalar - * \sa class CwiseUnaryOp, Cwise::rsqrt() - */ -template<typename Scalar> struct scalar_rsqrt_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_rsqrt_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return Scalar(1)/sqrt(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::prsqrt(a); } -}; - -template<typename Scalar> -struct functor_traits<scalar_rsqrt_op<Scalar> > -{ enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasRsqrt - }; -}; - - -/** \internal - * \brief Template functor to compute the cosine of a scalar - * \sa class CwiseUnaryOp, ArrayBase::cos() - */ -template<typename Scalar> struct scalar_cos_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_cos_op) - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return cos(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::pcos(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_cos_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasCos - }; -}; - -/** \internal - * \brief Template functor to compute the sine of a scalar - * \sa class CwiseUnaryOp, ArrayBase::sin() - */ -template<typename Scalar> struct scalar_sin_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sin_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return sin(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::psin(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_sin_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasSin - }; -}; - - -/** \internal - * \brief Template functor to compute the tan of a scalar - * \sa class CwiseUnaryOp, ArrayBase::tan() - */ -template<typename Scalar> struct scalar_tan_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_tan_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return tan(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::ptan(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_tan_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasTan - }; -}; - -/** \internal - * \brief Template functor to compute the arc cosine of a scalar - * \sa class CwiseUnaryOp, ArrayBase::acos() - */ -template<typename Scalar> struct scalar_acos_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_acos_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return acos(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::pacos(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_acos_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasACos - }; -}; - -/** \internal - * \brief Template functor to compute the arc sine of a scalar - * \sa class CwiseUnaryOp, ArrayBase::asin() - */ -template<typename Scalar> struct scalar_asin_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_asin_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return asin(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::pasin(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_asin_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasASin - }; -}; - - -/** \internal - * \brief Template functor to compute the atan of a scalar - * \sa class CwiseUnaryOp, ArrayBase::atan() - */ -template<typename Scalar> struct scalar_atan_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_atan_op) - inline const Scalar operator() (const Scalar& a) const { return atan(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::patan(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_atan_op<Scalar> > -{ - enum { - Cost = 5 * NumTraits<Scalar>::MulCost, - PacketAccess = packet_traits<Scalar>::HasATan - }; -}; - - /** \internal - * \brief Template functor to compute the tanh of a scalar - * \sa class CwiseUnaryOp, ArrayBase::tanh() - */ -template<typename Scalar> struct scalar_tanh_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { using std::tanh; return tanh(a); } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::ptanh(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_tanh_op<Scalar> > -{ - enum { - Cost = 6 * NumTraits<Scalar>::MulCost + 4 * NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasTanH - }; -}; - -/** \internal - * \brief Template functor to compute the natural log of the absolute value of Gamma of a scalar - * \sa class CwiseUnaryOp, Cwise::lgamma() - */ -template<typename Scalar> struct scalar_lgamma_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_lgamma_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { -#if defined(__CUDA_ARCH__) - return lgamma(a); -#else - using numext::lgamma; return lgamma(a); -#endif - } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::plgamma(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_lgamma_op<Scalar> > -{ - enum { - // Guesstimate - Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasLGamma - }; -}; - -/** \internal - * \brief Template functor to compute the Gauss error function of a scalar - * \sa class CwiseUnaryOp, Cwise::erf() - */ -template<typename Scalar> struct scalar_erf_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_erf_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { -#if defined(__CUDA_ARCH__) - return erf(a); -#else - using numext::erf; return erf(a); -#endif - } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::perf(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_erf_op<Scalar> > -{ - enum { - // Guesstimate - Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasErf - }; -}; - -/** \internal - * \brief Template functor to compute the Complementary Error Function of a scalar - * \sa class CwiseUnaryOp, Cwise::erfc() - */ -template<typename Scalar> struct scalar_erfc_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_erfc_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { -#if defined(__CUDA_ARCH__) - return erfc(a); -#else - using numext::erfc; return erfc(a); -#endif - } - typedef typename packet_traits<Scalar>::type Packet; - inline Packet packetOp(const Packet& a) const { return internal::perfc(a); } -}; -template<typename Scalar> -struct functor_traits<scalar_erfc_op<Scalar> > -{ - enum { - // Guesstimate - Cost = 10 * NumTraits<Scalar>::MulCost + 5 * NumTraits<Scalar>::AddCost, - PacketAccess = packet_traits<Scalar>::HasErfc - }; -}; - - - /** \internal - * \brief Template functor to compute the sigmoid of a scalar - * \sa class CwiseUnaryOp, ArrayBase::sigmoid() - */ -template <typename T> -struct scalar_sigmoid_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const { - const T one = T(1); - return one / (one + std::exp(-x)); - } - - template <typename Packet> - inline Packet packetOp(const Packet& x) const { - const Packet one = pset1<Packet>(1); - return pdiv(one, padd(one, pexp(pnegate(x)))); - } -}; - -template <typename T> -struct functor_traits<scalar_sigmoid_op<T> > { - enum { - Cost = NumTraits<T>::AddCost * 2 + NumTraits<T>::MulCost * 6, - PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasDiv && - packet_traits<T>::HasNegate && packet_traits<T>::HasExp - }; -}; - -/** \internal - * \brief Template functor to compute the inverse of a scalar - * \sa class CwiseUnaryOp, Cwise::inverse() - */ -template<typename Scalar> -struct scalar_inverse_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_inverse_op) - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; } - template<typename Packet> - inline const Packet packetOp(const Packet& a) const - { return internal::pdiv(pset1<Packet>(Scalar(1)),a); } -}; -template<typename Scalar> -struct functor_traits<scalar_inverse_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; }; - -/** \internal - * \brief Template functor to compute the square of a scalar - * \sa class CwiseUnaryOp, Cwise::square() - */ -template<typename Scalar> -struct scalar_square_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_square_op) - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a; } - template<typename Packet> - inline const Packet packetOp(const Packet& a) const - { return internal::pmul(a,a); } -}; -template<typename Scalar> -struct functor_traits<scalar_square_op<Scalar> > -{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; }; - -/** \internal - * \brief Template functor to compute the cube of a scalar - * \sa class CwiseUnaryOp, Cwise::cube() - */ -template<typename Scalar> -struct scalar_cube_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_cube_op) - EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a*a; } - template<typename Packet> - inline const Packet packetOp(const Packet& a) const - { return internal::pmul(a,pmul(a,a)); } -}; -template<typename Scalar> -struct functor_traits<scalar_cube_op<Scalar> > -{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; }; - - -/** \internal - * \brief Template functor to compute the signum of a scalar - * \sa class CwiseUnaryOp, Cwise::sign() - */ -template<typename Scalar,bool iscpx=(NumTraits<Scalar>::IsComplex!=0) > struct scalar_sign_op; -template<typename Scalar> -struct scalar_sign_op<Scalar,false> { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const - { - return Scalar( (a>Scalar(0)) - (a<Scalar(0)) ); - } -}; -template<typename Scalar> -struct scalar_sign_op<Scalar,true> { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sign_op) - EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const - { - typename NumTraits<Scalar>::Real aa = std::abs(a); - return (aa==0) ? Scalar(0) : (a/aa); - } -}; -template<typename Scalar> -struct functor_traits<scalar_sign_op<Scalar> > -{ enum { - Cost = - NumTraits<Scalar>::IsComplex - ? ( 8*NumTraits<Scalar>::MulCost ) // roughly - : ( 3*NumTraits<Scalar>::AddCost), - PacketAccess = false, - }; -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_FUNCTORS_H diff --git a/third_party/eigen3/Eigen/src/Core/products/CoeffBasedProduct.h b/third_party/eigen3/Eigen/src/Core/products/CoeffBasedProduct.h deleted file mode 100644 index 35a6e36e81..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/CoeffBasedProduct.h +++ /dev/null @@ -1,454 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COEFFBASED_PRODUCT_H -#define EIGEN_COEFFBASED_PRODUCT_H - -namespace Eigen { - -namespace internal { - -/********************************************************************************* -* Coefficient based product implementation. -* It is designed for the following use cases: -* - small fixed sizes -* - lazy products -*********************************************************************************/ - -/* Since the all the dimensions of the product are small, here we can rely - * on the generic Assign mechanism to evaluate the product per coeff (or packet). - * - * Note that here the inner-loops should always be unrolled. - */ - -template<int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar> -struct product_coeff_impl; - -template<int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct product_packet_impl; - -template<typename LhsNested, typename RhsNested, int NestingFlags> -struct traits<CoeffBasedProduct<LhsNested,RhsNested,NestingFlags> > -{ - typedef MatrixXpr XprKind; - typedef typename remove_all<LhsNested>::type _LhsNested; - typedef typename remove_all<RhsNested>::type _RhsNested; - typedef typename scalar_product_traits<typename _LhsNested::Scalar, typename _RhsNested::Scalar>::ReturnType Scalar; - typedef typename promote_storage_type<typename traits<_LhsNested>::StorageKind, - typename traits<_RhsNested>::StorageKind>::ret StorageKind; - typedef typename promote_index_type<typename traits<_LhsNested>::Index, - typename traits<_RhsNested>::Index>::type Index; - - enum { - LhsCoeffReadCost = _LhsNested::CoeffReadCost, - RhsCoeffReadCost = _RhsNested::CoeffReadCost, - LhsFlags = _LhsNested::Flags, - RhsFlags = _RhsNested::Flags, - - RowsAtCompileTime = _LhsNested::RowsAtCompileTime, - ColsAtCompileTime = _RhsNested::ColsAtCompileTime, - InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime), - - MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime, - MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime, - - LhsRowMajor = LhsFlags & RowMajorBit, - RhsRowMajor = RhsFlags & RowMajorBit, - - SameType = is_same<typename _LhsNested::Scalar,typename _RhsNested::Scalar>::value, - - CanVectorizeRhs = RhsRowMajor && (RhsFlags & PacketAccessBit) - && (ColsAtCompileTime == Dynamic - || ( (ColsAtCompileTime % packet_traits<Scalar>::size) == 0 - && (RhsFlags&AlignedBit) - ) - ), - - CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) - && (RowsAtCompileTime == Dynamic - || ( (RowsAtCompileTime % packet_traits<Scalar>::size) == 0 - && (LhsFlags&AlignedBit) - ) - ), - - EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1 - : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0 - : (RhsRowMajor && !CanVectorizeLhs), - - Flags = ((unsigned int)(LhsFlags | RhsFlags) & HereditaryBits & ~RowMajorBit) - | (EvalToRowMajor ? RowMajorBit : 0) - | NestingFlags - | (CanVectorizeLhs ? (LhsFlags & AlignedBit) : 0) - | (CanVectorizeRhs ? (RhsFlags & AlignedBit) : 0) - // TODO enable vectorization for mixed types - | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0), - - CoeffReadCost = InnerSize == Dynamic ? Dynamic - : InnerSize * (NumTraits<Scalar>::MulCost + LhsCoeffReadCost + RhsCoeffReadCost) - + (InnerSize - 1) * NumTraits<Scalar>::AddCost, - - /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside - * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner - * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect - * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI. - */ - CanVectorizeInner = SameType - && LhsRowMajor - && (!RhsRowMajor) - && (LhsFlags & RhsFlags & ActualPacketAccessBit) - && (LhsFlags & RhsFlags & AlignedBit) - && (InnerSize % packet_traits<Scalar>::size == 0) - }; -}; - -} // end namespace internal - -template<typename LhsNested, typename RhsNested, int NestingFlags> -class CoeffBasedProduct - : internal::no_assignment_operator, - public MatrixBase<CoeffBasedProduct<LhsNested, RhsNested, NestingFlags> > -{ - public: - - typedef MatrixBase<CoeffBasedProduct> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(CoeffBasedProduct) - typedef typename Base::PlainObject PlainObject; - - private: - - typedef typename internal::traits<CoeffBasedProduct>::_LhsNested _LhsNested; - typedef typename internal::traits<CoeffBasedProduct>::_RhsNested _RhsNested; - - enum { - PacketSize = internal::packet_traits<Scalar>::size, - InnerSize = internal::traits<CoeffBasedProduct>::InnerSize, - Unroll = CoeffReadCost != Dynamic && CoeffReadCost <= EIGEN_UNROLLING_LIMIT, - CanVectorizeInner = internal::traits<CoeffBasedProduct>::CanVectorizeInner - }; - - typedef internal::product_coeff_impl<CanVectorizeInner ? InnerVectorizedTraversal : DefaultTraversal, - Unroll ? InnerSize-1 : Dynamic, - _LhsNested, _RhsNested, Scalar> ScalarCoeffImpl; - - typedef CoeffBasedProduct<LhsNested,RhsNested,NestByRefBit> LazyCoeffBasedProductType; - - public: - - EIGEN_DEVICE_FUNC - inline CoeffBasedProduct(const CoeffBasedProduct& other) - : Base(), m_lhs(other.m_lhs), m_rhs(other.m_rhs) - {} - - template<typename Lhs, typename Rhs> - EIGEN_DEVICE_FUNC - inline CoeffBasedProduct(const Lhs& lhs, const Rhs& rhs) - : m_lhs(lhs), m_rhs(rhs) - { - // we don't allow taking products of matrices of different real types, as that wouldn't be vectorizable. - // We still allow to mix T and complex<T>. - EIGEN_STATIC_ASSERT((internal::scalar_product_traits<typename Lhs::RealScalar, typename Rhs::RealScalar>::Defined), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - eigen_assert(lhs.cols() == rhs.rows() - && "invalid matrix product" - && "if you wanted a coeff-wise or a dot product use the respective explicit functions"); - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const - { - Scalar res; - ScalarCoeffImpl::run(row, col, m_lhs, m_rhs, res); - return res; - } - - /* Allow index-based non-packet access. It is impossible though to allow index-based packed access, - * which is why we don't set the LinearAccessBit. - */ - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE const Scalar coeff(Index index) const - { - Scalar res; - const Index row = RowsAtCompileTime == 1 ? 0 : index; - const Index col = RowsAtCompileTime == 1 ? index : 0; - ScalarCoeffImpl::run(row, col, m_lhs, m_rhs, res); - return res; - } - - template<int LoadMode> - EIGEN_STRONG_INLINE const PacketScalar packet(Index row, Index col) const - { - PacketScalar res; - internal::product_packet_impl<Flags&RowMajorBit ? RowMajor : ColMajor, - Unroll ? InnerSize-1 : Dynamic, - _LhsNested, _RhsNested, PacketScalar, LoadMode> - ::run(row, col, m_lhs, m_rhs, res); - return res; - } - - // Implicit conversion to the nested type (trigger the evaluation of the product) - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE operator const PlainObject& () const - { - m_result.lazyAssign(*this); - return m_result; - } - - EIGEN_DEVICE_FUNC const _LhsNested& lhs() const { return m_lhs; } - EIGEN_DEVICE_FUNC const _RhsNested& rhs() const { return m_rhs; } - - EIGEN_DEVICE_FUNC - const Diagonal<const LazyCoeffBasedProductType,0> diagonal() const - { return reinterpret_cast<const LazyCoeffBasedProductType&>(*this); } - - template<int DiagonalIndex> - EIGEN_DEVICE_FUNC - const Diagonal<const LazyCoeffBasedProductType,DiagonalIndex> diagonal() const - { return reinterpret_cast<const LazyCoeffBasedProductType&>(*this); } - - EIGEN_DEVICE_FUNC - const Diagonal<const LazyCoeffBasedProductType, DynamicIndex> diagonal(Index index) const { - return Diagonal<const LazyCoeffBasedProductType, DynamicIndex>( - reinterpret_cast<const LazyCoeffBasedProductType&>(*this), index); - } - - protected: - typename internal::add_const_on_value_type<LhsNested>::type m_lhs; - typename internal::add_const_on_value_type<RhsNested>::type m_rhs; - - mutable PlainObject m_result; -}; - -namespace internal { - -// here we need to overload the nested rule for products -// such that the nested type is a const reference to a plain matrix -template<typename Lhs, typename Rhs, int N, typename PlainObject> -struct nested<CoeffBasedProduct<Lhs,Rhs,EvalBeforeNestingBit|EvalBeforeAssigningBit>, N, PlainObject> -{ - typedef PlainObject const& type; -}; - -/*************************************************************************** -* Normal product .coeff() implementation (with meta-unrolling) -***************************************************************************/ - -/************************************** -*** Scalar path - no vectorization *** -**************************************/ - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar> -struct product_coeff_impl<DefaultTraversal, UnrollingIndex, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::Index Index; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res) - { - product_coeff_impl<DefaultTraversal, UnrollingIndex-1, Lhs, Rhs, RetScalar>::run(row, col, lhs, rhs, res); - res += lhs.coeff(row, UnrollingIndex) * rhs.coeff(UnrollingIndex, col); - } -}; - -template<typename Lhs, typename Rhs, typename RetScalar> -struct product_coeff_impl<DefaultTraversal, 0, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::Index Index; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res) - { - res = lhs.coeff(row, 0) * rhs.coeff(0, col); - } -}; - -template<typename Lhs, typename Rhs, typename RetScalar> -struct product_coeff_impl<DefaultTraversal, Dynamic, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::Index Index; - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar& res) - { - eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix"); - res = lhs.coeff(row, 0) * rhs.coeff(0, col); - for(Index i = 1; i < lhs.cols(); ++i) - res += lhs.coeff(row, i) * rhs.coeff(i, col); - } -}; - -/******************************************* -*** Scalar path with inner vectorization *** -*******************************************/ - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet> -struct product_coeff_vectorized_unroller -{ - typedef typename Lhs::Index Index; - enum { PacketSize = packet_traits<typename Lhs::Scalar>::size }; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres) - { - product_coeff_vectorized_unroller<UnrollingIndex-PacketSize, Lhs, Rhs, Packet>::run(row, col, lhs, rhs, pres); - pres = padd(pres, pmul( lhs.template packet<Aligned>(row, UnrollingIndex) , rhs.template packet<Aligned>(UnrollingIndex, col) )); - } -}; - -template<typename Lhs, typename Rhs, typename Packet> -struct product_coeff_vectorized_unroller<0, Lhs, Rhs, Packet> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::PacketScalar &pres) - { - pres = pmul(lhs.template packet<Aligned>(row, 0) , rhs.template packet<Aligned>(0, col)); - } -}; - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar> -struct product_coeff_impl<InnerVectorizedTraversal, UnrollingIndex, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::PacketScalar Packet; - typedef typename Lhs::Index Index; - enum { PacketSize = packet_traits<typename Lhs::Scalar>::size }; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, RetScalar &res) - { - Packet pres; - product_coeff_vectorized_unroller<UnrollingIndex+1-PacketSize, Lhs, Rhs, Packet>::run(row, col, lhs, rhs, pres); - res = predux(pres); - } -}; - -template<typename Lhs, typename Rhs, int LhsRows = Lhs::RowsAtCompileTime, int RhsCols = Rhs::ColsAtCompileTime> -struct product_coeff_vectorized_dyn_selector -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) - { - res = lhs.row(row).transpose().cwiseProduct(rhs.col(col)).sum(); - } -}; - -// NOTE the 3 following specializations are because taking .col(0) on a vector is a bit slower -// NOTE maybe they are now useless since we have a specialization for Block<Matrix> -template<typename Lhs, typename Rhs, int RhsCols> -struct product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,RhsCols> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index /*row*/, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) - { - res = lhs.transpose().cwiseProduct(rhs.col(col)).sum(); - } -}; - -template<typename Lhs, typename Rhs, int LhsRows> -struct product_coeff_vectorized_dyn_selector<Lhs,Rhs,LhsRows,1> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) - { - res = lhs.row(row).transpose().cwiseProduct(rhs).sum(); - } -}; - -template<typename Lhs, typename Rhs> -struct product_coeff_vectorized_dyn_selector<Lhs,Rhs,1,1> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) - { - res = lhs.transpose().cwiseProduct(rhs).sum(); - } -}; - -template<typename Lhs, typename Rhs, typename RetScalar> -struct product_coeff_impl<InnerVectorizedTraversal, Dynamic, Lhs, Rhs, RetScalar> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, typename Lhs::Scalar &res) - { - product_coeff_vectorized_dyn_selector<Lhs,Rhs>::run(row, col, lhs, rhs, res); - } -}; - -/******************* -*** Packet path *** -*******************/ - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res) - { - product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, res); - res = pmadd(pset1<Packet>(lhs.coeff(row, UnrollingIndex)), rhs.template packet<LoadMode>(UnrollingIndex, col), res); - } -}; - -template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res) - { - product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, res); - res = pmadd(lhs.template packet<LoadMode>(row, UnrollingIndex), pset1<Packet>(rhs.coeff(UnrollingIndex, col)), res); - } -}; - -template<typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res) - { - res = pmul(pset1<Packet>(lhs.coeff(row, 0)),rhs.template packet<LoadMode>(0, col)); - } -}; - -template<typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet &res) - { - res = pmul(lhs.template packet<LoadMode>(row, 0), pset1<Packet>(rhs.coeff(0, col))); - } -}; - -template<typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet& res) - { - eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix"); - res = pmul(pset1<Packet>(lhs.coeff(row, 0)),rhs.template packet<LoadMode>(0, col)); - for(Index i = 1; i < lhs.cols(); ++i) - res = pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode>(i, col), res); - } -}; - -template<typename Lhs, typename Rhs, typename Packet, int LoadMode> -struct product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode> -{ - typedef typename Lhs::Index Index; - static EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Packet& res) - { - eigen_assert(lhs.cols()>0 && "you are using a non initialized matrix"); - res = pmul(lhs.template packet<LoadMode>(row, 0), pset1<Packet>(rhs.coeff(0, col))); - for(Index i = 1; i < lhs.cols(); ++i) - res = pmadd(lhs.template packet<LoadMode>(row, i), pset1<Packet>(rhs.coeff(i, col)), res); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_COEFFBASED_PRODUCT_H diff --git a/third_party/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/third_party/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h deleted file mode 100644 index 80bd6aa0e6..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/GeneralBlockPanelKernel.h +++ /dev/null @@ -1,2197 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERAL_BLOCK_PANEL_H -#define EIGEN_GENERAL_BLOCK_PANEL_H - - -namespace Eigen { - -namespace internal { - -template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs=false, bool _ConjRhs=false> -class gebp_traits; - - -/** \internal \returns b if a<=0, and returns a otherwise. */ -inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b) -{ - return a<=0 ? b : a; -} - -#if EIGEN_ARCH_i386_OR_x86_64 -const std::ptrdiff_t defaultL1CacheSize = 32*1024; -const std::ptrdiff_t defaultL2CacheSize = 256*1024; -const std::ptrdiff_t defaultL3CacheSize = 2*1024*1024; -#else -const std::ptrdiff_t defaultL1CacheSize = 16*1024; -const std::ptrdiff_t defaultL2CacheSize = 512*1024; -const std::ptrdiff_t defaultL3CacheSize = 512*1024; -#endif - -/** \internal */ -inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1, std::ptrdiff_t* l2, std::ptrdiff_t* l3) -{ - static bool m_cache_sizes_initialized = false; - static std::ptrdiff_t m_l1CacheSize = 0; - static std::ptrdiff_t m_l2CacheSize = 0; - static std::ptrdiff_t m_l3CacheSize = 0; - - if(EIGEN_UNLIKELY(!m_cache_sizes_initialized)) - { - int l1CacheSize, l2CacheSize, l3CacheSize; - queryCacheSizes(l1CacheSize, l2CacheSize, l3CacheSize); - m_l1CacheSize = manage_caching_sizes_helper(l1CacheSize, defaultL1CacheSize); - m_l2CacheSize = manage_caching_sizes_helper(l2CacheSize, defaultL2CacheSize); - m_l3CacheSize = manage_caching_sizes_helper(l3CacheSize, defaultL3CacheSize); - m_cache_sizes_initialized = true; - } - - if(EIGEN_UNLIKELY(action==SetAction)) - { - // set the cpu cache size and cache all block sizes from a global cache size in byte - eigen_internal_assert(l1!=0 && l2!=0); - m_l1CacheSize = *l1; - m_l2CacheSize = *l2; - m_l3CacheSize = *l3; - } - else if(EIGEN_LIKELY(action==GetAction)) - { - eigen_internal_assert(l1!=0 && l2!=0); - *l1 = m_l1CacheSize; - *l2 = m_l2CacheSize; - *l3 = m_l3CacheSize; - } - else - { - eigen_internal_assert(false); - } -} - -#define CEIL(a, b) ((a)+(b)-1)/(b) - -/* Helper for computeProductBlockingSizes. - * - * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar, - * this function computes the blocking size parameters along the respective dimensions - * for matrix products and related algorithms. The blocking sizes depends on various - * parameters: - * - the L1 and L2 cache sizes, - * - the register level blocking sizes defined by gebp_traits, - * - the number of scalars that fit into a packet (when vectorization is enabled). - * - * \sa setCpuCacheSizes */ -template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index> -void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index num_threads = 1) -{ - // Explanations: - // Let's recall the product algorithms form kc x nc horizontal panels B' on the rhs and - // mc x kc blocks A' on the lhs. A' has to fit into L2 cache. Moreover, B' is processed - // per kc x nr vertical small panels where nr is the blocking size along the n dimension - // at the register level. For vectorization purpose, these small vertical panels are unpacked, - // e.g., each coefficient is replicated to fit a packet. This small vertical panel has to - // stay in L1 cache. - typedef gebp_traits<LhsScalar,RhsScalar> Traits; - typedef typename Traits::ResScalar ResScalar; - enum { - kdiv = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)), - ksub = Traits::mr * Traits::nr * sizeof(ResScalar), - k_mask = (0xffffffff/8)*8, - - mr = Traits::mr, - mr_mask = (0xffffffff/mr)*mr, - - nr = Traits::nr, - nr_mask = (0xffffffff/nr)*nr - }; - - std::ptrdiff_t l1, l2, l3; - manage_caching_sizes(GetAction, &l1, &l2, &l3); - - // Increasing k gives us more time to prefetch the content of the "C" - // registers. However once the latency is hidden there is no point in - // increasing the value of k, so we'll cap it at 320 (value determined - // experimentally). - const Index k_cache = (std::min<Index>)((l1-ksub)/kdiv, 320); - if (k_cache < k) { - k = k_cache & k_mask; - eigen_assert(k > 0); - } - - const Index n_cache = (l2-l1) / (nr * sizeof(RhsScalar) * k); - Index n_per_thread = CEIL(n, num_threads); - if (n_cache <= n_per_thread) { - // Don't exceed the capacity of the l2 cache. - if (n_cache < nr) { - n = nr; - } else { - n = n_cache & nr_mask; - eigen_assert(n > 0); - } - } else { - n = (std::min<Index>)(n, (n_per_thread + nr - 1) & nr_mask); - } - - if (l3 > l2) { - // l3 is shared between all cores, so we'll give each thread its own chunk of l3. - const Index m_cache = (l3-l2) / (sizeof(LhsScalar) * k * num_threads); - const Index m_per_thread = CEIL(m, num_threads); - if(m_cache < m_per_thread && m_cache >= static_cast<Index>(mr)) { - m = m_cache & mr_mask; - eigen_assert(m > 0); - } else { - m = (std::min<Index>)(m, (m_per_thread + mr - 1) & mr_mask); - } - } -} - -template <typename Index> -bool useSpecificBlockingSizes(Index& k, Index& m, Index& n) -{ -#ifdef EIGEN_TEST_SPECIFIC_BLOCKING_SIZES - if (EIGEN_TEST_SPECIFIC_BLOCKING_SIZES) { - k = std::min<Index>(k, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K); - m = std::min<Index>(m, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M); - n = std::min<Index>(n, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N); - return true; - } -#else - EIGEN_UNUSED_VARIABLE(k) - EIGEN_UNUSED_VARIABLE(m) - EIGEN_UNUSED_VARIABLE(n) -#endif - return false; -} - -/** \brief Computes the blocking parameters for a m x k times k x n matrix product - * - * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension. - * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension. - * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension. - * - * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar, - * this function computes the blocking size parameters along the respective dimensions - * for matrix products and related algorithms. - * - * The blocking size parameters may be evaluated: - * - either by a heuristic based on cache sizes; - * - or using fixed prescribed values (for testing purposes). - * - * \sa setCpuCacheSizes */ - -template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index> -void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1) -{ - if (!k || !m || !n) { - return; - } - - if (!useSpecificBlockingSizes(k, m, n)) { - evaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor>(k, m, n, num_threads); - } - -#if !EIGEN_ARCH_i386_OR_x86_64 - // The following code rounds k,m,n down to the nearest multiple of register-level blocking sizes. - // We should always do that, and in upstream Eigen we always do that. - // Unfortunately, we can't do that in Google3 on x86[-64] because this makes tiny differences in results and - // we have some unfortunate tests require very specific relative errors which fail because of that, - // at least //learning/laser/algorithms/wals:wals_batch_solver_test. - // Note that this wouldn't make any difference if we had been using only correctly rounded values, - // but we've not! See how in evaluateProductBlockingSizesHeuristic, we do the rounding down by - // bit-masking, e.g. mr_mask = (0xffffffff/mr)*mr, implicitly assuming that mr is always a power of - // two, which is not the case with the 3px4 kernel. - typedef gebp_traits<LhsScalar,RhsScalar> Traits; - enum { - kr = 8, - mr = Traits::mr, - nr = Traits::nr - }; - if (k > kr) k -= k % kr; - if (m > mr) m -= m % mr; - if (n > nr) n -= n % nr; -#endif -} - -template<typename LhsScalar, typename RhsScalar, typename Index> -inline void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads) -{ - computeProductBlockingSizes<LhsScalar,RhsScalar,1>(k, m, n, num_threads); -} - -#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_CJMADD - #define CJMADD(CJ,A,B,C,T) C = CJ.pmadd(A,B,C); -#else - - // FIXME (a bit overkill maybe ?) - - template<typename CJ, typename A, typename B, typename C, typename T> struct gebp_madd_selector { - EIGEN_ALWAYS_INLINE static void run(const CJ& cj, A& a, B& b, C& c, T& /*t*/) - { - c = cj.pmadd(a,b,c); - } - }; - - template<typename CJ, typename T> struct gebp_madd_selector<CJ,T,T,T,T> { - EIGEN_ALWAYS_INLINE static void run(const CJ& cj, T& a, T& b, T& c, T& t) - { - t = b; t = cj.pmul(a,t); c = padd(c,t); - } - }; - - template<typename CJ, typename A, typename B, typename C, typename T> - EIGEN_STRONG_INLINE void gebp_madd(const CJ& cj, A& a, B& b, C& c, T& t) - { - gebp_madd_selector<CJ,A,B,C,T>::run(cj,a,b,c,t); - } - - #define CJMADD(CJ,A,B,C,T) gebp_madd(CJ,A,B,C,T); -// #define CJMADD(CJ,A,B,C,T) T = B; T = CJ.pmul(A,T); C = padd(C,T); -#endif - -/* Vectorization logic - * real*real: unpack rhs to constant packets, ... - * - * cd*cd : unpack rhs to (b_r,b_r), (b_i,b_i), mul to get (a_r b_r,a_i b_r) (a_r b_i,a_i b_i), - * storing each res packet into two packets (2x2), - * at the end combine them: swap the second and addsub them - * cf*cf : same but with 2x4 blocks - * cplx*real : unpack rhs to constant packets, ... - * real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual - */ -template<typename _LhsScalar, typename _RhsScalar, bool _ConjLhs, bool _ConjRhs> -class gebp_traits -{ -public: - typedef _LhsScalar LhsScalar; - typedef _RhsScalar RhsScalar; - typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - - enum { - ConjLhs = _ConjLhs, - ConjRhs = _ConjRhs, - Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable, - LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, - RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, - ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, - - NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, - - // register block size along the N direction must be 1 or 4 - nr = 4, - - // register block size along the M direction (currently, this one cannot be modified) - default_mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*LhsPacketSize, -#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX) - // we assume 16 registers - mr = Vectorizable ? 3*LhsPacketSize : default_mr, -#else - mr = default_mr, -#endif - - LhsProgress = LhsPacketSize, - RhsProgress = 1 - }; - - typedef typename packet_traits<LhsScalar>::type _LhsPacket; - typedef typename packet_traits<RhsScalar>::type _RhsPacket; - typedef typename packet_traits<ResScalar>::type _ResPacket; - - typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; - typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; - typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; - - typedef ResPacket AccPacket; - - EIGEN_STRONG_INLINE void initAcc(AccPacket& p) - { - p = pset1<ResPacket>(ResScalar(0)); - } - - EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, RhsPacket& b0, RhsPacket& b1, RhsPacket& b2, RhsPacket& b3) - { - pbroadcast4(b, b0, b1, b2, b3); - } - -// EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, RhsPacket& b0, RhsPacket& b1) -// { -// pbroadcast2(b, b0, b1); -// } - - template<typename RhsPacketType> - EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const - { - dest = pset1<RhsPacketType>(*b); - } - - EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const - { - dest = ploadquad<RhsPacket>(b); - } - - template<typename LhsPacketType> - EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacketType& dest) const - { - dest = pload<LhsPacketType>(a); - } - - template<typename LhsPacketType> - EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const - { - dest = ploadu<LhsPacketType>(a); - } - - template<typename LhsPacketType, typename RhsPacketType, typename AccPacketType> - EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, AccPacketType& tmp) const - { - // It would be a lot cleaner to call pmadd all the time. Unfortunately if we - // let gcc allocate the register in which to store the result of the pmul - // (in the case where there is no FMA) gcc fails to figure out how to avoid - // spilling register. -#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD - EIGEN_UNUSED_VARIABLE(tmp); - c = pmadd(a,b,c); -#else - tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp); -#endif - } - - EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const - { - r = pmadd(c,alpha,r); - } - - template<typename ResPacketHalf> - EIGEN_STRONG_INLINE void acc(const ResPacketHalf& c, const ResPacketHalf& alpha, ResPacketHalf& r) const - { - r = pmadd(c,alpha,r); - } - -protected: -// conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj; -// conj_helper<LhsPacket,RhsPacket,ConjLhs,ConjRhs> pcj; -}; - -template<typename RealScalar, bool _ConjLhs> -class gebp_traits<std::complex<RealScalar>, RealScalar, _ConjLhs, false> -{ -public: - typedef std::complex<RealScalar> LhsScalar; - typedef RealScalar RhsScalar; - typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - - enum { - ConjLhs = _ConjLhs, - ConjRhs = false, - Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable, - LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, - RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, - ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, - - NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, - nr = 4, -#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX) - // we assume 16 registers - mr = 3*LhsPacketSize, -#else - mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*LhsPacketSize, -#endif - - LhsProgress = LhsPacketSize, - RhsProgress = 1 - }; - - typedef typename packet_traits<LhsScalar>::type _LhsPacket; - typedef typename packet_traits<RhsScalar>::type _RhsPacket; - typedef typename packet_traits<ResScalar>::type _ResPacket; - - typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; - typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; - typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; - - typedef ResPacket AccPacket; - - EIGEN_STRONG_INLINE void initAcc(AccPacket& p) - { - p = pset1<ResPacket>(ResScalar(0)); - } - - EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const - { - dest = pset1<RhsPacket>(*b); - } - - EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const - { - dest = pset1<RhsPacket>(*b); - } - - EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const - { - dest = pload<LhsPacket>(a); - } - - EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacket& dest) const - { - dest = ploadu<LhsPacket>(a); - } - - EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, RhsPacket& b0, RhsPacket& b1, RhsPacket& b2, RhsPacket& b3) - { - pbroadcast4(b, b0, b1, b2, b3); - } - -// EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, RhsPacket& b0, RhsPacket& b1) -// { -// pbroadcast2(b, b0, b1); -// } - - EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const - { - madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type()); - } - - EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const - { -#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD - EIGEN_UNUSED_VARIABLE(tmp); - c.v = pmadd(a.v,b,c.v); -#else - tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp); -#endif - } - - EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const - { - c += a * b; - } - - EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const - { - r = cj.pmadd(c,alpha,r); - } - -protected: - conj_helper<ResPacket,ResPacket,ConjLhs,false> cj; -}; - -template<typename Packet> -struct DoublePacket -{ - Packet first; - Packet second; -}; - -template<typename Packet> -DoublePacket<Packet> padd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b) -{ - DoublePacket<Packet> res; - res.first = padd(a.first, b.first); - res.second = padd(a.second,b.second); - return res; -} - -template<typename Packet> -const DoublePacket<Packet>& predux4(const DoublePacket<Packet> &a) -{ - return a; -} - -template<typename Packet> struct unpacket_traits<DoublePacket<Packet> > { typedef DoublePacket<Packet> half; }; -// template<typename Packet> -// DoublePacket<Packet> pmadd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b) -// { -// DoublePacket<Packet> res; -// res.first = padd(a.first, b.first); -// res.second = padd(a.second,b.second); -// return res; -// } - -template<typename RealScalar, bool _ConjLhs, bool _ConjRhs> -class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, _ConjLhs, _ConjRhs > -{ -public: - typedef std::complex<RealScalar> Scalar; - typedef std::complex<RealScalar> LhsScalar; - typedef std::complex<RealScalar> RhsScalar; - typedef std::complex<RealScalar> ResScalar; - - enum { - ConjLhs = _ConjLhs, - ConjRhs = _ConjRhs, - Vectorizable = packet_traits<RealScalar>::Vectorizable - && packet_traits<Scalar>::Vectorizable, - RealPacketSize = Vectorizable ? packet_traits<RealScalar>::size : 1, - ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, - LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, - RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, - - // FIXME: should depend on NumberOfRegisters - nr = 4, - mr = ResPacketSize, - - LhsProgress = ResPacketSize, - RhsProgress = 1 - }; - - typedef typename packet_traits<RealScalar>::type RealPacket; - typedef typename packet_traits<Scalar>::type ScalarPacket; - typedef DoublePacket<RealPacket> DoublePacketType; - - typedef typename conditional<Vectorizable,RealPacket, Scalar>::type LhsPacket; - typedef typename conditional<Vectorizable,DoublePacketType,Scalar>::type RhsPacket; - typedef typename conditional<Vectorizable,ScalarPacket,Scalar>::type ResPacket; - typedef typename conditional<Vectorizable,DoublePacketType,Scalar>::type AccPacket; - - EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); } - - EIGEN_STRONG_INLINE void initAcc(DoublePacketType& p) - { - p.first = pset1<RealPacket>(RealScalar(0)); - p.second = pset1<RealPacket>(RealScalar(0)); - } - - // Scalar path - EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ResPacket& dest) const - { - dest = pset1<ResPacket>(*b); - } - - // Vectorized path - EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacketType& dest) const - { - dest.first = pset1<RealPacket>(real(*b)); - dest.second = pset1<RealPacket>(imag(*b)); - } - - EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, ResPacket& dest) const - { - loadRhs(b,dest); - } - EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, DoublePacketType& dest) const - { - eigen_internal_assert(unpacket_traits<ScalarPacket>::size<=4); - loadRhs(b,dest); - } - - EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, RhsPacket& b0, RhsPacket& b1, RhsPacket& b2, RhsPacket& b3) - { - // FIXME not sure that's the best way to implement it! - loadRhs(b+0, b0); - loadRhs(b+1, b1); - loadRhs(b+2, b2); - loadRhs(b+3, b3); - } - - // Vectorized path - EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, DoublePacketType& b0, DoublePacketType& b1) - { - // FIXME not sure that's the best way to implement it! - loadRhs(b+0, b0); - loadRhs(b+1, b1); - } - - // Scalar path - EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, RhsScalar& b0, RhsScalar& b1) - { - // FIXME not sure that's the best way to implement it! - loadRhs(b+0, b0); - loadRhs(b+1, b1); - } - - // nothing special here - EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const - { - dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a)); - } - - EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacket& dest) const - { - dest = ploadu<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a)); - } - - EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, DoublePacketType& c, RhsPacket& /*tmp*/) const - { - c.first = padd(pmul(a,b.first), c.first); - c.second = padd(pmul(a,b.second),c.second); - } - - EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/) const - { - c = cj.pmadd(a,b,c); - } - - EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; } - - EIGEN_STRONG_INLINE void acc(const DoublePacketType& c, const ResPacket& alpha, ResPacket& r) const - { - // assemble c - ResPacket tmp; - if((!ConjLhs)&&(!ConjRhs)) - { - tmp = pcplxflip(pconj(ResPacket(c.second))); - tmp = padd(ResPacket(c.first),tmp); - } - else if((!ConjLhs)&&(ConjRhs)) - { - tmp = pconj(pcplxflip(ResPacket(c.second))); - tmp = padd(ResPacket(c.first),tmp); - } - else if((ConjLhs)&&(!ConjRhs)) - { - tmp = pcplxflip(ResPacket(c.second)); - tmp = padd(pconj(ResPacket(c.first)),tmp); - } - else if((ConjLhs)&&(ConjRhs)) - { - tmp = pcplxflip(ResPacket(c.second)); - tmp = psub(pconj(ResPacket(c.first)),tmp); - } - - r = pmadd(tmp,alpha,r); - } - -protected: - conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj; -}; - -template<typename RealScalar, bool _ConjRhs> -class gebp_traits<RealScalar, std::complex<RealScalar>, false, _ConjRhs > -{ -public: - typedef std::complex<RealScalar> Scalar; - typedef RealScalar LhsScalar; - typedef Scalar RhsScalar; - typedef Scalar ResScalar; - - enum { - ConjLhs = false, - ConjRhs = _ConjRhs, - Vectorizable = packet_traits<RealScalar>::Vectorizable - && packet_traits<Scalar>::Vectorizable, - LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, - RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, - ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1, - - NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, - // FIXME: should depend on NumberOfRegisters - nr = 4, - mr = (EIGEN_PLAIN_ENUM_MIN(16,NumberOfRegisters)/2/nr)*ResPacketSize, - - LhsProgress = ResPacketSize, - RhsProgress = 1 - }; - - typedef typename packet_traits<LhsScalar>::type _LhsPacket; - typedef typename packet_traits<RhsScalar>::type _RhsPacket; - typedef typename packet_traits<ResScalar>::type _ResPacket; - - typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; - typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; - typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; - - typedef ResPacket AccPacket; - - EIGEN_STRONG_INLINE void initAcc(AccPacket& p) - { - p = pset1<ResPacket>(ResScalar(0)); - } - - EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const - { - dest = pset1<RhsPacket>(*b); - } - - void broadcastRhs(const RhsScalar* b, RhsPacket& b0, RhsPacket& b1, RhsPacket& b2, RhsPacket& b3) - { - pbroadcast4(b, b0, b1, b2, b3); - } - -// EIGEN_STRONG_INLINE void broadcastRhs(const RhsScalar* b, RhsPacket& b0, RhsPacket& b1) -// { -// // FIXME not sure that's the best way to implement it! -// b0 = pload1<RhsPacket>(b+0); -// b1 = pload1<RhsPacket>(b+1); -// } - - EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const - { - dest = ploaddup<LhsPacket>(a); - } - - EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const - { - eigen_internal_assert(unpacket_traits<RhsPacket>::size<=4); - loadRhs(b,dest); - } - - EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacket& dest) const - { - dest = ploaddup<LhsPacket>(a); - } - - EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp) const - { - madd_impl(a, b, c, tmp, typename conditional<Vectorizable,true_type,false_type>::type()); - } - - EIGEN_STRONG_INLINE void madd_impl(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& tmp, const true_type&) const - { -#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD - EIGEN_UNUSED_VARIABLE(tmp); - c.v = pmadd(a,b.v,c.v); -#else - tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp); -#endif - - } - - EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const - { - c += a * b; - } - - EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const - { - r = cj.pmadd(alpha,c,r); - } - -protected: - conj_helper<ResPacket,ResPacket,false,ConjRhs> cj; -}; - -// helper for the rotating kernel below -template <typename GebpKernel, bool UseRotatingKernel = GebpKernel::UseRotatingKernel> -struct PossiblyRotatingKernelHelper -{ - // default implementation, not rotating - - typedef typename GebpKernel::Traits Traits; - typedef typename Traits::RhsScalar RhsScalar; - typedef typename Traits::RhsPacket RhsPacket; - typedef typename Traits::AccPacket AccPacket; - - const Traits& traits; - EIGEN_ALWAYS_INLINE PossiblyRotatingKernelHelper(const Traits& t) : traits(t) {} - - - template <size_t K, size_t Index> EIGEN_ALWAYS_INLINE - void loadOrRotateRhs(RhsPacket& to, const RhsScalar* from) const - { - traits.loadRhs(from + (Index+4*K)*Traits::RhsProgress, to); - } - - EIGEN_ALWAYS_INLINE void unrotateResult(AccPacket&, - AccPacket&, - AccPacket&, - AccPacket&) - { - } -}; - -// rotating implementation -template <typename GebpKernel> -struct PossiblyRotatingKernelHelper<GebpKernel, true> -{ - typedef typename GebpKernel::Traits Traits; - typedef typename Traits::RhsScalar RhsScalar; - typedef typename Traits::RhsPacket RhsPacket; - typedef typename Traits::AccPacket AccPacket; - - const Traits& traits; - EIGEN_ALWAYS_INLINE PossiblyRotatingKernelHelper(const Traits& t) : traits(t) {} - - template <size_t K, size_t Index> EIGEN_ALWAYS_INLINE - void loadOrRotateRhs(RhsPacket& to, const RhsScalar* from) const - { - if (Index == 0) { - to = pload<RhsPacket>(from + 4*K*Traits::RhsProgress); - } else { - EIGEN_ASM_COMMENT("Do not reorder code, we're very tight on registers"); - to = protate<1>(to); - } - } - - EIGEN_ALWAYS_INLINE void unrotateResult(AccPacket& res0, - AccPacket& res1, - AccPacket& res2, - AccPacket& res3) - { - PacketBlock<AccPacket> resblock; - resblock.packet[0] = res0; - resblock.packet[1] = res1; - resblock.packet[2] = res2; - resblock.packet[3] = res3; - ptranspose(resblock); - resblock.packet[3] = protate<1>(resblock.packet[3]); - resblock.packet[2] = protate<2>(resblock.packet[2]); - resblock.packet[1] = protate<3>(resblock.packet[1]); - ptranspose(resblock); - res0 = resblock.packet[0]; - res1 = resblock.packet[1]; - res2 = resblock.packet[2]; - res3 = resblock.packet[3]; - } -}; - -/* optimized GEneral packed Block * packed Panel product kernel - * - * Mixing type logic: C += A * B - * | A | B | comments - * |real |cplx | no vectorization yet, would require to pack A with duplication - * |cplx |real | easy vectorization - */ -template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs> -struct gebp_kernel -{ - typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> Traits; - typedef typename Traits::ResScalar ResScalar; - typedef typename Traits::LhsPacket LhsPacket; - typedef typename Traits::RhsPacket RhsPacket; - typedef typename Traits::ResPacket ResPacket; - typedef typename Traits::AccPacket AccPacket; - - typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs> SwappedTraits; - typedef typename SwappedTraits::ResScalar SResScalar; - typedef typename SwappedTraits::LhsPacket SLhsPacket; - typedef typename SwappedTraits::RhsPacket SRhsPacket; - typedef typename SwappedTraits::ResPacket SResPacket; - typedef typename SwappedTraits::AccPacket SAccPacket; - - typedef typename DataMapper::LinearMapper LinearMapper; - - enum { - Vectorizable = Traits::Vectorizable, - LhsProgress = Traits::LhsProgress, - RhsProgress = Traits::RhsProgress, - ResPacketSize = Traits::ResPacketSize - }; - - EIGEN_DONT_INLINE - void operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB, - Index rows, Index depth, Index cols, ResScalar alpha, - Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0); - - static const bool UseRotatingKernel = - EIGEN_ARCH_ARM && - internal::is_same<LhsScalar, float>::value && - internal::is_same<RhsScalar, float>::value && - internal::is_same<ResScalar, float>::value && - Traits::LhsPacketSize == 4 && - Traits::RhsPacketSize == 4 && - Traits::ResPacketSize == 4; -}; - -template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs> -EIGEN_DONT_INLINE -void gebp_kernel<LhsScalar, RhsScalar, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> - ::operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB, - Index rows, Index depth, Index cols, ResScalar alpha, - Index strideA, Index strideB, Index offsetA, Index offsetB) - { - Traits traits; - SwappedTraits straits; - - if(strideA==-1) strideA = depth; - if(strideB==-1) strideB = depth; - conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj; - Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0; - const Index peeled_mc3 = mr>=3*Traits::LhsProgress ? (rows/(3*LhsProgress))*(3*LhsProgress) : 0; - const Index peeled_mc2 = mr>=2*Traits::LhsProgress ? peeled_mc3+((rows-peeled_mc3)/(2*LhsProgress))*(2*LhsProgress) : 0; - const Index peeled_mc1 = mr>=1*Traits::LhsProgress ? (rows/(1*LhsProgress))*(1*LhsProgress) : 0; - enum { pk = 8 }; // NOTE Such a large peeling factor is important for large matrices (~ +5% when >1000 on Haswell) - const Index peeled_kc = depth & ~(pk-1); - const Index prefetch_res_offset = 0; -// const Index depth2 = depth & ~1; - - //---------- Process 3 * LhsProgress rows at once ---------- - // This corresponds to 3*LhsProgress x nr register blocks. - // Usually, make sense only with FMA - if(mr>=3*Traits::LhsProgress) - { - PossiblyRotatingKernelHelper<gebp_kernel> possiblyRotatingKernelHelper(traits); - - // loops on each largest micro horizontal panel of lhs (3*Traits::LhsProgress x depth) - for(Index i=0; i<peeled_mc3; i+=3*Traits::LhsProgress) - { - // loops on each largest micro vertical panel of rhs (depth * nr) - for(Index j2=0; j2<packet_cols4; j2+=nr) - { - // We select a 3*Traits::LhsProgress x nr micro block of res which is entirely - // stored into 3 x nr registers. - - const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*Traits::LhsProgress)]; - prefetch(&blA[0]); - const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr]; - prefetch(&blB[0]); - LhsPacket A0, A1; - - // gets res block as register - AccPacket C0, C1, C2, C3, - C4, C5, C6, C7, - C8, C9, C10, C11; - traits.initAcc(C0); traits.initAcc(C1); traits.initAcc(C2); traits.initAcc(C3); - traits.initAcc(C4); traits.initAcc(C5); traits.initAcc(C6); traits.initAcc(C7); - traits.initAcc(C8); traits.initAcc(C9); traits.initAcc(C10); traits.initAcc(C11); - - LinearMapper r0 = res.getLinearMapper(i, j2 + 0); - LinearMapper r1 = res.getLinearMapper(i, j2 + 1); - LinearMapper r2 = res.getLinearMapper(i, j2 + 2); - LinearMapper r3 = res.getLinearMapper(i, j2 + 3); - - r0.prefetch(0); - r1.prefetch(0); - r2.prefetch(0); - r3.prefetch(0); - - // performs "inner" products - for(Index k=0; k<peeled_kc; k+=pk) - { - EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX4"); - RhsPacket B_0, T0; - LhsPacket A2; - -#define EIGEN_GEBP_ONESTEP(K) \ - do { \ - EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX4"); \ - EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \ - internal::prefetch(blA+(3*K+16)*LhsProgress); \ - if (EIGEN_ARCH_ARM) internal::prefetch(blB+(4*K+16)*RhsProgress); /* Bug 953 */ \ - traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \ - traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \ - traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \ - possiblyRotatingKernelHelper.template loadOrRotateRhs<K, 0>(B_0, blB); \ - traits.madd(A0, B_0, C0, T0); \ - traits.madd(A1, B_0, C4, T0); \ - traits.madd(A2, B_0, C8, B_0); \ - possiblyRotatingKernelHelper.template loadOrRotateRhs<K, 1>(B_0, blB); \ - traits.madd(A0, B_0, C1, T0); \ - traits.madd(A1, B_0, C5, T0); \ - traits.madd(A2, B_0, C9, B_0); \ - possiblyRotatingKernelHelper.template loadOrRotateRhs<K, 2>(B_0, blB); \ - traits.madd(A0, B_0, C2, T0); \ - traits.madd(A1, B_0, C6, T0); \ - traits.madd(A2, B_0, C10, B_0); \ - possiblyRotatingKernelHelper.template loadOrRotateRhs<K, 3>(B_0, blB); \ - traits.madd(A0, B_0, C3 , T0); \ - traits.madd(A1, B_0, C7, T0); \ - traits.madd(A2, B_0, C11, B_0); \ - EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX4"); \ - } while(false) - - internal::prefetch(blB); - EIGEN_GEBP_ONESTEP(0); - EIGEN_GEBP_ONESTEP(1); - EIGEN_GEBP_ONESTEP(2); - EIGEN_GEBP_ONESTEP(3); - EIGEN_GEBP_ONESTEP(4); - EIGEN_GEBP_ONESTEP(5); - EIGEN_GEBP_ONESTEP(6); - EIGEN_GEBP_ONESTEP(7); - - blB += pk*4*RhsProgress; - blA += pk*3*Traits::LhsProgress; - - EIGEN_ASM_COMMENT("end gebp micro kernel 3pX4"); - } - // process remaining peeled loop - for(Index k=peeled_kc; k<depth; k++) - { - RhsPacket B_0, T0; - LhsPacket A2; - EIGEN_GEBP_ONESTEP(0); - blB += 4*RhsProgress; - blA += 3*Traits::LhsProgress; - } -#undef EIGEN_GEBP_ONESTEP - - possiblyRotatingKernelHelper.unrotateResult(C0, C1, C2, C3); - possiblyRotatingKernelHelper.unrotateResult(C4, C5, C6, C7); - possiblyRotatingKernelHelper.unrotateResult(C8, C9, C10, C11); - - ResPacket R0, R1, R2; - ResPacket alphav = pset1<ResPacket>(alpha); - - R0 = r0.loadPacket(0 * Traits::ResPacketSize); - R1 = r0.loadPacket(1 * Traits::ResPacketSize); - R2 = r0.loadPacket(2 * Traits::ResPacketSize); - traits.acc(C0, alphav, R0); - traits.acc(C4, alphav, R1); - traits.acc(C8, alphav, R2); - r0.storePacket(0 * Traits::ResPacketSize, R0); - r0.storePacket(1 * Traits::ResPacketSize, R1); - r0.storePacket(2 * Traits::ResPacketSize, R2); - - R0 = r1.loadPacket(0 * Traits::ResPacketSize); - R1 = r1.loadPacket(1 * Traits::ResPacketSize); - R2 = r1.loadPacket(2 * Traits::ResPacketSize); - traits.acc(C1, alphav, R0); - traits.acc(C5, alphav, R1); - traits.acc(C9, alphav, R2); - r1.storePacket(0 * Traits::ResPacketSize, R0); - r1.storePacket(1 * Traits::ResPacketSize, R1); - r1.storePacket(2 * Traits::ResPacketSize, R2); - - R0 = r2.loadPacket(0 * Traits::ResPacketSize); - R1 = r2.loadPacket(1 * Traits::ResPacketSize); - R2 = r2.loadPacket(2 * Traits::ResPacketSize); - traits.acc(C2, alphav, R0); - traits.acc(C6, alphav, R1); - traits.acc(C10, alphav, R2); - r2.storePacket(0 * Traits::ResPacketSize, R0); - r2.storePacket(1 * Traits::ResPacketSize, R1); - r2.storePacket(2 * Traits::ResPacketSize, R2); - - R0 = r3.loadPacket(0 * Traits::ResPacketSize); - R1 = r3.loadPacket(1 * Traits::ResPacketSize); - R2 = r3.loadPacket(2 * Traits::ResPacketSize); - traits.acc(C3, alphav, R0); - traits.acc(C7, alphav, R1); - traits.acc(C11, alphav, R2); - r3.storePacket(0 * Traits::ResPacketSize, R0); - r3.storePacket(1 * Traits::ResPacketSize, R1); - r3.storePacket(2 * Traits::ResPacketSize, R2); - } - - // Deal with remaining columns of the rhs - for(Index j2=packet_cols4; j2<cols; j2++) - { - // One column at a time - const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*Traits::LhsProgress)]; - prefetch(&blA[0]); - const RhsScalar* blB = &blockB[j2*strideB+offsetB]; - prefetch(&blB[0]); - // gets res block as register - AccPacket C0, C4, C8; - traits.initAcc(C0); - traits.initAcc(C4); - traits.initAcc(C8); - - LinearMapper r0 = res.getLinearMapper(i, j2); - r0.prefetch(0); - LhsPacket A0, A1, A2; - - // performs "inner" products - for(Index k=0; k<peeled_kc; k+=pk) - { - EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX1"); - RhsPacket B_0; -#define EIGEN_GEBGP_ONESTEP(K) \ - do { \ - EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX1"); \ - EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \ - traits.loadLhs(&blA[(0+3*K)*LhsProgress], A0); \ - traits.loadLhs(&blA[(1+3*K)*LhsProgress], A1); \ - traits.loadLhs(&blA[(2+3*K)*LhsProgress], A2); \ - traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \ - traits.madd(A0, B_0, C0, B_0); \ - traits.madd(A1, B_0, C4, B_0); \ - traits.madd(A2, B_0, C8, B_0); \ - EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX1"); \ - } while(false) - - EIGEN_GEBGP_ONESTEP(0); - EIGEN_GEBGP_ONESTEP(1); - EIGEN_GEBGP_ONESTEP(2); - EIGEN_GEBGP_ONESTEP(3); - EIGEN_GEBGP_ONESTEP(4); - EIGEN_GEBGP_ONESTEP(5); - EIGEN_GEBGP_ONESTEP(6); - EIGEN_GEBGP_ONESTEP(7); - - blB += pk*RhsProgress; - blA += pk*3*Traits::LhsProgress; - - EIGEN_ASM_COMMENT("end gebp micro kernel 3pX1"); - } - - // process remaining peeled loop - for(Index k=peeled_kc; k<depth; k++) - { - RhsPacket B_0; - EIGEN_GEBGP_ONESTEP(0); - blB += RhsProgress; - blA += 3*Traits::LhsProgress; - } -#undef EIGEN_GEBGP_ONESTEP - ResPacket R0, R1, R2; - ResPacket alphav = pset1<ResPacket>(alpha); - - R0 = r0.loadPacket(0 * Traits::ResPacketSize); - R1 = r0.loadPacket(1 * Traits::ResPacketSize); - R2 = r0.loadPacket(2 * Traits::ResPacketSize); - traits.acc(C0, alphav, R0); - traits.acc(C4, alphav, R1); - traits.acc(C8, alphav, R2); - r0.storePacket(0 * Traits::ResPacketSize, R0); - r0.storePacket(1 * Traits::ResPacketSize, R1); - r0.storePacket(2 * Traits::ResPacketSize, R2); - } - } - } - - //---------- Process 2 * LhsProgress rows at once ---------- - if(mr>=2*Traits::LhsProgress) - { - // loops on each largest micro horizontal panel of lhs (2*LhsProgress x depth) - for(Index i=peeled_mc3; i<peeled_mc2; i+=2*LhsProgress) - { - // loops on each largest micro vertical panel of rhs (depth * nr) - for(Index j2=0; j2<packet_cols4; j2+=nr) - { - // We select a 2*Traits::LhsProgress x nr micro block of res which is entirely - // stored into 2 x nr registers. - - const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)]; - prefetch(&blA[0]); - const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr]; - prefetch(&blB[0]); - - // gets res block as register - AccPacket C0, C1, C2, C3, - C4, C5, C6, C7; - traits.initAcc(C0); traits.initAcc(C1); traits.initAcc(C2); traits.initAcc(C3); - traits.initAcc(C4); traits.initAcc(C5); traits.initAcc(C6); traits.initAcc(C7); - - LinearMapper r0 = res.getLinearMapper(i, j2 + 0); - LinearMapper r1 = res.getLinearMapper(i, j2 + 1); - LinearMapper r2 = res.getLinearMapper(i, j2 + 2); - LinearMapper r3 = res.getLinearMapper(i, j2 + 3); - - r0.prefetch(prefetch_res_offset); - r1.prefetch(prefetch_res_offset); - r2.prefetch(prefetch_res_offset); - r3.prefetch(prefetch_res_offset); - - LhsPacket A0, A1; - - // performs "inner" products - for(Index k=0; k<peeled_kc; k+=pk) - { - EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX4"); - RhsPacket B_0, B1, B2, B3, T0; - - // The 2 ASM comments in the #define are intended to prevent gcc - // from optimizing the code accross steps since it ends up spilling - // registers in this case. - #define EIGEN_GEBGP_ONESTEP(K) \ - do { \ - EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX4"); \ - EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \ - traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \ - traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \ - traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], B_0, B1, B2, B3); \ - traits.madd(A0, B_0, C0, T0); \ - traits.madd(A1, B_0, C4, B_0); \ - traits.madd(A0, B1, C1, T0); \ - traits.madd(A1, B1, C5, B1); \ - traits.madd(A0, B2, C2, T0); \ - traits.madd(A1, B2, C6, B2); \ - traits.madd(A0, B3, C3, T0); \ - traits.madd(A1, B3, C7, B3); \ - EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX4"); \ - } while(false) - - prefetch(&blB[pk*4*RhsProgress]); - EIGEN_GEBGP_ONESTEP(0); - EIGEN_GEBGP_ONESTEP(1); - EIGEN_GEBGP_ONESTEP(2); - EIGEN_GEBGP_ONESTEP(3); - EIGEN_GEBGP_ONESTEP(4); - EIGEN_GEBGP_ONESTEP(5); - EIGEN_GEBGP_ONESTEP(6); - EIGEN_GEBGP_ONESTEP(7); - - blB += pk*4*RhsProgress; - blA += pk*(2*Traits::LhsProgress); - - EIGEN_ASM_COMMENT("end gebp micro kernel 2pX4"); - } - // process remaining peeled loop - for(Index k=peeled_kc; k<depth; k++) - { - RhsPacket B_0, B1, B2, B3, T0; - EIGEN_GEBGP_ONESTEP(0); - blB += 4*RhsProgress; - blA += 2*Traits::LhsProgress; - } -#undef EIGEN_GEBGP_ONESTEP - - ResPacket R0, R1, R2, R3; - ResPacket alphav = pset1<ResPacket>(alpha); - - R0 = r0.loadPacket(0 * Traits::ResPacketSize); - R1 = r0.loadPacket(1 * Traits::ResPacketSize); - R2 = r1.loadPacket(0 * Traits::ResPacketSize); - R3 = r1.loadPacket(1 * Traits::ResPacketSize); - traits.acc(C0, alphav, R0); - traits.acc(C4, alphav, R1); - traits.acc(C1, alphav, R2); - traits.acc(C5, alphav, R3); - r0.storePacket(0 * Traits::ResPacketSize, R0); - r0.storePacket(1 * Traits::ResPacketSize, R1); - r1.storePacket(0 * Traits::ResPacketSize, R2); - r1.storePacket(1 * Traits::ResPacketSize, R3); - - R0 = r2.loadPacket(0 * Traits::ResPacketSize); - R1 = r2.loadPacket(1 * Traits::ResPacketSize); - R2 = r3.loadPacket(0 * Traits::ResPacketSize); - R3 = r3.loadPacket(1 * Traits::ResPacketSize); - traits.acc(C2, alphav, R0); - traits.acc(C6, alphav, R1); - traits.acc(C3, alphav, R2); - traits.acc(C7, alphav, R3); - r2.storePacket(0 * Traits::ResPacketSize, R0); - r2.storePacket(1 * Traits::ResPacketSize, R1); - r3.storePacket(0 * Traits::ResPacketSize, R2); - r3.storePacket(1 * Traits::ResPacketSize, R3); - } - - // Deal with remaining columns of the rhs - for(Index j2=packet_cols4; j2<cols; j2++) - { - // One column at a time - const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)]; - prefetch(&blA[0]); - const RhsScalar* blB = &blockB[j2*strideB+offsetB]; - prefetch(&blB[0]); - - // gets res block as register - AccPacket C0, C4; - traits.initAcc(C0); - traits.initAcc(C4); - - LinearMapper r0 = res.getLinearMapper(i, j2); - r0.prefetch(prefetch_res_offset); - LhsPacket A0, A1; - - // performs "inner" products - for(Index k=0; k<peeled_kc; k+=pk) - { - EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX1"); - RhsPacket B_0, B1; - -#define EIGEN_GEBGP_ONESTEP(K) \ - do { \ - EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX1"); \ - EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \ - traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0); \ - traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1); \ - traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \ - traits.madd(A0, B_0, C0, B1); \ - traits.madd(A1, B_0, C4, B_0); \ - EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX1"); \ - } while(false) - - EIGEN_GEBGP_ONESTEP(0); - EIGEN_GEBGP_ONESTEP(1); - EIGEN_GEBGP_ONESTEP(2); - EIGEN_GEBGP_ONESTEP(3); - EIGEN_GEBGP_ONESTEP(4); - EIGEN_GEBGP_ONESTEP(5); - EIGEN_GEBGP_ONESTEP(6); - EIGEN_GEBGP_ONESTEP(7); - - blB += pk*RhsProgress; - blA += pk*2*Traits::LhsProgress; - - EIGEN_ASM_COMMENT("end gebp micro kernel 2pX1"); - } - - // process remaining peeled loop - for(Index k=peeled_kc; k<depth; k++) - { - RhsPacket B_0, B1; - EIGEN_GEBGP_ONESTEP(0); - blB += RhsProgress; - blA += 2*Traits::LhsProgress; - } -#undef EIGEN_GEBGP_ONESTEP - ResPacket R0, R1; - ResPacket alphav = pset1<ResPacket>(alpha); - - R0 = r0.loadPacket(0 * Traits::ResPacketSize); - R1 = r0.loadPacket(1 * Traits::ResPacketSize); - traits.acc(C0, alphav, R0); - traits.acc(C4, alphav, R1); - r0.storePacket(0 * Traits::ResPacketSize, R0); - r0.storePacket(1 * Traits::ResPacketSize, R1); - } - } - } - //---------- Process 1 * LhsProgress rows at once ---------- - if(mr>=1*Traits::LhsProgress) - { - // loops on each largest micro horizontal panel of lhs (1*LhsProgress x depth) - for(Index i=peeled_mc2; i<peeled_mc1; i+=1*LhsProgress) - { - // loops on each largest micro vertical panel of rhs (depth * nr) - for(Index j2=0; j2<packet_cols4; j2+=nr) - { - // We select a 1*Traits::LhsProgress x nr micro block of res which is entirely - // stored into 1 x nr registers. - - const LhsScalar* blA = &blockA[i*strideA+offsetA*(1*Traits::LhsProgress)]; - prefetch(&blA[0]); - const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr]; - prefetch(&blB[0]); - - // gets res block as register - AccPacket C0, C1, C2, C3; - traits.initAcc(C0); - traits.initAcc(C1); - traits.initAcc(C2); - traits.initAcc(C3); - - LinearMapper r0 = res.getLinearMapper(i, j2 + 0); - LinearMapper r1 = res.getLinearMapper(i, j2 + 1); - LinearMapper r2 = res.getLinearMapper(i, j2 + 2); - LinearMapper r3 = res.getLinearMapper(i, j2 + 3); - - r0.prefetch(prefetch_res_offset); - r1.prefetch(prefetch_res_offset); - r2.prefetch(prefetch_res_offset); - r3.prefetch(prefetch_res_offset); - LhsPacket A0; - - // performs "inner" products - for(Index k=0; k<peeled_kc; k+=pk) - { - EIGEN_ASM_COMMENT("begin gebp micro kernel 1pX4"); - RhsPacket B_0, B1, B2, B3; - -#define EIGEN_GEBGP_ONESTEP(K) \ - do { \ - EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1pX4"); \ - EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \ - traits.loadLhs(&blA[(0+1*K)*LhsProgress], A0); \ - traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], B_0, B1, B2, B3); \ - traits.madd(A0, B_0, C0, B_0); \ - traits.madd(A0, B1, C1, B1); \ - traits.madd(A0, B2, C2, B2); \ - traits.madd(A0, B3, C3, B3); \ - EIGEN_ASM_COMMENT("end step of gebp micro kernel 1pX4"); \ - } while(false) - - EIGEN_GEBGP_ONESTEP(0); - EIGEN_GEBGP_ONESTEP(1); - EIGEN_GEBGP_ONESTEP(2); - EIGEN_GEBGP_ONESTEP(3); - EIGEN_GEBGP_ONESTEP(4); - EIGEN_GEBGP_ONESTEP(5); - EIGEN_GEBGP_ONESTEP(6); - EIGEN_GEBGP_ONESTEP(7); - - blB += pk*4*RhsProgress; - blA += pk*1*LhsProgress; - - EIGEN_ASM_COMMENT("end gebp micro kernel 1pX4"); - } - // process remaining peeled loop - for(Index k=peeled_kc; k<depth; k++) - { - RhsPacket B_0, B1, B2, B3; - EIGEN_GEBGP_ONESTEP(0); - blB += 4*RhsProgress; - blA += 1*LhsProgress; - } -#undef EIGEN_GEBGP_ONESTEP - - ResPacket R0, R1; - ResPacket alphav = pset1<ResPacket>(alpha); - - R0 = r0.loadPacket(0 * Traits::ResPacketSize); - R1 = r1.loadPacket(0 * Traits::ResPacketSize); - traits.acc(C0, alphav, R0); - traits.acc(C1, alphav, R1); - r0.storePacket(0 * Traits::ResPacketSize, R0); - r1.storePacket(0 * Traits::ResPacketSize, R1); - - R0 = r2.loadPacket(0 * Traits::ResPacketSize); - R1 = r3.loadPacket(0 * Traits::ResPacketSize); - traits.acc(C2, alphav, R0); - traits.acc(C3, alphav, R1); - r2.storePacket(0 * Traits::ResPacketSize, R0); - r3.storePacket(0 * Traits::ResPacketSize, R1); - } - - // Deal with remaining columns of the rhs - for(Index j2=packet_cols4; j2<cols; j2++) - { - // One column at a time - const LhsScalar* blA = &blockA[i*strideA+offsetA*(1*Traits::LhsProgress)]; - prefetch(&blA[0]); - const RhsScalar* blB = &blockB[j2*strideB+offsetB]; - prefetch(&blB[0]); - - // gets res block as register - AccPacket C0; - traits.initAcc(C0); - - LinearMapper r0 = res.getLinearMapper(i, j2); - LhsPacket A0; - - // performs "inner" products - for(Index k=0; k<peeled_kc; k+=pk) - { - EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX1"); - RhsPacket B_0; - -#define EIGEN_GEBGP_ONESTEP(K) \ - do { \ - EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX1"); \ - EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \ - traits.loadLhs(&blA[(0+1*K)*LhsProgress], A0); \ - traits.loadRhs(&blB[(0+K)*RhsProgress], B_0); \ - traits.madd(A0, B_0, C0, B_0); \ - EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX1"); \ - } while(false) - - EIGEN_GEBGP_ONESTEP(0); - EIGEN_GEBGP_ONESTEP(1); - EIGEN_GEBGP_ONESTEP(2); - EIGEN_GEBGP_ONESTEP(3); - EIGEN_GEBGP_ONESTEP(4); - EIGEN_GEBGP_ONESTEP(5); - EIGEN_GEBGP_ONESTEP(6); - EIGEN_GEBGP_ONESTEP(7); - - blB += pk*RhsProgress; - blA += pk*1*Traits::LhsProgress; - - EIGEN_ASM_COMMENT("end gebp micro kernel 2pX1"); - } - - // process remaining peeled loop - for(Index k=peeled_kc; k<depth; k++) - { - RhsPacket B_0; - EIGEN_GEBGP_ONESTEP(0); - blB += RhsProgress; - blA += 1*Traits::LhsProgress; - } -#undef EIGEN_GEBGP_ONESTEP - ResPacket R0; - ResPacket alphav = pset1<ResPacket>(alpha); - R0 = r0.loadPacket(0 * Traits::ResPacketSize); - traits.acc(C0, alphav, R0); - r0.storePacket(0 * Traits::ResPacketSize, R0); - } - } - } - //---------- Process remaining rows, 1 by 1 ---------- - for(Index i=peeled_mc1; i<rows; i+=1) - { - // loop on each panel of the rhs - for(Index j2=0; j2<packet_cols4; j2+=nr) - { - const LhsScalar* blA = &blockA[i*strideA+offsetA]; - prefetch(&blA[0]); - const RhsScalar* blB = &blockB[j2*strideB+offsetB*nr]; - prefetch(&blB[0]); - - if( (SwappedTraits::LhsProgress % 4)==0 ) - { - // NOTE The following piece of code wont work for 512 bit registers - SAccPacket C0, C1, C2, C3; - straits.initAcc(C0); - straits.initAcc(C1); - straits.initAcc(C2); - straits.initAcc(C3); - - const Index spk = (std::max)(1,SwappedTraits::LhsProgress/4); - const Index endk = (depth/spk)*spk; - const Index endk4 = (depth/(spk*4))*(spk*4); - - Index k=0; - for(; k<endk4; k+=4*spk) - { - prefetch(&blB[4*SwappedTraits::LhsProgress]); - - SLhsPacket A0,A1,A2,A3; - SRhsPacket B_0,B_1,B_2,B_3; - - straits.loadLhsUnaligned(blB+0*SwappedTraits::LhsProgress, A0); - straits.loadLhsUnaligned(blB+1*SwappedTraits::LhsProgress, A1); - straits.loadRhsQuad(blA+0*spk, B_0); - straits.loadRhsQuad(blA+1*spk, B_1); - straits.madd(A0,B_0,C0,B_0); - straits.madd(A1,B_1,C1,B_1); - - straits.loadLhsUnaligned(blB+2*SwappedTraits::LhsProgress, A2); - straits.loadLhsUnaligned(blB+3*SwappedTraits::LhsProgress, A3); - straits.loadRhsQuad(blA+2*spk, B_2); - straits.loadRhsQuad(blA+3*spk, B_3); - straits.madd(A2,B_2,C2,B_2); - straits.madd(A3,B_3,C3,B_3); - - blB += 4*SwappedTraits::LhsProgress; - blA += 4*spk; - } - C0 = padd(padd(C0,C1),padd(C2,C3)); - for(; k<endk; k+=spk) - { - SLhsPacket A0; - SRhsPacket B_0; - - straits.loadLhsUnaligned(blB, A0); - straits.loadRhsQuad(blA, B_0); - straits.madd(A0,B_0,C0,B_0); - - blB += SwappedTraits::LhsProgress; - blA += spk; - } - if(SwappedTraits::LhsProgress==8) - { - // Special case where we have to first reduce the accumulation register C0 - typedef typename conditional<SwappedTraits::LhsProgress==8,typename unpacket_traits<SResPacket>::half,SResPacket>::type SResPacketHalf; - typedef typename conditional<SwappedTraits::LhsProgress==8,typename unpacket_traits<SLhsPacket>::half,SLhsPacket>::type SLhsPacketHalf; - typedef typename conditional<SwappedTraits::LhsProgress==8,typename unpacket_traits<SLhsPacket>::half,SRhsPacket>::type SRhsPacketHalf; - typedef typename conditional<SwappedTraits::LhsProgress==8,typename unpacket_traits<SAccPacket>::half,SAccPacket>::type SAccPacketHalf; - - SResPacketHalf R = res.template gatherPacket<SResPacketHalf>(i, j2); - SResPacketHalf alphav = pset1<SResPacketHalf>(alpha); - - if(depth-endk>0) - { - // We have to handle the last row of the rhs which corresponds to a half-packet - SLhsPacketHalf a0; - SRhsPacketHalf b0; - straits.loadLhsUnaligned(blB, a0); - straits.loadRhs(blA, b0); - SAccPacketHalf c0 = predux4(C0); - straits.madd(a0,b0,c0,b0); - straits.acc(c0, alphav, R); - } - else - { - straits.acc(predux4(C0), alphav, R); - } - res.scatterPacket(i, j2, R); - } - else - { - SResPacket R = res.template gatherPacket<SResPacket>(i, j2); - SResPacket alphav = pset1<SResPacket>(alpha); - straits.acc(C0, alphav, R); - res.scatterPacket(i, j2, R); - } - } - else // scalar path - { - // get a 1 x 4 res block as registers - ResScalar C0(0), C1(0), C2(0), C3(0); - - for(Index k=0; k<depth; k++) - { - LhsScalar A0 = blA[k]; - RhsScalar B_0 = blB[0]; - RhsScalar B_1 = blB[1]; - CJMADD(cj,A0,B_0,C0, B_0); - CJMADD(cj,A0,B_1,C1, B_1); - RhsScalar B_2 = blB[2]; - RhsScalar B_3 = blB[3]; - CJMADD(cj,A0,B_2,C2, B_2); - CJMADD(cj,A0,B_3,C3, B_3); - - blB += 4; - } - res(i, j2 + 0) += alpha * C0; - res(i, j2 + 1) += alpha * C1; - res(i, j2 + 2) += alpha * C2; - res(i, j2 + 3) += alpha * C3; - } - } - - // remaining columns - for(Index j2=packet_cols4; j2<cols; j2++) - { - const LhsScalar* blA = &blockA[i*strideA+offsetA]; - // prefetch(blA); - // gets a 1 x 1 res block as registers - ResScalar C0(0); - const RhsScalar* blB = &blockB[j2*strideB+offsetB]; - for(Index k=0; k<depth; k++) - { - LhsScalar A0 = blA[k]; - RhsScalar B_0 = blB[k]; - CJMADD(cj, A0, B_0, C0, B_0); - } - res(i, j2) += alpha * C0; - } - } - } - - -#undef CJMADD - -// pack a block of the lhs -// The traversal is as follow (mr==4): -// 0 4 8 12 ... -// 1 5 9 13 ... -// 2 6 10 14 ... -// 3 7 11 15 ... -// -// 16 20 24 28 ... -// 17 21 25 29 ... -// 18 22 26 30 ... -// 19 23 27 31 ... -// -// 32 33 34 35 ... -// 36 36 38 39 ... -template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, bool Conjugate, bool PanelMode> -struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, ColMajor, Conjugate, PanelMode> -{ - typedef typename DataMapper::LinearMapper LinearMapper; - EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0); -}; - -template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, bool Conjugate, bool PanelMode> -EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, ColMajor, Conjugate, PanelMode> - ::operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset) -{ - typedef typename packet_traits<Scalar>::type Packet; - enum { PacketSize = packet_traits<Scalar>::size }; - - EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS"); - EIGEN_UNUSED_VARIABLE(stride); - EIGEN_UNUSED_VARIABLE(offset); - eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); - eigen_assert( ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) || (Pack1<=4) ); - conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; - - const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0; - const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0; - const Index peeled_mc1 = Pack1>=1*PacketSize ? (rows/(1*PacketSize))*(1*PacketSize) : 0; - const Index peeled_mc0 = Pack2>=1*PacketSize ? peeled_mc1 - : Pack2>1 ? (rows/Pack2)*Pack2 : 0; - - Index i=0; - - // Pack 3 packets - if(Pack1>=3*PacketSize) - { - if(PanelMode) - { - for(; i<peeled_mc3; i+=3*PacketSize) - { - blockA += (3*PacketSize) * offset; - - for(Index k=0; k<depth; k++) - { - Packet A, B, C; - A = lhs.loadPacket(i+0*PacketSize, k); - B = lhs.loadPacket(i+1*PacketSize, k); - C = lhs.loadPacket(i+2*PacketSize, k); - pstore(blockA+0*PacketSize, cj.pconj(A)); - pstore(blockA+1*PacketSize, cj.pconj(B)); - pstore(blockA+2*PacketSize, cj.pconj(C)); - blockA += 3*PacketSize; - } - blockA += (3*PacketSize) * (stride-offset-depth); - } - } - else - { - // Read the data from DRAM as sequentially as possible. We're writing to - // SRAM so the order of the writes shouldn't impact performance. - for(Index k=0; k<depth; k++) - { - Scalar* localBlockA = blockA + 3*PacketSize*k; - for(Index local_i = i; local_i<peeled_mc3; local_i+=3*PacketSize) - { - Packet A, B, C; - A = lhs.loadPacket(local_i+0*PacketSize, k); - B = lhs.loadPacket(local_i+1*PacketSize, k); - C = lhs.loadPacket(local_i+2*PacketSize, k); - pstore(localBlockA+0*PacketSize, cj.pconj(A)); - pstore(localBlockA+1*PacketSize, cj.pconj(B)); - pstore(localBlockA+2*PacketSize, cj.pconj(C)); - localBlockA += 3*PacketSize*depth; - } - } - blockA += depth*peeled_mc3; - i = peeled_mc3; - } - } - // Pack 2 packets - if(Pack1>=2*PacketSize) - { - if(PanelMode) - { - for(; i<peeled_mc2; i+=2*PacketSize) - { - blockA += (2*PacketSize) * offset; - - for(Index k=0; k<depth; k++) - { - Packet A, B; - A = lhs.loadPacket(i+0*PacketSize, k); - B = lhs.loadPacket(i+1*PacketSize, k); - pstore(blockA+0*PacketSize, cj.pconj(A)); - pstore(blockA+1*PacketSize, cj.pconj(B)); - blockA += 2*PacketSize; - } - blockA += (2*PacketSize) * (stride-offset-depth); - } - } - else - { - // Read the data from RAM as sequentially as possible. - for(Index k=0; k<depth; k++) - { - Scalar* localBlockA = blockA + 2*PacketSize*k; - for(Index local_i = i; local_i<peeled_mc2; local_i+=2*PacketSize) - { - Packet A, B; - A = lhs.loadPacket(local_i+0*PacketSize, k); - B = lhs.loadPacket(local_i+1*PacketSize, k); - pstore(localBlockA+0*PacketSize, cj.pconj(A)); - pstore(localBlockA+1*PacketSize, cj.pconj(B)); - localBlockA += 2*PacketSize*depth; - } - } - blockA += depth*(peeled_mc2-i); - i = peeled_mc2; - } - } - // Pack 1 packets - if(Pack1>=1*PacketSize) - { - if(PanelMode) - { - for(; i<peeled_mc1; i+=1*PacketSize) - { - blockA += (1*PacketSize) * offset; - - for(Index k=0; k<depth; k++) - { - Packet A; - A = lhs.loadPacket(i+0*PacketSize, k); - pstore(blockA, cj.pconj(A)); - blockA+=PacketSize; - } - blockA += (1*PacketSize) * (stride-offset-depth); - } - } - else - { - // Read the data from RAM as sequentially as possible. - for(Index k=0; k<depth; k++) - { - Scalar* localBlockA = blockA + PacketSize*k; - for(Index local_i = i; local_i<peeled_mc1; local_i+=1*PacketSize) - { - Packet A; - A = lhs.loadPacket(local_i+0*PacketSize, k); - pstore(localBlockA, cj.pconj(A)); - localBlockA += PacketSize*depth; - } - } - blockA += depth*(peeled_mc1-i); - i = peeled_mc1; - } - } - // Pack scalars - if(Pack2<PacketSize && Pack2>1) - { - for(; i<peeled_mc0; i+=Pack2) - { - if (PanelMode) { - blockA += Pack2 * offset; - } - - for(Index k=0; k<depth; k++) { - const LinearMapper dm0 = lhs.getLinearMapper(i, k); - for(Index w=0; w<Pack2; w++) { - *blockA = cj(dm0(w)); - blockA += 1; - } - } - - if(PanelMode) blockA += Pack2 * (stride-offset-depth); - } - } - for(; i<rows; i++) - { - if(PanelMode) blockA += offset; - for(Index k=0; k<depth; k++) { - *blockA = cj(lhs(i, k)); - blockA += 1; - } - if(PanelMode) blockA += (stride-offset-depth); - } -} - -template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, bool Conjugate, bool PanelMode> -struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, RowMajor, Conjugate, PanelMode> -{ - typedef typename DataMapper::LinearMapper LinearMapper; - EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0); -}; - -template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, bool Conjugate, bool PanelMode> -EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, RowMajor, Conjugate, PanelMode> - ::operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset) -{ - typedef typename packet_traits<Scalar>::type Packet; - enum { PacketSize = packet_traits<Scalar>::size }; - - EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS"); - EIGEN_UNUSED_VARIABLE(stride); - EIGEN_UNUSED_VARIABLE(offset); - eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); - conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; - -// const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0; -// const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0; -// const Index peeled_mc1 = Pack1>=1*PacketSize ? (rows/(1*PacketSize))*(1*PacketSize) : 0; - - int pack = Pack1; - Index i = 0; - while(pack>0) - { - Index remaining_rows = rows-i; - Index peeled_mc = i+(remaining_rows/pack)*pack; - for(; i<peeled_mc; i+=pack) - { - if(PanelMode) blockA += pack * offset; - - const Index peeled_k = (depth/PacketSize)*PacketSize; - Index k=0; - if(pack>=PacketSize) - { - for(; k<peeled_k; k+=PacketSize) - { - for (Index m = 0; m < pack; m += PacketSize) - { - PacketBlock<Packet> kernel; - for (int p = 0; p < PacketSize; ++p) kernel.packet[p] = lhs.loadPacket(i+p+m, k); - ptranspose(kernel); - for (int p = 0; p < PacketSize; ++p) pstore(blockA+m+(pack)*p, cj.pconj(kernel.packet[p])); - } - blockA += PacketSize*pack; - } - } - for(; k<depth; k++) - { - Index w=0; - for(; w<pack-3; w+=4) - { - Scalar a(cj(lhs(i+w+0, k))), - b(cj(lhs(i+w+1, k))), - c(cj(lhs(i+w+2, k))), - d(cj(lhs(i+w+3, k))); - blockA[0] = a; - blockA[1] = b; - blockA[2] = c; - blockA[3] = d; - blockA += 4; - } - if(pack%4) - for(;w<pack;++w) { - *blockA = cj(lhs(i+w, k)); - blockA += 1; - } - } - - if(PanelMode) blockA += pack * (stride-offset-depth); - } - - pack -= PacketSize; - if(pack<Pack2 && (pack+PacketSize)!=Pack2) - pack = Pack2; - } - - for(; i<rows; i++) - { - if(PanelMode) blockA += offset; - for(Index k=0; k<depth; k++) { - *blockA = cj(lhs(i, k)); - blockA += 1; - } - if(PanelMode) blockA += (stride-offset-depth); - } -} - -// copy a complete panel of the rhs -// this version is optimized for column major matrices -// The traversal order is as follow: (nr==4): -// 0 1 2 3 12 13 14 15 24 27 -// 4 5 6 7 16 17 18 19 25 28 -// 8 9 10 11 20 21 22 23 26 29 -// . . . . . . . . . . -template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode> -struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode> -{ - typedef typename packet_traits<Scalar>::type Packet; - typedef typename DataMapper::LinearMapper LinearMapper; - enum { PacketSize = packet_traits<Scalar>::size }; - EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0); -}; - -template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode> -EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode> -::operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) -{ - EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR"); - EIGEN_UNUSED_VARIABLE(stride); - EIGEN_UNUSED_VARIABLE(offset); - eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); - conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; - Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0; - Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0; - const Index peeled_k = (depth/PacketSize)*PacketSize; -// if(nr>=8) -// { -// for(Index j2=0; j2<packet_cols8; j2+=8) -// { -// // skip what we have before -// if(PanelMode) count += 8 * offset; -// const Scalar* b0 = &rhs[(j2+0)*rhsStride]; -// const Scalar* b1 = &rhs[(j2+1)*rhsStride]; -// const Scalar* b2 = &rhs[(j2+2)*rhsStride]; -// const Scalar* b3 = &rhs[(j2+3)*rhsStride]; -// const Scalar* b4 = &rhs[(j2+4)*rhsStride]; -// const Scalar* b5 = &rhs[(j2+5)*rhsStride]; -// const Scalar* b6 = &rhs[(j2+6)*rhsStride]; -// const Scalar* b7 = &rhs[(j2+7)*rhsStride]; -// Index k=0; -// if(PacketSize==8) // TODO enbale vectorized transposition for PacketSize==4 -// { -// for(; k<peeled_k; k+=PacketSize) { -// PacketBlock<Packet> kernel; -// for (int p = 0; p < PacketSize; ++p) { -// kernel.packet[p] = ploadu<Packet>(&rhs[(j2+p)*rhsStride+k]); -// } -// ptranspose(kernel); -// for (int p = 0; p < PacketSize; ++p) { -// pstoreu(blockB+count, cj.pconj(kernel.packet[p])); -// count+=PacketSize; -// } -// } -// } -// for(; k<depth; k++) -// { -// blockB[count+0] = cj(b0[k]); -// blockB[count+1] = cj(b1[k]); -// blockB[count+2] = cj(b2[k]); -// blockB[count+3] = cj(b3[k]); -// blockB[count+4] = cj(b4[k]); -// blockB[count+5] = cj(b5[k]); -// blockB[count+6] = cj(b6[k]); -// blockB[count+7] = cj(b7[k]); -// count += 8; -// } -// // skip what we have after -// if(PanelMode) count += 8 * (stride-offset-depth); -// } -// } - - if(nr>=4) - { - for(Index j2=packet_cols8; j2<packet_cols4; j2+=4) - { - // skip what we have before - if(PanelMode) blockB += 4 * offset; - - // TODO: each of these makes a copy of the stride :( - const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0); - const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1); - const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2); - const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3); - - Index k=0; - if((PacketSize%4)==0) // TODO enable vectorized transposition for PacketSize==2 ?? - { - for(; k<peeled_k; k+=PacketSize) { - PacketBlock<Packet, 4> kernel; - kernel.packet[0] = dm0.loadPacket(k); - kernel.packet[1] = dm1.loadPacket(k); - kernel.packet[2] = dm2.loadPacket(k); - kernel.packet[3] = dm3.loadPacket(k); - ptranspose(kernel); - pstoreu(blockB+0*PacketSize, cj.pconj(kernel.packet[0])); - pstoreu(blockB+1*PacketSize, cj.pconj(kernel.packet[1])); - pstoreu(blockB+2*PacketSize, cj.pconj(kernel.packet[2])); - pstoreu(blockB+3*PacketSize, cj.pconj(kernel.packet[3])); - blockB+=4*PacketSize; - } - } - for(; k<depth; k++) - { - blockB[0] = cj(dm0(k)); - blockB[1] = cj(dm1(k)); - blockB[2] = cj(dm2(k)); - blockB[3] = cj(dm3(k)); - blockB += 4; - } - // skip what we have after - if(PanelMode) blockB += 4 * (stride-offset-depth); - } - } - - // copy the remaining columns one at a time (nr==1) - for(Index j2=packet_cols4; j2<cols; ++j2) - { - const LinearMapper dm0 = rhs.getLinearMapper(0, j2); - if(PanelMode) blockB += offset; - for(Index k=0; k<depth; k++) - { - *blockB = cj(dm0(k)); - blockB += 1; - } - if(PanelMode) blockB += (stride-offset-depth); - } -} - -// this version is optimized for row major matrices -template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode> -struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode> -{ - typedef typename packet_traits<Scalar>::type Packet; - typedef typename packet_traits<Scalar>::half HalfPacket; - typedef typename DataMapper::LinearMapper LinearMapper; - enum { - PacketSize = packet_traits<Scalar>::size, - HalfPacketSize = packet_traits<Scalar>::HasHalfPacket ? unpacket_traits<typename packet_traits<Scalar>::half>::size : 0 - }; - EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0); -}; - -template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode> -EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode> - ::operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset) -{ - EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR"); - EIGEN_UNUSED_VARIABLE(stride); - EIGEN_UNUSED_VARIABLE(offset); - eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride)); - conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; - Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0; - Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0; - -// if(nr>=8) -// { -// for(Index j2=0; j2<packet_cols8; j2+=8) -// { -// // skip what we have before -// if(PanelMode) count += 8 * offset; -// for(Index k=0; k<depth; k++) -// { -// if (PacketSize==8) { -// Packet A = ploadu<Packet>(&rhs[k*rhsStride + j2]); -// pstoreu(blockB+count, cj.pconj(A)); -// } else if (PacketSize==4) { -// Packet A = ploadu<Packet>(&rhs[k*rhsStride + j2]); -// Packet B = ploadu<Packet>(&rhs[k*rhsStride + j2 + PacketSize]); -// pstoreu(blockB+count, cj.pconj(A)); -// pstoreu(blockB+count+PacketSize, cj.pconj(B)); -// } else { -// const Scalar* b0 = &rhs[k*rhsStride + j2]; -// blockB[count+0] = cj(b0[0]); -// blockB[count+1] = cj(b0[1]); -// blockB[count+2] = cj(b0[2]); -// blockB[count+3] = cj(b0[3]); -// blockB[count+4] = cj(b0[4]); -// blockB[count+5] = cj(b0[5]); -// blockB[count+6] = cj(b0[6]); -// blockB[count+7] = cj(b0[7]); -// } -// count += 8; -// } -// // skip what we have after -// if(PanelMode) count += 8 * (stride-offset-depth); -// } -// } - if(nr>=4) - { - for(Index j2=packet_cols8; j2<packet_cols4; j2+=4) - { - // skip what we have before - if(PanelMode) blockB += 4 * offset; - for(Index k=0; k<depth; k++) - { - if (PacketSize==4) { - Packet A = rhs.loadPacket(k, j2); - pstore(blockB, cj.pconj(A)); - blockB += PacketSize; - } - else if (HalfPacketSize==4) { - HalfPacket A = rhs.loadHalfPacket(k, j2); - pstore<Scalar, HalfPacket>(blockB, cj.pconj(A)); - blockB += HalfPacketSize; - } - else { - const LinearMapper dm0 = rhs.getLinearMapper(k, j2); - blockB[0] = cj(dm0(0)); - blockB[1] = cj(dm0(1)); - blockB[2] = cj(dm0(2)); - blockB[3] = cj(dm0(3)); - blockB += 4; - } - } - // skip what we have after - if(PanelMode) blockB += 4 * (stride-offset-depth); - } - } - // copy the remaining columns one at a time (nr==1) - for(Index j2=packet_cols4; j2<cols; ++j2) - { - if(PanelMode) blockB += offset; - for(Index k=0; k<depth; k++) - { - *blockB = cj(rhs(k, j2)); - blockB += 1; - } - if(PanelMode) blockB += stride-offset-depth; - } -} - -} // end namespace internal - -/** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters. - * \sa setCpuCacheSize */ -inline std::ptrdiff_t l1CacheSize() -{ - std::ptrdiff_t l1, l2, l3; - internal::manage_caching_sizes(GetAction, &l1, &l2, &l3); - return l1; -} - -/** \returns the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters. - * \sa setCpuCacheSize */ -inline std::ptrdiff_t l2CacheSize() -{ - std::ptrdiff_t l1, l2, l3; - internal::manage_caching_sizes(GetAction, &l1, &l2, &l3); - return l2; -} - -/** \returns the currently set level 3 cpu cache size (in bytes) used to estimate the ideal blocking size parameters. - * \sa setCpuCacheSize */ -inline std::ptrdiff_t l3CacheSize() -{ - std::ptrdiff_t l1, l2, l3; - internal::manage_caching_sizes(GetAction, &l1, &l2, &l3); - return l3; -} - -/** Set the cpu L1 and L2 cache sizes (in bytes). - * These values are use to adjust the size of the blocks - * for the algorithms working per blocks. - * - * \sa computeProductBlockingSizes */ -inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2, std::ptrdiff_t l3) -{ - internal::manage_caching_sizes(SetAction, &l1, &l2, &l3); -} - -} // end namespace Eigen - -#endif // EIGEN_GENERAL_BLOCK_PANEL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h b/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h deleted file mode 100644 index c3715b1a39..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix.h +++ /dev/null @@ -1,465 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERAL_MATRIX_MATRIX_H -#define EIGEN_GENERAL_MATRIX_MATRIX_H - -namespace Eigen { - -namespace internal { - -template<typename _LhsScalar, typename _RhsScalar> class level3_blocking; - -/* Specialization for a row-major destination matrix => simple transposition of the product */ -template< - typename Index, - typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, - typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs> -struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor> -{ - typedef gebp_traits<RhsScalar,LhsScalar> Traits; - - typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - static EIGEN_STRONG_INLINE void run( - Index rows, Index cols, Index depth, - const LhsScalar* lhs, Index lhsStride, - const RhsScalar* rhs, Index rhsStride, - ResScalar* res, Index resStride, - ResScalar alpha, - level3_blocking<RhsScalar,LhsScalar>& blocking, - GemmParallelInfo<Index>* info = 0) - { - // transpose the product such that the result is column major - general_matrix_matrix_product<Index, - RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs, - LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs, - ColMajor> - ::run(cols,rows,depth,rhs,rhsStride,lhs,lhsStride,res,resStride,alpha,blocking,info); - } -}; - -/* Specialization for a col-major destination matrix - * => Blocking algorithm following Goto's paper */ -template< - typename Index, - typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, - typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs> -struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor> -{ - -typedef gebp_traits<LhsScalar,RhsScalar> Traits; - -typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; -static void run(Index rows, Index cols, Index depth, - const LhsScalar* _lhs, Index lhsStride, - const RhsScalar* _rhs, Index rhsStride, - ResScalar* _res, Index resStride, - ResScalar alpha, - level3_blocking<LhsScalar,RhsScalar>& blocking, - GemmParallelInfo<Index>* info = 0) -{ - typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper; - typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper; - typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor> ResMapper; - LhsMapper lhs(_lhs,lhsStride); - RhsMapper rhs(_rhs,rhsStride); - ResMapper res(_res, resStride); - - Index kc = blocking.kc(); // cache block size along the K direction - Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction - Index nc = (std::min)(cols,blocking.nc()); // cache block size along the N direction - - gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs; - gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs; - gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp; - -#ifdef EIGEN_HAS_OPENMP - if(info) - { - // this is the parallel version! - Index tid = omp_get_thread_num(); - Index threads = omp_get_num_threads(); - - LhsScalar* blockA = blocking.blockA(); - eigen_internal_assert(blockA!=0); - - std::size_t sizeB = kc*nc; - ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, 0); - - // For each horizontal panel of the rhs, and corresponding vertical panel of the lhs... - for(Index k=0; k<depth; k+=kc) - { - const Index actual_kc = (std::min)(k+kc,depth)-k; // => rows of B', and cols of the A' - - // In order to reduce the chance that a thread has to wait for the other, - // let's start by packing B'. - pack_rhs(blockB, rhs.getSubMapper(k,0), actual_kc, nc); - - // Pack A_k to A' in a parallel fashion: - // each thread packs the sub block A_k,i to A'_i where i is the thread id. - - // However, before copying to A'_i, we have to make sure that no other thread is still using it, - // i.e., we test that info[tid].users equals 0. - // Then, we set info[tid].users to the number of threads to mark that all other threads are going to use it. - while(info[tid].users!=0) {} - info[tid].users += threads; - - pack_lhs(blockA+info[tid].lhs_start*actual_kc, lhs.getSubMapper(info[tid].lhs_start,k), actual_kc, info[tid].lhs_length); - - // Notify the other threads that the part A'_i is ready to go. - info[tid].sync = k; - - // Computes C_i += A' * B' per A'_i - for(Index shift=0; shift<threads; ++shift) - { - Index i = (tid+shift)%threads; - - // At this point we have to make sure that A'_i has been updated by the thread i, - // we use testAndSetOrdered to mimic a volatile access. - // However, no need to wait for the B' part which has been updated by the current thread! - if (shift>0) { - while(info[i].sync!=k) { - } - } - - gebp(res.getSubMapper(info[i].lhs_start, 0), blockA+info[i].lhs_start*actual_kc, blockB, info[i].lhs_length, actual_kc, nc, alpha); - } - - // Then keep going as usual with the remaining B' - for(Index j=nc; j<cols; j+=nc) - { - const Index actual_nc = (std::min)(j+nc,cols)-j; - - // pack B_k,j to B' - pack_rhs(blockB, rhs.getSubMapper(k,j), actual_kc, actual_nc); - - // C_j += A' * B' - gebp(res.getSubMapper(0, j), blockA, blockB, rows, actual_kc, actual_nc, alpha); - } - - // Release all the sub blocks A'_i of A' for the current thread, - // i.e., we simply decrement the number of users by 1 - #pragma omp critical - { - for(Index i=0; i<threads; ++i) - #pragma omp atomic - --(info[i].users); - } - } - } - else -#endif // EIGEN_HAS_OPENMP - { - EIGEN_UNUSED_VARIABLE(info); - - // this is the sequential version! - std::size_t sizeA = kc*mc; - std::size_t sizeB = kc*nc; - - ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA()); - ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB()); - - const bool pack_rhs_once = mc!=rows && kc==depth && nc==cols; - - // For each horizontal panel of the rhs, and corresponding panel of the lhs... - for(Index i2=0; i2<rows; i2+=mc) - { - const Index actual_mc = (std::min)(i2+mc,rows)-i2; - - for(Index k2=0; k2<depth; k2+=kc) - { - const Index actual_kc = (std::min)(k2+kc,depth)-k2; - - // OK, here we have selected one horizontal panel of rhs and one vertical panel of lhs. - // => Pack lhs's panel into a sequential chunk of memory (L2/L3 caching) - // Note that this panel will be read as many times as the number of blocks in the rhs's - // horizontal panel which is, in practice, a very low number. - pack_lhs(blockA, lhs.getSubMapper(i2,k2), actual_kc, actual_mc); - - // For each kc x nc block of the rhs's horizontal panel... - for(Index j2=0; j2<cols; j2+=nc) - { - const Index actual_nc = (std::min)(j2+nc,cols)-j2; - - // We pack the rhs's block into a sequential chunk of memory (L2 caching) - // Note that this block will be read a very high number of times, which is equal to the number of - // micro horizontal panel of the large rhs's panel (e.g., rows/12 times). - if((!pack_rhs_once) || i2==0) - pack_rhs(blockB, rhs.getSubMapper(k2,j2), actual_kc, actual_nc); - - // Everything is packed, we can now call the panel * block kernel: - gebp(res.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, alpha); - } - } - } - } -} - -}; - -/********************************************************************************* -* Specialization of GeneralProduct<> for "large" GEMM, i.e., -* implementation of the high level wrapper to general_matrix_matrix_product -**********************************************************************************/ - -template<typename Lhs, typename Rhs> -struct traits<GeneralProduct<Lhs,Rhs,GemmProduct> > - : traits<ProductBase<GeneralProduct<Lhs,Rhs,GemmProduct>, Lhs, Rhs> > -{}; - -template<typename Scalar, typename Index, typename Gemm, typename Lhs, typename Rhs, typename Dest, typename BlockingType> -struct gemm_functor -{ - gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, const Scalar& actualAlpha, BlockingType& blocking) - : m_lhs(lhs), m_rhs(rhs), m_dest(dest), m_actualAlpha(actualAlpha), m_blocking(blocking) - {} - - void initParallelSession() const - { - m_blocking.allocateA(); - } - - void operator() (Index row, Index rows, Index col=0, Index cols=-1, GemmParallelInfo<Index>* info=0) const - { - if(cols==-1) - cols = m_rhs.cols(); - - Gemm::run(rows, cols, m_lhs.cols(), - /*(const Scalar*)*/&m_lhs.coeffRef(row,0), m_lhs.outerStride(), - /*(const Scalar*)*/&m_rhs.coeffRef(0,col), m_rhs.outerStride(), - (Scalar*)&(m_dest.coeffRef(row,col)), m_dest.outerStride(), - m_actualAlpha, m_blocking, info); - } - - typedef typename Gemm::Traits Traits; - - protected: - const Lhs& m_lhs; - const Rhs& m_rhs; - Dest& m_dest; - Scalar m_actualAlpha; - BlockingType& m_blocking; -}; - -template<int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor=1, -bool FiniteAtCompileTime = MaxRows!=Dynamic && MaxCols!=Dynamic && MaxDepth != Dynamic> class gemm_blocking_space; - -template<typename _LhsScalar, typename _RhsScalar> -class level3_blocking -{ - typedef _LhsScalar LhsScalar; - typedef _RhsScalar RhsScalar; - - protected: - LhsScalar* m_blockA; - RhsScalar* m_blockB; - - DenseIndex m_mc; - DenseIndex m_nc; - DenseIndex m_kc; - - public: - - level3_blocking() - : m_blockA(0), m_blockB(0), m_mc(0), m_nc(0), m_kc(0) - {} - - inline DenseIndex mc() const { return m_mc; } - inline DenseIndex nc() const { return m_nc; } - inline DenseIndex kc() const { return m_kc; } - - inline LhsScalar* blockA() { return m_blockA; } - inline RhsScalar* blockB() { return m_blockB; } -}; - -template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor> -class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, KcFactor, true> - : public level3_blocking< - typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type, - typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type> -{ - enum { - Transpose = StorageOrder==RowMajor, - ActualRows = Transpose ? MaxCols : MaxRows, - ActualCols = Transpose ? MaxRows : MaxCols - }; - typedef typename conditional<Transpose,_RhsScalar,_LhsScalar>::type LhsScalar; - typedef typename conditional<Transpose,_LhsScalar,_RhsScalar>::type RhsScalar; - typedef gebp_traits<LhsScalar,RhsScalar> Traits; - enum { - SizeA = ActualRows * MaxDepth, - SizeB = ActualCols * MaxDepth - }; - - EIGEN_ALIGN_DEFAULT LhsScalar m_staticA[SizeA]; - EIGEN_ALIGN_DEFAULT RhsScalar m_staticB[SizeB]; - - public: - - gemm_blocking_space(DenseIndex /*rows*/, DenseIndex /*cols*/, DenseIndex /*depth*/, int /*num_threads*/, bool /*full_rows = false*/) - { - this->m_mc = ActualRows; - this->m_nc = ActualCols; - this->m_kc = MaxDepth; - this->m_blockA = m_staticA; - this->m_blockB = m_staticB; - } - - inline void allocateA() {} - inline void allocateB() {} - inline void allocateAll() {} -}; - -template<int StorageOrder, typename _LhsScalar, typename _RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor> -class gemm_blocking_space<StorageOrder,_LhsScalar,_RhsScalar,MaxRows, MaxCols, MaxDepth, KcFactor, false> - : public level3_blocking< - typename conditional<StorageOrder==RowMajor,_RhsScalar,_LhsScalar>::type, - typename conditional<StorageOrder==RowMajor,_LhsScalar,_RhsScalar>::type> -{ - enum { - Transpose = StorageOrder==RowMajor - }; - typedef typename conditional<Transpose,_RhsScalar,_LhsScalar>::type LhsScalar; - typedef typename conditional<Transpose,_LhsScalar,_RhsScalar>::type RhsScalar; - typedef gebp_traits<LhsScalar,RhsScalar> Traits; - - DenseIndex m_sizeA; - DenseIndex m_sizeB; - - public: - - gemm_blocking_space(DenseIndex rows, DenseIndex cols, DenseIndex depth, DenseIndex num_threads, bool l3_blocking) - { - this->m_mc = Transpose ? cols : rows; - this->m_nc = Transpose ? rows : cols; - this->m_kc = depth; - - if(l3_blocking) - { - computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, this->m_nc, num_threads); - } - else // no l3 blocking - { - DenseIndex m = this->m_mc; - DenseIndex n = this->m_nc; - computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, m, n, num_threads); - } - - m_sizeA = this->m_mc * this->m_kc; - m_sizeB = this->m_kc * this->m_nc; - } - - void allocateA() - { - if(this->m_blockA==0) - this->m_blockA = aligned_new<LhsScalar>(m_sizeA); - } - - void allocateB() - { - if(this->m_blockB==0) - this->m_blockB = aligned_new<RhsScalar>(m_sizeB); - } - - void allocateAll() - { - allocateA(); - allocateB(); - } - - ~gemm_blocking_space() - { - aligned_delete(this->m_blockA, m_sizeA); - aligned_delete(this->m_blockB, m_sizeB); - } -}; - -} // end namespace internal - -template<typename Lhs, typename Rhs> -class GeneralProduct<Lhs, Rhs, GemmProduct> - : public ProductBase<GeneralProduct<Lhs,Rhs,GemmProduct>, Lhs, Rhs> -{ - enum { - MaxDepthAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(Lhs::MaxColsAtCompileTime,Rhs::MaxRowsAtCompileTime) - }; - public: - EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct) - - typedef typename Lhs::Scalar LhsScalar; - typedef typename Rhs::Scalar RhsScalar; - typedef Scalar ResScalar; - - GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) - { - typedef internal::scalar_product_op<LhsScalar,RhsScalar> BinOp; - EIGEN_CHECK_BINARY_COMPATIBILIY(BinOp,LhsScalar,RhsScalar); - } - - template<typename Dest> - inline void evalTo(Dest& dst) const - { - if((m_rhs.rows()+dst.rows()+dst.cols())<20 && m_rhs.rows()>0) - dst.noalias() = m_lhs .lazyProduct( m_rhs ); - else - { - dst.setZero(); - scaleAndAddTo(dst,Scalar(1)); - } - } - - template<typename Dest> - inline void addTo(Dest& dst) const - { - if((m_rhs.rows()+dst.rows()+dst.cols())<20 && m_rhs.rows()>0) - dst.noalias() += m_lhs .lazyProduct( m_rhs ); - else - scaleAndAddTo(dst,Scalar(1)); - } - - template<typename Dest> - inline void subTo(Dest& dst) const - { - if((m_rhs.rows()+dst.rows()+dst.cols())<20 && m_rhs.rows()>0) - dst.noalias() -= m_lhs .lazyProduct( m_rhs ); - else - scaleAndAddTo(dst,Scalar(-1)); - } - - template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const - { - eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols()); - - typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs); - typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs); - - Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) - * RhsBlasTraits::extractScalarFactor(m_rhs); - - typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,LhsScalar,RhsScalar, - Dest::MaxRowsAtCompileTime,Dest::MaxColsAtCompileTime,MaxDepthAtCompileTime> BlockingType; - - typedef internal::gemm_functor< - Scalar, Index, - internal::general_matrix_matrix_product< - Index, - LhsScalar, (_ActualLhsType::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(LhsBlasTraits::NeedToConjugate), - RhsScalar, (_ActualRhsType::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate), - (Dest::Flags&RowMajorBit) ? RowMajor : ColMajor>, - _ActualLhsType, _ActualRhsType, Dest, BlockingType> GemmFunctor; - - BlockingType blocking(dst.rows(), dst.cols(), lhs.cols(), 1, true); - - internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime>32 || Dest::MaxRowsAtCompileTime==Dynamic)>(GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), this->rows(), this->cols(), Dest::Flags&RowMajorBit); - } -}; - -} // end namespace Eigen - -#endif // EIGEN_GENERAL_MATRIX_MATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h deleted file mode 100644 index e4c10e88d1..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h +++ /dev/null @@ -1,285 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H -#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H - -namespace Eigen { - -template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjLhs, bool ConjRhs> -struct selfadjoint_rank1_update; - -namespace internal { - -/********************************************************************** -* This file implements a general A * B product while -* evaluating only one triangular part of the product. -* This is more general version of self adjoint product (C += A A^T) -* as the level 3 SYRK Blas routine. -**********************************************************************/ - -// forward declarations (defined at the end of this file) -template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int UpLo> -struct tribb_kernel; - -/* Optimized matrix-matrix product evaluating only one triangular half */ -template <typename Index, - typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, - typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, - int ResStorageOrder, int UpLo, int Version = Specialized> -struct general_matrix_matrix_triangular_product; - -// as usual if the result is row major => we transpose the product -template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, - typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int UpLo, int Version> -struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,UpLo,Version> -{ - typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride, - const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resStride, const ResScalar& alpha) - { - general_matrix_matrix_triangular_product<Index, - RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs, - LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs, - ColMajor, UpLo==Lower?Upper:Lower> - ::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resStride,alpha); - } -}; - -template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, - typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, int UpLo, int Version> -struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,UpLo,Version> -{ - typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride, - const RhsScalar* _rhs, Index rhsStride, ResScalar* _res, Index resStride, const ResScalar& alpha) - { - typedef gebp_traits<LhsScalar,RhsScalar> Traits; - - typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper; - typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper; - typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor> ResMapper; - LhsMapper lhs(_lhs,lhsStride); - RhsMapper rhs(_rhs,rhsStride); - ResMapper res(_res, resStride); - - Index kc = depth; // cache block size along the K direction - Index mc = size; // cache block size along the M direction - Index nc = size; // cache block size along the N direction - computeProductBlockingSizes<LhsScalar,RhsScalar>(kc, mc, nc, Index(1)); - // !!! mc must be a multiple of nr: - if(mc > Traits::nr) - mc = (mc/Traits::nr)*Traits::nr; - - ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, kc*mc, 0); - ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, kc*size, 0); - - gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs; - gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs; - gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp; - tribb_kernel<LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs, UpLo> sybb; - - for(Index k2=0; k2<depth; k2+=kc) - { - const Index actual_kc = (std::min)(k2+kc,depth)-k2; - - // note that the actual rhs is the transpose/adjoint of mat - pack_rhs(blockB, rhs.getSubMapper(k2,0), actual_kc, size); - - for(Index i2=0; i2<size; i2+=mc) - { - const Index actual_mc = (std::min)(i2+mc,size)-i2; - - pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc); - - // the selected actual_mc * size panel of res is split into three different part: - // 1 - before the diagonal => processed with gebp or skipped - // 2 - the actual_mc x actual_mc symmetric block => processed with a special kernel - // 3 - after the diagonal => processed with gebp or skipped - if (UpLo==Lower) - gebp(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, - (std::min)(size,i2), alpha, -1, -1, 0, 0); - - - sybb(_res+resStride*i2 + i2, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha); - - if (UpLo==Upper) - { - Index j2 = i2+actual_mc; - gebp(res.getSubMapper(i2, j2), blockA, blockB+actual_kc*j2, actual_mc, - actual_kc, (std::max)(Index(0), size-j2), alpha, -1, -1, 0, 0); - } - } - } - } -}; - -// Optimized packed Block * packed Block product kernel evaluating only one given triangular part -// This kernel is built on top of the gebp kernel: -// - the current destination block is processed per panel of actual_mc x BlockSize -// where BlockSize is set to the minimal value allowing gebp to be as fast as possible -// - then, as usual, each panel is split into three parts along the diagonal, -// the sub blocks above and below the diagonal are processed as usual, -// while the triangular block overlapping the diagonal is evaluated into a -// small temporary buffer which is then accumulated into the result using a -// triangular traversal. -template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int UpLo> -struct tribb_kernel -{ - typedef gebp_traits<LhsScalar,RhsScalar,ConjLhs,ConjRhs> Traits; - typedef typename Traits::ResScalar ResScalar; - - enum { - BlockSize = EIGEN_PLAIN_ENUM_MAX(mr,nr) - }; - void operator()(ResScalar* _res, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, const ResScalar& alpha) - { - typedef blas_data_mapper<ResScalar, Index, ColMajor> ResMapper; - ResMapper res(_res, resStride); - gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel; - - Matrix<ResScalar,BlockSize,BlockSize,ColMajor> buffer; - - // let's process the block per panel of actual_mc x BlockSize, - // again, each is split into three parts, etc. - for (Index j=0; j<size; j+=BlockSize) - { - Index actualBlockSize = std::min<Index>(BlockSize,size - j); - const RhsScalar* actual_b = blockB+j*depth; - - if(UpLo==Upper) - gebp_kernel(res.getSubMapper(0, j), blockA, actual_b, j, depth, actualBlockSize, alpha, - -1, -1, 0, 0); - - // selfadjoint micro block - { - Index i = j; - buffer.setZero(); - // 1 - apply the kernel on the temporary buffer - gebp_kernel(ResMapper(buffer.data(), BlockSize), blockA+depth*i, actual_b, actualBlockSize, depth, actualBlockSize, alpha, - -1, -1, 0, 0); - // 2 - triangular accumulation - for(Index j1=0; j1<actualBlockSize; ++j1) - { - ResScalar* r = &res(i, j + j1); - for(Index i1=UpLo==Lower ? j1 : 0; - UpLo==Lower ? i1<actualBlockSize : i1<=j1; ++i1) - r[i1] += buffer(i1,j1); - } - } - - if(UpLo==Lower) - { - Index i = j+actualBlockSize; - gebp_kernel(res.getSubMapper(i, j), blockA+depth*i, actual_b, size-i, - depth, actualBlockSize, alpha, -1, -1, 0, 0); - } - } - } -}; - -} // end namespace internal - -// high level API - -template<typename MatrixType, typename ProductType, int UpLo, bool IsOuterProduct> -struct general_product_to_triangular_selector; - - -template<typename MatrixType, typename ProductType, int UpLo> -struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,true> -{ - static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha) - { - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - - typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs; - typedef internal::blas_traits<Lhs> LhsBlasTraits; - typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs; - typedef typename internal::remove_all<ActualLhs>::type _ActualLhs; - typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs()); - - typedef typename internal::remove_all<typename ProductType::RhsNested>::type Rhs; - typedef internal::blas_traits<Rhs> RhsBlasTraits; - typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs; - typedef typename internal::remove_all<ActualRhs>::type _ActualRhs; - typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs()); - - Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived()); - - enum { - StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor, - UseLhsDirectly = _ActualLhs::InnerStrideAtCompileTime==1, - UseRhsDirectly = _ActualRhs::InnerStrideAtCompileTime==1 - }; - - internal::gemv_static_vector_if<Scalar,Lhs::SizeAtCompileTime,Lhs::MaxSizeAtCompileTime,!UseLhsDirectly> static_lhs; - ei_declare_aligned_stack_constructed_variable(Scalar, actualLhsPtr, actualLhs.size(), - (UseLhsDirectly ? const_cast<Scalar*>(actualLhs.data()) : static_lhs.data())); - if(!UseLhsDirectly) Map<typename _ActualLhs::PlainObject>(actualLhsPtr, actualLhs.size()) = actualLhs; - - internal::gemv_static_vector_if<Scalar,Rhs::SizeAtCompileTime,Rhs::MaxSizeAtCompileTime,!UseRhsDirectly> static_rhs; - ei_declare_aligned_stack_constructed_variable(Scalar, actualRhsPtr, actualRhs.size(), - (UseRhsDirectly ? const_cast<Scalar*>(actualRhs.data()) : static_rhs.data())); - if(!UseRhsDirectly) Map<typename _ActualRhs::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs; - - - selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo, - LhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex, - RhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex> - ::run(actualLhs.size(), mat.data(), mat.outerStride(), actualLhsPtr, actualRhsPtr, actualAlpha); - } -}; - -template<typename MatrixType, typename ProductType, int UpLo> -struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false> -{ - static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha) - { - typedef typename MatrixType::Index Index; - - typedef typename internal::remove_all<typename ProductType::LhsNested>::type Lhs; - typedef internal::blas_traits<Lhs> LhsBlasTraits; - typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs; - typedef typename internal::remove_all<ActualLhs>::type _ActualLhs; - typename internal::add_const_on_value_type<ActualLhs>::type actualLhs = LhsBlasTraits::extract(prod.lhs()); - - typedef typename internal::remove_all<typename ProductType::RhsNested>::type Rhs; - typedef internal::blas_traits<Rhs> RhsBlasTraits; - typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs; - typedef typename internal::remove_all<ActualRhs>::type _ActualRhs; - typename internal::add_const_on_value_type<ActualRhs>::type actualRhs = RhsBlasTraits::extract(prod.rhs()); - - typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived()); - - internal::general_matrix_matrix_triangular_product<Index, - typename Lhs::Scalar, _ActualLhs::Flags&RowMajorBit ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate, - typename Rhs::Scalar, _ActualRhs::Flags&RowMajorBit ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate, - MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo> - ::run(mat.cols(), actualLhs.cols(), - &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &actualRhs.coeffRef(0,0), actualRhs.outerStride(), - mat.data(), mat.outerStride(), actualAlpha); - } -}; - -template<typename MatrixType, unsigned int UpLo> -template<typename ProductDerived, typename _Lhs, typename _Rhs> -TriangularView<MatrixType,UpLo>& TriangularView<MatrixType,UpLo>::assignProduct(const ProductBase<ProductDerived, _Lhs,_Rhs>& prod, const Scalar& alpha) -{ - eigen_assert(m_matrix.rows() == prod.rows() && m_matrix.cols() == prod.cols()); - - general_product_to_triangular_selector<MatrixType, ProductDerived, UpLo, (_Lhs::ColsAtCompileTime==1) || (_Rhs::RowsAtCompileTime==1)>::run(m_matrix.const_cast_derived(), prod.derived(), alpha); - - return *this; -} - -} // end namespace Eigen - -#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H diff --git a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_MKL.h b/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_MKL.h deleted file mode 100644 index 3deed068e3..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_MKL.h +++ /dev/null @@ -1,146 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Level 3 BLAS SYRK/HERK implementation. - ******************************************************************************** -*/ - -#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H -#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H - -namespace Eigen { - -namespace internal { - -template <typename Index, typename Scalar, int AStorageOrder, bool ConjugateA, int ResStorageOrder, int UpLo> -struct general_matrix_matrix_rankupdate : - general_matrix_matrix_triangular_product< - Index,Scalar,AStorageOrder,ConjugateA,Scalar,AStorageOrder,ConjugateA,ResStorageOrder,UpLo,BuiltIn> {}; - - -// try to go to BLAS specialization -#define EIGEN_MKL_RANKUPDATE_SPECIALIZE(Scalar) \ -template <typename Index, int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs, int UpLo> \ -struct general_matrix_matrix_triangular_product<Index,Scalar,LhsStorageOrder,ConjugateLhs, \ - Scalar,RhsStorageOrder,ConjugateRhs,ColMajor,UpLo,Specialized> { \ - static EIGEN_STRONG_INLINE void run(Index size, Index depth,const Scalar* lhs, Index lhsStride, \ - const Scalar* rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) \ - { \ - if (lhs==rhs) { \ - general_matrix_matrix_rankupdate<Index,Scalar,LhsStorageOrder,ConjugateLhs,ColMajor,UpLo> \ - ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha); \ - } else { \ - general_matrix_matrix_triangular_product<Index, \ - Scalar, LhsStorageOrder, ConjugateLhs, \ - Scalar, RhsStorageOrder, ConjugateRhs, \ - ColMajor, UpLo, BuiltIn> \ - ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha); \ - } \ - } \ -}; - -EIGEN_MKL_RANKUPDATE_SPECIALIZE(double) -//EIGEN_MKL_RANKUPDATE_SPECIALIZE(dcomplex) -EIGEN_MKL_RANKUPDATE_SPECIALIZE(float) -//EIGEN_MKL_RANKUPDATE_SPECIALIZE(scomplex) - -// SYRK for float/double -#define EIGEN_MKL_RANKUPDATE_R(EIGTYPE, MKLTYPE, MKLFUNC) \ -template <typename Index, int AStorageOrder, bool ConjugateA, int UpLo> \ -struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \ - enum { \ - IsLower = (UpLo&Lower) == Lower, \ - LowUp = IsLower ? Lower : Upper, \ - conjA = ((AStorageOrder==ColMajor) && ConjugateA) ? 1 : 0 \ - }; \ - static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \ - const EIGTYPE* rhs, Index rhsStride, EIGTYPE* res, Index resStride, EIGTYPE alpha) \ - { \ - /* typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;*/ \ -\ - MKL_INT lda=lhsStride, ldc=resStride, n=size, k=depth; \ - char uplo=(IsLower) ? 'L' : 'U', trans=(AStorageOrder==RowMajor) ? 'T':'N'; \ - MKLTYPE alpha_, beta_; \ -\ -/* Set alpha_ & beta_ */ \ - assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \ - assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(beta_, EIGTYPE(1)); \ - MKLFUNC(&uplo, &trans, &n, &k, &alpha_, lhs, &lda, &beta_, res, &ldc); \ - } \ -}; - -// HERK for complex data -#define EIGEN_MKL_RANKUPDATE_C(EIGTYPE, MKLTYPE, RTYPE, MKLFUNC) \ -template <typename Index, int AStorageOrder, bool ConjugateA, int UpLo> \ -struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \ - enum { \ - IsLower = (UpLo&Lower) == Lower, \ - LowUp = IsLower ? Lower : Upper, \ - conjA = (((AStorageOrder==ColMajor) && ConjugateA) || ((AStorageOrder==RowMajor) && !ConjugateA)) ? 1 : 0 \ - }; \ - static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \ - const EIGTYPE* rhs, Index rhsStride, EIGTYPE* res, Index resStride, EIGTYPE alpha) \ - { \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, AStorageOrder> MatrixType; \ -\ - MKL_INT lda=lhsStride, ldc=resStride, n=size, k=depth; \ - char uplo=(IsLower) ? 'L' : 'U', trans=(AStorageOrder==RowMajor) ? 'C':'N'; \ - RTYPE alpha_, beta_; \ - const EIGTYPE* a_ptr; \ -\ -/* Set alpha_ & beta_ */ \ -/* assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); */\ -/* assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(beta_, EIGTYPE(1));*/ \ - alpha_ = alpha.real(); \ - beta_ = 1.0; \ -/* Copy with conjugation in some cases*/ \ - MatrixType a; \ - if (conjA) { \ - Map<const MatrixType, 0, OuterStride<> > mapA(lhs,n,k,OuterStride<>(lhsStride)); \ - a = mapA.conjugate(); \ - lda = a.outerStride(); \ - a_ptr = a.data(); \ - } else a_ptr=lhs; \ - MKLFUNC(&uplo, &trans, &n, &k, &alpha_, (MKLTYPE*)a_ptr, &lda, &beta_, (MKLTYPE*)res, &ldc); \ - } \ -}; - - -EIGEN_MKL_RANKUPDATE_R(double, double, dsyrk) -EIGEN_MKL_RANKUPDATE_R(float, float, ssyrk) - -//EIGEN_MKL_RANKUPDATE_C(dcomplex, MKL_Complex16, double, zherk) -//EIGEN_MKL_RANKUPDATE_C(scomplex, MKL_Complex8, double, cherk) - - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_MKL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_MKL.h b/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_MKL.h deleted file mode 100644 index 060af328eb..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixMatrix_MKL.h +++ /dev/null @@ -1,118 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * General matrix-matrix product functionality based on ?GEMM. - ******************************************************************************** -*/ - -#ifndef EIGEN_GENERAL_MATRIX_MATRIX_MKL_H -#define EIGEN_GENERAL_MATRIX_MATRIX_MKL_H - -namespace Eigen { - -namespace internal { - -/********************************************************************** -* This file implements general matrix-matrix multiplication using BLAS -* gemm function via partial specialization of -* general_matrix_matrix_product::run(..) method for float, double, -* std::complex<float> and std::complex<double> types -**********************************************************************/ - -// gemm specialization - -#define GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, MKLTYPE, MKLPREFIX) \ -template< \ - typename Index, \ - int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs> \ -struct general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor> \ -{ \ -static void run(Index rows, Index cols, Index depth, \ - const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsStride, \ - EIGTYPE* res, Index resStride, \ - EIGTYPE alpha, \ - level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/, \ - GemmParallelInfo<Index>* /*info = 0*/) \ -{ \ - using std::conj; \ -\ - char transa, transb; \ - MKL_INT m, n, k, lda, ldb, ldc; \ - const EIGTYPE *a, *b; \ - MKLTYPE alpha_, beta_; \ - MatrixX##EIGPREFIX a_tmp, b_tmp; \ - EIGTYPE myone(1);\ -\ -/* Set transpose options */ \ - transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \ - transb = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \ -\ -/* Set m, n, k */ \ - m = (MKL_INT)rows; \ - n = (MKL_INT)cols; \ - k = (MKL_INT)depth; \ -\ -/* Set alpha_ & beta_ */ \ - assign_scalar_eig2mkl(alpha_, alpha); \ - assign_scalar_eig2mkl(beta_, myone); \ -\ -/* Set lda, ldb, ldc */ \ - lda = (MKL_INT)lhsStride; \ - ldb = (MKL_INT)rhsStride; \ - ldc = (MKL_INT)resStride; \ -\ -/* Set a, b, c */ \ - if ((LhsStorageOrder==ColMajor) && (ConjugateLhs)) { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,k,OuterStride<>(lhsStride)); \ - a_tmp = lhs.conjugate(); \ - a = a_tmp.data(); \ - lda = a_tmp.outerStride(); \ - } else a = _lhs; \ -\ - if ((RhsStorageOrder==ColMajor) && (ConjugateRhs)) { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,k,n,OuterStride<>(rhsStride)); \ - b_tmp = rhs.conjugate(); \ - b = b_tmp.data(); \ - ldb = b_tmp.outerStride(); \ - } else b = _rhs; \ -\ - MKLPREFIX##gemm(&transa, &transb, &m, &n, &k, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ -}}; - -GEMM_SPECIALIZATION(double, d, double, d) -GEMM_SPECIALIZATION(float, f, float, s) -GEMM_SPECIALIZATION(dcomplex, cd, MKL_Complex16, z) -GEMM_SPECIALIZATION(scomplex, cf, MKL_Complex8, c) - -} // end namespase internal - -} // end namespace Eigen - -#endif // EIGEN_GENERAL_MATRIX_MATRIX_MKL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h b/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h deleted file mode 100644 index cb67d5d0a9..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixVector.h +++ /dev/null @@ -1,618 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERAL_MATRIX_VECTOR_H -#define EIGEN_GENERAL_MATRIX_VECTOR_H - -namespace Eigen { - -namespace internal { - -/* Optimized col-major matrix * vector product: - * This algorithm processes 4 columns at onces that allows to both reduce - * the number of load/stores of the result by a factor 4 and to reduce - * the instruction dependency. Moreover, we know that all bands have the - * same alignment pattern. - * - * Mixing type logic: C += alpha * A * B - * | A | B |alpha| comments - * |real |cplx |cplx | no vectorization - * |real |cplx |real | alpha is converted to a cplx when calling the run function, no vectorization - * |cplx |real |cplx | invalid, the caller has to do tmp: = A * B; C += alpha*tmp - * |cplx |real |real | optimal case, vectorization possible via real-cplx mul - * - * Accesses to the matrix coefficients follow the following logic: - * - * - if all columns have the same alignment then - * - if the columns have the same alignment as the result vector, then easy! (-> AllAligned case) - * - otherwise perform unaligned loads only (-> NoneAligned case) - * - otherwise - * - if even columns have the same alignment then - * // odd columns are guaranteed to have the same alignment too - * - if even or odd columns have the same alignment as the result, then - * // for a register size of 2 scalars, this is guarantee to be the case (e.g., SSE with double) - * - perform half aligned and half unaligned loads (-> EvenAligned case) - * - otherwise perform unaligned loads only (-> NoneAligned case) - * - otherwise, if the register size is 4 scalars (e.g., SSE with float) then - * - one over 4 consecutive columns is guaranteed to be aligned with the result vector, - * perform simple aligned loads for this column and aligned loads plus re-alignment for the other. (-> FirstAligned case) - * // this re-alignment is done by the palign function implemented for SSE in Eigen/src/Core/arch/SSE/PacketMath.h - * - otherwise, - * // if we get here, this means the register size is greater than 4 (e.g., AVX with floats), - * // we currently fall back to the NoneAligned case - * - * The same reasoning apply for the transposed case. - * - * The last case (PacketSize>4) could probably be improved by generalizing the FirstAligned case, but since we do not support AVX yet... - * One might also wonder why in the EvenAligned case we perform unaligned loads instead of using the aligned-loads plus re-alignment - * strategy as in the FirstAligned case. The reason is that we observed that unaligned loads on a 8 byte boundary are not too slow - * compared to unaligned loads on a 4 byte boundary. - * - */ -template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version> -struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version> -{ - typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - -enum { - Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable - && int(packet_traits<LhsScalar>::size)==int(packet_traits<RhsScalar>::size), - LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, - RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, - ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1 -}; - -typedef typename packet_traits<LhsScalar>::type _LhsPacket; -typedef typename packet_traits<RhsScalar>::type _RhsPacket; -typedef typename packet_traits<ResScalar>::type _ResPacket; - -typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; -typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; -typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; - -EIGEN_DONT_INLINE static void run( - Index rows, Index cols, - const LhsMapper& lhs, - const RhsMapper& rhs, - ResScalar* res, Index resIncr, - RhsScalar alpha); -}; - -template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version> -EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>::run( - Index rows, Index cols, - const LhsMapper& lhs, - const RhsMapper& rhs, - ResScalar* res, Index resIncr, - RhsScalar alpha) -{ - EIGEN_UNUSED_VARIABLE(resIncr); - eigen_internal_assert(resIncr==1); - #ifdef _EIGEN_ACCUMULATE_PACKETS - #error _EIGEN_ACCUMULATE_PACKETS has already been defined - #endif - #define _EIGEN_ACCUMULATE_PACKETS(Alignment0,Alignment13,Alignment2) \ - pstore(&res[j], \ - padd(pload<ResPacket>(&res[j]), \ - padd( \ - padd(pcj.pmul(lhs0.template load<LhsPacket, Alignment0>(j), ptmp0), \ - pcj.pmul(lhs1.template load<LhsPacket, Alignment13>(j), ptmp1)), \ - padd(pcj.pmul(lhs2.template load<LhsPacket, Alignment2>(j), ptmp2), \ - pcj.pmul(lhs3.template load<LhsPacket, Alignment13>(j), ptmp3)) ))) - - typedef typename LhsMapper::VectorMapper LhsScalars; - - conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj; - conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj; - if(ConjugateRhs) - alpha = numext::conj(alpha); - - enum { AllAligned = 0, EvenAligned, FirstAligned, NoneAligned }; - const Index columnsAtOnce = 4; - const Index peels = 2; - const Index LhsPacketAlignedMask = LhsPacketSize-1; - const Index ResPacketAlignedMask = ResPacketSize-1; -// const Index PeelAlignedMask = ResPacketSize*peels-1; - const Index size = rows; - - const Index lhsStride = lhs.stride(); - - // How many coeffs of the result do we have to skip to be aligned. - // Here we assume data are at least aligned on the base scalar type. - Index alignedStart = internal::first_aligned(res,size); - Index alignedSize = ResPacketSize>1 ? alignedStart + ((size-alignedStart) & ~ResPacketAlignedMask) : 0; - const Index peeledSize = alignedSize - RhsPacketSize*peels - RhsPacketSize + 1; - - const Index alignmentStep = LhsPacketSize>1 ? (LhsPacketSize - lhsStride % LhsPacketSize) & LhsPacketAlignedMask : 0; - Index alignmentPattern = alignmentStep==0 ? AllAligned - : alignmentStep==(LhsPacketSize/2) ? EvenAligned - : FirstAligned; - - // we cannot assume the first element is aligned because of sub-matrices - const Index lhsAlignmentOffset = lhs.firstAligned(size); - - // find how many columns do we have to skip to be aligned with the result (if possible) - Index skipColumns = 0; - // if the data cannot be aligned (TODO add some compile time tests when possible, e.g. for floats) - if( (lhsAlignmentOffset < 0) || (lhsAlignmentOffset == size) || (size_t(res)%sizeof(ResScalar)) ) - { - alignedSize = 0; - alignedStart = 0; - alignmentPattern = NoneAligned; - } - else if(LhsPacketSize > 4) - { - // TODO: extend the code to support aligned loads whenever possible when LhsPacketSize > 4. - // Currently, it seems to be better to perform unaligned loads anyway - alignmentPattern = NoneAligned; - } - else if (LhsPacketSize>1) - { - // eigen_internal_assert(size_t(firstLhs+lhsAlignmentOffset)%sizeof(LhsPacket)==0 || size<LhsPacketSize); - - while (skipColumns<LhsPacketSize && - alignedStart != ((lhsAlignmentOffset + alignmentStep*skipColumns)%LhsPacketSize)) - ++skipColumns; - if (skipColumns==LhsPacketSize) - { - // nothing can be aligned, no need to skip any column - alignmentPattern = NoneAligned; - skipColumns = 0; - } - else - { - skipColumns = (std::min)(skipColumns,cols); - // note that the skiped columns are processed later. - } - - /* eigen_internal_assert( (alignmentPattern==NoneAligned) - || (skipColumns + columnsAtOnce >= cols) - || LhsPacketSize > size - || (size_t(firstLhs+alignedStart+lhsStride*skipColumns)%sizeof(LhsPacket))==0);*/ - } - else if(Vectorizable) - { - alignedStart = 0; - alignedSize = size; - alignmentPattern = AllAligned; - } - - const Index offset1 = (FirstAligned && alignmentStep==1?3:1); - const Index offset3 = (FirstAligned && alignmentStep==1?1:3); - - Index columnBound = ((cols-skipColumns)/columnsAtOnce)*columnsAtOnce + skipColumns; - for (Index i=skipColumns; i<columnBound; i+=columnsAtOnce) - { - RhsPacket ptmp0 = pset1<RhsPacket>(alpha*rhs(i, 0)), - ptmp1 = pset1<RhsPacket>(alpha*rhs(i+offset1, 0)), - ptmp2 = pset1<RhsPacket>(alpha*rhs(i+2, 0)), - ptmp3 = pset1<RhsPacket>(alpha*rhs(i+offset3, 0)); - - // this helps a lot generating better binary code - const LhsScalars lhs0 = lhs.getVectorMapper(0, i+0), lhs1 = lhs.getVectorMapper(0, i+offset1), - lhs2 = lhs.getVectorMapper(0, i+2), lhs3 = lhs.getVectorMapper(0, i+offset3); - - if (Vectorizable) - { - /* explicit vectorization */ - // process initial unaligned coeffs - for (Index j=0; j<alignedStart; ++j) - { - res[j] = cj.pmadd(lhs0(j), pfirst(ptmp0), res[j]); - res[j] = cj.pmadd(lhs1(j), pfirst(ptmp1), res[j]); - res[j] = cj.pmadd(lhs2(j), pfirst(ptmp2), res[j]); - res[j] = cj.pmadd(lhs3(j), pfirst(ptmp3), res[j]); - } - - if (alignedSize>alignedStart) - { - switch(alignmentPattern) - { - case AllAligned: - for (Index j = alignedStart; j<alignedSize; j+=ResPacketSize) - _EIGEN_ACCUMULATE_PACKETS(Aligned,Aligned,Aligned); - break; - case EvenAligned: - for (Index j = alignedStart; j<alignedSize; j+=ResPacketSize) - _EIGEN_ACCUMULATE_PACKETS(Aligned,Unaligned,Aligned); - break; - case FirstAligned: - { - Index j = alignedStart; - if(peels>1) - { - LhsPacket A00, A01, A02, A03, A10, A11, A12, A13; - ResPacket T0, T1; - - A01 = lhs1.template load<LhsPacket, Aligned>(alignedStart-1); - A02 = lhs2.template load<LhsPacket, Aligned>(alignedStart-2); - A03 = lhs3.template load<LhsPacket, Aligned>(alignedStart-3); - - for (; j<peeledSize; j+=peels*ResPacketSize) - { - A11 = lhs1.template load<LhsPacket, Aligned>(j-1+LhsPacketSize); palign<1>(A01,A11); - A12 = lhs2.template load<LhsPacket, Aligned>(j-2+LhsPacketSize); palign<2>(A02,A12); - A13 = lhs3.template load<LhsPacket, Aligned>(j-3+LhsPacketSize); palign<3>(A03,A13); - - A00 = lhs0.template load<LhsPacket, Aligned>(j); - A10 = lhs0.template load<LhsPacket, Aligned>(j+LhsPacketSize); - T0 = pcj.pmadd(A00, ptmp0, pload<ResPacket>(&res[j])); - T1 = pcj.pmadd(A10, ptmp0, pload<ResPacket>(&res[j+ResPacketSize])); - - T0 = pcj.pmadd(A01, ptmp1, T0); - A01 = lhs1.template load<LhsPacket, Aligned>(j-1+2*LhsPacketSize); palign<1>(A11,A01); - T0 = pcj.pmadd(A02, ptmp2, T0); - A02 = lhs2.template load<LhsPacket, Aligned>(j-2+2*LhsPacketSize); palign<2>(A12,A02); - T0 = pcj.pmadd(A03, ptmp3, T0); - pstore(&res[j],T0); - A03 = lhs3.template load<LhsPacket, Aligned>(j-3+2*LhsPacketSize); palign<3>(A13,A03); - T1 = pcj.pmadd(A11, ptmp1, T1); - T1 = pcj.pmadd(A12, ptmp2, T1); - T1 = pcj.pmadd(A13, ptmp3, T1); - pstore(&res[j+ResPacketSize],T1); - } - } - for (; j<alignedSize; j+=ResPacketSize) - _EIGEN_ACCUMULATE_PACKETS(Aligned,Unaligned,Unaligned); - break; - } - default: - for (Index j = alignedStart; j<alignedSize; j+=ResPacketSize) - _EIGEN_ACCUMULATE_PACKETS(Unaligned,Unaligned,Unaligned); - break; - } - } - } // end explicit vectorization - - /* process remaining coeffs (or all if there is no explicit vectorization) */ - for (Index j=alignedSize; j<size; ++j) - { - res[j] = cj.pmadd(lhs0(j), pfirst(ptmp0), res[j]); - res[j] = cj.pmadd(lhs1(j), pfirst(ptmp1), res[j]); - res[j] = cj.pmadd(lhs2(j), pfirst(ptmp2), res[j]); - res[j] = cj.pmadd(lhs3(j), pfirst(ptmp3), res[j]); - } - } - - // process remaining first and last columns (at most columnsAtOnce-1) - Index end = cols; - Index start = columnBound; - do - { - for (Index k=start; k<end; ++k) - { - RhsPacket ptmp0 = pset1<RhsPacket>(alpha*rhs(k, 0)); - const LhsScalars lhs0 = lhs.getVectorMapper(0, k); - - if (Vectorizable) - { - /* explicit vectorization */ - // process first unaligned result's coeffs - for (Index j=0; j<alignedStart; ++j) - res[j] += cj.pmul(lhs0(j), pfirst(ptmp0)); - // process aligned result's coeffs - if (lhs0.template aligned<LhsPacket>(alignedStart)) - for (Index i = alignedStart;i<alignedSize;i+=ResPacketSize) - pstore(&res[i], pcj.pmadd(lhs0.template load<LhsPacket, Aligned>(i), ptmp0, pload<ResPacket>(&res[i]))); - else - for (Index i = alignedStart;i<alignedSize;i+=ResPacketSize) - pstore(&res[i], pcj.pmadd(lhs0.template load<LhsPacket, Unaligned>(i), ptmp0, pload<ResPacket>(&res[i]))); - } - - // process remaining scalars (or all if no explicit vectorization) - for (Index i=alignedSize; i<size; ++i) - res[i] += cj.pmul(lhs0(i), pfirst(ptmp0)); - } - if (skipColumns) - { - start = 0; - end = skipColumns; - skipColumns = 0; - } - else - break; - } while(Vectorizable); - #undef _EIGEN_ACCUMULATE_PACKETS -} - -/* Optimized row-major matrix * vector product: - * This algorithm processes 4 rows at onces that allows to both reduce - * the number of load/stores of the result by a factor 4 and to reduce - * the instruction dependency. Moreover, we know that all bands have the - * same alignment pattern. - * - * Mixing type logic: - * - alpha is always a complex (or converted to a complex) - * - no vectorization - */ -template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version> -struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version> -{ -typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - -enum { - Vectorizable = packet_traits<LhsScalar>::Vectorizable && packet_traits<RhsScalar>::Vectorizable - && int(packet_traits<LhsScalar>::size)==int(packet_traits<RhsScalar>::size), - LhsPacketSize = Vectorizable ? packet_traits<LhsScalar>::size : 1, - RhsPacketSize = Vectorizable ? packet_traits<RhsScalar>::size : 1, - ResPacketSize = Vectorizable ? packet_traits<ResScalar>::size : 1 -}; - -typedef typename packet_traits<LhsScalar>::type _LhsPacket; -typedef typename packet_traits<RhsScalar>::type _RhsPacket; -typedef typename packet_traits<ResScalar>::type _ResPacket; - -typedef typename conditional<Vectorizable,_LhsPacket,LhsScalar>::type LhsPacket; -typedef typename conditional<Vectorizable,_RhsPacket,RhsScalar>::type RhsPacket; -typedef typename conditional<Vectorizable,_ResPacket,ResScalar>::type ResPacket; - -EIGEN_DONT_INLINE static void run( - Index rows, Index cols, - const LhsMapper& lhs, - const RhsMapper& rhs, - ResScalar* res, Index resIncr, - ResScalar alpha); -}; - -template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version> -EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>::run( - Index rows, Index cols, - const LhsMapper& lhs, - const RhsMapper& rhs, - ResScalar* res, Index resIncr, - ResScalar alpha) -{ - eigen_internal_assert(rhs.stride()==1); - - #ifdef _EIGEN_ACCUMULATE_PACKETS - #error _EIGEN_ACCUMULATE_PACKETS has already been defined - #endif - - #define _EIGEN_ACCUMULATE_PACKETS(Alignment0,Alignment13,Alignment2) {\ - RhsPacket b = rhs.getVectorMapper(j, 0).template load<RhsPacket, Aligned>(0); \ - ptmp0 = pcj.pmadd(lhs0.template load<LhsPacket, Alignment0>(j), b, ptmp0); \ - ptmp1 = pcj.pmadd(lhs1.template load<LhsPacket, Alignment13>(j), b, ptmp1); \ - ptmp2 = pcj.pmadd(lhs2.template load<LhsPacket, Alignment2>(j), b, ptmp2); \ - ptmp3 = pcj.pmadd(lhs3.template load<LhsPacket, Alignment13>(j), b, ptmp3); } - - conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj; - conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj; - - typedef typename LhsMapper::VectorMapper LhsScalars; - - enum { AllAligned=0, EvenAligned=1, FirstAligned=2, NoneAligned=3 }; - const Index rowsAtOnce = 4; - const Index peels = 2; - const Index RhsPacketAlignedMask = RhsPacketSize-1; - const Index LhsPacketAlignedMask = LhsPacketSize-1; - const Index depth = cols; - const Index lhsStride = lhs.stride(); - - // How many coeffs of the result do we have to skip to be aligned. - // Here we assume data are at least aligned on the base scalar type - // if that's not the case then vectorization is discarded, see below. - Index alignedStart = rhs.firstAligned(depth); - Index alignedSize = RhsPacketSize>1 ? alignedStart + ((depth-alignedStart) & ~RhsPacketAlignedMask) : 0; - const Index peeledSize = alignedSize - RhsPacketSize*peels - RhsPacketSize + 1; - - const Index alignmentStep = LhsPacketSize>1 ? (LhsPacketSize - lhsStride % LhsPacketSize) & LhsPacketAlignedMask : 0; - Index alignmentPattern = alignmentStep==0 ? AllAligned - : alignmentStep==(LhsPacketSize/2) ? EvenAligned - : FirstAligned; - - // we cannot assume the first element is aligned because of sub-matrices - const Index lhsAlignmentOffset = lhs.firstAligned(depth); - const Index rhsAlignmentOffset = rhs.firstAligned(rows); - - // find how many rows do we have to skip to be aligned with rhs (if possible) - Index skipRows = 0; - // if the data cannot be aligned (TODO add some compile time tests when possible, e.g. for floats) - if( (sizeof(LhsScalar)!=sizeof(RhsScalar)) - || (lhsAlignmentOffset < 0) || (lhsAlignmentOffset == depth) - || (rhsAlignmentOffset < 0) || (rhsAlignmentOffset == rows)) - { - alignedSize = 0; - alignedStart = 0; - alignmentPattern = NoneAligned; - } - else if(LhsPacketSize > 4) - { - // TODO: extend the code to support aligned loads whenever possible when LhsPacketSize > 4. - alignmentPattern = NoneAligned; - } - else if (LhsPacketSize>1) - { - // eigen_internal_assert(size_t(firstLhs+lhsAlignmentOffset)%sizeof(LhsPacket)==0 || depth<LhsPacketSize); - - while (skipRows<LhsPacketSize && - alignedStart != ((lhsAlignmentOffset + alignmentStep*skipRows)%LhsPacketSize)) - ++skipRows; - if (skipRows==LhsPacketSize) - { - // nothing can be aligned, no need to skip any column - alignmentPattern = NoneAligned; - skipRows = 0; - } - else - { - skipRows = (std::min)(skipRows,Index(rows)); - // note that the skiped columns are processed later. - } - /* eigen_internal_assert( alignmentPattern==NoneAligned - || LhsPacketSize==1 - || (skipRows + rowsAtOnce >= rows) - || LhsPacketSize > depth - || (size_t(firstLhs+alignedStart+lhsStride*skipRows)%sizeof(LhsPacket))==0);*/ - } - else if(Vectorizable) - { - alignedStart = 0; - alignedSize = depth; - alignmentPattern = AllAligned; - } - - const Index offset1 = (FirstAligned && alignmentStep==1?3:1); - const Index offset3 = (FirstAligned && alignmentStep==1?1:3); - - Index rowBound = ((rows-skipRows)/rowsAtOnce)*rowsAtOnce + skipRows; - for (Index i=skipRows; i<rowBound; i+=rowsAtOnce) - { - EIGEN_ALIGN_DEFAULT ResScalar tmp0 = ResScalar(0); - ResScalar tmp1 = ResScalar(0), tmp2 = ResScalar(0), tmp3 = ResScalar(0); - - // this helps the compiler generating good binary code - const LhsScalars lhs0 = lhs.getVectorMapper(i+0, 0), lhs1 = lhs.getVectorMapper(i+offset1, 0), - lhs2 = lhs.getVectorMapper(i+2, 0), lhs3 = lhs.getVectorMapper(i+offset3, 0); - - if (Vectorizable) - { - /* explicit vectorization */ - ResPacket ptmp0 = pset1<ResPacket>(ResScalar(0)), ptmp1 = pset1<ResPacket>(ResScalar(0)), - ptmp2 = pset1<ResPacket>(ResScalar(0)), ptmp3 = pset1<ResPacket>(ResScalar(0)); - - // process initial unaligned coeffs - // FIXME this loop get vectorized by the compiler ! - for (Index j=0; j<alignedStart; ++j) - { - RhsScalar b = rhs(j, 0); - tmp0 += cj.pmul(lhs0(j),b); tmp1 += cj.pmul(lhs1(j),b); - tmp2 += cj.pmul(lhs2(j),b); tmp3 += cj.pmul(lhs3(j),b); - } - - if (alignedSize>alignedStart) - { - switch(alignmentPattern) - { - case AllAligned: - for (Index j = alignedStart; j<alignedSize; j+=RhsPacketSize) - _EIGEN_ACCUMULATE_PACKETS(Aligned,Aligned,Aligned); - break; - case EvenAligned: - for (Index j = alignedStart; j<alignedSize; j+=RhsPacketSize) - _EIGEN_ACCUMULATE_PACKETS(Aligned,Unaligned,Aligned); - break; - case FirstAligned: - { - Index j = alignedStart; - if (peels>1) - { - /* Here we proccess 4 rows with with two peeled iterations to hide - * the overhead of unaligned loads. Moreover unaligned loads are handled - * using special shift/move operations between the two aligned packets - * overlaping the desired unaligned packet. This is *much* more efficient - * than basic unaligned loads. - */ - LhsPacket A01, A02, A03, A11, A12, A13; - A01 = lhs1.template load<LhsPacket, Aligned>(alignedStart-1); - A02 = lhs2.template load<LhsPacket, Aligned>(alignedStart-2); - A03 = lhs3.template load<LhsPacket, Aligned>(alignedStart-3); - - for (; j<peeledSize; j+=peels*RhsPacketSize) - { - RhsPacket b = rhs.getVectorMapper(j, 0).template load<RhsPacket, Aligned>(0); - A11 = lhs1.template load<LhsPacket, Aligned>(j-1+LhsPacketSize); palign<1>(A01,A11); - A12 = lhs2.template load<LhsPacket, Aligned>(j-2+LhsPacketSize); palign<2>(A02,A12); - A13 = lhs3.template load<LhsPacket, Aligned>(j-3+LhsPacketSize); palign<3>(A03,A13); - - ptmp0 = pcj.pmadd(lhs0.template load<LhsPacket, Aligned>(j), b, ptmp0); - ptmp1 = pcj.pmadd(A01, b, ptmp1); - A01 = lhs1.template load<LhsPacket, Aligned>(j-1+2*LhsPacketSize); palign<1>(A11,A01); - ptmp2 = pcj.pmadd(A02, b, ptmp2); - A02 = lhs2.template load<LhsPacket, Aligned>(j-2+2*LhsPacketSize); palign<2>(A12,A02); - ptmp3 = pcj.pmadd(A03, b, ptmp3); - A03 = lhs3.template load<LhsPacket, Aligned>(j-3+2*LhsPacketSize); palign<3>(A13,A03); - - b = rhs.getVectorMapper(j+RhsPacketSize, 0).template load<RhsPacket, Aligned>(0); - ptmp0 = pcj.pmadd(lhs0.template load<LhsPacket, Aligned>(j+LhsPacketSize), b, ptmp0); - ptmp1 = pcj.pmadd(A11, b, ptmp1); - ptmp2 = pcj.pmadd(A12, b, ptmp2); - ptmp3 = pcj.pmadd(A13, b, ptmp3); - } - } - for (; j<alignedSize; j+=RhsPacketSize) - _EIGEN_ACCUMULATE_PACKETS(Aligned,Unaligned,Unaligned); - break; - } - default: - for (Index j = alignedStart; j<alignedSize; j+=RhsPacketSize) - _EIGEN_ACCUMULATE_PACKETS(Unaligned,Unaligned,Unaligned); - break; - } - tmp0 += predux(ptmp0); - tmp1 += predux(ptmp1); - tmp2 += predux(ptmp2); - tmp3 += predux(ptmp3); - } - } // end explicit vectorization - - // process remaining coeffs (or all if no explicit vectorization) - // FIXME this loop get vectorized by the compiler ! - for (Index j=alignedSize; j<depth; ++j) - { - RhsScalar b = rhs(j, 0); - tmp0 += cj.pmul(lhs0(j),b); tmp1 += cj.pmul(lhs1(j),b); - tmp2 += cj.pmul(lhs2(j),b); tmp3 += cj.pmul(lhs3(j),b); - } - res[i*resIncr] += alpha*tmp0; - res[(i+offset1)*resIncr] += alpha*tmp1; - res[(i+2)*resIncr] += alpha*tmp2; - res[(i+offset3)*resIncr] += alpha*tmp3; - } - - // process remaining first and last rows (at most columnsAtOnce-1) - Index end = rows; - Index start = rowBound; - do - { - for (Index i=start; i<end; ++i) - { - EIGEN_ALIGN_DEFAULT ResScalar tmp0 = ResScalar(0); - ResPacket ptmp0 = pset1<ResPacket>(tmp0); - const LhsScalars lhs0 = lhs.getVectorMapper(i, 0); - // process first unaligned result's coeffs - // FIXME this loop get vectorized by the compiler ! - for (Index j=0; j<alignedStart; ++j) - tmp0 += cj.pmul(lhs0(j), rhs(j, 0)); - - if (alignedSize>alignedStart) - { - // process aligned rhs coeffs - if (lhs0.template aligned<LhsPacket>(alignedStart)) - for (Index j = alignedStart;j<alignedSize;j+=RhsPacketSize) - ptmp0 = pcj.pmadd(lhs0.template load<LhsPacket, Aligned>(j), rhs.getVectorMapper(j, 0).template load<RhsPacket, Aligned>(0), ptmp0); - else - for (Index j = alignedStart;j<alignedSize;j+=RhsPacketSize) - ptmp0 = pcj.pmadd(lhs0.template load<LhsPacket, Unaligned>(j), rhs.getVectorMapper(j, 0).template load<RhsPacket, Aligned>(0), ptmp0); - tmp0 += predux(ptmp0); - } - - // process remaining scalars - // FIXME this loop get vectorized by the compiler ! - for (Index j=alignedSize; j<depth; ++j) - tmp0 += cj.pmul(lhs0(j), rhs(j, 0)); - res[i*resIncr] += alpha*tmp0; - } - if (skipRows) - { - start = 0; - end = skipRows; - skipRows = 0; - } - else - break; - } while(Vectorizable); - - #undef _EIGEN_ACCUMULATE_PACKETS -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_GENERAL_MATRIX_VECTOR_H diff --git a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixVector_MKL.h b/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixVector_MKL.h deleted file mode 100644 index 1cb9fe6b5a..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/GeneralMatrixVector_MKL.h +++ /dev/null @@ -1,131 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * General matrix-vector product functionality based on ?GEMV. - ******************************************************************************** -*/ - -#ifndef EIGEN_GENERAL_MATRIX_VECTOR_MKL_H -#define EIGEN_GENERAL_MATRIX_VECTOR_MKL_H - -namespace Eigen { - -namespace internal { - -/********************************************************************** -* This file implements general matrix-vector multiplication using BLAS -* gemv function via partial specialization of -* general_matrix_vector_product::run(..) method for float, double, -* std::complex<float> and std::complex<double> types -**********************************************************************/ - -// gemv specialization - -template<typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar, bool ConjugateRhs> -struct general_matrix_vector_product_gemv : - general_matrix_vector_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,ConjugateRhs,BuiltIn> {}; - -#define EIGEN_MKL_GEMV_SPECIALIZE(Scalar) \ -template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \ -struct general_matrix_vector_product<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs,Specialized> { \ -static void run( \ - Index rows, Index cols, \ - const Scalar* lhs, Index lhsStride, \ - const Scalar* rhs, Index rhsIncr, \ - Scalar* res, Index resIncr, Scalar alpha) \ -{ \ - if (ConjugateLhs) { \ - general_matrix_vector_product<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs,BuiltIn>::run( \ - rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \ - } else { \ - general_matrix_vector_product_gemv<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \ - rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \ - } \ -} \ -}; \ -template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \ -struct general_matrix_vector_product<Index,Scalar,RowMajor,ConjugateLhs,Scalar,ConjugateRhs,Specialized> { \ -static void run( \ - Index rows, Index cols, \ - const Scalar* lhs, Index lhsStride, \ - const Scalar* rhs, Index rhsIncr, \ - Scalar* res, Index resIncr, Scalar alpha) \ -{ \ - general_matrix_vector_product_gemv<Index,Scalar,RowMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \ - rows, cols, lhs, lhsStride, rhs, rhsIncr, res, resIncr, alpha); \ -} \ -}; \ - -EIGEN_MKL_GEMV_SPECIALIZE(double) -EIGEN_MKL_GEMV_SPECIALIZE(float) -EIGEN_MKL_GEMV_SPECIALIZE(dcomplex) -EIGEN_MKL_GEMV_SPECIALIZE(scomplex) - -#define EIGEN_MKL_GEMV_SPECIALIZATION(EIGTYPE,MKLTYPE,MKLPREFIX) \ -template<typename Index, int LhsStorageOrder, bool ConjugateLhs, bool ConjugateRhs> \ -struct general_matrix_vector_product_gemv<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,ConjugateRhs> \ -{ \ -typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> GEMVVector;\ -\ -static void run( \ - Index rows, Index cols, \ - const EIGTYPE* lhs, Index lhsStride, \ - const EIGTYPE* rhs, Index rhsIncr, \ - EIGTYPE* res, Index resIncr, EIGTYPE alpha) \ -{ \ - MKL_INT m=rows, n=cols, lda=lhsStride, incx=rhsIncr, incy=resIncr; \ - MKLTYPE alpha_, beta_; \ - const EIGTYPE *x_ptr, myone(1); \ - char trans=(LhsStorageOrder==ColMajor) ? 'N' : (ConjugateLhs) ? 'C' : 'T'; \ - if (LhsStorageOrder==RowMajor) { \ - m=cols; \ - n=rows; \ - }\ - assign_scalar_eig2mkl(alpha_, alpha); \ - assign_scalar_eig2mkl(beta_, myone); \ - GEMVVector x_tmp; \ - if (ConjugateRhs) { \ - Map<const GEMVVector, 0, InnerStride<> > map_x(rhs,cols,1,InnerStride<>(incx)); \ - x_tmp=map_x.conjugate(); \ - x_ptr=x_tmp.data(); \ - incx=1; \ - } else x_ptr=rhs; \ - MKLPREFIX##gemv(&trans, &m, &n, &alpha_, (const MKLTYPE*)lhs, &lda, (const MKLTYPE*)x_ptr, &incx, &beta_, (MKLTYPE*)res, &incy); \ -}\ -}; - -EIGEN_MKL_GEMV_SPECIALIZATION(double, double, d) -EIGEN_MKL_GEMV_SPECIALIZATION(float, float, s) -EIGEN_MKL_GEMV_SPECIALIZATION(dcomplex, MKL_Complex16, z) -EIGEN_MKL_GEMV_SPECIALIZATION(scomplex, MKL_Complex8, c) - -} // end namespase internal - -} // end namespace Eigen - -#endif // EIGEN_GENERAL_MATRIX_VECTOR_MKL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/Parallelizer.h b/third_party/eigen3/Eigen/src/Core/products/Parallelizer.h deleted file mode 100644 index 837e69415b..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/Parallelizer.h +++ /dev/null @@ -1,158 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PARALLELIZER_H -#define EIGEN_PARALLELIZER_H - -namespace Eigen { - -namespace internal { - -/** \internal */ -inline void manage_multi_threading(Action action, int* v) -{ - static EIGEN_UNUSED int m_maxThreads = -1; - - if(action==SetAction) - { - eigen_internal_assert(v!=0); - m_maxThreads = *v; - } - else if(action==GetAction) - { - eigen_internal_assert(v!=0); - #ifdef EIGEN_HAS_OPENMP - if(m_maxThreads>0) - *v = m_maxThreads; - else - *v = omp_get_max_threads(); - #else - *v = 1; - #endif - } - else - { - eigen_internal_assert(false); - } -} - -} - -/** Must be call first when calling Eigen from multiple threads */ -inline void initParallel() -{ - int nbt; - internal::manage_multi_threading(GetAction, &nbt); - std::ptrdiff_t l1, l2, l3; - internal::manage_caching_sizes(GetAction, &l1, &l2, &l3); -} - -/** \returns the max number of threads reserved for Eigen - * \sa setNbThreads */ -inline int nbThreads() -{ - int ret; - internal::manage_multi_threading(GetAction, &ret); - return ret; -} - -/** Sets the max number of threads reserved for Eigen - * \sa nbThreads */ -inline void setNbThreads(int v) -{ - internal::manage_multi_threading(SetAction, &v); -} - -namespace internal { - -template<typename Index> struct GemmParallelInfo -{ - GemmParallelInfo() : sync(-1), users(0), lhs_start(0), lhs_length(0) {} - - int volatile sync; - int volatile users; - - Index lhs_start; - Index lhs_length; -}; - -template<bool Condition, typename Functor, typename Index> -void parallelize_gemm(const Functor& func, Index rows, Index cols, bool transpose) -{ - // TODO when EIGEN_USE_BLAS is defined, - // we should still enable OMP for other scalar types -#if !(defined (EIGEN_HAS_OPENMP)) || defined (EIGEN_USE_BLAS) - // FIXME the transpose variable is only needed to properly split - // the matrix product when multithreading is enabled. This is a temporary - // fix to support row-major destination matrices. This whole - // parallelizer mechanism has to be redisigned anyway. - EIGEN_UNUSED_VARIABLE(transpose); - func(0,rows, 0,cols); -#else - - // Dynamically check whether we should enable or disable OpenMP. - // The conditions are: - // - the max number of threads we can create is greater than 1 - // - we are not already in a parallel code - // - the sizes are large enough - - // 1- are we already in a parallel session? - // FIXME omp_get_num_threads()>1 only works for openmp, what if the user does not use openmp? - if((!Condition) || (omp_get_num_threads()>1)) - return func(0,rows, 0,cols); - - Index size = transpose ? rows : cols; - - // 2- compute the maximal number of threads from the size of the product: - // FIXME this has to be fine tuned - Index max_threads = std::max<Index>(1,size / 32); - - // 3 - compute the number of threads we are going to use - Index threads = std::min<Index>(nbThreads(), max_threads); - - if(threads==1) - return func(0,rows, 0,cols); - - Eigen::initParallel(); - func.initParallelSession(); - - if(transpose) - std::swap(rows,cols); - - Index blockCols = (cols / threads) & ~Index(0x3); - Index blockRows = (rows / threads); - blockRows = (blockRows/Functor::Traits::mr)*Functor::Traits::mr; - - GemmParallelInfo<Index>* info = new GemmParallelInfo<Index>[threads]; - - #pragma omp parallel num_threads(threads) - { - Index i = omp_get_thread_num(); - Index r0 = i*blockRows; - Index actualBlockRows = (i+1==threads) ? rows-r0 : blockRows; - - Index c0 = i*blockCols; - Index actualBlockCols = (i+1==threads) ? cols-c0 : blockCols; - - info[i].lhs_start = r0; - info[i].lhs_length = actualBlockRows; - - if(transpose) func(c0, actualBlockCols, 0, rows, info); - else func(0, rows, c0, actualBlockCols, info); - } - - delete[] info; -#endif -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PARALLELIZER_H diff --git a/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h deleted file mode 100644 index 4a60ef7dc5..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix.h +++ /dev/null @@ -1,523 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_H -#define EIGEN_SELFADJOINT_MATRIX_MATRIX_H - -namespace Eigen { - -namespace internal { - -// pack a selfadjoint block diagonal for use with the gebp_kernel -template<typename Scalar, typename Index, int Pack1, int Pack2_dummy, int StorageOrder> -struct symm_pack_lhs -{ - template<int BlockRows> inline - void pack(Scalar* blockA, const const_blas_data_mapper<Scalar,Index,StorageOrder>& lhs, Index cols, Index i, Index& count) - { - // normal copy - for(Index k=0; k<i; k++) - for(Index w=0; w<BlockRows; w++) - blockA[count++] = lhs(i+w,k); // normal - // symmetric copy - Index h = 0; - for(Index k=i; k<i+BlockRows; k++) - { - for(Index w=0; w<h; w++) - blockA[count++] = numext::conj(lhs(k, i+w)); // transposed - - blockA[count++] = numext::real(lhs(k,k)); // real (diagonal) - - for(Index w=h+1; w<BlockRows; w++) - blockA[count++] = lhs(i+w, k); // normal - ++h; - } - // transposed copy - for(Index k=i+BlockRows; k<cols; k++) - for(Index w=0; w<BlockRows; w++) - blockA[count++] = numext::conj(lhs(k, i+w)); // transposed - } - void operator()(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows) - { - enum { PacketSize = packet_traits<Scalar>::size }; - const_blas_data_mapper<Scalar,Index,StorageOrder> lhs(_lhs,lhsStride); - Index count = 0; - //Index peeled_mc3 = (rows/Pack1)*Pack1; - - const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0; - const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0; - const Index peeled_mc1 = Pack1>=1*PacketSize ? (rows/(1*PacketSize))*(1*PacketSize) : 0; - - if(Pack1>=3*PacketSize) - for(Index i=0; i<peeled_mc3; i+=3*PacketSize) - pack<3*PacketSize>(blockA, lhs, cols, i, count); - - if(Pack1>=2*PacketSize) - for(Index i=peeled_mc3; i<peeled_mc2; i+=2*PacketSize) - pack<2*PacketSize>(blockA, lhs, cols, i, count); - - if(Pack1>=1*PacketSize) - for(Index i=peeled_mc2; i<peeled_mc1; i+=1*PacketSize) - pack<1*PacketSize>(blockA, lhs, cols, i, count); - - // do the same with mr==1 - for(Index i=peeled_mc1; i<rows; i++) - { - for(Index k=0; k<i; k++) - blockA[count++] = lhs(i, k); // normal - - blockA[count++] = numext::real(lhs(i, i)); // real (diagonal) - - for(Index k=i+1; k<cols; k++) - blockA[count++] = numext::conj(lhs(k, i)); // transposed - } - } -}; - -template<typename Scalar, typename Index, int nr, int StorageOrder> -struct symm_pack_rhs -{ - enum { PacketSize = packet_traits<Scalar>::size }; - void operator()(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2) - { - Index end_k = k2 + rows; - Index count = 0; - const_blas_data_mapper<Scalar,Index,StorageOrder> rhs(_rhs,rhsStride); - Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0; - Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0; - - // first part: normal case - for(Index j2=0; j2<k2; j2+=nr) - { - for(Index k=k2; k<end_k; k++) - { - blockB[count+0] = rhs(k,j2+0); - blockB[count+1] = rhs(k,j2+1); - if (nr>=4) - { - blockB[count+2] = rhs(k,j2+2); - blockB[count+3] = rhs(k,j2+3); - } - if (nr>=8) - { - blockB[count+4] = rhs(k,j2+4); - blockB[count+5] = rhs(k,j2+5); - blockB[count+6] = rhs(k,j2+6); - blockB[count+7] = rhs(k,j2+7); - } - count += nr; - } - } - - // second part: diagonal block - Index end8 = nr>=8 ? (std::min)(k2+rows,packet_cols8) : k2; - if(nr>=8) - { - for(Index j2=k2; j2<end8; j2+=8) - { - // again we can split vertically in three different parts (transpose, symmetric, normal) - // transpose - for(Index k=k2; k<j2; k++) - { - blockB[count+0] = numext::conj(rhs(j2+0,k)); - blockB[count+1] = numext::conj(rhs(j2+1,k)); - blockB[count+2] = numext::conj(rhs(j2+2,k)); - blockB[count+3] = numext::conj(rhs(j2+3,k)); - blockB[count+4] = numext::conj(rhs(j2+4,k)); - blockB[count+5] = numext::conj(rhs(j2+5,k)); - blockB[count+6] = numext::conj(rhs(j2+6,k)); - blockB[count+7] = numext::conj(rhs(j2+7,k)); - count += 8; - } - // symmetric - Index h = 0; - for(Index k=j2; k<j2+8; k++) - { - // normal - for (Index w=0 ; w<h; ++w) - blockB[count+w] = rhs(k,j2+w); - - blockB[count+h] = numext::real(rhs(k,k)); - - // transpose - for (Index w=h+1 ; w<8; ++w) - blockB[count+w] = numext::conj(rhs(j2+w,k)); - count += 8; - ++h; - } - // normal - for(Index k=j2+8; k<end_k; k++) - { - blockB[count+0] = rhs(k,j2+0); - blockB[count+1] = rhs(k,j2+1); - blockB[count+2] = rhs(k,j2+2); - blockB[count+3] = rhs(k,j2+3); - blockB[count+4] = rhs(k,j2+4); - blockB[count+5] = rhs(k,j2+5); - blockB[count+6] = rhs(k,j2+6); - blockB[count+7] = rhs(k,j2+7); - count += 8; - } - } - } - if(nr>=4) - { - for(Index j2=end8; j2<(std::min)(k2+rows,packet_cols4); j2+=4) - { - // again we can split vertically in three different parts (transpose, symmetric, normal) - // transpose - for(Index k=k2; k<j2; k++) - { - blockB[count+0] = numext::conj(rhs(j2+0,k)); - blockB[count+1] = numext::conj(rhs(j2+1,k)); - blockB[count+2] = numext::conj(rhs(j2+2,k)); - blockB[count+3] = numext::conj(rhs(j2+3,k)); - count += 4; - } - // symmetric - Index h = 0; - for(Index k=j2; k<j2+4; k++) - { - // normal - for (Index w=0 ; w<h; ++w) - blockB[count+w] = rhs(k,j2+w); - - blockB[count+h] = numext::real(rhs(k,k)); - - // transpose - for (Index w=h+1 ; w<4; ++w) - blockB[count+w] = numext::conj(rhs(j2+w,k)); - count += 4; - ++h; - } - // normal - for(Index k=j2+4; k<end_k; k++) - { - blockB[count+0] = rhs(k,j2+0); - blockB[count+1] = rhs(k,j2+1); - blockB[count+2] = rhs(k,j2+2); - blockB[count+3] = rhs(k,j2+3); - count += 4; - } - } - } - - // third part: transposed - if(nr>=8) - { - for(Index j2=k2+rows; j2<packet_cols8; j2+=8) - { - for(Index k=k2; k<end_k; k++) - { - blockB[count+0] = numext::conj(rhs(j2+0,k)); - blockB[count+1] = numext::conj(rhs(j2+1,k)); - blockB[count+2] = numext::conj(rhs(j2+2,k)); - blockB[count+3] = numext::conj(rhs(j2+3,k)); - blockB[count+4] = numext::conj(rhs(j2+4,k)); - blockB[count+5] = numext::conj(rhs(j2+5,k)); - blockB[count+6] = numext::conj(rhs(j2+6,k)); - blockB[count+7] = numext::conj(rhs(j2+7,k)); - count += 8; - } - } - } - if(nr>=4) - { - for(Index j2=(std::max)(packet_cols8,k2+rows); j2<packet_cols4; j2+=4) - { - for(Index k=k2; k<end_k; k++) - { - blockB[count+0] = numext::conj(rhs(j2+0,k)); - blockB[count+1] = numext::conj(rhs(j2+1,k)); - blockB[count+2] = numext::conj(rhs(j2+2,k)); - blockB[count+3] = numext::conj(rhs(j2+3,k)); - count += 4; - } - } - } - - // copy the remaining columns one at a time (=> the same with nr==1) - for(Index j2=packet_cols4; j2<cols; ++j2) - { - // transpose - Index half = (std::min)(end_k,j2); - for(Index k=k2; k<half; k++) - { - blockB[count] = numext::conj(rhs(j2,k)); - count += 1; - } - - if(half==j2 && half<k2+rows) - { - blockB[count] = numext::real(rhs(j2,j2)); - count += 1; - } - else - half--; - - // normal - for(Index k=half+1; k<k2+rows; k++) - { - blockB[count] = rhs(k,j2); - count += 1; - } - } - } -}; - -/* Optimized selfadjoint matrix * matrix (_SYMM) product built on top of - * the general matrix matrix product. - */ -template <typename Scalar, typename Index, - int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs, - int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs, - int ResStorageOrder> -struct product_selfadjoint_matrix; - -template <typename Scalar, typename Index, - int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs, - int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs> -struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,LhsSelfAdjoint,ConjugateLhs, RhsStorageOrder,RhsSelfAdjoint,ConjugateRhs,RowMajor> -{ - - static EIGEN_STRONG_INLINE void run( - Index rows, Index cols, - const Scalar* lhs, Index lhsStride, - const Scalar* rhs, Index rhsStride, - Scalar* res, Index resStride, - const Scalar& alpha) - { - product_selfadjoint_matrix<Scalar, Index, - EIGEN_LOGICAL_XOR(RhsSelfAdjoint,RhsStorageOrder==RowMajor) ? ColMajor : RowMajor, - RhsSelfAdjoint, NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(RhsSelfAdjoint,ConjugateRhs), - EIGEN_LOGICAL_XOR(LhsSelfAdjoint,LhsStorageOrder==RowMajor) ? ColMajor : RowMajor, - LhsSelfAdjoint, NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsSelfAdjoint,ConjugateLhs), - ColMajor> - ::run(cols, rows, rhs, rhsStride, lhs, lhsStride, res, resStride, alpha); - } -}; - -template <typename Scalar, typename Index, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs> -struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor> -{ - - static EIGEN_DONT_INLINE void run( - Index rows, Index cols, - const Scalar* _lhs, Index lhsStride, - const Scalar* _rhs, Index rhsStride, - Scalar* res, Index resStride, - const Scalar& alpha); -}; - -template <typename Scalar, typename Index, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs> -EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor>::run( - Index rows, Index cols, - const Scalar* _lhs, Index lhsStride, - const Scalar* _rhs, Index rhsStride, - Scalar* _res, Index resStride, - const Scalar& alpha) - { - Index size = rows; - - typedef gebp_traits<Scalar,Scalar> Traits; - - typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper; - typedef const_blas_data_mapper<Scalar, Index, (LhsStorageOrder == RowMajor) ? ColMajor : RowMajor> LhsTransposeMapper; - typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper; - typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor> ResMapper; - LhsMapper lhs(_lhs,lhsStride); - LhsTransposeMapper lhs_transpose(_lhs,lhsStride); - RhsMapper rhs(_rhs,rhsStride); - ResMapper res(_res, resStride); - - Index kc = size; // cache block size along the K direction - Index mc = rows; // cache block size along the M direction - Index nc = cols; // cache block size along the N direction - computeProductBlockingSizes<Scalar,Scalar>(kc, mc, nc, Index(1)); - // kc must smaller than mc - kc = (std::min)(kc,mc); - - std::size_t sizeB = kc*cols; - ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); - ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); - Scalar* blockB = allocatedBlockB; - - gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel; - symm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs; - gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs; - gemm_pack_lhs<Scalar, Index, LhsTransposeMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder==RowMajor?ColMajor:RowMajor, true> pack_lhs_transposed; - - for(Index k2=0; k2<size; k2+=kc) - { - const Index actual_kc = (std::min)(k2+kc,size)-k2; - - // we have selected one row panel of rhs and one column panel of lhs - // pack rhs's panel into a sequential chunk of memory - // and expand each coeff to a constant packet for further reuse - pack_rhs(blockB, rhs.getSubMapper(k2,0), actual_kc, cols); - - // the select lhs's panel has to be split in three different parts: - // 1 - the transposed panel above the diagonal block => transposed packed copy - // 2 - the diagonal block => special packed copy - // 3 - the panel below the diagonal block => generic packed copy - for(Index i2=0; i2<k2; i2+=mc) - { - const Index actual_mc = (std::min)(i2+mc,k2)-i2; - // transposed packed copy - pack_lhs_transposed(blockA, lhs_transpose.getSubMapper(i2, k2), actual_kc, actual_mc); - - gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha); - } - // the block diagonal - { - const Index actual_mc = (std::min)(k2+kc,size)-k2; - // symmetric packed copy - pack_lhs(blockA, &lhs(k2,k2), lhsStride, actual_kc, actual_mc); - - gebp_kernel(res.getSubMapper(k2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha); - } - - for(Index i2=k2+kc; i2<size; i2+=mc) - { - const Index actual_mc = (std::min)(i2+mc,size)-i2; - gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder,false>() - (blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc); - - gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha); - } - } - } - -// matrix * selfadjoint product -template <typename Scalar, typename Index, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs> -struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor> -{ - - static EIGEN_DONT_INLINE void run( - Index rows, Index cols, - const Scalar* _lhs, Index lhsStride, - const Scalar* _rhs, Index rhsStride, - Scalar* res, Index resStride, - const Scalar& alpha); -}; - -template <typename Scalar, typename Index, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs> -EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor>::run( - Index rows, Index cols, - const Scalar* _lhs, Index lhsStride, - const Scalar* _rhs, Index rhsStride, - Scalar* _res, Index resStride, - const Scalar& alpha) - { - Index size = cols; - - typedef gebp_traits<Scalar,Scalar> Traits; - - typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper; - typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor> ResMapper; - LhsMapper lhs(_lhs,lhsStride); - ResMapper res(_res,resStride); - - Index kc = size; // cache block size along the K direction - Index mc = rows; // cache block size along the M direction - Index nc = cols; // cache block size along the N direction - computeProductBlockingSizes<Scalar,Scalar>(kc, mc, nc, Index(1)); - std::size_t sizeB = kc*cols; - ei_declare_aligned_stack_constructed_variable(Scalar, blockA, kc*mc, 0); - ei_declare_aligned_stack_constructed_variable(Scalar, allocatedBlockB, sizeB, 0); - Scalar* blockB = allocatedBlockB; - - gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel; - gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs; - symm_pack_rhs<Scalar, Index, Traits::nr,RhsStorageOrder> pack_rhs; - - for(Index k2=0; k2<size; k2+=kc) - { - const Index actual_kc = (std::min)(k2+kc,size)-k2; - - pack_rhs(blockB, _rhs, rhsStride, actual_kc, cols, k2); - - // => GEPP - for(Index i2=0; i2<rows; i2+=mc) - { - const Index actual_mc = (std::min)(i2+mc,rows)-i2; - pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc); - - gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha); - } - } - } - -} // end namespace internal - -/*************************************************************************** -* Wrapper to product_selfadjoint_matrix -***************************************************************************/ - -namespace internal { -template<typename Lhs, int LhsMode, typename Rhs, int RhsMode> -struct traits<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false> > - : traits<ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false>, Lhs, Rhs> > -{}; -} - -template<typename Lhs, int LhsMode, typename Rhs, int RhsMode> -struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false> - : public ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,RhsMode,false>, Lhs, Rhs > -{ - EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix) - - SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} - - enum { - LhsIsUpper = (LhsMode&(Upper|Lower))==Upper, - LhsIsSelfAdjoint = (LhsMode&SelfAdjoint)==SelfAdjoint, - RhsIsUpper = (RhsMode&(Upper|Lower))==Upper, - RhsIsSelfAdjoint = (RhsMode&SelfAdjoint)==SelfAdjoint - }; - - template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const - { - eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols()); - - typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs); - typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs); - - Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) - * RhsBlasTraits::extractScalarFactor(m_rhs); - - internal::product_selfadjoint_matrix<Scalar, Index, - EIGEN_LOGICAL_XOR(LhsIsUpper, - internal::traits<Lhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, LhsIsSelfAdjoint, - NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(LhsIsUpper,bool(LhsBlasTraits::NeedToConjugate)), - EIGEN_LOGICAL_XOR(RhsIsUpper, - internal::traits<Rhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, RhsIsSelfAdjoint, - NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(RhsIsUpper,bool(RhsBlasTraits::NeedToConjugate)), - internal::traits<Dest>::Flags&RowMajorBit ? RowMajor : ColMajor> - ::run( - lhs.rows(), rhs.cols(), // sizes - &lhs.coeffRef(0,0), lhs.outerStride(), // lhs info - &rhs.coeffRef(0,0), rhs.outerStride(), // rhs info - &dst.coeffRef(0,0), dst.outerStride(), // result info - actualAlpha // alpha - ); - } -}; - -} // end namespace Eigen - -#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_MKL.h b/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_MKL.h deleted file mode 100644 index dfa687fefe..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixMatrix_MKL.h +++ /dev/null @@ -1,295 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -// - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM. - ******************************************************************************** -*/ - -#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H -#define EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H - -namespace Eigen { - -namespace internal { - - -/* Optimized selfadjoint matrix * matrix (?SYMM/?HEMM) product */ - -#define EIGEN_MKL_SYMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ -template <typename Index, \ - int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs> \ -struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor> \ -{\ -\ - static void run( \ - Index rows, Index cols, \ - const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsStride, \ - EIGTYPE* res, Index resStride, \ - EIGTYPE alpha) \ - { \ - char side='L', uplo='L'; \ - MKL_INT m, n, lda, ldb, ldc; \ - const EIGTYPE *a, *b; \ - MKLTYPE alpha_, beta_; \ - MatrixX##EIGPREFIX b_tmp; \ - EIGTYPE myone(1);\ -\ -/* Set transpose options */ \ -/* Set m, n, k */ \ - m = (MKL_INT)rows; \ - n = (MKL_INT)cols; \ -\ -/* Set alpha_ & beta_ */ \ - assign_scalar_eig2mkl(alpha_, alpha); \ - assign_scalar_eig2mkl(beta_, myone); \ -\ -/* Set lda, ldb, ldc */ \ - lda = (MKL_INT)lhsStride; \ - ldb = (MKL_INT)rhsStride; \ - ldc = (MKL_INT)resStride; \ -\ -/* Set a, b, c */ \ - if (LhsStorageOrder==RowMajor) uplo='U'; \ - a = _lhs; \ -\ - if (RhsStorageOrder==RowMajor) { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \ - b_tmp = rhs.adjoint(); \ - b = b_tmp.data(); \ - ldb = b_tmp.outerStride(); \ - } else b = _rhs; \ -\ - MKLPREFIX##symm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ -\ - } \ -}; - - -#define EIGEN_MKL_HEMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ -template <typename Index, \ - int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs> \ -struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor> \ -{\ - static void run( \ - Index rows, Index cols, \ - const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsStride, \ - EIGTYPE* res, Index resStride, \ - EIGTYPE alpha) \ - { \ - char side='L', uplo='L'; \ - MKL_INT m, n, lda, ldb, ldc; \ - const EIGTYPE *a, *b; \ - MKLTYPE alpha_, beta_; \ - MatrixX##EIGPREFIX b_tmp; \ - Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> a_tmp; \ - EIGTYPE myone(1); \ -\ -/* Set transpose options */ \ -/* Set m, n, k */ \ - m = (MKL_INT)rows; \ - n = (MKL_INT)cols; \ -\ -/* Set alpha_ & beta_ */ \ - assign_scalar_eig2mkl(alpha_, alpha); \ - assign_scalar_eig2mkl(beta_, myone); \ -\ -/* Set lda, ldb, ldc */ \ - lda = (MKL_INT)lhsStride; \ - ldb = (MKL_INT)rhsStride; \ - ldc = (MKL_INT)resStride; \ -\ -/* Set a, b, c */ \ - if (((LhsStorageOrder==ColMajor) && ConjugateLhs) || ((LhsStorageOrder==RowMajor) && (!ConjugateLhs))) { \ - Map<const Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder>, 0, OuterStride<> > lhs(_lhs,m,m,OuterStride<>(lhsStride)); \ - a_tmp = lhs.conjugate(); \ - a = a_tmp.data(); \ - lda = a_tmp.outerStride(); \ - } else a = _lhs; \ - if (LhsStorageOrder==RowMajor) uplo='U'; \ -\ - if (RhsStorageOrder==ColMajor && (!ConjugateRhs)) { \ - b = _rhs; } \ - else { \ - if (RhsStorageOrder==ColMajor && ConjugateRhs) { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,m,n,OuterStride<>(rhsStride)); \ - b_tmp = rhs.conjugate(); \ - } else \ - if (ConjugateRhs) { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \ - b_tmp = rhs.adjoint(); \ - } else { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \ - b_tmp = rhs.transpose(); \ - } \ - b = b_tmp.data(); \ - ldb = b_tmp.outerStride(); \ - } \ -\ - MKLPREFIX##hemm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ -\ - } \ -}; - -EIGEN_MKL_SYMM_L(double, double, d, d) -EIGEN_MKL_SYMM_L(float, float, f, s) -EIGEN_MKL_HEMM_L(dcomplex, MKL_Complex16, cd, z) -EIGEN_MKL_HEMM_L(scomplex, MKL_Complex8, cf, c) - - -/* Optimized matrix * selfadjoint matrix (?SYMM/?HEMM) product */ - -#define EIGEN_MKL_SYMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ -template <typename Index, \ - int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs> \ -struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor> \ -{\ -\ - static void run( \ - Index rows, Index cols, \ - const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsStride, \ - EIGTYPE* res, Index resStride, \ - EIGTYPE alpha) \ - { \ - char side='R', uplo='L'; \ - MKL_INT m, n, lda, ldb, ldc; \ - const EIGTYPE *a, *b; \ - MKLTYPE alpha_, beta_; \ - MatrixX##EIGPREFIX b_tmp; \ - EIGTYPE myone(1);\ -\ -/* Set m, n, k */ \ - m = (MKL_INT)rows; \ - n = (MKL_INT)cols; \ -\ -/* Set alpha_ & beta_ */ \ - assign_scalar_eig2mkl(alpha_, alpha); \ - assign_scalar_eig2mkl(beta_, myone); \ -\ -/* Set lda, ldb, ldc */ \ - lda = (MKL_INT)rhsStride; \ - ldb = (MKL_INT)lhsStride; \ - ldc = (MKL_INT)resStride; \ -\ -/* Set a, b, c */ \ - if (RhsStorageOrder==RowMajor) uplo='U'; \ - a = _rhs; \ -\ - if (LhsStorageOrder==RowMajor) { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(rhsStride)); \ - b_tmp = lhs.adjoint(); \ - b = b_tmp.data(); \ - ldb = b_tmp.outerStride(); \ - } else b = _lhs; \ -\ - MKLPREFIX##symm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ -\ - } \ -}; - - -#define EIGEN_MKL_HEMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ -template <typename Index, \ - int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs> \ -struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor> \ -{\ - static void run( \ - Index rows, Index cols, \ - const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsStride, \ - EIGTYPE* res, Index resStride, \ - EIGTYPE alpha) \ - { \ - char side='R', uplo='L'; \ - MKL_INT m, n, lda, ldb, ldc; \ - const EIGTYPE *a, *b; \ - MKLTYPE alpha_, beta_; \ - MatrixX##EIGPREFIX b_tmp; \ - Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> a_tmp; \ - EIGTYPE myone(1); \ -\ -/* Set m, n, k */ \ - m = (MKL_INT)rows; \ - n = (MKL_INT)cols; \ -\ -/* Set alpha_ & beta_ */ \ - assign_scalar_eig2mkl(alpha_, alpha); \ - assign_scalar_eig2mkl(beta_, myone); \ -\ -/* Set lda, ldb, ldc */ \ - lda = (MKL_INT)rhsStride; \ - ldb = (MKL_INT)lhsStride; \ - ldc = (MKL_INT)resStride; \ -\ -/* Set a, b, c */ \ - if (((RhsStorageOrder==ColMajor) && ConjugateRhs) || ((RhsStorageOrder==RowMajor) && (!ConjugateRhs))) { \ - Map<const Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder>, 0, OuterStride<> > rhs(_rhs,n,n,OuterStride<>(rhsStride)); \ - a_tmp = rhs.conjugate(); \ - a = a_tmp.data(); \ - lda = a_tmp.outerStride(); \ - } else a = _rhs; \ - if (RhsStorageOrder==RowMajor) uplo='U'; \ -\ - if (LhsStorageOrder==ColMajor && (!ConjugateLhs)) { \ - b = _lhs; } \ - else { \ - if (LhsStorageOrder==ColMajor && ConjugateLhs) { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,n,OuterStride<>(lhsStride)); \ - b_tmp = lhs.conjugate(); \ - } else \ - if (ConjugateLhs) { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \ - b_tmp = lhs.adjoint(); \ - } else { \ - Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \ - b_tmp = lhs.transpose(); \ - } \ - b = b_tmp.data(); \ - ldb = b_tmp.outerStride(); \ - } \ -\ - MKLPREFIX##hemm(&side, &uplo, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)b, &ldb, &beta_, (MKLTYPE*)res, &ldc); \ - } \ -}; - -EIGEN_MKL_SYMM_R(double, double, d, d) -EIGEN_MKL_SYMM_R(float, float, f, s) -EIGEN_MKL_HEMM_R(dcomplex, MKL_Complex16, cd, z) -EIGEN_MKL_HEMM_R(scomplex, MKL_Complex8, cf, c) - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_MKL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h b/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h deleted file mode 100644 index fdc81205ab..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector.h +++ /dev/null @@ -1,281 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_H -#define EIGEN_SELFADJOINT_MATRIX_VECTOR_H - -namespace Eigen { - -namespace internal { - -/* Optimized selfadjoint matrix * vector product: - * This algorithm processes 2 columns at onces that allows to both reduce - * the number of load/stores of the result by a factor 2 and to reduce - * the instruction dependency. - */ - -template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version=Specialized> -struct selfadjoint_matrix_vector_product; - -template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version> -struct selfadjoint_matrix_vector_product - -{ -static EIGEN_DONT_INLINE void run( - Index size, - const Scalar* lhs, Index lhsStride, - const Scalar* _rhs, Index rhsIncr, - Scalar* res, - Scalar alpha); -}; - -template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version> -EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Version>::run( - Index size, - const Scalar* lhs, Index lhsStride, - const Scalar* _rhs, Index rhsIncr, - Scalar* res, - Scalar alpha) -{ - typedef typename packet_traits<Scalar>::type Packet; - const Index PacketSize = sizeof(Packet)/sizeof(Scalar); - - enum { - IsRowMajor = StorageOrder==RowMajor ? 1 : 0, - IsLower = UpLo == Lower ? 1 : 0, - FirstTriangular = IsRowMajor == IsLower - }; - - conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> cj0; - conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1; - conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex, ConjugateRhs> cjd; - - conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0; - conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1; - - Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha; - - // FIXME this copy is now handled outside product_selfadjoint_vector, so it could probably be removed. - // if the rhs is not sequentially stored in memory we copy it to a temporary buffer, - // this is because we need to extract packets - ei_declare_aligned_stack_constructed_variable(Scalar,rhs,size,rhsIncr==1 ? const_cast<Scalar*>(_rhs) : 0); - if (rhsIncr!=1) - { - const Scalar* it = _rhs; - for (Index i=0; i<size; ++i, it+=rhsIncr) - rhs[i] = *it; - } - - Index bound = (std::max)(Index(0),size-8) & 0xfffffffe; - if (FirstTriangular) - bound = size - bound; - - for (Index j=FirstTriangular ? bound : 0; - j<(FirstTriangular ? size : bound);j+=2) - { - const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride; - const Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride; - - Scalar t0 = cjAlpha * rhs[j]; - Packet ptmp0 = pset1<Packet>(t0); - Scalar t1 = cjAlpha * rhs[j+1]; - Packet ptmp1 = pset1<Packet>(t1); - - Scalar t2(0); - Packet ptmp2 = pset1<Packet>(t2); - Scalar t3(0); - Packet ptmp3 = pset1<Packet>(t3); - - size_t starti = FirstTriangular ? 0 : j+2; - size_t endi = FirstTriangular ? j : size; - size_t alignedStart = (starti) + internal::first_aligned(&res[starti], endi-starti); - size_t alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize); - - // TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed - res[j] += cjd.pmul(numext::real(A0[j]), t0); - res[j+1] += cjd.pmul(numext::real(A1[j+1]), t1); - if(FirstTriangular) - { - res[j] += cj0.pmul(A1[j], t1); - t3 += cj1.pmul(A1[j], rhs[j]); - } - else - { - res[j+1] += cj0.pmul(A0[j+1],t0); - t2 += cj1.pmul(A0[j+1], rhs[j+1]); - } - - for (size_t i=starti; i<alignedStart; ++i) - { - res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1); - t2 += cj1.pmul(A0[i], rhs[i]); - t3 += cj1.pmul(A1[i], rhs[i]); - } - // Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up) - // gcc 4.2 does this optimization automatically. - const Scalar* EIGEN_RESTRICT a0It = A0 + alignedStart; - const Scalar* EIGEN_RESTRICT a1It = A1 + alignedStart; - const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart; - Scalar* EIGEN_RESTRICT resIt = res + alignedStart; - for (size_t i=alignedStart; i<alignedEnd; i+=PacketSize) - { - Packet A0i = ploadu<Packet>(a0It); a0It += PacketSize; - Packet A1i = ploadu<Packet>(a1It); a1It += PacketSize; - Packet Bi = ploadu<Packet>(rhsIt); rhsIt += PacketSize; // FIXME should be aligned in most cases - Packet Xi = pload <Packet>(resIt); - - Xi = pcj0.pmadd(A0i,ptmp0, pcj0.pmadd(A1i,ptmp1,Xi)); - ptmp2 = pcj1.pmadd(A0i, Bi, ptmp2); - ptmp3 = pcj1.pmadd(A1i, Bi, ptmp3); - pstore(resIt,Xi); resIt += PacketSize; - } - for (size_t i=alignedEnd; i<endi; i++) - { - res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1); - t2 += cj1.pmul(A0[i], rhs[i]); - t3 += cj1.pmul(A1[i], rhs[i]); - } - - res[j] += alpha * (t2 + predux(ptmp2)); - res[j+1] += alpha * (t3 + predux(ptmp3)); - } - for (Index j=FirstTriangular ? 0 : bound;j<(FirstTriangular ? bound : size);j++) - { - const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride; - - Scalar t1 = cjAlpha * rhs[j]; - Scalar t2(0); - // TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed - res[j] += cjd.pmul(numext::real(A0[j]), t1); - for (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++) - { - res[i] += cj0.pmul(A0[i], t1); - t2 += cj1.pmul(A0[i], rhs[i]); - } - res[j] += alpha * t2; - } -} - -} // end namespace internal - -/*************************************************************************** -* Wrapper to product_selfadjoint_vector -***************************************************************************/ - -namespace internal { -template<typename Lhs, int LhsMode, typename Rhs> -struct traits<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true> > - : traits<ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>, Lhs, Rhs> > -{}; -} - -template<typename Lhs, int LhsMode, typename Rhs> -struct SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true> - : public ProductBase<SelfadjointProductMatrix<Lhs,LhsMode,false,Rhs,0,true>, Lhs, Rhs > -{ - EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix) - - enum { - LhsUpLo = LhsMode&(Upper|Lower) - }; - - SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} - - template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const - { - typedef typename Dest::Scalar ResScalar; - typedef typename Base::RhsScalar RhsScalar; - typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest; - - eigen_assert(dest.rows()==m_lhs.rows() && dest.cols()==m_rhs.cols()); - - typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs); - typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs); - - Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) - * RhsBlasTraits::extractScalarFactor(m_rhs); - - enum { - EvalToDest = (Dest::InnerStrideAtCompileTime==1), - UseRhs = (_ActualRhsType::InnerStrideAtCompileTime==1) - }; - - internal::gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,!EvalToDest> static_dest; - internal::gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!UseRhs> static_rhs; - - ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), - EvalToDest ? dest.data() : static_dest.data()); - - ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,rhs.size(), - UseRhs ? const_cast<RhsScalar*>(rhs.data()) : static_rhs.data()); - - if(!EvalToDest) - { - #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN - int size = dest.size(); - EIGEN_DENSE_STORAGE_CTOR_PLUGIN - #endif - MappedDest(actualDestPtr, dest.size()) = dest; - } - - if(!UseRhs) - { - #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN - int size = rhs.size(); - EIGEN_DENSE_STORAGE_CTOR_PLUGIN - #endif - Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, rhs.size()) = rhs; - } - - - internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<_ActualLhsType>::Flags&RowMajorBit) ? RowMajor : ColMajor, int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run - ( - lhs.rows(), // size - &lhs.coeffRef(0,0), lhs.outerStride(), // lhs info - actualRhsPtr, 1, // rhs info - actualDestPtr, // result info - actualAlpha // scale factor - ); - - if(!EvalToDest) - dest = MappedDest(actualDestPtr, dest.size()); - } -}; - -namespace internal { -template<typename Lhs, typename Rhs, int RhsMode> -struct traits<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false> > - : traits<ProductBase<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>, Lhs, Rhs> > -{}; -} - -template<typename Lhs, typename Rhs, int RhsMode> -struct SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false> - : public ProductBase<SelfadjointProductMatrix<Lhs,0,true,Rhs,RhsMode,false>, Lhs, Rhs > -{ - EIGEN_PRODUCT_PUBLIC_INTERFACE(SelfadjointProductMatrix) - - enum { - RhsUpLo = RhsMode&(Upper|Lower) - }; - - SelfadjointProductMatrix(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} - - template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const - { - // let's simply transpose the product - Transpose<Dest> destT(dest); - SelfadjointProductMatrix<Transpose<const Rhs>, int(RhsUpLo)==Upper ? Lower : Upper, false, - Transpose<const Lhs>, 0, true>(m_rhs.transpose(), m_lhs.transpose()).scaleAndAddTo(destT, alpha); - } -}; - -} // end namespace Eigen - -#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_H diff --git a/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_MKL.h b/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_MKL.h deleted file mode 100644 index 86684b66d9..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/SelfadjointMatrixVector_MKL.h +++ /dev/null @@ -1,114 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Selfadjoint matrix-vector product functionality based on ?SYMV/HEMV. - ******************************************************************************** -*/ - -#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H -#define EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H - -namespace Eigen { - -namespace internal { - -/********************************************************************** -* This file implements selfadjoint matrix-vector multiplication using BLAS -**********************************************************************/ - -// symv/hemv specialization - -template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> -struct selfadjoint_matrix_vector_product_symv : - selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn> {}; - -#define EIGEN_MKL_SYMV_SPECIALIZE(Scalar) \ -template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \ -struct selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Specialized> { \ -static void run( \ - Index size, const Scalar* lhs, Index lhsStride, \ - const Scalar* _rhs, Index rhsIncr, Scalar* res, Scalar alpha) { \ - enum {\ - IsColMajor = StorageOrder==ColMajor \ - }; \ - if (IsColMajor == ConjugateLhs) {\ - selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn>::run( \ - size, lhs, lhsStride, _rhs, rhsIncr, res, alpha); \ - } else {\ - selfadjoint_matrix_vector_product_symv<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs>::run( \ - size, lhs, lhsStride, _rhs, rhsIncr, res, alpha); \ - }\ - } \ -}; \ - -EIGEN_MKL_SYMV_SPECIALIZE(double) -EIGEN_MKL_SYMV_SPECIALIZE(float) -EIGEN_MKL_SYMV_SPECIALIZE(dcomplex) -EIGEN_MKL_SYMV_SPECIALIZE(scomplex) - -#define EIGEN_MKL_SYMV_SPECIALIZATION(EIGTYPE,MKLTYPE,MKLFUNC) \ -template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \ -struct selfadjoint_matrix_vector_product_symv<EIGTYPE,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs> \ -{ \ -typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> SYMVVector;\ -\ -static void run( \ -Index size, const EIGTYPE* lhs, Index lhsStride, \ -const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* res, EIGTYPE alpha) \ -{ \ - enum {\ - IsRowMajor = StorageOrder==RowMajor ? 1 : 0, \ - IsLower = UpLo == Lower ? 1 : 0 \ - }; \ - MKL_INT n=size, lda=lhsStride, incx=rhsIncr, incy=1; \ - MKLTYPE alpha_, beta_; \ - const EIGTYPE *x_ptr, myone(1); \ - char uplo=(IsRowMajor) ? (IsLower ? 'U' : 'L') : (IsLower ? 'L' : 'U'); \ - assign_scalar_eig2mkl(alpha_, alpha); \ - assign_scalar_eig2mkl(beta_, myone); \ - SYMVVector x_tmp; \ - if (ConjugateRhs) { \ - Map<const SYMVVector, 0, InnerStride<> > map_x(_rhs,size,1,InnerStride<>(incx)); \ - x_tmp=map_x.conjugate(); \ - x_ptr=x_tmp.data(); \ - incx=1; \ - } else x_ptr=_rhs; \ - MKLFUNC(&uplo, &n, &alpha_, (const MKLTYPE*)lhs, &lda, (const MKLTYPE*)x_ptr, &incx, &beta_, (MKLTYPE*)res, &incy); \ -}\ -}; - -EIGEN_MKL_SYMV_SPECIALIZATION(double, double, dsymv) -EIGEN_MKL_SYMV_SPECIALIZATION(float, float, ssymv) -EIGEN_MKL_SYMV_SPECIALIZATION(dcomplex, MKL_Complex16, zhemv) -EIGEN_MKL_SYMV_SPECIALIZATION(scomplex, MKL_Complex8, chemv) - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_MKL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/SelfadjointProduct.h b/third_party/eigen3/Eigen/src/Core/products/SelfadjointProduct.h deleted file mode 100644 index 6ca4ae6c0f..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/SelfadjointProduct.h +++ /dev/null @@ -1,123 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SELFADJOINT_PRODUCT_H -#define EIGEN_SELFADJOINT_PRODUCT_H - -/********************************************************************** -* This file implements a self adjoint product: C += A A^T updating only -* half of the selfadjoint matrix C. -* It corresponds to the level 3 SYRK and level 2 SYR Blas routines. -**********************************************************************/ - -namespace Eigen { - - -template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs> -struct selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo,ConjLhs,ConjRhs> -{ - static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha) - { - internal::conj_if<ConjRhs> cj; - typedef Map<const Matrix<Scalar,Dynamic,1> > OtherMap; - typedef typename internal::conditional<ConjLhs,typename OtherMap::ConjugateReturnType,const OtherMap&>::type ConjLhsType; - for (Index i=0; i<size; ++i) - { - Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+(UpLo==Lower ? i : 0), (UpLo==Lower ? size-i : (i+1))) - += (alpha * cj(vecY[i])) * ConjLhsType(OtherMap(vecX+(UpLo==Lower ? i : 0),UpLo==Lower ? size-i : (i+1))); - } - } -}; - -template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs> -struct selfadjoint_rank1_update<Scalar,Index,RowMajor,UpLo,ConjLhs,ConjRhs> -{ - static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha) - { - selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo==Lower?Upper:Lower,ConjRhs,ConjLhs>::run(size,mat,stride,vecY,vecX,alpha); - } -}; - -template<typename MatrixType, typename OtherType, int UpLo, bool OtherIsVector = OtherType::IsVectorAtCompileTime> -struct selfadjoint_product_selector; - -template<typename MatrixType, typename OtherType, int UpLo> -struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,true> -{ - static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha) - { - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - typedef internal::blas_traits<OtherType> OtherBlasTraits; - typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType; - typedef typename internal::remove_all<ActualOtherType>::type _ActualOtherType; - typename internal::add_const_on_value_type<ActualOtherType>::type actualOther = OtherBlasTraits::extract(other.derived()); - - Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived()); - - enum { - StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor, - UseOtherDirectly = _ActualOtherType::InnerStrideAtCompileTime==1 - }; - internal::gemv_static_vector_if<Scalar,OtherType::SizeAtCompileTime,OtherType::MaxSizeAtCompileTime,!UseOtherDirectly> static_other; - - ei_declare_aligned_stack_constructed_variable(Scalar, actualOtherPtr, other.size(), - (UseOtherDirectly ? const_cast<Scalar*>(actualOther.data()) : static_other.data())); - - if(!UseOtherDirectly) - Map<typename _ActualOtherType::PlainObject>(actualOtherPtr, actualOther.size()) = actualOther; - - selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo, - OtherBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex, - (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex> - ::run(other.size(), mat.data(), mat.outerStride(), actualOtherPtr, actualOtherPtr, actualAlpha); - } -}; - -template<typename MatrixType, typename OtherType, int UpLo> -struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,false> -{ - static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha) - { - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - typedef internal::blas_traits<OtherType> OtherBlasTraits; - typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType; - typedef typename internal::remove_all<ActualOtherType>::type _ActualOtherType; - typename internal::add_const_on_value_type<ActualOtherType>::type actualOther = OtherBlasTraits::extract(other.derived()); - - Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived()); - - enum { IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0 }; - - internal::general_matrix_matrix_triangular_product<Index, - Scalar, _ActualOtherType::Flags&RowMajorBit ? RowMajor : ColMajor, OtherBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex, - Scalar, _ActualOtherType::Flags&RowMajorBit ? ColMajor : RowMajor, (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex, - MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor, UpLo> - ::run(mat.cols(), actualOther.cols(), - &actualOther.coeffRef(0,0), actualOther.outerStride(), &actualOther.coeffRef(0,0), actualOther.outerStride(), - mat.data(), mat.outerStride(), actualAlpha); - } -}; - -// high level API - -template<typename MatrixType, unsigned int UpLo> -template<typename DerivedU> -SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo> -::rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha) -{ - selfadjoint_product_selector<MatrixType,DerivedU,UpLo>::run(_expression().const_cast_derived(), u.derived(), alpha); - - return *this; -} - -} // end namespace Eigen - -#endif // EIGEN_SELFADJOINT_PRODUCT_H diff --git a/third_party/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h b/third_party/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h deleted file mode 100644 index 8594a97cea..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/SelfadjointRank2Update.h +++ /dev/null @@ -1,93 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SELFADJOINTRANK2UPTADE_H -#define EIGEN_SELFADJOINTRANK2UPTADE_H - -namespace Eigen { - -namespace internal { - -/* Optimized selfadjoint matrix += alpha * uv' + conj(alpha)*vu' - * It corresponds to the Level2 syr2 BLAS routine - */ - -template<typename Scalar, typename Index, typename UType, typename VType, int UpLo> -struct selfadjoint_rank2_update_selector; - -template<typename Scalar, typename Index, typename UType, typename VType> -struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Lower> -{ - static void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha) - { - const Index size = u.size(); - for (Index i=0; i<size; ++i) - { - Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+i, size-i) += - (numext::conj(alpha) * numext::conj(u.coeff(i))) * v.tail(size-i) - + (alpha * numext::conj(v.coeff(i))) * u.tail(size-i); - } - } -}; - -template<typename Scalar, typename Index, typename UType, typename VType> -struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Upper> -{ - static void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha) - { - const Index size = u.size(); - for (Index i=0; i<size; ++i) - Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i, i+1) += - (numext::conj(alpha) * numext::conj(u.coeff(i))) * v.head(i+1) - + (alpha * numext::conj(v.coeff(i))) * u.head(i+1); - } -}; - -template<bool Cond, typename T> struct conj_expr_if - : conditional<!Cond, const T&, - CwiseUnaryOp<scalar_conjugate_op<typename traits<T>::Scalar>,T> > {}; - -} // end namespace internal - -template<typename MatrixType, unsigned int UpLo> -template<typename DerivedU, typename DerivedV> -SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo> -::rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha) -{ - typedef internal::blas_traits<DerivedU> UBlasTraits; - typedef typename UBlasTraits::DirectLinearAccessType ActualUType; - typedef typename internal::remove_all<ActualUType>::type _ActualUType; - typename internal::add_const_on_value_type<ActualUType>::type actualU = UBlasTraits::extract(u.derived()); - - typedef internal::blas_traits<DerivedV> VBlasTraits; - typedef typename VBlasTraits::DirectLinearAccessType ActualVType; - typedef typename internal::remove_all<ActualVType>::type _ActualVType; - typename internal::add_const_on_value_type<ActualVType>::type actualV = VBlasTraits::extract(v.derived()); - - // If MatrixType is row major, then we use the routine for lower triangular in the upper triangular case and - // vice versa, and take the complex conjugate of all coefficients and vector entries. - - enum { IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0 }; - Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived()) - * numext::conj(VBlasTraits::extractScalarFactor(v.derived())); - if (IsRowMajor) - actualAlpha = numext::conj(actualAlpha); - - internal::selfadjoint_rank2_update_selector<Scalar, Index, - typename internal::remove_all<typename internal::conj_expr_if<IsRowMajor ^ UBlasTraits::NeedToConjugate,_ActualUType>::type>::type, - typename internal::remove_all<typename internal::conj_expr_if<IsRowMajor ^ VBlasTraits::NeedToConjugate,_ActualVType>::type>::type, - (IsRowMajor ? int(UpLo==Upper ? Lower : Upper) : UpLo)> - ::run(_expression().const_cast_derived().data(),_expression().outerStride(),actualU,actualV,actualAlpha); - - return *this; -} - -} // end namespace Eigen - -#endif // EIGEN_SELFADJOINTRANK2UPTADE_H diff --git a/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h b/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h deleted file mode 100644 index 4cbb79da0c..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix.h +++ /dev/null @@ -1,434 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_H -#define EIGEN_TRIANGULAR_MATRIX_MATRIX_H - -namespace Eigen { - -namespace internal { - -// template<typename Scalar, int mr, int StorageOrder, bool Conjugate, int Mode> -// struct gemm_pack_lhs_triangular -// { -// Matrix<Scalar,mr,mr, -// void operator()(Scalar* blockA, const EIGEN_RESTRICT Scalar* _lhs, int lhsStride, int depth, int rows) -// { -// conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj; -// const_blas_data_mapper<Scalar, StorageOrder> lhs(_lhs,lhsStride); -// int count = 0; -// const int peeled_mc = (rows/mr)*mr; -// for(int i=0; i<peeled_mc; i+=mr) -// { -// for(int k=0; k<depth; k++) -// for(int w=0; w<mr; w++) -// blockA[count++] = cj(lhs(i+w, k)); -// } -// for(int i=peeled_mc; i<rows; i++) -// { -// for(int k=0; k<depth; k++) -// blockA[count++] = cj(lhs(i, k)); -// } -// } -// }; - -/* Optimized triangular matrix * matrix (_TRMM++) product built on top of - * the general matrix matrix product. - */ -template <typename Scalar, typename Index, - int Mode, bool LhsIsTriangular, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs, - int ResStorageOrder, int Version = Specialized> -struct product_triangular_matrix_matrix; - -template <typename Scalar, typename Index, - int Mode, bool LhsIsTriangular, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs, int Version> -struct product_triangular_matrix_matrix<Scalar,Index,Mode,LhsIsTriangular, - LhsStorageOrder,ConjugateLhs, - RhsStorageOrder,ConjugateRhs,RowMajor,Version> -{ - static EIGEN_STRONG_INLINE void run( - Index rows, Index cols, Index depth, - const Scalar* lhs, Index lhsStride, - const Scalar* rhs, Index rhsStride, - Scalar* res, Index resStride, - const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking) - { - product_triangular_matrix_matrix<Scalar, Index, - (Mode&(UnitDiag|ZeroDiag)) | ((Mode&Upper) ? Lower : Upper), - (!LhsIsTriangular), - RhsStorageOrder==RowMajor ? ColMajor : RowMajor, - ConjugateRhs, - LhsStorageOrder==RowMajor ? ColMajor : RowMajor, - ConjugateLhs, - ColMajor> - ::run(cols, rows, depth, rhs, rhsStride, lhs, lhsStride, res, resStride, alpha, blocking); - } -}; - -// implements col-major += alpha * op(triangular) * op(general) -template <typename Scalar, typename Index, int Mode, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs, int Version> -struct product_triangular_matrix_matrix<Scalar,Index,Mode,true, - LhsStorageOrder,ConjugateLhs, - RhsStorageOrder,ConjugateRhs,ColMajor,Version> -{ - - typedef gebp_traits<Scalar,Scalar> Traits; - enum { - SmallPanelWidth = 2 * EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr), - IsLower = (Mode&Lower) == Lower, - SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1 - }; - - static EIGEN_DONT_INLINE void run( - Index _rows, Index _cols, Index _depth, - const Scalar* _lhs, Index lhsStride, - const Scalar* _rhs, Index rhsStride, - Scalar* res, Index resStride, - const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking); -}; - -template <typename Scalar, typename Index, int Mode, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs, int Version> -EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true, - LhsStorageOrder,ConjugateLhs, - RhsStorageOrder,ConjugateRhs,ColMajor,Version>::run( - Index _rows, Index _cols, Index _depth, - const Scalar* _lhs, Index lhsStride, - const Scalar* _rhs, Index rhsStride, - Scalar* _res, Index resStride, - const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking) - { - // strip zeros - Index diagSize = (std::min)(_rows,_depth); - Index rows = IsLower ? _rows : diagSize; - Index depth = IsLower ? diagSize : _depth; - Index cols = _cols; - - typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper; - typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper; - typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor> ResMapper; - LhsMapper lhs(_lhs,lhsStride); - RhsMapper rhs(_rhs,rhsStride); - ResMapper res(_res, resStride); - - Index kc = blocking.kc(); // cache block size along the K direction - Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction - - std::size_t sizeA = kc*mc; - std::size_t sizeB = kc*cols; - - ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA()); - ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB()); - - Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,LhsStorageOrder> triangularBuffer; - triangularBuffer.setZero(); - if((Mode&ZeroDiag)==ZeroDiag) - triangularBuffer.diagonal().setZero(); - else - triangularBuffer.diagonal().setOnes(); - - gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel; - gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs; - gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs; - - for(Index k2=IsLower ? depth : 0; - IsLower ? k2>0 : k2<depth; - IsLower ? k2-=kc : k2+=kc) - { - Index actual_kc = (std::min)(IsLower ? k2 : depth-k2, kc); - Index actual_k2 = IsLower ? k2-actual_kc : k2; - - // align blocks with the end of the triangular part for trapezoidal lhs - if((!IsLower)&&(k2<rows)&&(k2+actual_kc>rows)) - { - actual_kc = rows-k2; - k2 = k2+actual_kc-kc; - } - - pack_rhs(blockB, rhs.getSubMapper(actual_k2,0), actual_kc, cols); - - // the selected lhs's panel has to be split in three different parts: - // 1 - the part which is zero => skip it - // 2 - the diagonal block => special kernel - // 3 - the dense panel below (lower case) or above (upper case) the diagonal block => GEPP - - // the block diagonal, if any: - if(IsLower || actual_k2<rows) - { - // for each small vertical panels of lhs - for (Index k1=0; k1<actual_kc; k1+=SmallPanelWidth) - { - Index actualPanelWidth = std::min<Index>(actual_kc-k1, SmallPanelWidth); - Index lengthTarget = IsLower ? actual_kc-k1-actualPanelWidth : k1; - Index startBlock = actual_k2+k1; - Index blockBOffset = k1; - - // => GEBP with the micro triangular block - // The trick is to pack this micro block while filling the opposite triangular part with zeros. - // To this end we do an extra triangular copy to a small temporary buffer - for (Index k=0;k<actualPanelWidth;++k) - { - if (SetDiag) - triangularBuffer.coeffRef(k,k) = lhs(startBlock+k,startBlock+k); - for (Index i=IsLower ? k+1 : 0; IsLower ? i<actualPanelWidth : i<k; ++i) - triangularBuffer.coeffRef(i,k) = lhs(startBlock+i,startBlock+k); - } - pack_lhs(blockA, LhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()), actualPanelWidth, actualPanelWidth); - - gebp_kernel(res.getSubMapper(startBlock, 0), blockA, blockB, - actualPanelWidth, actualPanelWidth, cols, alpha, - actualPanelWidth, actual_kc, 0, blockBOffset); - - // GEBP with remaining micro panel - if (lengthTarget>0) - { - Index startTarget = IsLower ? actual_k2+k1+actualPanelWidth : actual_k2; - - pack_lhs(blockA, lhs.getSubMapper(startTarget,startBlock), actualPanelWidth, lengthTarget); - - gebp_kernel(res.getSubMapper(startTarget, 0), blockA, blockB, - lengthTarget, actualPanelWidth, cols, alpha, - actualPanelWidth, actual_kc, 0, blockBOffset); - } - } - } - // the part below (lower case) or above (upper case) the diagonal => GEPP - { - Index start = IsLower ? k2 : 0; - Index end = IsLower ? rows : (std::min)(actual_k2,rows); - for(Index i2=start; i2<end; i2+=mc) - { - const Index actual_mc = (std::min)(i2+mc,end)-i2; - gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr,Traits::LhsProgress, LhsStorageOrder,false>() - (blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc); - - gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, - actual_kc, cols, alpha, -1, -1, 0, 0); - } - } - } - } - -// implements col-major += alpha * op(general) * op(triangular) -template <typename Scalar, typename Index, int Mode, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs, int Version> -struct product_triangular_matrix_matrix<Scalar,Index,Mode,false, - LhsStorageOrder,ConjugateLhs, - RhsStorageOrder,ConjugateRhs,ColMajor,Version> -{ - typedef gebp_traits<Scalar,Scalar> Traits; - enum { - SmallPanelWidth = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr), - IsLower = (Mode&Lower) == Lower, - SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1 - }; - - static EIGEN_DONT_INLINE void run( - Index _rows, Index _cols, Index _depth, - const Scalar* _lhs, Index lhsStride, - const Scalar* _rhs, Index rhsStride, - Scalar* res, Index resStride, - const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking); -}; - -template <typename Scalar, typename Index, int Mode, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs, int Version> -EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false, - LhsStorageOrder,ConjugateLhs, - RhsStorageOrder,ConjugateRhs,ColMajor,Version>::run( - Index _rows, Index _cols, Index _depth, - const Scalar* _lhs, Index lhsStride, - const Scalar* _rhs, Index rhsStride, - Scalar* _res, Index resStride, - const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking) - { - // strip zeros - Index diagSize = (std::min)(_cols,_depth); - Index rows = _rows; - Index depth = IsLower ? _depth : diagSize; - Index cols = IsLower ? diagSize : _cols; - - typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper; - typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper; - typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor> ResMapper; - LhsMapper lhs(_lhs,lhsStride); - RhsMapper rhs(_rhs,rhsStride); - ResMapper res(_res, resStride); - - Index kc = blocking.kc(); // cache block size along the K direction - Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction - - std::size_t sizeA = kc*mc; - std::size_t sizeB = kc*cols+EIGEN_ALIGN_BYTES/sizeof(Scalar); - - ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA()); - ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB()); - - Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,RhsStorageOrder> triangularBuffer; - triangularBuffer.setZero(); - if((Mode&ZeroDiag)==ZeroDiag) - triangularBuffer.diagonal().setZero(); - else - triangularBuffer.diagonal().setOnes(); - - gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel; - gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs; - gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs; - gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder,false,true> pack_rhs_panel; - - for(Index k2=IsLower ? 0 : depth; - IsLower ? k2<depth : k2>0; - IsLower ? k2+=kc : k2-=kc) - { - Index actual_kc = (std::min)(IsLower ? depth-k2 : k2, kc); - Index actual_k2 = IsLower ? k2 : k2-actual_kc; - - // align blocks with the end of the triangular part for trapezoidal rhs - if(IsLower && (k2<cols) && (actual_k2+actual_kc>cols)) - { - actual_kc = cols-k2; - k2 = actual_k2 + actual_kc - kc; - } - - // remaining size - Index rs = IsLower ? (std::min)(cols,actual_k2) : cols - k2; - // size of the triangular part - Index ts = (IsLower && actual_k2>=cols) ? 0 : actual_kc; - - Scalar* geb = blockB+ts*ts; - geb = geb + internal::first_aligned(geb,EIGEN_ALIGN_BYTES/sizeof(Scalar)); - - pack_rhs(geb, rhs.getSubMapper(actual_k2,IsLower ? 0 : k2), actual_kc, rs); - - // pack the triangular part of the rhs padding the unrolled blocks with zeros - if(ts>0) - { - for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth) - { - Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth); - Index actual_j2 = actual_k2 + j2; - Index panelOffset = IsLower ? j2+actualPanelWidth : 0; - Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2; - // general part - pack_rhs_panel(blockB+j2*actual_kc, - rhs.getSubMapper(actual_k2+panelOffset, actual_j2), - panelLength, actualPanelWidth, - actual_kc, panelOffset); - - // append the triangular part via a temporary buffer - for (Index j=0;j<actualPanelWidth;++j) - { - if (SetDiag) - triangularBuffer.coeffRef(j,j) = rhs(actual_j2+j,actual_j2+j); - for (Index k=IsLower ? j+1 : 0; IsLower ? k<actualPanelWidth : k<j; ++k) - triangularBuffer.coeffRef(k,j) = rhs(actual_j2+k,actual_j2+j); - } - - pack_rhs_panel(blockB+j2*actual_kc, - RhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()), - actualPanelWidth, actualPanelWidth, - actual_kc, j2); - } - } - - for (Index i2=0; i2<rows; i2+=mc) - { - const Index actual_mc = (std::min)(mc,rows-i2); - pack_lhs(blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc); - - // triangular kernel - if(ts>0) - { - for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth) - { - Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth); - Index panelLength = IsLower ? actual_kc-j2 : j2+actualPanelWidth; - Index blockOffset = IsLower ? j2 : 0; - - gebp_kernel(res.getSubMapper(i2, actual_k2 + j2), - blockA, blockB+j2*actual_kc, - actual_mc, panelLength, actualPanelWidth, - alpha, - actual_kc, actual_kc, // strides - blockOffset, blockOffset);// offsets - } - } - gebp_kernel(res.getSubMapper(i2, IsLower ? 0 : k2), - blockA, geb, actual_mc, actual_kc, rs, - alpha, - -1, -1, 0, 0); - } - } - } - -/*************************************************************************** -* Wrapper to product_triangular_matrix_matrix -***************************************************************************/ - -template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs> -struct traits<TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false> > - : traits<ProductBase<TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false>, Lhs, Rhs> > -{}; - -} // end namespace internal - -template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs> -struct TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false> - : public ProductBase<TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,false>, Lhs, Rhs > -{ - EIGEN_PRODUCT_PUBLIC_INTERFACE(TriangularProduct) - - TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} - - template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const - { - typename internal::add_const_on_value_type<ActualLhsType>::type lhs = LhsBlasTraits::extract(m_lhs); - typename internal::add_const_on_value_type<ActualRhsType>::type rhs = RhsBlasTraits::extract(m_rhs); - - Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(m_lhs) - * RhsBlasTraits::extractScalarFactor(m_rhs); - - typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar, - Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,4> BlockingType; - - enum { IsLower = (Mode&Lower) == Lower }; - Index stripedRows = ((!LhsIsTriangular) || (IsLower)) ? lhs.rows() : (std::min)(lhs.rows(),lhs.cols()); - Index stripedCols = ((LhsIsTriangular) || (!IsLower)) ? rhs.cols() : (std::min)(rhs.cols(),rhs.rows()); - Index stripedDepth = LhsIsTriangular ? ((!IsLower) ? lhs.cols() : (std::min)(lhs.cols(),lhs.rows())) - : ((IsLower) ? rhs.rows() : (std::min)(rhs.rows(),rhs.cols())); - - BlockingType blocking(stripedRows, stripedCols, stripedDepth, 1, false); - - internal::product_triangular_matrix_matrix<Scalar, Index, - Mode, LhsIsTriangular, - (internal::traits<_ActualLhsType>::Flags&RowMajorBit) ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate, - (internal::traits<_ActualRhsType>::Flags&RowMajorBit) ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate, - (internal::traits<Dest >::Flags&RowMajorBit) ? RowMajor : ColMajor> - ::run( - stripedRows, stripedCols, stripedDepth, // sizes - &lhs.coeffRef(0,0), lhs.outerStride(), // lhs info - &rhs.coeffRef(0,0), rhs.outerStride(), // rhs info - &dst.coeffRef(0,0), dst.outerStride(), // result info - actualAlpha, blocking - ); - } -}; - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h b/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h deleted file mode 100644 index ba41a1c99f..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h +++ /dev/null @@ -1,309 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Triangular matrix * matrix product functionality based on ?TRMM. - ******************************************************************************** -*/ - -#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H -#define EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H - -namespace Eigen { - -namespace internal { - - -template <typename Scalar, typename Index, - int Mode, bool LhsIsTriangular, - int LhsStorageOrder, bool ConjugateLhs, - int RhsStorageOrder, bool ConjugateRhs, - int ResStorageOrder> -struct product_triangular_matrix_matrix_trmm : - product_triangular_matrix_matrix<Scalar,Index,Mode, - LhsIsTriangular,LhsStorageOrder,ConjugateLhs, - RhsStorageOrder, ConjugateRhs, ResStorageOrder, BuiltIn> {}; - - -// try to go to BLAS specialization -#define EIGEN_MKL_TRMM_SPECIALIZE(Scalar, LhsIsTriangular) \ -template <typename Index, int Mode, \ - int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs> \ -struct product_triangular_matrix_matrix<Scalar,Index, Mode, LhsIsTriangular, \ - LhsStorageOrder,ConjugateLhs, RhsStorageOrder,ConjugateRhs,ColMajor,Specialized> { \ - static inline void run(Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride,\ - const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha, level3_blocking<Scalar,Scalar>& blocking) { \ - product_triangular_matrix_matrix_trmm<Scalar,Index,Mode, \ - LhsIsTriangular,LhsStorageOrder,ConjugateLhs, \ - RhsStorageOrder, ConjugateRhs, ColMajor>::run( \ - _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \ - } \ -}; - -EIGEN_MKL_TRMM_SPECIALIZE(double, true) -EIGEN_MKL_TRMM_SPECIALIZE(double, false) -EIGEN_MKL_TRMM_SPECIALIZE(dcomplex, true) -EIGEN_MKL_TRMM_SPECIALIZE(dcomplex, false) -EIGEN_MKL_TRMM_SPECIALIZE(float, true) -EIGEN_MKL_TRMM_SPECIALIZE(float, false) -EIGEN_MKL_TRMM_SPECIALIZE(scomplex, true) -EIGEN_MKL_TRMM_SPECIALIZE(scomplex, false) - -// implements col-major += alpha * op(triangular) * op(general) -#define EIGEN_MKL_TRMM_L(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ -template <typename Index, int Mode, \ - int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs> \ -struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \ - LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \ -{ \ - enum { \ - IsLower = (Mode&Lower) == Lower, \ - SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \ - IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ - IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ - LowUp = IsLower ? Lower : Upper, \ - conjA = ((LhsStorageOrder==ColMajor) && ConjugateLhs) ? 1 : 0 \ - }; \ -\ - static void run( \ - Index _rows, Index _cols, Index _depth, \ - const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsStride, \ - EIGTYPE* res, Index resStride, \ - EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \ - { \ - Index diagSize = (std::min)(_rows,_depth); \ - Index rows = IsLower ? _rows : diagSize; \ - Index depth = IsLower ? diagSize : _depth; \ - Index cols = _cols; \ -\ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \ -\ -/* Non-square case - doesn't fit to MKL ?TRMM. Fall to default triangular product or call MKL ?GEMM*/ \ - if (rows != depth) { \ -\ - int nthr = mkl_domain_get_max_threads(MKL_BLAS); \ -\ - if (((nthr==1) && (((std::max)(rows,depth)-diagSize)/(double)diagSize < 0.5))) { \ - /* Most likely no benefit to call TRMM or GEMM from MKL*/ \ - product_triangular_matrix_matrix<EIGTYPE,Index,Mode,true, \ - LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, BuiltIn>::run( \ - _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \ - /*std::cout << "TRMM_L: A is not square! Go to Eigen TRMM implementation!\n";*/ \ - } else { \ - /* Make sense to call GEMM */ \ - Map<const MatrixLhs, 0, OuterStride<> > lhsMap(_lhs,rows,depth,OuterStride<>(lhsStride)); \ - MatrixLhs aa_tmp=lhsMap.template triangularView<Mode>(); \ - MKL_INT aStride = aa_tmp.outerStride(); \ - gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth); \ - general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor>::run( \ - rows, cols, depth, aa_tmp.data(), aStride, _rhs, rhsStride, res, resStride, alpha, gemm_blocking, 0); \ -\ - /*std::cout << "TRMM_L: A is not square! Go to MKL GEMM implementation! " << nthr<<" \n";*/ \ - } \ - return; \ - } \ - char side = 'L', transa, uplo, diag = 'N'; \ - EIGTYPE *b; \ - const EIGTYPE *a; \ - MKL_INT m, n, lda, ldb; \ - MKLTYPE alpha_; \ -\ -/* Set alpha_*/ \ - assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \ -\ -/* Set m, n */ \ - m = (MKL_INT)diagSize; \ - n = (MKL_INT)cols; \ -\ -/* Set trans */ \ - transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \ -\ -/* Set b, ldb */ \ - Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols,OuterStride<>(rhsStride)); \ - MatrixX##EIGPREFIX b_tmp; \ -\ - if (ConjugateRhs) b_tmp = rhs.conjugate(); else b_tmp = rhs; \ - b = b_tmp.data(); \ - ldb = b_tmp.outerStride(); \ -\ -/* Set uplo */ \ - uplo = IsLower ? 'L' : 'U'; \ - if (LhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \ -/* Set a, lda */ \ - Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \ - MatrixLhs a_tmp; \ -\ - if ((conjA!=0) || (SetDiag==0)) { \ - if (conjA) a_tmp = lhs.conjugate(); else a_tmp = lhs; \ - if (IsZeroDiag) \ - a_tmp.diagonal().setZero(); \ - else if (IsUnitDiag) \ - a_tmp.diagonal().setOnes();\ - a = a_tmp.data(); \ - lda = a_tmp.outerStride(); \ - } else { \ - a = _lhs; \ - lda = lhsStride; \ - } \ - /*std::cout << "TRMM_L: A is square! Go to MKL TRMM implementation! \n";*/ \ -/* call ?trmm*/ \ - MKLPREFIX##trmm(&side, &uplo, &transa, &diag, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (MKLTYPE*)b, &ldb); \ -\ -/* Add op(a_triangular)*b into res*/ \ - Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \ - res_tmp=res_tmp+b_tmp; \ - } \ -}; - -EIGEN_MKL_TRMM_L(double, double, d, d) -EIGEN_MKL_TRMM_L(dcomplex, MKL_Complex16, cd, z) -EIGEN_MKL_TRMM_L(float, float, f, s) -EIGEN_MKL_TRMM_L(scomplex, MKL_Complex8, cf, c) - -// implements col-major += alpha * op(general) * op(triangular) -#define EIGEN_MKL_TRMM_R(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ -template <typename Index, int Mode, \ - int LhsStorageOrder, bool ConjugateLhs, \ - int RhsStorageOrder, bool ConjugateRhs> \ -struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \ - LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \ -{ \ - enum { \ - IsLower = (Mode&Lower) == Lower, \ - SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \ - IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ - IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ - LowUp = IsLower ? Lower : Upper, \ - conjA = ((RhsStorageOrder==ColMajor) && ConjugateRhs) ? 1 : 0 \ - }; \ -\ - static void run( \ - Index _rows, Index _cols, Index _depth, \ - const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsStride, \ - EIGTYPE* res, Index resStride, \ - EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \ - { \ - Index diagSize = (std::min)(_cols,_depth); \ - Index rows = _rows; \ - Index depth = IsLower ? _depth : diagSize; \ - Index cols = IsLower ? diagSize : _cols; \ -\ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \ -\ -/* Non-square case - doesn't fit to MKL ?TRMM. Fall to default triangular product or call MKL ?GEMM*/ \ - if (cols != depth) { \ -\ - int nthr = mkl_domain_get_max_threads(MKL_BLAS); \ -\ - if ((nthr==1) && (((std::max)(cols,depth)-diagSize)/(double)diagSize < 0.5)) { \ - /* Most likely no benefit to call TRMM or GEMM from MKL*/ \ - product_triangular_matrix_matrix<EIGTYPE,Index,Mode,false, \ - LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, BuiltIn>::run( \ - _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \ - /*std::cout << "TRMM_R: A is not square! Go to Eigen TRMM implementation!\n";*/ \ - } else { \ - /* Make sense to call GEMM */ \ - Map<const MatrixRhs, 0, OuterStride<> > rhsMap(_rhs,depth,cols, OuterStride<>(rhsStride)); \ - MatrixRhs aa_tmp=rhsMap.template triangularView<Mode>(); \ - MKL_INT aStride = aa_tmp.outerStride(); \ - gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth); \ - general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor>::run( \ - rows, cols, depth, _lhs, lhsStride, aa_tmp.data(), aStride, res, resStride, alpha, gemm_blocking, 0); \ -\ - /*std::cout << "TRMM_R: A is not square! Go to MKL GEMM implementation! " << nthr<<" \n";*/ \ - } \ - return; \ - } \ - char side = 'R', transa, uplo, diag = 'N'; \ - EIGTYPE *b; \ - const EIGTYPE *a; \ - MKL_INT m, n, lda, ldb; \ - MKLTYPE alpha_; \ -\ -/* Set alpha_*/ \ - assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \ -\ -/* Set m, n */ \ - m = (MKL_INT)rows; \ - n = (MKL_INT)diagSize; \ -\ -/* Set trans */ \ - transa = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \ -\ -/* Set b, ldb */ \ - Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \ - MatrixX##EIGPREFIX b_tmp; \ -\ - if (ConjugateLhs) b_tmp = lhs.conjugate(); else b_tmp = lhs; \ - b = b_tmp.data(); \ - ldb = b_tmp.outerStride(); \ -\ -/* Set uplo */ \ - uplo = IsLower ? 'L' : 'U'; \ - if (RhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \ -/* Set a, lda */ \ - Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols, OuterStride<>(rhsStride)); \ - MatrixRhs a_tmp; \ -\ - if ((conjA!=0) || (SetDiag==0)) { \ - if (conjA) a_tmp = rhs.conjugate(); else a_tmp = rhs; \ - if (IsZeroDiag) \ - a_tmp.diagonal().setZero(); \ - else if (IsUnitDiag) \ - a_tmp.diagonal().setOnes();\ - a = a_tmp.data(); \ - lda = a_tmp.outerStride(); \ - } else { \ - a = _rhs; \ - lda = rhsStride; \ - } \ - /*std::cout << "TRMM_R: A is square! Go to MKL TRMM implementation! \n";*/ \ -/* call ?trmm*/ \ - MKLPREFIX##trmm(&side, &uplo, &transa, &diag, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (MKLTYPE*)b, &ldb); \ -\ -/* Add op(a_triangular)*b into res*/ \ - Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \ - res_tmp=res_tmp+b_tmp; \ - } \ -}; - -EIGEN_MKL_TRMM_R(double, double, d, d) -EIGEN_MKL_TRMM_R(dcomplex, MKL_Complex16, cd, z) -EIGEN_MKL_TRMM_R(float, float, f, s) -EIGEN_MKL_TRMM_R(scomplex, MKL_Complex8, cf, c) - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_MKL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h b/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h deleted file mode 100644 index 9863076958..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixVector.h +++ /dev/null @@ -1,354 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRIANGULARMATRIXVECTOR_H -#define EIGEN_TRIANGULARMATRIXVECTOR_H - -namespace Eigen { - -namespace internal { - -template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder, int Version=Specialized> -struct triangular_matrix_vector_product; - -template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version> -struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version> -{ - typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - enum { - IsLower = ((Mode&Lower)==Lower), - HasUnitDiag = (Mode & UnitDiag)==UnitDiag, - HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag - }; - static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride, - const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha); -}; - -template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version> -EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version> - ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride, - const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha) - { - static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH; - Index size = (std::min)(_rows,_cols); - Index rows = IsLower ? _rows : (std::min)(_rows,_cols); - Index cols = IsLower ? (std::min)(_rows,_cols) : _cols; - - typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap; - const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride)); - typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs); - - typedef Map<const Matrix<RhsScalar,Dynamic,1>, 0, InnerStride<> > RhsMap; - const RhsMap rhs(_rhs,cols,InnerStride<>(rhsIncr)); - typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs); - - typedef Map<Matrix<ResScalar,Dynamic,1> > ResMap; - ResMap res(_res,rows); - - typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper; - typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper; - - for (Index pi=0; pi<size; pi+=PanelWidth) - { - Index actualPanelWidth = (std::min)(PanelWidth, size-pi); - for (Index k=0; k<actualPanelWidth; ++k) - { - Index i = pi + k; - Index s = IsLower ? ((HasUnitDiag||HasZeroDiag) ? i+1 : i ) : pi; - Index r = IsLower ? actualPanelWidth-k : k+1; - if ((!(HasUnitDiag||HasZeroDiag)) || (--r)>0) - res.segment(s,r) += (alpha * cjRhs.coeff(i)) * cjLhs.col(i).segment(s,r); - if (HasUnitDiag) - res.coeffRef(i) += alpha * cjRhs.coeff(i); - } - Index r = IsLower ? rows - pi - actualPanelWidth : pi; - if (r>0) - { - Index s = IsLower ? pi+actualPanelWidth : 0; - general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs,BuiltIn>::run( - r, actualPanelWidth, - LhsMapper(&lhs.coeffRef(s,pi), lhsStride), - RhsMapper(&rhs.coeffRef(pi), rhsIncr), - &res.coeffRef(s), resIncr, alpha); - } - } - if((!IsLower) && cols>size) - { - general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs>::run( - rows, cols-size, - LhsMapper(&lhs.coeffRef(0,size), lhsStride), - RhsMapper(&rhs.coeffRef(size), rhsIncr), - _res, resIncr, alpha); - } - } - -template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version> -struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version> -{ - typedef typename scalar_product_traits<LhsScalar, RhsScalar>::ReturnType ResScalar; - enum { - IsLower = ((Mode&Lower)==Lower), - HasUnitDiag = (Mode & UnitDiag)==UnitDiag, - HasZeroDiag = (Mode & ZeroDiag)==ZeroDiag - }; - static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride, - const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha); -}; - -template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version> -EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version> - ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride, - const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha) - { - static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH; - Index diagSize = (std::min)(_rows,_cols); - Index rows = IsLower ? _rows : diagSize; - Index cols = IsLower ? diagSize : _cols; - - typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap; - const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride)); - typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs); - - typedef Map<const Matrix<RhsScalar,Dynamic,1> > RhsMap; - const RhsMap rhs(_rhs,cols); - typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs); - - typedef Map<Matrix<ResScalar,Dynamic,1>, 0, InnerStride<> > ResMap; - ResMap res(_res,rows,InnerStride<>(resIncr)); - - typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper; - typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper; - - for (Index pi=0; pi<diagSize; pi+=PanelWidth) - { - Index actualPanelWidth = (std::min)(PanelWidth, diagSize-pi); - for (Index k=0; k<actualPanelWidth; ++k) - { - Index i = pi + k; - Index s = IsLower ? pi : ((HasUnitDiag||HasZeroDiag) ? i+1 : i); - Index r = IsLower ? k+1 : actualPanelWidth-k; - if ((!(HasUnitDiag||HasZeroDiag)) || (--r)>0) - res.coeffRef(i) += alpha * (cjLhs.row(i).segment(s,r).cwiseProduct(cjRhs.segment(s,r).transpose())).sum(); - if (HasUnitDiag) - res.coeffRef(i) += alpha * cjRhs.coeff(i); - } - Index r = IsLower ? pi : cols - pi - actualPanelWidth; - if (r>0) - { - Index s = IsLower ? 0 : pi + actualPanelWidth; - general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs,BuiltIn>::run( - actualPanelWidth, r, - LhsMapper(&lhs.coeffRef(pi,s), lhsStride), - RhsMapper(&rhs.coeffRef(s), rhsIncr), - &res.coeffRef(pi), resIncr, alpha); - } - } - if(IsLower && rows>diagSize) - { - general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs>::run( - rows-diagSize, cols, - LhsMapper(&lhs.coeffRef(diagSize,0), lhsStride), - RhsMapper(&rhs.coeffRef(0), rhsIncr), - &res.coeffRef(diagSize), resIncr, alpha); - } - } - -/*************************************************************************** -* Wrapper to product_triangular_vector -***************************************************************************/ - -template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs> -struct traits<TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,true> > - : traits<ProductBase<TriangularProduct<Mode,LhsIsTriangular,Lhs,false,Rhs,true>, Lhs, Rhs> > -{}; - -template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs> -struct traits<TriangularProduct<Mode,LhsIsTriangular,Lhs,true,Rhs,false> > - : traits<ProductBase<TriangularProduct<Mode,LhsIsTriangular,Lhs,true,Rhs,false>, Lhs, Rhs> > -{}; - - -template<int StorageOrder> -struct trmv_selector; - -} // end namespace internal - -template<int Mode, typename Lhs, typename Rhs> -struct TriangularProduct<Mode,true,Lhs,false,Rhs,true> - : public ProductBase<TriangularProduct<Mode,true,Lhs,false,Rhs,true>, Lhs, Rhs > -{ - EIGEN_PRODUCT_PUBLIC_INTERFACE(TriangularProduct) - - TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} - - template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const - { - eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols()); - - internal::trmv_selector<(int(internal::traits<Lhs>::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(*this, dst, alpha); - } -}; - -template<int Mode, typename Lhs, typename Rhs> -struct TriangularProduct<Mode,false,Lhs,true,Rhs,false> - : public ProductBase<TriangularProduct<Mode,false,Lhs,true,Rhs,false>, Lhs, Rhs > -{ - EIGEN_PRODUCT_PUBLIC_INTERFACE(TriangularProduct) - - TriangularProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) {} - - template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const - { - eigen_assert(dst.rows()==m_lhs.rows() && dst.cols()==m_rhs.cols()); - - typedef TriangularProduct<(Mode & (UnitDiag|ZeroDiag)) | ((Mode & Lower) ? Upper : Lower),true,Transpose<const Rhs>,false,Transpose<const Lhs>,true> TriangularProductTranspose; - Transpose<Dest> dstT(dst); - internal::trmv_selector<(int(internal::traits<Rhs>::Flags)&RowMajorBit) ? ColMajor : RowMajor>::run( - TriangularProductTranspose(m_rhs.transpose(),m_lhs.transpose()), dstT, alpha); - } -}; - -namespace internal { - -// TODO: find a way to factorize this piece of code with gemv_selector since the logic is exactly the same. - -template<> struct trmv_selector<ColMajor> -{ - template<int Mode, typename Lhs, typename Rhs, typename Dest> - static void run(const TriangularProduct<Mode,true,Lhs,false,Rhs,true>& prod, Dest& dest, const typename TriangularProduct<Mode,true,Lhs,false,Rhs,true>::Scalar& alpha) - { - typedef TriangularProduct<Mode,true,Lhs,false,Rhs,true> ProductType; - typedef typename ProductType::Index Index; - typedef typename ProductType::LhsScalar LhsScalar; - typedef typename ProductType::RhsScalar RhsScalar; - typedef typename ProductType::Scalar ResScalar; - typedef typename ProductType::RealScalar RealScalar; - typedef typename ProductType::ActualLhsType ActualLhsType; - typedef typename ProductType::ActualRhsType ActualRhsType; - typedef typename ProductType::LhsBlasTraits LhsBlasTraits; - typedef typename ProductType::RhsBlasTraits RhsBlasTraits; - typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest; - - typename internal::add_const_on_value_type<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs()); - typename internal::add_const_on_value_type<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs()); - - ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) - * RhsBlasTraits::extractScalarFactor(prod.rhs()); - - enum { - // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1 - // on, the other hand it is good for the cache to pack the vector anyways... - EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1, - ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex), - MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal - }; - - gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest; - - bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0)); - bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible; - - RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha); - - ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), - evalToDest ? dest.data() : static_dest.data()); - - if(!evalToDest) - { - #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN - Index size = dest.size(); - EIGEN_DENSE_STORAGE_CTOR_PLUGIN - #endif - if(!alphaIsCompatible) - { - MappedDest(actualDestPtr, dest.size()).setZero(); - compatibleAlpha = RhsScalar(1); - } - else - MappedDest(actualDestPtr, dest.size()) = dest; - } - - internal::triangular_matrix_vector_product - <Index,Mode, - LhsScalar, LhsBlasTraits::NeedToConjugate, - RhsScalar, RhsBlasTraits::NeedToConjugate, - ColMajor> - ::run(actualLhs.rows(),actualLhs.cols(), - actualLhs.data(),actualLhs.outerStride(), - actualRhs.data(),actualRhs.innerStride(), - actualDestPtr,1,compatibleAlpha); - - if (!evalToDest) - { - if(!alphaIsCompatible) - dest += actualAlpha * MappedDest(actualDestPtr, dest.size()); - else - dest = MappedDest(actualDestPtr, dest.size()); - } - } -}; - -template<> struct trmv_selector<RowMajor> -{ - template<int Mode, typename Lhs, typename Rhs, typename Dest> - static void run(const TriangularProduct<Mode,true,Lhs,false,Rhs,true>& prod, Dest& dest, const typename TriangularProduct<Mode,true,Lhs,false,Rhs,true>::Scalar& alpha) - { - typedef TriangularProduct<Mode,true,Lhs,false,Rhs,true> ProductType; - typedef typename ProductType::LhsScalar LhsScalar; - typedef typename ProductType::RhsScalar RhsScalar; - typedef typename ProductType::Scalar ResScalar; - typedef typename ProductType::Index Index; - typedef typename ProductType::ActualLhsType ActualLhsType; - typedef typename ProductType::ActualRhsType ActualRhsType; - typedef typename ProductType::_ActualRhsType _ActualRhsType; - typedef typename ProductType::LhsBlasTraits LhsBlasTraits; - typedef typename ProductType::RhsBlasTraits RhsBlasTraits; - - typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs()); - typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs()); - - ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) - * RhsBlasTraits::extractScalarFactor(prod.rhs()); - - enum { - DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1 - }; - - gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs; - - ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(), - DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data()); - - if(!DirectlyUseRhs) - { - #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN - int size = actualRhs.size(); - EIGEN_DENSE_STORAGE_CTOR_PLUGIN - #endif - Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs; - } - - internal::triangular_matrix_vector_product - <Index,Mode, - LhsScalar, LhsBlasTraits::NeedToConjugate, - RhsScalar, RhsBlasTraits::NeedToConjugate, - RowMajor> - ::run(actualLhs.rows(),actualLhs.cols(), - actualLhs.data(),actualLhs.outerStride(), - actualRhsPtr,1, - dest.data(),dest.innerStride(), - actualAlpha); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULARMATRIXVECTOR_H diff --git a/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixVector_MKL.h b/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixVector_MKL.h deleted file mode 100644 index 09f110da71..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/TriangularMatrixVector_MKL.h +++ /dev/null @@ -1,247 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Triangular matrix-vector product functionality based on ?TRMV. - ******************************************************************************** -*/ - -#ifndef EIGEN_TRIANGULAR_MATRIX_VECTOR_MKL_H -#define EIGEN_TRIANGULAR_MATRIX_VECTOR_MKL_H - -namespace Eigen { - -namespace internal { - -/********************************************************************** -* This file implements triangular matrix-vector multiplication using BLAS -**********************************************************************/ - -// trmv/hemv specialization - -template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder> -struct triangular_matrix_vector_product_trmv : - triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,StorageOrder,BuiltIn> {}; - -#define EIGEN_MKL_TRMV_SPECIALIZE(Scalar) \ -template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \ -struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor,Specialized> { \ - static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \ - const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \ - triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor>::run( \ - _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \ - } \ -}; \ -template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \ -struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor,Specialized> { \ - static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \ - const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \ - triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor>::run( \ - _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \ - } \ -}; - -EIGEN_MKL_TRMV_SPECIALIZE(double) -EIGEN_MKL_TRMV_SPECIALIZE(float) -EIGEN_MKL_TRMV_SPECIALIZE(dcomplex) -EIGEN_MKL_TRMV_SPECIALIZE(scomplex) - -// implements col-major: res += alpha * op(triangular) * vector -#define EIGEN_MKL_TRMV_CM(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ -template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \ -struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor> { \ - enum { \ - IsLower = (Mode&Lower) == Lower, \ - SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \ - IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ - IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ - LowUp = IsLower ? Lower : Upper \ - }; \ - static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \ - { \ - if (ConjLhs || IsZeroDiag) { \ - triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor,BuiltIn>::run( \ - _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \ - return; \ - }\ - Index size = (std::min)(_rows,_cols); \ - Index rows = IsLower ? _rows : size; \ - Index cols = IsLower ? size : _cols; \ -\ - typedef VectorX##EIGPREFIX VectorRhs; \ - EIGTYPE *x, *y;\ -\ -/* Set x*/ \ - Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \ - VectorRhs x_tmp; \ - if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \ - x = x_tmp.data(); \ -\ -/* Square part handling */\ -\ - char trans, uplo, diag; \ - MKL_INT m, n, lda, incx, incy; \ - EIGTYPE const *a; \ - MKLTYPE alpha_, beta_; \ - assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \ - assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(beta_, EIGTYPE(1)); \ -\ -/* Set m, n */ \ - n = (MKL_INT)size; \ - lda = lhsStride; \ - incx = 1; \ - incy = resIncr; \ -\ -/* Set uplo, trans and diag*/ \ - trans = 'N'; \ - uplo = IsLower ? 'L' : 'U'; \ - diag = IsUnitDiag ? 'U' : 'N'; \ -\ -/* call ?TRMV*/ \ - MKLPREFIX##trmv(&uplo, &trans, &diag, &n, (const MKLTYPE*)_lhs, &lda, (MKLTYPE*)x, &incx); \ -\ -/* Add op(a_tr)rhs into res*/ \ - MKLPREFIX##axpy(&n, &alpha_,(const MKLTYPE*)x, &incx, (MKLTYPE*)_res, &incy); \ -/* Non-square case - doesn't fit to MKL ?TRMV. Fall to default triangular product*/ \ - if (size<(std::max)(rows,cols)) { \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic> MatrixLhs; \ - if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \ - x = x_tmp.data(); \ - if (size<rows) { \ - y = _res + size*resIncr; \ - a = _lhs + size; \ - m = rows-size; \ - n = size; \ - } \ - else { \ - x += size; \ - y = _res; \ - a = _lhs + size*lda; \ - m = size; \ - n = cols-size; \ - } \ - MKLPREFIX##gemv(&trans, &m, &n, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)x, &incx, &beta_, (MKLTYPE*)y, &incy); \ - } \ - } \ -}; - -EIGEN_MKL_TRMV_CM(double, double, d, d) -EIGEN_MKL_TRMV_CM(dcomplex, MKL_Complex16, cd, z) -EIGEN_MKL_TRMV_CM(float, float, f, s) -EIGEN_MKL_TRMV_CM(scomplex, MKL_Complex8, cf, c) - -// implements row-major: res += alpha * op(triangular) * vector -#define EIGEN_MKL_TRMV_RM(EIGTYPE, MKLTYPE, EIGPREFIX, MKLPREFIX) \ -template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \ -struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor> { \ - enum { \ - IsLower = (Mode&Lower) == Lower, \ - SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \ - IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ - IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ - LowUp = IsLower ? Lower : Upper \ - }; \ - static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \ - const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \ - { \ - if (IsZeroDiag) { \ - triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor,BuiltIn>::run( \ - _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \ - return; \ - }\ - Index size = (std::min)(_rows,_cols); \ - Index rows = IsLower ? _rows : size; \ - Index cols = IsLower ? size : _cols; \ -\ - typedef VectorX##EIGPREFIX VectorRhs; \ - EIGTYPE *x, *y;\ -\ -/* Set x*/ \ - Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \ - VectorRhs x_tmp; \ - if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \ - x = x_tmp.data(); \ -\ -/* Square part handling */\ -\ - char trans, uplo, diag; \ - MKL_INT m, n, lda, incx, incy; \ - EIGTYPE const *a; \ - MKLTYPE alpha_, beta_; \ - assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(alpha_, alpha); \ - assign_scalar_eig2mkl<MKLTYPE, EIGTYPE>(beta_, EIGTYPE(1)); \ -\ -/* Set m, n */ \ - n = (MKL_INT)size; \ - lda = lhsStride; \ - incx = 1; \ - incy = resIncr; \ -\ -/* Set uplo, trans and diag*/ \ - trans = ConjLhs ? 'C' : 'T'; \ - uplo = IsLower ? 'U' : 'L'; \ - diag = IsUnitDiag ? 'U' : 'N'; \ -\ -/* call ?TRMV*/ \ - MKLPREFIX##trmv(&uplo, &trans, &diag, &n, (const MKLTYPE*)_lhs, &lda, (MKLTYPE*)x, &incx); \ -\ -/* Add op(a_tr)rhs into res*/ \ - MKLPREFIX##axpy(&n, &alpha_,(const MKLTYPE*)x, &incx, (MKLTYPE*)_res, &incy); \ -/* Non-square case - doesn't fit to MKL ?TRMV. Fall to default triangular product*/ \ - if (size<(std::max)(rows,cols)) { \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic> MatrixLhs; \ - if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \ - x = x_tmp.data(); \ - if (size<rows) { \ - y = _res + size*resIncr; \ - a = _lhs + size*lda; \ - m = rows-size; \ - n = size; \ - } \ - else { \ - x += size; \ - y = _res; \ - a = _lhs + size; \ - m = size; \ - n = cols-size; \ - } \ - MKLPREFIX##gemv(&trans, &n, &m, &alpha_, (const MKLTYPE*)a, &lda, (const MKLTYPE*)x, &incx, &beta_, (MKLTYPE*)y, &incy); \ - } \ - } \ -}; - -EIGEN_MKL_TRMV_RM(double, double, d, d) -EIGEN_MKL_TRMV_RM(dcomplex, MKL_Complex16, cd, z) -EIGEN_MKL_TRMV_RM(float, float, f, s) -EIGEN_MKL_TRMV_RM(scomplex, MKL_Complex8, cf, c) - -} // end namespase internal - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULAR_MATRIX_VECTOR_MKL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h b/third_party/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h deleted file mode 100644 index f5de67c59f..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/TriangularSolverMatrix.h +++ /dev/null @@ -1,331 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_H -#define EIGEN_TRIANGULAR_SOLVER_MATRIX_H - -namespace Eigen { - -namespace internal { - -// if the rhs is row major, let's transpose the product -template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder> -struct triangular_solve_matrix<Scalar,Index,Side,Mode,Conjugate,TriStorageOrder,RowMajor> -{ - static void run( - Index size, Index cols, - const Scalar* tri, Index triStride, - Scalar* _other, Index otherStride, - level3_blocking<Scalar,Scalar>& blocking) - { - triangular_solve_matrix< - Scalar, Index, Side==OnTheLeft?OnTheRight:OnTheLeft, - (Mode&UnitDiag) | ((Mode&Upper) ? Lower : Upper), - NumTraits<Scalar>::IsComplex && Conjugate, - TriStorageOrder==RowMajor ? ColMajor : RowMajor, ColMajor> - ::run(size, cols, tri, triStride, _other, otherStride, blocking); - } -}; - -/* Optimized triangular solver with multiple right hand side and the triangular matrix on the left - */ -template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder> -struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor> -{ - static EIGEN_DONT_INLINE void run( - Index size, Index otherSize, - const Scalar* _tri, Index triStride, - Scalar* _other, Index otherStride, - level3_blocking<Scalar,Scalar>& blocking); -}; -template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder> -EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor>::run( - Index size, Index otherSize, - const Scalar* _tri, Index triStride, - Scalar* _other, Index otherStride, - level3_blocking<Scalar,Scalar>& blocking) - { - Index cols = otherSize; - - typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> TriMapper; - typedef blas_data_mapper<Scalar, Index, ColMajor> OtherMapper; - TriMapper tri(_tri, triStride); - OtherMapper other(_other, otherStride); - - typedef gebp_traits<Scalar,Scalar> Traits; - - enum { - SmallPanelWidth = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr), - IsLower = (Mode&Lower) == Lower - }; - - Index kc = blocking.kc(); // cache block size along the K direction - Index mc = (std::min)(size,blocking.mc()); // cache block size along the M direction - - std::size_t sizeA = kc*mc; - std::size_t sizeB = kc*cols; - - ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA()); - ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB()); - - conj_if<Conjugate> conj; - gebp_kernel<Scalar, Scalar, Index, OtherMapper, Traits::mr, Traits::nr, Conjugate, false> gebp_kernel; - gemm_pack_lhs<Scalar, Index, TriMapper, Traits::mr, Traits::LhsProgress, TriStorageOrder> pack_lhs; - gemm_pack_rhs<Scalar, Index, OtherMapper, Traits::nr, ColMajor, false, true> pack_rhs; - - // the goal here is to subdivise the Rhs panels such that we keep some cache - // coherence when accessing the rhs elements - std::ptrdiff_t l1, l2, l3; - manage_caching_sizes(GetAction, &l1, &l2, &l3); - Index subcols = cols>0 ? l2/(4 * sizeof(Scalar) * otherStride) : 0; - subcols = std::max<Index>((subcols/Traits::nr)*Traits::nr, Traits::nr); - - for(Index k2=IsLower ? 0 : size; - IsLower ? k2<size : k2>0; - IsLower ? k2+=kc : k2-=kc) - { - const Index actual_kc = (std::min)(IsLower ? size-k2 : k2, kc); - - // We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel, - // and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into - // A11 (the triangular part) and A21 the remaining rectangular part. - // Then the high level algorithm is: - // - B = R1 => general block copy (done during the next step) - // - R1 = A11^-1 B => tricky part - // - update B from the new R1 => actually this has to be performed continuously during the above step - // - R2 -= A21 * B => GEPP - - // The tricky part: compute R1 = A11^-1 B while updating B from R1 - // The idea is to split A11 into multiple small vertical panels. - // Each panel can be split into a small triangular part T1k which is processed without optimization, - // and the remaining small part T2k which is processed using gebp with appropriate block strides - for(Index j2=0; j2<cols; j2+=subcols) - { - Index actual_cols = (std::min)(cols-j2,subcols); - // for each small vertical panels [T1k^T, T2k^T]^T of lhs - for (Index k1=0; k1<actual_kc; k1+=SmallPanelWidth) - { - Index actualPanelWidth = std::min<Index>(actual_kc-k1, SmallPanelWidth); - // tr solve - for (Index k=0; k<actualPanelWidth; ++k) - { - // TODO write a small kernel handling this (can be shared with trsv) - Index i = IsLower ? k2+k1+k : k2-k1-k-1; - Index s = IsLower ? k2+k1 : i+1; - Index rs = actualPanelWidth - k - 1; // remaining size - - Scalar a = (Mode & UnitDiag) ? Scalar(1) : Scalar(1)/conj(tri(i,i)); - for (Index j=j2; j<j2+actual_cols; ++j) - { - if (TriStorageOrder==RowMajor) - { - Scalar b(0); - const Scalar* l = &tri(i,s); - Scalar* r = &other(s,j); - for (Index i3=0; i3<k; ++i3) - b += conj(l[i3]) * r[i3]; - - other(i,j) = (other(i,j) - b)*a; - } - else - { - Index s = IsLower ? i+1 : i-rs; - Scalar b = (other(i,j) *= a); - Scalar* r = &other(s,j); - const Scalar* l = &tri(s,i); - for (Index i3=0;i3<rs;++i3) - r[i3] -= b * conj(l[i3]); - } - } - } - - Index lengthTarget = actual_kc-k1-actualPanelWidth; - Index startBlock = IsLower ? k2+k1 : k2-k1-actualPanelWidth; - Index blockBOffset = IsLower ? k1 : lengthTarget; - - // update the respective rows of B from other - pack_rhs(blockB+actual_kc*j2, other.getSubMapper(startBlock,j2), actualPanelWidth, actual_cols, actual_kc, blockBOffset); - - // GEBP - if (lengthTarget>0) - { - Index startTarget = IsLower ? k2+k1+actualPanelWidth : k2-actual_kc; - - pack_lhs(blockA, tri.getSubMapper(startTarget,startBlock), actualPanelWidth, lengthTarget); - - gebp_kernel(other.getSubMapper(startTarget,j2), blockA, blockB+actual_kc*j2, lengthTarget, actualPanelWidth, actual_cols, Scalar(-1), - actualPanelWidth, actual_kc, 0, blockBOffset); - } - } - } - - // R2 -= A21 * B => GEPP - { - Index start = IsLower ? k2+kc : 0; - Index end = IsLower ? size : k2-kc; - for(Index i2=start; i2<end; i2+=mc) - { - const Index actual_mc = (std::min)(mc,end-i2); - if (actual_mc>0) - { - pack_lhs(blockA, tri.getSubMapper(i2, IsLower ? k2 : k2-kc), actual_kc, actual_mc); - - gebp_kernel(other.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1), -1, -1, 0, 0); - } - } - } - } - } - -/* Optimized triangular solver with multiple left hand sides and the trinagular matrix on the right - */ -template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder> -struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor> -{ - static EIGEN_DONT_INLINE void run( - Index size, Index otherSize, - const Scalar* _tri, Index triStride, - Scalar* _other, Index otherStride, - level3_blocking<Scalar,Scalar>& blocking); -}; -template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder> -EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor>::run( - Index size, Index otherSize, - const Scalar* _tri, Index triStride, - Scalar* _other, Index otherStride, - level3_blocking<Scalar,Scalar>& blocking) - { - Index rows = otherSize; - - typedef blas_data_mapper<Scalar, Index, ColMajor> LhsMapper; - typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> RhsMapper; - LhsMapper lhs(_other, otherStride); - RhsMapper rhs(_tri, triStride); - - typedef gebp_traits<Scalar,Scalar> Traits; - enum { - RhsStorageOrder = TriStorageOrder, - SmallPanelWidth = EIGEN_PLAIN_ENUM_MAX(Traits::mr,Traits::nr), - IsLower = (Mode&Lower) == Lower - }; - - Index kc = blocking.kc(); // cache block size along the K direction - Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction - - std::size_t sizeA = kc*mc; - std::size_t sizeB = kc*size; - - ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA()); - ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB()); - - conj_if<Conjugate> conj; - gebp_kernel<Scalar, Scalar, Index, LhsMapper, Traits::mr, Traits::nr, false, Conjugate> gebp_kernel; - gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs; - gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder,false,true> pack_rhs_panel; - gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, ColMajor, false, true> pack_lhs_panel; - - for(Index k2=IsLower ? size : 0; - IsLower ? k2>0 : k2<size; - IsLower ? k2-=kc : k2+=kc) - { - const Index actual_kc = (std::min)(IsLower ? k2 : size-k2, kc); - Index actual_k2 = IsLower ? k2-actual_kc : k2 ; - - Index startPanel = IsLower ? 0 : k2+actual_kc; - Index rs = IsLower ? actual_k2 : size - actual_k2 - actual_kc; - Scalar* geb = blockB+actual_kc*actual_kc; - - if (rs>0) pack_rhs(geb, rhs.getSubMapper(actual_k2,startPanel), actual_kc, rs); - - // triangular packing (we only pack the panels off the diagonal, - // neglecting the blocks overlapping the diagonal - { - for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth) - { - Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth); - Index actual_j2 = actual_k2 + j2; - Index panelOffset = IsLower ? j2+actualPanelWidth : 0; - Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2; - - if (panelLength>0) - pack_rhs_panel(blockB+j2*actual_kc, - rhs.getSubMapper(actual_k2+panelOffset, actual_j2), - panelLength, actualPanelWidth, - actual_kc, panelOffset); - } - } - - for(Index i2=0; i2<rows; i2+=mc) - { - const Index actual_mc = (std::min)(mc,rows-i2); - - // triangular solver kernel - { - // for each small block of the diagonal (=> vertical panels of rhs) - for (Index j2 = IsLower - ? (actual_kc - ((actual_kc%SmallPanelWidth) ? Index(actual_kc%SmallPanelWidth) - : Index(SmallPanelWidth))) - : 0; - IsLower ? j2>=0 : j2<actual_kc; - IsLower ? j2-=SmallPanelWidth : j2+=SmallPanelWidth) - { - Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth); - Index absolute_j2 = actual_k2 + j2; - Index panelOffset = IsLower ? j2+actualPanelWidth : 0; - Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2; - - // GEBP - if(panelLength>0) - { - gebp_kernel(lhs.getSubMapper(i2,absolute_j2), - blockA, blockB+j2*actual_kc, - actual_mc, panelLength, actualPanelWidth, - Scalar(-1), - actual_kc, actual_kc, // strides - panelOffset, panelOffset); // offsets - } - - // unblocked triangular solve - for (Index k=0; k<actualPanelWidth; ++k) - { - Index j = IsLower ? absolute_j2+actualPanelWidth-k-1 : absolute_j2+k; - - Scalar* r = &lhs(i2,j); - for (Index k3=0; k3<k; ++k3) - { - Scalar b = conj(rhs(IsLower ? j+1+k3 : absolute_j2+k3,j)); - Scalar* a = &lhs(i2,IsLower ? j+1+k3 : absolute_j2+k3); - for (Index i=0; i<actual_mc; ++i) - r[i] -= a[i] * b; - } - Scalar b = (Mode & UnitDiag) ? Scalar(1) : Scalar(1)/conj(rhs(j,j)); - for (Index i=0; i<actual_mc; ++i) - r[i] *= b; - } - - // pack the just computed part of lhs to A - pack_lhs_panel(blockA, LhsMapper(_other+absolute_j2*otherStride+i2, otherStride), - actualPanelWidth, actual_mc, - actual_kc, j2); - } - } - - if (rs>0) - gebp_kernel(lhs.getSubMapper(i2, startPanel), blockA, geb, - actual_mc, actual_kc, rs, Scalar(-1), - -1, -1, 0, 0); - } - } - } - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_H diff --git a/third_party/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_MKL.h b/third_party/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_MKL.h deleted file mode 100644 index 6a0bb83393..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/TriangularSolverMatrix_MKL.h +++ /dev/null @@ -1,155 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Triangular matrix * matrix product functionality based on ?TRMM. - ******************************************************************************** -*/ - -#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H -#define EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H - -namespace Eigen { - -namespace internal { - -// implements LeftSide op(triangular)^-1 * general -#define EIGEN_MKL_TRSM_L(EIGTYPE, MKLTYPE, MKLPREFIX) \ -template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \ -struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor> \ -{ \ - enum { \ - IsLower = (Mode&Lower) == Lower, \ - IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ - IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ - conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \ - }; \ - static void run( \ - Index size, Index otherSize, \ - const EIGTYPE* _tri, Index triStride, \ - EIGTYPE* _other, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \ - { \ - MKL_INT m = size, n = otherSize, lda, ldb; \ - char side = 'L', uplo, diag='N', transa; \ - /* Set alpha_ */ \ - MKLTYPE alpha; \ - EIGTYPE myone(1); \ - assign_scalar_eig2mkl(alpha, myone); \ - ldb = otherStride;\ -\ - const EIGTYPE *a; \ -/* Set trans */ \ - transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \ -/* Set uplo */ \ - uplo = IsLower ? 'L' : 'U'; \ - if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \ -/* Set a, lda */ \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \ - Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \ - MatrixTri a_tmp; \ -\ - if (conjA) { \ - a_tmp = tri.conjugate(); \ - a = a_tmp.data(); \ - lda = a_tmp.outerStride(); \ - } else { \ - a = _tri; \ - lda = triStride; \ - } \ - if (IsUnitDiag) diag='U'; \ -/* call ?trsm*/ \ - MKLPREFIX##trsm(&side, &uplo, &transa, &diag, &m, &n, &alpha, (const MKLTYPE*)a, &lda, (MKLTYPE*)_other, &ldb); \ - } \ -}; - -EIGEN_MKL_TRSM_L(double, double, d) -EIGEN_MKL_TRSM_L(dcomplex, MKL_Complex16, z) -EIGEN_MKL_TRSM_L(float, float, s) -EIGEN_MKL_TRSM_L(scomplex, MKL_Complex8, c) - - -// implements RightSide general * op(triangular)^-1 -#define EIGEN_MKL_TRSM_R(EIGTYPE, MKLTYPE, MKLPREFIX) \ -template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \ -struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor> \ -{ \ - enum { \ - IsLower = (Mode&Lower) == Lower, \ - IsUnitDiag = (Mode&UnitDiag) ? 1 : 0, \ - IsZeroDiag = (Mode&ZeroDiag) ? 1 : 0, \ - conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \ - }; \ - static void run( \ - Index size, Index otherSize, \ - const EIGTYPE* _tri, Index triStride, \ - EIGTYPE* _other, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \ - { \ - MKL_INT m = otherSize, n = size, lda, ldb; \ - char side = 'R', uplo, diag='N', transa; \ - /* Set alpha_ */ \ - MKLTYPE alpha; \ - EIGTYPE myone(1); \ - assign_scalar_eig2mkl(alpha, myone); \ - ldb = otherStride;\ -\ - const EIGTYPE *a; \ -/* Set trans */ \ - transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \ -/* Set uplo */ \ - uplo = IsLower ? 'L' : 'U'; \ - if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \ -/* Set a, lda */ \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \ - Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \ - MatrixTri a_tmp; \ -\ - if (conjA) { \ - a_tmp = tri.conjugate(); \ - a = a_tmp.data(); \ - lda = a_tmp.outerStride(); \ - } else { \ - a = _tri; \ - lda = triStride; \ - } \ - if (IsUnitDiag) diag='U'; \ -/* call ?trsm*/ \ - MKLPREFIX##trsm(&side, &uplo, &transa, &diag, &m, &n, &alpha, (const MKLTYPE*)a, &lda, (MKLTYPE*)_other, &ldb); \ - /*std::cout << "TRMS_L specialization!\n";*/ \ - } \ -}; - -EIGEN_MKL_TRSM_R(double, double, d) -EIGEN_MKL_TRSM_R(dcomplex, MKL_Complex16, z) -EIGEN_MKL_TRSM_R(float, float, s) -EIGEN_MKL_TRSM_R(scomplex, MKL_Complex8, c) - - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_MKL_H diff --git a/third_party/eigen3/Eigen/src/Core/products/TriangularSolverVector.h b/third_party/eigen3/Eigen/src/Core/products/TriangularSolverVector.h deleted file mode 100644 index b994759b26..0000000000 --- a/third_party/eigen3/Eigen/src/Core/products/TriangularSolverVector.h +++ /dev/null @@ -1,145 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRIANGULAR_SOLVER_VECTOR_H -#define EIGEN_TRIANGULAR_SOLVER_VECTOR_H - -namespace Eigen { - -namespace internal { - -template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate, int StorageOrder> -struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheRight, Mode, Conjugate, StorageOrder> -{ - static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) - { - triangular_solve_vector<LhsScalar,RhsScalar,Index,OnTheLeft, - ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag), - Conjugate,StorageOrder==RowMajor?ColMajor:RowMajor - >::run(size, _lhs, lhsStride, rhs); - } -}; - -// forward and backward substitution, row-major, rhs is a vector -template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate> -struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, RowMajor> -{ - enum { - IsLower = ((Mode&Lower)==Lower) - }; - static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) - { - typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap; - const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride)); - - typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper; - typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper; - - typename internal::conditional< - Conjugate, - const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>,LhsMap>, - const LhsMap&> - ::type cjLhs(lhs); - static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH; - for(Index pi=IsLower ? 0 : size; - IsLower ? pi<size : pi>0; - IsLower ? pi+=PanelWidth : pi-=PanelWidth) - { - Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth); - - Index r = IsLower ? pi : size - pi; // remaining size - if (r > 0) - { - // let's directly call the low level product function because: - // 1 - it is faster to compile - // 2 - it is slighlty faster at runtime - Index startRow = IsLower ? pi : pi-actualPanelWidth; - Index startCol = IsLower ? 0 : pi; - - general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,Conjugate,RhsScalar,RhsMapper,false>::run( - actualPanelWidth, r, - LhsMapper(&lhs.coeffRef(startRow,startCol), lhsStride), - RhsMapper(rhs + startCol, 1), - rhs + startRow, 1, - RhsScalar(-1)); - } - - for(Index k=0; k<actualPanelWidth; ++k) - { - Index i = IsLower ? pi+k : pi-k-1; - Index s = IsLower ? pi : i+1; - if (k>0) - rhs[i] -= (cjLhs.row(i).segment(s,k).transpose().cwiseProduct(Map<const Matrix<RhsScalar,Dynamic,1> >(rhs+s,k))).sum(); - - if(!(Mode & UnitDiag)) - rhs[i] /= cjLhs(i,i); - } - } - } -}; - -// forward and backward substitution, column-major, rhs is a vector -template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate> -struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, ColMajor> -{ - enum { - IsLower = ((Mode&Lower)==Lower) - }; - static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs) - { - typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap; - const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride)); - typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper; - typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper; - typename internal::conditional<Conjugate, - const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>,LhsMap>, - const LhsMap& - >::type cjLhs(lhs); - static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH; - - for(Index pi=IsLower ? 0 : size; - IsLower ? pi<size : pi>0; - IsLower ? pi+=PanelWidth : pi-=PanelWidth) - { - Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth); - Index startBlock = IsLower ? pi : pi-actualPanelWidth; - Index endBlock = IsLower ? pi + actualPanelWidth : 0; - - for(Index k=0; k<actualPanelWidth; ++k) - { - Index i = IsLower ? pi+k : pi-k-1; - if(!(Mode & UnitDiag)) - rhs[i] /= cjLhs.coeff(i,i); - - Index r = actualPanelWidth - k - 1; // remaining size - Index s = IsLower ? i+1 : i-r; - if (r>0) - Map<Matrix<RhsScalar,Dynamic,1> >(rhs+s,r) -= rhs[i] * cjLhs.col(i).segment(s,r); - } - Index r = IsLower ? size - endBlock : startBlock; // remaining size - if (r > 0) - { - // let's directly call the low level product function because: - // 1 - it is faster to compile - // 2 - it is slighlty faster at runtime - general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,Conjugate,RhsScalar,RhsMapper,false>::run( - r, actualPanelWidth, - LhsMapper(&lhs.coeffRef(endBlock,startBlock), lhsStride), - RhsMapper(rhs+startBlock, 1), - rhs+endBlock, 1, RhsScalar(-1)); - } - } - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULAR_SOLVER_VECTOR_H diff --git a/third_party/eigen3/Eigen/src/Core/util/BlasUtil.h b/third_party/eigen3/Eigen/src/Core/util/BlasUtil.h deleted file mode 100644 index bbaff8dd0e..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/BlasUtil.h +++ /dev/null @@ -1,237 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BLASUTIL_H -#define EIGEN_BLASUTIL_H - -// This file contains many lightweight helper classes used to -// implement and control fast level 2 and level 3 BLAS-like routines. - -namespace Eigen { - -namespace internal { - -// forward declarations -template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs=false, bool ConjugateRhs=false> -struct gebp_kernel; - -template<typename Scalar, typename Index, typename DataMapper, int nr, int StorageOrder, bool Conjugate = false, bool PanelMode=false> -struct gemm_pack_rhs; - -template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, int StorageOrder, bool Conjugate = false, bool PanelMode = false> -struct gemm_pack_lhs; - -template< - typename Index, - typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs, - typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs, - int ResStorageOrder> -struct general_matrix_matrix_product; - -template<typename Index, typename LhsScalar, typename LhsMapper, int LhsStorageOrder, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version=Specialized> -struct general_matrix_vector_product; - - -template<bool Conjugate> struct conj_if; - -template<> struct conj_if<true> { - template<typename T> - inline T operator()(const T& x) { return numext::conj(x); } - template<typename T> - inline T pconj(const T& x) { return internal::pconj(x); } -}; - -template<> struct conj_if<false> { - template<typename T> - inline const T& operator()(const T& x) { return x; } - template<typename T> - inline const T& pconj(const T& x) { return x; } -}; - -template<typename Scalar> struct conj_helper<Scalar,Scalar,false,false> -{ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const Scalar& y, const Scalar& c) const { return internal::pmadd(x,y,c); } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const { return internal::pmul(x,y); } -}; - -template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::complex<RealScalar>, false,true> -{ - typedef std::complex<RealScalar> Scalar; - EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const Scalar& y, const Scalar& c) const - { return c + pmul(x,y); } - - EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const - { return Scalar(numext::real(x)*numext::real(y) + numext::imag(x)*numext::imag(y), numext::imag(x)*numext::real(y) - numext::real(x)*numext::imag(y)); } -}; - -template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::complex<RealScalar>, true,false> -{ - typedef std::complex<RealScalar> Scalar; - EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const Scalar& y, const Scalar& c) const - { return c + pmul(x,y); } - - EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const - { return Scalar(numext::real(x)*numext::real(y) + numext::imag(x)*numext::imag(y), numext::real(x)*numext::imag(y) - numext::imag(x)*numext::real(y)); } -}; - -template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::complex<RealScalar>, true,true> -{ - typedef std::complex<RealScalar> Scalar; - EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const Scalar& y, const Scalar& c) const - { return c + pmul(x,y); } - - EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const - { return Scalar(numext::real(x)*numext::real(y) - numext::imag(x)*numext::imag(y), - numext::real(x)*numext::imag(y) - numext::imag(x)*numext::real(y)); } -}; - -template<typename RealScalar,bool Conj> struct conj_helper<std::complex<RealScalar>, RealScalar, Conj,false> -{ - typedef std::complex<RealScalar> Scalar; - EIGEN_STRONG_INLINE Scalar pmadd(const Scalar& x, const RealScalar& y, const Scalar& c) const - { return padd(c, pmul(x,y)); } - EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const RealScalar& y) const - { return conj_if<Conj>()(x)*y; } -}; - -template<typename RealScalar,bool Conj> struct conj_helper<RealScalar, std::complex<RealScalar>, false,Conj> -{ - typedef std::complex<RealScalar> Scalar; - EIGEN_STRONG_INLINE Scalar pmadd(const RealScalar& x, const Scalar& y, const Scalar& c) const - { return padd(c, pmul(x,y)); } - EIGEN_STRONG_INLINE Scalar pmul(const RealScalar& x, const Scalar& y) const - { return x*conj_if<Conj>()(y); } -}; - -template<typename From,typename To> struct get_factor { - EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE To run(const From& x) { return x; } -}; - -template<typename Scalar> struct get_factor<Scalar,typename NumTraits<Scalar>::Real> { - EIGEN_DEVICE_FUNC - static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) { return numext::real(x); } -}; - - -/* Helper class to analyze the factors of a Product expression. - * In particular it allows to pop out operator-, scalar multiples, - * and conjugate */ -template<typename XprType> struct blas_traits -{ - typedef typename traits<XprType>::Scalar Scalar; - typedef const XprType& ExtractType; - typedef XprType _ExtractType; - enum { - IsComplex = NumTraits<Scalar>::IsComplex, - IsTransposed = false, - NeedToConjugate = false, - HasUsableDirectAccess = ( (int(XprType::Flags)&DirectAccessBit) - && ( bool(XprType::IsVectorAtCompileTime) - || int(inner_stride_at_compile_time<XprType>::ret) == 1) - ) ? 1 : 0 - }; - typedef typename conditional<bool(HasUsableDirectAccess), - ExtractType, - typename _ExtractType::PlainObject - >::type DirectLinearAccessType; - static inline ExtractType extract(const XprType& x) { return x; } - static inline const Scalar extractScalarFactor(const XprType&) { return Scalar(1); } -}; - -// pop conjugate -template<typename Scalar, typename NestedXpr> -struct blas_traits<CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> > - : blas_traits<NestedXpr> -{ - typedef blas_traits<NestedXpr> Base; - typedef CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> XprType; - typedef typename Base::ExtractType ExtractType; - - enum { - IsComplex = NumTraits<Scalar>::IsComplex, - NeedToConjugate = Base::NeedToConjugate ? 0 : IsComplex - }; - static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); } - static inline Scalar extractScalarFactor(const XprType& x) { return conj(Base::extractScalarFactor(x.nestedExpression())); } -}; - -// pop scalar multiple -template<typename Scalar, typename NestedXpr> -struct blas_traits<CwiseUnaryOp<scalar_multiple_op<Scalar>, NestedXpr> > - : blas_traits<NestedXpr> -{ - typedef blas_traits<NestedXpr> Base; - typedef CwiseUnaryOp<scalar_multiple_op<Scalar>, NestedXpr> XprType; - typedef typename Base::ExtractType ExtractType; - static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); } - static inline Scalar extractScalarFactor(const XprType& x) - { return x.functor().m_other * Base::extractScalarFactor(x.nestedExpression()); } -}; - -// pop opposite -template<typename Scalar, typename NestedXpr> -struct blas_traits<CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> > - : blas_traits<NestedXpr> -{ - typedef blas_traits<NestedXpr> Base; - typedef CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> XprType; - typedef typename Base::ExtractType ExtractType; - static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); } - static inline Scalar extractScalarFactor(const XprType& x) - { return - Base::extractScalarFactor(x.nestedExpression()); } -}; - -// pop/push transpose -template<typename NestedXpr> -struct blas_traits<Transpose<NestedXpr> > - : blas_traits<NestedXpr> -{ - typedef typename NestedXpr::Scalar Scalar; - typedef blas_traits<NestedXpr> Base; - typedef Transpose<NestedXpr> XprType; - typedef Transpose<const typename Base::_ExtractType> ExtractType; // const to get rid of a compile error; anyway blas traits are only used on the RHS - typedef Transpose<const typename Base::_ExtractType> _ExtractType; - typedef typename conditional<bool(Base::HasUsableDirectAccess), - ExtractType, - typename ExtractType::PlainObject - >::type DirectLinearAccessType; - enum { - IsTransposed = Base::IsTransposed ? 0 : 1 - }; - static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); } - static inline Scalar extractScalarFactor(const XprType& x) { return Base::extractScalarFactor(x.nestedExpression()); } -}; - -template<typename T> -struct blas_traits<const T> - : blas_traits<T> -{}; - -template<typename T, bool HasUsableDirectAccess=blas_traits<T>::HasUsableDirectAccess> -struct extract_data_selector { - static const typename T::Scalar* run(const T& m) - { - return blas_traits<T>::extract(m).data(); - } -}; - -template<typename T> -struct extract_data_selector<T,false> { - static typename T::Scalar* run(const T&) { return 0; } -}; - -template<typename T> const typename T::Scalar* extract_data(const T& m) -{ - return extract_data_selector<T>::run(m); -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_BLASUTIL_H diff --git a/third_party/eigen3/Eigen/src/Core/util/Constants.h b/third_party/eigen3/Eigen/src/Core/util/Constants.h deleted file mode 100644 index 75b91cdceb..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/Constants.h +++ /dev/null @@ -1,469 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CONSTANTS_H -#define EIGEN_CONSTANTS_H - -namespace Eigen { - -/** This value means that a positive quantity (e.g., a size) is not known at compile-time, and that instead the value is - * stored in some runtime variable. - * - * Changing the value of Dynamic breaks the ABI, as Dynamic is often used as a template parameter for Matrix. - */ -const int Dynamic = -1; - -/** This value means that a signed quantity (e.g., a signed index) is not known at compile-time, and that instead its value - * has to be specified at runtime. - */ -const int DynamicIndex = 0xffffff; - -/** This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>(). - * The value Infinity there means the L-infinity norm. - */ -const int Infinity = -1; - -/** \defgroup flags Flags - * \ingroup Core_Module - * - * These are the possible bits which can be OR'ed to constitute the flags of a matrix or - * expression. - * - * It is important to note that these flags are a purely compile-time notion. They are a compile-time property of - * an expression type, implemented as enum's. They are not stored in memory at runtime, and they do not incur any - * runtime overhead. - * - * \sa MatrixBase::Flags - */ - -/** \ingroup flags - * - * for a matrix, this means that the storage order is row-major. - * If this bit is not set, the storage order is column-major. - * For an expression, this determines the storage order of - * the matrix created by evaluation of that expression. - * \sa \ref TopicStorageOrders */ -const unsigned int RowMajorBit = 0x1; - -/** \ingroup flags - * - * means the expression should be evaluated by the calling expression */ -const unsigned int EvalBeforeNestingBit = 0x2; - -/** \ingroup flags - * - * means the expression should be evaluated before any assignment */ -const unsigned int EvalBeforeAssigningBit = 0x4; - -/** \ingroup flags - * - * Short version: means the expression might be vectorized - * - * Long version: means that the coefficients can be handled by packets - * and start at a memory location whose alignment meets the requirements - * of the present CPU architecture for optimized packet access. In the fixed-size - * case, there is the additional condition that it be possible to access all the - * coefficients by packets (this implies the requirement that the size be a multiple of 16 bytes, - * and that any nontrivial strides don't break the alignment). In the dynamic-size case, - * there is no such condition on the total size and strides, so it might not be possible to access - * all coeffs by packets. - * - * \note This bit can be set regardless of whether vectorization is actually enabled. - * To check for actual vectorizability, see \a ActualPacketAccessBit. - */ -const unsigned int PacketAccessBit = 0x8; - -#ifdef EIGEN_VECTORIZE -/** \ingroup flags - * - * If vectorization is enabled (EIGEN_VECTORIZE is defined) this constant - * is set to the value \a PacketAccessBit. - * - * If vectorization is not enabled (EIGEN_VECTORIZE is not defined) this constant - * is set to the value 0. - */ -const unsigned int ActualPacketAccessBit = PacketAccessBit; -#else -const unsigned int ActualPacketAccessBit = 0x0; -#endif - -/** \ingroup flags - * - * Short version: means the expression can be seen as 1D vector. - * - * Long version: means that one can access the coefficients - * of this expression by coeff(int), and coeffRef(int) in the case of a lvalue expression. These - * index-based access methods are guaranteed - * to not have to do any runtime computation of a (row, col)-pair from the index, so that it - * is guaranteed that whenever it is available, index-based access is at least as fast as - * (row,col)-based access. Expressions for which that isn't possible don't have the LinearAccessBit. - * - * If both PacketAccessBit and LinearAccessBit are set, then the - * packets of this expression can be accessed by packet(int), and writePacket(int) in the case of a - * lvalue expression. - * - * Typically, all vector expressions have the LinearAccessBit, but there is one exception: - * Product expressions don't have it, because it would be troublesome for vectorization, even when the - * Product is a vector expression. Thus, vector Product expressions allow index-based coefficient access but - * not index-based packet access, so they don't have the LinearAccessBit. - */ -const unsigned int LinearAccessBit = 0x10; - -/** \ingroup flags - * - * Means the expression has a coeffRef() method, i.e. is writable as its individual coefficients are directly addressable. - * This rules out read-only expressions. - * - * Note that DirectAccessBit and LvalueBit are mutually orthogonal, as there are examples of expression having one but note - * the other: - * \li writable expressions that don't have a very simple memory layout as a strided array, have LvalueBit but not DirectAccessBit - * \li Map-to-const expressions, for example Map<const Matrix>, have DirectAccessBit but not LvalueBit - * - * Expressions having LvalueBit also have their coeff() method returning a const reference instead of returning a new value. - */ -const unsigned int LvalueBit = 0x20; - -/** \ingroup flags - * - * Means that the underlying array of coefficients can be directly accessed as a plain strided array. The memory layout - * of the array of coefficients must be exactly the natural one suggested by rows(), cols(), - * outerStride(), innerStride(), and the RowMajorBit. This rules out expressions such as Diagonal, whose coefficients, - * though referencable, do not have such a regular memory layout. - * - * See the comment on LvalueBit for an explanation of how LvalueBit and DirectAccessBit are mutually orthogonal. - */ -const unsigned int DirectAccessBit = 0x40; - -/** \ingroup flags - * - * means the first coefficient packet is guaranteed to be aligned. - * An expression cannot has the AlignedBit without the PacketAccessBit flag. - * In other words, this means we are allow to perform an aligned packet access to the first element regardless - * of the expression kind: - * \code - * expression.packet<Aligned>(0); - * \endcode - */ -const unsigned int AlignedBit = 0x80; - -const unsigned int NestByRefBit = 0x100; - -// list of flags that are inherited by default -const unsigned int HereditaryBits = RowMajorBit - | EvalBeforeNestingBit - | EvalBeforeAssigningBit; - -/** \defgroup enums Enumerations - * \ingroup Core_Module - * - * Various enumerations used in %Eigen. Many of these are used as template parameters. - */ - -/** \ingroup enums - * Enum containing possible values for the \p Mode parameter of - * MatrixBase::selfadjointView() and MatrixBase::triangularView(). */ -enum { - /** View matrix as a lower triangular matrix. */ - Lower=0x1, - /** View matrix as an upper triangular matrix. */ - Upper=0x2, - /** %Matrix has ones on the diagonal; to be used in combination with #Lower or #Upper. */ - UnitDiag=0x4, - /** %Matrix has zeros on the diagonal; to be used in combination with #Lower or #Upper. */ - ZeroDiag=0x8, - /** View matrix as a lower triangular matrix with ones on the diagonal. */ - UnitLower=UnitDiag|Lower, - /** View matrix as an upper triangular matrix with ones on the diagonal. */ - UnitUpper=UnitDiag|Upper, - /** View matrix as a lower triangular matrix with zeros on the diagonal. */ - StrictlyLower=ZeroDiag|Lower, - /** View matrix as an upper triangular matrix with zeros on the diagonal. */ - StrictlyUpper=ZeroDiag|Upper, - /** Used in BandMatrix and SelfAdjointView to indicate that the matrix is self-adjoint. */ - SelfAdjoint=0x10, - /** Used to support symmetric, non-selfadjoint, complex matrices. */ - Symmetric=0x20 -}; - -/** \ingroup enums - * Enum for indicating whether an object is aligned or not. */ -enum { - /** Object is not correctly aligned for vectorization. */ - Unaligned=0, - /** Object is aligned for vectorization. */ - Aligned=1 -}; - -/** \ingroup enums - * Enum used by DenseBase::corner() in Eigen2 compatibility mode. */ -// FIXME after the corner() API change, this was not needed anymore, except by AlignedBox -// TODO: find out what to do with that. Adapt the AlignedBox API ? -enum CornerType { TopLeft, TopRight, BottomLeft, BottomRight }; - -/** \ingroup enums - * Enum containing possible values for the \p Direction parameter of - * Reverse, PartialReduxExpr and VectorwiseOp. */ -enum DirectionType { - /** For Reverse, all columns are reversed; - * for PartialReduxExpr and VectorwiseOp, act on columns. */ - Vertical, - /** For Reverse, all rows are reversed; - * for PartialReduxExpr and VectorwiseOp, act on rows. */ - Horizontal, - /** For Reverse, both rows and columns are reversed; - * not used for PartialReduxExpr and VectorwiseOp. */ - BothDirections -}; - -/** \internal \ingroup enums - * Enum to specify how to traverse the entries of a matrix. */ -enum { - /** \internal Default traversal, no vectorization, no index-based access */ - DefaultTraversal, - /** \internal No vectorization, use index-based access to have only one for loop instead of 2 nested loops */ - LinearTraversal, - /** \internal Equivalent to a slice vectorization for fixed-size matrices having good alignment - * and good size */ - InnerVectorizedTraversal, - /** \internal Vectorization path using a single loop plus scalar loops for the - * unaligned boundaries */ - LinearVectorizedTraversal, - /** \internal Generic vectorization path using one vectorized loop per row/column with some - * scalar loops to handle the unaligned boundaries */ - SliceVectorizedTraversal, - /** \internal Special case to properly handle incompatible scalar types or other defecting cases*/ - InvalidTraversal, - /** \internal Evaluate all entries at once */ - AllAtOnceTraversal -}; - -/** \internal \ingroup enums - * Enum to specify whether to unroll loops when traversing over the entries of a matrix. */ -enum { - /** \internal Do not unroll loops. */ - NoUnrolling, - /** \internal Unroll only the inner loop, but not the outer loop. */ - InnerUnrolling, - /** \internal Unroll both the inner and the outer loop. If there is only one loop, - * because linear traversal is used, then unroll that loop. */ - CompleteUnrolling -}; - -/** \internal \ingroup enums - * Enum to specify whether to use the default (built-in) implementation or the specialization. */ -enum { - Specialized, - BuiltIn -}; - -/** \ingroup enums - * Enum containing possible values for the \p _Options template parameter of - * Matrix, Array and BandMatrix. */ -enum { - /** Storage order is column major (see \ref TopicStorageOrders). */ - ColMajor = 0, - /** Storage order is row major (see \ref TopicStorageOrders). */ - RowMajor = 0x1, // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that - /** Align the matrix itself if it is vectorizable fixed-size */ - AutoAlign = 0, - /** Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation - DontAlign = 0x2, - AllocateDefault = 0, - AllocateUVM = 0x8 -}; - -/** \ingroup enums - * Enum for specifying whether to apply or solve on the left or right. */ -enum { - /** Apply transformation on the left. */ - OnTheLeft = 1, - /** Apply transformation on the right. */ - OnTheRight = 2 -}; - -/* the following used to be written as: - * - * struct NoChange_t {}; - * namespace { - * EIGEN_UNUSED NoChange_t NoChange; - * } - * - * on the ground that it feels dangerous to disambiguate overloaded functions on enum/integer types. - * However, this leads to "variable declared but never referenced" warnings on Intel Composer XE, - * and we do not know how to get rid of them (bug 450). - */ - -enum NoChange_t { NoChange }; -enum Sequential_t { Sequential }; -enum Default_t { Default }; - -/** \internal \ingroup enums - * Used in AmbiVector. */ -enum { - IsDense = 0, - IsSparse -}; - -/** \ingroup enums - * Used as template parameter in DenseCoeffBase and MapBase to indicate - * which accessors should be provided. */ -enum AccessorLevels { - /** Read-only access via a member function. */ - ReadOnlyAccessors, - /** Read/write access via member functions. */ - WriteAccessors, - /** Direct read-only access to the coefficients. */ - DirectAccessors, - /** Direct read/write access to the coefficients. */ - DirectWriteAccessors -}; - -/** \ingroup enums - * Enum with options to give to various decompositions. */ -enum DecompositionOptions { - /** \internal Not used (meant for LDLT?). */ - Pivoting = 0x01, - /** \internal Not used (meant for LDLT?). */ - NoPivoting = 0x02, - /** Used in JacobiSVD to indicate that the square matrix U is to be computed. */ - ComputeFullU = 0x04, - /** Used in JacobiSVD to indicate that the thin matrix U is to be computed. */ - ComputeThinU = 0x08, - /** Used in JacobiSVD to indicate that the square matrix V is to be computed. */ - ComputeFullV = 0x10, - /** Used in JacobiSVD to indicate that the thin matrix V is to be computed. */ - ComputeThinV = 0x20, - /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify - * that only the eigenvalues are to be computed and not the eigenvectors. */ - EigenvaluesOnly = 0x40, - /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify - * that both the eigenvalues and the eigenvectors are to be computed. */ - ComputeEigenvectors = 0x80, - /** \internal */ - EigVecMask = EigenvaluesOnly | ComputeEigenvectors, - /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should - * solve the generalized eigenproblem \f$ Ax = \lambda B x \f$. */ - Ax_lBx = 0x100, - /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should - * solve the generalized eigenproblem \f$ ABx = \lambda x \f$. */ - ABx_lx = 0x200, - /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should - * solve the generalized eigenproblem \f$ BAx = \lambda x \f$. */ - BAx_lx = 0x400, - /** \internal */ - GenEigMask = Ax_lBx | ABx_lx | BAx_lx -}; - -/** \ingroup enums - * Possible values for the \p QRPreconditioner template parameter of JacobiSVD. */ -enum QRPreconditioners { - /** Do not specify what is to be done if the SVD of a non-square matrix is asked for. */ - NoQRPreconditioner, - /** Use a QR decomposition without pivoting as the first step. */ - HouseholderQRPreconditioner, - /** Use a QR decomposition with column pivoting as the first step. */ - ColPivHouseholderQRPreconditioner, - /** Use a QR decomposition with full pivoting as the first step. */ - FullPivHouseholderQRPreconditioner -}; - -#ifdef Success -#error The preprocessor symbol 'Success' is defined, possibly by the X11 header file X.h -#endif - -/** \ingroup enums - * Enum for reporting the status of a computation. */ -enum ComputationInfo { - /** Computation was successful. */ - Success = 0, - /** The provided data did not satisfy the prerequisites. */ - NumericalIssue = 1, - /** Iterative procedure did not converge. */ - NoConvergence = 2, - /** The inputs are invalid, or the algorithm has been improperly called. - * When assertions are enabled, such errors trigger an assert. */ - InvalidInput = 3 -}; - -/** \ingroup enums - * Enum used to specify how a particular transformation is stored in a matrix. - * \sa Transform, Hyperplane::transform(). */ -enum TransformTraits { - /** Transformation is an isometry. */ - Isometry = 0x1, - /** Transformation is an affine transformation stored as a (Dim+1)^2 matrix whose last row is - * assumed to be [0 ... 0 1]. */ - Affine = 0x2, - /** Transformation is an affine transformation stored as a (Dim) x (Dim+1) matrix. */ - AffineCompact = 0x10 | Affine, - /** Transformation is a general projective transformation stored as a (Dim+1)^2 matrix. */ - Projective = 0x20 -}; - -/** \internal \ingroup enums - * Enum used to choose between implementation depending on the computer architecture. */ -namespace Architecture -{ - enum Type { - Generic = 0x0, - SSE = 0x1, - AltiVec = 0x2, - VSX = 0x3, - NEON = 0x4, -#if defined EIGEN_VECTORIZE_SSE - Target = SSE -#elif defined EIGEN_VECTORIZE_ALTIVEC - Target = AltiVec -#elif defined EIGEN_VECTORIZE_VSX - Target = VSX -#elif defined EIGEN_VECTORIZE_NEON - Target = NEON -#else - Target = Generic -#endif - }; -} - -/** \internal \ingroup enums - * Enum used as template parameter in GeneralProduct. */ -enum { CoeffBasedProductMode, LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct, GemmProduct }; - -/** \internal \ingroup enums - * Enum used in experimental parallel implementation. */ -enum Action {GetAction, SetAction}; - -/** The type used to identify a dense storage. */ -struct Dense {}; - -/** The type used to identify a matrix expression */ -struct MatrixXpr {}; - -/** The type used to identify an array expression */ -struct ArrayXpr {}; - -namespace internal { - -/** \internal - * Constants for comparison functors - */ -enum ComparisonName { - cmp_EQ = 0, - cmp_LT = 1, - cmp_LE = 2, - cmp_UNORD = 3, - cmp_NEQ = 4, - cmp_GT = 5, - cmp_GE = 6 -}; -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_CONSTANTS_H diff --git a/third_party/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h b/third_party/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h deleted file mode 100644 index 6a0bf0629c..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/DisableStupidWarnings.h +++ /dev/null @@ -1,40 +0,0 @@ -#ifndef EIGEN_WARNINGS_DISABLED -#define EIGEN_WARNINGS_DISABLED - -#ifdef _MSC_VER - // 4100 - unreferenced formal parameter (occurred e.g. in aligned_allocator::destroy(pointer p)) - // 4101 - unreferenced local variable - // 4127 - conditional expression is constant - // 4181 - qualifier applied to reference type ignored - // 4211 - nonstandard extension used : redefined extern to static - // 4244 - 'argument' : conversion from 'type1' to 'type2', possible loss of data - // 4273 - QtAlignedMalloc, inconsistent DLL linkage - // 4324 - structure was padded due to declspec(align()) - // 4512 - assignment operator could not be generated - // 4522 - 'class' : multiple assignment operators specified - // 4700 - uninitialized local variable 'xyz' used - // 4717 - 'function' : recursive on all control paths, function will cause runtime stack overflow - #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS - #pragma warning( push ) - #endif - #pragma warning( disable : 4100 4101 4127 4181 4211 4244 4273 4324 4512 4522 4700 4717 ) -#elif defined __INTEL_COMPILER - // 2196 - routine is both "inline" and "noinline" ("noinline" assumed) - // ICC 12 generates this warning even without any inline keyword, when defining class methods 'inline' i.e. inside of class body - // typedef that may be a reference type. - // 279 - controlling expression is constant - // ICC 12 generates this warning on assert(constant_expression_depending_on_template_params) and frankly this is a legitimate use case. - #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS - #pragma warning push - #endif - #pragma warning disable 2196 279 -#elif defined __clang__ - // -Wconstant-logical-operand - warning: use of logical && with constant operand; switch to bitwise & or remove constant - // this is really a stupid warning as it warns on compile-time expressions involving enums - #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS - #pragma clang diagnostic push - #endif - #pragma clang diagnostic ignored "-Wconstant-logical-operand" -#endif - -#endif // not EIGEN_WARNINGS_DISABLED diff --git a/third_party/eigen3/Eigen/src/Core/util/ForwardDeclarations.h b/third_party/eigen3/Eigen/src/Core/util/ForwardDeclarations.h deleted file mode 100644 index f8cd6e47ee..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/ForwardDeclarations.h +++ /dev/null @@ -1,308 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_FORWARDDECLARATIONS_H -#define EIGEN_FORWARDDECLARATIONS_H - -namespace Eigen { -namespace internal { - -template<typename T> struct traits; - -// here we say once and for all that traits<const T> == traits<T> -// When constness must affect traits, it has to be constness on template parameters on which T itself depends. -// For example, traits<Map<const T> > != traits<Map<T> >, but -// traits<const Map<T> > == traits<Map<T> > -template<typename T> struct traits<const T> : traits<T> {}; - -template<typename Derived> struct has_direct_access -{ - enum { ret = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0 }; -}; - -template<typename Derived> struct accessors_level -{ - enum { has_direct_access = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0, - has_write_access = (traits<Derived>::Flags & LvalueBit) ? 1 : 0, - value = has_direct_access ? (has_write_access ? DirectWriteAccessors : DirectAccessors) - : (has_write_access ? WriteAccessors : ReadOnlyAccessors) - }; -}; - -} // end namespace internal - -template<typename T> struct NumTraits; - -template<typename Derived> struct EigenBase; -template<typename Derived> class DenseBase; -template<typename Derived> class PlainObjectBase; - - -template<typename Derived, - int Level = internal::accessors_level<Derived>::value > -class DenseCoeffsBase; - -template<typename _Scalar, int _Rows, int _Cols, - int _Options = AutoAlign | -#if EIGEN_GNUC_AT(3,4) - // workaround a bug in at least gcc 3.4.6 - // the innermost ?: ternary operator is misparsed. We write it slightly - // differently and this makes gcc 3.4.6 happy, but it's ugly. - // The error would only show up with EIGEN_DEFAULT_TO_ROW_MAJOR is defined - // (when EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION is RowMajor) - ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor - : !(_Cols==1 && _Rows!=1) ? EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION - : Eigen::ColMajor ), -#else - ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor - : (_Cols==1 && _Rows!=1) ? Eigen::ColMajor - : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ), -#endif - int _MaxRows = _Rows, - int _MaxCols = _Cols -> class Matrix; - -template<typename Derived> class MatrixBase; -template<typename Derived> class ArrayBase; - -template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged; -template<typename ExpressionType, template <typename> class StorageBase > class NoAlias; -template<typename ExpressionType> class NestByValue; -template<typename ExpressionType> class ForceAlignedAccess; -template<typename ExpressionType> class SwapWrapper; - -template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false> class Block; - -template<typename MatrixType, int Size=Dynamic> class VectorBlock; -template<typename MatrixType> class Transpose; -template<typename MatrixType> class Conjugate; -template<typename NullaryOp, typename MatrixType> class CwiseNullaryOp; -template<typename UnaryOp, typename MatrixType> class CwiseUnaryOp; -template<typename ViewOp, typename MatrixType> class CwiseUnaryView; -template<typename BinaryOp, typename Lhs, typename Rhs> class CwiseBinaryOp; -template<typename BinOp, typename Lhs, typename Rhs> class SelfCwiseBinaryOp; -template<typename Derived, typename Lhs, typename Rhs> class ProductBase; -template<typename Lhs, typename Rhs> class Product; -template<typename Lhs, typename Rhs, int Mode> class GeneralProduct; -template<typename Lhs, typename Rhs, int NestingFlags> class CoeffBasedProduct; - -template<typename Derived> class DiagonalBase; -template<typename _DiagonalVectorType> class DiagonalWrapper; -template<typename _Scalar, int SizeAtCompileTime, int MaxSizeAtCompileTime=SizeAtCompileTime> class DiagonalMatrix; -template<typename MatrixType, typename DiagonalType, int ProductOrder> class DiagonalProduct; -template<typename MatrixType, int Index = 0> class Diagonal; -template<int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType=int> class PermutationMatrix; -template<int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType=int> class Transpositions; -template<typename Derived> class PermutationBase; -template<typename Derived> class TranspositionsBase; -template<typename _IndicesType> class PermutationWrapper; -template<typename _IndicesType> class TranspositionsWrapper; - -template<typename Derived, - int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors -> class MapBase; -template<int InnerStrideAtCompileTime, int OuterStrideAtCompileTime> class Stride; -template<typename MatrixType, int MapOptions=Unaligned, typename StrideType = Stride<0,0> > class Map; - -template<typename Derived> class TriangularBase; -template<typename MatrixType, unsigned int Mode> class TriangularView; -template<typename MatrixType, unsigned int Mode> class SelfAdjointView; -template<typename MatrixType> class SparseView; -template<typename ExpressionType> class WithFormat; -template<typename MatrixType> struct CommaInitializer; -template<typename Derived> class ReturnByValue; -template<typename ExpressionType> class ArrayWrapper; -template<typename ExpressionType> class MatrixWrapper; - -namespace internal { -template<typename DecompositionType, typename Rhs> struct solve_retval_base; -template<typename DecompositionType, typename Rhs> struct solve_retval; -template<typename DecompositionType> struct kernel_retval_base; -template<typename DecompositionType> struct kernel_retval; -template<typename DecompositionType> struct image_retval_base; -template<typename DecompositionType> struct image_retval; -} // end namespace internal - -namespace internal { -template<typename _Scalar, int Rows=Dynamic, int Cols=Dynamic, int Supers=Dynamic, int Subs=Dynamic, int Options=0> class BandMatrix; -} - -namespace internal { -template<typename Lhs, typename Rhs> struct product_type; -} - -template<typename Lhs, typename Rhs, - int ProductType = internal::product_type<Lhs,Rhs>::value> -struct ProductReturnType; - -// this is a workaround for sun CC -template<typename Lhs, typename Rhs> struct LazyProductReturnType; - -namespace internal { - -// Provides scalar/packet-wise product and product with accumulation -// with optional conjugation of the arguments. -template<typename LhsScalar, typename RhsScalar, bool ConjLhs=false, bool ConjRhs=false> struct conj_helper; - -template<typename Scalar> struct scalar_sum_op; -template<typename Scalar> struct scalar_difference_op; -template<typename LhsScalar,typename RhsScalar> struct scalar_conj_product_op; -template<typename Scalar> struct scalar_opposite_op; -template<typename Scalar> struct scalar_conjugate_op; -template<typename Scalar> struct scalar_real_op; -template<typename Scalar> struct scalar_imag_op; -template<typename Scalar> struct scalar_abs_op; -template<typename Scalar> struct scalar_abs2_op; -template<typename Scalar> struct scalar_sqrt_op; -template<typename Scalar> struct scalar_rsqrt_op; -template<typename Scalar> struct scalar_exp_op; -template<typename Scalar> struct scalar_log_op; -template<typename Scalar> struct scalar_cos_op; -template<typename Scalar> struct scalar_sin_op; -template<typename Scalar> struct scalar_acos_op; -template<typename Scalar> struct scalar_asin_op; -template<typename Scalar> struct scalar_tan_op; -template<typename Scalar> struct scalar_pow_op; -template<typename Scalar> struct scalar_inverse_op; -template<typename Scalar> struct scalar_square_op; -template<typename Scalar> struct scalar_cube_op; -template<typename Scalar, typename NewType> struct scalar_cast_op; -template<typename Scalar> struct scalar_multiple_op; -template<typename Scalar> struct scalar_quotient1_op; -template<typename Scalar> struct scalar_min_op; -template<typename Scalar> struct scalar_max_op; -template<typename Scalar> struct scalar_random_op; -template<typename Scalar> struct scalar_add_op; -template<typename Scalar> struct scalar_constant_op; -template<typename Scalar> struct scalar_identity_op; - -template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_product_op; -template<typename LhsScalar,typename RhsScalar> struct scalar_multiple2_op; -template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_quotient_op; - -} // end namespace internal - -struct IOFormat; - -// Array module -template<typename _Scalar, int _Rows, int _Cols, - int _Options = AutoAlign | -#if EIGEN_GNUC_AT(3,4) - // workaround a bug in at least gcc 3.4.6 - // the innermost ?: ternary operator is misparsed. We write it slightly - // differently and this makes gcc 3.4.6 happy, but it's ugly. - // The error would only show up with EIGEN_DEFAULT_TO_ROW_MAJOR is defined - // (when EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION is RowMajor) - ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor - : !(_Cols==1 && _Rows!=1) ? EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION - : Eigen::ColMajor ), -#else - ( (_Rows==1 && _Cols!=1) ? Eigen::RowMajor - : (_Cols==1 && _Rows!=1) ? Eigen::ColMajor - : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ), -#endif - int _MaxRows = _Rows, int _MaxCols = _Cols> class Array; -template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType> class Select; -template<typename MatrixType, typename BinaryOp, int Direction> class PartialReduxExpr; -template<typename ExpressionType, int Direction> class VectorwiseOp; -template<typename MatrixType,int RowFactor,int ColFactor> class Replicate; -template<typename MatrixType, int Direction = BothDirections> class Reverse; - -template<typename MatrixType> class FullPivLU; -template<typename MatrixType> class PartialPivLU; -namespace internal { -template<typename MatrixType> struct inverse_impl; -} -template<typename MatrixType> class HouseholderQR; -template<typename MatrixType> class ColPivHouseholderQR; -template<typename MatrixType> class FullPivHouseholderQR; -template<typename MatrixType, int QRPreconditioner = ColPivHouseholderQRPreconditioner> class JacobiSVD; -template<typename MatrixType, int UpLo = Lower> class LLT; -template<typename MatrixType, int UpLo = Lower> class LDLT; -template<typename VectorsType, typename CoeffsType, int Side=OnTheLeft> class HouseholderSequence; -template<typename Scalar> class JacobiRotation; - -// Geometry module: -template<typename Derived, int _Dim> class RotationBase; -template<typename Lhs, typename Rhs> class Cross; -template<typename Derived> class QuaternionBase; -template<typename Scalar> class Rotation2D; -template<typename Scalar> class AngleAxis; -template<typename Scalar,int Dim> class Translation; - -#ifdef EIGEN2_SUPPORT -template<typename Derived, int _Dim> class eigen2_RotationBase; -template<typename Lhs, typename Rhs> class eigen2_Cross; -template<typename Scalar> class eigen2_Quaternion; -template<typename Scalar> class eigen2_Rotation2D; -template<typename Scalar> class eigen2_AngleAxis; -template<typename Scalar,int Dim> class eigen2_Transform; -template <typename _Scalar, int _AmbientDim> class eigen2_ParametrizedLine; -template <typename _Scalar, int _AmbientDim> class eigen2_Hyperplane; -template<typename Scalar,int Dim> class eigen2_Translation; -template<typename Scalar,int Dim> class eigen2_Scaling; -#endif - -#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS -template<typename Scalar> class Quaternion; -template<typename Scalar,int Dim> class Transform; -template <typename _Scalar, int _AmbientDim> class ParametrizedLine; -template <typename _Scalar, int _AmbientDim> class Hyperplane; -template<typename Scalar,int Dim> class Scaling; -#endif - -#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS -template<typename Scalar, int Options = AutoAlign> class Quaternion; -template<typename Scalar,int Dim,int Mode,int _Options=AutoAlign> class Transform; -template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class ParametrizedLine; -template <typename _Scalar, int _AmbientDim, int Options=AutoAlign> class Hyperplane; -template<typename Scalar> class UniformScaling; -template<typename MatrixType,int Direction> class Homogeneous; -#endif - -// MatrixFunctions module -template<typename Derived> struct MatrixExponentialReturnValue; -template<typename Derived> class MatrixFunctionReturnValue; -template<typename Derived> class MatrixSquareRootReturnValue; -template<typename Derived> class MatrixLogarithmReturnValue; -template<typename Derived> class MatrixPowerReturnValue; -template<typename Derived> class MatrixComplexPowerReturnValue; - -namespace internal { -template <typename Scalar> -struct stem_function -{ - typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar; - typedef ComplexScalar type(ComplexScalar, int); -}; -} - - -#ifdef EIGEN2_SUPPORT -template<typename ExpressionType> class Cwise; -template<typename MatrixType> class Minor; -template<typename MatrixType> class LU; -template<typename MatrixType> class QR; -template<typename MatrixType> class SVD; -namespace internal { -template<typename MatrixType, unsigned int Mode> struct eigen2_part_return_type; -} -#endif - -// SpecialFunctions forward declarations -namespace internal { -template <typename Scalar> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar __lgamma(Scalar x); -template <typename Scalar> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar __erf(Scalar x); -template <typename Scalar> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar __erfc(Scalar x); -} - -} // end namespace Eigen - -#endif // EIGEN_FORWARDDECLARATIONS_H diff --git a/third_party/eigen3/Eigen/src/Core/util/MKL_support.h b/third_party/eigen3/Eigen/src/Core/util/MKL_support.h deleted file mode 100644 index 8acca9c8c5..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/MKL_support.h +++ /dev/null @@ -1,126 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Include file with common MKL declarations - ******************************************************************************** -*/ - -#ifndef EIGEN_MKL_SUPPORT_H -#define EIGEN_MKL_SUPPORT_H - -#ifdef EIGEN_USE_MKL_ALL - #ifndef EIGEN_USE_BLAS - #define EIGEN_USE_BLAS - #endif - #ifndef EIGEN_USE_LAPACKE - #define EIGEN_USE_LAPACKE - #endif - #ifndef EIGEN_USE_MKL_VML - #define EIGEN_USE_MKL_VML - #endif -#endif - -#ifdef EIGEN_USE_LAPACKE_STRICT - #define EIGEN_USE_LAPACKE -#endif - -#if defined(EIGEN_USE_BLAS) || defined(EIGEN_USE_LAPACKE) || defined(EIGEN_USE_MKL_VML) - #define EIGEN_USE_MKL -#endif - -#if defined EIGEN_USE_MKL -# include <mkl.h> -/*Check IMKL version for compatibility: < 10.3 is not usable with Eigen*/ -# ifndef INTEL_MKL_VERSION -# undef EIGEN_USE_MKL /* INTEL_MKL_VERSION is not even defined on older versions */ -# elif INTEL_MKL_VERSION < 100305 /* the intel-mkl-103-release-notes say this was when the lapacke.h interface was added*/ -# undef EIGEN_USE_MKL -# endif -# ifndef EIGEN_USE_MKL - /*If the MKL version is too old, undef everything*/ -# undef EIGEN_USE_MKL_ALL -# undef EIGEN_USE_BLAS -# undef EIGEN_USE_LAPACKE -# undef EIGEN_USE_MKL_VML -# undef EIGEN_USE_LAPACKE_STRICT -# undef EIGEN_USE_LAPACKE -# endif -#endif - -#if defined EIGEN_USE_MKL -#include <mkl_lapacke.h> -#define EIGEN_MKL_VML_THRESHOLD 128 - -namespace Eigen { - -typedef std::complex<double> dcomplex; -typedef std::complex<float> scomplex; - -namespace internal { - -template<typename MKLType, typename EigenType> -static inline void assign_scalar_eig2mkl(MKLType& mklScalar, const EigenType& eigenScalar) { - mklScalar=eigenScalar; -} - -template<typename MKLType, typename EigenType> -static inline void assign_conj_scalar_eig2mkl(MKLType& mklScalar, const EigenType& eigenScalar) { - mklScalar=eigenScalar; -} - -template <> -inline void assign_scalar_eig2mkl<MKL_Complex16,dcomplex>(MKL_Complex16& mklScalar, const dcomplex& eigenScalar) { - mklScalar.real=eigenScalar.real(); - mklScalar.imag=eigenScalar.imag(); -} - -template <> -inline void assign_scalar_eig2mkl<MKL_Complex8,scomplex>(MKL_Complex8& mklScalar, const scomplex& eigenScalar) { - mklScalar.real=eigenScalar.real(); - mklScalar.imag=eigenScalar.imag(); -} - -template <> -inline void assign_conj_scalar_eig2mkl<MKL_Complex16,dcomplex>(MKL_Complex16& mklScalar, const dcomplex& eigenScalar) { - mklScalar.real=eigenScalar.real(); - mklScalar.imag=-eigenScalar.imag(); -} - -template <> -inline void assign_conj_scalar_eig2mkl<MKL_Complex8,scomplex>(MKL_Complex8& mklScalar, const scomplex& eigenScalar) { - mklScalar.real=eigenScalar.real(); - mklScalar.imag=-eigenScalar.imag(); -} - -} // end namespace internal - -} // end namespace Eigen - -#endif - -#endif // EIGEN_MKL_SUPPORT_H diff --git a/third_party/eigen3/Eigen/src/Core/util/Macros.h b/third_party/eigen3/Eigen/src/Core/util/Macros.h deleted file mode 100644 index b531327afb..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/Macros.h +++ /dev/null @@ -1,744 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MACROS_H -#define EIGEN_MACROS_H - -#define EIGEN_WORLD_VERSION 3 -#define EIGEN_MAJOR_VERSION 2 -#define EIGEN_MINOR_VERSION 90 - -#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \ - (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \ - EIGEN_MINOR_VERSION>=z)))) - -// Compiler identification, EIGEN_COMP_* -/// \internal EIGEN_COMP_GNUC set to 1 for all compilers compatible with GCC -#ifdef __GNUC__ - #define EIGEN_COMP_GNUC 1 -#else - #define EIGEN_COMP_GNUC 0 -#endif - -/// \internal EIGEN_COMP_CLANG set to 1 if the compiler is clang (alias for __clang__) -#if defined(__clang__) - #define EIGEN_COMP_CLANG 1 -#else - #define EIGEN_COMP_CLANG 0 -#endif - - -/// \internal EIGEN_COMP_LLVM set to 1 if the compiler backend is llvm -#if defined(__llvm__) - #define EIGEN_COMP_LLVM 1 -#else - #define EIGEN_COMP_LLVM 0 -#endif - -/// \internal EIGEN_COMP_ICC set to __INTEL_COMPILER if the compiler is Intel compiler, 0 otherwise -#if defined(__INTEL_COMPILER) - #define EIGEN_COMP_ICC __INTEL_COMPILER -#else - #define EIGEN_COMP_ICC 0 -#endif - -/// \internal EIGEN_COMP_MINGW set to 1 if the compiler is mingw -#if defined(__MINGW32__) - #define EIGEN_COMP_MINGW 1 -#else - #define EIGEN_COMP_MINGW 0 -#endif - -/// \internal EIGEN_COMP_SUNCC set to 1 if the compiler is Solaris Studio -#if defined(__SUNPRO_CC) - #define EIGEN_COMP_SUNCC 1 -#else - #define EIGEN_COMP_SUNCC 0 -#endif - -/// \internal EIGEN_COMP_MSVC set to _MSC_VER if the compiler is Microsoft Visual C++, 0 otherwise. -#if defined(_MSC_VER) - #define EIGEN_COMP_MSVC _MSC_VER -#else - #define EIGEN_COMP_MSVC 0 -#endif - -/// \internal EIGEN_COMP_MSVC_STRICT set to 1 if the compiler is really Microsoft Visual C++ and not ,e.g., ICC -#if EIGEN_COMP_MSVC && !(EIGEN_COMP_ICC) - #define EIGEN_COMP_MSVC_STRICT 1 -#else - #define EIGEN_COMP_MSVC_STRICT 0 -#endif - -/// \internal EIGEN_COMP_IBM set to 1 if the compiler is IBM XL C++ -#if defined(__IBMCPP__) || defined(__xlc__) - #define EIGEN_COMP_IBM 1 -#else - #define EIGEN_COMP_IBM 0 -#endif - -/// \internal EIGEN_COMP_PGI set to 1 if the compiler is Portland Group Compiler -#if defined(__PGI) - #define EIGEN_COMP_PGI 1 -#else - #define EIGEN_COMP_PGI 0 -#endif - -/// \internal EIGEN_COMP_ARM set to 1 if the compiler is ARM Compiler -#if defined(__CC_ARM) || defined(__ARMCC_VERSION) - #define EIGEN_COMP_ARM 1 -#else - #define EIGEN_COMP_ARM 0 -#endif - - -/// \internal EIGEN_GNUC_STRICT set to 1 if the compiler is really GCC and not a compatible compiler (e.g., ICC, clang, mingw, etc.) -#if EIGEN_COMP_GNUC && !(EIGEN_COMP_CLANG || EIGEN_COMP_CLANG || EIGEN_COMP_MINGW || EIGEN_COMP_PGI || EIGEN_COMP_IBM || EIGEN_COMP_ARM ) - #define EIGEN_COMP_GNUC_STRICT 1 -#else - #define EIGEN_COMP_GNUC_STRICT 0 -#endif - - -#if EIGEN_COMP_GNUC - #define EIGEN_GNUC_AT_LEAST(x,y) ((__GNUC__==x && __GNUC_MINOR__>=y) || __GNUC__>x) - #define EIGEN_GNUC_AT_MOST(x,y) ((__GNUC__==x && __GNUC_MINOR__<=y) || __GNUC__<x) - #define EIGEN_GNUC_AT(x,y) ( __GNUC__==x && __GNUC_MINOR__==y ) -#else - #define EIGEN_GNUC_AT_LEAST(x,y) 0 - #define EIGEN_GNUC_AT_MOST(x,y) 0 - #define EIGEN_GNUC_AT(x,y) 0 -#endif - -// FIXME: could probably be removed as we do not support gcc 3.x anymore -#if EIGEN_COMP_GNUC && (__GNUC__ <= 3) -#define EIGEN_GCC3_OR_OLDER 1 -#else -#define EIGEN_GCC3_OR_OLDER 0 -#endif - - -// Architecture identification, EIGEN_ARCH_* - -#if defined(__x86_64__) || defined(_M_X64) || defined(__amd64) - #define EIGEN_ARCH_x86_64 1 -#else - #define EIGEN_ARCH_x86_64 0 -#endif - -#if defined(__i386__) || defined(_M_IX86) || defined(_X86_) || defined(__i386) - #define EIGEN_ARCH_i386 1 -#else - #define EIGEN_ARCH_i386 0 -#endif - -#if EIGEN_ARCH_x86_64 || EIGEN_ARCH_i386 - #define EIGEN_ARCH_i386_OR_x86_64 1 -#else - #define EIGEN_ARCH_i386_OR_x86_64 0 -#endif - -/// \internal EIGEN_ARCH_ARM set to 1 if the architecture is ARM -#if defined(__arm__) - #define EIGEN_ARCH_ARM 1 -#else - #define EIGEN_ARCH_ARM 0 -#endif - -/// \internal EIGEN_ARCH_ARM64 set to 1 if the architecture is ARM64 -#if defined(__aarch64__) - #define EIGEN_ARCH_ARM64 1 -#else - #define EIGEN_ARCH_ARM64 0 -#endif - -#if EIGEN_ARCH_ARM || EIGEN_ARCH_ARM64 - #define EIGEN_ARCH_ARM_OR_ARM64 1 -#else - #define EIGEN_ARCH_ARM_OR_ARM64 0 -#endif - -/// \internal EIGEN_ARCH_MIPS set to 1 if the architecture is MIPS -#if defined(__mips__) || defined(__mips) - #define EIGEN_ARCH_MIPS 1 -#else - #define EIGEN_ARCH_MIPS 0 -#endif - -/// \internal EIGEN_ARCH_SPARC set to 1 if the architecture is SPARC -#if defined(__sparc__) || defined(__sparc) - #define EIGEN_ARCH_SPARC 1 -#else - #define EIGEN_ARCH_SPARC 0 -#endif - -/// \internal EIGEN_ARCH_IA64 set to 1 if the architecture is Intel Itanium -#if defined(__ia64__) - #define EIGEN_ARCH_IA64 1 -#else - #define EIGEN_ARCH_IA64 0 -#endif - -/// \internal EIGEN_ARCH_PPC set to 1 if the architecture is PowerPC -#if defined(__powerpc__) || defined(__ppc__) || defined(_M_PPC) - #define EIGEN_ARCH_PPC 1 -#else - #define EIGEN_ARCH_PPC 0 -#endif - - - -// Operating system identification, EIGEN_OS_* - -/// \internal EIGEN_OS_UNIX set to 1 if the OS is a unix variant -#if defined(__unix__) || defined(__unix) - #define EIGEN_OS_UNIX 1 -#else - #define EIGEN_OS_UNIX 0 -#endif - -/// \internal EIGEN_OS_LINUX set to 1 if the OS is based on Linux kernel -#if defined(__linux__) - #define EIGEN_OS_LINUX 1 -#else - #define EIGEN_OS_LINUX 0 -#endif - -/// \internal EIGEN_OS_ANDROID set to 1 if the OS is Android -// note: ANDROID is defined when using ndk_build, __ANDROID__ is defined when using a standalone toolchain. -#if defined(__ANDROID__) || defined(ANDROID) - #define EIGEN_OS_ANDROID 1 -#else - #define EIGEN_OS_ANDROID 0 -#endif - -/// \internal EIGEN_OS_GNULINUX set to 1 if the OS is GNU Linux and not Linux-based OS (e.g., not android) -#if defined(__gnu_linux__) && !(EIGEN_OS_ANDROID) - #define EIGEN_OS_GNULINUX 1 -#else - #define EIGEN_OS_GNULINUX 0 -#endif - -/// \internal EIGEN_OS_BSD set to 1 if the OS is a BSD variant -#if defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__) || defined(__bsdi__) || defined(__DragonFly__) - #define EIGEN_OS_BSD 1 -#else - #define EIGEN_OS_BSD 0 -#endif - -/// \internal EIGEN_OS_MAC set to 1 if the OS is MacOS -#if defined(__APPLE__) - #define EIGEN_OS_MAC 1 -#else - #define EIGEN_OS_MAC 0 -#endif - -/// \internal EIGEN_OS_QNX set to 1 if the OS is QNX -#if defined(__QNX__) - #define EIGEN_OS_QNX 1 -#else - #define EIGEN_OS_QNX 0 -#endif - -/// \internal EIGEN_OS_WIN set to 1 if the OS is Windows based -#if defined(_WIN32) - #define EIGEN_OS_WIN 1 -#else - #define EIGEN_OS_WIN 0 -#endif - -/// \internal EIGEN_OS_WIN64 set to 1 if the OS is Windows 64bits -#if defined(_WIN64) - #define EIGEN_OS_WIN64 1 -#else - #define EIGEN_OS_WIN64 0 -#endif - -/// \internal EIGEN_OS_WINCE set to 1 if the OS is Windows CE -#if defined(_WIN32_WCE) - #define EIGEN_OS_WINCE 1 -#else - #define EIGEN_OS_WINCE 0 -#endif - -/// \internal EIGEN_OS_CYGWIN set to 1 if the OS is Windows/Cygwin -#if defined(__CYGWIN__) - #define EIGEN_OS_CYGWIN 1 -#else - #define EIGEN_OS_CYGWIN 0 -#endif - -/// \internal EIGEN_OS_WIN_STRICT set to 1 if the OS is really Windows and not some variants -#if EIGEN_OS_WIN && !( EIGEN_OS_WINCE || EIGEN_OS_CYGWIN ) - #define EIGEN_OS_WIN_STRICT 1 -#else - #define EIGEN_OS_WIN_STRICT 0 -#endif - - - - -#if EIGEN_GNUC_AT_MOST(4,3) && !EIGEN_COMP_CLANG - // see bug 89 - #define EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO 0 -#else - #define EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO 1 -#endif - -// 16 byte alignment is only useful for vectorization. Since it affects the ABI, we need to enable -// 16 byte alignment on all platforms where vectorization might be enabled. In theory we could always -// enable alignment, but it can be a cause of problems on some platforms, so we just disable it in -// certain common platform (compiler+architecture combinations) to avoid these problems. -// Only static alignment is really problematic (relies on nonstandard compiler extensions), -// try to keep heap alignment even when we have to disable static alignment. -#if EIGEN_COMP_GNUC && !(EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64 || EIGEN_ARCH_PPC || EIGEN_ARCH_IA64) -#define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1 -#elif EIGEN_ARCH_ARM_OR_ARM64 && EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_AT_MOST(4, 6) -// Old versions of GCC on ARM, at least 4.4, were once seen to have buggy static alignment support. -// Not sure which version fixed it, hopefully it doesn't affect 4.7, which is still somewhat in use. -// 4.8 and newer seem definitely unaffected. -#define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1 -#else -#define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 0 -#endif - -// static alignment is completely disabled with GCC 3, Sun Studio, and QCC/QNX -#if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT \ - && !EIGEN_GCC3_OR_OLDER \ - && !EIGEN_COMP_SUNCC \ - && !EIGEN_OS_QNX - #define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 1 -#else - #define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 0 -#endif - -// Defined the boundary (in bytes) on which the data needs to be aligned. Note -// that unless EIGEN_ALIGN is defined and not equal to 0, the data may not be -// aligned at all regardless of the value of this #define. -#define EIGEN_ALIGN_BYTES 16 - -#ifdef EIGEN_DONT_ALIGN - #ifndef EIGEN_DONT_ALIGN_STATICALLY - #define EIGEN_DONT_ALIGN_STATICALLY - #endif - #define EIGEN_ALIGN 0 -#elif !defined(EIGEN_DONT_VECTORIZE) - #if defined(__AVX__) - #undef EIGEN_ALIGN_BYTES - #define EIGEN_ALIGN_BYTES 32 - #endif - #define EIGEN_ALIGN 1 -#else - #define EIGEN_ALIGN 0 -#endif - -#define EIGEN_MAX_ALIGN_BYTES EIGEN_ALIGN_BYTES - - -// This macro can be used to prevent from macro expansion, e.g.: -// std::max EIGEN_NOT_A_MACRO(a,b) -#define EIGEN_NOT_A_MACRO - -// EIGEN_ALIGN_STATICALLY is the true test whether we want to align arrays on the stack or not. It takes into account both the user choice to explicitly disable -// alignment (EIGEN_DONT_ALIGN_STATICALLY) and the architecture config (EIGEN_ARCH_WANTS_STACK_ALIGNMENT). Henceforth, only EIGEN_ALIGN_STATICALLY should be used. -#if EIGEN_ARCH_WANTS_STACK_ALIGNMENT && !defined(EIGEN_DONT_ALIGN_STATICALLY) - #define EIGEN_ALIGN_STATICALLY 1 -#else - #define EIGEN_ALIGN_STATICALLY 0 - #ifndef EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT - #define EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT - #endif -#endif - -#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR -#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::RowMajor -#else -#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::ColMajor -#endif - -#ifndef EIGEN_DEFAULT_DENSE_INDEX_TYPE -#define EIGEN_DEFAULT_DENSE_INDEX_TYPE std::ptrdiff_t -#endif - -// Cross compiler wrapper around LLVM's __has_builtin -#ifdef __has_builtin -# define EIGEN_HAS_BUILTIN(x) __has_builtin(x) -#else -# define EIGEN_HAS_BUILTIN(x) 0 -#endif - -// A Clang feature extension to determine compiler features. -// We use it to determine 'cxx_rvalue_references' -#ifndef __has_feature -# define __has_feature(x) 0 -#endif - -#if __cplusplus > 199711L -#define EIGEN_HAS_VARIADIC_TEMPLATES 1 -#endif - -// Does the compiler support const expressions? -#if __cplusplus > 199711L && !defined(__NVCC__) && !defined(GOOGLE_LIBCXX) && !defined(__APPLE__) -#define EIGEN_HAS_CONSTEXPR 1 -#endif - -/** Allows to disable some optimizations which might affect the accuracy of the result. - * Such optimization are enabled by default, and set EIGEN_FAST_MATH to 0 to disable them. - * They currently include: - * - single precision Cwise::sin() and Cwise::cos() when SSE vectorization is enabled. - */ -#ifndef EIGEN_FAST_MATH -#define EIGEN_FAST_MATH 1 -#endif - -#define EIGEN_DEBUG_VAR(x) std::cerr << #x << " = " << x << std::endl; - -// concatenate two tokens -#define EIGEN_CAT2(a,b) a ## b -#define EIGEN_CAT(a,b) EIGEN_CAT2(a,b) - -// convert a token to a string -#define EIGEN_MAKESTRING2(a) #a -#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a) - -// EIGEN_STRONG_INLINE is a stronger version of the inline, using __forceinline on MSVC, -// but it still doesn't use GCC's always_inline. This is useful in (common) situations where MSVC needs forceinline -// but GCC is still doing fine with just inline. -#if EIGEN_COMP_MSVC || EIGEN_COMP_ICC -#define EIGEN_STRONG_INLINE __forceinline -#else -#define EIGEN_STRONG_INLINE inline -#endif - -// EIGEN_ALWAYS_INLINE is the stronget, it has the effect of making the function inline and adding every possible -// attribute to maximize inlining. This should only be used when really necessary: in particular, -// it uses __attribute__((always_inline)) on GCC, which most of the time is useless and can severely harm compile times. -// FIXME with the always_inline attribute, -// gcc 3.4.x reports the following compilation error: -// Eval.h:91: sorry, unimplemented: inlining failed in call to 'const Eigen::Eval<Derived> Eigen::MatrixBase<Scalar, Derived>::eval() const' -// : function body not available -#if EIGEN_GNUC_AT_LEAST(4,0) -#define EIGEN_ALWAYS_INLINE __attribute__((always_inline)) inline -#else -#define EIGEN_ALWAYS_INLINE EIGEN_STRONG_INLINE -#endif - -#if EIGEN_COMP_GNUC -#define EIGEN_DONT_INLINE __attribute__((noinline)) -#elif EIGEN_COMP_MSVC -#define EIGEN_DONT_INLINE __declspec(noinline) -#else -#define EIGEN_DONT_INLINE -#endif - -#if EIGEN_COMP_GNUC -#define EIGEN_PERMISSIVE_EXPR __extension__ -#else -#define EIGEN_PERMISSIVE_EXPR -#endif - -#if EIGEN_COMP_GNUC -#define EIGEN_LIKELY(x) __builtin_expect((x), 1) -#define EIGEN_UNLIKELY(x) __builtin_expect((x), 0) -#else -#define EIGEN_LIKELY(x) (x) -#define EIGEN_UNLIKELY(x) (x) -#endif - -// this macro allows to get rid of linking errors about multiply defined functions. -// - static is not very good because it prevents definitions from different object files to be merged. -// So static causes the resulting linked executable to be bloated with multiple copies of the same function. -// - inline is not perfect either as it unwantedly hints the compiler toward inlining the function. -#define EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS -#define EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS inline - -#ifdef NDEBUG -# ifndef EIGEN_NO_DEBUG -# define EIGEN_NO_DEBUG -# endif -#endif - -#if !defined(EIGEN_NO_CHECK) || (!defined(EIGEN_NO_DEBUG) && !EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO) - // Custom assertion code that works regardless of the compilation mode. - #include <cstdlib> // for abort - #include <iostream> // for std::cerr - - namespace Eigen { - namespace internal { - // trivial function copying a bool. Must be EIGEN_DONT_INLINE, so we implement it after including Eigen headers. - // see bug 89. - namespace { - EIGEN_DONT_INLINE bool copy_bool(bool b) { return b; } - } - inline void assert_fail(const char *condition, const char *function, const char *file, int line) - { - copy_bool(true); // dummy call to avoid warnings about unused functions. - std::cerr << "assertion failed: " << condition << " in function " << function << " at " << file << ":" << line << std::endl; - abort(); - } - } - } - #define eigen_internal_check(x) \ - do { \ - if(!Eigen::internal::copy_bool(x)) \ - Eigen::internal::assert_fail(EIGEN_MAKESTRING(x), __PRETTY_FUNCTION__, __FILE__, __LINE__); \ - } while(false) -#endif - -#ifdef EIGEN_NO_CHECK - #define eigen_check(x) -#else - #define eigen_check(x) eigen_internal_check(x) -#endif - -// eigen_plain_assert is where we implement the workaround for the assert() bug in GCC <= 4.3, see bug 89 -#ifdef EIGEN_NO_DEBUG - #define eigen_plain_assert(x) -#else - #if EIGEN_SAFE_TO_USE_STANDARD_ASSERT_MACRO - namespace Eigen { - namespace internal { - inline bool copy_bool(bool b) { return b; } - } - } - #define eigen_plain_assert(x) assert(x) - #else - // work around bug 89 - #define eigen_plain_assert(x) eigen_internal_check(x) - #endif -#endif - -// eigen_assert can be overridden -#ifndef eigen_assert -#define eigen_assert(x) eigen_plain_assert(x) -#endif - -#ifdef EIGEN_INTERNAL_DEBUGGING -#define eigen_internal_assert(x) eigen_assert(x) -#else -#define eigen_internal_assert(x) -#endif - -#ifdef EIGEN_NO_DEBUG -#define EIGEN_ONLY_USED_FOR_DEBUG(x) (void)x -#else -#define EIGEN_ONLY_USED_FOR_DEBUG(x) -#endif - -#ifndef EIGEN_NO_DEPRECATED_WARNING - #if EIGEN_COMP_GNUC - #define EIGEN_DEPRECATED __attribute__((deprecated)) - #elif (defined _MSC_VER) - #define EIGEN_DEPRECATED __declspec(deprecated) - #else - #define EIGEN_DEPRECATED - #endif -#else - #define EIGEN_DEPRECATED -#endif - -#if EIGEN_COMP_GNUC -#define EIGEN_UNUSED __attribute__((unused)) -#else -#define EIGEN_UNUSED -#endif - -// Suppresses 'unused variable' warnings. -namespace Eigen { - namespace internal { - template<typename T> void ignore_unused_variable(const T&) {} - } -} -#define EIGEN_UNUSED_VARIABLE(var) Eigen::internal::ignore_unused_variable(var); - -#if !defined(EIGEN_ASM_COMMENT) - #if EIGEN_COMP_GNUC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64) - #define EIGEN_ASM_COMMENT(X) asm("#" X) - #else - #define EIGEN_ASM_COMMENT(X) - #endif -#endif - -/* EIGEN_ALIGN_TO_BOUNDARY(n) forces data to be n-byte aligned. This is used to satisfy SIMD requirements. - * However, we do that EVEN if vectorization (EIGEN_VECTORIZE) is disabled, - * so that vectorization doesn't affect binary compatibility. - * - * If we made alignment depend on whether or not EIGEN_VECTORIZE is defined, it would be impossible to link - * vectorized and non-vectorized code. - */ -#if (defined __CUDACC__) - #define EIGEN_ALIGN_TO_BOUNDARY(n) __align__(n) -#elif EIGEN_COMP_GNUC || EIGEN_COMP_PGI || EIGEN_COMP_IBM || EIGEN_COMP_ARM - #define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n))) -#elif EIGEN_COMP_MSVC - #define EIGEN_ALIGN_TO_BOUNDARY(n) __declspec(align(n)) -#elif EIGEN_COMP_SUNCC - // FIXME not sure about this one: - #define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n))) -#else - #error Please tell me what is the equivalent of __attribute__((aligned(n))) for your compiler -#endif - -#define EIGEN_ALIGN16 EIGEN_ALIGN_TO_BOUNDARY(16) -#define EIGEN_ALIGN32 EIGEN_ALIGN_TO_BOUNDARY(32) -#define EIGEN_ALIGN_DEFAULT EIGEN_ALIGN_TO_BOUNDARY(EIGEN_ALIGN_BYTES) -#define EIGEN_ALIGN_MAX EIGEN_ALIGN_DEFAULT - -#if EIGEN_ALIGN_STATICALLY -#define EIGEN_USER_ALIGN_TO_BOUNDARY(n) EIGEN_ALIGN_TO_BOUNDARY(n) -#define EIGEN_USER_ALIGN16 EIGEN_ALIGN16 -#define EIGEN_USER_ALIGN32 EIGEN_ALIGN32 -#define EIGEN_USER_ALIGN_DEFAULT EIGEN_ALIGN_DEFAULT -#else -#define EIGEN_USER_ALIGN_TO_BOUNDARY(n) -#define EIGEN_USER_ALIGN16 -#define EIGEN_USER_ALIGN32 -#define EIGEN_USER_ALIGN_DEFAULT -#endif - -#ifdef EIGEN_DONT_USE_RESTRICT_KEYWORD - #define EIGEN_RESTRICT -#endif -#ifndef EIGEN_RESTRICT - #define EIGEN_RESTRICT __restrict -#endif - -#ifndef EIGEN_STACK_ALLOCATION_LIMIT -#define EIGEN_STACK_ALLOCATION_LIMIT 20000 -#endif - -#ifndef EIGEN_DEFAULT_IO_FORMAT -#ifdef EIGEN_MAKING_DOCS -// format used in Eigen's documentation -// needed to define it here as escaping characters in CMake add_definition's argument seems very problematic. -#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat(3, 0, " ", "\n", "", "") -#else -#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat() -#endif -#endif - -// just an empty macro ! -#define EIGEN_EMPTY - -#if EIGEN_COMP_MSVC_STRICT - #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \ - using Base::operator =; -#elif EIGEN_COMP_CLANG // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653) - #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \ - using Base::operator =; \ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \ - template <typename OtherDerived> \ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; } -#else - #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \ - using Base::operator =; \ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) \ - { \ - Base::operator=(other); \ - return *this; \ - } -#endif - -#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived) EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) - -/** -* Just a side note. Commenting within defines works only by documenting -* behind the object (via '!<'). Comments cannot be multi-line and thus -* we have these extra long lines. What is confusing doxygen over here is -* that we use '\' and basically have a bunch of typedefs with their -* documentation in a single line. -**/ - -#define EIGEN_GENERIC_PUBLIC_INTERFACE(Derived) \ - typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; /*!< \brief Numeric type, e.g. float, double, int or std::complex<float>. */ \ - typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; /*!< \brief The underlying numeric type for composed scalar types. \details In cases where Scalar is e.g. std::complex<T>, T were corresponding to RealScalar. */ \ - typedef typename Base::CoeffReturnType CoeffReturnType; /*!< \brief The return type for coefficient access. \details Depending on whether the object allows direct coefficient access (e.g. for a MatrixXd), this type is either 'const Scalar&' or simply 'Scalar' for objects that do not allow direct coefficient access. */ \ - typedef typename Eigen::internal::nested<Derived>::type Nested; \ - typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \ - typedef typename Eigen::internal::traits<Derived>::Index Index; \ - enum { RowsAtCompileTime = Eigen::internal::traits<Derived>::RowsAtCompileTime, \ - ColsAtCompileTime = Eigen::internal::traits<Derived>::ColsAtCompileTime, \ - Flags = Eigen::internal::traits<Derived>::Flags, \ - CoeffReadCost = Eigen::internal::traits<Derived>::CoeffReadCost, \ - SizeAtCompileTime = Base::SizeAtCompileTime, \ - MaxSizeAtCompileTime = Base::MaxSizeAtCompileTime, \ - IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; - - -#define EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \ - typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; /*!< \brief Numeric type, e.g. float, double, int or std::complex<float>. */ \ - typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; /*!< \brief The underlying numeric type for composed scalar types. \details In cases where Scalar is e.g. std::complex<T>, T were corresponding to RealScalar. */ \ - typedef typename Base::PacketScalar PacketScalar; \ - typedef typename Base::CoeffReturnType CoeffReturnType; /*!< \brief The return type for coefficient access. \details Depending on whether the object allows direct coefficient access (e.g. for a MatrixXd), this type is either 'const Scalar&' or simply 'Scalar' for objects that do not allow direct coefficient access. */ \ - typedef typename Eigen::internal::nested<Derived>::type Nested; \ - typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \ - typedef typename Eigen::internal::traits<Derived>::Index Index; \ - enum { RowsAtCompileTime = Eigen::internal::traits<Derived>::RowsAtCompileTime, \ - ColsAtCompileTime = Eigen::internal::traits<Derived>::ColsAtCompileTime, \ - MaxRowsAtCompileTime = Eigen::internal::traits<Derived>::MaxRowsAtCompileTime, \ - MaxColsAtCompileTime = Eigen::internal::traits<Derived>::MaxColsAtCompileTime, \ - Flags = Eigen::internal::traits<Derived>::Flags, \ - CoeffReadCost = Eigen::internal::traits<Derived>::CoeffReadCost, \ - SizeAtCompileTime = Base::SizeAtCompileTime, \ - MaxSizeAtCompileTime = Base::MaxSizeAtCompileTime, \ - IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; \ - using Base::derived; \ - using Base::const_cast_derived; - - -#define EIGEN_PLAIN_ENUM_MIN(a,b) (((int)a <= (int)b) ? (int)a : (int)b) -#define EIGEN_PLAIN_ENUM_MAX(a,b) (((int)a >= (int)b) ? (int)a : (int)b) - -// EIGEN_SIZE_MIN_PREFER_DYNAMIC gives the min between compile-time sizes. 0 has absolute priority, followed by 1, -// followed by Dynamic, followed by other finite values. The reason for giving Dynamic the priority over -// finite values is that min(3, Dynamic) should be Dynamic, since that could be anything between 0 and 3. -#define EIGEN_SIZE_MIN_PREFER_DYNAMIC(a,b) (((int)a == 0 || (int)b == 0) ? 0 \ - : ((int)a == 1 || (int)b == 1) ? 1 \ - : ((int)a == Dynamic || (int)b == Dynamic) ? Dynamic \ - : ((int)a <= (int)b) ? (int)a : (int)b) - -// EIGEN_SIZE_MIN_PREFER_FIXED is a variant of EIGEN_SIZE_MIN_PREFER_DYNAMIC comparing MaxSizes. The difference is that finite values -// now have priority over Dynamic, so that min(3, Dynamic) gives 3. Indeed, whatever the actual value is -// (between 0 and 3), it is not more than 3. -#define EIGEN_SIZE_MIN_PREFER_FIXED(a,b) (((int)a == 0 || (int)b == 0) ? 0 \ - : ((int)a == 1 || (int)b == 1) ? 1 \ - : ((int)a == Dynamic && (int)b == Dynamic) ? Dynamic \ - : ((int)a == Dynamic) ? (int)b \ - : ((int)b == Dynamic) ? (int)a \ - : ((int)a <= (int)b) ? (int)a : (int)b) - -// see EIGEN_SIZE_MIN_PREFER_DYNAMIC. No need for a separate variant for MaxSizes here. -#define EIGEN_SIZE_MAX(a,b) (((int)a == Dynamic || (int)b == Dynamic) ? Dynamic \ - : ((int)a >= (int)b) ? (int)a : (int)b) - -#define EIGEN_LOGICAL_XOR(a,b) (((a) || (b)) && !((a) && (b))) - -#define EIGEN_IMPLIES(a,b) (!(a) || (b)) - -#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR) \ - template<typename OtherDerived> \ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> \ - (METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \ - { \ - return CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); \ - } - -// the expression type of a cwise product -#define EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS) \ - CwiseBinaryOp< \ - internal::scalar_product_op< \ - typename internal::traits<LHS>::Scalar, \ - typename internal::traits<RHS>::Scalar \ - >, \ - const LHS, \ - const RHS \ - > - -#endif // EIGEN_MACROS_H diff --git a/third_party/eigen3/Eigen/src/Core/util/MatrixMapper.h b/third_party/eigen3/Eigen/src/Core/util/MatrixMapper.h deleted file mode 100644 index ec2ad018ff..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/MatrixMapper.h +++ /dev/null @@ -1,155 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Eric Martin <eric@ericmart.in> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MATRIXMAPPER_H -#define EIGEN_MATRIXMAPPER_H - -// To support both matrices and tensors, we need a way to abstractly access an -// element of a matrix (where the matrix might be an implicitly flattened -// tensor). This file abstracts the logic needed to access elements in a row -// major or column major matrix. - -namespace Eigen { - -namespace internal { - -template<typename Scalar, typename Index> -class BlasVectorMapper { - public: - EIGEN_ALWAYS_INLINE BlasVectorMapper(Scalar *data) : m_data(data) {} - - EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const { - return m_data[i]; - } - template <typename Packet, int AlignmentType> - EIGEN_ALWAYS_INLINE Packet load(Index i) const { - return ploadt<Packet, AlignmentType>(m_data + i); - } - - template <typename Packet> - bool aligned(Index i) const { - return (size_t(m_data+i)%sizeof(Packet))==0; - } - - protected: - Scalar* m_data; -}; - -// We need a fast way to iterate down columns (if column major) that doesn't -// involves performing a multiplication for each lookup. -template<typename Scalar, typename Index, int AlignmentType> -class BlasLinearMapper { - public: - typedef typename packet_traits<Scalar>::type Packet; - typedef typename packet_traits<Scalar>::half HalfPacket; - - EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar *data) : m_data(data) {} - - EIGEN_ALWAYS_INLINE void prefetch(int i) const { - internal::prefetch(&operator()(i)); - } - - EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const { - return m_data[i]; - } - - EIGEN_ALWAYS_INLINE Packet loadPacket(Index i) const { - return ploadt<Packet, AlignmentType>(m_data + i); - } - - EIGEN_ALWAYS_INLINE HalfPacket loadHalfPacket(Index i) const { - return ploadt<HalfPacket, AlignmentType>(m_data + i); - } - - EIGEN_ALWAYS_INLINE void storePacket(Index i, Packet p) const { - pstoret<Scalar, Packet, AlignmentType>(m_data + i, p); - } - - protected: - Scalar* m_data; -}; - -// This mapper allows access into matrix by coordinates i and j. -template<typename Scalar, typename Index, int StorageOrder, int AlignmentType = Unaligned> -class blas_data_mapper { - public: - typedef typename packet_traits<Scalar>::type Packet; - typedef typename packet_traits<Scalar>::half HalfPacket; - - typedef BlasLinearMapper<Scalar, Index, AlignmentType> LinearMapper; - typedef BlasVectorMapper<Scalar, Index> VectorMapper; - - EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride) : m_data(data), m_stride(stride) {} - - EIGEN_ALWAYS_INLINE blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType> - getSubMapper(Index i, Index j) const { - return blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType>(&operator()(i, j), m_stride); - } - - EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const { - return LinearMapper(&operator()(i, j)); - } - - EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const { - return VectorMapper(&operator()(i, j)); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const { - return m_data[StorageOrder==RowMajor ? j + i*m_stride : i + j*m_stride]; - } - - EIGEN_ALWAYS_INLINE Packet loadPacket(Index i, Index j) const { - return ploadt<Packet, AlignmentType>(&operator()(i, j)); - } - - EIGEN_ALWAYS_INLINE HalfPacket loadHalfPacket(Index i, Index j) const { - return ploadt<HalfPacket, AlignmentType>(&operator()(i, j)); - } - - template<typename SubPacket> - EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, SubPacket p) const { - pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride); - } - - template<typename SubPacket> - EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const { - return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride); - } - - const Index stride() const { return m_stride; } - - Index firstAligned(Index size) const { - if (size_t(m_data)%sizeof(Scalar)) { - return -1; - } - return internal::first_aligned(m_data, size); - } - - protected: - Scalar* EIGEN_RESTRICT m_data; - const Index m_stride; -}; - -// This is just a convienent way to work with -// blas_data_mapper<const Scalar, Index, StorageOrder> -template<typename Scalar, typename Index, int StorageOrder> -class const_blas_data_mapper : public blas_data_mapper<const Scalar, Index, StorageOrder> { - public: - EIGEN_ALWAYS_INLINE const_blas_data_mapper(const Scalar *data, Index stride) : blas_data_mapper<const Scalar, Index, StorageOrder>(data, stride) {} - - EIGEN_ALWAYS_INLINE const_blas_data_mapper<Scalar, Index, StorageOrder> getSubMapper(Index i, Index j) const { - return const_blas_data_mapper<Scalar, Index, StorageOrder>(&(this->operator()(i, j)), this->m_stride); - } -}; - -} // end namespace internal -} // end namespace eigen - -#endif //EIGEN_MATRIXMAPPER_H diff --git a/third_party/eigen3/Eigen/src/Core/util/Memory.h b/third_party/eigen3/Eigen/src/Core/util/Memory.h deleted file mode 100644 index 03a699177a..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/Memory.h +++ /dev/null @@ -1,984 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009 Kenneth Riddile <kfriddile@yahoo.com> -// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com> -// Copyright (C) 2010 Thomas Capricelli <orzel@freehackers.org> -// Copyright (C) 2013 Pavel Holoborodko <pavel@holoborodko.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -/***************************************************************************** -*** Platform checks for aligned malloc functions *** -*****************************************************************************/ - -#ifndef EIGEN_MEMORY_H -#define EIGEN_MEMORY_H - -// See bug 554 (http://eigen.tuxfamily.org/bz/show_bug.cgi?id=554) -// It seems to be unsafe to check _POSIX_ADVISORY_INFO without including unistd.h first. -// Currently, let's include it only on unix systems: -#if defined(__unix__) || defined(__unix) - #include <unistd.h> - #if ((defined __QNXNTO__) || (defined _GNU_SOURCE) || ((defined _XOPEN_SOURCE) && (_XOPEN_SOURCE >= 600))) && (defined _POSIX_ADVISORY_INFO) && (_POSIX_ADVISORY_INFO > 0) - #define EIGEN_HAS_POSIX_MEMALIGN 1 - #endif -#endif - -#ifndef EIGEN_HAS_POSIX_MEMALIGN - #define EIGEN_HAS_POSIX_MEMALIGN 0 -#endif - -#if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_AVX - #define EIGEN_HAS_MM_MALLOC 1 -#else - #define EIGEN_HAS_MM_MALLOC 0 -#endif - -namespace Eigen { - -namespace internal { - -EIGEN_DEVICE_FUNC inline void throw_std_bad_alloc() -{ -#ifndef __CUDA_ARCH__ - #ifdef EIGEN_EXCEPTIONS - throw std::bad_alloc(); - #else - std::size_t huge = static_cast<std::size_t>(-1); - new int[huge]; - #endif -#endif -} - -/***************************************************************************** -*** Implementation of handmade aligned functions *** -*****************************************************************************/ - -/* ----- Hand made implementations of aligned malloc/free and realloc ----- */ - -/** \internal Like malloc, but the returned pointer is guaranteed to be 16-byte aligned. - * Fast, but wastes 16 additional bytes of memory. Does not throw any exception. - */ -inline void* handmade_aligned_malloc(std::size_t size) -{ - void *original = std::malloc(size+EIGEN_ALIGN_BYTES); - if (original == 0) return 0; - void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(EIGEN_ALIGN_BYTES-1))) + EIGEN_ALIGN_BYTES); - *(reinterpret_cast<void**>(aligned) - 1) = original; - return aligned; -} - -/** \internal Frees memory allocated with handmade_aligned_malloc */ -inline void handmade_aligned_free(void *ptr) -{ - if (ptr) std::free(*(reinterpret_cast<void**>(ptr) - 1)); -} - -/** \internal - * \brief Reallocates aligned memory. - * Since we know that our handmade version is based on std::realloc - * we can use std::realloc to implement efficient reallocation. - */ -inline void* handmade_aligned_realloc(void* ptr, std::size_t size, std::size_t = 0) -{ - if (ptr == 0) return handmade_aligned_malloc(size); - void *original = *(reinterpret_cast<void**>(ptr) - 1); - std::ptrdiff_t previous_offset = static_cast<char *>(ptr)-static_cast<char *>(original); - original = std::realloc(original,size+EIGEN_ALIGN_BYTES); - if (original == 0) return 0; - void *aligned = reinterpret_cast<void*>((reinterpret_cast<std::size_t>(original) & ~(std::size_t(EIGEN_ALIGN_BYTES-1))) + EIGEN_ALIGN_BYTES); - void *previous_aligned = static_cast<char *>(original)+previous_offset; - if(aligned!=previous_aligned) - std::memmove(aligned, previous_aligned, size); - - *(reinterpret_cast<void**>(aligned) - 1) = original; - return aligned; -} - -/***************************************************************************** -*** Implementation of generic aligned realloc (when no realloc can be used)*** -*****************************************************************************/ - -EIGEN_DEVICE_FUNC void* aligned_malloc(std::size_t size); -EIGEN_DEVICE_FUNC void aligned_free(void *ptr); - -/** \internal - * \brief Reallocates aligned memory. - * Allows reallocation with aligned ptr types. This implementation will - * always create a new memory chunk and copy the old data. - */ -inline void* generic_aligned_realloc(void* ptr, size_t size, size_t old_size) -{ - if (ptr==0) - return aligned_malloc(size); - - if (size==0) - { - aligned_free(ptr); - return 0; - } - - void* newptr = aligned_malloc(size); - if (newptr == 0) - { - #ifdef EIGEN_HAS_ERRNO - errno = ENOMEM; // according to the standard - #endif - return 0; - } - - if (ptr != 0) - { - std::memcpy(newptr, ptr, (std::min)(size,old_size)); - aligned_free(ptr); - } - - return newptr; -} - -/***************************************************************************** -*** Implementation of portable aligned versions of malloc/free/realloc *** -*****************************************************************************/ - -#ifdef EIGEN_NO_MALLOC -EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed() -{ - eigen_assert(false && "heap allocation is forbidden (EIGEN_NO_MALLOC is defined)"); -} -#elif defined EIGEN_RUNTIME_NO_MALLOC -EIGEN_DEVICE_FUNC inline bool is_malloc_allowed_impl(bool update, bool new_value = false) -{ - static bool value = true; - if (update == 1) - value = new_value; - return value; -} -EIGEN_DEVICE_FUNC inline bool is_malloc_allowed() { return is_malloc_allowed_impl(false); } -EIGEN_DEVICE_FUNC inline bool set_is_malloc_allowed(bool new_value) { return is_malloc_allowed_impl(true, new_value); } -EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed() -{ - eigen_assert(is_malloc_allowed() && "heap allocation is forbidden (EIGEN_RUNTIME_NO_MALLOC is defined and g_is_malloc_allowed is false)"); -} -#else -EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed() -{} -#endif - -/** \internal Allocates \a size bytes. The returned pointer is guaranteed to have 16 or 32 bytes alignment depending on the requirements. - * On allocation error, the returned pointer is null, and std::bad_alloc is thrown. - */ -EIGEN_DEVICE_FUNC -inline void* aligned_malloc(size_t size) -{ - check_that_malloc_is_allowed(); - - void *result; - #if !EIGEN_ALIGN - result = std::malloc(size); - #elif EIGEN_HAS_POSIX_MEMALIGN - if(posix_memalign(&result, EIGEN_ALIGN_BYTES, size)) result = 0; - #elif EIGEN_HAS_MM_MALLOC - result = _mm_malloc(size, EIGEN_ALIGN_BYTES); - #elif defined(_MSC_VER) && (!defined(_WIN32_WCE)) - result = _aligned_malloc(size, EIGEN_ALIGN_BYTES); - #else - result = handmade_aligned_malloc(size); - #endif - - if(!result && size) - throw_std_bad_alloc(); - - return result; -} - -/** \internal Frees memory allocated with aligned_malloc. */ -EIGEN_DEVICE_FUNC -inline void aligned_free(void *ptr) -{ - #if !EIGEN_ALIGN - std::free(ptr); - #elif EIGEN_HAS_POSIX_MEMALIGN - std::free(ptr); - #elif EIGEN_HAS_MM_MALLOC - _mm_free(ptr); - #elif defined(_MSC_VER) && (!defined(_WIN32_WCE)) - _aligned_free(ptr); - #else - handmade_aligned_free(ptr); - #endif -} - -/** -* \internal -* \brief Reallocates an aligned block of memory. -* \throws std::bad_alloc on allocation failure -**/ -inline void* aligned_realloc(void *ptr, size_t new_size, size_t old_size) -{ - EIGEN_UNUSED_VARIABLE(old_size); - - void *result; -#if !EIGEN_ALIGN - result = std::realloc(ptr,new_size); -#elif EIGEN_HAS_POSIX_MEMALIGN - result = generic_aligned_realloc(ptr,new_size,old_size); -#elif EIGEN_HAS_MM_MALLOC - // The defined(_mm_free) is just here to verify that this MSVC version - // implements _mm_malloc/_mm_free based on the corresponding _aligned_ - // functions. This may not always be the case and we just try to be safe. - #if EIGEN_OS_WIN_STRICT && defined(_mm_free) - result = _aligned_realloc(ptr,new_size,EIGEN_ALIGN_BYTES); - #else - result = generic_aligned_realloc(ptr,new_size,old_size); - #endif -#elif EIGEN_OS_WIN_STRICT - result = _aligned_realloc(ptr,new_size,EIGEN_ALIGN_BYTES); -#else - result = handmade_aligned_realloc(ptr,new_size,old_size); -#endif - - if (!result && new_size) - throw_std_bad_alloc(); - - return result; -} - -/***************************************************************************** -*** Implementation of conditionally aligned functions *** -*****************************************************************************/ - -/** \internal Allocates \a size bytes. If Align is true, then the returned ptr is 16-byte-aligned. - * On allocation error, the returned pointer is null, and a std::bad_alloc is thrown. - */ -template<bool Align> EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc(size_t size) -{ - return aligned_malloc(size); -} - -template<> EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc<false>(size_t size) -{ - check_that_malloc_is_allowed(); - - void *result = std::malloc(size); - if(!result && size) - throw_std_bad_alloc(); - return result; -} - -/** \internal Frees memory allocated with conditional_aligned_malloc */ -template<bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_free(void *ptr) -{ - aligned_free(ptr); -} - -template<> EIGEN_DEVICE_FUNC inline void conditional_aligned_free<false>(void *ptr) -{ - std::free(ptr); -} - -template<bool Align> inline void* conditional_aligned_realloc(void* ptr, size_t new_size, size_t old_size) -{ - return aligned_realloc(ptr, new_size, old_size); -} - -template<> inline void* conditional_aligned_realloc<false>(void* ptr, size_t new_size, size_t) -{ - return std::realloc(ptr, new_size); -} - -/***************************************************************************** -*** Construction/destruction of array elements *** -*****************************************************************************/ - -/** \internal Constructs the elements of an array. - * The \a size parameter tells on how many objects to call the constructor of T. - */ -template<typename T> EIGEN_DEVICE_FUNC inline T* construct_elements_of_array(T *ptr, size_t size) -{ - for (size_t i=0; i < size; ++i) ::new (ptr + i) T; - return ptr; -} - -/** \internal Destructs the elements of an array. - * The \a size parameters tells on how many objects to call the destructor of T. - */ -template<typename T> EIGEN_DEVICE_FUNC inline void destruct_elements_of_array(T *ptr, size_t size) -{ - // always destruct an array starting from the end. - if(ptr) - while(size) ptr[--size].~T(); -} - -/***************************************************************************** -*** Implementation of aligned new/delete-like functions *** -*****************************************************************************/ - -template<typename T> -EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void check_size_for_overflow(size_t size) -{ - if(size > size_t(-1) / sizeof(T)) - throw_std_bad_alloc(); -} - -/** \internal Allocates \a size objects of type T. The returned pointer is guaranteed to have 16 bytes alignment. - * On allocation error, the returned pointer is undefined, but a std::bad_alloc is thrown. - * The default constructor of T is called. - */ -template<typename T> EIGEN_DEVICE_FUNC inline T* aligned_new(size_t size) -{ - check_size_for_overflow<T>(size); - T *result = reinterpret_cast<T*>(aligned_malloc(sizeof(T)*size)); - return construct_elements_of_array(result, size); -} - -template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new(size_t size) -{ - check_size_for_overflow<T>(size); - T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size)); - return construct_elements_of_array(result, size); -} - -template<typename T> EIGEN_DEVICE_FUNC inline T* allocate_uvm(size_t size) -{ -#if defined(EIGEN_USE_GPU) && defined(__CUDA_ARCH__) - return (T*)malloc(size); -#elif defined(EIGEN_USE_GPU) && defined(__NVCC__) - T* result = NULL; - if (cudaMallocManaged(&result, size) != cudaSuccess) { - throw_std_bad_alloc(); - } - return result; -#else - return reinterpret_cast<T*>(conditional_aligned_malloc<true>(sizeof(T)*size)); -#endif -} - -template<typename T> EIGEN_DEVICE_FUNC void deallocate_uvm(T* ptr) -{ -#if defined(EIGEN_USE_GPU) && defined(__CUDA_ARCH__) - free(ptr); -#elif defined(EIGEN_USE_GPU) && defined(__NVCC__) - if (cudaFree(ptr) != cudaSuccess) { - throw_std_bad_alloc(); - } -#else - return conditional_aligned_free<true>(ptr); -#endif -} - -/** \internal Deletes objects constructed with aligned_new - * The \a size parameters tells on how many objects to call the destructor of T. - */ -template<typename T> EIGEN_DEVICE_FUNC inline void aligned_delete(T *ptr, size_t size) -{ - destruct_elements_of_array<T>(ptr, size); - aligned_free(ptr); -} - -/** \internal Deletes objects constructed with conditional_aligned_new - * The \a size parameters tells on how many objects to call the destructor of T. - */ -template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete(T *ptr, size_t size) -{ - destruct_elements_of_array<T>(ptr, size); - conditional_aligned_free<Align>(ptr); -} - -template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_realloc_new(T* pts, size_t new_size, size_t old_size) -{ - check_size_for_overflow<T>(new_size); - check_size_for_overflow<T>(old_size); - if(new_size < old_size) - destruct_elements_of_array(pts+new_size, old_size-new_size); - T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size)); - if(new_size > old_size) - construct_elements_of_array(result+old_size, new_size-old_size); - return result; -} - - -template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new_auto(size_t size) -{ - check_size_for_overflow<T>(size); - T *result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size)); - if(NumTraits<T>::RequireInitialization) - construct_elements_of_array(result, size); - return result; -} - -template<typename T, bool Align, bool UseUVM> EIGEN_DEVICE_FUNC inline T* conditional_managed_new_auto(size_t size) -{ - check_size_for_overflow<T>(size); - T *result; - if (UseUVM) { - result = allocate_uvm<T>(size*sizeof(T)); - } - else { - result = reinterpret_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size)); - } - if(NumTraits<T>::RequireInitialization) - construct_elements_of_array(result, size); - return result; -} - -template<typename T, bool Align, bool UseUVM> EIGEN_DEVICE_FUNC inline void conditional_managed_delete_auto(T* ptr, size_t size) -{ - if(NumTraits<T>::RequireInitialization) - destruct_elements_of_array<T>(ptr, size); - if (UseUVM) { - deallocate_uvm(ptr); - } - else { - conditional_aligned_free<Align>(ptr); - } -} - -template<typename T, bool Align> inline T* conditional_aligned_realloc_new_auto(T* pts, size_t new_size, size_t old_size) -{ - check_size_for_overflow<T>(new_size); - check_size_for_overflow<T>(old_size); - if(NumTraits<T>::RequireInitialization && (new_size < old_size)) - destruct_elements_of_array(pts+new_size, old_size-new_size); - T *result = reinterpret_cast<T*>(conditional_aligned_realloc<Align>(reinterpret_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size)); - if(NumTraits<T>::RequireInitialization && (new_size > old_size)) - construct_elements_of_array(result+old_size, new_size-old_size); - return result; -} - -template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete_auto(T *ptr, size_t size) -{ - if(NumTraits<T>::RequireInitialization) - destruct_elements_of_array<T>(ptr, size); - conditional_aligned_free<Align>(ptr); -} - -/****************************************************************************/ - -/** \internal Returns the index of the first element of the array that is well aligned for vectorization. - * - * \param array the address of the start of the array - * \param size the size of the array - * - * \note If no element of the array is well aligned, the size of the array is returned. Typically, - * for example with SSE, "well aligned" means 16-byte-aligned. If vectorization is disabled or if the - * packet size for the given scalar type is 1, then everything is considered well-aligned. - * - * \note If the scalar type is vectorizable, we rely on the following assumptions: sizeof(Scalar) is a - * power of 2, the packet size in bytes is also a power of 2, and is a multiple of sizeof(Scalar). On the - * other hand, we do not assume that the array address is a multiple of sizeof(Scalar), as that fails for - * example with Scalar=double on certain 32-bit platforms, see bug #79. - * - * There is also the variant first_aligned(const MatrixBase&) defined in DenseCoeffsBase.h. - */ -template<typename Scalar, typename Index> -inline Index first_aligned(const Scalar* array, Index size) -{ - enum { PacketSize = packet_traits<Scalar>::size, - PacketAlignedMask = PacketSize-1 - }; - - if(PacketSize==1) - { - // Either there is no vectorization, or a packet consists of exactly 1 scalar so that all elements - // of the array have the same alignment. - return 0; - } - else if(size_t(array) & (sizeof(Scalar)-1)) - { - // There is vectorization for this scalar type, but the array is not aligned to the size of a single scalar. - // Consequently, no element of the array is well aligned. - return size; - } - else - { - return std::min<Index>( (PacketSize - (Index((size_t(array)/sizeof(Scalar))) & PacketAlignedMask)) - & PacketAlignedMask, size); - } -} - -/** \internal Returns the smallest integer multiple of \a base and greater or equal to \a size - */ -template<typename Index> -inline Index first_multiple(Index size, Index base) -{ - return ((size+base-1)/base)*base; -} - -// std::copy is much slower than memcpy, so let's introduce a smart_copy which -// use memcpy on trivial types, i.e., on types that does not require an initialization ctor. -template<typename T, bool UseMemcpy> struct smart_copy_helper; - -template<typename T> EIGEN_DEVICE_FUNC void smart_copy(const T* start, const T* end, T* target) -{ - smart_copy_helper<T,!NumTraits<T>::RequireInitialization>::run(start, end, target); -} - -template<typename T> struct smart_copy_helper<T,true> { - static inline EIGEN_DEVICE_FUNC void run(const T* start, const T* end, T* target) - { memcpy(target, start, std::ptrdiff_t(end)-std::ptrdiff_t(start)); } -}; - -template<typename T> struct smart_copy_helper<T,false> { - static inline EIGEN_DEVICE_FUNC void run(const T* start, const T* end, T* target) - { std::copy(start, end, target); } -}; - -// intelligent memmove. falls back to std::memmove for POD types, uses std::copy otherwise. -template<typename T, bool UseMemmove> struct smart_memmove_helper; - -template<typename T> void smart_memmove(const T* start, const T* end, T* target) -{ - smart_memmove_helper<T,!NumTraits<T>::RequireInitialization>::run(start, end, target); -} - -template<typename T> struct smart_memmove_helper<T,true> { - static inline void run(const T* start, const T* end, T* target) - { std::memmove(target, start, std::ptrdiff_t(end)-std::ptrdiff_t(start)); } -}; - -template<typename T> struct smart_memmove_helper<T,false> { - static inline void run(const T* start, const T* end, T* target) - { - if (uintptr_t(target) < uintptr_t(start)) - { - std::copy(start, end, target); - } - else - { - std::ptrdiff_t count = (std::ptrdiff_t(end)-std::ptrdiff_t(start)) / sizeof(T); - std::copy_backward(start, end, target + count); - } - } -}; - - -/***************************************************************************** -*** Implementation of runtime stack allocation (falling back to malloc) *** -*****************************************************************************/ - -// you can overwrite Eigen's default behavior regarding alloca by defining EIGEN_ALLOCA -// to the appropriate stack allocation function -#ifndef EIGEN_ALLOCA - #if (defined __linux__) || (defined __APPLE__) - #define EIGEN_ALLOCA alloca - #elif defined(_MSC_VER) - #define EIGEN_ALLOCA _alloca - #endif -#endif - -// This helper class construct the allocated memory, and takes care of destructing and freeing the handled data -// at destruction time. In practice this helper class is mainly useful to avoid memory leak in case of exceptions. -template<typename T> class aligned_stack_memory_handler -{ - public: - /* Creates a stack_memory_handler responsible for the buffer \a ptr of size \a size. - * Note that \a ptr can be 0 regardless of the other parameters. - * This constructor takes care of constructing/initializing the elements of the buffer if required by the scalar type T (see NumTraits<T>::RequireInitialization). - * In this case, the buffer elements will also be destructed when this handler will be destructed. - * Finally, if \a dealloc is true, then the pointer \a ptr is freed. - **/ - aligned_stack_memory_handler(T* ptr, size_t size, bool dealloc) - : m_ptr(ptr), m_size(size), m_deallocate(dealloc) - { - if(NumTraits<T>::RequireInitialization && m_ptr) - Eigen::internal::construct_elements_of_array(m_ptr, size); - } - ~aligned_stack_memory_handler() - { - if(NumTraits<T>::RequireInitialization && m_ptr) - Eigen::internal::destruct_elements_of_array<T>(m_ptr, m_size); - if(m_deallocate) - Eigen::internal::aligned_free(m_ptr); - } - protected: - T* m_ptr; - size_t m_size; - bool m_deallocate; -}; - -} // end namespace internal - -/** \internal - * Declares, allocates and construct an aligned buffer named NAME of SIZE elements of type TYPE on the stack - * if SIZE is smaller than EIGEN_STACK_ALLOCATION_LIMIT, and if stack allocation is supported by the platform - * (currently, this is Linux and Visual Studio only). Otherwise the memory is allocated on the heap. - * The allocated buffer is automatically deleted when exiting the scope of this declaration. - * If BUFFER is non null, then the declared variable is simply an alias for BUFFER, and no allocation/deletion occurs. - * Here is an example: - * \code - * { - * ei_declare_aligned_stack_constructed_variable(float,data,size,0); - * // use data[0] to data[size-1] - * } - * \endcode - * The underlying stack allocation function can controlled with the EIGEN_ALLOCA preprocessor token. - */ -#ifdef EIGEN_ALLOCA - // The native alloca() that comes with llvm aligns buffer on 16 bytes even when AVX is enabled. -#if defined(__arm__) || defined(_WIN32) || EIGEN_ALIGN_BYTES > 16 - #define EIGEN_ALIGNED_ALLOCA(SIZE) reinterpret_cast<void*>((reinterpret_cast<size_t>(EIGEN_ALLOCA(SIZE+EIGEN_ALIGN_BYTES)) & ~(size_t(EIGEN_ALIGN_BYTES-1))) + EIGEN_ALIGN_BYTES) - #else - #define EIGEN_ALIGNED_ALLOCA EIGEN_ALLOCA - #endif - - #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \ - Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \ - TYPE* NAME = (BUFFER)!=0 ? (BUFFER) \ - : reinterpret_cast<TYPE*>( \ - (sizeof(TYPE)*SIZE<=EIGEN_STACK_ALLOCATION_LIMIT) ? EIGEN_ALIGNED_ALLOCA(sizeof(TYPE)*SIZE) \ - : Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE) ); \ - Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,sizeof(TYPE)*SIZE>EIGEN_STACK_ALLOCATION_LIMIT) - -#else - - #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \ - Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \ - TYPE* NAME = (BUFFER)!=0 ? BUFFER : reinterpret_cast<TYPE*>(Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE)); \ - Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,true) - -#endif - - -/***************************************************************************** -*** Implementation of EIGEN_MAKE_ALIGNED_OPERATOR_NEW [_IF] *** -*****************************************************************************/ - -#if EIGEN_ALIGN - #ifdef EIGEN_EXCEPTIONS - #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \ - void* operator new(size_t size, const std::nothrow_t&) throw() { \ - try { return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); } \ - catch (...) { return 0; } \ - return 0; \ - } - #else - #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \ - void* operator new(size_t size, const std::nothrow_t&) throw() { \ - return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \ - } - #endif - - #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) \ - void *operator new(size_t size) { \ - return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \ - } \ - void *operator new[](size_t size) { \ - return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \ - } \ - void operator delete(void * ptr) throw() { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \ - void operator delete[](void * ptr) throw() { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \ - /* in-place new and delete. since (at least afaik) there is no actual */ \ - /* memory allocated we can safely let the default implementation handle */ \ - /* this particular case. */ \ - static void *operator new(size_t size, void *ptr) { return ::operator new(size,ptr); } \ - static void *operator new[](size_t size, void* ptr) { return ::operator new[](size,ptr); } \ - void operator delete(void * memory, void *ptr) throw() { return ::operator delete(memory,ptr); } \ - void operator delete[](void * memory, void *ptr) throw() { return ::operator delete[](memory,ptr); } \ - /* nothrow-new (returns zero instead of std::bad_alloc) */ \ - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \ - void operator delete(void *ptr, const std::nothrow_t&) throw() { \ - Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); \ - } \ - typedef void eigen_aligned_operator_new_marker_type; -#else - #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) -#endif - -#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(true) -#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar,Size) \ - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(((Size)!=Eigen::Dynamic) && ((sizeof(Scalar)*(Size))%EIGEN_ALIGN_BYTES==0))) - -/****************************************************************************/ - -/** \class aligned_allocator -* \ingroup Core_Module -* -* \brief STL compatible allocator to use with with 16 byte aligned types -* -* Example: -* \code -* // Matrix4f requires 16 bytes alignment: -* std::map< int, Matrix4f, std::less<int>, -* aligned_allocator<std::pair<const int, Matrix4f> > > my_map_mat4; -* // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator: -* std::map< int, Vector3f > my_map_vec3; -* \endcode -* -* \sa \ref TopicStlContainers. -*/ -template<class T> -class aligned_allocator : public std::allocator<T> -{ -public: - typedef size_t size_type; - typedef std::ptrdiff_t difference_type; - typedef T* pointer; - typedef const T* const_pointer; - typedef T& reference; - typedef const T& const_reference; - typedef T value_type; - - template<class U> - struct rebind - { - typedef aligned_allocator<U> other; - }; - - aligned_allocator() : std::allocator<T>() {} - - aligned_allocator(const aligned_allocator& other) : std::allocator<T>(other) {} - - template<class U> - aligned_allocator(const aligned_allocator<U>& other) : std::allocator<T>(other) {} - - ~aligned_allocator() {} - - pointer allocate(size_type num, const void* /*hint*/ = 0) - { - internal::check_size_for_overflow<T>(num); - return static_cast<pointer>( internal::aligned_malloc(num * sizeof(T)) ); - } - - void deallocate(pointer p, size_type /*num*/) - { - internal::aligned_free(p); - } -}; - -//---------- Cache sizes ---------- - -#if !defined(EIGEN_NO_CPUID) -# if EIGEN_COMP_GNUC && EIGEN_ARCH_i386_OR_x86_64 -# if defined(__PIC__) && EIGEN_ARCH_i386 - // Case for x86 with PIC -# define EIGEN_CPUID(abcd,func,id) \ - __asm__ __volatile__ ("xchgl %%ebx, %k1;cpuid; xchgl %%ebx,%k1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id)); -# elif defined(__PIC__) && EIGEN_ARCH_x86_64 - // Case for x64 with PIC. In theory this is only a problem with recent gcc and with medium or large code model, not with the default small code model. - // However, we cannot detect which code model is used, and the xchg overhead is negligible anyway. -# define EIGEN_CPUID(abcd,func,id) \ - __asm__ __volatile__ ("xchg{q}\t{%%}rbx, %q1; cpuid; xchg{q}\t{%%}rbx, %q1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id)); -# else - // Case for x86_64 or x86 w/o PIC -# define EIGEN_CPUID(abcd,func,id) \ - __asm__ __volatile__ ("cpuid": "=a" (abcd[0]), "=b" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id) ); -# endif -# elif EIGEN_COMP_MSVC -# if (EIGEN_COMP_MSVC > 1500) && EIGEN_ARCH_i386_OR_x86_64 -# define EIGEN_CPUID(abcd,func,id) __cpuidex((int*)abcd,func,id) -# endif -# endif -#endif - -namespace internal { - -#ifdef EIGEN_CPUID - -inline bool cpuid_is_vendor(int abcd[4], const char* vendor) -{ - return abcd[1]==(reinterpret_cast<const int*>(vendor))[0] && abcd[3]==(reinterpret_cast<const int*>(vendor))[1] && abcd[2]==(reinterpret_cast<const int*>(vendor))[2]; -} - -inline void queryCacheSizes_intel_direct(int& l1, int& l2, int& l3) -{ - int abcd[4]; - l1 = l2 = l3 = 0; - int cache_id = 0; - int cache_type = 0; - do { - abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0; - EIGEN_CPUID(abcd,0x4,cache_id); - cache_type = (abcd[0] & 0x0F) >> 0; - if(cache_type==1||cache_type==3) // data or unified cache - { - int cache_level = (abcd[0] & 0xE0) >> 5; // A[7:5] - int ways = (abcd[1] & 0xFFC00000) >> 22; // B[31:22] - int partitions = (abcd[1] & 0x003FF000) >> 12; // B[21:12] - int line_size = (abcd[1] & 0x00000FFF) >> 0; // B[11:0] - int sets = (abcd[2]); // C[31:0] - - int cache_size = (ways+1) * (partitions+1) * (line_size+1) * (sets+1); - - switch(cache_level) - { - case 1: l1 = cache_size; break; - case 2: l2 = cache_size; break; - case 3: l3 = cache_size; break; - default: break; - } - } - cache_id++; - } while(cache_type>0 && cache_id<16); -} - -inline void queryCacheSizes_intel_codes(int& l1, int& l2, int& l3) -{ - int abcd[4]; - abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0; - l1 = l2 = l3 = 0; - EIGEN_CPUID(abcd,0x00000002,0); - unsigned char * bytes = reinterpret_cast<unsigned char *>(abcd)+2; - bool check_for_p2_core2 = false; - for(int i=0; i<14; ++i) - { - switch(bytes[i]) - { - case 0x0A: l1 = 8; break; // 0Ah data L1 cache, 8 KB, 2 ways, 32 byte lines - case 0x0C: l1 = 16; break; // 0Ch data L1 cache, 16 KB, 4 ways, 32 byte lines - case 0x0E: l1 = 24; break; // 0Eh data L1 cache, 24 KB, 6 ways, 64 byte lines - case 0x10: l1 = 16; break; // 10h data L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64) - case 0x15: l1 = 16; break; // 15h code L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64) - case 0x2C: l1 = 32; break; // 2Ch data L1 cache, 32 KB, 8 ways, 64 byte lines - case 0x30: l1 = 32; break; // 30h code L1 cache, 32 KB, 8 ways, 64 byte lines - case 0x60: l1 = 16; break; // 60h data L1 cache, 16 KB, 8 ways, 64 byte lines, sectored - case 0x66: l1 = 8; break; // 66h data L1 cache, 8 KB, 4 ways, 64 byte lines, sectored - case 0x67: l1 = 16; break; // 67h data L1 cache, 16 KB, 4 ways, 64 byte lines, sectored - case 0x68: l1 = 32; break; // 68h data L1 cache, 32 KB, 4 ways, 64 byte lines, sectored - case 0x1A: l2 = 96; break; // code and data L2 cache, 96 KB, 6 ways, 64 byte lines (IA-64) - case 0x22: l3 = 512; break; // code and data L3 cache, 512 KB, 4 ways (!), 64 byte lines, dual-sectored - case 0x23: l3 = 1024; break; // code and data L3 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored - case 0x25: l3 = 2048; break; // code and data L3 cache, 2048 KB, 8 ways, 64 byte lines, dual-sectored - case 0x29: l3 = 4096; break; // code and data L3 cache, 4096 KB, 8 ways, 64 byte lines, dual-sectored - case 0x39: l2 = 128; break; // code and data L2 cache, 128 KB, 4 ways, 64 byte lines, sectored - case 0x3A: l2 = 192; break; // code and data L2 cache, 192 KB, 6 ways, 64 byte lines, sectored - case 0x3B: l2 = 128; break; // code and data L2 cache, 128 KB, 2 ways, 64 byte lines, sectored - case 0x3C: l2 = 256; break; // code and data L2 cache, 256 KB, 4 ways, 64 byte lines, sectored - case 0x3D: l2 = 384; break; // code and data L2 cache, 384 KB, 6 ways, 64 byte lines, sectored - case 0x3E: l2 = 512; break; // code and data L2 cache, 512 KB, 4 ways, 64 byte lines, sectored - case 0x40: l2 = 0; break; // no integrated L2 cache (P6 core) or L3 cache (P4 core) - case 0x41: l2 = 128; break; // code and data L2 cache, 128 KB, 4 ways, 32 byte lines - case 0x42: l2 = 256; break; // code and data L2 cache, 256 KB, 4 ways, 32 byte lines - case 0x43: l2 = 512; break; // code and data L2 cache, 512 KB, 4 ways, 32 byte lines - case 0x44: l2 = 1024; break; // code and data L2 cache, 1024 KB, 4 ways, 32 byte lines - case 0x45: l2 = 2048; break; // code and data L2 cache, 2048 KB, 4 ways, 32 byte lines - case 0x46: l3 = 4096; break; // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines - case 0x47: l3 = 8192; break; // code and data L3 cache, 8192 KB, 8 ways, 64 byte lines - case 0x48: l2 = 3072; break; // code and data L2 cache, 3072 KB, 12 ways, 64 byte lines - case 0x49: if(l2!=0) l3 = 4096; else {check_for_p2_core2=true; l3 = l2 = 4096;} break;// code and data L3 cache, 4096 KB, 16 ways, 64 byte lines (P4) or L2 for core2 - case 0x4A: l3 = 6144; break; // code and data L3 cache, 6144 KB, 12 ways, 64 byte lines - case 0x4B: l3 = 8192; break; // code and data L3 cache, 8192 KB, 16 ways, 64 byte lines - case 0x4C: l3 = 12288; break; // code and data L3 cache, 12288 KB, 12 ways, 64 byte lines - case 0x4D: l3 = 16384; break; // code and data L3 cache, 16384 KB, 16 ways, 64 byte lines - case 0x4E: l2 = 6144; break; // code and data L2 cache, 6144 KB, 24 ways, 64 byte lines - case 0x78: l2 = 1024; break; // code and data L2 cache, 1024 KB, 4 ways, 64 byte lines - case 0x79: l2 = 128; break; // code and data L2 cache, 128 KB, 8 ways, 64 byte lines, dual-sectored - case 0x7A: l2 = 256; break; // code and data L2 cache, 256 KB, 8 ways, 64 byte lines, dual-sectored - case 0x7B: l2 = 512; break; // code and data L2 cache, 512 KB, 8 ways, 64 byte lines, dual-sectored - case 0x7C: l2 = 1024; break; // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored - case 0x7D: l2 = 2048; break; // code and data L2 cache, 2048 KB, 8 ways, 64 byte lines - case 0x7E: l2 = 256; break; // code and data L2 cache, 256 KB, 8 ways, 128 byte lines, sect. (IA-64) - case 0x7F: l2 = 512; break; // code and data L2 cache, 512 KB, 2 ways, 64 byte lines - case 0x80: l2 = 512; break; // code and data L2 cache, 512 KB, 8 ways, 64 byte lines - case 0x81: l2 = 128; break; // code and data L2 cache, 128 KB, 8 ways, 32 byte lines - case 0x82: l2 = 256; break; // code and data L2 cache, 256 KB, 8 ways, 32 byte lines - case 0x83: l2 = 512; break; // code and data L2 cache, 512 KB, 8 ways, 32 byte lines - case 0x84: l2 = 1024; break; // code and data L2 cache, 1024 KB, 8 ways, 32 byte lines - case 0x85: l2 = 2048; break; // code and data L2 cache, 2048 KB, 8 ways, 32 byte lines - case 0x86: l2 = 512; break; // code and data L2 cache, 512 KB, 4 ways, 64 byte lines - case 0x87: l2 = 1024; break; // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines - case 0x88: l3 = 2048; break; // code and data L3 cache, 2048 KB, 4 ways, 64 byte lines (IA-64) - case 0x89: l3 = 4096; break; // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines (IA-64) - case 0x8A: l3 = 8192; break; // code and data L3 cache, 8192 KB, 4 ways, 64 byte lines (IA-64) - case 0x8D: l3 = 3072; break; // code and data L3 cache, 3072 KB, 12 ways, 128 byte lines (IA-64) - - default: break; - } - } - if(check_for_p2_core2 && l2 == l3) - l3 = 0; - l1 *= 1024; - l2 *= 1024; - l3 *= 1024; -} - -inline void queryCacheSizes_intel(int& l1, int& l2, int& l3, int max_std_funcs) -{ - if(max_std_funcs>=4) - queryCacheSizes_intel_direct(l1,l2,l3); - else - queryCacheSizes_intel_codes(l1,l2,l3); -} - -inline void queryCacheSizes_amd(int& l1, int& l2, int& l3) -{ - int abcd[4]; - abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0; - EIGEN_CPUID(abcd,0x80000005,0); - l1 = (abcd[2] >> 24) * 1024; // C[31:24] = L1 size in KB - abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0; - EIGEN_CPUID(abcd,0x80000006,0); - l2 = (abcd[2] >> 16) * 1024; // C[31;16] = l2 cache size in KB - l3 = ((abcd[3] & 0xFFFC000) >> 18) * 512 * 1024; // D[31;18] = l3 cache size in 512KB -} -#endif - -/** \internal - * Queries and returns the cache sizes in Bytes of the L1, L2, and L3 data caches respectively */ -inline void queryCacheSizes(int& l1, int& l2, int& l3) -{ - #ifdef EIGEN_CPUID - int abcd[4]; - - // identify the CPU vendor - EIGEN_CPUID(abcd,0x0,0); - int max_std_funcs = abcd[1]; - if(cpuid_is_vendor(abcd,"GenuineIntel")) - queryCacheSizes_intel(l1,l2,l3,max_std_funcs); - else if(cpuid_is_vendor(abcd,"AuthenticAMD") || cpuid_is_vendor(abcd,"AMDisbetter!")) - queryCacheSizes_amd(l1,l2,l3); - else - // by default let's use Intel's API - queryCacheSizes_intel(l1,l2,l3,max_std_funcs); - - // here is the list of other vendors: -// ||cpuid_is_vendor(abcd,"VIA VIA VIA ") -// ||cpuid_is_vendor(abcd,"CyrixInstead") -// ||cpuid_is_vendor(abcd,"CentaurHauls") -// ||cpuid_is_vendor(abcd,"GenuineTMx86") -// ||cpuid_is_vendor(abcd,"TransmetaCPU") -// ||cpuid_is_vendor(abcd,"RiseRiseRise") -// ||cpuid_is_vendor(abcd,"Geode by NSC") -// ||cpuid_is_vendor(abcd,"SiS SiS SiS ") -// ||cpuid_is_vendor(abcd,"UMC UMC UMC ") -// ||cpuid_is_vendor(abcd,"NexGenDriven") - #else - l1 = l2 = l3 = -1; - #endif -} - -/** \internal - * \returns the size in Bytes of the L1 data cache */ -inline int queryL1CacheSize() -{ - int l1(-1), l2, l3; - queryCacheSizes(l1,l2,l3); - return l1; -} - -inline int queryL2CacheSize() -{ - int l1, l2(-1), l3; - queryCacheSizes(l1,l2,l3); - return l2; -} - -/** \internal - * \returns the size in Bytes of the L2 or L3 cache if this later is present */ -inline int queryTopLevelCacheSize() -{ - int l1, l2(-1), l3(-1); - queryCacheSizes(l1,l2,l3); - return (std::max)(l2,l3); -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_MEMORY_H diff --git a/third_party/eigen3/Eigen/src/Core/util/Meta.h b/third_party/eigen3/Eigen/src/Core/util/Meta.h deleted file mode 100644 index 7576b32689..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/Meta.h +++ /dev/null @@ -1,334 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_META_H -#define EIGEN_META_H - -#if defined(__CUDA_ARCH__) && !defined(__GCUDACC__) -#include <math_constants.h> -#endif - -namespace Eigen { - -namespace internal { - -/** \internal - * \file Meta.h - * This file contains generic metaprogramming classes which are not specifically related to Eigen. - * \note In case you wonder, yes we're aware that Boost already provides all these features, - * we however don't want to add a dependency to Boost. - */ - -struct true_type { enum { value = 1 }; }; -struct false_type { enum { value = 0 }; }; - -template<bool Condition, typename Then, typename Else> -struct conditional { typedef Then type; }; - -template<typename Then, typename Else> -struct conditional <false, Then, Else> { typedef Else type; }; - -template<typename T, typename U> struct is_same { enum { value = 0 }; }; -template<typename T> struct is_same<T,T> { enum { value = 1 }; }; - -template<typename T> struct remove_reference { typedef T type; }; -template<typename T> struct remove_reference<T&> { typedef T type; }; - -template<typename T> struct remove_pointer { typedef T type; }; -template<typename T> struct remove_pointer<T*> { typedef T type; }; -template<typename T> struct remove_pointer<T*const> { typedef T type; }; - -template <class T> struct remove_const { typedef T type; }; -template <class T> struct remove_const<const T> { typedef T type; }; -template <class T> struct remove_const<const T[]> { typedef T type[]; }; -template <class T, unsigned int Size> struct remove_const<const T[Size]> { typedef T type[Size]; }; - -template<typename T> struct remove_all { typedef T type; }; -template<typename T> struct remove_all<const T> { typedef typename remove_all<T>::type type; }; -template<typename T> struct remove_all<T const&> { typedef typename remove_all<T>::type type; }; -template<typename T> struct remove_all<T&> { typedef typename remove_all<T>::type type; }; -template<typename T> struct remove_all<T const*> { typedef typename remove_all<T>::type type; }; -template<typename T> struct remove_all<T*> { typedef typename remove_all<T>::type type; }; - -template<typename T> struct is_arithmetic { enum { value = false }; }; -template<> struct is_arithmetic<float> { enum { value = true }; }; -template<> struct is_arithmetic<double> { enum { value = true }; }; -template<> struct is_arithmetic<long double> { enum { value = true }; }; -template<> struct is_arithmetic<bool> { enum { value = true }; }; -template<> struct is_arithmetic<char> { enum { value = true }; }; -template<> struct is_arithmetic<signed char> { enum { value = true }; }; -template<> struct is_arithmetic<unsigned char> { enum { value = true }; }; -template<> struct is_arithmetic<signed short> { enum { value = true }; }; -template<> struct is_arithmetic<unsigned short>{ enum { value = true }; }; -template<> struct is_arithmetic<signed int> { enum { value = true }; }; -template<> struct is_arithmetic<unsigned int> { enum { value = true }; }; -template<> struct is_arithmetic<signed long> { enum { value = true }; }; -template<> struct is_arithmetic<unsigned long> { enum { value = true }; }; - -template <typename T> struct add_const { typedef const T type; }; -template <typename T> struct add_const<T&> { typedef T& type; }; - -template <typename T> struct is_const { enum { value = 0 }; }; -template <typename T> struct is_const<T const> { enum { value = 1 }; }; - -template<typename T> struct add_const_on_value_type { typedef const T type; }; -template<typename T> struct add_const_on_value_type<T&> { typedef T const& type; }; -template<typename T> struct add_const_on_value_type<T*> { typedef T const* type; }; -template<typename T> struct add_const_on_value_type<T* const> { typedef T const* const type; }; -template<typename T> struct add_const_on_value_type<T const* const> { typedef T const* const type; }; - -/** \internal Allows to enable/disable an overload - * according to a compile time condition. - */ -template<bool Condition, typename T> struct enable_if; - -template<typename T> struct enable_if<true,T> -{ typedef T type; }; - -#if defined(__CUDA_ARCH__) && !defined(__GCUDACC__) - -namespace device { - -template<typename T> struct numeric_limits -{ - EIGEN_DEVICE_FUNC - static T epsilon() { return 0; } - static T max() { assert(false && "Max not suppoted for this type"); } - static T lowest() { assert(false && "Lowest not suppoted for this type"); } -}; -template<> struct numeric_limits<float> -{ - EIGEN_DEVICE_FUNC - static float epsilon() { return __FLT_EPSILON__; } - EIGEN_DEVICE_FUNC - static float max() { return CUDART_MAX_NORMAL_F; } - EIGEN_DEVICE_FUNC - static float lowest() { return -CUDART_MAX_NORMAL_F; } -}; -template<> struct numeric_limits<double> -{ - EIGEN_DEVICE_FUNC - static double epsilon() { return __DBL_EPSILON__; } - EIGEN_DEVICE_FUNC - static double max() { return CUDART_INF; } - EIGEN_DEVICE_FUNC - static double lowest() { return -CUDART_INF; } -}; -template<> struct numeric_limits<int> -{ - EIGEN_DEVICE_FUNC - static int epsilon() { return 0; } - EIGEN_DEVICE_FUNC - static int max() { return INT_MAX; } - EIGEN_DEVICE_FUNC - static int lowest() { return INT_MIN; } -}; -template<> struct numeric_limits<long> -{ - EIGEN_DEVICE_FUNC - static long epsilon() { return 0; } - EIGEN_DEVICE_FUNC - static long max() { return LONG_MAX; } - EIGEN_DEVICE_FUNC - static long lowest() { return LONG_MIN; } -}; -template<> struct numeric_limits<long long> -{ - EIGEN_DEVICE_FUNC - static long long epsilon() { return 0; } - EIGEN_DEVICE_FUNC - static long long max() { return LLONG_MAX; } - EIGEN_DEVICE_FUNC - static long long lowest() { return LLONG_MIN; } -}; - -} - -#endif - -/** \internal - * A base class do disable default copy ctor and copy assignement operator. - */ -class noncopyable -{ - noncopyable(const noncopyable&); - const noncopyable& operator=(const noncopyable&); -protected: - noncopyable() {} - ~noncopyable() {} -}; - - -/** \internal - * Convenient struct to get the result type of a unary or binary functor. - * - * It supports both the current STL mechanism (using the result_type member) as well as - * upcoming next STL generation (using a templated result member). - * If none of these members is provided, then the type of the first argument is returned. FIXME, that behavior is a pretty bad hack. - */ -template<typename T> struct result_of {}; - -struct has_none {int a[1];}; -struct has_std_result_type {int a[2];}; -struct has_tr1_result {int a[3];}; - -template<typename Func, typename ArgType, int SizeOf=sizeof(has_none)> -struct unary_result_of_select {typedef ArgType type;}; - -template<typename Func, typename ArgType> -struct unary_result_of_select<Func, ArgType, sizeof(has_std_result_type)> {typedef typename Func::result_type type;}; - -template<typename Func, typename ArgType> -struct unary_result_of_select<Func, ArgType, sizeof(has_tr1_result)> {typedef typename Func::template result<Func(ArgType)>::type type;}; - -template<typename Func, typename ArgType> -struct result_of<Func(ArgType)> { - template<typename T> - static has_std_result_type testFunctor(T const *, typename T::result_type const * = 0); - template<typename T> - static has_tr1_result testFunctor(T const *, typename T::template result<T(ArgType)>::type const * = 0); - static has_none testFunctor(...); - - // note that the following indirection is needed for gcc-3.3 - enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))}; - typedef typename unary_result_of_select<Func, ArgType, FunctorType>::type type; -}; - -template<typename Func, typename ArgType0, typename ArgType1, int SizeOf=sizeof(has_none)> -struct binary_result_of_select {typedef ArgType0 type;}; - -template<typename Func, typename ArgType0, typename ArgType1> -struct binary_result_of_select<Func, ArgType0, ArgType1, sizeof(has_std_result_type)> -{typedef typename Func::result_type type;}; - -template<typename Func, typename ArgType0, typename ArgType1> -struct binary_result_of_select<Func, ArgType0, ArgType1, sizeof(has_tr1_result)> -{typedef typename Func::template result<Func(ArgType0,ArgType1)>::type type;}; - -template<typename Func, typename ArgType0, typename ArgType1> -struct result_of<Func(ArgType0,ArgType1)> { - template<typename T> - static has_std_result_type testFunctor(T const *, typename T::result_type const * = 0); - template<typename T> - static has_tr1_result testFunctor(T const *, typename T::template result<T(ArgType0,ArgType1)>::type const * = 0); - static has_none testFunctor(...); - - // note that the following indirection is needed for gcc-3.3 - enum {FunctorType = sizeof(testFunctor(static_cast<Func*>(0)))}; - typedef typename binary_result_of_select<Func, ArgType0, ArgType1, FunctorType>::type type; -}; - -/** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer. - * Usage example: \code meta_sqrt<1023>::ret \endcode - */ -template<int Y, - int InfX = 0, - int SupX = ((Y==1) ? 1 : Y/2), - bool Done = ((SupX-InfX)<=1 ? true : ((SupX*SupX <= Y) && ((SupX+1)*(SupX+1) > Y))) > - // use ?: instead of || just to shut up a stupid gcc 4.3 warning -class meta_sqrt -{ - enum { - MidX = (InfX+SupX)/2, - TakeInf = MidX*MidX > Y ? 1 : 0, - NewInf = int(TakeInf) ? InfX : int(MidX), - NewSup = int(TakeInf) ? int(MidX) : SupX - }; - public: - enum { ret = meta_sqrt<Y,NewInf,NewSup>::ret }; -}; - -template<int Y, int InfX, int SupX> -class meta_sqrt<Y, InfX, SupX, true> { public: enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; }; - -/** \internal determines whether the product of two numeric types is allowed and what the return type is */ -template<typename T, typename U> struct scalar_product_traits -{ - enum { Defined = 0 }; -}; - -template<typename T> struct scalar_product_traits<T,T> -{ - enum { - // Cost = NumTraits<T>::MulCost, - Defined = 1 - }; - typedef T ReturnType; -}; - -template<typename T> struct scalar_product_traits<T, const T> -{ - enum { - // Cost = NumTraits<T>::MulCost, - Defined = 1 - }; - typedef T ReturnType; -}; - -template<typename T> struct scalar_product_traits<const T, T> -{ - enum { - // Cost = NumTraits<T>::MulCost, - Defined = 1 - }; - typedef T ReturnType; -}; - -template<typename T> struct scalar_product_traits<T,std::complex<T> > -{ - enum { - // Cost = 2*NumTraits<T>::MulCost, - Defined = 1 - }; - typedef std::complex<T> ReturnType; -}; - -template<typename T> struct scalar_product_traits<std::complex<T>, T> -{ - enum { - // Cost = 2*NumTraits<T>::MulCost, - Defined = 1 - }; - typedef std::complex<T> ReturnType; -}; - -// FIXME quick workaround around current limitation of result_of -// template<typename Scalar, typename ArgType0, typename ArgType1> -// struct result_of<scalar_product_op<Scalar>(ArgType0,ArgType1)> { -// typedef typename scalar_product_traits<typename remove_all<ArgType0>::type, typename remove_all<ArgType1>::type>::ReturnType type; -// }; - -template<typename T> struct is_diagonal -{ enum { ret = false }; }; - -template<typename T> struct is_diagonal<DiagonalBase<T> > -{ enum { ret = true }; }; - -template<typename T> struct is_diagonal<DiagonalWrapper<T> > -{ enum { ret = true }; }; - -template<typename T, int S> struct is_diagonal<DiagonalMatrix<T,S> > -{ enum { ret = true }; }; - -} // end namespace internal - -namespace numext { - -#if defined(__CUDA_ARCH__) -template<typename T> EIGEN_DEVICE_FUNC void swap(T &a, T &b) { T tmp = b; b = a; a = tmp; } -#else -template<typename T> EIGEN_STRONG_INLINE void swap(T &a, T &b) { std::swap(a,b); } -#endif - -} // end namespace numext - -} // end namespace Eigen - -#endif // EIGEN_META_H diff --git a/third_party/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h b/third_party/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h deleted file mode 100644 index 5ddfbd4aa6..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/ReenableStupidWarnings.h +++ /dev/null @@ -1,14 +0,0 @@ -#ifdef EIGEN_WARNINGS_DISABLED -#undef EIGEN_WARNINGS_DISABLED - -#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS - #ifdef _MSC_VER - #pragma warning( pop ) - #elif defined __INTEL_COMPILER - #pragma warning pop - #elif defined __clang__ - #pragma clang diagnostic pop - #endif -#endif - -#endif // EIGEN_WARNINGS_DISABLED diff --git a/third_party/eigen3/Eigen/src/Core/util/StaticAssert.h b/third_party/eigen3/Eigen/src/Core/util/StaticAssert.h deleted file mode 100644 index 461c52fba9..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/StaticAssert.h +++ /dev/null @@ -1,207 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_STATIC_ASSERT_H -#define EIGEN_STATIC_ASSERT_H - -/* Some notes on Eigen's static assertion mechanism: - * - * - in EIGEN_STATIC_ASSERT(CONDITION,MSG) the parameter CONDITION must be a compile time boolean - * expression, and MSG an enum listed in struct internal::static_assertion<true> - * - * - define EIGEN_NO_STATIC_ASSERT to disable them (and save compilation time) - * in that case, the static assertion is converted to the following runtime assert: - * eigen_assert(CONDITION && "MSG") - * - * - currently EIGEN_STATIC_ASSERT can only be used in function scope - * - */ - -#ifndef EIGEN_NO_STATIC_ASSERT - - #if defined(__GXX_EXPERIMENTAL_CXX0X__) || (EIGEN_COMP_MSVC >= 1600) - - // if native static_assert is enabled, let's use it - #define EIGEN_STATIC_ASSERT(X,MSG) static_assert(X,#MSG); - - #else // not CXX0X - - namespace Eigen { - - namespace internal { - - template<bool condition> - struct static_assertion {}; - - template<> - struct static_assertion<true> - { - enum { - YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX, - YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES, - YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES, - THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE, - THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE, - THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE, - YOU_MADE_A_PROGRAMMING_MISTAKE, - EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT, - EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE, - YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR, - YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR, - UNALIGNED_LOAD_AND_STORE_OPERATIONS_UNIMPLEMENTED_ON_ALTIVEC, - THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES, - FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED, - NUMERIC_TYPE_MUST_BE_REAL, - COEFFICIENT_WRITE_ACCESS_TO_SELFADJOINT_NOT_SUPPORTED, - WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED, - THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE, - INVALID_MATRIX_PRODUCT, - INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS, - INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION, - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY, - THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES, - THIS_METHOD_IS_ONLY_FOR_ROW_MAJOR_MATRICES, - INVALID_MATRIX_TEMPLATE_PARAMETERS, - INVALID_MATRIXBASE_TEMPLATE_PARAMETERS, - BOTH_MATRICES_MUST_HAVE_THE_SAME_STORAGE_ORDER, - THIS_METHOD_IS_ONLY_FOR_DIAGONAL_MATRIX, - THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE, - THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_WITH_DIRECT_MEMORY_ACCESS_SUCH_AS_MAP_OR_PLAIN_MATRICES, - YOU_ALREADY_SPECIFIED_THIS_STRIDE, - INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION, - THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD, - PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1, - THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS, - YOU_CANNOT_MIX_ARRAYS_AND_MATRICES, - YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION, - THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY, - YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT, - THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS, - THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL, - THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES, - YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED, - YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED, - THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE, - THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH, - OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG, - THIS_TYPE_IS_NOT_SUPPORTED - }; - }; - - } // end namespace internal - - } // end namespace Eigen - - // Specialized implementation for MSVC to avoid "conditional - // expression is constant" warnings. This implementation doesn't - // appear to work under GCC, hence the multiple implementations. - #if EIGEN_COMP_MSVC - - #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \ - {Eigen::internal::static_assertion<bool(CONDITION)>::MSG;} - - #else - // In some cases clang interprets bool(CONDITION) as function declaration - #define EIGEN_STATIC_ASSERT(CONDITION,MSG) \ - if (Eigen::internal::static_assertion<static_cast<bool>(CONDITION)>::MSG) {} - - #endif - - #endif // not CXX0X - -#else // EIGEN_NO_STATIC_ASSERT - - #define EIGEN_STATIC_ASSERT(CONDITION,MSG) eigen_assert((CONDITION) && #MSG); - -#endif // EIGEN_NO_STATIC_ASSERT - - -// static assertion failing if the type \a TYPE is not a vector type -#define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE) \ - EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime, \ - YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX) - -// static assertion failing if the type \a TYPE is not fixed-size -#define EIGEN_STATIC_ASSERT_FIXED_SIZE(TYPE) \ - EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime!=Eigen::Dynamic, \ - YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR) - -// static assertion failing if the type \a TYPE is not dynamic-size -#define EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(TYPE) \ - EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime==Eigen::Dynamic, \ - YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR) - -// static assertion failing if the type \a TYPE is not a vector type of the given size -#define EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(TYPE, SIZE) \ - EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime && TYPE::SizeAtCompileTime==SIZE, \ - THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE) - -// static assertion failing if the type \a TYPE is not a vector type of the given size -#define EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(TYPE, ROWS, COLS) \ - EIGEN_STATIC_ASSERT(TYPE::RowsAtCompileTime==ROWS && TYPE::ColsAtCompileTime==COLS, \ - THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE) - -// static assertion failing if the two vector expression types are not compatible (same fixed-size or dynamic size) -#define EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(TYPE0,TYPE1) \ - EIGEN_STATIC_ASSERT( \ - (int(TYPE0::SizeAtCompileTime)==Eigen::Dynamic \ - || int(TYPE1::SizeAtCompileTime)==Eigen::Dynamic \ - || int(TYPE0::SizeAtCompileTime)==int(TYPE1::SizeAtCompileTime)),\ - YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES) - -#define EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1) \ - ( \ - (int(TYPE0::SizeAtCompileTime)==0 && int(TYPE1::SizeAtCompileTime)==0) \ - || (\ - (int(TYPE0::RowsAtCompileTime)==Eigen::Dynamic \ - || int(TYPE1::RowsAtCompileTime)==Eigen::Dynamic \ - || int(TYPE0::RowsAtCompileTime)==int(TYPE1::RowsAtCompileTime)) \ - && (int(TYPE0::ColsAtCompileTime)==Eigen::Dynamic \ - || int(TYPE1::ColsAtCompileTime)==Eigen::Dynamic \ - || int(TYPE0::ColsAtCompileTime)==int(TYPE1::ColsAtCompileTime))\ - ) \ - ) - -#ifdef EIGEN2_SUPPORT - #define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \ - eigen_assert(!NumTraits<Scalar>::IsInteger); -#else - #define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \ - EIGEN_STATIC_ASSERT(!NumTraits<TYPE>::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES) -#endif - - -// static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different sizes -#define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0,TYPE1) \ - EIGEN_STATIC_ASSERT( \ - EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1),\ - YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES) - -#define EIGEN_STATIC_ASSERT_SIZE_1x1(TYPE) \ - EIGEN_STATIC_ASSERT((TYPE::RowsAtCompileTime == 1 || TYPE::RowsAtCompileTime == Dynamic) && \ - (TYPE::ColsAtCompileTime == 1 || TYPE::ColsAtCompileTime == Dynamic), \ - THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS) - -#define EIGEN_STATIC_ASSERT_LVALUE(Derived) \ - EIGEN_STATIC_ASSERT(internal::is_lvalue<Derived>::value, \ - THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY) - -#define EIGEN_STATIC_ASSERT_ARRAYXPR(Derived) \ - EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Derived>::XprKind, ArrayXpr>::value), \ - THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES) - -#define EIGEN_STATIC_ASSERT_SAME_XPR_KIND(Derived1, Derived2) \ - EIGEN_STATIC_ASSERT((internal::is_same<typename internal::traits<Derived1>::XprKind, \ - typename internal::traits<Derived2>::XprKind \ - >::value), \ - YOU_CANNOT_MIX_ARRAYS_AND_MATRICES) - - -#endif // EIGEN_STATIC_ASSERT_H diff --git a/third_party/eigen3/Eigen/src/Core/util/XprHelper.h b/third_party/eigen3/Eigen/src/Core/util/XprHelper.h deleted file mode 100644 index 13285909b4..0000000000 --- a/third_party/eigen3/Eigen/src/Core/util/XprHelper.h +++ /dev/null @@ -1,481 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_XPRHELPER_H -#define EIGEN_XPRHELPER_H - -// just a workaround because GCC seems to not really like empty structs -// FIXME: gcc 4.3 generates bad code when strict-aliasing is enabled -// so currently we simply disable this optimization for gcc 4.3 -#if EIGEN_COMP_GNUC && !EIGEN_GNUC_AT(4,3) - #define EIGEN_EMPTY_STRUCT_CTOR(X) \ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X() {} \ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE X(const X& ) {} -#else - #define EIGEN_EMPTY_STRUCT_CTOR(X) -#endif - -namespace Eigen { - -typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex; - -namespace internal { - -//classes inheriting no_assignment_operator don't generate a default operator=. -class no_assignment_operator -{ - private: - no_assignment_operator& operator=(const no_assignment_operator&); -}; - -/** \internal return the index type with the largest number of bits */ -template<typename I1, typename I2> -struct promote_index_type -{ - typedef typename conditional<(sizeof(I1)<sizeof(I2)), I2, I1>::type type; -}; - -/** \internal If the template parameter Value is Dynamic, this class is just a wrapper around a T variable that - * can be accessed using value() and setValue(). - * Otherwise, this class is an empty structure and value() just returns the template parameter Value. - */ -template<typename T, int Value> class variable_if_dynamic -{ - public: - EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamic) - EIGEN_DEVICE_FUNC explicit variable_if_dynamic(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); } - EIGEN_DEVICE_FUNC static T value() { return T(Value); } - EIGEN_DEVICE_FUNC void setValue(T) {} -}; - -template<typename T> class variable_if_dynamic<T, Dynamic> -{ - T m_value; - EIGEN_DEVICE_FUNC variable_if_dynamic() { eigen_assert(false); } - public: - EIGEN_DEVICE_FUNC explicit variable_if_dynamic(T value) : m_value(value) {} - EIGEN_DEVICE_FUNC T value() const { return m_value; } - EIGEN_DEVICE_FUNC void setValue(T value) { m_value = value; } -}; - -/** \internal like variable_if_dynamic but for DynamicIndex - */ -template<typename T, int Value> class variable_if_dynamicindex -{ - public: - EIGEN_EMPTY_STRUCT_CTOR(variable_if_dynamicindex) - EIGEN_DEVICE_FUNC explicit variable_if_dynamicindex(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); } - EIGEN_DEVICE_FUNC static T value() { return T(Value); } - EIGEN_DEVICE_FUNC void setValue(T) {} -}; - -template<typename T> class variable_if_dynamicindex<T, DynamicIndex> -{ - T m_value; - EIGEN_DEVICE_FUNC variable_if_dynamicindex() { eigen_assert(false); } - public: - EIGEN_DEVICE_FUNC explicit variable_if_dynamicindex(T value) : m_value(value) {} - EIGEN_DEVICE_FUNC T value() const { return m_value; } - EIGEN_DEVICE_FUNC void setValue(T value) { m_value = value; } -}; - -template<typename T> struct functor_traits -{ - enum - { - Cost = 10, - PacketAccess = false, - IsRepeatable = false - }; -}; - -template<typename T> struct packet_traits; - -template<typename T> struct unpacket_traits -{ - typedef T type; - typedef T half; - enum {size=1}; -}; - -template<typename _Scalar, int _Rows, int _Cols, - int _Options = AutoAlign | - ( (_Rows==1 && _Cols!=1) ? RowMajor - : (_Cols==1 && _Rows!=1) ? ColMajor - : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ), - int _MaxRows = _Rows, - int _MaxCols = _Cols -> class make_proper_matrix_type -{ - enum { - IsColVector = _Cols==1 && _Rows!=1, - IsRowVector = _Rows==1 && _Cols!=1, - Options = IsColVector ? (_Options | ColMajor) & ~RowMajor - : IsRowVector ? (_Options | RowMajor) & ~ColMajor - : _Options - }; - public: - typedef Matrix<_Scalar, _Rows, _Cols, Options, _MaxRows, _MaxCols> type; -}; - -template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols> -class compute_matrix_flags -{ - enum { - row_major_bit = Options&RowMajor ? RowMajorBit : 0, - is_dynamic_size_storage = MaxRows==Dynamic || MaxCols==Dynamic, - - aligned_bit = - ( - ((Options&DontAlign)==0) - && ( -#if EIGEN_ALIGN_STATICALLY - ((!is_dynamic_size_storage) && (((MaxCols*MaxRows*int(sizeof(Scalar))) % EIGEN_ALIGN_BYTES) == 0)) -#else - 0 -#endif - - || - -#if EIGEN_ALIGN - is_dynamic_size_storage -#else - 0 -#endif - - ) - ) ? AlignedBit : 0, - packet_access_bit = packet_traits<Scalar>::Vectorizable && aligned_bit ? PacketAccessBit : 0 - }; - - public: - enum { ret = LinearAccessBit | LvalueBit | DirectAccessBit | NestByRefBit | packet_access_bit | row_major_bit | aligned_bit }; -}; - -template<int _Rows, int _Cols> struct size_at_compile_time -{ - enum { ret = (_Rows==Dynamic || _Cols==Dynamic) ? Dynamic : _Rows * _Cols }; -}; - -/* plain_matrix_type : the difference from eval is that plain_matrix_type is always a plain matrix type, - * whereas eval is a const reference in the case of a matrix - */ - -template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_matrix_type; -template<typename T, typename BaseClassType> struct plain_matrix_type_dense; -template<typename T> struct plain_matrix_type<T,Dense> -{ - typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind>::type type; -}; - -template<typename T> struct plain_matrix_type_dense<T,MatrixXpr> -{ - typedef Matrix<typename traits<T>::Scalar, - traits<T>::RowsAtCompileTime, - traits<T>::ColsAtCompileTime, - AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor), - traits<T>::MaxRowsAtCompileTime, - traits<T>::MaxColsAtCompileTime - > type; -}; - -template<typename T> struct plain_matrix_type_dense<T,ArrayXpr> -{ - typedef Array<typename traits<T>::Scalar, - traits<T>::RowsAtCompileTime, - traits<T>::ColsAtCompileTime, - AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor), - traits<T>::MaxRowsAtCompileTime, - traits<T>::MaxColsAtCompileTime - > type; -}; - -/* eval : the return type of eval(). For matrices, this is just a const reference - * in order to avoid a useless copy - */ - -template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct eval; - -template<typename T> struct eval<T,Dense> -{ - typedef typename plain_matrix_type<T>::type type; -// typedef typename T::PlainObject type; -// typedef T::Matrix<typename traits<T>::Scalar, -// traits<T>::RowsAtCompileTime, -// traits<T>::ColsAtCompileTime, -// AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor), -// traits<T>::MaxRowsAtCompileTime, -// traits<T>::MaxColsAtCompileTime -// > type; -}; - -// for matrices, no need to evaluate, just use a const reference to avoid a useless copy -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -struct eval<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense> -{ - typedef const Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type; -}; - -template<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols> -struct eval<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense> -{ - typedef const Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>& type; -}; - - - -/* plain_matrix_type_column_major : same as plain_matrix_type but guaranteed to be column-major - */ -template<typename T> struct plain_matrix_type_column_major -{ - enum { Rows = traits<T>::RowsAtCompileTime, - Cols = traits<T>::ColsAtCompileTime, - MaxRows = traits<T>::MaxRowsAtCompileTime, - MaxCols = traits<T>::MaxColsAtCompileTime - }; - typedef Matrix<typename traits<T>::Scalar, - Rows, - Cols, - (MaxRows==1&&MaxCols!=1) ? RowMajor : ColMajor, - MaxRows, - MaxCols - > type; -}; - -/* plain_matrix_type_row_major : same as plain_matrix_type but guaranteed to be row-major - */ -template<typename T> struct plain_matrix_type_row_major -{ - enum { Rows = traits<T>::RowsAtCompileTime, - Cols = traits<T>::ColsAtCompileTime, - MaxRows = traits<T>::MaxRowsAtCompileTime, - MaxCols = traits<T>::MaxColsAtCompileTime - }; - typedef Matrix<typename traits<T>::Scalar, - Rows, - Cols, - (MaxCols==1&&MaxRows!=1) ? RowMajor : ColMajor, - MaxRows, - MaxCols - > type; -}; - -// we should be able to get rid of this one too -template<typename T> struct must_nest_by_value { enum { ret = false }; }; - -/** \internal The reference selector for template expressions. The idea is that we don't - * need to use references for expressions since they are light weight proxy - * objects which should generate no copying overhead. */ -template <typename T> -struct ref_selector -{ - typedef typename conditional< - bool(traits<T>::Flags & NestByRefBit), - T const&, - const T - >::type type; -}; - -/** \internal Adds the const qualifier on the value-type of T2 if and only if T1 is a const type */ -template<typename T1, typename T2> -struct transfer_constness -{ - typedef typename conditional< - bool(internal::is_const<T1>::value), - typename internal::add_const_on_value_type<T2>::type, - T2 - >::type type; -}; - -/** \internal Determines how a given expression should be nested into another one. - * For example, when you do a * (b+c), Eigen will determine how the expression b+c should be - * nested into the bigger product expression. The choice is between nesting the expression b+c as-is, or - * evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is - * a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes - * many coefficient accesses in the nested expressions -- as is the case with matrix product for example. - * - * \param T the type of the expression being nested - * \param n the number of coefficient accesses in the nested expression for each coefficient access in the bigger expression. - * - * Note that if no evaluation occur, then the constness of T is preserved. - * - * Example. Suppose that a, b, and c are of type Matrix3d. The user forms the expression a*(b+c). - * b+c is an expression "sum of matrices", which we will denote by S. In order to determine how to nest it, - * the Product expression uses: nested<S, 3>::type, which turns out to be Matrix3d because the internal logic of - * nested determined that in this case it was better to evaluate the expression b+c into a temporary. On the other hand, - * since a is of type Matrix3d, the Product expression nests it as nested<Matrix3d, 3>::type, which turns out to be - * const Matrix3d&, because the internal logic of nested determined that since a was already a matrix, there was no point - * in copying it into another matrix. - */ -template<typename T, int n=1, typename PlainObject = typename eval<T>::type> struct nested -{ - enum { - // for the purpose of this test, to keep it reasonably simple, we arbitrarily choose a value of Dynamic values. - // the choice of 10000 makes it larger than any practical fixed value and even most dynamic values. - // in extreme cases where these assumptions would be wrong, we would still at worst suffer performance issues - // (poor choice of temporaries). - // it's important that this value can still be squared without integer overflowing. - DynamicAsInteger = 10000, - ScalarReadCost = NumTraits<typename traits<T>::Scalar>::ReadCost, - ScalarReadCostAsInteger = ScalarReadCost == Dynamic ? int(DynamicAsInteger) : int(ScalarReadCost), - CoeffReadCost = traits<T>::CoeffReadCost, - CoeffReadCostAsInteger = CoeffReadCost == Dynamic ? int(DynamicAsInteger) : int(CoeffReadCost), - NAsInteger = n == Dynamic ? int(DynamicAsInteger) : n, - CostEvalAsInteger = (NAsInteger+1) * ScalarReadCostAsInteger + CoeffReadCostAsInteger, - CostNoEvalAsInteger = NAsInteger * CoeffReadCostAsInteger - }; - - typedef typename conditional< - ( (int(traits<T>::Flags) & EvalBeforeNestingBit) || - int(CostEvalAsInteger) < int(CostNoEvalAsInteger) - ), - PlainObject, - typename ref_selector<T>::type - >::type type; -}; - -template<typename T> -EIGEN_DEVICE_FUNC -T* const_cast_ptr(const T* ptr) -{ - return const_cast<T*>(ptr); -} - -template<typename Derived, typename XprKind = typename traits<Derived>::XprKind> -struct dense_xpr_base -{ - /* dense_xpr_base should only ever be used on dense expressions, thus falling either into the MatrixXpr or into the ArrayXpr cases */ -}; - -template<typename Derived> -struct dense_xpr_base<Derived, MatrixXpr> -{ - typedef MatrixBase<Derived> type; -}; - -template<typename Derived> -struct dense_xpr_base<Derived, ArrayXpr> -{ - typedef ArrayBase<Derived> type; -}; - -/** \internal Helper base class to add a scalar multiple operator - * overloads for complex types */ -template<typename Derived,typename Scalar,typename OtherScalar, - bool EnableIt = !is_same<Scalar,OtherScalar>::value > -struct special_scalar_op_base : public DenseCoeffsBase<Derived> -{ - // dummy operator* so that the - // "using special_scalar_op_base::operator*" compiles - void operator*() const; -}; - -template<typename Derived,typename Scalar,typename OtherScalar> -struct special_scalar_op_base<Derived,Scalar,OtherScalar,true> : public DenseCoeffsBase<Derived> -{ - const CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived> - operator*(const OtherScalar& scalar) const - { - return CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived> - (*static_cast<const Derived*>(this), scalar_multiple2_op<Scalar,OtherScalar>(scalar)); - } - - inline friend const CwiseUnaryOp<scalar_multiple2_op<Scalar,OtherScalar>, Derived> - operator*(const OtherScalar& scalar, const Derived& matrix) - { return static_cast<const special_scalar_op_base&>(matrix).operator*(scalar); } -}; - -template<typename XprType, typename CastType> struct cast_return_type -{ - typedef typename XprType::Scalar CurrentScalarType; - typedef typename remove_all<CastType>::type _CastType; - typedef typename _CastType::Scalar NewScalarType; - typedef typename conditional<is_same<CurrentScalarType,NewScalarType>::value, - const XprType&,CastType>::type type; -}; - -template <typename A, typename B> struct promote_storage_type; - -template <typename A> struct promote_storage_type<A,A> -{ - typedef A ret; -}; -template <typename A> struct promote_storage_type<A, const A> -{ - typedef A ret; -}; -template <typename A> struct promote_storage_type<const A, A> -{ - typedef A ret; -}; - - - -/** \internal gives the plain matrix or array type to store a row/column/diagonal of a matrix type. - * \param Scalar optional parameter allowing to pass a different scalar type than the one of the MatrixType. - */ -template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar> -struct plain_row_type -{ - typedef Matrix<Scalar, 1, ExpressionType::ColsAtCompileTime, - ExpressionType::PlainObject::Options | RowMajor, 1, ExpressionType::MaxColsAtCompileTime> MatrixRowType; - typedef Array<Scalar, 1, ExpressionType::ColsAtCompileTime, - ExpressionType::PlainObject::Options | RowMajor, 1, ExpressionType::MaxColsAtCompileTime> ArrayRowType; - - typedef typename conditional< - is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value, - MatrixRowType, - ArrayRowType - >::type type; -}; - -template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar> -struct plain_col_type -{ - typedef Matrix<Scalar, ExpressionType::RowsAtCompileTime, 1, - ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> MatrixColType; - typedef Array<Scalar, ExpressionType::RowsAtCompileTime, 1, - ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> ArrayColType; - - typedef typename conditional< - is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value, - MatrixColType, - ArrayColType - >::type type; -}; - -template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar> -struct plain_diag_type -{ - enum { diag_size = EIGEN_SIZE_MIN_PREFER_DYNAMIC(ExpressionType::RowsAtCompileTime, ExpressionType::ColsAtCompileTime), - max_diag_size = EIGEN_SIZE_MIN_PREFER_FIXED(ExpressionType::MaxRowsAtCompileTime, ExpressionType::MaxColsAtCompileTime) - }; - typedef Matrix<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> MatrixDiagType; - typedef Array<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> ArrayDiagType; - - typedef typename conditional< - is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value, - MatrixDiagType, - ArrayDiagType - >::type type; -}; - -template<typename ExpressionType> -struct is_lvalue -{ - enum { value = !bool(is_const<ExpressionType>::value) && - bool(traits<ExpressionType>::Flags & LvalueBit) }; -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_XPRHELPER_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Block.h b/third_party/eigen3/Eigen/src/Eigen2Support/Block.h deleted file mode 100644 index 604456f40e..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Block.h +++ /dev/null @@ -1,126 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BLOCK2_H -#define EIGEN_BLOCK2_H - -namespace Eigen { - -/** \returns a dynamic-size expression of a corner of *this. - * - * \param type the type of corner. Can be \a Eigen::TopLeft, \a Eigen::TopRight, - * \a Eigen::BottomLeft, \a Eigen::BottomRight. - * \param cRows the number of rows in the corner - * \param cCols the number of columns in the corner - * - * Example: \include MatrixBase_corner_enum_int_int.cpp - * Output: \verbinclude MatrixBase_corner_enum_int_int.out - * - * \note Even though the returned expression has dynamic size, in the case - * when it is applied to a fixed-size matrix, it inherits a fixed maximal size, - * which means that evaluating it does not cause a dynamic memory allocation. - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<typename Derived> -inline Block<Derived> DenseBase<Derived> - ::corner(CornerType type, Index cRows, Index cCols) -{ - switch(type) - { - default: - eigen_assert(false && "Bad corner type."); - case TopLeft: - return Block<Derived>(derived(), 0, 0, cRows, cCols); - case TopRight: - return Block<Derived>(derived(), 0, cols() - cCols, cRows, cCols); - case BottomLeft: - return Block<Derived>(derived(), rows() - cRows, 0, cRows, cCols); - case BottomRight: - return Block<Derived>(derived(), rows() - cRows, cols() - cCols, cRows, cCols); - } -} - -/** This is the const version of corner(CornerType, Index, Index).*/ -template<typename Derived> -inline const Block<Derived> -DenseBase<Derived>::corner(CornerType type, Index cRows, Index cCols) const -{ - switch(type) - { - default: - eigen_assert(false && "Bad corner type."); - case TopLeft: - return Block<Derived>(derived(), 0, 0, cRows, cCols); - case TopRight: - return Block<Derived>(derived(), 0, cols() - cCols, cRows, cCols); - case BottomLeft: - return Block<Derived>(derived(), rows() - cRows, 0, cRows, cCols); - case BottomRight: - return Block<Derived>(derived(), rows() - cRows, cols() - cCols, cRows, cCols); - } -} - -/** \returns a fixed-size expression of a corner of *this. - * - * \param type the type of corner. Can be \a Eigen::TopLeft, \a Eigen::TopRight, - * \a Eigen::BottomLeft, \a Eigen::BottomRight. - * - * The template parameters CRows and CCols arethe number of rows and columns in the corner. - * - * Example: \include MatrixBase_template_int_int_corner_enum.cpp - * Output: \verbinclude MatrixBase_template_int_int_corner_enum.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<typename Derived> -template<int CRows, int CCols> -inline Block<Derived, CRows, CCols> -DenseBase<Derived>::corner(CornerType type) -{ - switch(type) - { - default: - eigen_assert(false && "Bad corner type."); - case TopLeft: - return Block<Derived, CRows, CCols>(derived(), 0, 0); - case TopRight: - return Block<Derived, CRows, CCols>(derived(), 0, cols() - CCols); - case BottomLeft: - return Block<Derived, CRows, CCols>(derived(), rows() - CRows, 0); - case BottomRight: - return Block<Derived, CRows, CCols>(derived(), rows() - CRows, cols() - CCols); - } -} - -/** This is the const version of corner<int, int>(CornerType).*/ -template<typename Derived> -template<int CRows, int CCols> -inline const Block<Derived, CRows, CCols> -DenseBase<Derived>::corner(CornerType type) const -{ - switch(type) - { - default: - eigen_assert(false && "Bad corner type."); - case TopLeft: - return Block<Derived, CRows, CCols>(derived(), 0, 0); - case TopRight: - return Block<Derived, CRows, CCols>(derived(), 0, cols() - CCols); - case BottomLeft: - return Block<Derived, CRows, CCols>(derived(), rows() - CRows, 0); - case BottomRight: - return Block<Derived, CRows, CCols>(derived(), rows() - CRows, cols() - CCols); - } -} - -} // end namespace Eigen - -#endif // EIGEN_BLOCK2_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Cwise.h b/third_party/eigen3/Eigen/src/Eigen2Support/Cwise.h deleted file mode 100644 index d95009b6e2..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Cwise.h +++ /dev/null @@ -1,192 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CWISE_H -#define EIGEN_CWISE_H - -namespace Eigen { - -/** \internal - * convenient macro to defined the return type of a cwise binary operation */ -#define EIGEN_CWISE_BINOP_RETURN_TYPE(OP) \ - CwiseBinaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType, OtherDerived> - -/** \internal - * convenient macro to defined the return type of a cwise unary operation */ -#define EIGEN_CWISE_UNOP_RETURN_TYPE(OP) \ - CwiseUnaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType> - -/** \internal - * convenient macro to defined the return type of a cwise comparison to a scalar */ -#define EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(OP) \ - CwiseBinaryOp<OP<typename internal::traits<ExpressionType>::Scalar>, ExpressionType, \ - typename ExpressionType::ConstantReturnType > - -/** \class Cwise - * - * \brief Pseudo expression providing additional coefficient-wise operations - * - * \param ExpressionType the type of the object on which to do coefficient-wise operations - * - * This class represents an expression with additional coefficient-wise features. - * It is the return type of MatrixBase::cwise() - * and most of the time this is the only way it is used. - * - * Example: \include MatrixBase_cwise_const.cpp - * Output: \verbinclude MatrixBase_cwise_const.out - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_CWISE_PLUGIN. - * - * \sa MatrixBase::cwise() const, MatrixBase::cwise() - */ -template<typename ExpressionType> class Cwise -{ - public: - - typedef typename internal::traits<ExpressionType>::Scalar Scalar; - typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret, - ExpressionType, const ExpressionType&>::type ExpressionTypeNested; - typedef CwiseUnaryOp<internal::scalar_add_op<Scalar>, ExpressionType> ScalarAddReturnType; - - inline Cwise(const ExpressionType& matrix) : m_matrix(matrix) {} - - /** \internal */ - inline const ExpressionType& _expression() const { return m_matrix; } - - template<typename OtherDerived> - const EIGEN_CWISE_PRODUCT_RETURN_TYPE(ExpressionType,OtherDerived) - operator*(const MatrixBase<OtherDerived> &other) const; - - template<typename OtherDerived> - const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op) - operator/(const MatrixBase<OtherDerived> &other) const; - - /** \deprecated ArrayBase::min() */ - template<typename OtherDerived> - const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op) - (min)(const MatrixBase<OtherDerived> &other) const - { return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)(_expression(), other.derived()); } - - /** \deprecated ArrayBase::max() */ - template<typename OtherDerived> - const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op) - (max)(const MatrixBase<OtherDerived> &other) const - { return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)(_expression(), other.derived()); } - - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs_op) abs() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs2_op) abs2() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_square_op) square() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cube_op) cube() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_inverse_op) inverse() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sqrt_op) sqrt() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_exp_op) exp() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_log_op) log() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cos_op) cos() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sin_op) sin() const; - const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_pow_op) pow(const Scalar& exponent) const; - - const ScalarAddReturnType - operator+(const Scalar& scalar) const; - - /** \relates Cwise */ - friend const ScalarAddReturnType - operator+(const Scalar& scalar, const Cwise& mat) - { return mat + scalar; } - - ExpressionType& operator+=(const Scalar& scalar); - - const ScalarAddReturnType - operator-(const Scalar& scalar) const; - - ExpressionType& operator-=(const Scalar& scalar); - - template<typename OtherDerived> - inline ExpressionType& operator*=(const MatrixBase<OtherDerived> &other); - - template<typename OtherDerived> - inline ExpressionType& operator/=(const MatrixBase<OtherDerived> &other); - - template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less) - operator<(const MatrixBase<OtherDerived>& other) const; - - template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less_equal) - operator<=(const MatrixBase<OtherDerived>& other) const; - - template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater) - operator>(const MatrixBase<OtherDerived>& other) const; - - template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater_equal) - operator>=(const MatrixBase<OtherDerived>& other) const; - - template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::equal_to) - operator==(const MatrixBase<OtherDerived>& other) const; - - template<typename OtherDerived> const EIGEN_CWISE_BINOP_RETURN_TYPE(std::not_equal_to) - operator!=(const MatrixBase<OtherDerived>& other) const; - - // comparisons to a scalar value - const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less) - operator<(Scalar s) const; - - const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less_equal) - operator<=(Scalar s) const; - - const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater) - operator>(Scalar s) const; - - const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater_equal) - operator>=(Scalar s) const; - - const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::equal_to) - operator==(Scalar s) const; - - const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::not_equal_to) - operator!=(Scalar s) const; - - // allow to extend Cwise outside Eigen - #ifdef EIGEN_CWISE_PLUGIN - #include EIGEN_CWISE_PLUGIN - #endif - - protected: - ExpressionTypeNested m_matrix; -}; - - -/** \returns a Cwise wrapper of *this providing additional coefficient-wise operations - * - * Example: \include MatrixBase_cwise_const.cpp - * Output: \verbinclude MatrixBase_cwise_const.out - * - * \sa class Cwise, cwise() - */ -template<typename Derived> -inline const Cwise<Derived> MatrixBase<Derived>::cwise() const -{ - return derived(); -} - -/** \returns a Cwise wrapper of *this providing additional coefficient-wise operations - * - * Example: \include MatrixBase_cwise.cpp - * Output: \verbinclude MatrixBase_cwise.out - * - * \sa class Cwise, cwise() const - */ -template<typename Derived> -inline Cwise<Derived> MatrixBase<Derived>::cwise() -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_CWISE_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/CwiseOperators.h b/third_party/eigen3/Eigen/src/Eigen2Support/CwiseOperators.h deleted file mode 100644 index 482f306485..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/CwiseOperators.h +++ /dev/null @@ -1,298 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ARRAY_CWISE_OPERATORS_H -#define EIGEN_ARRAY_CWISE_OPERATORS_H - -namespace Eigen { - -/*************************************************************************** -* The following functions were defined in Core -***************************************************************************/ - - -/** \deprecated ArrayBase::abs() */ -template<typename ExpressionType> -EIGEN_STRONG_INLINE const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs_op) -Cwise<ExpressionType>::abs() const -{ - return _expression(); -} - -/** \deprecated ArrayBase::abs2() */ -template<typename ExpressionType> -EIGEN_STRONG_INLINE const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs2_op) -Cwise<ExpressionType>::abs2() const -{ - return _expression(); -} - -/** \deprecated ArrayBase::exp() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_exp_op) -Cwise<ExpressionType>::exp() const -{ - return _expression(); -} - -/** \deprecated ArrayBase::log() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_log_op) -Cwise<ExpressionType>::log() const -{ - return _expression(); -} - -/** \deprecated ArrayBase::operator*() */ -template<typename ExpressionType> -template<typename OtherDerived> -EIGEN_STRONG_INLINE const EIGEN_CWISE_PRODUCT_RETURN_TYPE(ExpressionType,OtherDerived) -Cwise<ExpressionType>::operator*(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_CWISE_PRODUCT_RETURN_TYPE(ExpressionType,OtherDerived)(_expression(), other.derived()); -} - -/** \deprecated ArrayBase::operator/() */ -template<typename ExpressionType> -template<typename OtherDerived> -EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op) -Cwise<ExpressionType>::operator/(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_quotient_op)(_expression(), other.derived()); -} - -/** \deprecated ArrayBase::operator*=() */ -template<typename ExpressionType> -template<typename OtherDerived> -inline ExpressionType& Cwise<ExpressionType>::operator*=(const MatrixBase<OtherDerived> &other) -{ - return m_matrix.const_cast_derived() = *this * other; -} - -/** \deprecated ArrayBase::operator/=() */ -template<typename ExpressionType> -template<typename OtherDerived> -inline ExpressionType& Cwise<ExpressionType>::operator/=(const MatrixBase<OtherDerived> &other) -{ - return m_matrix.const_cast_derived() = *this / other; -} - -/*************************************************************************** -* The following functions were defined in Array -***************************************************************************/ - -// -- unary operators -- - -/** \deprecated ArrayBase::sqrt() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sqrt_op) -Cwise<ExpressionType>::sqrt() const -{ - return _expression(); -} - -/** \deprecated ArrayBase::cos() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cos_op) -Cwise<ExpressionType>::cos() const -{ - return _expression(); -} - - -/** \deprecated ArrayBase::sin() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_sin_op) -Cwise<ExpressionType>::sin() const -{ - return _expression(); -} - - -/** \deprecated ArrayBase::log() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_pow_op) -Cwise<ExpressionType>::pow(const Scalar& exponent) const -{ - return EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_pow_op)(_expression(), internal::scalar_pow_op<Scalar>(exponent)); -} - - -/** \deprecated ArrayBase::inverse() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_inverse_op) -Cwise<ExpressionType>::inverse() const -{ - return _expression(); -} - -/** \deprecated ArrayBase::square() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_square_op) -Cwise<ExpressionType>::square() const -{ - return _expression(); -} - -/** \deprecated ArrayBase::cube() */ -template<typename ExpressionType> -inline const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_cube_op) -Cwise<ExpressionType>::cube() const -{ - return _expression(); -} - - -// -- binary operators -- - -/** \deprecated ArrayBase::operator<() */ -template<typename ExpressionType> -template<typename OtherDerived> -inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less) -Cwise<ExpressionType>::operator<(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_CWISE_BINOP_RETURN_TYPE(std::less)(_expression(), other.derived()); -} - -/** \deprecated ArrayBase::<=() */ -template<typename ExpressionType> -template<typename OtherDerived> -inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::less_equal) -Cwise<ExpressionType>::operator<=(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_CWISE_BINOP_RETURN_TYPE(std::less_equal)(_expression(), other.derived()); -} - -/** \deprecated ArrayBase::operator>() */ -template<typename ExpressionType> -template<typename OtherDerived> -inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater) -Cwise<ExpressionType>::operator>(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater)(_expression(), other.derived()); -} - -/** \deprecated ArrayBase::operator>=() */ -template<typename ExpressionType> -template<typename OtherDerived> -inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater_equal) -Cwise<ExpressionType>::operator>=(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_CWISE_BINOP_RETURN_TYPE(std::greater_equal)(_expression(), other.derived()); -} - -/** \deprecated ArrayBase::operator==() */ -template<typename ExpressionType> -template<typename OtherDerived> -inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::equal_to) -Cwise<ExpressionType>::operator==(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_CWISE_BINOP_RETURN_TYPE(std::equal_to)(_expression(), other.derived()); -} - -/** \deprecated ArrayBase::operator!=() */ -template<typename ExpressionType> -template<typename OtherDerived> -inline const EIGEN_CWISE_BINOP_RETURN_TYPE(std::not_equal_to) -Cwise<ExpressionType>::operator!=(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_CWISE_BINOP_RETURN_TYPE(std::not_equal_to)(_expression(), other.derived()); -} - -// comparisons to scalar value - -/** \deprecated ArrayBase::operator<(Scalar) */ -template<typename ExpressionType> -inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less) -Cwise<ExpressionType>::operator<(Scalar s) const -{ - return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less)(_expression(), - typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s)); -} - -/** \deprecated ArrayBase::operator<=(Scalar) */ -template<typename ExpressionType> -inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less_equal) -Cwise<ExpressionType>::operator<=(Scalar s) const -{ - return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::less_equal)(_expression(), - typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s)); -} - -/** \deprecated ArrayBase::operator>(Scalar) */ -template<typename ExpressionType> -inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater) -Cwise<ExpressionType>::operator>(Scalar s) const -{ - return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater)(_expression(), - typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s)); -} - -/** \deprecated ArrayBase::operator>=(Scalar) */ -template<typename ExpressionType> -inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater_equal) -Cwise<ExpressionType>::operator>=(Scalar s) const -{ - return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::greater_equal)(_expression(), - typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s)); -} - -/** \deprecated ArrayBase::operator==(Scalar) */ -template<typename ExpressionType> -inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::equal_to) -Cwise<ExpressionType>::operator==(Scalar s) const -{ - return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::equal_to)(_expression(), - typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s)); -} - -/** \deprecated ArrayBase::operator!=(Scalar) */ -template<typename ExpressionType> -inline const EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::not_equal_to) -Cwise<ExpressionType>::operator!=(Scalar s) const -{ - return EIGEN_CWISE_COMP_TO_SCALAR_RETURN_TYPE(std::not_equal_to)(_expression(), - typename ExpressionType::ConstantReturnType(_expression().rows(), _expression().cols(), s)); -} - -// scalar addition - -/** \deprecated ArrayBase::operator+(Scalar) */ -template<typename ExpressionType> -inline const typename Cwise<ExpressionType>::ScalarAddReturnType -Cwise<ExpressionType>::operator+(const Scalar& scalar) const -{ - return typename Cwise<ExpressionType>::ScalarAddReturnType(m_matrix, internal::scalar_add_op<Scalar>(scalar)); -} - -/** \deprecated ArrayBase::operator+=(Scalar) */ -template<typename ExpressionType> -inline ExpressionType& Cwise<ExpressionType>::operator+=(const Scalar& scalar) -{ - return m_matrix.const_cast_derived() = *this + scalar; -} - -/** \deprecated ArrayBase::operator-(Scalar) */ -template<typename ExpressionType> -inline const typename Cwise<ExpressionType>::ScalarAddReturnType -Cwise<ExpressionType>::operator-(const Scalar& scalar) const -{ - return *this + (-scalar); -} - -/** \deprecated ArrayBase::operator-=(Scalar) */ -template<typename ExpressionType> -inline ExpressionType& Cwise<ExpressionType>::operator-=(const Scalar& scalar) -{ - return m_matrix.const_cast_derived() = *this - scalar; -} - -} // end namespace Eigen - -#endif // EIGEN_ARRAY_CWISE_OPERATORS_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/AlignedBox.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/AlignedBox.h deleted file mode 100644 index 2e4309dd94..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/AlignedBox.h +++ /dev/null @@ -1,159 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * \nonstableyet - * - * \class AlignedBox - * - * \brief An axis aligned box - * - * \param _Scalar the type of the scalar coefficients - * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic. - * - * This class represents an axis aligned box as a pair of the minimal and maximal corners. - */ -template <typename _Scalar, int _AmbientDim> -class AlignedBox -{ -public: -EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1) - enum { AmbientDimAtCompileTime = _AmbientDim }; - typedef _Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType; - - /** Default constructor initializing a null box. */ - inline AlignedBox() - { if (AmbientDimAtCompileTime!=Dynamic) setNull(); } - - /** Constructs a null box with \a _dim the dimension of the ambient space. */ - inline explicit AlignedBox(int _dim) : m_min(_dim), m_max(_dim) - { setNull(); } - - /** Constructs a box with extremities \a _min and \a _max. */ - inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_min(_min), m_max(_max) {} - - /** Constructs a box containing a single point \a p. */ - inline explicit AlignedBox(const VectorType& p) : m_min(p), m_max(p) {} - - ~AlignedBox() {} - - /** \returns the dimension in which the box holds */ - inline int dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size()-1 : AmbientDimAtCompileTime; } - - /** \returns true if the box is null, i.e, empty. */ - inline bool isNull() const { return (m_min.cwise() > m_max).any(); } - - /** Makes \c *this a null/empty box. */ - inline void setNull() - { - m_min.setConstant( (std::numeric_limits<Scalar>::max)()); - m_max.setConstant(-(std::numeric_limits<Scalar>::max)()); - } - - /** \returns the minimal corner */ - inline const VectorType& (min)() const { return m_min; } - /** \returns a non const reference to the minimal corner */ - inline VectorType& (min)() { return m_min; } - /** \returns the maximal corner */ - inline const VectorType& (max)() const { return m_max; } - /** \returns a non const reference to the maximal corner */ - inline VectorType& (max)() { return m_max; } - - /** \returns true if the point \a p is inside the box \c *this. */ - inline bool contains(const VectorType& p) const - { return (m_min.cwise()<=p).all() && (p.cwise()<=m_max).all(); } - - /** \returns true if the box \a b is entirely inside the box \c *this. */ - inline bool contains(const AlignedBox& b) const - { return (m_min.cwise()<=(b.min)()).all() && ((b.max)().cwise()<=m_max).all(); } - - /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */ - inline AlignedBox& extend(const VectorType& p) - { m_min = (m_min.cwise().min)(p); m_max = (m_max.cwise().max)(p); return *this; } - - /** Extends \c *this such that it contains the box \a b and returns a reference to \c *this. */ - inline AlignedBox& extend(const AlignedBox& b) - { m_min = (m_min.cwise().min)(b.m_min); m_max = (m_max.cwise().max)(b.m_max); return *this; } - - /** Clamps \c *this by the box \a b and returns a reference to \c *this. */ - inline AlignedBox& clamp(const AlignedBox& b) - { m_min = (m_min.cwise().max)(b.m_min); m_max = (m_max.cwise().min)(b.m_max); return *this; } - - /** Translate \c *this by the vector \a t and returns a reference to \c *this. */ - inline AlignedBox& translate(const VectorType& t) - { m_min += t; m_max += t; return *this; } - - /** \returns the squared distance between the point \a p and the box \c *this, - * and zero if \a p is inside the box. - * \sa exteriorDistance() - */ - inline Scalar squaredExteriorDistance(const VectorType& p) const; - - /** \returns the distance between the point \a p and the box \c *this, - * and zero if \a p is inside the box. - * \sa squaredExteriorDistance() - */ - inline Scalar exteriorDistance(const VectorType& p) const - { return ei_sqrt(squaredExteriorDistance(p)); } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<AlignedBox, - AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type cast() const - { - return typename internal::cast_return_type<AlignedBox, - AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type(*this); - } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other) - { - m_min = (other.min)().template cast<Scalar>(); - m_max = (other.max)().template cast<Scalar>(); - } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const AlignedBox& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return m_min.isApprox(other.m_min, prec) && m_max.isApprox(other.m_max, prec); } - -protected: - - VectorType m_min, m_max; -}; - -template<typename Scalar,int AmbiantDim> -inline Scalar AlignedBox<Scalar,AmbiantDim>::squaredExteriorDistance(const VectorType& p) const -{ - Scalar dist2(0); - Scalar aux; - for (int k=0; k<dim(); ++k) - { - if ((aux = (p[k]-m_min[k]))<Scalar(0)) - dist2 += aux*aux; - else if ( (aux = (m_max[k]-p[k]))<Scalar(0)) - dist2 += aux*aux; - } - return dist2; -} - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/All.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/All.h deleted file mode 100644 index e0b00fcccc..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/All.h +++ /dev/null @@ -1,115 +0,0 @@ -#ifndef EIGEN2_GEOMETRY_MODULE_H -#define EIGEN2_GEOMETRY_MODULE_H - -#include <limits> - -#ifndef M_PI -#define M_PI 3.14159265358979323846 -#endif - -#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS -#include "RotationBase.h" -#include "Rotation2D.h" -#include "Quaternion.h" -#include "AngleAxis.h" -#include "Transform.h" -#include "Translation.h" -#include "Scaling.h" -#include "AlignedBox.h" -#include "Hyperplane.h" -#include "ParametrizedLine.h" -#endif - - -#define RotationBase eigen2_RotationBase -#define Rotation2D eigen2_Rotation2D -#define Rotation2Df eigen2_Rotation2Df -#define Rotation2Dd eigen2_Rotation2Dd - -#define Quaternion eigen2_Quaternion -#define Quaternionf eigen2_Quaternionf -#define Quaterniond eigen2_Quaterniond - -#define AngleAxis eigen2_AngleAxis -#define AngleAxisf eigen2_AngleAxisf -#define AngleAxisd eigen2_AngleAxisd - -#define Transform eigen2_Transform -#define Transform2f eigen2_Transform2f -#define Transform2d eigen2_Transform2d -#define Transform3f eigen2_Transform3f -#define Transform3d eigen2_Transform3d - -#define Translation eigen2_Translation -#define Translation2f eigen2_Translation2f -#define Translation2d eigen2_Translation2d -#define Translation3f eigen2_Translation3f -#define Translation3d eigen2_Translation3d - -#define Scaling eigen2_Scaling -#define Scaling2f eigen2_Scaling2f -#define Scaling2d eigen2_Scaling2d -#define Scaling3f eigen2_Scaling3f -#define Scaling3d eigen2_Scaling3d - -#define AlignedBox eigen2_AlignedBox - -#define Hyperplane eigen2_Hyperplane -#define ParametrizedLine eigen2_ParametrizedLine - -#define ei_toRotationMatrix eigen2_ei_toRotationMatrix -#define ei_quaternion_assign_impl eigen2_ei_quaternion_assign_impl -#define ei_transform_product_impl eigen2_ei_transform_product_impl - -#include "RotationBase.h" -#include "Rotation2D.h" -#include "Quaternion.h" -#include "AngleAxis.h" -#include "Transform.h" -#include "Translation.h" -#include "Scaling.h" -#include "AlignedBox.h" -#include "Hyperplane.h" -#include "ParametrizedLine.h" - -#undef ei_toRotationMatrix -#undef ei_quaternion_assign_impl -#undef ei_transform_product_impl - -#undef RotationBase -#undef Rotation2D -#undef Rotation2Df -#undef Rotation2Dd - -#undef Quaternion -#undef Quaternionf -#undef Quaterniond - -#undef AngleAxis -#undef AngleAxisf -#undef AngleAxisd - -#undef Transform -#undef Transform2f -#undef Transform2d -#undef Transform3f -#undef Transform3d - -#undef Translation -#undef Translation2f -#undef Translation2d -#undef Translation3f -#undef Translation3d - -#undef Scaling -#undef Scaling2f -#undef Scaling2d -#undef Scaling3f -#undef Scaling3d - -#undef AlignedBox - -#undef Hyperplane -#undef ParametrizedLine - -#endif // EIGEN2_GEOMETRY_MODULE_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/AngleAxis.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/AngleAxis.h deleted file mode 100644 index a0b4ac44e7..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/AngleAxis.h +++ /dev/null @@ -1,228 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class AngleAxis - * - * \brief Represents a 3D rotation as a rotation angle around an arbitrary 3D axis - * - * \param _Scalar the scalar type, i.e., the type of the coefficients. - * - * The following two typedefs are provided for convenience: - * \li \c AngleAxisf for \c float - * \li \c AngleAxisd for \c double - * - * \addexample AngleAxisForEuler \label How to define a rotation from Euler-angles - * - * Combined with MatrixBase::Unit{X,Y,Z}, AngleAxis can be used to easily - * mimic Euler-angles. Here is an example: - * \include AngleAxis_mimic_euler.cpp - * Output: \verbinclude AngleAxis_mimic_euler.out - * - * \note This class is not aimed to be used to store a rotation transformation, - * but rather to make easier the creation of other rotation (Quaternion, rotation Matrix) - * and transformation objects. - * - * \sa class Quaternion, class Transform, MatrixBase::UnitX() - */ - -template<typename _Scalar> struct ei_traits<AngleAxis<_Scalar> > -{ - typedef _Scalar Scalar; -}; - -template<typename _Scalar> -class AngleAxis : public RotationBase<AngleAxis<_Scalar>,3> -{ - typedef RotationBase<AngleAxis<_Scalar>,3> Base; - -public: - - using Base::operator*; - - enum { Dim = 3 }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - typedef Matrix<Scalar,3,3> Matrix3; - typedef Matrix<Scalar,3,1> Vector3; - typedef Quaternion<Scalar> QuaternionType; - -protected: - - Vector3 m_axis; - Scalar m_angle; - -public: - - /** Default constructor without initialization. */ - AngleAxis() {} - - /** Constructs and initialize the angle-axis rotation from an \a angle in radian - * and an \a axis which must be normalized. */ - template<typename Derived> - inline AngleAxis(Scalar angle, const MatrixBase<Derived>& axis) : m_axis(axis), m_angle(angle) - { - using std::sqrt; - using std::abs; - // since we compare against 1, this is equal to computing the relative error - eigen_assert( abs(m_axis.derived().squaredNorm() - 1) < sqrt( NumTraits<Scalar>::dummy_precision() ) ); - } - - /** Constructs and initialize the angle-axis rotation from a quaternion \a q. */ - inline AngleAxis(const QuaternionType& q) { *this = q; } - - /** Constructs and initialize the angle-axis rotation from a 3x3 rotation matrix. */ - template<typename Derived> - inline explicit AngleAxis(const MatrixBase<Derived>& m) { *this = m; } - - Scalar angle() const { return m_angle; } - Scalar& angle() { return m_angle; } - - const Vector3& axis() const { return m_axis; } - Vector3& axis() { return m_axis; } - - /** Concatenates two rotations */ - inline QuaternionType operator* (const AngleAxis& other) const - { return QuaternionType(*this) * QuaternionType(other); } - - /** Concatenates two rotations */ - inline QuaternionType operator* (const QuaternionType& other) const - { return QuaternionType(*this) * other; } - - /** Concatenates two rotations */ - friend inline QuaternionType operator* (const QuaternionType& a, const AngleAxis& b) - { return a * QuaternionType(b); } - - /** Concatenates two rotations */ - inline Matrix3 operator* (const Matrix3& other) const - { return toRotationMatrix() * other; } - - /** Concatenates two rotations */ - inline friend Matrix3 operator* (const Matrix3& a, const AngleAxis& b) - { return a * b.toRotationMatrix(); } - - /** Applies rotation to vector */ - inline Vector3 operator* (const Vector3& other) const - { return toRotationMatrix() * other; } - - /** \returns the inverse rotation, i.e., an angle-axis with opposite rotation angle */ - AngleAxis inverse() const - { return AngleAxis(-m_angle, m_axis); } - - AngleAxis& operator=(const QuaternionType& q); - template<typename Derived> - AngleAxis& operator=(const MatrixBase<Derived>& m); - - template<typename Derived> - AngleAxis& fromRotationMatrix(const MatrixBase<Derived>& m); - Matrix3 toRotationMatrix(void) const; - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type cast() const - { return typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other) - { - m_axis = other.axis().template cast<Scalar>(); - m_angle = Scalar(other.angle()); - } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const AngleAxis& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return m_axis.isApprox(other.m_axis, prec) && ei_isApprox(m_angle,other.m_angle, prec); } -}; - -/** \ingroup Geometry_Module - * single precision angle-axis type */ -typedef AngleAxis<float> AngleAxisf; -/** \ingroup Geometry_Module - * double precision angle-axis type */ -typedef AngleAxis<double> AngleAxisd; - -/** Set \c *this from a quaternion. - * The axis is normalized. - */ -template<typename Scalar> -AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionType& q) -{ - Scalar n2 = q.vec().squaredNorm(); - if (n2 < precision<Scalar>()*precision<Scalar>()) - { - m_angle = 0; - m_axis << 1, 0, 0; - } - else - { - m_angle = 2*std::acos(q.w()); - m_axis = q.vec() / ei_sqrt(n2); - - using std::sqrt; - using std::abs; - // since we compare against 1, this is equal to computing the relative error - eigen_assert( abs(m_axis.derived().squaredNorm() - 1) < sqrt( NumTraits<Scalar>::dummy_precision() ) ); - } - return *this; -} - -/** Set \c *this from a 3x3 rotation matrix \a mat. - */ -template<typename Scalar> -template<typename Derived> -AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const MatrixBase<Derived>& mat) -{ - // Since a direct conversion would not be really faster, - // let's use the robust Quaternion implementation: - return *this = QuaternionType(mat); -} - -/** Constructs and \returns an equivalent 3x3 rotation matrix. - */ -template<typename Scalar> -typename AngleAxis<Scalar>::Matrix3 -AngleAxis<Scalar>::toRotationMatrix(void) const -{ - Matrix3 res; - Vector3 sin_axis = ei_sin(m_angle) * m_axis; - Scalar c = ei_cos(m_angle); - Vector3 cos1_axis = (Scalar(1)-c) * m_axis; - - Scalar tmp; - tmp = cos1_axis.x() * m_axis.y(); - res.coeffRef(0,1) = tmp - sin_axis.z(); - res.coeffRef(1,0) = tmp + sin_axis.z(); - - tmp = cos1_axis.x() * m_axis.z(); - res.coeffRef(0,2) = tmp + sin_axis.y(); - res.coeffRef(2,0) = tmp - sin_axis.y(); - - tmp = cos1_axis.y() * m_axis.z(); - res.coeffRef(1,2) = tmp - sin_axis.x(); - res.coeffRef(2,1) = tmp + sin_axis.x(); - - res.diagonal() = (cos1_axis.cwise() * m_axis).cwise() + c; - - return res; -} - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Hyperplane.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Hyperplane.h deleted file mode 100644 index b95bf00ecf..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Hyperplane.h +++ /dev/null @@ -1,254 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Hyperplane - * - * \brief A hyperplane - * - * A hyperplane is an affine subspace of dimension n-1 in a space of dimension n. - * For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane. - * - * \param _Scalar the scalar type, i.e., the type of the coefficients - * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic. - * Notice that the dimension of the hyperplane is _AmbientDim-1. - * - * This class represents an hyperplane as the zero set of the implicit equation - * \f$ n \cdot x + d = 0 \f$ where \f$ n \f$ is a unit normal vector of the plane (linear part) - * and \f$ d \f$ is the distance (offset) to the origin. - */ -template <typename _Scalar, int _AmbientDim> -class Hyperplane -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1) - enum { AmbientDimAtCompileTime = _AmbientDim }; - typedef _Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType; - typedef Matrix<Scalar,int(AmbientDimAtCompileTime)==Dynamic - ? Dynamic - : int(AmbientDimAtCompileTime)+1,1> Coefficients; - typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType; - - /** Default constructor without initialization */ - inline Hyperplane() {} - - /** Constructs a dynamic-size hyperplane with \a _dim the dimension - * of the ambient space */ - inline explicit Hyperplane(int _dim) : m_coeffs(_dim+1) {} - - /** Construct a plane from its normal \a n and a point \a e onto the plane. - * \warning the vector normal is assumed to be normalized. - */ - inline Hyperplane(const VectorType& n, const VectorType& e) - : m_coeffs(n.size()+1) - { - normal() = n; - offset() = -e.eigen2_dot(n); - } - - /** Constructs a plane from its normal \a n and distance to the origin \a d - * such that the algebraic equation of the plane is \f$ n \cdot x + d = 0 \f$. - * \warning the vector normal is assumed to be normalized. - */ - inline Hyperplane(const VectorType& n, Scalar d) - : m_coeffs(n.size()+1) - { - normal() = n; - offset() = d; - } - - /** Constructs a hyperplane passing through the two points. If the dimension of the ambient space - * is greater than 2, then there isn't uniqueness, so an arbitrary choice is made. - */ - static inline Hyperplane Through(const VectorType& p0, const VectorType& p1) - { - Hyperplane result(p0.size()); - result.normal() = (p1 - p0).unitOrthogonal(); - result.offset() = -result.normal().eigen2_dot(p0); - return result; - } - - /** Constructs a hyperplane passing through the three points. The dimension of the ambient space - * is required to be exactly 3. - */ - static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2) - { - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3) - Hyperplane result(p0.size()); - result.normal() = (p2 - p0).cross(p1 - p0).normalized(); - result.offset() = -result.normal().eigen2_dot(p0); - return result; - } - - /** Constructs a hyperplane passing through the parametrized line \a parametrized. - * If the dimension of the ambient space is greater than 2, then there isn't uniqueness, - * so an arbitrary choice is made. - */ - // FIXME to be consitent with the rest this could be implemented as a static Through function ?? - explicit Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized) - { - normal() = parametrized.direction().unitOrthogonal(); - offset() = -normal().eigen2_dot(parametrized.origin()); - } - - ~Hyperplane() {} - - /** \returns the dimension in which the plane holds */ - inline int dim() const { return int(AmbientDimAtCompileTime)==Dynamic ? m_coeffs.size()-1 : int(AmbientDimAtCompileTime); } - - /** normalizes \c *this */ - void normalize(void) - { - m_coeffs /= normal().norm(); - } - - /** \returns the signed distance between the plane \c *this and a point \a p. - * \sa absDistance() - */ - inline Scalar signedDistance(const VectorType& p) const { return p.eigen2_dot(normal()) + offset(); } - - /** \returns the absolute distance between the plane \c *this and a point \a p. - * \sa signedDistance() - */ - inline Scalar absDistance(const VectorType& p) const { return ei_abs(signedDistance(p)); } - - /** \returns the projection of a point \a p onto the plane \c *this. - */ - inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); } - - /** \returns a constant reference to the unit normal vector of the plane, which corresponds - * to the linear part of the implicit equation. - */ - inline const NormalReturnType normal() const { return NormalReturnType(*const_cast<Coefficients*>(&m_coeffs),0,0,dim(),1); } - - /** \returns a non-constant reference to the unit normal vector of the plane, which corresponds - * to the linear part of the implicit equation. - */ - inline NormalReturnType normal() { return NormalReturnType(m_coeffs,0,0,dim(),1); } - - /** \returns the distance to the origin, which is also the "constant term" of the implicit equation - * \warning the vector normal is assumed to be normalized. - */ - inline const Scalar& offset() const { return m_coeffs.coeff(dim()); } - - /** \returns a non-constant reference to the distance to the origin, which is also the constant part - * of the implicit equation */ - inline Scalar& offset() { return m_coeffs(dim()); } - - /** \returns a constant reference to the coefficients c_i of the plane equation: - * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$ - */ - inline const Coefficients& coeffs() const { return m_coeffs; } - - /** \returns a non-constant reference to the coefficients c_i of the plane equation: - * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$ - */ - inline Coefficients& coeffs() { return m_coeffs; } - - /** \returns the intersection of *this with \a other. - * - * \warning The ambient space must be a plane, i.e. have dimension 2, so that \c *this and \a other are lines. - * - * \note If \a other is approximately parallel to *this, this method will return any point on *this. - */ - VectorType intersection(const Hyperplane& other) - { - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2) - Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0); - // since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests - // whether the two lines are approximately parallel. - if(ei_isMuchSmallerThan(det, Scalar(1))) - { // special case where the two lines are approximately parallel. Pick any point on the first line. - if(ei_abs(coeffs().coeff(1))>ei_abs(coeffs().coeff(0))) - return VectorType(coeffs().coeff(1), -coeffs().coeff(2)/coeffs().coeff(1)-coeffs().coeff(0)); - else - return VectorType(-coeffs().coeff(2)/coeffs().coeff(0)-coeffs().coeff(1), coeffs().coeff(0)); - } - else - { // general case - Scalar invdet = Scalar(1) / det; - return VectorType(invdet*(coeffs().coeff(1)*other.coeffs().coeff(2)-other.coeffs().coeff(1)*coeffs().coeff(2)), - invdet*(other.coeffs().coeff(0)*coeffs().coeff(2)-coeffs().coeff(0)*other.coeffs().coeff(2))); - } - } - - /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this. - * - * \param mat the Dim x Dim transformation matrix - * \param traits specifies whether the matrix \a mat represents an Isometry - * or a more generic Affine transformation. The default is Affine. - */ - template<typename XprType> - inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine) - { - if (traits==Affine) - normal() = mat.inverse().transpose() * normal(); - else if (traits==Isometry) - normal() = mat * normal(); - else - { - ei_assert("invalid traits value in Hyperplane::transform()"); - } - return *this; - } - - /** Applies the transformation \a t to \c *this and returns a reference to \c *this. - * - * \param t the transformation of dimension Dim - * \param traits specifies whether the transformation \a t represents an Isometry - * or a more generic Affine transformation. The default is Affine. - * Other kind of transformations are not supported. - */ - inline Hyperplane& transform(const Transform<Scalar,AmbientDimAtCompileTime>& t, - TransformTraits traits = Affine) - { - transform(t.linear(), traits); - offset() -= t.translation().eigen2_dot(normal()); - return *this; - } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Hyperplane, - Hyperplane<NewScalarType,AmbientDimAtCompileTime> >::type cast() const - { - return typename internal::cast_return_type<Hyperplane, - Hyperplane<NewScalarType,AmbientDimAtCompileTime> >::type(*this); - } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Hyperplane(const Hyperplane<OtherScalarType,AmbientDimAtCompileTime>& other) - { m_coeffs = other.coeffs().template cast<Scalar>(); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Hyperplane& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return m_coeffs.isApprox(other.m_coeffs, prec); } - -protected: - - Coefficients m_coeffs; -}; - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/ParametrizedLine.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/ParametrizedLine.h deleted file mode 100644 index 9b57b7e0bb..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/ParametrizedLine.h +++ /dev/null @@ -1,141 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class ParametrizedLine - * - * \brief A parametrized line - * - * A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit - * direction vector \f$ \mathbf{d} \f$ such that the line corresponds to - * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ l \in \mathbf{R} \f$. - * - * \param _Scalar the scalar type, i.e., the type of the coefficients - * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic. - */ -template <typename _Scalar, int _AmbientDim> -class ParametrizedLine -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim) - enum { AmbientDimAtCompileTime = _AmbientDim }; - typedef _Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType; - - /** Default constructor without initialization */ - inline ParametrizedLine() {} - - /** Constructs a dynamic-size line with \a _dim the dimension - * of the ambient space */ - inline explicit ParametrizedLine(int _dim) : m_origin(_dim), m_direction(_dim) {} - - /** Initializes a parametrized line of direction \a direction and origin \a origin. - * \warning the vector direction is assumed to be normalized. - */ - ParametrizedLine(const VectorType& origin, const VectorType& direction) - : m_origin(origin), m_direction(direction) {} - - explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane); - - /** Constructs a parametrized line going from \a p0 to \a p1. */ - static inline ParametrizedLine Through(const VectorType& p0, const VectorType& p1) - { return ParametrizedLine(p0, (p1-p0).normalized()); } - - ~ParametrizedLine() {} - - /** \returns the dimension in which the line holds */ - inline int dim() const { return m_direction.size(); } - - const VectorType& origin() const { return m_origin; } - VectorType& origin() { return m_origin; } - - const VectorType& direction() const { return m_direction; } - VectorType& direction() { return m_direction; } - - /** \returns the squared distance of a point \a p to its projection onto the line \c *this. - * \sa distance() - */ - RealScalar squaredDistance(const VectorType& p) const - { - VectorType diff = p-origin(); - return (diff - diff.eigen2_dot(direction())* direction()).squaredNorm(); - } - /** \returns the distance of a point \a p to its projection onto the line \c *this. - * \sa squaredDistance() - */ - RealScalar distance(const VectorType& p) const { return ei_sqrt(squaredDistance(p)); } - - /** \returns the projection of a point \a p onto the line \c *this. */ - VectorType projection(const VectorType& p) const - { return origin() + (p-origin()).eigen2_dot(direction()) * direction(); } - - Scalar intersection(const Hyperplane<_Scalar, _AmbientDim>& hyperplane); - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<ParametrizedLine, - ParametrizedLine<NewScalarType,AmbientDimAtCompileTime> >::type cast() const - { - return typename internal::cast_return_type<ParametrizedLine, - ParametrizedLine<NewScalarType,AmbientDimAtCompileTime> >::type(*this); - } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit ParametrizedLine(const ParametrizedLine<OtherScalarType,AmbientDimAtCompileTime>& other) - { - m_origin = other.origin().template cast<Scalar>(); - m_direction = other.direction().template cast<Scalar>(); - } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const ParametrizedLine& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec); } - -protected: - - VectorType m_origin, m_direction; -}; - -/** Constructs a parametrized line from a 2D hyperplane - * - * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line - */ -template <typename _Scalar, int _AmbientDim> -inline ParametrizedLine<_Scalar, _AmbientDim>::ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim>& hyperplane) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2) - direction() = hyperplane.normal().unitOrthogonal(); - origin() = -hyperplane.normal()*hyperplane.offset(); -} - -/** \returns the parameter value of the intersection between \c *this and the given hyperplane - */ -template <typename _Scalar, int _AmbientDim> -inline _Scalar ParametrizedLine<_Scalar, _AmbientDim>::intersection(const Hyperplane<_Scalar, _AmbientDim>& hyperplane) -{ - return -(hyperplane.offset()+origin().eigen2_dot(hyperplane.normal())) - /(direction().eigen2_dot(hyperplane.normal())); -} - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Quaternion.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Quaternion.h deleted file mode 100644 index 4b6390cf1d..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Quaternion.h +++ /dev/null @@ -1,495 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -template<typename Other, - int OtherRows=Other::RowsAtCompileTime, - int OtherCols=Other::ColsAtCompileTime> -struct ei_quaternion_assign_impl; - -/** \geometry_module \ingroup Geometry_Module - * - * \class Quaternion - * - * \brief The quaternion class used to represent 3D orientations and rotations - * - * \param _Scalar the scalar type, i.e., the type of the coefficients - * - * This class represents a quaternion \f$ w+xi+yj+zk \f$ that is a convenient representation of - * orientations and rotations of objects in three dimensions. Compared to other representations - * like Euler angles or 3x3 matrices, quatertions offer the following advantages: - * \li \b compact storage (4 scalars) - * \li \b efficient to compose (28 flops), - * \li \b stable spherical interpolation - * - * The following two typedefs are provided for convenience: - * \li \c Quaternionf for \c float - * \li \c Quaterniond for \c double - * - * \sa class AngleAxis, class Transform - */ - -template<typename _Scalar> struct ei_traits<Quaternion<_Scalar> > -{ - typedef _Scalar Scalar; -}; - -template<typename _Scalar> -class Quaternion : public RotationBase<Quaternion<_Scalar>,3> -{ - typedef RotationBase<Quaternion<_Scalar>,3> Base; - -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,4) - - using Base::operator*; - - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - - /** the type of the Coefficients 4-vector */ - typedef Matrix<Scalar, 4, 1> Coefficients; - /** the type of a 3D vector */ - typedef Matrix<Scalar,3,1> Vector3; - /** the equivalent rotation matrix type */ - typedef Matrix<Scalar,3,3> Matrix3; - /** the equivalent angle-axis type */ - typedef AngleAxis<Scalar> AngleAxisType; - - /** \returns the \c x coefficient */ - inline Scalar x() const { return m_coeffs.coeff(0); } - /** \returns the \c y coefficient */ - inline Scalar y() const { return m_coeffs.coeff(1); } - /** \returns the \c z coefficient */ - inline Scalar z() const { return m_coeffs.coeff(2); } - /** \returns the \c w coefficient */ - inline Scalar w() const { return m_coeffs.coeff(3); } - - /** \returns a reference to the \c x coefficient */ - inline Scalar& x() { return m_coeffs.coeffRef(0); } - /** \returns a reference to the \c y coefficient */ - inline Scalar& y() { return m_coeffs.coeffRef(1); } - /** \returns a reference to the \c z coefficient */ - inline Scalar& z() { return m_coeffs.coeffRef(2); } - /** \returns a reference to the \c w coefficient */ - inline Scalar& w() { return m_coeffs.coeffRef(3); } - - /** \returns a read-only vector expression of the imaginary part (x,y,z) */ - inline const Block<const Coefficients,3,1> vec() const { return m_coeffs.template start<3>(); } - - /** \returns a vector expression of the imaginary part (x,y,z) */ - inline Block<Coefficients,3,1> vec() { return m_coeffs.template start<3>(); } - - /** \returns a read-only vector expression of the coefficients (x,y,z,w) */ - inline const Coefficients& coeffs() const { return m_coeffs; } - - /** \returns a vector expression of the coefficients (x,y,z,w) */ - inline Coefficients& coeffs() { return m_coeffs; } - - /** Default constructor leaving the quaternion uninitialized. */ - inline Quaternion() {} - - /** Constructs and initializes the quaternion \f$ w+xi+yj+zk \f$ from - * its four coefficients \a w, \a x, \a y and \a z. - * - * \warning Note the order of the arguments: the real \a w coefficient first, - * while internally the coefficients are stored in the following order: - * [\c x, \c y, \c z, \c w] - */ - inline Quaternion(Scalar w, Scalar x, Scalar y, Scalar z) - { m_coeffs << x, y, z, w; } - - /** Copy constructor */ - inline Quaternion(const Quaternion& other) { m_coeffs = other.m_coeffs; } - - /** Constructs and initializes a quaternion from the angle-axis \a aa */ - explicit inline Quaternion(const AngleAxisType& aa) { *this = aa; } - - /** Constructs and initializes a quaternion from either: - * - a rotation matrix expression, - * - a 4D vector expression representing quaternion coefficients. - * \sa operator=(MatrixBase<Derived>) - */ - template<typename Derived> - explicit inline Quaternion(const MatrixBase<Derived>& other) { *this = other; } - - Quaternion& operator=(const Quaternion& other); - Quaternion& operator=(const AngleAxisType& aa); - template<typename Derived> - Quaternion& operator=(const MatrixBase<Derived>& m); - - /** \returns a quaternion representing an identity rotation - * \sa MatrixBase::Identity() - */ - static inline Quaternion Identity() { return Quaternion(1, 0, 0, 0); } - - /** \sa Quaternion::Identity(), MatrixBase::setIdentity() - */ - inline Quaternion& setIdentity() { m_coeffs << 0, 0, 0, 1; return *this; } - - /** \returns the squared norm of the quaternion's coefficients - * \sa Quaternion::norm(), MatrixBase::squaredNorm() - */ - inline Scalar squaredNorm() const { return m_coeffs.squaredNorm(); } - - /** \returns the norm of the quaternion's coefficients - * \sa Quaternion::squaredNorm(), MatrixBase::norm() - */ - inline Scalar norm() const { return m_coeffs.norm(); } - - /** Normalizes the quaternion \c *this - * \sa normalized(), MatrixBase::normalize() */ - inline void normalize() { m_coeffs.normalize(); } - /** \returns a normalized version of \c *this - * \sa normalize(), MatrixBase::normalized() */ - inline Quaternion normalized() const { return Quaternion(m_coeffs.normalized()); } - - /** \returns the dot product of \c *this and \a other - * Geometrically speaking, the dot product of two unit quaternions - * corresponds to the cosine of half the angle between the two rotations. - * \sa angularDistance() - */ - inline Scalar eigen2_dot(const Quaternion& other) const { return m_coeffs.eigen2_dot(other.m_coeffs); } - - inline Scalar angularDistance(const Quaternion& other) const; - - Matrix3 toRotationMatrix(void) const; - - template<typename Derived1, typename Derived2> - Quaternion& setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b); - - inline Quaternion operator* (const Quaternion& q) const; - inline Quaternion& operator*= (const Quaternion& q); - - Quaternion inverse(void) const; - Quaternion conjugate(void) const; - - Quaternion slerp(Scalar t, const Quaternion& other) const; - - template<typename Derived> - Vector3 operator* (const MatrixBase<Derived>& vec) const; - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Quaternion,Quaternion<NewScalarType> >::type cast() const - { return typename internal::cast_return_type<Quaternion,Quaternion<NewScalarType> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Quaternion(const Quaternion<OtherScalarType>& other) - { m_coeffs = other.coeffs().template cast<Scalar>(); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Quaternion& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return m_coeffs.isApprox(other.m_coeffs, prec); } - -protected: - Coefficients m_coeffs; -}; - -/** \ingroup Geometry_Module - * single precision quaternion type */ -typedef Quaternion<float> Quaternionf; -/** \ingroup Geometry_Module - * double precision quaternion type */ -typedef Quaternion<double> Quaterniond; - -// Generic Quaternion * Quaternion product -template<typename Scalar> inline Quaternion<Scalar> -ei_quaternion_product(const Quaternion<Scalar>& a, const Quaternion<Scalar>& b) -{ - return Quaternion<Scalar> - ( - a.w() * b.w() - a.x() * b.x() - a.y() * b.y() - a.z() * b.z(), - a.w() * b.x() + a.x() * b.w() + a.y() * b.z() - a.z() * b.y(), - a.w() * b.y() + a.y() * b.w() + a.z() * b.x() - a.x() * b.z(), - a.w() * b.z() + a.z() * b.w() + a.x() * b.y() - a.y() * b.x() - ); -} - -/** \returns the concatenation of two rotations as a quaternion-quaternion product */ -template <typename Scalar> -inline Quaternion<Scalar> Quaternion<Scalar>::operator* (const Quaternion& other) const -{ - return ei_quaternion_product(*this,other); -} - -/** \sa operator*(Quaternion) */ -template <typename Scalar> -inline Quaternion<Scalar>& Quaternion<Scalar>::operator*= (const Quaternion& other) -{ - return (*this = *this * other); -} - -/** Rotation of a vector by a quaternion. - * \remarks If the quaternion is used to rotate several points (>1) - * then it is much more efficient to first convert it to a 3x3 Matrix. - * Comparison of the operation cost for n transformations: - * - Quaternion: 30n - * - Via a Matrix3: 24 + 15n - */ -template <typename Scalar> -template<typename Derived> -inline typename Quaternion<Scalar>::Vector3 -Quaternion<Scalar>::operator* (const MatrixBase<Derived>& v) const -{ - // Note that this algorithm comes from the optimization by hand - // of the conversion to a Matrix followed by a Matrix/Vector product. - // It appears to be much faster than the common algorithm found - // in the litterature (30 versus 39 flops). It also requires two - // Vector3 as temporaries. - Vector3 uv; - uv = 2 * this->vec().cross(v); - return v + this->w() * uv + this->vec().cross(uv); -} - -template<typename Scalar> -inline Quaternion<Scalar>& Quaternion<Scalar>::operator=(const Quaternion& other) -{ - m_coeffs = other.m_coeffs; - return *this; -} - -/** Set \c *this from an angle-axis \a aa and returns a reference to \c *this - */ -template<typename Scalar> -inline Quaternion<Scalar>& Quaternion<Scalar>::operator=(const AngleAxisType& aa) -{ - Scalar ha = Scalar(0.5)*aa.angle(); // Scalar(0.5) to suppress precision loss warnings - this->w() = ei_cos(ha); - this->vec() = ei_sin(ha) * aa.axis(); - return *this; -} - -/** Set \c *this from the expression \a xpr: - * - if \a xpr is a 4x1 vector, then \a xpr is assumed to be a quaternion - * - if \a xpr is a 3x3 matrix, then \a xpr is assumed to be rotation matrix - * and \a xpr is converted to a quaternion - */ -template<typename Scalar> -template<typename Derived> -inline Quaternion<Scalar>& Quaternion<Scalar>::operator=(const MatrixBase<Derived>& xpr) -{ - ei_quaternion_assign_impl<Derived>::run(*this, xpr.derived()); - return *this; -} - -/** Convert the quaternion to a 3x3 rotation matrix */ -template<typename Scalar> -inline typename Quaternion<Scalar>::Matrix3 -Quaternion<Scalar>::toRotationMatrix(void) const -{ - // NOTE if inlined, then gcc 4.2 and 4.4 get rid of the temporary (not gcc 4.3 !!) - // if not inlined then the cost of the return by value is huge ~ +35%, - // however, not inlining this function is an order of magnitude slower, so - // it has to be inlined, and so the return by value is not an issue - Matrix3 res; - - const Scalar tx = Scalar(2)*this->x(); - const Scalar ty = Scalar(2)*this->y(); - const Scalar tz = Scalar(2)*this->z(); - const Scalar twx = tx*this->w(); - const Scalar twy = ty*this->w(); - const Scalar twz = tz*this->w(); - const Scalar txx = tx*this->x(); - const Scalar txy = ty*this->x(); - const Scalar txz = tz*this->x(); - const Scalar tyy = ty*this->y(); - const Scalar tyz = tz*this->y(); - const Scalar tzz = tz*this->z(); - - res.coeffRef(0,0) = Scalar(1)-(tyy+tzz); - res.coeffRef(0,1) = txy-twz; - res.coeffRef(0,2) = txz+twy; - res.coeffRef(1,0) = txy+twz; - res.coeffRef(1,1) = Scalar(1)-(txx+tzz); - res.coeffRef(1,2) = tyz-twx; - res.coeffRef(2,0) = txz-twy; - res.coeffRef(2,1) = tyz+twx; - res.coeffRef(2,2) = Scalar(1)-(txx+tyy); - - return res; -} - -/** Sets *this to be a quaternion representing a rotation sending the vector \a a to the vector \a b. - * - * \returns a reference to *this. - * - * Note that the two input vectors do \b not have to be normalized. - */ -template<typename Scalar> -template<typename Derived1, typename Derived2> -inline Quaternion<Scalar>& Quaternion<Scalar>::setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b) -{ - Vector3 v0 = a.normalized(); - Vector3 v1 = b.normalized(); - Scalar c = v0.eigen2_dot(v1); - - // if dot == 1, vectors are the same - if (ei_isApprox(c,Scalar(1))) - { - // set to identity - this->w() = 1; this->vec().setZero(); - return *this; - } - // if dot == -1, vectors are opposites - if (ei_isApprox(c,Scalar(-1))) - { - this->vec() = v0.unitOrthogonal(); - this->w() = 0; - return *this; - } - - Vector3 axis = v0.cross(v1); - Scalar s = ei_sqrt((Scalar(1)+c)*Scalar(2)); - Scalar invs = Scalar(1)/s; - this->vec() = axis * invs; - this->w() = s * Scalar(0.5); - - return *this; -} - -/** \returns the multiplicative inverse of \c *this - * Note that in most cases, i.e., if you simply want the opposite rotation, - * and/or the quaternion is normalized, then it is enough to use the conjugate. - * - * \sa Quaternion::conjugate() - */ -template <typename Scalar> -inline Quaternion<Scalar> Quaternion<Scalar>::inverse() const -{ - // FIXME should this function be called multiplicativeInverse and conjugate() be called inverse() or opposite() ?? - Scalar n2 = this->squaredNorm(); - if (n2 > 0) - return Quaternion(conjugate().coeffs() / n2); - else - { - // return an invalid result to flag the error - return Quaternion(Coefficients::Zero()); - } -} - -/** \returns the conjugate of the \c *this which is equal to the multiplicative inverse - * if the quaternion is normalized. - * The conjugate of a quaternion represents the opposite rotation. - * - * \sa Quaternion::inverse() - */ -template <typename Scalar> -inline Quaternion<Scalar> Quaternion<Scalar>::conjugate() const -{ - return Quaternion(this->w(),-this->x(),-this->y(),-this->z()); -} - -/** \returns the angle (in radian) between two rotations - * \sa eigen2_dot() - */ -template <typename Scalar> -inline Scalar Quaternion<Scalar>::angularDistance(const Quaternion& other) const -{ - double d = ei_abs(this->eigen2_dot(other)); - if (d>=1.0) - return 0; - return Scalar(2) * std::acos(d); -} - -/** \returns the spherical linear interpolation between the two quaternions - * \c *this and \a other at the parameter \a t - */ -template <typename Scalar> -Quaternion<Scalar> Quaternion<Scalar>::slerp(Scalar t, const Quaternion& other) const -{ - static const Scalar one = Scalar(1) - machine_epsilon<Scalar>(); - Scalar d = this->eigen2_dot(other); - Scalar absD = ei_abs(d); - - Scalar scale0; - Scalar scale1; - - if (absD>=one) - { - scale0 = Scalar(1) - t; - scale1 = t; - } - else - { - // theta is the angle between the 2 quaternions - Scalar theta = std::acos(absD); - Scalar sinTheta = ei_sin(theta); - - scale0 = ei_sin( ( Scalar(1) - t ) * theta) / sinTheta; - scale1 = ei_sin( ( t * theta) ) / sinTheta; - if (d<0) - scale1 = -scale1; - } - - return Quaternion<Scalar>(scale0 * coeffs() + scale1 * other.coeffs()); -} - -// set from a rotation matrix -template<typename Other> -struct ei_quaternion_assign_impl<Other,3,3> -{ - typedef typename Other::Scalar Scalar; - static inline void run(Quaternion<Scalar>& q, const Other& mat) - { - // This algorithm comes from "Quaternion Calculus and Fast Animation", - // Ken Shoemake, 1987 SIGGRAPH course notes - Scalar t = mat.trace(); - if (t > 0) - { - t = ei_sqrt(t + Scalar(1.0)); - q.w() = Scalar(0.5)*t; - t = Scalar(0.5)/t; - q.x() = (mat.coeff(2,1) - mat.coeff(1,2)) * t; - q.y() = (mat.coeff(0,2) - mat.coeff(2,0)) * t; - q.z() = (mat.coeff(1,0) - mat.coeff(0,1)) * t; - } - else - { - int i = 0; - if (mat.coeff(1,1) > mat.coeff(0,0)) - i = 1; - if (mat.coeff(2,2) > mat.coeff(i,i)) - i = 2; - int j = (i+1)%3; - int k = (j+1)%3; - - t = ei_sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + Scalar(1.0)); - q.coeffs().coeffRef(i) = Scalar(0.5) * t; - t = Scalar(0.5)/t; - q.w() = (mat.coeff(k,j)-mat.coeff(j,k))*t; - q.coeffs().coeffRef(j) = (mat.coeff(j,i)+mat.coeff(i,j))*t; - q.coeffs().coeffRef(k) = (mat.coeff(k,i)+mat.coeff(i,k))*t; - } - } -}; - -// set from a vector of coefficients assumed to be a quaternion -template<typename Other> -struct ei_quaternion_assign_impl<Other,4,1> -{ - typedef typename Other::Scalar Scalar; - static inline void run(Quaternion<Scalar>& q, const Other& vec) - { - q.coeffs() = vec; - } -}; - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Rotation2D.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Rotation2D.h deleted file mode 100644 index 19b8582a1b..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Rotation2D.h +++ /dev/null @@ -1,145 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Rotation2D - * - * \brief Represents a rotation/orientation in a 2 dimensional space. - * - * \param _Scalar the scalar type, i.e., the type of the coefficients - * - * This class is equivalent to a single scalar representing a counter clock wise rotation - * as a single angle in radian. It provides some additional features such as the automatic - * conversion from/to a 2x2 rotation matrix. Moreover this class aims to provide a similar - * interface to Quaternion in order to facilitate the writing of generic algorithms - * dealing with rotations. - * - * \sa class Quaternion, class Transform - */ -template<typename _Scalar> struct ei_traits<Rotation2D<_Scalar> > -{ - typedef _Scalar Scalar; -}; - -template<typename _Scalar> -class Rotation2D : public RotationBase<Rotation2D<_Scalar>,2> -{ - typedef RotationBase<Rotation2D<_Scalar>,2> Base; - -public: - - using Base::operator*; - - enum { Dim = 2 }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - typedef Matrix<Scalar,2,1> Vector2; - typedef Matrix<Scalar,2,2> Matrix2; - -protected: - - Scalar m_angle; - -public: - - /** Construct a 2D counter clock wise rotation from the angle \a a in radian. */ - inline Rotation2D(Scalar a) : m_angle(a) {} - - /** \returns the rotation angle */ - inline Scalar angle() const { return m_angle; } - - /** \returns a read-write reference to the rotation angle */ - inline Scalar& angle() { return m_angle; } - - /** \returns the inverse rotation */ - inline Rotation2D inverse() const { return -m_angle; } - - /** Concatenates two rotations */ - inline Rotation2D operator*(const Rotation2D& other) const - { return m_angle + other.m_angle; } - - /** Concatenates two rotations */ - inline Rotation2D& operator*=(const Rotation2D& other) - { return m_angle += other.m_angle; return *this; } - - /** Applies the rotation to a 2D vector */ - Vector2 operator* (const Vector2& vec) const - { return toRotationMatrix() * vec; } - - template<typename Derived> - Rotation2D& fromRotationMatrix(const MatrixBase<Derived>& m); - Matrix2 toRotationMatrix(void) const; - - /** \returns the spherical interpolation between \c *this and \a other using - * parameter \a t. It is in fact equivalent to a linear interpolation. - */ - inline Rotation2D slerp(Scalar t, const Rotation2D& other) const - { return m_angle * (1-t) + other.angle() * t; } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type cast() const - { return typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other) - { - m_angle = Scalar(other.angle()); - } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Rotation2D& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return ei_isApprox(m_angle,other.m_angle, prec); } -}; - -/** \ingroup Geometry_Module - * single precision 2D rotation type */ -typedef Rotation2D<float> Rotation2Df; -/** \ingroup Geometry_Module - * double precision 2D rotation type */ -typedef Rotation2D<double> Rotation2Dd; - -/** Set \c *this from a 2x2 rotation matrix \a mat. - * In other words, this function extract the rotation angle - * from the rotation matrix. - */ -template<typename Scalar> -template<typename Derived> -Rotation2D<Scalar>& Rotation2D<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat) -{ - EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime==2 && Derived::ColsAtCompileTime==2,YOU_MADE_A_PROGRAMMING_MISTAKE) - m_angle = ei_atan2(mat.coeff(1,0), mat.coeff(0,0)); - return *this; -} - -/** Constructs and \returns an equivalent 2x2 rotation matrix. - */ -template<typename Scalar> -typename Rotation2D<Scalar>::Matrix2 -Rotation2D<Scalar>::toRotationMatrix(void) const -{ - Scalar sinA = ei_sin(m_angle); - Scalar cosA = ei_cos(m_angle); - return (Matrix2() << cosA, -sinA, sinA, cosA).finished(); -} - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/RotationBase.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/RotationBase.h deleted file mode 100644 index b1c8f38da9..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/RotationBase.h +++ /dev/null @@ -1,123 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -// this file aims to contains the various representations of rotation/orientation -// in 2D and 3D space excepted Matrix and Quaternion. - -/** \class RotationBase - * - * \brief Common base class for compact rotation representations - * - * \param Derived is the derived type, i.e., a rotation type - * \param _Dim the dimension of the space - */ -template<typename Derived, int _Dim> -class RotationBase -{ - public: - enum { Dim = _Dim }; - /** the scalar type of the coefficients */ - typedef typename ei_traits<Derived>::Scalar Scalar; - - /** corresponding linear transformation matrix type */ - typedef Matrix<Scalar,Dim,Dim> RotationMatrixType; - - inline const Derived& derived() const { return *static_cast<const Derived*>(this); } - inline Derived& derived() { return *static_cast<Derived*>(this); } - - /** \returns an equivalent rotation matrix */ - inline RotationMatrixType toRotationMatrix() const { return derived().toRotationMatrix(); } - - /** \returns the inverse rotation */ - inline Derived inverse() const { return derived().inverse(); } - - /** \returns the concatenation of the rotation \c *this with a translation \a t */ - inline Transform<Scalar,Dim> operator*(const Translation<Scalar,Dim>& t) const - { return toRotationMatrix() * t; } - - /** \returns the concatenation of the rotation \c *this with a scaling \a s */ - inline RotationMatrixType operator*(const Scaling<Scalar,Dim>& s) const - { return toRotationMatrix() * s; } - - /** \returns the concatenation of the rotation \c *this with an affine transformation \a t */ - inline Transform<Scalar,Dim> operator*(const Transform<Scalar,Dim>& t) const - { return toRotationMatrix() * t; } -}; - -/** \geometry_module - * - * Constructs a Dim x Dim rotation matrix from the rotation \a r - */ -template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols> -template<typename OtherDerived> -Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols> -::Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r) -{ - EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim)) - *this = r.toRotationMatrix(); -} - -/** \geometry_module - * - * Set a Dim x Dim rotation matrix from the rotation \a r - */ -template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols> -template<typename OtherDerived> -Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>& -Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols> -::operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r) -{ - EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim)) - return *this = r.toRotationMatrix(); -} - -/** \internal - * - * Helper function to return an arbitrary rotation object to a rotation matrix. - * - * \param Scalar the numeric type of the matrix coefficients - * \param Dim the dimension of the current space - * - * It returns a Dim x Dim fixed size matrix. - * - * Default specializations are provided for: - * - any scalar type (2D), - * - any matrix expression, - * - any type based on RotationBase (e.g., Quaternion, AngleAxis, Rotation2D) - * - * Currently ei_toRotationMatrix is only used by Transform. - * - * \sa class Transform, class Rotation2D, class Quaternion, class AngleAxis - */ -template<typename Scalar, int Dim> -static inline Matrix<Scalar,2,2> ei_toRotationMatrix(const Scalar& s) -{ - EIGEN_STATIC_ASSERT(Dim==2,YOU_MADE_A_PROGRAMMING_MISTAKE) - return Rotation2D<Scalar>(s).toRotationMatrix(); -} - -template<typename Scalar, int Dim, typename OtherDerived> -static inline Matrix<Scalar,Dim,Dim> ei_toRotationMatrix(const RotationBase<OtherDerived,Dim>& r) -{ - return r.toRotationMatrix(); -} - -template<typename Scalar, int Dim, typename OtherDerived> -static inline const MatrixBase<OtherDerived>& ei_toRotationMatrix(const MatrixBase<OtherDerived>& mat) -{ - EIGEN_STATIC_ASSERT(OtherDerived::RowsAtCompileTime==Dim && OtherDerived::ColsAtCompileTime==Dim, - YOU_MADE_A_PROGRAMMING_MISTAKE) - return mat; -} - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Scaling.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Scaling.h deleted file mode 100644 index b8fa6cd3f6..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Scaling.h +++ /dev/null @@ -1,167 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Scaling - * - * \brief Represents a possibly non uniform scaling transformation - * - * \param _Scalar the scalar type, i.e., the type of the coefficients. - * \param _Dim the dimension of the space, can be a compile time value or Dynamic - * - * \note This class is not aimed to be used to store a scaling transformation, - * but rather to make easier the constructions and updates of Transform objects. - * - * \sa class Translation, class Transform - */ -template<typename _Scalar, int _Dim> -class Scaling -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim) - /** dimension of the space */ - enum { Dim = _Dim }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - /** corresponding vector type */ - typedef Matrix<Scalar,Dim,1> VectorType; - /** corresponding linear transformation matrix type */ - typedef Matrix<Scalar,Dim,Dim> LinearMatrixType; - /** corresponding translation type */ - typedef Translation<Scalar,Dim> TranslationType; - /** corresponding affine transformation type */ - typedef Transform<Scalar,Dim> TransformType; - -protected: - - VectorType m_coeffs; - -public: - - /** Default constructor without initialization. */ - Scaling() {} - /** Constructs and initialize a uniform scaling transformation */ - explicit inline Scaling(const Scalar& s) { m_coeffs.setConstant(s); } - /** 2D only */ - inline Scaling(const Scalar& sx, const Scalar& sy) - { - ei_assert(Dim==2); - m_coeffs.x() = sx; - m_coeffs.y() = sy; - } - /** 3D only */ - inline Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz) - { - ei_assert(Dim==3); - m_coeffs.x() = sx; - m_coeffs.y() = sy; - m_coeffs.z() = sz; - } - /** Constructs and initialize the scaling transformation from a vector of scaling coefficients */ - explicit inline Scaling(const VectorType& coeffs) : m_coeffs(coeffs) {} - - const VectorType& coeffs() const { return m_coeffs; } - VectorType& coeffs() { return m_coeffs; } - - /** Concatenates two scaling */ - inline Scaling operator* (const Scaling& other) const - { return Scaling(coeffs().cwise() * other.coeffs()); } - - /** Concatenates a scaling and a translation */ - inline TransformType operator* (const TranslationType& t) const; - - /** Concatenates a scaling and an affine transformation */ - inline TransformType operator* (const TransformType& t) const; - - /** Concatenates a scaling and a linear transformation matrix */ - // TODO returns an expression - inline LinearMatrixType operator* (const LinearMatrixType& other) const - { return coeffs().asDiagonal() * other; } - - /** Concatenates a linear transformation matrix and a scaling */ - // TODO returns an expression - friend inline LinearMatrixType operator* (const LinearMatrixType& other, const Scaling& s) - { return other * s.coeffs().asDiagonal(); } - - template<typename Derived> - inline LinearMatrixType operator*(const RotationBase<Derived,Dim>& r) const - { return *this * r.toRotationMatrix(); } - - /** Applies scaling to vector */ - inline VectorType operator* (const VectorType& other) const - { return coeffs().asDiagonal() * other; } - - /** \returns the inverse scaling */ - inline Scaling inverse() const - { return Scaling(coeffs().cwise().inverse()); } - - inline Scaling& operator=(const Scaling& other) - { - m_coeffs = other.m_coeffs; - return *this; - } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Scaling,Scaling<NewScalarType,Dim> >::type cast() const - { return typename internal::cast_return_type<Scaling,Scaling<NewScalarType,Dim> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Scaling(const Scaling<OtherScalarType,Dim>& other) - { m_coeffs = other.coeffs().template cast<Scalar>(); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Scaling& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return m_coeffs.isApprox(other.m_coeffs, prec); } - -}; - -/** \addtogroup Geometry_Module */ -//@{ -typedef Scaling<float, 2> Scaling2f; -typedef Scaling<double,2> Scaling2d; -typedef Scaling<float, 3> Scaling3f; -typedef Scaling<double,3> Scaling3d; -//@} - -template<typename Scalar, int Dim> -inline typename Scaling<Scalar,Dim>::TransformType -Scaling<Scalar,Dim>::operator* (const TranslationType& t) const -{ - TransformType res; - res.matrix().setZero(); - res.linear().diagonal() = coeffs(); - res.translation() = m_coeffs.cwise() * t.vector(); - res(Dim,Dim) = Scalar(1); - return res; -} - -template<typename Scalar, int Dim> -inline typename Scaling<Scalar,Dim>::TransformType -Scaling<Scalar,Dim>::operator* (const TransformType& t) const -{ - TransformType res = t; - res.prescale(m_coeffs); - return res; -} - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Transform.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Transform.h deleted file mode 100644 index fab60b251d..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Transform.h +++ /dev/null @@ -1,786 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -// Note that we have to pass Dim and HDim because it is not allowed to use a template -// parameter to define a template specialization. To be more precise, in the following -// specializations, it is not allowed to use Dim+1 instead of HDim. -template< typename Other, - int Dim, - int HDim, - int OtherRows=Other::RowsAtCompileTime, - int OtherCols=Other::ColsAtCompileTime> -struct ei_transform_product_impl; - -/** \geometry_module \ingroup Geometry_Module - * - * \class Transform - * - * \brief Represents an homogeneous transformation in a N dimensional space - * - * \param _Scalar the scalar type, i.e., the type of the coefficients - * \param _Dim the dimension of the space - * - * The homography is internally represented and stored as a (Dim+1)^2 matrix which - * is available through the matrix() method. - * - * Conversion methods from/to Qt's QMatrix and QTransform are available if the - * preprocessor token EIGEN_QT_SUPPORT is defined. - * - * \sa class Matrix, class Quaternion - */ -template<typename _Scalar, int _Dim> -class Transform -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim==Dynamic ? Dynamic : (_Dim+1)*(_Dim+1)) - enum { - Dim = _Dim, ///< space dimension in which the transformation holds - HDim = _Dim+1 ///< size of a respective homogeneous vector - }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - /** type of the matrix used to represent the transformation */ - typedef Matrix<Scalar,HDim,HDim> MatrixType; - /** type of the matrix used to represent the linear part of the transformation */ - typedef Matrix<Scalar,Dim,Dim> LinearMatrixType; - /** type of read/write reference to the linear part of the transformation */ - typedef Block<MatrixType,Dim,Dim> LinearPart; - /** type of read/write reference to the linear part of the transformation */ - typedef const Block<const MatrixType,Dim,Dim> ConstLinearPart; - /** type of a vector */ - typedef Matrix<Scalar,Dim,1> VectorType; - /** type of a read/write reference to the translation part of the rotation */ - typedef Block<MatrixType,Dim,1> TranslationPart; - /** type of a read/write reference to the translation part of the rotation */ - typedef const Block<const MatrixType,Dim,1> ConstTranslationPart; - /** corresponding translation type */ - typedef Translation<Scalar,Dim> TranslationType; - /** corresponding scaling transformation type */ - typedef Scaling<Scalar,Dim> ScalingType; - -protected: - - MatrixType m_matrix; - -public: - - /** Default constructor without initialization of the coefficients. */ - inline Transform() { } - - inline Transform(const Transform& other) - { - m_matrix = other.m_matrix; - } - - inline explicit Transform(const TranslationType& t) { *this = t; } - inline explicit Transform(const ScalingType& s) { *this = s; } - template<typename Derived> - inline explicit Transform(const RotationBase<Derived, Dim>& r) { *this = r; } - - inline Transform& operator=(const Transform& other) - { m_matrix = other.m_matrix; return *this; } - - template<typename OtherDerived, bool BigMatrix> // MSVC 2005 will commit suicide if BigMatrix has a default value - struct construct_from_matrix - { - static inline void run(Transform *transform, const MatrixBase<OtherDerived>& other) - { - transform->matrix() = other; - } - }; - - template<typename OtherDerived> struct construct_from_matrix<OtherDerived, true> - { - static inline void run(Transform *transform, const MatrixBase<OtherDerived>& other) - { - transform->linear() = other; - transform->translation().setZero(); - transform->matrix()(Dim,Dim) = Scalar(1); - transform->matrix().template block<1,Dim>(Dim,0).setZero(); - } - }; - - /** Constructs and initializes a transformation from a Dim^2 or a (Dim+1)^2 matrix. */ - template<typename OtherDerived> - inline explicit Transform(const MatrixBase<OtherDerived>& other) - { - construct_from_matrix<OtherDerived, int(OtherDerived::RowsAtCompileTime) == Dim>::run(this, other); - } - - /** Set \c *this from a (Dim+1)^2 matrix. */ - template<typename OtherDerived> - inline Transform& operator=(const MatrixBase<OtherDerived>& other) - { m_matrix = other; return *this; } - - #ifdef EIGEN_QT_SUPPORT - inline Transform(const QMatrix& other); - inline Transform& operator=(const QMatrix& other); - inline QMatrix toQMatrix(void) const; - inline Transform(const QTransform& other); - inline Transform& operator=(const QTransform& other); - inline QTransform toQTransform(void) const; - #endif - - /** shortcut for m_matrix(row,col); - * \sa MatrixBase::operaror(int,int) const */ - inline Scalar operator() (int row, int col) const { return m_matrix(row,col); } - /** shortcut for m_matrix(row,col); - * \sa MatrixBase::operaror(int,int) */ - inline Scalar& operator() (int row, int col) { return m_matrix(row,col); } - - /** \returns a read-only expression of the transformation matrix */ - inline const MatrixType& matrix() const { return m_matrix; } - /** \returns a writable expression of the transformation matrix */ - inline MatrixType& matrix() { return m_matrix; } - - /** \returns a read-only expression of the linear (linear) part of the transformation */ - inline ConstLinearPart linear() const { return m_matrix.template block<Dim,Dim>(0,0); } - /** \returns a writable expression of the linear (linear) part of the transformation */ - inline LinearPart linear() { return m_matrix.template block<Dim,Dim>(0,0); } - - /** \returns a read-only expression of the translation vector of the transformation */ - inline ConstTranslationPart translation() const { return m_matrix.template block<Dim,1>(0,Dim); } - /** \returns a writable expression of the translation vector of the transformation */ - inline TranslationPart translation() { return m_matrix.template block<Dim,1>(0,Dim); } - - /** \returns an expression of the product between the transform \c *this and a matrix expression \a other - * - * The right hand side \a other might be either: - * \li a vector of size Dim, - * \li an homogeneous vector of size Dim+1, - * \li a transformation matrix of size Dim+1 x Dim+1. - */ - // note: this function is defined here because some compilers cannot find the respective declaration - template<typename OtherDerived> - inline const typename ei_transform_product_impl<OtherDerived,_Dim,_Dim+1>::ResultType - operator * (const MatrixBase<OtherDerived> &other) const - { return ei_transform_product_impl<OtherDerived,Dim,HDim>::run(*this,other.derived()); } - - /** \returns the product expression of a transformation matrix \a a times a transform \a b - * The transformation matrix \a a must have a Dim+1 x Dim+1 sizes. */ - template<typename OtherDerived> - friend inline const typename ProductReturnType<OtherDerived,MatrixType>::Type - operator * (const MatrixBase<OtherDerived> &a, const Transform &b) - { return a.derived() * b.matrix(); } - - /** Contatenates two transformations */ - inline const Transform - operator * (const Transform& other) const - { return Transform(m_matrix * other.matrix()); } - - /** \sa MatrixBase::setIdentity() */ - void setIdentity() { m_matrix.setIdentity(); } - static const typename MatrixType::IdentityReturnType Identity() - { - return MatrixType::Identity(); - } - - template<typename OtherDerived> - inline Transform& scale(const MatrixBase<OtherDerived> &other); - - template<typename OtherDerived> - inline Transform& prescale(const MatrixBase<OtherDerived> &other); - - inline Transform& scale(Scalar s); - inline Transform& prescale(Scalar s); - - template<typename OtherDerived> - inline Transform& translate(const MatrixBase<OtherDerived> &other); - - template<typename OtherDerived> - inline Transform& pretranslate(const MatrixBase<OtherDerived> &other); - - template<typename RotationType> - inline Transform& rotate(const RotationType& rotation); - - template<typename RotationType> - inline Transform& prerotate(const RotationType& rotation); - - Transform& shear(Scalar sx, Scalar sy); - Transform& preshear(Scalar sx, Scalar sy); - - inline Transform& operator=(const TranslationType& t); - inline Transform& operator*=(const TranslationType& t) { return translate(t.vector()); } - inline Transform operator*(const TranslationType& t) const; - - inline Transform& operator=(const ScalingType& t); - inline Transform& operator*=(const ScalingType& s) { return scale(s.coeffs()); } - inline Transform operator*(const ScalingType& s) const; - friend inline Transform operator*(const LinearMatrixType& mat, const Transform& t) - { - Transform res = t; - res.matrix().row(Dim) = t.matrix().row(Dim); - res.matrix().template block<Dim,HDim>(0,0) = (mat * t.matrix().template block<Dim,HDim>(0,0)).lazy(); - return res; - } - - template<typename Derived> - inline Transform& operator=(const RotationBase<Derived,Dim>& r); - template<typename Derived> - inline Transform& operator*=(const RotationBase<Derived,Dim>& r) { return rotate(r.toRotationMatrix()); } - template<typename Derived> - inline Transform operator*(const RotationBase<Derived,Dim>& r) const; - - LinearMatrixType rotation() const; - template<typename RotationMatrixType, typename ScalingMatrixType> - void computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const; - template<typename ScalingMatrixType, typename RotationMatrixType> - void computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const; - - template<typename PositionDerived, typename OrientationType, typename ScaleDerived> - Transform& fromPositionOrientationScale(const MatrixBase<PositionDerived> &position, - const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale); - - inline const MatrixType inverse(TransformTraits traits = Affine) const; - - /** \returns a const pointer to the column major internal matrix */ - const Scalar* data() const { return m_matrix.data(); } - /** \returns a non-const pointer to the column major internal matrix */ - Scalar* data() { return m_matrix.data(); } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim> >::type cast() const - { return typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Transform(const Transform<OtherScalarType,Dim>& other) - { m_matrix = other.matrix().template cast<Scalar>(); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Transform& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return m_matrix.isApprox(other.m_matrix, prec); } - - #ifdef EIGEN_TRANSFORM_PLUGIN - #include EIGEN_TRANSFORM_PLUGIN - #endif - -protected: - -}; - -/** \ingroup Geometry_Module */ -typedef Transform<float,2> Transform2f; -/** \ingroup Geometry_Module */ -typedef Transform<float,3> Transform3f; -/** \ingroup Geometry_Module */ -typedef Transform<double,2> Transform2d; -/** \ingroup Geometry_Module */ -typedef Transform<double,3> Transform3d; - -/************************** -*** Optional QT support *** -**************************/ - -#ifdef EIGEN_QT_SUPPORT -/** Initialises \c *this from a QMatrix assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim> -Transform<Scalar,Dim>::Transform(const QMatrix& other) -{ - *this = other; -} - -/** Set \c *this from a QMatrix assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim> -Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const QMatrix& other) -{ - EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - m_matrix << other.m11(), other.m21(), other.dx(), - other.m12(), other.m22(), other.dy(), - 0, 0, 1; - return *this; -} - -/** \returns a QMatrix from \c *this assuming the dimension is 2. - * - * \warning this convertion might loss data if \c *this is not affine - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim> -QMatrix Transform<Scalar,Dim>::toQMatrix(void) const -{ - EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - return QMatrix(m_matrix.coeff(0,0), m_matrix.coeff(1,0), - m_matrix.coeff(0,1), m_matrix.coeff(1,1), - m_matrix.coeff(0,2), m_matrix.coeff(1,2)); -} - -/** Initialises \c *this from a QTransform assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim> -Transform<Scalar,Dim>::Transform(const QTransform& other) -{ - *this = other; -} - -/** Set \c *this from a QTransform assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim> -Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const QTransform& other) -{ - EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - m_matrix << other.m11(), other.m21(), other.dx(), - other.m12(), other.m22(), other.dy(), - other.m13(), other.m23(), other.m33(); - return *this; -} - -/** \returns a QTransform from \c *this assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim> -QTransform Transform<Scalar,Dim>::toQTransform(void) const -{ - EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0), m_matrix.coeff(2,0), - m_matrix.coeff(0,1), m_matrix.coeff(1,1), m_matrix.coeff(2,1), - m_matrix.coeff(0,2), m_matrix.coeff(1,2), m_matrix.coeff(2,2)); -} -#endif - -/********************* -*** Procedural API *** -*********************/ - -/** Applies on the right the non uniform scale transformation represented - * by the vector \a other to \c *this and returns a reference to \c *this. - * \sa prescale() - */ -template<typename Scalar, int Dim> -template<typename OtherDerived> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::scale(const MatrixBase<OtherDerived> &other) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim)) - linear() = (linear() * other.asDiagonal()).lazy(); - return *this; -} - -/** Applies on the right a uniform scale of a factor \a c to \c *this - * and returns a reference to \c *this. - * \sa prescale(Scalar) - */ -template<typename Scalar, int Dim> -inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::scale(Scalar s) -{ - linear() *= s; - return *this; -} - -/** Applies on the left the non uniform scale transformation represented - * by the vector \a other to \c *this and returns a reference to \c *this. - * \sa scale() - */ -template<typename Scalar, int Dim> -template<typename OtherDerived> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::prescale(const MatrixBase<OtherDerived> &other) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim)) - m_matrix.template block<Dim,HDim>(0,0) = (other.asDiagonal() * m_matrix.template block<Dim,HDim>(0,0)).lazy(); - return *this; -} - -/** Applies on the left a uniform scale of a factor \a c to \c *this - * and returns a reference to \c *this. - * \sa scale(Scalar) - */ -template<typename Scalar, int Dim> -inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::prescale(Scalar s) -{ - m_matrix.template corner<Dim,HDim>(TopLeft) *= s; - return *this; -} - -/** Applies on the right the translation matrix represented by the vector \a other - * to \c *this and returns a reference to \c *this. - * \sa pretranslate() - */ -template<typename Scalar, int Dim> -template<typename OtherDerived> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::translate(const MatrixBase<OtherDerived> &other) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim)) - translation() += linear() * other; - return *this; -} - -/** Applies on the left the translation matrix represented by the vector \a other - * to \c *this and returns a reference to \c *this. - * \sa translate() - */ -template<typename Scalar, int Dim> -template<typename OtherDerived> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::pretranslate(const MatrixBase<OtherDerived> &other) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim)) - translation() += other; - return *this; -} - -/** Applies on the right the rotation represented by the rotation \a rotation - * to \c *this and returns a reference to \c *this. - * - * The template parameter \a RotationType is the type of the rotation which - * must be known by ei_toRotationMatrix<>. - * - * Natively supported types includes: - * - any scalar (2D), - * - a Dim x Dim matrix expression, - * - a Quaternion (3D), - * - a AngleAxis (3D) - * - * This mechanism is easily extendable to support user types such as Euler angles, - * or a pair of Quaternion for 4D rotations. - * - * \sa rotate(Scalar), class Quaternion, class AngleAxis, prerotate(RotationType) - */ -template<typename Scalar, int Dim> -template<typename RotationType> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::rotate(const RotationType& rotation) -{ - linear() *= ei_toRotationMatrix<Scalar,Dim>(rotation); - return *this; -} - -/** Applies on the left the rotation represented by the rotation \a rotation - * to \c *this and returns a reference to \c *this. - * - * See rotate() for further details. - * - * \sa rotate() - */ -template<typename Scalar, int Dim> -template<typename RotationType> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::prerotate(const RotationType& rotation) -{ - m_matrix.template block<Dim,HDim>(0,0) = ei_toRotationMatrix<Scalar,Dim>(rotation) - * m_matrix.template block<Dim,HDim>(0,0); - return *this; -} - -/** Applies on the right the shear transformation represented - * by the vector \a other to \c *this and returns a reference to \c *this. - * \warning 2D only. - * \sa preshear() - */ -template<typename Scalar, int Dim> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::shear(Scalar sx, Scalar sy) -{ - EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - VectorType tmp = linear().col(0)*sy + linear().col(1); - linear() << linear().col(0) + linear().col(1)*sx, tmp; - return *this; -} - -/** Applies on the left the shear transformation represented - * by the vector \a other to \c *this and returns a reference to \c *this. - * \warning 2D only. - * \sa shear() - */ -template<typename Scalar, int Dim> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::preshear(Scalar sx, Scalar sy) -{ - EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - m_matrix.template block<Dim,HDim>(0,0) = LinearMatrixType(1, sx, sy, 1) * m_matrix.template block<Dim,HDim>(0,0); - return *this; -} - -/****************************************************** -*** Scaling, Translation and Rotation compatibility *** -******************************************************/ - -template<typename Scalar, int Dim> -inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const TranslationType& t) -{ - linear().setIdentity(); - translation() = t.vector(); - m_matrix.template block<1,Dim>(Dim,0).setZero(); - m_matrix(Dim,Dim) = Scalar(1); - return *this; -} - -template<typename Scalar, int Dim> -inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const TranslationType& t) const -{ - Transform res = *this; - res.translate(t.vector()); - return res; -} - -template<typename Scalar, int Dim> -inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const ScalingType& s) -{ - m_matrix.setZero(); - linear().diagonal() = s.coeffs(); - m_matrix.coeffRef(Dim,Dim) = Scalar(1); - return *this; -} - -template<typename Scalar, int Dim> -inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const ScalingType& s) const -{ - Transform res = *this; - res.scale(s.coeffs()); - return res; -} - -template<typename Scalar, int Dim> -template<typename Derived> -inline Transform<Scalar,Dim>& Transform<Scalar,Dim>::operator=(const RotationBase<Derived,Dim>& r) -{ - linear() = ei_toRotationMatrix<Scalar,Dim>(r); - translation().setZero(); - m_matrix.template block<1,Dim>(Dim,0).setZero(); - m_matrix.coeffRef(Dim,Dim) = Scalar(1); - return *this; -} - -template<typename Scalar, int Dim> -template<typename Derived> -inline Transform<Scalar,Dim> Transform<Scalar,Dim>::operator*(const RotationBase<Derived,Dim>& r) const -{ - Transform res = *this; - res.rotate(r.derived()); - return res; -} - -/************************ -*** Special functions *** -************************/ - -/** \returns the rotation part of the transformation - * \nonstableyet - * - * \svd_module - * - * \sa computeRotationScaling(), computeScalingRotation(), class SVD - */ -template<typename Scalar, int Dim> -typename Transform<Scalar,Dim>::LinearMatrixType -Transform<Scalar,Dim>::rotation() const -{ - LinearMatrixType result; - computeRotationScaling(&result, (LinearMatrixType*)0); - return result; -} - - -/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being - * not necessarily positive. - * - * If either pointer is zero, the corresponding computation is skipped. - * - * \nonstableyet - * - * \svd_module - * - * \sa computeScalingRotation(), rotation(), class SVD - */ -template<typename Scalar, int Dim> -template<typename RotationMatrixType, typename ScalingMatrixType> -void Transform<Scalar,Dim>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const -{ - JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU|ComputeFullV); - Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant(); // so x has absolute value 1 - Matrix<Scalar, Dim, 1> sv(svd.singularValues()); - sv.coeffRef(0) *= x; - if(scaling) - { - scaling->noalias() = svd.matrixV() * sv.asDiagonal() * svd.matrixV().adjoint(); - } - if(rotation) - { - LinearMatrixType m(svd.matrixU()); - m.col(0) /= x; - rotation->noalias() = m * svd.matrixV().adjoint(); - } -} - -/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being - * not necessarily positive. - * - * If either pointer is zero, the corresponding computation is skipped. - * - * \nonstableyet - * - * \svd_module - * - * \sa computeRotationScaling(), rotation(), class SVD - */ -template<typename Scalar, int Dim> -template<typename ScalingMatrixType, typename RotationMatrixType> -void Transform<Scalar,Dim>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const -{ - JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU|ComputeFullV); - Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant(); // so x has absolute value 1 - Matrix<Scalar, Dim, 1> sv(svd.singularValues()); - sv.coeffRef(0) *= x; - if(scaling) - { - scaling->noalias() = svd.matrixU() * sv.asDiagonal() * svd.matrixU().adjoint(); - } - if(rotation) - { - LinearMatrixType m(svd.matrixU()); - m.col(0) /= x; - rotation->noalias() = m * svd.matrixV().adjoint(); - } -} - -/** Convenient method to set \c *this from a position, orientation and scale - * of a 3D object. - */ -template<typename Scalar, int Dim> -template<typename PositionDerived, typename OrientationType, typename ScaleDerived> -Transform<Scalar,Dim>& -Transform<Scalar,Dim>::fromPositionOrientationScale(const MatrixBase<PositionDerived> &position, - const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale) -{ - linear() = ei_toRotationMatrix<Scalar,Dim>(orientation); - linear() *= scale.asDiagonal(); - translation() = position; - m_matrix.template block<1,Dim>(Dim,0).setZero(); - m_matrix(Dim,Dim) = Scalar(1); - return *this; -} - -/** \nonstableyet - * - * \returns the inverse transformation matrix according to some given knowledge - * on \c *this. - * - * \param traits allows to optimize the inversion process when the transformion - * is known to be not a general transformation. The possible values are: - * - Projective if the transformation is not necessarily affine, i.e., if the - * last row is not guaranteed to be [0 ... 0 1] - * - Affine is the default, the last row is assumed to be [0 ... 0 1] - * - Isometry if the transformation is only a concatenations of translations - * and rotations. - * - * \warning unless \a traits is always set to NoShear or NoScaling, this function - * requires the generic inverse method of MatrixBase defined in the LU module. If - * you forget to include this module, then you will get hard to debug linking errors. - * - * \sa MatrixBase::inverse() - */ -template<typename Scalar, int Dim> -inline const typename Transform<Scalar,Dim>::MatrixType -Transform<Scalar,Dim>::inverse(TransformTraits traits) const -{ - if (traits == Projective) - { - return m_matrix.inverse(); - } - else - { - MatrixType res; - if (traits == Affine) - { - res.template corner<Dim,Dim>(TopLeft) = linear().inverse(); - } - else if (traits == Isometry) - { - res.template corner<Dim,Dim>(TopLeft) = linear().transpose(); - } - else - { - ei_assert("invalid traits value in Transform::inverse()"); - } - // translation and remaining parts - res.template corner<Dim,1>(TopRight) = - res.template corner<Dim,Dim>(TopLeft) * translation(); - res.template corner<1,Dim>(BottomLeft).setZero(); - res.coeffRef(Dim,Dim) = Scalar(1); - return res; - } -} - -/***************************************************** -*** Specializations of operator* with a MatrixBase *** -*****************************************************/ - -template<typename Other, int Dim, int HDim> -struct ei_transform_product_impl<Other,Dim,HDim, HDim,HDim> -{ - typedef Transform<typename Other::Scalar,Dim> TransformType; - typedef typename TransformType::MatrixType MatrixType; - typedef typename ProductReturnType<MatrixType,Other>::Type ResultType; - static ResultType run(const TransformType& tr, const Other& other) - { return tr.matrix() * other; } -}; - -template<typename Other, int Dim, int HDim> -struct ei_transform_product_impl<Other,Dim,HDim, Dim,Dim> -{ - typedef Transform<typename Other::Scalar,Dim> TransformType; - typedef typename TransformType::MatrixType MatrixType; - typedef TransformType ResultType; - static ResultType run(const TransformType& tr, const Other& other) - { - TransformType res; - res.translation() = tr.translation(); - res.matrix().row(Dim) = tr.matrix().row(Dim); - res.linear() = (tr.linear() * other).lazy(); - return res; - } -}; - -template<typename Other, int Dim, int HDim> -struct ei_transform_product_impl<Other,Dim,HDim, HDim,1> -{ - typedef Transform<typename Other::Scalar,Dim> TransformType; - typedef typename TransformType::MatrixType MatrixType; - typedef typename ProductReturnType<MatrixType,Other>::Type ResultType; - static ResultType run(const TransformType& tr, const Other& other) - { return tr.matrix() * other; } -}; - -template<typename Other, int Dim, int HDim> -struct ei_transform_product_impl<Other,Dim,HDim, Dim,1> -{ - typedef typename Other::Scalar Scalar; - typedef Transform<Scalar,Dim> TransformType; - typedef Matrix<Scalar,Dim,1> ResultType; - static ResultType run(const TransformType& tr, const Other& other) - { return ((tr.linear() * other) + tr.translation()) - * (Scalar(1) / ( (tr.matrix().template block<1,Dim>(Dim,0) * other).coeff(0) + tr.matrix().coeff(Dim,Dim))); } -}; - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Translation.h b/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Translation.h deleted file mode 100644 index 2b9859f6f4..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Geometry/Translation.h +++ /dev/null @@ -1,184 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// no include guard, we'll include this twice from All.h from Eigen2Support, and it's internal anyway - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Translation - * - * \brief Represents a translation transformation - * - * \param _Scalar the scalar type, i.e., the type of the coefficients. - * \param _Dim the dimension of the space, can be a compile time value or Dynamic - * - * \note This class is not aimed to be used to store a translation transformation, - * but rather to make easier the constructions and updates of Transform objects. - * - * \sa class Scaling, class Transform - */ -template<typename _Scalar, int _Dim> -class Translation -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim) - /** dimension of the space */ - enum { Dim = _Dim }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - /** corresponding vector type */ - typedef Matrix<Scalar,Dim,1> VectorType; - /** corresponding linear transformation matrix type */ - typedef Matrix<Scalar,Dim,Dim> LinearMatrixType; - /** corresponding scaling transformation type */ - typedef Scaling<Scalar,Dim> ScalingType; - /** corresponding affine transformation type */ - typedef Transform<Scalar,Dim> TransformType; - -protected: - - VectorType m_coeffs; - -public: - - /** Default constructor without initialization. */ - Translation() {} - /** */ - inline Translation(const Scalar& sx, const Scalar& sy) - { - ei_assert(Dim==2); - m_coeffs.x() = sx; - m_coeffs.y() = sy; - } - /** */ - inline Translation(const Scalar& sx, const Scalar& sy, const Scalar& sz) - { - ei_assert(Dim==3); - m_coeffs.x() = sx; - m_coeffs.y() = sy; - m_coeffs.z() = sz; - } - /** Constructs and initialize the scaling transformation from a vector of scaling coefficients */ - explicit inline Translation(const VectorType& vector) : m_coeffs(vector) {} - - const VectorType& vector() const { return m_coeffs; } - VectorType& vector() { return m_coeffs; } - - /** Concatenates two translation */ - inline Translation operator* (const Translation& other) const - { return Translation(m_coeffs + other.m_coeffs); } - - /** Concatenates a translation and a scaling */ - inline TransformType operator* (const ScalingType& other) const; - - /** Concatenates a translation and a linear transformation */ - inline TransformType operator* (const LinearMatrixType& linear) const; - - template<typename Derived> - inline TransformType operator*(const RotationBase<Derived,Dim>& r) const - { return *this * r.toRotationMatrix(); } - - /** Concatenates a linear transformation and a translation */ - // its a nightmare to define a templated friend function outside its declaration - friend inline TransformType operator* (const LinearMatrixType& linear, const Translation& t) - { - TransformType res; - res.matrix().setZero(); - res.linear() = linear; - res.translation() = linear * t.m_coeffs; - res.matrix().row(Dim).setZero(); - res(Dim,Dim) = Scalar(1); - return res; - } - - /** Concatenates a translation and an affine transformation */ - inline TransformType operator* (const TransformType& t) const; - - /** Applies translation to vector */ - inline VectorType operator* (const VectorType& other) const - { return m_coeffs + other; } - - /** \returns the inverse translation (opposite) */ - Translation inverse() const { return Translation(-m_coeffs); } - - Translation& operator=(const Translation& other) - { - m_coeffs = other.m_coeffs; - return *this; - } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type cast() const - { return typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Translation(const Translation<OtherScalarType,Dim>& other) - { m_coeffs = other.vector().template cast<Scalar>(); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Translation& other, typename NumTraits<Scalar>::Real prec = precision<Scalar>()) const - { return m_coeffs.isApprox(other.m_coeffs, prec); } - -}; - -/** \addtogroup Geometry_Module */ -//@{ -typedef Translation<float, 2> Translation2f; -typedef Translation<double,2> Translation2d; -typedef Translation<float, 3> Translation3f; -typedef Translation<double,3> Translation3d; -//@} - - -template<typename Scalar, int Dim> -inline typename Translation<Scalar,Dim>::TransformType -Translation<Scalar,Dim>::operator* (const ScalingType& other) const -{ - TransformType res; - res.matrix().setZero(); - res.linear().diagonal() = other.coeffs(); - res.translation() = m_coeffs; - res(Dim,Dim) = Scalar(1); - return res; -} - -template<typename Scalar, int Dim> -inline typename Translation<Scalar,Dim>::TransformType -Translation<Scalar,Dim>::operator* (const LinearMatrixType& linear) const -{ - TransformType res; - res.matrix().setZero(); - res.linear() = linear; - res.translation() = m_coeffs; - res.matrix().row(Dim).setZero(); - res(Dim,Dim) = Scalar(1); - return res; -} - -template<typename Scalar, int Dim> -inline typename Translation<Scalar,Dim>::TransformType -Translation<Scalar,Dim>::operator* (const TransformType& t) const -{ - TransformType res = t; - res.pretranslate(m_coeffs); - return res; -} - -} // end namespace Eigen diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/LU.h b/third_party/eigen3/Eigen/src/Eigen2Support/LU.h deleted file mode 100644 index 49f19ad76e..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/LU.h +++ /dev/null @@ -1,120 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_LU_H -#define EIGEN2_LU_H - -namespace Eigen { - -template<typename MatrixType> -class LU : public FullPivLU<MatrixType> -{ - public: - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef Matrix<int, 1, MatrixType::ColsAtCompileTime, MatrixType::Options, 1, MatrixType::MaxColsAtCompileTime> IntRowVectorType; - typedef Matrix<int, MatrixType::RowsAtCompileTime, 1, MatrixType::Options, MatrixType::MaxRowsAtCompileTime, 1> IntColVectorType; - typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime, MatrixType::Options, 1, MatrixType::MaxColsAtCompileTime> RowVectorType; - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1, MatrixType::Options, MatrixType::MaxRowsAtCompileTime, 1> ColVectorType; - - typedef Matrix<typename MatrixType::Scalar, - MatrixType::ColsAtCompileTime, // the number of rows in the "kernel matrix" is the number of cols of the original matrix - // so that the product "matrix * kernel = zero" makes sense - Dynamic, // we don't know at compile-time the dimension of the kernel - MatrixType::Options, - MatrixType::MaxColsAtCompileTime, // see explanation for 2nd template parameter - MatrixType::MaxColsAtCompileTime // the kernel is a subspace of the domain space, whose dimension is the number - // of columns of the original matrix - > KernelResultType; - - typedef Matrix<typename MatrixType::Scalar, - MatrixType::RowsAtCompileTime, // the image is a subspace of the destination space, whose dimension is the number - // of rows of the original matrix - Dynamic, // we don't know at compile time the dimension of the image (the rank) - MatrixType::Options, - MatrixType::MaxRowsAtCompileTime, // the image matrix will consist of columns from the original matrix, - MatrixType::MaxColsAtCompileTime // so it has the same number of rows and at most as many columns. - > ImageResultType; - - typedef FullPivLU<MatrixType> Base; - - template<typename T> - explicit LU(const T& t) : Base(t), m_originalMatrix(t) {} - - template<typename OtherDerived, typename ResultType> - bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const - { - *result = static_cast<const Base*>(this)->solve(b); - return true; - } - - template<typename ResultType> - inline void computeInverse(ResultType *result) const - { - solve(MatrixType::Identity(this->rows(), this->cols()), result); - } - - template<typename KernelMatrixType> - void computeKernel(KernelMatrixType *result) const - { - *result = static_cast<const Base*>(this)->kernel(); - } - - template<typename ImageMatrixType> - void computeImage(ImageMatrixType *result) const - { - *result = static_cast<const Base*>(this)->image(m_originalMatrix); - } - - const ImageResultType image() const - { - return static_cast<const Base*>(this)->image(m_originalMatrix); - } - - const MatrixType& m_originalMatrix; -}; - -#if EIGEN2_SUPPORT_STAGE < STAGE20_RESOLVE_API_CONFLICTS -/** \lu_module - * - * Synonym of partialPivLu(). - * - * \return the partial-pivoting LU decomposition of \c *this. - * - * \sa class PartialPivLU - */ -template<typename Derived> -inline const LU<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::lu() const -{ - return LU<PlainObject>(eval()); -} -#endif - -#ifdef EIGEN2_SUPPORT -/** \lu_module - * - * Synonym of partialPivLu(). - * - * \return the partial-pivoting LU decomposition of \c *this. - * - * \sa class PartialPivLU - */ -template<typename Derived> -inline const LU<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::eigen2_lu() const -{ - return LU<PlainObject>(eval()); -} -#endif - -} // end namespace Eigen - -#endif // EIGEN2_LU_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Lazy.h b/third_party/eigen3/Eigen/src/Eigen2Support/Lazy.h deleted file mode 100644 index 593fc78e6d..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Lazy.h +++ /dev/null @@ -1,71 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_LAZY_H -#define EIGEN_LAZY_H - -namespace Eigen { - -/** \deprecated it is only used by lazy() which is deprecated - * - * \returns an expression of *this with added flags - * - * Example: \include MatrixBase_marked.cpp - * Output: \verbinclude MatrixBase_marked.out - * - * \sa class Flagged, extract(), part() - */ -template<typename Derived> -template<unsigned int Added> -inline const Flagged<Derived, Added, 0> -MatrixBase<Derived>::marked() const -{ - return derived(); -} - -/** \deprecated use MatrixBase::noalias() - * - * \returns an expression of *this with the EvalBeforeAssigningBit flag removed. - * - * Example: \include MatrixBase_lazy.cpp - * Output: \verbinclude MatrixBase_lazy.out - * - * \sa class Flagged, marked() - */ -template<typename Derived> -inline const Flagged<Derived, 0, EvalBeforeAssigningBit> -MatrixBase<Derived>::lazy() const -{ - return derived(); -} - - -/** \internal - * Overloaded to perform an efficient C += (A*B).lazy() */ -template<typename Derived> -template<typename ProductDerived, typename Lhs, typename Rhs> -Derived& MatrixBase<Derived>::operator+=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0, - EvalBeforeAssigningBit>& other) -{ - other._expression().derived().addTo(derived()); return derived(); -} - -/** \internal - * Overloaded to perform an efficient C -= (A*B).lazy() */ -template<typename Derived> -template<typename ProductDerived, typename Lhs, typename Rhs> -Derived& MatrixBase<Derived>::operator-=(const Flagged<ProductBase<ProductDerived, Lhs,Rhs>, 0, - EvalBeforeAssigningBit>& other) -{ - other._expression().derived().subTo(derived()); return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_LAZY_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/LeastSquares.h b/third_party/eigen3/Eigen/src/Eigen2Support/LeastSquares.h deleted file mode 100644 index 0e6fdb4889..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/LeastSquares.h +++ /dev/null @@ -1,170 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_LEASTSQUARES_H -#define EIGEN2_LEASTSQUARES_H - -namespace Eigen { - -/** \ingroup LeastSquares_Module - * - * \leastsquares_module - * - * For a set of points, this function tries to express - * one of the coords as a linear (affine) function of the other coords. - * - * This is best explained by an example. This function works in full - * generality, for points in a space of arbitrary dimension, and also over - * the complex numbers, but for this example we will work in dimension 3 - * over the real numbers (doubles). - * - * So let us work with the following set of 5 points given by their - * \f$(x,y,z)\f$ coordinates: - * @code - Vector3d points[5]; - points[0] = Vector3d( 3.02, 6.89, -4.32 ); - points[1] = Vector3d( 2.01, 5.39, -3.79 ); - points[2] = Vector3d( 2.41, 6.01, -4.01 ); - points[3] = Vector3d( 2.09, 5.55, -3.86 ); - points[4] = Vector3d( 2.58, 6.32, -4.10 ); - * @endcode - * Suppose that we want to express the second coordinate (\f$y\f$) as a linear - * expression in \f$x\f$ and \f$z\f$, that is, - * \f[ y=ax+bz+c \f] - * for some constants \f$a,b,c\f$. Thus, we want to find the best possible - * constants \f$a,b,c\f$ so that the plane of equation \f$y=ax+bz+c\f$ fits - * best the five above points. To do that, call this function as follows: - * @code - Vector3d coeffs; // will store the coefficients a, b, c - linearRegression( - 5, - &points, - &coeffs, - 1 // the coord to express as a function of - // the other ones. 0 means x, 1 means y, 2 means z. - ); - * @endcode - * Now the vector \a coeffs is approximately - * \f$( 0.495 , -1.927 , -2.906 )\f$. - * Thus, we get \f$a=0.495, b = -1.927, c = -2.906\f$. Let us check for - * instance how near points[0] is from the plane of equation \f$y=ax+bz+c\f$. - * Looking at the coords of points[0], we see that: - * \f[ax+bz+c = 0.495 * 3.02 + (-1.927) * (-4.32) + (-2.906) = 6.91.\f] - * On the other hand, we have \f$y=6.89\f$. We see that the values - * \f$6.91\f$ and \f$6.89\f$ - * are near, so points[0] is very near the plane of equation \f$y=ax+bz+c\f$. - * - * Let's now describe precisely the parameters: - * @param numPoints the number of points - * @param points the array of pointers to the points on which to perform the linear regression - * @param result pointer to the vector in which to store the result. - This vector must be of the same type and size as the - data points. The meaning of its coords is as follows. - For brevity, let \f$n=Size\f$, - \f$r_i=result[i]\f$, - and \f$f=funcOfOthers\f$. Denote by - \f$x_0,\ldots,x_{n-1}\f$ - the n coordinates in the n-dimensional space. - Then the resulting equation is: - \f[ x_f = r_0 x_0 + \cdots + r_{f-1}x_{f-1} - + r_{f+1}x_{f+1} + \cdots + r_{n-1}x_{n-1} + r_n. \f] - * @param funcOfOthers Determines which coord to express as a function of the - others. Coords are numbered starting from 0, so that a - value of 0 means \f$x\f$, 1 means \f$y\f$, - 2 means \f$z\f$, ... - * - * \sa fitHyperplane() - */ -template<typename VectorType> -void linearRegression(int numPoints, - VectorType **points, - VectorType *result, - int funcOfOthers ) -{ - typedef typename VectorType::Scalar Scalar; - typedef Hyperplane<Scalar, VectorType::SizeAtCompileTime> HyperplaneType; - const int size = points[0]->size(); - result->resize(size); - HyperplaneType h(size); - fitHyperplane(numPoints, points, &h); - for(int i = 0; i < funcOfOthers; i++) - result->coeffRef(i) = - h.coeffs()[i] / h.coeffs()[funcOfOthers]; - for(int i = funcOfOthers; i < size; i++) - result->coeffRef(i) = - h.coeffs()[i+1] / h.coeffs()[funcOfOthers]; -} - -/** \ingroup LeastSquares_Module - * - * \leastsquares_module - * - * This function is quite similar to linearRegression(), so we refer to the - * documentation of this function and only list here the differences. - * - * The main difference from linearRegression() is that this function doesn't - * take a \a funcOfOthers argument. Instead, it finds a general equation - * of the form - * \f[ r_0 x_0 + \cdots + r_{n-1}x_{n-1} + r_n = 0, \f] - * where \f$n=Size\f$, \f$r_i=retCoefficients[i]\f$, and we denote by - * \f$x_0,\ldots,x_{n-1}\f$ the n coordinates in the n-dimensional space. - * - * Thus, the vector \a retCoefficients has size \f$n+1\f$, which is another - * difference from linearRegression(). - * - * In practice, this function performs an hyper-plane fit in a total least square sense - * via the following steps: - * 1 - center the data to the mean - * 2 - compute the covariance matrix - * 3 - pick the eigenvector corresponding to the smallest eigenvalue of the covariance matrix - * The ratio of the smallest eigenvalue and the second one gives us a hint about the relevance - * of the solution. This value is optionally returned in \a soundness. - * - * \sa linearRegression() - */ -template<typename VectorType, typename HyperplaneType> -void fitHyperplane(int numPoints, - VectorType **points, - HyperplaneType *result, - typename NumTraits<typename VectorType::Scalar>::Real* soundness = 0) -{ - typedef typename VectorType::Scalar Scalar; - typedef Matrix<Scalar,VectorType::SizeAtCompileTime,VectorType::SizeAtCompileTime> CovMatrixType; - EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType) - ei_assert(numPoints >= 1); - int size = points[0]->size(); - ei_assert(size+1 == result->coeffs().size()); - - // compute the mean of the data - VectorType mean = VectorType::Zero(size); - for(int i = 0; i < numPoints; ++i) - mean += *(points[i]); - mean /= numPoints; - - // compute the covariance matrix - CovMatrixType covMat = CovMatrixType::Zero(size, size); - VectorType remean = VectorType::Zero(size); - for(int i = 0; i < numPoints; ++i) - { - VectorType diff = (*(points[i]) - mean).conjugate(); - covMat += diff * diff.adjoint(); - } - - // now we just have to pick the eigen vector with smallest eigen value - SelfAdjointEigenSolver<CovMatrixType> eig(covMat); - result->normal() = eig.eigenvectors().col(0); - if (soundness) - *soundness = eig.eigenvalues().coeff(0)/eig.eigenvalues().coeff(1); - - // let's compute the constant coefficient such that the - // plane pass trough the mean point: - result->offset() = - (result->normal().cwise()* mean).sum(); -} - -} // end namespace Eigen - -#endif // EIGEN2_LEASTSQUARES_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Macros.h b/third_party/eigen3/Eigen/src/Eigen2Support/Macros.h deleted file mode 100644 index 351c32afb6..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Macros.h +++ /dev/null @@ -1,20 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_MACROS_H -#define EIGEN2_MACROS_H - -#define ei_assert eigen_assert -#define ei_internal_assert eigen_internal_assert - -#define EIGEN_ALIGN_128 EIGEN_ALIGN16 - -#define EIGEN_ARCH_WANTS_ALIGNMENT EIGEN_ALIGN_STATICALLY - -#endif // EIGEN2_MACROS_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/MathFunctions.h b/third_party/eigen3/Eigen/src/Eigen2Support/MathFunctions.h deleted file mode 100644 index 3544af2538..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/MathFunctions.h +++ /dev/null @@ -1,57 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_MATH_FUNCTIONS_H -#define EIGEN2_MATH_FUNCTIONS_H - -namespace Eigen { - -template<typename T> inline typename NumTraits<T>::Real ei_real(const T& x) { return numext::real(x); } -template<typename T> inline typename NumTraits<T>::Real ei_imag(const T& x) { return numext::imag(x); } -template<typename T> inline T ei_conj(const T& x) { return numext::conj(x); } -template<typename T> inline typename NumTraits<T>::Real ei_abs (const T& x) { using std::abs; return abs(x); } -template<typename T> inline typename NumTraits<T>::Real ei_abs2(const T& x) { return numext::abs2(x); } -template<typename T> inline T ei_sqrt(const T& x) { using std::sqrt; return sqrt(x); } -template<typename T> inline T ei_exp (const T& x) { using std::exp; return exp(x); } -template<typename T> inline T ei_log (const T& x) { using std::log; return log(x); } -template<typename T> inline T ei_sin (const T& x) { using std::sin; return sin(x); } -template<typename T> inline T ei_cos (const T& x) { using std::cos; return cos(x); } -template<typename T> inline T ei_atan2(const T& x,const T& y) { using std::atan2; return atan2(x,y); } -template<typename T> inline T ei_pow (const T& x,const T& y) { return numext::pow(x,y); } -template<typename T> inline T ei_random () { return internal::random<T>(); } -template<typename T> inline T ei_random (const T& x, const T& y) { return internal::random(x, y); } - -template<typename T> inline T precision () { return NumTraits<T>::dummy_precision(); } -template<typename T> inline T machine_epsilon () { return NumTraits<T>::epsilon(); } - - -template<typename Scalar, typename OtherScalar> -inline bool ei_isMuchSmallerThan(const Scalar& x, const OtherScalar& y, - typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) -{ - return internal::isMuchSmallerThan(x, y, precision); -} - -template<typename Scalar> -inline bool ei_isApprox(const Scalar& x, const Scalar& y, - typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) -{ - return internal::isApprox(x, y, precision); -} - -template<typename Scalar> -inline bool ei_isApproxOrLessThan(const Scalar& x, const Scalar& y, - typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) -{ - return internal::isApproxOrLessThan(x, y, precision); -} - -} // end namespace Eigen - -#endif // EIGEN2_MATH_FUNCTIONS_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Memory.h b/third_party/eigen3/Eigen/src/Eigen2Support/Memory.h deleted file mode 100644 index f86372b6b5..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Memory.h +++ /dev/null @@ -1,45 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_MEMORY_H -#define EIGEN2_MEMORY_H - -namespace Eigen { - -inline void* ei_aligned_malloc(size_t size) { return internal::aligned_malloc(size); } -inline void ei_aligned_free(void *ptr) { internal::aligned_free(ptr); } -inline void* ei_aligned_realloc(void *ptr, size_t new_size, size_t old_size) { return internal::aligned_realloc(ptr, new_size, old_size); } -inline void* ei_handmade_aligned_malloc(size_t size) { return internal::handmade_aligned_malloc(size); } -inline void ei_handmade_aligned_free(void *ptr) { internal::handmade_aligned_free(ptr); } - -template<bool Align> inline void* ei_conditional_aligned_malloc(size_t size) -{ - return internal::conditional_aligned_malloc<Align>(size); -} -template<bool Align> inline void ei_conditional_aligned_free(void *ptr) -{ - internal::conditional_aligned_free<Align>(ptr); -} -template<bool Align> inline void* ei_conditional_aligned_realloc(void* ptr, size_t new_size, size_t old_size) -{ - return internal::conditional_aligned_realloc<Align>(ptr, new_size, old_size); -} - -template<typename T> inline T* ei_aligned_new(size_t size) -{ - return internal::aligned_new<T>(size); -} -template<typename T> inline void ei_aligned_delete(T *ptr, size_t size) -{ - return internal::aligned_delete(ptr, size); -} - -} // end namespace Eigen - -#endif // EIGEN2_MACROS_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Meta.h b/third_party/eigen3/Eigen/src/Eigen2Support/Meta.h deleted file mode 100644 index fa37cfc961..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Meta.h +++ /dev/null @@ -1,75 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_META_H -#define EIGEN2_META_H - -namespace Eigen { - -template<typename T> -struct ei_traits : internal::traits<T> -{}; - -struct ei_meta_true { enum { ret = 1 }; }; -struct ei_meta_false { enum { ret = 0 }; }; - -template<bool Condition, typename Then, typename Else> -struct ei_meta_if { typedef Then ret; }; - -template<typename Then, typename Else> -struct ei_meta_if <false, Then, Else> { typedef Else ret; }; - -template<typename T, typename U> struct ei_is_same_type { enum { ret = 0 }; }; -template<typename T> struct ei_is_same_type<T,T> { enum { ret = 1 }; }; - -template<typename T> struct ei_unref { typedef T type; }; -template<typename T> struct ei_unref<T&> { typedef T type; }; - -template<typename T> struct ei_unpointer { typedef T type; }; -template<typename T> struct ei_unpointer<T*> { typedef T type; }; -template<typename T> struct ei_unpointer<T*const> { typedef T type; }; - -template<typename T> struct ei_unconst { typedef T type; }; -template<typename T> struct ei_unconst<const T> { typedef T type; }; -template<typename T> struct ei_unconst<T const &> { typedef T & type; }; -template<typename T> struct ei_unconst<T const *> { typedef T * type; }; - -template<typename T> struct ei_cleantype { typedef T type; }; -template<typename T> struct ei_cleantype<const T> { typedef typename ei_cleantype<T>::type type; }; -template<typename T> struct ei_cleantype<const T&> { typedef typename ei_cleantype<T>::type type; }; -template<typename T> struct ei_cleantype<T&> { typedef typename ei_cleantype<T>::type type; }; -template<typename T> struct ei_cleantype<const T*> { typedef typename ei_cleantype<T>::type type; }; -template<typename T> struct ei_cleantype<T*> { typedef typename ei_cleantype<T>::type type; }; - -/** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer. - * Usage example: \code ei_meta_sqrt<1023>::ret \endcode - */ -template<int Y, - int InfX = 0, - int SupX = ((Y==1) ? 1 : Y/2), - bool Done = ((SupX-InfX)<=1 ? true : ((SupX*SupX <= Y) && ((SupX+1)*(SupX+1) > Y))) > - // use ?: instead of || just to shut up a stupid gcc 4.3 warning -class ei_meta_sqrt -{ - enum { - MidX = (InfX+SupX)/2, - TakeInf = MidX*MidX > Y ? 1 : 0, - NewInf = int(TakeInf) ? InfX : int(MidX), - NewSup = int(TakeInf) ? int(MidX) : SupX - }; - public: - enum { ret = ei_meta_sqrt<Y,NewInf,NewSup>::ret }; -}; - -template<int Y, int InfX, int SupX> -class ei_meta_sqrt<Y, InfX, SupX, true> { public: enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; }; - -} // end namespace Eigen - -#endif // EIGEN2_META_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/Minor.h b/third_party/eigen3/Eigen/src/Eigen2Support/Minor.h deleted file mode 100644 index 4cded5734f..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/Minor.h +++ /dev/null @@ -1,117 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MINOR_H -#define EIGEN_MINOR_H - -namespace Eigen { - -/** - * \class Minor - * - * \brief Expression of a minor - * - * \param MatrixType the type of the object in which we are taking a minor - * - * This class represents an expression of a minor. It is the return - * type of MatrixBase::minor() and most of the time this is the only way it - * is used. - * - * \sa MatrixBase::minor() - */ - -namespace internal { -template<typename MatrixType> -struct traits<Minor<MatrixType> > - : traits<MatrixType> -{ - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested; - typedef typename MatrixType::StorageKind StorageKind; - enum { - RowsAtCompileTime = (MatrixType::RowsAtCompileTime != Dynamic) ? - int(MatrixType::RowsAtCompileTime) - 1 : Dynamic, - ColsAtCompileTime = (MatrixType::ColsAtCompileTime != Dynamic) ? - int(MatrixType::ColsAtCompileTime) - 1 : Dynamic, - MaxRowsAtCompileTime = (MatrixType::MaxRowsAtCompileTime != Dynamic) ? - int(MatrixType::MaxRowsAtCompileTime) - 1 : Dynamic, - MaxColsAtCompileTime = (MatrixType::MaxColsAtCompileTime != Dynamic) ? - int(MatrixType::MaxColsAtCompileTime) - 1 : Dynamic, - Flags = _MatrixTypeNested::Flags & (HereditaryBits | LvalueBit), - CoeffReadCost = _MatrixTypeNested::CoeffReadCost // minor is used typically on tiny matrices, - // where loops are unrolled and the 'if' evaluates at compile time - }; -}; -} - -template<typename MatrixType> class Minor - : public MatrixBase<Minor<MatrixType> > -{ - public: - - typedef MatrixBase<Minor> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Minor) - - inline Minor(const MatrixType& matrix, - Index row, Index col) - : m_matrix(matrix), m_row(row), m_col(col) - { - eigen_assert(row >= 0 && row < matrix.rows() - && col >= 0 && col < matrix.cols()); - } - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Minor) - - inline Index rows() const { return m_matrix.rows() - 1; } - inline Index cols() const { return m_matrix.cols() - 1; } - - inline Scalar& coeffRef(Index row, Index col) - { - return m_matrix.const_cast_derived().coeffRef(row + (row >= m_row), col + (col >= m_col)); - } - - inline const Scalar coeff(Index row, Index col) const - { - return m_matrix.coeff(row + (row >= m_row), col + (col >= m_col)); - } - - protected: - const typename MatrixType::Nested m_matrix; - const Index m_row, m_col; -}; - -/** - * \return an expression of the (\a row, \a col)-minor of *this, - * i.e. an expression constructed from *this by removing the specified - * row and column. - * - * Example: \include MatrixBase_minor.cpp - * Output: \verbinclude MatrixBase_minor.out - * - * \sa class Minor - */ -template<typename Derived> -inline Minor<Derived> -MatrixBase<Derived>::minor(Index row, Index col) -{ - return Minor<Derived>(derived(), row, col); -} - -/** - * This is the const version of minor(). */ -template<typename Derived> -inline const Minor<Derived> -MatrixBase<Derived>::minor(Index row, Index col) const -{ - return Minor<Derived>(derived(), row, col); -} - -} // end namespace Eigen - -#endif // EIGEN_MINOR_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/QR.h b/third_party/eigen3/Eigen/src/Eigen2Support/QR.h deleted file mode 100644 index 2042c98510..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/QR.h +++ /dev/null @@ -1,67 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_QR_H -#define EIGEN2_QR_H - -namespace Eigen { - -template<typename MatrixType> -class QR : public HouseholderQR<MatrixType> -{ - public: - - typedef HouseholderQR<MatrixType> Base; - typedef Block<const MatrixType, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> MatrixRBlockType; - - QR() : Base() {} - - template<typename T> - explicit QR(const T& t) : Base(t) {} - - template<typename OtherDerived, typename ResultType> - bool solve(const MatrixBase<OtherDerived>& b, ResultType *result) const - { - *result = static_cast<const Base*>(this)->solve(b); - return true; - } - - MatrixType matrixQ(void) const { - MatrixType ret = MatrixType::Identity(this->rows(), this->cols()); - ret = this->householderQ() * ret; - return ret; - } - - bool isFullRank() const { - return true; - } - - const TriangularView<MatrixRBlockType, UpperTriangular> - matrixR(void) const - { - int cols = this->cols(); - return MatrixRBlockType(this->matrixQR(), 0, 0, cols, cols).template triangularView<UpperTriangular>(); - } -}; - -/** \return the QR decomposition of \c *this. - * - * \sa class QR - */ -template<typename Derived> -const QR<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::qr() const -{ - return QR<PlainObject>(eval()); -} - -} // end namespace Eigen - -#endif // EIGEN2_QR_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/SVD.h b/third_party/eigen3/Eigen/src/Eigen2Support/SVD.h deleted file mode 100644 index 3d03d2288d..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/SVD.h +++ /dev/null @@ -1,637 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_SVD_H -#define EIGEN2_SVD_H - -namespace Eigen { - -/** \ingroup SVD_Module - * \nonstableyet - * - * \class SVD - * - * \brief Standard SVD decomposition of a matrix and associated features - * - * \param MatrixType the type of the matrix of which we are computing the SVD decomposition - * - * This class performs a standard SVD decomposition of a real matrix A of size \c M x \c N - * with \c M \>= \c N. - * - * - * \sa MatrixBase::SVD() - */ -template<typename MatrixType> class SVD -{ - private: - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - - enum { - PacketSize = internal::packet_traits<Scalar>::size, - AlignmentMask = int(PacketSize)-1, - MinSize = EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime) - }; - - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVector; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> RowVector; - - typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MinSize> MatrixUType; - typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> MatrixVType; - typedef Matrix<Scalar, MinSize, 1> SingularValuesType; - - public: - - SVD() {} // a user who relied on compiler-generated default compiler reported problems with MSVC in 2.0.7 - - SVD(const MatrixType& matrix) - : m_matU(matrix.rows(), (std::min)(matrix.rows(), matrix.cols())), - m_matV(matrix.cols(),matrix.cols()), - m_sigma((std::min)(matrix.rows(),matrix.cols())) - { - compute(matrix); - } - - template<typename OtherDerived, typename ResultType> - bool solve(const MatrixBase<OtherDerived> &b, ResultType* result) const; - - const MatrixUType& matrixU() const { return m_matU; } - const SingularValuesType& singularValues() const { return m_sigma; } - const MatrixVType& matrixV() const { return m_matV; } - - void compute(const MatrixType& matrix); - SVD& sort(); - - template<typename UnitaryType, typename PositiveType> - void computeUnitaryPositive(UnitaryType *unitary, PositiveType *positive) const; - template<typename PositiveType, typename UnitaryType> - void computePositiveUnitary(PositiveType *positive, UnitaryType *unitary) const; - template<typename RotationType, typename ScalingType> - void computeRotationScaling(RotationType *unitary, ScalingType *positive) const; - template<typename ScalingType, typename RotationType> - void computeScalingRotation(ScalingType *positive, RotationType *unitary) const; - - protected: - /** \internal */ - MatrixUType m_matU; - /** \internal */ - MatrixVType m_matV; - /** \internal */ - SingularValuesType m_sigma; -}; - -/** Computes / recomputes the SVD decomposition A = U S V^* of \a matrix - * - * \note this code has been adapted from JAMA (public domain) - */ -template<typename MatrixType> -void SVD<MatrixType>::compute(const MatrixType& matrix) -{ - const int m = matrix.rows(); - const int n = matrix.cols(); - const int nu = (std::min)(m,n); - ei_assert(m>=n && "In Eigen 2.0, SVD only works for MxN matrices with M>=N. Sorry!"); - ei_assert(m>1 && "In Eigen 2.0, SVD doesn't work on 1x1 matrices"); - - m_matU.resize(m, nu); - m_matU.setZero(); - m_sigma.resize((std::min)(m,n)); - m_matV.resize(n,n); - - RowVector e(n); - ColVector work(m); - MatrixType matA(matrix); - const bool wantu = true; - const bool wantv = true; - int i=0, j=0, k=0; - - // Reduce A to bidiagonal form, storing the diagonal elements - // in s and the super-diagonal elements in e. - int nct = (std::min)(m-1,n); - int nrt = (std::max)(0,(std::min)(n-2,m)); - for (k = 0; k < (std::max)(nct,nrt); ++k) - { - if (k < nct) - { - // Compute the transformation for the k-th column and - // place the k-th diagonal in m_sigma[k]. - m_sigma[k] = matA.col(k).end(m-k).norm(); - if (m_sigma[k] != 0.0) // FIXME - { - if (matA(k,k) < 0.0) - m_sigma[k] = -m_sigma[k]; - matA.col(k).end(m-k) /= m_sigma[k]; - matA(k,k) += 1.0; - } - m_sigma[k] = -m_sigma[k]; - } - - for (j = k+1; j < n; ++j) - { - if ((k < nct) && (m_sigma[k] != 0.0)) - { - // Apply the transformation. - Scalar t = matA.col(k).end(m-k).eigen2_dot(matA.col(j).end(m-k)); // FIXME dot product or cwise prod + .sum() ?? - t = -t/matA(k,k); - matA.col(j).end(m-k) += t * matA.col(k).end(m-k); - } - - // Place the k-th row of A into e for the - // subsequent calculation of the row transformation. - e[j] = matA(k,j); - } - - // Place the transformation in U for subsequent back multiplication. - if (wantu & (k < nct)) - m_matU.col(k).end(m-k) = matA.col(k).end(m-k); - - if (k < nrt) - { - // Compute the k-th row transformation and place the - // k-th super-diagonal in e[k]. - e[k] = e.end(n-k-1).norm(); - if (e[k] != 0.0) - { - if (e[k+1] < 0.0) - e[k] = -e[k]; - e.end(n-k-1) /= e[k]; - e[k+1] += 1.0; - } - e[k] = -e[k]; - if ((k+1 < m) & (e[k] != 0.0)) - { - // Apply the transformation. - work.end(m-k-1) = matA.corner(BottomRight,m-k-1,n-k-1) * e.end(n-k-1); - for (j = k+1; j < n; ++j) - matA.col(j).end(m-k-1) += (-e[j]/e[k+1]) * work.end(m-k-1); - } - - // Place the transformation in V for subsequent back multiplication. - if (wantv) - m_matV.col(k).end(n-k-1) = e.end(n-k-1); - } - } - - - // Set up the final bidiagonal matrix or order p. - int p = (std::min)(n,m+1); - if (nct < n) - m_sigma[nct] = matA(nct,nct); - if (m < p) - m_sigma[p-1] = 0.0; - if (nrt+1 < p) - e[nrt] = matA(nrt,p-1); - e[p-1] = 0.0; - - // If required, generate U. - if (wantu) - { - for (j = nct; j < nu; ++j) - { - m_matU.col(j).setZero(); - m_matU(j,j) = 1.0; - } - for (k = nct-1; k >= 0; k--) - { - if (m_sigma[k] != 0.0) - { - for (j = k+1; j < nu; ++j) - { - Scalar t = m_matU.col(k).end(m-k).eigen2_dot(m_matU.col(j).end(m-k)); // FIXME is it really a dot product we want ? - t = -t/m_matU(k,k); - m_matU.col(j).end(m-k) += t * m_matU.col(k).end(m-k); - } - m_matU.col(k).end(m-k) = - m_matU.col(k).end(m-k); - m_matU(k,k) = Scalar(1) + m_matU(k,k); - if (k-1>0) - m_matU.col(k).start(k-1).setZero(); - } - else - { - m_matU.col(k).setZero(); - m_matU(k,k) = 1.0; - } - } - } - - // If required, generate V. - if (wantv) - { - for (k = n-1; k >= 0; k--) - { - if ((k < nrt) & (e[k] != 0.0)) - { - for (j = k+1; j < nu; ++j) - { - Scalar t = m_matV.col(k).end(n-k-1).eigen2_dot(m_matV.col(j).end(n-k-1)); // FIXME is it really a dot product we want ? - t = -t/m_matV(k+1,k); - m_matV.col(j).end(n-k-1) += t * m_matV.col(k).end(n-k-1); - } - } - m_matV.col(k).setZero(); - m_matV(k,k) = 1.0; - } - } - - // Main iteration loop for the singular values. - int pp = p-1; - int iter = 0; - Scalar eps = ei_pow(Scalar(2),ei_is_same_type<Scalar,float>::ret ? Scalar(-23) : Scalar(-52)); - while (p > 0) - { - int k=0; - int kase=0; - - // Here is where a test for too many iterations would go. - - // This section of the program inspects for - // negligible elements in the s and e arrays. On - // completion the variables kase and k are set as follows. - - // kase = 1 if s(p) and e[k-1] are negligible and k<p - // kase = 2 if s(k) is negligible and k<p - // kase = 3 if e[k-1] is negligible, k<p, and - // s(k), ..., s(p) are not negligible (qr step). - // kase = 4 if e(p-1) is negligible (convergence). - - for (k = p-2; k >= -1; --k) - { - if (k == -1) - break; - if (ei_abs(e[k]) <= eps*(ei_abs(m_sigma[k]) + ei_abs(m_sigma[k+1]))) - { - e[k] = 0.0; - break; - } - } - if (k == p-2) - { - kase = 4; - } - else - { - int ks; - for (ks = p-1; ks >= k; --ks) - { - if (ks == k) - break; - Scalar t = (ks != p ? ei_abs(e[ks]) : Scalar(0)) + (ks != k+1 ? ei_abs(e[ks-1]) : Scalar(0)); - if (ei_abs(m_sigma[ks]) <= eps*t) - { - m_sigma[ks] = 0.0; - break; - } - } - if (ks == k) - { - kase = 3; - } - else if (ks == p-1) - { - kase = 1; - } - else - { - kase = 2; - k = ks; - } - } - ++k; - - // Perform the task indicated by kase. - switch (kase) - { - - // Deflate negligible s(p). - case 1: - { - Scalar f(e[p-2]); - e[p-2] = 0.0; - for (j = p-2; j >= k; --j) - { - Scalar t(numext::hypot(m_sigma[j],f)); - Scalar cs(m_sigma[j]/t); - Scalar sn(f/t); - m_sigma[j] = t; - if (j != k) - { - f = -sn*e[j-1]; - e[j-1] = cs*e[j-1]; - } - if (wantv) - { - for (i = 0; i < n; ++i) - { - t = cs*m_matV(i,j) + sn*m_matV(i,p-1); - m_matV(i,p-1) = -sn*m_matV(i,j) + cs*m_matV(i,p-1); - m_matV(i,j) = t; - } - } - } - } - break; - - // Split at negligible s(k). - case 2: - { - Scalar f(e[k-1]); - e[k-1] = 0.0; - for (j = k; j < p; ++j) - { - Scalar t(numext::hypot(m_sigma[j],f)); - Scalar cs( m_sigma[j]/t); - Scalar sn(f/t); - m_sigma[j] = t; - f = -sn*e[j]; - e[j] = cs*e[j]; - if (wantu) - { - for (i = 0; i < m; ++i) - { - t = cs*m_matU(i,j) + sn*m_matU(i,k-1); - m_matU(i,k-1) = -sn*m_matU(i,j) + cs*m_matU(i,k-1); - m_matU(i,j) = t; - } - } - } - } - break; - - // Perform one qr step. - case 3: - { - // Calculate the shift. - Scalar scale = (std::max)((std::max)((std::max)((std::max)( - ei_abs(m_sigma[p-1]),ei_abs(m_sigma[p-2])),ei_abs(e[p-2])), - ei_abs(m_sigma[k])),ei_abs(e[k])); - Scalar sp = m_sigma[p-1]/scale; - Scalar spm1 = m_sigma[p-2]/scale; - Scalar epm1 = e[p-2]/scale; - Scalar sk = m_sigma[k]/scale; - Scalar ek = e[k]/scale; - Scalar b = ((spm1 + sp)*(spm1 - sp) + epm1*epm1)/Scalar(2); - Scalar c = (sp*epm1)*(sp*epm1); - Scalar shift(0); - if ((b != 0.0) || (c != 0.0)) - { - shift = ei_sqrt(b*b + c); - if (b < 0.0) - shift = -shift; - shift = c/(b + shift); - } - Scalar f = (sk + sp)*(sk - sp) + shift; - Scalar g = sk*ek; - - // Chase zeros. - - for (j = k; j < p-1; ++j) - { - Scalar t = numext::hypot(f,g); - Scalar cs = f/t; - Scalar sn = g/t; - if (j != k) - e[j-1] = t; - f = cs*m_sigma[j] + sn*e[j]; - e[j] = cs*e[j] - sn*m_sigma[j]; - g = sn*m_sigma[j+1]; - m_sigma[j+1] = cs*m_sigma[j+1]; - if (wantv) - { - for (i = 0; i < n; ++i) - { - t = cs*m_matV(i,j) + sn*m_matV(i,j+1); - m_matV(i,j+1) = -sn*m_matV(i,j) + cs*m_matV(i,j+1); - m_matV(i,j) = t; - } - } - t = numext::hypot(f,g); - cs = f/t; - sn = g/t; - m_sigma[j] = t; - f = cs*e[j] + sn*m_sigma[j+1]; - m_sigma[j+1] = -sn*e[j] + cs*m_sigma[j+1]; - g = sn*e[j+1]; - e[j+1] = cs*e[j+1]; - if (wantu && (j < m-1)) - { - for (i = 0; i < m; ++i) - { - t = cs*m_matU(i,j) + sn*m_matU(i,j+1); - m_matU(i,j+1) = -sn*m_matU(i,j) + cs*m_matU(i,j+1); - m_matU(i,j) = t; - } - } - } - e[p-2] = f; - iter = iter + 1; - } - break; - - // Convergence. - case 4: - { - // Make the singular values positive. - if (m_sigma[k] <= 0.0) - { - m_sigma[k] = m_sigma[k] < Scalar(0) ? -m_sigma[k] : Scalar(0); - if (wantv) - m_matV.col(k).start(pp+1) = -m_matV.col(k).start(pp+1); - } - - // Order the singular values. - while (k < pp) - { - if (m_sigma[k] >= m_sigma[k+1]) - break; - Scalar t = m_sigma[k]; - m_sigma[k] = m_sigma[k+1]; - m_sigma[k+1] = t; - if (wantv && (k < n-1)) - m_matV.col(k).swap(m_matV.col(k+1)); - if (wantu && (k < m-1)) - m_matU.col(k).swap(m_matU.col(k+1)); - ++k; - } - iter = 0; - p--; - } - break; - } // end big switch - } // end iterations -} - -template<typename MatrixType> -SVD<MatrixType>& SVD<MatrixType>::sort() -{ - int mu = m_matU.rows(); - int mv = m_matV.rows(); - int n = m_matU.cols(); - - for (int i=0; i<n; ++i) - { - int k = i; - Scalar p = m_sigma.coeff(i); - - for (int j=i+1; j<n; ++j) - { - if (m_sigma.coeff(j) > p) - { - k = j; - p = m_sigma.coeff(j); - } - } - if (k != i) - { - m_sigma.coeffRef(k) = m_sigma.coeff(i); // i.e. - m_sigma.coeffRef(i) = p; // swaps the i-th and the k-th elements - - int j = mu; - for(int s=0; j!=0; ++s, --j) - std::swap(m_matU.coeffRef(s,i), m_matU.coeffRef(s,k)); - - j = mv; - for (int s=0; j!=0; ++s, --j) - std::swap(m_matV.coeffRef(s,i), m_matV.coeffRef(s,k)); - } - } - return *this; -} - -/** \returns the solution of \f$ A x = b \f$ using the current SVD decomposition of A. - * The parts of the solution corresponding to zero singular values are ignored. - * - * \sa MatrixBase::svd(), LU::solve(), LLT::solve() - */ -template<typename MatrixType> -template<typename OtherDerived, typename ResultType> -bool SVD<MatrixType>::solve(const MatrixBase<OtherDerived> &b, ResultType* result) const -{ - ei_assert(b.rows() == m_matU.rows()); - - Scalar maxVal = m_sigma.cwise().abs().maxCoeff(); - for (int j=0; j<b.cols(); ++j) - { - Matrix<Scalar,MatrixUType::RowsAtCompileTime,1> aux = m_matU.transpose() * b.col(j); - - for (int i = 0; i <m_matU.cols(); ++i) - { - Scalar si = m_sigma.coeff(i); - if (ei_isMuchSmallerThan(ei_abs(si),maxVal)) - aux.coeffRef(i) = 0; - else - aux.coeffRef(i) /= si; - } - - result->col(j) = m_matV * aux; - } - return true; -} - -/** Computes the polar decomposition of the matrix, as a product unitary x positive. - * - * If either pointer is zero, the corresponding computation is skipped. - * - * Only for square matrices. - * - * \sa computePositiveUnitary(), computeRotationScaling() - */ -template<typename MatrixType> -template<typename UnitaryType, typename PositiveType> -void SVD<MatrixType>::computeUnitaryPositive(UnitaryType *unitary, - PositiveType *positive) const -{ - ei_assert(m_matU.cols() == m_matV.cols() && "Polar decomposition is only for square matrices"); - if(unitary) *unitary = m_matU * m_matV.adjoint(); - if(positive) *positive = m_matV * m_sigma.asDiagonal() * m_matV.adjoint(); -} - -/** Computes the polar decomposition of the matrix, as a product positive x unitary. - * - * If either pointer is zero, the corresponding computation is skipped. - * - * Only for square matrices. - * - * \sa computeUnitaryPositive(), computeRotationScaling() - */ -template<typename MatrixType> -template<typename UnitaryType, typename PositiveType> -void SVD<MatrixType>::computePositiveUnitary(UnitaryType *positive, - PositiveType *unitary) const -{ - ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices"); - if(unitary) *unitary = m_matU * m_matV.adjoint(); - if(positive) *positive = m_matU * m_sigma.asDiagonal() * m_matU.adjoint(); -} - -/** decomposes the matrix as a product rotation x scaling, the scaling being - * not necessarily positive. - * - * If either pointer is zero, the corresponding computation is skipped. - * - * This method requires the Geometry module. - * - * \sa computeScalingRotation(), computeUnitaryPositive() - */ -template<typename MatrixType> -template<typename RotationType, typename ScalingType> -void SVD<MatrixType>::computeRotationScaling(RotationType *rotation, ScalingType *scaling) const -{ - ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices"); - Scalar x = (m_matU * m_matV.adjoint()).determinant(); // so x has absolute value 1 - Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> sv(m_sigma); - sv.coeffRef(0) *= x; - if(scaling) scaling->lazyAssign(m_matV * sv.asDiagonal() * m_matV.adjoint()); - if(rotation) - { - MatrixType m(m_matU); - m.col(0) /= x; - rotation->lazyAssign(m * m_matV.adjoint()); - } -} - -/** decomposes the matrix as a product scaling x rotation, the scaling being - * not necessarily positive. - * - * If either pointer is zero, the corresponding computation is skipped. - * - * This method requires the Geometry module. - * - * \sa computeRotationScaling(), computeUnitaryPositive() - */ -template<typename MatrixType> -template<typename ScalingType, typename RotationType> -void SVD<MatrixType>::computeScalingRotation(ScalingType *scaling, RotationType *rotation) const -{ - ei_assert(m_matU.rows() == m_matV.rows() && "Polar decomposition is only for square matrices"); - Scalar x = (m_matU * m_matV.adjoint()).determinant(); // so x has absolute value 1 - Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> sv(m_sigma); - sv.coeffRef(0) *= x; - if(scaling) scaling->lazyAssign(m_matU * sv.asDiagonal() * m_matU.adjoint()); - if(rotation) - { - MatrixType m(m_matU); - m.col(0) /= x; - rotation->lazyAssign(m * m_matV.adjoint()); - } -} - - -/** \svd_module - * \returns the SVD decomposition of \c *this - */ -template<typename Derived> -inline SVD<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::svd() const -{ - return SVD<PlainObject>(derived()); -} - -} // end namespace Eigen - -#endif // EIGEN2_SVD_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/TriangularSolver.h b/third_party/eigen3/Eigen/src/Eigen2Support/TriangularSolver.h deleted file mode 100644 index ebbeb3b495..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/TriangularSolver.h +++ /dev/null @@ -1,42 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRIANGULAR_SOLVER2_H -#define EIGEN_TRIANGULAR_SOLVER2_H - -namespace Eigen { - -const unsigned int UnitDiagBit = UnitDiag; -const unsigned int SelfAdjointBit = SelfAdjoint; -const unsigned int UpperTriangularBit = Upper; -const unsigned int LowerTriangularBit = Lower; - -const unsigned int UpperTriangular = Upper; -const unsigned int LowerTriangular = Lower; -const unsigned int UnitUpperTriangular = UnitUpper; -const unsigned int UnitLowerTriangular = UnitLower; - -template<typename ExpressionType, unsigned int Added, unsigned int Removed> -template<typename OtherDerived> -typename ExpressionType::PlainObject -Flagged<ExpressionType,Added,Removed>::solveTriangular(const MatrixBase<OtherDerived>& other) const -{ - return m_matrix.template triangularView<Added>().solve(other.derived()); -} - -template<typename ExpressionType, unsigned int Added, unsigned int Removed> -template<typename OtherDerived> -void Flagged<ExpressionType,Added,Removed>::solveTriangularInPlace(const MatrixBase<OtherDerived>& other) const -{ - m_matrix.template triangularView<Added>().solveInPlace(other.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_TRIANGULAR_SOLVER2_H diff --git a/third_party/eigen3/Eigen/src/Eigen2Support/VectorBlock.h b/third_party/eigen3/Eigen/src/Eigen2Support/VectorBlock.h deleted file mode 100644 index 71a8080a9f..0000000000 --- a/third_party/eigen3/Eigen/src/Eigen2Support/VectorBlock.h +++ /dev/null @@ -1,94 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN2_VECTORBLOCK_H -#define EIGEN2_VECTORBLOCK_H - -namespace Eigen { - -/** \deprecated use DenseMase::head(Index) */ -template<typename Derived> -inline VectorBlock<Derived> -MatrixBase<Derived>::start(Index size) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return VectorBlock<Derived>(derived(), 0, size); -} - -/** \deprecated use DenseMase::head(Index) */ -template<typename Derived> -inline const VectorBlock<const Derived> -MatrixBase<Derived>::start(Index size) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return VectorBlock<const Derived>(derived(), 0, size); -} - -/** \deprecated use DenseMase::tail(Index) */ -template<typename Derived> -inline VectorBlock<Derived> -MatrixBase<Derived>::end(Index size) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return VectorBlock<Derived>(derived(), this->size() - size, size); -} - -/** \deprecated use DenseMase::tail(Index) */ -template<typename Derived> -inline const VectorBlock<const Derived> -MatrixBase<Derived>::end(Index size) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return VectorBlock<const Derived>(derived(), this->size() - size, size); -} - -/** \deprecated use DenseMase::head() */ -template<typename Derived> -template<int Size> -inline VectorBlock<Derived,Size> -MatrixBase<Derived>::start() -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return VectorBlock<Derived,Size>(derived(), 0); -} - -/** \deprecated use DenseMase::head() */ -template<typename Derived> -template<int Size> -inline const VectorBlock<const Derived,Size> -MatrixBase<Derived>::start() const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return VectorBlock<const Derived,Size>(derived(), 0); -} - -/** \deprecated use DenseMase::tail() */ -template<typename Derived> -template<int Size> -inline VectorBlock<Derived,Size> -MatrixBase<Derived>::end() -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return VectorBlock<Derived, Size>(derived(), size() - Size); -} - -/** \deprecated use DenseMase::tail() */ -template<typename Derived> -template<int Size> -inline const VectorBlock<const Derived,Size> -MatrixBase<Derived>::end() const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return VectorBlock<const Derived, Size>(derived(), size() - Size); -} - -} // end namespace Eigen - -#endif // EIGEN2_VECTORBLOCK_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/third_party/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h deleted file mode 100644 index af434bc9bd..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/ComplexEigenSolver.h +++ /dev/null @@ -1,333 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Claire Maurice -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COMPLEX_EIGEN_SOLVER_H -#define EIGEN_COMPLEX_EIGEN_SOLVER_H - -#include "./ComplexSchur.h" - -namespace Eigen { - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class ComplexEigenSolver - * - * \brief Computes eigenvalues and eigenvectors of general complex matrices - * - * \tparam _MatrixType the type of the matrix of which we are - * computing the eigendecomposition; this is expected to be an - * instantiation of the Matrix class template. - * - * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars - * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v - * \f$. If \f$ D \f$ is a diagonal matrix with the eigenvalues on - * the diagonal, and \f$ V \f$ is a matrix with the eigenvectors as - * its columns, then \f$ A V = V D \f$. The matrix \f$ V \f$ is - * almost always invertible, in which case we have \f$ A = V D V^{-1} - * \f$. This is called the eigendecomposition. - * - * The main function in this class is compute(), which computes the - * eigenvalues and eigenvectors of a given function. The - * documentation for that function contains an example showing the - * main features of the class. - * - * \sa class EigenSolver, class SelfAdjointEigenSolver - */ -template<typename _MatrixType> class ComplexEigenSolver -{ - public: - - /** \brief Synonym for the template parameter \p _MatrixType. */ - typedef _MatrixType MatrixType; - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - - /** \brief Scalar type for matrices of type #MatrixType. */ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - - /** \brief Complex scalar type for #MatrixType. - * - * This is \c std::complex<Scalar> if #Scalar is real (e.g., - * \c float or \c double) and just \c Scalar if #Scalar is - * complex. - */ - typedef std::complex<RealScalar> ComplexScalar; - - /** \brief Type for vector of eigenvalues as returned by eigenvalues(). - * - * This is a column vector with entries of type #ComplexScalar. - * The length of the vector is the size of #MatrixType. - */ - typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options&(~RowMajor), MaxColsAtCompileTime, 1> EigenvalueType; - - /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). - * - * This is a square matrix with entries of type #ComplexScalar. - * The size is the same as the size of #MatrixType. - */ - typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorType; - - /** \brief Default constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via compute(). - */ - ComplexEigenSolver() - : m_eivec(), - m_eivalues(), - m_schur(), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_matX() - {} - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa ComplexEigenSolver() - */ - ComplexEigenSolver(Index size) - : m_eivec(size, size), - m_eivalues(size), - m_schur(size), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_matX(size, size) - {} - - /** \brief Constructor; computes eigendecomposition of given matrix. - * - * \param[in] matrix Square matrix whose eigendecomposition is to be computed. - * \param[in] computeEigenvectors If true, both the eigenvectors and the - * eigenvalues are computed; if false, only the eigenvalues are - * computed. - * - * This constructor calls compute() to compute the eigendecomposition. - */ - ComplexEigenSolver(const MatrixType& matrix, bool computeEigenvectors = true) - : m_eivec(matrix.rows(),matrix.cols()), - m_eivalues(matrix.cols()), - m_schur(matrix.rows()), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_matX(matrix.rows(),matrix.cols()) - { - compute(matrix, computeEigenvectors); - } - - /** \brief Returns the eigenvectors of given matrix. - * - * \returns A const reference to the matrix whose columns are the eigenvectors. - * - * \pre Either the constructor - * ComplexEigenSolver(const MatrixType& matrix, bool) or the member - * function compute(const MatrixType& matrix, bool) has been called before - * to compute the eigendecomposition of a matrix, and - * \p computeEigenvectors was set to true (the default). - * - * This function returns a matrix whose columns are the eigenvectors. Column - * \f$ k \f$ is an eigenvector corresponding to eigenvalue number \f$ k - * \f$ as returned by eigenvalues(). The eigenvectors are normalized to - * have (Euclidean) norm equal to one. The matrix returned by this - * function is the matrix \f$ V \f$ in the eigendecomposition \f$ A = V D - * V^{-1} \f$, if it exists. - * - * Example: \include ComplexEigenSolver_eigenvectors.cpp - * Output: \verbinclude ComplexEigenSolver_eigenvectors.out - */ - const EigenvectorType& eigenvectors() const - { - eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized."); - eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues."); - return m_eivec; - } - - /** \brief Returns the eigenvalues of given matrix. - * - * \returns A const reference to the column vector containing the eigenvalues. - * - * \pre Either the constructor - * ComplexEigenSolver(const MatrixType& matrix, bool) or the member - * function compute(const MatrixType& matrix, bool) has been called before - * to compute the eigendecomposition of a matrix. - * - * This function returns a column vector containing the - * eigenvalues. Eigenvalues are repeated according to their - * algebraic multiplicity, so there are as many eigenvalues as - * rows in the matrix. The eigenvalues are not sorted in any particular - * order. - * - * Example: \include ComplexEigenSolver_eigenvalues.cpp - * Output: \verbinclude ComplexEigenSolver_eigenvalues.out - */ - const EigenvalueType& eigenvalues() const - { - eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized."); - return m_eivalues; - } - - /** \brief Computes eigendecomposition of given matrix. - * - * \param[in] matrix Square matrix whose eigendecomposition is to be computed. - * \param[in] computeEigenvectors If true, both the eigenvectors and the - * eigenvalues are computed; if false, only the eigenvalues are - * computed. - * \returns Reference to \c *this - * - * This function computes the eigenvalues of the complex matrix \p matrix. - * The eigenvalues() function can be used to retrieve them. If - * \p computeEigenvectors is true, then the eigenvectors are also computed - * and can be retrieved by calling eigenvectors(). - * - * The matrix is first reduced to Schur form using the - * ComplexSchur class. The Schur decomposition is then used to - * compute the eigenvalues and eigenvectors. - * - * The cost of the computation is dominated by the cost of the - * Schur decomposition, which is \f$ O(n^3) \f$ where \f$ n \f$ - * is the size of the matrix. - * - * Example: \include ComplexEigenSolver_compute.cpp - * Output: \verbinclude ComplexEigenSolver_compute.out - */ - ComplexEigenSolver& compute(const MatrixType& matrix, bool computeEigenvectors = true); - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, \c NoConvergence otherwise. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized."); - return m_schur.info(); - } - - /** \brief Sets the maximum number of iterations allowed. */ - ComplexEigenSolver& setMaxIterations(Index maxIters) - { - m_schur.setMaxIterations(maxIters); - return *this; - } - - /** \brief Returns the maximum number of iterations. */ - Index getMaxIterations() - { - return m_schur.getMaxIterations(); - } - - protected: - EigenvectorType m_eivec; - EigenvalueType m_eivalues; - ComplexSchur<MatrixType> m_schur; - bool m_isInitialized; - bool m_eigenvectorsOk; - EigenvectorType m_matX; - - private: - void doComputeEigenvectors(const RealScalar& matrixnorm); - void sortEigenvalues(bool computeEigenvectors); -}; - - -template<typename MatrixType> -ComplexEigenSolver<MatrixType>& -ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvectors) -{ - // this code is inspired from Jampack - eigen_assert(matrix.cols() == matrix.rows()); - - // Do a complex Schur decomposition, A = U T U^* - // The eigenvalues are on the diagonal of T. - m_schur.compute(matrix, computeEigenvectors); - - if(m_schur.info() == Success) - { - m_eivalues = m_schur.matrixT().diagonal(); - if(computeEigenvectors) - doComputeEigenvectors(matrix.norm()); - sortEigenvalues(computeEigenvectors); - } - - m_isInitialized = true; - m_eigenvectorsOk = computeEigenvectors; - return *this; -} - - -template<typename MatrixType> -void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(const RealScalar& matrixnorm) -{ - const Index n = m_eivalues.size(); - - // Compute X such that T = X D X^(-1), where D is the diagonal of T. - // The matrix X is unit triangular. - m_matX = EigenvectorType::Zero(n, n); - for(Index k=n-1 ; k>=0 ; k--) - { - m_matX.coeffRef(k,k) = ComplexScalar(1.0,0.0); - // Compute X(i,k) using the (i,k) entry of the equation X T = D X - for(Index i=k-1 ; i>=0 ; i--) - { - m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k); - if(k-i-1>0) - m_matX.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * m_matX.col(k).segment(i+1,k-i-1)).value(); - ComplexScalar z = m_schur.matrixT().coeff(i,i) - m_schur.matrixT().coeff(k,k); - if(z==ComplexScalar(0)) - { - // If the i-th and k-th eigenvalue are equal, then z equals 0. - // Use a small value instead, to prevent division by zero. - numext::real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm; - } - m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z; - } - } - - // Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1) - m_eivec.noalias() = m_schur.matrixU() * m_matX; - // .. and normalize the eigenvectors - for(Index k=0 ; k<n ; k++) - { - m_eivec.col(k).normalize(); - } -} - - -template<typename MatrixType> -void ComplexEigenSolver<MatrixType>::sortEigenvalues(bool computeEigenvectors) -{ - const Index n = m_eivalues.size(); - for (Index i=0; i<n; i++) - { - Index k; - m_eivalues.cwiseAbs().tail(n-i).minCoeff(&k); - if (k != 0) - { - k += i; - std::swap(m_eivalues[k],m_eivalues[i]); - if(computeEigenvectors) - m_eivec.col(i).swap(m_eivec.col(k)); - } - } -} - -} // end namespace Eigen - -#endif // EIGEN_COMPLEX_EIGEN_SOLVER_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h b/third_party/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h deleted file mode 100644 index 89e6cade33..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/ComplexSchur.h +++ /dev/null @@ -1,456 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Claire Maurice -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COMPLEX_SCHUR_H -#define EIGEN_COMPLEX_SCHUR_H - -#include "./HessenbergDecomposition.h" - -namespace Eigen { - -namespace internal { -template<typename MatrixType, bool IsComplex> struct complex_schur_reduce_to_hessenberg; -} - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class ComplexSchur - * - * \brief Performs a complex Schur decomposition of a real or complex square matrix - * - * \tparam _MatrixType the type of the matrix of which we are - * computing the Schur decomposition; this is expected to be an - * instantiation of the Matrix class template. - * - * Given a real or complex square matrix A, this class computes the - * Schur decomposition: \f$ A = U T U^*\f$ where U is a unitary - * complex matrix, and T is a complex upper triangular matrix. The - * diagonal of the matrix T corresponds to the eigenvalues of the - * matrix A. - * - * Call the function compute() to compute the Schur decomposition of - * a given matrix. Alternatively, you can use the - * ComplexSchur(const MatrixType&, bool) constructor which computes - * the Schur decomposition at construction time. Once the - * decomposition is computed, you can use the matrixU() and matrixT() - * functions to retrieve the matrices U and V in the decomposition. - * - * \note This code is inspired from Jampack - * - * \sa class RealSchur, class EigenSolver, class ComplexEigenSolver - */ -template<typename _MatrixType> class ComplexSchur -{ - public: - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - - /** \brief Scalar type for matrices of type \p _MatrixType. */ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - - /** \brief Complex scalar type for \p _MatrixType. - * - * This is \c std::complex<Scalar> if #Scalar is real (e.g., - * \c float or \c double) and just \c Scalar if #Scalar is - * complex. - */ - typedef std::complex<RealScalar> ComplexScalar; - - /** \brief Type for the matrices in the Schur decomposition. - * - * This is a square matrix with entries of type #ComplexScalar. - * The size is the same as the size of \p _MatrixType. - */ - typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> ComplexMatrixType; - - /** \brief Default constructor. - * - * \param [in] size Positive integer, size of the matrix whose Schur decomposition will be computed. - * - * The default constructor is useful in cases in which the user - * intends to perform decompositions via compute(). The \p size - * parameter is only used as a hint. It is not an error to give a - * wrong \p size, but it may impair performance. - * - * \sa compute() for an example. - */ - ComplexSchur(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime) - : m_matT(size,size), - m_matU(size,size), - m_hess(size), - m_isInitialized(false), - m_matUisUptodate(false), - m_maxIters(-1) - {} - - /** \brief Constructor; computes Schur decomposition of given matrix. - * - * \param[in] matrix Square matrix whose Schur decomposition is to be computed. - * \param[in] computeU If true, both T and U are computed; if false, only T is computed. - * - * This constructor calls compute() to compute the Schur decomposition. - * - * \sa matrixT() and matrixU() for examples. - */ - ComplexSchur(const MatrixType& matrix, bool computeU = true) - : m_matT(matrix.rows(),matrix.cols()), - m_matU(matrix.rows(),matrix.cols()), - m_hess(matrix.rows()), - m_isInitialized(false), - m_matUisUptodate(false), - m_maxIters(-1) - { - compute(matrix, computeU); - } - - /** \brief Returns the unitary matrix in the Schur decomposition. - * - * \returns A const reference to the matrix U. - * - * It is assumed that either the constructor - * ComplexSchur(const MatrixType& matrix, bool computeU) or the - * member function compute(const MatrixType& matrix, bool computeU) - * has been called before to compute the Schur decomposition of a - * matrix, and that \p computeU was set to true (the default - * value). - * - * Example: \include ComplexSchur_matrixU.cpp - * Output: \verbinclude ComplexSchur_matrixU.out - */ - const ComplexMatrixType& matrixU() const - { - eigen_assert(m_isInitialized && "ComplexSchur is not initialized."); - eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the ComplexSchur decomposition."); - return m_matU; - } - - /** \brief Returns the triangular matrix in the Schur decomposition. - * - * \returns A const reference to the matrix T. - * - * It is assumed that either the constructor - * ComplexSchur(const MatrixType& matrix, bool computeU) or the - * member function compute(const MatrixType& matrix, bool computeU) - * has been called before to compute the Schur decomposition of a - * matrix. - * - * Note that this function returns a plain square matrix. If you want to reference - * only the upper triangular part, use: - * \code schur.matrixT().triangularView<Upper>() \endcode - * - * Example: \include ComplexSchur_matrixT.cpp - * Output: \verbinclude ComplexSchur_matrixT.out - */ - const ComplexMatrixType& matrixT() const - { - eigen_assert(m_isInitialized && "ComplexSchur is not initialized."); - return m_matT; - } - - /** \brief Computes Schur decomposition of given matrix. - * - * \param[in] matrix Square matrix whose Schur decomposition is to be computed. - * \param[in] computeU If true, both T and U are computed; if false, only T is computed. - - * \returns Reference to \c *this - * - * The Schur decomposition is computed by first reducing the - * matrix to Hessenberg form using the class - * HessenbergDecomposition. The Hessenberg matrix is then reduced - * to triangular form by performing QR iterations with a single - * shift. The cost of computing the Schur decomposition depends - * on the number of iterations; as a rough guide, it may be taken - * on the number of iterations; as a rough guide, it may be taken - * to be \f$25n^3\f$ complex flops, or \f$10n^3\f$ complex flops - * if \a computeU is false. - * - * Example: \include ComplexSchur_compute.cpp - * Output: \verbinclude ComplexSchur_compute.out - * - * \sa compute(const MatrixType&, bool, Index) - */ - ComplexSchur& compute(const MatrixType& matrix, bool computeU = true); - - /** \brief Compute Schur decomposition from a given Hessenberg matrix - * \param[in] matrixH Matrix in Hessenberg form H - * \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T - * \param computeU Computes the matriX U of the Schur vectors - * \return Reference to \c *this - * - * This routine assumes that the matrix is already reduced in Hessenberg form matrixH - * using either the class HessenbergDecomposition or another mean. - * It computes the upper quasi-triangular matrix T of the Schur decomposition of H - * When computeU is true, this routine computes the matrix U such that - * A = U T U^T = (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix - * - * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix - * is not available, the user should give an identity matrix (Q.setIdentity()) - * - * \sa compute(const MatrixType&, bool) - */ - template<typename HessMatrixType, typename OrthMatrixType> - ComplexSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU=true); - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, \c NoConvergence otherwise. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "ComplexSchur is not initialized."); - return m_info; - } - - /** \brief Sets the maximum number of iterations allowed. - * - * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size - * of the matrix. - */ - ComplexSchur& setMaxIterations(Index maxIters) - { - m_maxIters = maxIters; - return *this; - } - - /** \brief Returns the maximum number of iterations. */ - Index getMaxIterations() - { - return m_maxIters; - } - - /** \brief Maximum number of iterations per row. - * - * If not otherwise specified, the maximum number of iterations is this number times the size of the - * matrix. It is currently set to 30. - */ - static const int m_maxIterationsPerRow = 30; - - protected: - ComplexMatrixType m_matT, m_matU; - HessenbergDecomposition<MatrixType> m_hess; - ComputationInfo m_info; - bool m_isInitialized; - bool m_matUisUptodate; - Index m_maxIters; - - private: - bool subdiagonalEntryIsNeglegible(Index i); - ComplexScalar computeShift(Index iu, Index iter); - void reduceToTriangularForm(bool computeU); - friend struct internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>; -}; - -/** If m_matT(i+1,i) is neglegible in floating point arithmetic - * compared to m_matT(i,i) and m_matT(j,j), then set it to zero and - * return true, else return false. */ -template<typename MatrixType> -inline bool ComplexSchur<MatrixType>::subdiagonalEntryIsNeglegible(Index i) -{ - RealScalar d = numext::norm1(m_matT.coeff(i,i)) + numext::norm1(m_matT.coeff(i+1,i+1)); - RealScalar sd = numext::norm1(m_matT.coeff(i+1,i)); - if (internal::isMuchSmallerThan(sd, d, NumTraits<RealScalar>::epsilon())) - { - m_matT.coeffRef(i+1,i) = ComplexScalar(0); - return true; - } - return false; -} - - -/** Compute the shift in the current QR iteration. */ -template<typename MatrixType> -typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::computeShift(Index iu, Index iter) -{ - using std::abs; - if (iter == 10 || iter == 20) - { - // exceptional shift, taken from http://www.netlib.org/eispack/comqr.f - return abs(numext::real(m_matT.coeff(iu,iu-1))) + abs(numext::real(m_matT.coeff(iu-1,iu-2))); - } - - // compute the shift as one of the eigenvalues of t, the 2x2 - // diagonal block on the bottom of the active submatrix - Matrix<ComplexScalar,2,2> t = m_matT.template block<2,2>(iu-1,iu-1); - RealScalar normt = t.cwiseAbs().sum(); - t /= normt; // the normalization by sf is to avoid under/overflow - - ComplexScalar b = t.coeff(0,1) * t.coeff(1,0); - ComplexScalar c = t.coeff(0,0) - t.coeff(1,1); - ComplexScalar disc = sqrt(c*c + RealScalar(4)*b); - ComplexScalar det = t.coeff(0,0) * t.coeff(1,1) - b; - ComplexScalar trace = t.coeff(0,0) + t.coeff(1,1); - ComplexScalar eival1 = (trace + disc) / RealScalar(2); - ComplexScalar eival2 = (trace - disc) / RealScalar(2); - - if(numext::norm1(eival1) > numext::norm1(eival2)) - eival2 = det / eival1; - else - eival1 = det / eival2; - - // choose the eigenvalue closest to the bottom entry of the diagonal - if(numext::norm1(eival1-t.coeff(1,1)) < numext::norm1(eival2-t.coeff(1,1))) - return normt * eival1; - else - return normt * eival2; -} - - -template<typename MatrixType> -ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::compute(const MatrixType& matrix, bool computeU) -{ - m_matUisUptodate = false; - eigen_assert(matrix.cols() == matrix.rows()); - - if(matrix.cols() == 1) - { - m_matT = matrix.template cast<ComplexScalar>(); - if(computeU) m_matU = ComplexMatrixType::Identity(1,1); - m_info = Success; - m_isInitialized = true; - m_matUisUptodate = computeU; - return *this; - } - - internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>::run(*this, matrix, computeU); - computeFromHessenberg(m_matT, m_matU, computeU); - return *this; -} - -template<typename MatrixType> -template<typename HessMatrixType, typename OrthMatrixType> -ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU) -{ - m_matT = matrixH; - if(computeU) - m_matU = matrixQ; - reduceToTriangularForm(computeU); - return *this; -} -namespace internal { - -/* Reduce given matrix to Hessenberg form */ -template<typename MatrixType, bool IsComplex> -struct complex_schur_reduce_to_hessenberg -{ - // this is the implementation for the case IsComplex = true - static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU) - { - _this.m_hess.compute(matrix); - _this.m_matT = _this.m_hess.matrixH(); - if(computeU) _this.m_matU = _this.m_hess.matrixQ(); - } -}; - -template<typename MatrixType> -struct complex_schur_reduce_to_hessenberg<MatrixType, false> -{ - static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU) - { - typedef typename ComplexSchur<MatrixType>::ComplexScalar ComplexScalar; - - // Note: m_hess is over RealScalar; m_matT and m_matU is over ComplexScalar - _this.m_hess.compute(matrix); - _this.m_matT = _this.m_hess.matrixH().template cast<ComplexScalar>(); - if(computeU) - { - // This may cause an allocation which seems to be avoidable - MatrixType Q = _this.m_hess.matrixQ(); - _this.m_matU = Q.template cast<ComplexScalar>(); - } - } -}; - -} // end namespace internal - -// Reduce the Hessenberg matrix m_matT to triangular form by QR iteration. -template<typename MatrixType> -void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU) -{ - Index maxIters = m_maxIters; - if (maxIters == -1) - maxIters = m_maxIterationsPerRow * m_matT.rows(); - - // The matrix m_matT is divided in three parts. - // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero. - // Rows il,...,iu is the part we are working on (the active submatrix). - // Rows iu+1,...,end are already brought in triangular form. - Index iu = m_matT.cols() - 1; - Index il; - Index iter = 0; // number of iterations we are working on the (iu,iu) element - Index totalIter = 0; // number of iterations for whole matrix - - while(true) - { - // find iu, the bottom row of the active submatrix - while(iu > 0) - { - if(!subdiagonalEntryIsNeglegible(iu-1)) break; - iter = 0; - --iu; - } - - // if iu is zero then we are done; the whole matrix is triangularized - if(iu==0) break; - - // if we spent too many iterations, we give up - iter++; - totalIter++; - if(totalIter > maxIters) break; - - // find il, the top row of the active submatrix - il = iu-1; - while(il > 0 && !subdiagonalEntryIsNeglegible(il-1)) - { - --il; - } - - /* perform the QR step using Givens rotations. The first rotation - creates a bulge; the (il+2,il) element becomes nonzero. This - bulge is chased down to the bottom of the active submatrix. */ - - ComplexScalar shift = computeShift(iu, iter); - JacobiRotation<ComplexScalar> rot; - rot.makeGivens(m_matT.coeff(il,il) - shift, m_matT.coeff(il+1,il)); - m_matT.rightCols(m_matT.cols()-il).applyOnTheLeft(il, il+1, rot.adjoint()); - m_matT.topRows((std::min)(il+2,iu)+1).applyOnTheRight(il, il+1, rot); - if(computeU) m_matU.applyOnTheRight(il, il+1, rot); - - for(Index i=il+1 ; i<iu ; i++) - { - rot.makeGivens(m_matT.coeffRef(i,i-1), m_matT.coeffRef(i+1,i-1), &m_matT.coeffRef(i,i-1)); - m_matT.coeffRef(i+1,i-1) = ComplexScalar(0); - m_matT.rightCols(m_matT.cols()-i).applyOnTheLeft(i, i+1, rot.adjoint()); - m_matT.topRows((std::min)(i+2,iu)+1).applyOnTheRight(i, i+1, rot); - if(computeU) m_matU.applyOnTheRight(i, i+1, rot); - } - } - - if(totalIter <= maxIters) - m_info = Success; - else - m_info = NoConvergence; - - m_isInitialized = true; - m_matUisUptodate = computeU; -} - -} // end namespace Eigen - -#endif // EIGEN_COMPLEX_SCHUR_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/ComplexSchur_MKL.h b/third_party/eigen3/Eigen/src/Eigenvalues/ComplexSchur_MKL.h deleted file mode 100644 index 91496ae5bd..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/ComplexSchur_MKL.h +++ /dev/null @@ -1,94 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Complex Schur needed to complex unsymmetrical eigenvalues/eigenvectors. - ******************************************************************************** -*/ - -#ifndef EIGEN_COMPLEX_SCHUR_MKL_H -#define EIGEN_COMPLEX_SCHUR_MKL_H - -#include "Eigen/src/Core/util/MKL_support.h" - -namespace Eigen { - -/** \internal Specialization for the data types supported by MKL */ - -#define EIGEN_MKL_SCHUR_COMPLEX(EIGTYPE, MKLTYPE, MKLPREFIX, MKLPREFIX_U, EIGCOLROW, MKLCOLROW) \ -template<> inline \ -ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \ -ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, bool computeU) \ -{ \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType; \ - typedef MatrixType::Scalar Scalar; \ - typedef MatrixType::RealScalar RealScalar; \ - typedef std::complex<RealScalar> ComplexScalar; \ -\ - eigen_assert(matrix.cols() == matrix.rows()); \ -\ - m_matUisUptodate = false; \ - if(matrix.cols() == 1) \ - { \ - m_matT = matrix.cast<ComplexScalar>(); \ - if(computeU) m_matU = ComplexMatrixType::Identity(1,1); \ - m_info = Success; \ - m_isInitialized = true; \ - m_matUisUptodate = computeU; \ - return *this; \ - } \ - lapack_int n = matrix.cols(), sdim, info; \ - lapack_int lda = matrix.outerStride(); \ - lapack_int matrix_order = MKLCOLROW; \ - char jobvs, sort='N'; \ - LAPACK_##MKLPREFIX_U##_SELECT1 select = 0; \ - jobvs = (computeU) ? 'V' : 'N'; \ - m_matU.resize(n, n); \ - lapack_int ldvs = m_matU.outerStride(); \ - m_matT = matrix; \ - Matrix<EIGTYPE, Dynamic, Dynamic> w; \ - w.resize(n, 1);\ - info = LAPACKE_##MKLPREFIX##gees( matrix_order, jobvs, sort, select, n, (MKLTYPE*)m_matT.data(), lda, &sdim, (MKLTYPE*)w.data(), (MKLTYPE*)m_matU.data(), ldvs ); \ - if(info == 0) \ - m_info = Success; \ - else \ - m_info = NoConvergence; \ -\ - m_isInitialized = true; \ - m_matUisUptodate = computeU; \ - return *this; \ -\ -} - -EIGEN_MKL_SCHUR_COMPLEX(dcomplex, MKL_Complex16, z, Z, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_SCHUR_COMPLEX(scomplex, MKL_Complex8, c, C, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_SCHUR_COMPLEX(dcomplex, MKL_Complex16, z, Z, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_SCHUR_COMPLEX(scomplex, MKL_Complex8, c, C, RowMajor, LAPACK_ROW_MAJOR) - -} // end namespace Eigen - -#endif // EIGEN_COMPLEX_SCHUR_MKL_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/EigenSolver.h b/third_party/eigen3/Eigen/src/Eigenvalues/EigenSolver.h deleted file mode 100644 index 1763fed197..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/EigenSolver.h +++ /dev/null @@ -1,629 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_EIGENSOLVER_H -#define EIGEN_EIGENSOLVER_H - -#include "./RealSchur.h" - -namespace Eigen { - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class EigenSolver - * - * \brief Computes eigenvalues and eigenvectors of general matrices - * - * \tparam _MatrixType the type of the matrix of which we are computing the - * eigendecomposition; this is expected to be an instantiation of the Matrix - * class template. Currently, only real matrices are supported. - * - * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars - * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v \f$. If - * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and - * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V = - * V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we - * have \f$ A = V D V^{-1} \f$. This is called the eigendecomposition. - * - * The eigenvalues and eigenvectors of a matrix may be complex, even when the - * matrix is real. However, we can choose real matrices \f$ V \f$ and \f$ D - * \f$ satisfying \f$ A V = V D \f$, just like the eigendecomposition, if the - * matrix \f$ D \f$ is not required to be diagonal, but if it is allowed to - * have blocks of the form - * \f[ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f] - * (where \f$ u \f$ and \f$ v \f$ are real numbers) on the diagonal. These - * blocks correspond to complex eigenvalue pairs \f$ u \pm iv \f$. We call - * this variant of the eigendecomposition the pseudo-eigendecomposition. - * - * Call the function compute() to compute the eigenvalues and eigenvectors of - * a given matrix. Alternatively, you can use the - * EigenSolver(const MatrixType&, bool) constructor which computes the - * eigenvalues and eigenvectors at construction time. Once the eigenvalue and - * eigenvectors are computed, they can be retrieved with the eigenvalues() and - * eigenvectors() functions. The pseudoEigenvalueMatrix() and - * pseudoEigenvectors() methods allow the construction of the - * pseudo-eigendecomposition. - * - * The documentation for EigenSolver(const MatrixType&, bool) contains an - * example of the typical use of this class. - * - * \note The implementation is adapted from - * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain). - * Their code is based on EISPACK. - * - * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver - */ -template<typename _MatrixType> class EigenSolver -{ - public: - - /** \brief Synonym for the template parameter \p _MatrixType. */ - typedef _MatrixType MatrixType; - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - - /** \brief Scalar type for matrices of type #MatrixType. */ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - - /** \brief Complex scalar type for #MatrixType. - * - * This is \c std::complex<Scalar> if #Scalar is real (e.g., - * \c float or \c double) and just \c Scalar if #Scalar is - * complex. - */ - typedef std::complex<RealScalar> ComplexScalar; - - /** \brief Type for vector of eigenvalues as returned by eigenvalues(). - * - * This is a column vector with entries of type #ComplexScalar. - * The length of the vector is the size of #MatrixType. - */ - typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType; - - /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). - * - * This is a square matrix with entries of type #ComplexScalar. - * The size is the same as the size of #MatrixType. - */ - typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType; - - /** \brief Default constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via EigenSolver::compute(const MatrixType&, bool). - * - * \sa compute() for an example. - */ - EigenSolver() : m_eivec(), m_eivalues(), m_isInitialized(false), m_realSchur(), m_matT(), m_tmp() {} - - /** \brief Default constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa EigenSolver() - */ - EigenSolver(Index size) - : m_eivec(size, size), - m_eivalues(size), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_realSchur(size), - m_matT(size, size), - m_tmp(size) - {} - - /** \brief Constructor; computes eigendecomposition of given matrix. - * - * \param[in] matrix Square matrix whose eigendecomposition is to be computed. - * \param[in] computeEigenvectors If true, both the eigenvectors and the - * eigenvalues are computed; if false, only the eigenvalues are - * computed. - * - * This constructor calls compute() to compute the eigenvalues - * and eigenvectors. - * - * Example: \include EigenSolver_EigenSolver_MatrixType.cpp - * Output: \verbinclude EigenSolver_EigenSolver_MatrixType.out - * - * \sa compute() - */ - EigenSolver(const MatrixType& matrix, bool computeEigenvectors = true) - : m_eivec(matrix.rows(), matrix.cols()), - m_eivalues(matrix.cols()), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_realSchur(matrix.cols()), - m_matT(matrix.rows(), matrix.cols()), - m_tmp(matrix.cols()) - { - compute(matrix, computeEigenvectors); - } - - /** \brief Returns the eigenvectors of given matrix. - * - * \returns %Matrix whose columns are the (possibly complex) eigenvectors. - * - * \pre Either the constructor - * EigenSolver(const MatrixType&,bool) or the member function - * compute(const MatrixType&, bool) has been called before, and - * \p computeEigenvectors was set to true (the default). - * - * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding - * to eigenvalue number \f$ k \f$ as returned by eigenvalues(). The - * eigenvectors are normalized to have (Euclidean) norm equal to one. The - * matrix returned by this function is the matrix \f$ V \f$ in the - * eigendecomposition \f$ A = V D V^{-1} \f$, if it exists. - * - * Example: \include EigenSolver_eigenvectors.cpp - * Output: \verbinclude EigenSolver_eigenvectors.out - * - * \sa eigenvalues(), pseudoEigenvectors() - */ - EigenvectorsType eigenvectors() const; - - /** \brief Returns the pseudo-eigenvectors of given matrix. - * - * \returns Const reference to matrix whose columns are the pseudo-eigenvectors. - * - * \pre Either the constructor - * EigenSolver(const MatrixType&,bool) or the member function - * compute(const MatrixType&, bool) has been called before, and - * \p computeEigenvectors was set to true (the default). - * - * The real matrix \f$ V \f$ returned by this function and the - * block-diagonal matrix \f$ D \f$ returned by pseudoEigenvalueMatrix() - * satisfy \f$ AV = VD \f$. - * - * Example: \include EigenSolver_pseudoEigenvectors.cpp - * Output: \verbinclude EigenSolver_pseudoEigenvectors.out - * - * \sa pseudoEigenvalueMatrix(), eigenvectors() - */ - const MatrixType& pseudoEigenvectors() const - { - eigen_assert(m_isInitialized && "EigenSolver is not initialized."); - eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues."); - return m_eivec; - } - - /** \brief Returns the block-diagonal matrix in the pseudo-eigendecomposition. - * - * \returns A block-diagonal matrix. - * - * \pre Either the constructor - * EigenSolver(const MatrixType&,bool) or the member function - * compute(const MatrixType&, bool) has been called before. - * - * The matrix \f$ D \f$ returned by this function is real and - * block-diagonal. The blocks on the diagonal are either 1-by-1 or 2-by-2 - * blocks of the form - * \f$ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f$. - * These blocks are not sorted in any particular order. - * The matrix \f$ D \f$ and the matrix \f$ V \f$ returned by - * pseudoEigenvectors() satisfy \f$ AV = VD \f$. - * - * \sa pseudoEigenvectors() for an example, eigenvalues() - */ - MatrixType pseudoEigenvalueMatrix() const; - - /** \brief Returns the eigenvalues of given matrix. - * - * \returns A const reference to the column vector containing the eigenvalues. - * - * \pre Either the constructor - * EigenSolver(const MatrixType&,bool) or the member function - * compute(const MatrixType&, bool) has been called before. - * - * The eigenvalues are repeated according to their algebraic multiplicity, - * so there are as many eigenvalues as rows in the matrix. The eigenvalues - * are not sorted in any particular order. - * - * Example: \include EigenSolver_eigenvalues.cpp - * Output: \verbinclude EigenSolver_eigenvalues.out - * - * \sa eigenvectors(), pseudoEigenvalueMatrix(), - * MatrixBase::eigenvalues() - */ - const EigenvalueType& eigenvalues() const - { - eigen_assert(m_isInitialized && "EigenSolver is not initialized."); - return m_eivalues; - } - - /** \brief Computes eigendecomposition of given matrix. - * - * \param[in] matrix Square matrix whose eigendecomposition is to be computed. - * \param[in] computeEigenvectors If true, both the eigenvectors and the - * eigenvalues are computed; if false, only the eigenvalues are - * computed. - * \returns Reference to \c *this - * - * This function computes the eigenvalues of the real matrix \p matrix. - * The eigenvalues() function can be used to retrieve them. If - * \p computeEigenvectors is true, then the eigenvectors are also computed - * and can be retrieved by calling eigenvectors(). - * - * The matrix is first reduced to real Schur form using the RealSchur - * class. The Schur decomposition is then used to compute the eigenvalues - * and eigenvectors. - * - * The cost of the computation is dominated by the cost of the - * Schur decomposition, which is very approximately \f$ 25n^3 \f$ - * (where \f$ n \f$ is the size of the matrix) if \p computeEigenvectors - * is true, and \f$ 10n^3 \f$ if \p computeEigenvectors is false. - * - * This method reuses of the allocated data in the EigenSolver object. - * - * Example: \include EigenSolver_compute.cpp - * Output: \verbinclude EigenSolver_compute.out - */ - EigenSolver& compute(const MatrixType& matrix, bool computeEigenvectors = true); - - /** \returns NumericalIssue if the input contains INF or NaN values or overflow occured. Returns Success otherwise. */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "EigenSolver is not initialized."); - return m_info; - } - - /** \brief Sets the maximum number of iterations allowed. */ - EigenSolver& setMaxIterations(Index maxIters) - { - m_realSchur.setMaxIterations(maxIters); - return *this; - } - - /** \brief Returns the maximum number of iterations. */ - Index getMaxIterations() - { - return m_realSchur.getMaxIterations(); - } - - private: - void doComputeEigenvectors(); - - protected: - MatrixType m_eivec; - EigenvalueType m_eivalues; - bool m_isInitialized; - bool m_eigenvectorsOk; - ComputationInfo m_info; - RealSchur<MatrixType> m_realSchur; - MatrixType m_matT; - - typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType; - ColumnVectorType m_tmp; -}; - -template<typename MatrixType> -MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const -{ - eigen_assert(m_isInitialized && "EigenSolver is not initialized."); - Index n = m_eivalues.rows(); - MatrixType matD = MatrixType::Zero(n,n); - for (Index i=0; i<n; ++i) - { - if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i)))) - matD.coeffRef(i,i) = numext::real(m_eivalues.coeff(i)); - else - { - matD.template block<2,2>(i,i) << numext::real(m_eivalues.coeff(i)), numext::imag(m_eivalues.coeff(i)), - -numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i)); - ++i; - } - } - return matD; -} - -template<typename MatrixType> -typename EigenSolver<MatrixType>::EigenvectorsType EigenSolver<MatrixType>::eigenvectors() const -{ - eigen_assert(m_isInitialized && "EigenSolver is not initialized."); - eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues."); - Index n = m_eivec.cols(); - EigenvectorsType matV(n,n); - for (Index j=0; j<n; ++j) - { - if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(j)), numext::real(m_eivalues.coeff(j))) || j+1==n) - { - // we have a real eigen value - matV.col(j) = m_eivec.col(j).template cast<ComplexScalar>(); - matV.col(j).normalize(); - } - else - { - // we have a pair of complex eigen values - for (Index i=0; i<n; ++i) - { - matV.coeffRef(i,j) = ComplexScalar(m_eivec.coeff(i,j), m_eivec.coeff(i,j+1)); - matV.coeffRef(i,j+1) = ComplexScalar(m_eivec.coeff(i,j), -m_eivec.coeff(i,j+1)); - } - matV.col(j).normalize(); - matV.col(j+1).normalize(); - ++j; - } - } - return matV; -} - -template<typename MatrixType> -EigenSolver<MatrixType>& -EigenSolver<MatrixType>::compute(const MatrixType& matrix, bool computeEigenvectors) -{ - using std::sqrt; - using std::abs; - using std::max; - using numext::isfinite; - eigen_assert(matrix.cols() == matrix.rows()); - - // Reduce to real Schur form. - m_realSchur.compute(matrix, computeEigenvectors); - - m_info = m_realSchur.info(); - - if (m_info == Success) - { - m_matT = m_realSchur.matrixT(); - if (computeEigenvectors) - m_eivec = m_realSchur.matrixU(); - - // Compute eigenvalues from matT - m_eivalues.resize(matrix.cols()); - Index i = 0; - while (i < matrix.cols()) - { - if (i == matrix.cols() - 1 || m_matT.coeff(i+1, i) == Scalar(0)) - { - m_eivalues.coeffRef(i) = m_matT.coeff(i, i); - if(!isfinite(m_eivalues.coeffRef(i))) - { - m_isInitialized = true; - m_eigenvectorsOk = false; - m_info = NumericalIssue; - return *this; - } - ++i; - } - else - { - Scalar p = Scalar(0.5) * (m_matT.coeff(i, i) - m_matT.coeff(i+1, i+1)); - Scalar z; - // Compute z = sqrt(abs(p * p + m_matT.coeff(i+1, i) * m_matT.coeff(i, i+1))); - // without overflow - { - Scalar t0 = m_matT.coeff(i+1, i); - Scalar t1 = m_matT.coeff(i, i+1); - Scalar maxval = (max)(abs(p),(max)(abs(t0),abs(t1))); - t0 /= maxval; - t1 /= maxval; - Scalar p0 = p/maxval; - z = maxval * sqrt(abs(p0 * p0 + t0 * t1)); - } - - m_eivalues.coeffRef(i) = ComplexScalar(m_matT.coeff(i+1, i+1) + p, z); - m_eivalues.coeffRef(i+1) = ComplexScalar(m_matT.coeff(i+1, i+1) + p, -z); - if(!(isfinite(m_eivalues.coeffRef(i)) && isfinite(m_eivalues.coeffRef(i+1)))) - { - m_isInitialized = true; - m_eigenvectorsOk = false; - m_info = NumericalIssue; - return *this; - } - i += 2; - } - } - - // Compute eigenvectors. - if (computeEigenvectors) - doComputeEigenvectors(); - } - - m_isInitialized = true; - m_eigenvectorsOk = computeEigenvectors; - - return *this; -} - -// Complex scalar division. -template<typename Scalar> -std::complex<Scalar> cdiv(const Scalar& xr, const Scalar& xi, const Scalar& yr, const Scalar& yi) -{ - using std::abs; - Scalar r,d; - if (abs(yr) > abs(yi)) - { - r = yi/yr; - d = yr + r*yi; - return std::complex<Scalar>((xr + r*xi)/d, (xi - r*xr)/d); - } - else - { - r = yr/yi; - d = yi + r*yr; - return std::complex<Scalar>((r*xr + xi)/d, (r*xi - xr)/d); - } -} - - -template<typename MatrixType> -void EigenSolver<MatrixType>::doComputeEigenvectors() -{ - using std::abs; - const Index size = m_eivec.cols(); - const Scalar eps = NumTraits<Scalar>::epsilon(); - - // inefficient! this is already computed in RealSchur - Scalar norm(0); - for (Index j = 0; j < size; ++j) - { - norm += m_matT.row(j).segment((std::max)(j-1,Index(0)), size-(std::max)(j-1,Index(0))).cwiseAbs().sum(); - } - - // Backsubstitute to find vectors of upper triangular form - if (norm == 0.0) - { - return; - } - - for (Index n = size-1; n >= 0; n--) - { - Scalar p = m_eivalues.coeff(n).real(); - Scalar q = m_eivalues.coeff(n).imag(); - - // Scalar vector - if (q == Scalar(0)) - { - Scalar lastr(0), lastw(0); - Index l = n; - - m_matT.coeffRef(n,n) = 1.0; - for (Index i = n-1; i >= 0; i--) - { - Scalar w = m_matT.coeff(i,i) - p; - Scalar r = m_matT.row(i).segment(l,n-l+1).dot(m_matT.col(n).segment(l, n-l+1)); - - if (m_eivalues.coeff(i).imag() < 0.0) - { - lastw = w; - lastr = r; - } - else - { - l = i; - if (m_eivalues.coeff(i).imag() == 0.0) - { - if (w != 0.0) - m_matT.coeffRef(i,n) = -r / w; - else - m_matT.coeffRef(i,n) = -r / (eps * norm); - } - else // Solve real equations - { - Scalar x = m_matT.coeff(i,i+1); - Scalar y = m_matT.coeff(i+1,i); - Scalar denom = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag(); - Scalar t = (x * lastr - lastw * r) / denom; - m_matT.coeffRef(i,n) = t; - if (abs(x) > abs(lastw)) - m_matT.coeffRef(i+1,n) = (-r - w * t) / x; - else - m_matT.coeffRef(i+1,n) = (-lastr - y * t) / lastw; - } - - // Overflow control - Scalar t = abs(m_matT.coeff(i,n)); - if ((eps * t) * t > Scalar(1)) - m_matT.col(n).tail(size-i) /= t; - } - } - } - else if (q < Scalar(0) && n > 0) // Complex vector - { - Scalar lastra(0), lastsa(0), lastw(0); - Index l = n-1; - - // Last vector component imaginary so matrix is triangular - if (abs(m_matT.coeff(n,n-1)) > abs(m_matT.coeff(n-1,n))) - { - m_matT.coeffRef(n-1,n-1) = q / m_matT.coeff(n,n-1); - m_matT.coeffRef(n-1,n) = -(m_matT.coeff(n,n) - p) / m_matT.coeff(n,n-1); - } - else - { - std::complex<Scalar> cc = cdiv<Scalar>(0.0,-m_matT.coeff(n-1,n),m_matT.coeff(n-1,n-1)-p,q); - m_matT.coeffRef(n-1,n-1) = numext::real(cc); - m_matT.coeffRef(n-1,n) = numext::imag(cc); - } - m_matT.coeffRef(n,n-1) = 0.0; - m_matT.coeffRef(n,n) = 1.0; - for (Index i = n-2; i >= 0; i--) - { - Scalar ra = m_matT.row(i).segment(l, n-l+1).dot(m_matT.col(n-1).segment(l, n-l+1)); - Scalar sa = m_matT.row(i).segment(l, n-l+1).dot(m_matT.col(n).segment(l, n-l+1)); - Scalar w = m_matT.coeff(i,i) - p; - - if (m_eivalues.coeff(i).imag() < 0.0) - { - lastw = w; - lastra = ra; - lastsa = sa; - } - else - { - l = i; - if (m_eivalues.coeff(i).imag() == RealScalar(0)) - { - std::complex<Scalar> cc = cdiv(-ra,-sa,w,q); - m_matT.coeffRef(i,n-1) = numext::real(cc); - m_matT.coeffRef(i,n) = numext::imag(cc); - } - else - { - // Solve complex equations - Scalar x = m_matT.coeff(i,i+1); - Scalar y = m_matT.coeff(i+1,i); - Scalar vr = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag() - q * q; - Scalar vi = (m_eivalues.coeff(i).real() - p) * Scalar(2) * q; - if ((vr == 0.0) && (vi == 0.0)) - vr = eps * norm * (abs(w) + abs(q) + abs(x) + abs(y) + abs(lastw)); - - std::complex<Scalar> cc = cdiv(x*lastra-lastw*ra+q*sa,x*lastsa-lastw*sa-q*ra,vr,vi); - m_matT.coeffRef(i,n-1) = numext::real(cc); - m_matT.coeffRef(i,n) = numext::imag(cc); - if (abs(x) > (abs(lastw) + abs(q))) - { - m_matT.coeffRef(i+1,n-1) = (-ra - w * m_matT.coeff(i,n-1) + q * m_matT.coeff(i,n)) / x; - m_matT.coeffRef(i+1,n) = (-sa - w * m_matT.coeff(i,n) - q * m_matT.coeff(i,n-1)) / x; - } - else - { - cc = cdiv(-lastra-y*m_matT.coeff(i,n-1),-lastsa-y*m_matT.coeff(i,n),lastw,q); - m_matT.coeffRef(i+1,n-1) = numext::real(cc); - m_matT.coeffRef(i+1,n) = numext::imag(cc); - } - } - - // Overflow control - Scalar t = numext::maxi(abs(m_matT.coeff(i,n-1)),abs(m_matT.coeff(i,n))); - if ((eps * t) * t > Scalar(1)) - m_matT.block(i, n-1, size-i, 2) /= t; - - } - } - - // We handled a pair of complex conjugate eigenvalues, so need to skip them both - n--; - } - else - { - eigen_assert(0 && "Internal bug in EigenSolver (INF or NaN has not been detected)"); // this should not happen - } - } - - // Back transformation to get eigenvectors of original matrix - for (Index j = size-1; j >= 0; j--) - { - m_tmp.noalias() = m_eivec.leftCols(j+1) * m_matT.col(j).segment(0, j+1); - m_eivec.col(j) = m_tmp; - } -} - -} // end namespace Eigen - -#endif // EIGEN_EIGENSOLVER_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h deleted file mode 100644 index dc240e13e1..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h +++ /dev/null @@ -1,341 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERALIZEDEIGENSOLVER_H -#define EIGEN_GENERALIZEDEIGENSOLVER_H - -#include "./RealQZ.h" - -namespace Eigen { - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class GeneralizedEigenSolver - * - * \brief Computes the generalized eigenvalues and eigenvectors of a pair of general matrices - * - * \tparam _MatrixType the type of the matrices of which we are computing the - * eigen-decomposition; this is expected to be an instantiation of the Matrix - * class template. Currently, only real matrices are supported. - * - * The generalized eigenvalues and eigenvectors of a matrix pair \f$ A \f$ and \f$ B \f$ are scalars - * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda Bv \f$. If - * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and - * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V = - * B V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we - * have \f$ A = B V D V^{-1} \f$. This is called the generalized eigen-decomposition. - * - * The generalized eigenvalues and eigenvectors of a matrix pair may be complex, even when the - * matrices are real. Moreover, the generalized eigenvalue might be infinite if the matrix B is - * singular. To workaround this difficulty, the eigenvalues are provided as a pair of complex \f$ \alpha \f$ - * and real \f$ \beta \f$ such that: \f$ \lambda_i = \alpha_i / \beta_i \f$. If \f$ \beta_i \f$ is (nearly) zero, - * then one can consider the well defined left eigenvalue \f$ \mu = \beta_i / \alpha_i\f$ such that: - * \f$ \mu_i A v_i = B v_i \f$, or even \f$ \mu_i u_i^T A = u_i^T B \f$ where \f$ u_i \f$ is - * called the left eigenvector. - * - * Call the function compute() to compute the generalized eigenvalues and eigenvectors of - * a given matrix pair. Alternatively, you can use the - * GeneralizedEigenSolver(const MatrixType&, const MatrixType&, bool) constructor which computes the - * eigenvalues and eigenvectors at construction time. Once the eigenvalue and - * eigenvectors are computed, they can be retrieved with the eigenvalues() and - * eigenvectors() functions. - * - * Here is an usage example of this class: - * Example: \include GeneralizedEigenSolver.cpp - * Output: \verbinclude GeneralizedEigenSolver.out - * - * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver - */ -template<typename _MatrixType> class GeneralizedEigenSolver -{ - public: - - /** \brief Synonym for the template parameter \p _MatrixType. */ - typedef _MatrixType MatrixType; - - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - - /** \brief Scalar type for matrices of type #MatrixType. */ - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - - /** \brief Complex scalar type for #MatrixType. - * - * This is \c std::complex<Scalar> if #Scalar is real (e.g., - * \c float or \c double) and just \c Scalar if #Scalar is - * complex. - */ - typedef std::complex<RealScalar> ComplexScalar; - - /** \brief Type for vector of real scalar values eigenvalues as returned by betas(). - * - * This is a column vector with entries of type #Scalar. - * The length of the vector is the size of #MatrixType. - */ - typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> VectorType; - - /** \brief Type for vector of complex scalar values eigenvalues as returned by betas(). - * - * This is a column vector with entries of type #ComplexScalar. - * The length of the vector is the size of #MatrixType. - */ - typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ComplexVectorType; - - /** \brief Expression type for the eigenvalues as returned by eigenvalues(). - */ - typedef CwiseBinaryOp<internal::scalar_quotient_op<ComplexScalar,Scalar>,ComplexVectorType,VectorType> EigenvalueType; - - /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). - * - * This is a square matrix with entries of type #ComplexScalar. - * The size is the same as the size of #MatrixType. - */ - typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType; - - /** \brief Default constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via EigenSolver::compute(const MatrixType&, bool). - * - * \sa compute() for an example. - */ - GeneralizedEigenSolver() : m_eivec(), m_alphas(), m_betas(), m_isInitialized(false), m_realQZ(), m_matS(), m_tmp() {} - - /** \brief Default constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa GeneralizedEigenSolver() - */ - GeneralizedEigenSolver(Index size) - : m_eivec(size, size), - m_alphas(size), - m_betas(size), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_realQZ(size), - m_matS(size, size), - m_tmp(size) - {} - - /** \brief Constructor; computes the generalized eigendecomposition of given matrix pair. - * - * \param[in] A Square matrix whose eigendecomposition is to be computed. - * \param[in] B Square matrix whose eigendecomposition is to be computed. - * \param[in] computeEigenvectors If true, both the eigenvectors and the - * eigenvalues are computed; if false, only the eigenvalues are computed. - * - * This constructor calls compute() to compute the generalized eigenvalues - * and eigenvectors. - * - * \sa compute() - */ - GeneralizedEigenSolver(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true) - : m_eivec(A.rows(), A.cols()), - m_alphas(A.cols()), - m_betas(A.cols()), - m_isInitialized(false), - m_eigenvectorsOk(false), - m_realQZ(A.cols()), - m_matS(A.rows(), A.cols()), - m_tmp(A.cols()) - { - compute(A, B, computeEigenvectors); - } - - /* \brief Returns the computed generalized eigenvectors. - * - * \returns %Matrix whose columns are the (possibly complex) eigenvectors. - * - * \pre Either the constructor - * GeneralizedEigenSolver(const MatrixType&,const MatrixType&, bool) or the member function - * compute(const MatrixType&, const MatrixType& bool) has been called before, and - * \p computeEigenvectors was set to true (the default). - * - * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding - * to eigenvalue number \f$ k \f$ as returned by eigenvalues(). The - * eigenvectors are normalized to have (Euclidean) norm equal to one. The - * matrix returned by this function is the matrix \f$ V \f$ in the - * generalized eigendecomposition \f$ A = B V D V^{-1} \f$, if it exists. - * - * \sa eigenvalues() - */ -// EigenvectorsType eigenvectors() const; - - /** \brief Returns an expression of the computed generalized eigenvalues. - * - * \returns An expression of the column vector containing the eigenvalues. - * - * It is a shortcut for \code this->alphas().cwiseQuotient(this->betas()); \endcode - * Not that betas might contain zeros. It is therefore not recommended to use this function, - * but rather directly deal with the alphas and betas vectors. - * - * \pre Either the constructor - * GeneralizedEigenSolver(const MatrixType&,const MatrixType&,bool) or the member function - * compute(const MatrixType&,const MatrixType&,bool) has been called before. - * - * The eigenvalues are repeated according to their algebraic multiplicity, - * so there are as many eigenvalues as rows in the matrix. The eigenvalues - * are not sorted in any particular order. - * - * \sa alphas(), betas(), eigenvectors() - */ - EigenvalueType eigenvalues() const - { - eigen_assert(m_isInitialized && "GeneralizedEigenSolver is not initialized."); - return EigenvalueType(m_alphas,m_betas); - } - - /** \returns A const reference to the vectors containing the alpha values - * - * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j). - * - * \sa betas(), eigenvalues() */ - ComplexVectorType alphas() const - { - eigen_assert(m_isInitialized && "GeneralizedEigenSolver is not initialized."); - return m_alphas; - } - - /** \returns A const reference to the vectors containing the beta values - * - * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j). - * - * \sa alphas(), eigenvalues() */ - VectorType betas() const - { - eigen_assert(m_isInitialized && "GeneralizedEigenSolver is not initialized."); - return m_betas; - } - - /** \brief Computes generalized eigendecomposition of given matrix. - * - * \param[in] A Square matrix whose eigendecomposition is to be computed. - * \param[in] B Square matrix whose eigendecomposition is to be computed. - * \param[in] computeEigenvectors If true, both the eigenvectors and the - * eigenvalues are computed; if false, only the eigenvalues are - * computed. - * \returns Reference to \c *this - * - * This function computes the eigenvalues of the real matrix \p matrix. - * The eigenvalues() function can be used to retrieve them. If - * \p computeEigenvectors is true, then the eigenvectors are also computed - * and can be retrieved by calling eigenvectors(). - * - * The matrix is first reduced to real generalized Schur form using the RealQZ - * class. The generalized Schur decomposition is then used to compute the eigenvalues - * and eigenvectors. - * - * The cost of the computation is dominated by the cost of the - * generalized Schur decomposition. - * - * This method reuses of the allocated data in the GeneralizedEigenSolver object. - */ - GeneralizedEigenSolver& compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true); - - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "EigenSolver is not initialized."); - return m_realQZ.info(); - } - - /** Sets the maximal number of iterations allowed. - */ - GeneralizedEigenSolver& setMaxIterations(Index maxIters) - { - m_realQZ.setMaxIterations(maxIters); - return *this; - } - - protected: - MatrixType m_eivec; - ComplexVectorType m_alphas; - VectorType m_betas; - bool m_isInitialized; - bool m_eigenvectorsOk; - RealQZ<MatrixType> m_realQZ; - MatrixType m_matS; - - typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType; - ColumnVectorType m_tmp; -}; - -//template<typename MatrixType> -//typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType GeneralizedEigenSolver<MatrixType>::eigenvectors() const -//{ -// eigen_assert(m_isInitialized && "EigenSolver is not initialized."); -// eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues."); -// Index n = m_eivec.cols(); -// EigenvectorsType matV(n,n); -// // TODO -// return matV; -//} - -template<typename MatrixType> -GeneralizedEigenSolver<MatrixType>& -GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors) -{ - using std::sqrt; - using std::abs; - eigen_assert(A.cols() == A.rows() && B.cols() == A.rows() && B.cols() == B.rows()); - - // Reduce to generalized real Schur form: - // A = Q S Z and B = Q T Z - m_realQZ.compute(A, B, computeEigenvectors); - - if (m_realQZ.info() == Success) - { - m_matS = m_realQZ.matrixS(); - if (computeEigenvectors) - m_eivec = m_realQZ.matrixZ().transpose(); - - // Compute eigenvalues from matS - m_alphas.resize(A.cols()); - m_betas.resize(A.cols()); - Index i = 0; - while (i < A.cols()) - { - if (i == A.cols() - 1 || m_matS.coeff(i+1, i) == Scalar(0)) - { - m_alphas.coeffRef(i) = m_matS.coeff(i, i); - m_betas.coeffRef(i) = m_realQZ.matrixT().coeff(i,i); - ++i; - } - else - { - Scalar p = Scalar(0.5) * (m_matS.coeff(i, i) - m_matS.coeff(i+1, i+1)); - Scalar z = sqrt(abs(p * p + m_matS.coeff(i+1, i) * m_matS.coeff(i, i+1))); - m_alphas.coeffRef(i) = ComplexScalar(m_matS.coeff(i+1, i+1) + p, z); - m_alphas.coeffRef(i+1) = ComplexScalar(m_matS.coeff(i+1, i+1) + p, -z); - - m_betas.coeffRef(i) = m_realQZ.matrixT().coeff(i,i); - m_betas.coeffRef(i+1) = m_realQZ.matrixT().coeff(i,i); - i += 2; - } - } - } - - m_isInitialized = true; - m_eigenvectorsOk = false;//computeEigenvectors; - - return *this; -} - -} // end namespace Eigen - -#endif // EIGEN_GENERALIZEDEIGENSOLVER_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h deleted file mode 100644 index 07bf1ea095..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h +++ /dev/null @@ -1,227 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H -#define EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H - -#include "./Tridiagonalization.h" - -namespace Eigen { - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class GeneralizedSelfAdjointEigenSolver - * - * \brief Computes eigenvalues and eigenvectors of the generalized selfadjoint eigen problem - * - * \tparam _MatrixType the type of the matrix of which we are computing the - * eigendecomposition; this is expected to be an instantiation of the Matrix - * class template. - * - * This class solves the generalized eigenvalue problem - * \f$ Av = \lambda Bv \f$. In this case, the matrix \f$ A \f$ should be - * selfadjoint and the matrix \f$ B \f$ should be positive definite. - * - * Only the \b lower \b triangular \b part of the input matrix is referenced. - * - * Call the function compute() to compute the eigenvalues and eigenvectors of - * a given matrix. Alternatively, you can use the - * GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int) - * constructor which computes the eigenvalues and eigenvectors at construction time. - * Once the eigenvalue and eigenvectors are computed, they can be retrieved with the eigenvalues() - * and eigenvectors() functions. - * - * The documentation for GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int) - * contains an example of the typical use of this class. - * - * \sa class SelfAdjointEigenSolver, class EigenSolver, class ComplexEigenSolver - */ -template<typename _MatrixType> -class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<_MatrixType> -{ - typedef SelfAdjointEigenSolver<_MatrixType> Base; - public: - - typedef typename Base::Index Index; - typedef _MatrixType MatrixType; - - /** \brief Default constructor for fixed-size matrices. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via compute(). This constructor - * can only be used if \p _MatrixType is a fixed-size matrix; use - * GeneralizedSelfAdjointEigenSolver(Index) for dynamic-size matrices. - */ - GeneralizedSelfAdjointEigenSolver() : Base() {} - - /** \brief Constructor, pre-allocates memory for dynamic-size matrices. - * - * \param [in] size Positive integer, size of the matrix whose - * eigenvalues and eigenvectors will be computed. - * - * This constructor is useful for dynamic-size matrices, when the user - * intends to perform decompositions via compute(). The \p size - * parameter is only used as a hint. It is not an error to give a wrong - * \p size, but it may impair performance. - * - * \sa compute() for an example - */ - GeneralizedSelfAdjointEigenSolver(Index size) - : Base(size) - {} - - /** \brief Constructor; computes generalized eigendecomposition of given matrix pencil. - * - * \param[in] matA Selfadjoint matrix in matrix pencil. - * Only the lower triangular part of the matrix is referenced. - * \param[in] matB Positive-definite matrix in matrix pencil. - * Only the lower triangular part of the matrix is referenced. - * \param[in] options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}. - * Default is #ComputeEigenvectors|#Ax_lBx. - * - * This constructor calls compute(const MatrixType&, const MatrixType&, int) - * to compute the eigenvalues and (if requested) the eigenvectors of the - * generalized eigenproblem \f$ Ax = \lambda B x \f$ with \a matA the - * selfadjoint matrix \f$ A \f$ and \a matB the positive definite matrix - * \f$ B \f$. Each eigenvector \f$ x \f$ satisfies the property - * \f$ x^* B x = 1 \f$. The eigenvectors are computed if - * \a options contains ComputeEigenvectors. - * - * In addition, the two following variants can be solved via \p options: - * - \c ABx_lx: \f$ ABx = \lambda x \f$ - * - \c BAx_lx: \f$ BAx = \lambda x \f$ - * - * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp - * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.out - * - * \sa compute(const MatrixType&, const MatrixType&, int) - */ - GeneralizedSelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB, - int options = ComputeEigenvectors|Ax_lBx) - : Base(matA.cols()) - { - compute(matA, matB, options); - } - - /** \brief Computes generalized eigendecomposition of given matrix pencil. - * - * \param[in] matA Selfadjoint matrix in matrix pencil. - * Only the lower triangular part of the matrix is referenced. - * \param[in] matB Positive-definite matrix in matrix pencil. - * Only the lower triangular part of the matrix is referenced. - * \param[in] options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}. - * Default is #ComputeEigenvectors|#Ax_lBx. - * - * \returns Reference to \c *this - * - * Accoring to \p options, this function computes eigenvalues and (if requested) - * the eigenvectors of one of the following three generalized eigenproblems: - * - \c Ax_lBx: \f$ Ax = \lambda B x \f$ - * - \c ABx_lx: \f$ ABx = \lambda x \f$ - * - \c BAx_lx: \f$ BAx = \lambda x \f$ - * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite - * matrix \f$ B \f$. - * In addition, each eigenvector \f$ x \f$ satisfies the property \f$ x^* B x = 1 \f$. - * - * The eigenvalues() function can be used to retrieve - * the eigenvalues. If \p options contains ComputeEigenvectors, then the - * eigenvectors are also computed and can be retrieved by calling - * eigenvectors(). - * - * The implementation uses LLT to compute the Cholesky decomposition - * \f$ B = LL^* \f$ and computes the classical eigendecomposition - * of the selfadjoint matrix \f$ L^{-1} A (L^*)^{-1} \f$ if \p options contains Ax_lBx - * and of \f$ L^{*} A L \f$ otherwise. This solves the - * generalized eigenproblem, because any solution of the generalized - * eigenproblem \f$ Ax = \lambda B x \f$ corresponds to a solution - * \f$ L^{-1} A (L^*)^{-1} (L^* x) = \lambda (L^* x) \f$ of the - * eigenproblem for \f$ L^{-1} A (L^*)^{-1} \f$. Similar statements - * can be made for the two other variants. - * - * Example: \include SelfAdjointEigenSolver_compute_MatrixType2.cpp - * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType2.out - * - * \sa GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int) - */ - GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB, - int options = ComputeEigenvectors|Ax_lBx); - - protected: - -}; - - -template<typename MatrixType> -GeneralizedSelfAdjointEigenSolver<MatrixType>& GeneralizedSelfAdjointEigenSolver<MatrixType>:: -compute(const MatrixType& matA, const MatrixType& matB, int options) -{ - eigen_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows()); - eigen_assert((options&~(EigVecMask|GenEigMask))==0 - && (options&EigVecMask)!=EigVecMask - && ((options&GenEigMask)==0 || (options&GenEigMask)==Ax_lBx - || (options&GenEigMask)==ABx_lx || (options&GenEigMask)==BAx_lx) - && "invalid option parameter"); - - bool computeEigVecs = ((options&EigVecMask)==0) || ((options&EigVecMask)==ComputeEigenvectors); - - // Compute the cholesky decomposition of matB = L L' = U'U - LLT<MatrixType> cholB(matB); - - int type = (options&GenEigMask); - if(type==0) - type = Ax_lBx; - - if(type==Ax_lBx) - { - // compute C = inv(L) A inv(L') - MatrixType matC = matA.template selfadjointView<Lower>(); - cholB.matrixL().template solveInPlace<OnTheLeft>(matC); - cholB.matrixU().template solveInPlace<OnTheRight>(matC); - - Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly ); - - // transform back the eigen vectors: evecs = inv(U) * evecs - if(computeEigVecs) - cholB.matrixU().solveInPlace(Base::m_eivec); - } - else if(type==ABx_lx) - { - // compute C = L' A L - MatrixType matC = matA.template selfadjointView<Lower>(); - matC = matC * cholB.matrixL(); - matC = cholB.matrixU() * matC; - - Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly); - - // transform back the eigen vectors: evecs = inv(U) * evecs - if(computeEigVecs) - cholB.matrixU().solveInPlace(Base::m_eivec); - } - else if(type==BAx_lx) - { - // compute C = L' A L - MatrixType matC = matA.template selfadjointView<Lower>(); - matC = matC * cholB.matrixL(); - matC = cholB.matrixU() * matC; - - Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly); - - // transform back the eigen vectors: evecs = L * evecs - if(computeEigVecs) - Base::m_eivec = cholB.matrixL() * Base::m_eivec; - } - - return *this; -} - -} // end namespace Eigen - -#endif // EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/third_party/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h deleted file mode 100644 index 3db0c0106c..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/HessenbergDecomposition.h +++ /dev/null @@ -1,373 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_HESSENBERGDECOMPOSITION_H -#define EIGEN_HESSENBERGDECOMPOSITION_H - -namespace Eigen { - -namespace internal { - -template<typename MatrixType> struct HessenbergDecompositionMatrixHReturnType; -template<typename MatrixType> -struct traits<HessenbergDecompositionMatrixHReturnType<MatrixType> > -{ - typedef MatrixType ReturnType; -}; - -} - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class HessenbergDecomposition - * - * \brief Reduces a square matrix to Hessenberg form by an orthogonal similarity transformation - * - * \tparam _MatrixType the type of the matrix of which we are computing the Hessenberg decomposition - * - * This class performs an Hessenberg decomposition of a matrix \f$ A \f$. In - * the real case, the Hessenberg decomposition consists of an orthogonal - * matrix \f$ Q \f$ and a Hessenberg matrix \f$ H \f$ such that \f$ A = Q H - * Q^T \f$. An orthogonal matrix is a matrix whose inverse equals its - * transpose (\f$ Q^{-1} = Q^T \f$). A Hessenberg matrix has zeros below the - * subdiagonal, so it is almost upper triangular. The Hessenberg decomposition - * of a complex matrix is \f$ A = Q H Q^* \f$ with \f$ Q \f$ unitary (that is, - * \f$ Q^{-1} = Q^* \f$). - * - * Call the function compute() to compute the Hessenberg decomposition of a - * given matrix. Alternatively, you can use the - * HessenbergDecomposition(const MatrixType&) constructor which computes the - * Hessenberg decomposition at construction time. Once the decomposition is - * computed, you can use the matrixH() and matrixQ() functions to construct - * the matrices H and Q in the decomposition. - * - * The documentation for matrixH() contains an example of the typical use of - * this class. - * - * \sa class ComplexSchur, class Tridiagonalization, \ref QR_Module "QR Module" - */ -template<typename _MatrixType> class HessenbergDecomposition -{ - public: - - /** \brief Synonym for the template parameter \p _MatrixType. */ - typedef _MatrixType MatrixType; - - enum { - Size = MatrixType::RowsAtCompileTime, - SizeMinusOne = Size == Dynamic ? Dynamic : Size - 1, - Options = MatrixType::Options, - MaxSize = MatrixType::MaxRowsAtCompileTime, - MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : MaxSize - 1 - }; - - /** \brief Scalar type for matrices of type #MatrixType. */ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - - /** \brief Type for vector of Householder coefficients. - * - * This is column vector with entries of type #Scalar. The length of the - * vector is one less than the size of #MatrixType, if it is a fixed-side - * type. - */ - typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType; - - /** \brief Return type of matrixQ() */ - typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType; - - typedef internal::HessenbergDecompositionMatrixHReturnType<MatrixType> MatrixHReturnType; - - /** \brief Default constructor; the decomposition will be computed later. - * - * \param [in] size The size of the matrix whose Hessenberg decomposition will be computed. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via compute(). The \p size parameter is only - * used as a hint. It is not an error to give a wrong \p size, but it may - * impair performance. - * - * \sa compute() for an example. - */ - HessenbergDecomposition(Index size = Size==Dynamic ? 2 : Size) - : m_matrix(size,size), - m_temp(size), - m_isInitialized(false) - { - if(size>1) - m_hCoeffs.resize(size-1); - } - - /** \brief Constructor; computes Hessenberg decomposition of given matrix. - * - * \param[in] matrix Square matrix whose Hessenberg decomposition is to be computed. - * - * This constructor calls compute() to compute the Hessenberg - * decomposition. - * - * \sa matrixH() for an example. - */ - HessenbergDecomposition(const MatrixType& matrix) - : m_matrix(matrix), - m_temp(matrix.rows()), - m_isInitialized(false) - { - if(matrix.rows()<2) - { - m_isInitialized = true; - return; - } - m_hCoeffs.resize(matrix.rows()-1,1); - _compute(m_matrix, m_hCoeffs, m_temp); - m_isInitialized = true; - } - - /** \brief Computes Hessenberg decomposition of given matrix. - * - * \param[in] matrix Square matrix whose Hessenberg decomposition is to be computed. - * \returns Reference to \c *this - * - * The Hessenberg decomposition is computed by bringing the columns of the - * matrix successively in the required form using Householder reflections - * (see, e.g., Algorithm 7.4.2 in Golub \& Van Loan, <i>%Matrix - * Computations</i>). The cost is \f$ 10n^3/3 \f$ flops, where \f$ n \f$ - * denotes the size of the given matrix. - * - * This method reuses of the allocated data in the HessenbergDecomposition - * object. - * - * Example: \include HessenbergDecomposition_compute.cpp - * Output: \verbinclude HessenbergDecomposition_compute.out - */ - HessenbergDecomposition& compute(const MatrixType& matrix) - { - m_matrix = matrix; - if(matrix.rows()<2) - { - m_isInitialized = true; - return *this; - } - m_hCoeffs.resize(matrix.rows()-1,1); - _compute(m_matrix, m_hCoeffs, m_temp); - m_isInitialized = true; - return *this; - } - - /** \brief Returns the Householder coefficients. - * - * \returns a const reference to the vector of Householder coefficients - * - * \pre Either the constructor HessenbergDecomposition(const MatrixType&) - * or the member function compute(const MatrixType&) has been called - * before to compute the Hessenberg decomposition of a matrix. - * - * The Householder coefficients allow the reconstruction of the matrix - * \f$ Q \f$ in the Hessenberg decomposition from the packed data. - * - * \sa packedMatrix(), \ref Householder_Module "Householder module" - */ - const CoeffVectorType& householderCoefficients() const - { - eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized."); - return m_hCoeffs; - } - - /** \brief Returns the internal representation of the decomposition - * - * \returns a const reference to a matrix with the internal representation - * of the decomposition. - * - * \pre Either the constructor HessenbergDecomposition(const MatrixType&) - * or the member function compute(const MatrixType&) has been called - * before to compute the Hessenberg decomposition of a matrix. - * - * The returned matrix contains the following information: - * - the upper part and lower sub-diagonal represent the Hessenberg matrix H - * - the rest of the lower part contains the Householder vectors that, combined with - * Householder coefficients returned by householderCoefficients(), - * allows to reconstruct the matrix Q as - * \f$ Q = H_{N-1} \ldots H_1 H_0 \f$. - * Here, the matrices \f$ H_i \f$ are the Householder transformations - * \f$ H_i = (I - h_i v_i v_i^T) \f$ - * where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and - * \f$ v_i \f$ is the Householder vector defined by - * \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$ - * with M the matrix returned by this function. - * - * See LAPACK for further details on this packed storage. - * - * Example: \include HessenbergDecomposition_packedMatrix.cpp - * Output: \verbinclude HessenbergDecomposition_packedMatrix.out - * - * \sa householderCoefficients() - */ - const MatrixType& packedMatrix() const - { - eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized."); - return m_matrix; - } - - /** \brief Reconstructs the orthogonal matrix Q in the decomposition - * - * \returns object representing the matrix Q - * - * \pre Either the constructor HessenbergDecomposition(const MatrixType&) - * or the member function compute(const MatrixType&) has been called - * before to compute the Hessenberg decomposition of a matrix. - * - * This function returns a light-weight object of template class - * HouseholderSequence. You can either apply it directly to a matrix or - * you can convert it to a matrix of type #MatrixType. - * - * \sa matrixH() for an example, class HouseholderSequence - */ - HouseholderSequenceType matrixQ() const - { - eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized."); - return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate()) - .setLength(m_matrix.rows() - 1) - .setShift(1); - } - - /** \brief Constructs the Hessenberg matrix H in the decomposition - * - * \returns expression object representing the matrix H - * - * \pre Either the constructor HessenbergDecomposition(const MatrixType&) - * or the member function compute(const MatrixType&) has been called - * before to compute the Hessenberg decomposition of a matrix. - * - * The object returned by this function constructs the Hessenberg matrix H - * when it is assigned to a matrix or otherwise evaluated. The matrix H is - * constructed from the packed matrix as returned by packedMatrix(): The - * upper part (including the subdiagonal) of the packed matrix contains - * the matrix H. It may sometimes be better to directly use the packed - * matrix instead of constructing the matrix H. - * - * Example: \include HessenbergDecomposition_matrixH.cpp - * Output: \verbinclude HessenbergDecomposition_matrixH.out - * - * \sa matrixQ(), packedMatrix() - */ - MatrixHReturnType matrixH() const - { - eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized."); - return MatrixHReturnType(*this); - } - - private: - - typedef Matrix<Scalar, 1, Size, Options | RowMajor, 1, MaxSize> VectorType; - typedef typename NumTraits<Scalar>::Real RealScalar; - static void _compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp); - - protected: - MatrixType m_matrix; - CoeffVectorType m_hCoeffs; - VectorType m_temp; - bool m_isInitialized; -}; - -/** \internal - * Performs a tridiagonal decomposition of \a matA in place. - * - * \param matA the input selfadjoint matrix - * \param hCoeffs returned Householder coefficients - * - * The result is written in the lower triangular part of \a matA. - * - * Implemented from Golub's "%Matrix Computations", algorithm 8.3.1. - * - * \sa packedMatrix() - */ -template<typename MatrixType> -void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp) -{ - eigen_assert(matA.rows()==matA.cols()); - Index n = matA.rows(); - temp.resize(n); - for (Index i = 0; i<n-1; ++i) - { - // let's consider the vector v = i-th column starting at position i+1 - Index remainingSize = n-i-1; - RealScalar beta; - Scalar h; - matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta); - matA.col(i).coeffRef(i+1) = beta; - hCoeffs.coeffRef(i) = h; - - // Apply similarity transformation to remaining columns, - // i.e., compute A = H A H' - - // A = H A - matA.bottomRightCorner(remainingSize, remainingSize) - .applyHouseholderOnTheLeft(matA.col(i).tail(remainingSize-1), h, &temp.coeffRef(0)); - - // A = A H' - matA.rightCols(remainingSize) - .applyHouseholderOnTheRight(matA.col(i).tail(remainingSize-1).conjugate(), numext::conj(h), &temp.coeffRef(0)); - } -} - -namespace internal { - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \brief Expression type for return value of HessenbergDecomposition::matrixH() - * - * \tparam MatrixType type of matrix in the Hessenberg decomposition - * - * Objects of this type represent the Hessenberg matrix in the Hessenberg - * decomposition of some matrix. The object holds a reference to the - * HessenbergDecomposition class until the it is assigned or evaluated for - * some other reason (the reference should remain valid during the life time - * of this object). This class is the return type of - * HessenbergDecomposition::matrixH(); there is probably no other use for this - * class. - */ -template<typename MatrixType> struct HessenbergDecompositionMatrixHReturnType -: public ReturnByValue<HessenbergDecompositionMatrixHReturnType<MatrixType> > -{ - typedef typename MatrixType::Index Index; - public: - /** \brief Constructor. - * - * \param[in] hess Hessenberg decomposition - */ - HessenbergDecompositionMatrixHReturnType(const HessenbergDecomposition<MatrixType>& hess) : m_hess(hess) { } - - /** \brief Hessenberg matrix in decomposition. - * - * \param[out] result Hessenberg matrix in decomposition \p hess which - * was passed to the constructor - */ - template <typename ResultType> - inline void evalTo(ResultType& result) const - { - result = m_hess.packedMatrix(); - Index n = result.rows(); - if (n>2) - result.bottomLeftCorner(n-2, n-2).template triangularView<Lower>().setZero(); - } - - Index rows() const { return m_hess.packedMatrix().rows(); } - Index cols() const { return m_hess.packedMatrix().cols(); } - - protected: - const HessenbergDecomposition<MatrixType>& m_hess; -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_HESSENBERGDECOMPOSITION_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/third_party/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h deleted file mode 100644 index 4fec8af0a3..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h +++ /dev/null @@ -1,160 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MATRIXBASEEIGENVALUES_H -#define EIGEN_MATRIXBASEEIGENVALUES_H - -namespace Eigen { - -namespace internal { - -template<typename Derived, bool IsComplex> -struct eigenvalues_selector -{ - // this is the implementation for the case IsComplex = true - static inline typename MatrixBase<Derived>::EigenvaluesReturnType const - run(const MatrixBase<Derived>& m) - { - typedef typename Derived::PlainObject PlainObject; - PlainObject m_eval(m); - return ComplexEigenSolver<PlainObject>(m_eval, false).eigenvalues(); - } -}; - -template<typename Derived> -struct eigenvalues_selector<Derived, false> -{ - static inline typename MatrixBase<Derived>::EigenvaluesReturnType const - run(const MatrixBase<Derived>& m) - { - typedef typename Derived::PlainObject PlainObject; - PlainObject m_eval(m); - return EigenSolver<PlainObject>(m_eval, false).eigenvalues(); - } -}; - -} // end namespace internal - -/** \brief Computes the eigenvalues of a matrix - * \returns Column vector containing the eigenvalues. - * - * \eigenvalues_module - * This function computes the eigenvalues with the help of the EigenSolver - * class (for real matrices) or the ComplexEigenSolver class (for complex - * matrices). - * - * The eigenvalues are repeated according to their algebraic multiplicity, - * so there are as many eigenvalues as rows in the matrix. - * - * The SelfAdjointView class provides a better algorithm for selfadjoint - * matrices. - * - * Example: \include MatrixBase_eigenvalues.cpp - * Output: \verbinclude MatrixBase_eigenvalues.out - * - * \sa EigenSolver::eigenvalues(), ComplexEigenSolver::eigenvalues(), - * SelfAdjointView::eigenvalues() - */ -template<typename Derived> -inline typename MatrixBase<Derived>::EigenvaluesReturnType -MatrixBase<Derived>::eigenvalues() const -{ - typedef typename internal::traits<Derived>::Scalar Scalar; - return internal::eigenvalues_selector<Derived, NumTraits<Scalar>::IsComplex>::run(derived()); -} - -/** \brief Computes the eigenvalues of a matrix - * \returns Column vector containing the eigenvalues. - * - * \eigenvalues_module - * This function computes the eigenvalues with the help of the - * SelfAdjointEigenSolver class. The eigenvalues are repeated according to - * their algebraic multiplicity, so there are as many eigenvalues as rows in - * the matrix. - * - * Example: \include SelfAdjointView_eigenvalues.cpp - * Output: \verbinclude SelfAdjointView_eigenvalues.out - * - * \sa SelfAdjointEigenSolver::eigenvalues(), MatrixBase::eigenvalues() - */ -template<typename MatrixType, unsigned int UpLo> -inline typename SelfAdjointView<MatrixType, UpLo>::EigenvaluesReturnType -SelfAdjointView<MatrixType, UpLo>::eigenvalues() const -{ - typedef typename SelfAdjointView<MatrixType, UpLo>::PlainObject PlainObject; - PlainObject thisAsMatrix(*this); - return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues(); -} - - - -/** \brief Computes the L2 operator norm - * \returns Operator norm of the matrix. - * - * \eigenvalues_module - * This function computes the L2 operator norm of a matrix, which is also - * known as the spectral norm. The norm of a matrix \f$ A \f$ is defined to be - * \f[ \|A\|_2 = \max_x \frac{\|Ax\|_2}{\|x\|_2} \f] - * where the maximum is over all vectors and the norm on the right is the - * Euclidean vector norm. The norm equals the largest singular value, which is - * the square root of the largest eigenvalue of the positive semi-definite - * matrix \f$ A^*A \f$. - * - * The current implementation uses the eigenvalues of \f$ A^*A \f$, as computed - * by SelfAdjointView::eigenvalues(), to compute the operator norm of a - * matrix. The SelfAdjointView class provides a better algorithm for - * selfadjoint matrices. - * - * Example: \include MatrixBase_operatorNorm.cpp - * Output: \verbinclude MatrixBase_operatorNorm.out - * - * \sa SelfAdjointView::eigenvalues(), SelfAdjointView::operatorNorm() - */ -template<typename Derived> -inline typename MatrixBase<Derived>::RealScalar -MatrixBase<Derived>::operatorNorm() const -{ - using std::sqrt; - typename Derived::PlainObject m_eval(derived()); - // FIXME if it is really guaranteed that the eigenvalues are already sorted, - // then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough. - return sqrt((m_eval*m_eval.adjoint()) - .eval() - .template selfadjointView<Lower>() - .eigenvalues() - .maxCoeff() - ); -} - -/** \brief Computes the L2 operator norm - * \returns Operator norm of the matrix. - * - * \eigenvalues_module - * This function computes the L2 operator norm of a self-adjoint matrix. For a - * self-adjoint matrix, the operator norm is the largest eigenvalue. - * - * The current implementation uses the eigenvalues of the matrix, as computed - * by eigenvalues(), to compute the operator norm of the matrix. - * - * Example: \include SelfAdjointView_operatorNorm.cpp - * Output: \verbinclude SelfAdjointView_operatorNorm.out - * - * \sa eigenvalues(), MatrixBase::operatorNorm() - */ -template<typename MatrixType, unsigned int UpLo> -inline typename SelfAdjointView<MatrixType, UpLo>::RealScalar -SelfAdjointView<MatrixType, UpLo>::operatorNorm() const -{ - return eigenvalues().cwiseAbs().maxCoeff(); -} - -} // end namespace Eigen - -#endif diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/RealQZ.h b/third_party/eigen3/Eigen/src/Eigenvalues/RealQZ.h deleted file mode 100644 index 5706eeebe9..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/RealQZ.h +++ /dev/null @@ -1,624 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Alexey Korepanov <kaikaikai@yandex.ru> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_REAL_QZ_H -#define EIGEN_REAL_QZ_H - -namespace Eigen { - - /** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class RealQZ - * - * \brief Performs a real QZ decomposition of a pair of square matrices - * - * \tparam _MatrixType the type of the matrix of which we are computing the - * real QZ decomposition; this is expected to be an instantiation of the - * Matrix class template. - * - * Given a real square matrices A and B, this class computes the real QZ - * decomposition: \f$ A = Q S Z \f$, \f$ B = Q T Z \f$ where Q and Z are - * real orthogonal matrixes, T is upper-triangular matrix, and S is upper - * quasi-triangular matrix. An orthogonal matrix is a matrix whose - * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular - * matrix is a block-triangular matrix whose diagonal consists of 1-by-1 - * blocks and 2-by-2 blocks where further reduction is impossible due to - * complex eigenvalues. - * - * The eigenvalues of the pencil \f$ A - z B \f$ can be obtained from - * 1x1 and 2x2 blocks on the diagonals of S and T. - * - * Call the function compute() to compute the real QZ decomposition of a - * given pair of matrices. Alternatively, you can use the - * RealQZ(const MatrixType& B, const MatrixType& B, bool computeQZ) - * constructor which computes the real QZ decomposition at construction - * time. Once the decomposition is computed, you can use the matrixS(), - * matrixT(), matrixQ() and matrixZ() functions to retrieve the matrices - * S, T, Q and Z in the decomposition. If computeQZ==false, some time - * is saved by not computing matrices Q and Z. - * - * Example: \include RealQZ_compute.cpp - * Output: \include RealQZ_compute.out - * - * \note The implementation is based on the algorithm in "Matrix Computations" - * by Gene H. Golub and Charles F. Van Loan, and a paper "An algorithm for - * generalized eigenvalue problems" by C.B.Moler and G.W.Stewart. - * - * \sa class RealSchur, class ComplexSchur, class EigenSolver, class ComplexEigenSolver - */ - - template<typename _MatrixType> class RealQZ - { - public: - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar; - typedef typename MatrixType::Index Index; - - typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType; - typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType; - - /** \brief Default constructor. - * - * \param [in] size Positive integer, size of the matrix whose QZ decomposition will be computed. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via compute(). The \p size parameter is only - * used as a hint. It is not an error to give a wrong \p size, but it may - * impair performance. - * - * \sa compute() for an example. - */ - RealQZ(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime) : - m_S(size, size), - m_T(size, size), - m_Q(size, size), - m_Z(size, size), - m_workspace(size*2), - m_maxIters(400), - m_isInitialized(false) - { } - - /** \brief Constructor; computes real QZ decomposition of given matrices - * - * \param[in] A Matrix A. - * \param[in] B Matrix B. - * \param[in] computeQZ If false, A and Z are not computed. - * - * This constructor calls compute() to compute the QZ decomposition. - */ - RealQZ(const MatrixType& A, const MatrixType& B, bool computeQZ = true) : - m_S(A.rows(),A.cols()), - m_T(A.rows(),A.cols()), - m_Q(A.rows(),A.cols()), - m_Z(A.rows(),A.cols()), - m_workspace(A.rows()*2), - m_maxIters(400), - m_isInitialized(false) { - compute(A, B, computeQZ); - } - - /** \brief Returns matrix Q in the QZ decomposition. - * - * \returns A const reference to the matrix Q. - */ - const MatrixType& matrixQ() const { - eigen_assert(m_isInitialized && "RealQZ is not initialized."); - eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition."); - return m_Q; - } - - /** \brief Returns matrix Z in the QZ decomposition. - * - * \returns A const reference to the matrix Z. - */ - const MatrixType& matrixZ() const { - eigen_assert(m_isInitialized && "RealQZ is not initialized."); - eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition."); - return m_Z; - } - - /** \brief Returns matrix S in the QZ decomposition. - * - * \returns A const reference to the matrix S. - */ - const MatrixType& matrixS() const { - eigen_assert(m_isInitialized && "RealQZ is not initialized."); - return m_S; - } - - /** \brief Returns matrix S in the QZ decomposition. - * - * \returns A const reference to the matrix S. - */ - const MatrixType& matrixT() const { - eigen_assert(m_isInitialized && "RealQZ is not initialized."); - return m_T; - } - - /** \brief Computes QZ decomposition of given matrix. - * - * \param[in] A Matrix A. - * \param[in] B Matrix B. - * \param[in] computeQZ If false, A and Z are not computed. - * \returns Reference to \c *this - */ - RealQZ& compute(const MatrixType& A, const MatrixType& B, bool computeQZ = true); - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, \c NoConvergence otherwise. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "RealQZ is not initialized."); - return m_info; - } - - /** \brief Returns number of performed QR-like iterations. - */ - Index iterations() const - { - eigen_assert(m_isInitialized && "RealQZ is not initialized."); - return m_global_iter; - } - - /** Sets the maximal number of iterations allowed to converge to one eigenvalue - * or decouple the problem. - */ - RealQZ& setMaxIterations(Index maxIters) - { - m_maxIters = maxIters; - return *this; - } - - private: - - MatrixType m_S, m_T, m_Q, m_Z; - Matrix<Scalar,Dynamic,1> m_workspace; - ComputationInfo m_info; - Index m_maxIters; - bool m_isInitialized; - bool m_computeQZ; - Scalar m_normOfT, m_normOfS; - Index m_global_iter; - - typedef Matrix<Scalar,3,1> Vector3s; - typedef Matrix<Scalar,2,1> Vector2s; - typedef Matrix<Scalar,2,2> Matrix2s; - typedef JacobiRotation<Scalar> JRs; - - void hessenbergTriangular(); - void computeNorms(); - Index findSmallSubdiagEntry(Index iu); - Index findSmallDiagEntry(Index f, Index l); - void splitOffTwoRows(Index i); - void pushDownZero(Index z, Index f, Index l); - void step(Index f, Index l, Index iter); - - }; // RealQZ - - /** \internal Reduces S and T to upper Hessenberg - triangular form */ - template<typename MatrixType> - void RealQZ<MatrixType>::hessenbergTriangular() - { - - const Index dim = m_S.cols(); - - // perform QR decomposition of T, overwrite T with R, save Q - HouseholderQR<MatrixType> qrT(m_T); - m_T = qrT.matrixQR(); - m_T.template triangularView<StrictlyLower>().setZero(); - m_Q = qrT.householderQ(); - // overwrite S with Q* S - m_S.applyOnTheLeft(m_Q.adjoint()); - // init Z as Identity - if (m_computeQZ) - m_Z = MatrixType::Identity(dim,dim); - // reduce S to upper Hessenberg with Givens rotations - for (Index j=0; j<=dim-3; j++) { - for (Index i=dim-1; i>=j+2; i--) { - JRs G; - // kill S(i,j) - if(m_S.coeff(i,j) != 0) - { - G.makeGivens(m_S.coeff(i-1,j), m_S.coeff(i,j), &m_S.coeffRef(i-1, j)); - m_S.coeffRef(i,j) = Scalar(0.0); - m_S.rightCols(dim-j-1).applyOnTheLeft(i-1,i,G.adjoint()); - m_T.rightCols(dim-i+1).applyOnTheLeft(i-1,i,G.adjoint()); - } - // update Q - if (m_computeQZ) - m_Q.applyOnTheRight(i-1,i,G); - // kill T(i,i-1) - if(m_T.coeff(i,i-1)!=Scalar(0)) - { - G.makeGivens(m_T.coeff(i,i), m_T.coeff(i,i-1), &m_T.coeffRef(i,i)); - m_T.coeffRef(i,i-1) = Scalar(0.0); - m_S.applyOnTheRight(i,i-1,G); - m_T.topRows(i).applyOnTheRight(i,i-1,G); - } - // update Z - if (m_computeQZ) - m_Z.applyOnTheLeft(i,i-1,G.adjoint()); - } - } - } - - /** \internal Computes vector L1 norms of S and T when in Hessenberg-Triangular form already */ - template<typename MatrixType> - inline void RealQZ<MatrixType>::computeNorms() - { - const Index size = m_S.cols(); - m_normOfS = Scalar(0.0); - m_normOfT = Scalar(0.0); - for (Index j = 0; j < size; ++j) - { - m_normOfS += m_S.col(j).segment(0, (std::min)(size,j+2)).cwiseAbs().sum(); - m_normOfT += m_T.row(j).segment(j, size - j).cwiseAbs().sum(); - } - } - - - /** \internal Look for single small sub-diagonal element S(res, res-1) and return res (or 0) */ - template<typename MatrixType> - inline typename MatrixType::Index RealQZ<MatrixType>::findSmallSubdiagEntry(Index iu) - { - using std::abs; - Index res = iu; - while (res > 0) - { - Scalar s = abs(m_S.coeff(res-1,res-1)) + abs(m_S.coeff(res,res)); - if (s == Scalar(0.0)) - s = m_normOfS; - if (abs(m_S.coeff(res,res-1)) < NumTraits<Scalar>::epsilon() * s) - break; - res--; - } - return res; - } - - /** \internal Look for single small diagonal element T(res, res) for res between f and l, and return res (or f-1) */ - template<typename MatrixType> - inline typename MatrixType::Index RealQZ<MatrixType>::findSmallDiagEntry(Index f, Index l) - { - using std::abs; - Index res = l; - while (res >= f) { - if (abs(m_T.coeff(res,res)) <= NumTraits<Scalar>::epsilon() * m_normOfT) - break; - res--; - } - return res; - } - - /** \internal decouple 2x2 diagonal block in rows i, i+1 if eigenvalues are real */ - template<typename MatrixType> - inline void RealQZ<MatrixType>::splitOffTwoRows(Index i) - { - using std::abs; - using std::sqrt; - const Index dim=m_S.cols(); - if (abs(m_S.coeff(i+1,i)==Scalar(0))) - return; - Index z = findSmallDiagEntry(i,i+1); - if (z==i-1) - { - // block of (S T^{-1}) - Matrix2s STi = m_T.template block<2,2>(i,i).template triangularView<Upper>(). - template solve<OnTheRight>(m_S.template block<2,2>(i,i)); - Scalar p = Scalar(0.5)*(STi(0,0)-STi(1,1)); - Scalar q = p*p + STi(1,0)*STi(0,1); - if (q>=0) { - Scalar z = sqrt(q); - // one QR-like iteration for ABi - lambda I - // is enough - when we know exact eigenvalue in advance, - // convergence is immediate - JRs G; - if (p>=0) - G.makeGivens(p + z, STi(1,0)); - else - G.makeGivens(p - z, STi(1,0)); - m_S.rightCols(dim-i).applyOnTheLeft(i,i+1,G.adjoint()); - m_T.rightCols(dim-i).applyOnTheLeft(i,i+1,G.adjoint()); - // update Q - if (m_computeQZ) - m_Q.applyOnTheRight(i,i+1,G); - - G.makeGivens(m_T.coeff(i+1,i+1), m_T.coeff(i+1,i)); - m_S.topRows(i+2).applyOnTheRight(i+1,i,G); - m_T.topRows(i+2).applyOnTheRight(i+1,i,G); - // update Z - if (m_computeQZ) - m_Z.applyOnTheLeft(i+1,i,G.adjoint()); - - m_S.coeffRef(i+1,i) = Scalar(0.0); - m_T.coeffRef(i+1,i) = Scalar(0.0); - } - } - else - { - pushDownZero(z,i,i+1); - } - } - - /** \internal use zero in T(z,z) to zero S(l,l-1), working in block f..l */ - template<typename MatrixType> - inline void RealQZ<MatrixType>::pushDownZero(Index z, Index f, Index l) - { - JRs G; - const Index dim = m_S.cols(); - for (Index zz=z; zz<l; zz++) - { - // push 0 down - Index firstColS = zz>f ? (zz-1) : zz; - G.makeGivens(m_T.coeff(zz, zz+1), m_T.coeff(zz+1, zz+1)); - m_S.rightCols(dim-firstColS).applyOnTheLeft(zz,zz+1,G.adjoint()); - m_T.rightCols(dim-zz).applyOnTheLeft(zz,zz+1,G.adjoint()); - m_T.coeffRef(zz+1,zz+1) = Scalar(0.0); - // update Q - if (m_computeQZ) - m_Q.applyOnTheRight(zz,zz+1,G); - // kill S(zz+1, zz-1) - if (zz>f) - { - G.makeGivens(m_S.coeff(zz+1, zz), m_S.coeff(zz+1,zz-1)); - m_S.topRows(zz+2).applyOnTheRight(zz, zz-1,G); - m_T.topRows(zz+1).applyOnTheRight(zz, zz-1,G); - m_S.coeffRef(zz+1,zz-1) = Scalar(0.0); - // update Z - if (m_computeQZ) - m_Z.applyOnTheLeft(zz,zz-1,G.adjoint()); - } - } - // finally kill S(l,l-1) - G.makeGivens(m_S.coeff(l,l), m_S.coeff(l,l-1)); - m_S.applyOnTheRight(l,l-1,G); - m_T.applyOnTheRight(l,l-1,G); - m_S.coeffRef(l,l-1)=Scalar(0.0); - // update Z - if (m_computeQZ) - m_Z.applyOnTheLeft(l,l-1,G.adjoint()); - } - - /** \internal QR-like iterative step for block f..l */ - template<typename MatrixType> - inline void RealQZ<MatrixType>::step(Index f, Index l, Index iter) - { - using std::abs; - const Index dim = m_S.cols(); - - // x, y, z - Scalar x, y, z; - if (iter==10) - { - // Wilkinson ad hoc shift - const Scalar - a11=m_S.coeff(f+0,f+0), a12=m_S.coeff(f+0,f+1), - a21=m_S.coeff(f+1,f+0), a22=m_S.coeff(f+1,f+1), a32=m_S.coeff(f+2,f+1), - b12=m_T.coeff(f+0,f+1), - b11i=Scalar(1.0)/m_T.coeff(f+0,f+0), - b22i=Scalar(1.0)/m_T.coeff(f+1,f+1), - a87=m_S.coeff(l-1,l-2), - a98=m_S.coeff(l-0,l-1), - b77i=Scalar(1.0)/m_T.coeff(l-2,l-2), - b88i=Scalar(1.0)/m_T.coeff(l-1,l-1); - Scalar ss = abs(a87*b77i) + abs(a98*b88i), - lpl = Scalar(1.5)*ss, - ll = ss*ss; - x = ll + a11*a11*b11i*b11i - lpl*a11*b11i + a12*a21*b11i*b22i - - a11*a21*b12*b11i*b11i*b22i; - y = a11*a21*b11i*b11i - lpl*a21*b11i + a21*a22*b11i*b22i - - a21*a21*b12*b11i*b11i*b22i; - z = a21*a32*b11i*b22i; - } - else if (iter==16) - { - // another exceptional shift - x = m_S.coeff(f,f)/m_T.coeff(f,f)-m_S.coeff(l,l)/m_T.coeff(l,l) + m_S.coeff(l,l-1)*m_T.coeff(l-1,l) / - (m_T.coeff(l-1,l-1)*m_T.coeff(l,l)); - y = m_S.coeff(f+1,f)/m_T.coeff(f,f); - z = 0; - } - else if (iter>23 && !(iter%8)) - { - // extremely exceptional shift - x = internal::random<Scalar>(-1.0,1.0); - y = internal::random<Scalar>(-1.0,1.0); - z = internal::random<Scalar>(-1.0,1.0); - } - else - { - // Compute the shifts: (x,y,z,0...) = (AB^-1 - l1 I) (AB^-1 - l2 I) e1 - // where l1 and l2 are the eigenvalues of the 2x2 matrix C = U V^-1 where - // U and V are 2x2 bottom right sub matrices of A and B. Thus: - // = AB^-1AB^-1 + l1 l2 I - (l1+l2)(AB^-1) - // = AB^-1AB^-1 + det(M) - tr(M)(AB^-1) - // Since we are only interested in having x, y, z with a correct ratio, we have: - const Scalar - a11 = m_S.coeff(f,f), a12 = m_S.coeff(f,f+1), - a21 = m_S.coeff(f+1,f), a22 = m_S.coeff(f+1,f+1), - a32 = m_S.coeff(f+2,f+1), - - a88 = m_S.coeff(l-1,l-1), a89 = m_S.coeff(l-1,l), - a98 = m_S.coeff(l,l-1), a99 = m_S.coeff(l,l), - - b11 = m_T.coeff(f,f), b12 = m_T.coeff(f,f+1), - b22 = m_T.coeff(f+1,f+1), - - b88 = m_T.coeff(l-1,l-1), b89 = m_T.coeff(l-1,l), - b99 = m_T.coeff(l,l); - - x = ( (a88/b88 - a11/b11)*(a99/b99 - a11/b11) - (a89/b99)*(a98/b88) + (a98/b88)*(b89/b99)*(a11/b11) ) * (b11/a21) - + a12/b22 - (a11/b11)*(b12/b22); - y = (a22/b22-a11/b11) - (a21/b11)*(b12/b22) - (a88/b88-a11/b11) - (a99/b99-a11/b11) + (a98/b88)*(b89/b99); - z = a32/b22; - } - - JRs G; - - for (Index k=f; k<=l-2; k++) - { - // variables for Householder reflections - Vector2s essential2; - Scalar tau, beta; - - Vector3s hr(x,y,z); - - // Q_k to annihilate S(k+1,k-1) and S(k+2,k-1) - hr.makeHouseholderInPlace(tau, beta); - essential2 = hr.template bottomRows<2>(); - Index fc=(std::max)(k-1,Index(0)); // first col to update - m_S.template middleRows<3>(k).rightCols(dim-fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data()); - m_T.template middleRows<3>(k).rightCols(dim-fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data()); - if (m_computeQZ) - m_Q.template middleCols<3>(k).applyHouseholderOnTheRight(essential2, tau, m_workspace.data()); - if (k>f) - m_S.coeffRef(k+2,k-1) = m_S.coeffRef(k+1,k-1) = Scalar(0.0); - - // Z_{k1} to annihilate T(k+2,k+1) and T(k+2,k) - hr << m_T.coeff(k+2,k+2),m_T.coeff(k+2,k),m_T.coeff(k+2,k+1); - hr.makeHouseholderInPlace(tau, beta); - essential2 = hr.template bottomRows<2>(); - { - Index lr = (std::min)(k+4,dim); // last row to update - Map<Matrix<Scalar,Dynamic,1> > tmp(m_workspace.data(),lr); - // S - tmp = m_S.template middleCols<2>(k).topRows(lr) * essential2; - tmp += m_S.col(k+2).head(lr); - m_S.col(k+2).head(lr) -= tau*tmp; - m_S.template middleCols<2>(k).topRows(lr) -= (tau*tmp) * essential2.adjoint(); - // T - tmp = m_T.template middleCols<2>(k).topRows(lr) * essential2; - tmp += m_T.col(k+2).head(lr); - m_T.col(k+2).head(lr) -= tau*tmp; - m_T.template middleCols<2>(k).topRows(lr) -= (tau*tmp) * essential2.adjoint(); - } - if (m_computeQZ) - { - // Z - Map<Matrix<Scalar,1,Dynamic> > tmp(m_workspace.data(),dim); - tmp = essential2.adjoint()*(m_Z.template middleRows<2>(k)); - tmp += m_Z.row(k+2); - m_Z.row(k+2) -= tau*tmp; - m_Z.template middleRows<2>(k) -= essential2 * (tau*tmp); - } - m_T.coeffRef(k+2,k) = m_T.coeffRef(k+2,k+1) = Scalar(0.0); - - // Z_{k2} to annihilate T(k+1,k) - G.makeGivens(m_T.coeff(k+1,k+1), m_T.coeff(k+1,k)); - m_S.applyOnTheRight(k+1,k,G); - m_T.applyOnTheRight(k+1,k,G); - // update Z - if (m_computeQZ) - m_Z.applyOnTheLeft(k+1,k,G.adjoint()); - m_T.coeffRef(k+1,k) = Scalar(0.0); - - // update x,y,z - x = m_S.coeff(k+1,k); - y = m_S.coeff(k+2,k); - if (k < l-2) - z = m_S.coeff(k+3,k); - } // loop over k - - // Q_{n-1} to annihilate y = S(l,l-2) - G.makeGivens(x,y); - m_S.applyOnTheLeft(l-1,l,G.adjoint()); - m_T.applyOnTheLeft(l-1,l,G.adjoint()); - if (m_computeQZ) - m_Q.applyOnTheRight(l-1,l,G); - m_S.coeffRef(l,l-2) = Scalar(0.0); - - // Z_{n-1} to annihilate T(l,l-1) - G.makeGivens(m_T.coeff(l,l),m_T.coeff(l,l-1)); - m_S.applyOnTheRight(l,l-1,G); - m_T.applyOnTheRight(l,l-1,G); - if (m_computeQZ) - m_Z.applyOnTheLeft(l,l-1,G.adjoint()); - m_T.coeffRef(l,l-1) = Scalar(0.0); - } - - - template<typename MatrixType> - RealQZ<MatrixType>& RealQZ<MatrixType>::compute(const MatrixType& A_in, const MatrixType& B_in, bool computeQZ) - { - - const Index dim = A_in.cols(); - - eigen_assert (A_in.rows()==dim && A_in.cols()==dim - && B_in.rows()==dim && B_in.cols()==dim - && "Need square matrices of the same dimension"); - - m_isInitialized = true; - m_computeQZ = computeQZ; - m_S = A_in; m_T = B_in; - m_workspace.resize(dim*2); - m_global_iter = 0; - - // entrance point: hessenberg triangular decomposition - hessenbergTriangular(); - // compute L1 vector norms of T, S into m_normOfS, m_normOfT - computeNorms(); - - Index l = dim-1, - f, - local_iter = 0; - - while (l>0 && local_iter<m_maxIters) - { - f = findSmallSubdiagEntry(l); - // now rows and columns f..l (including) decouple from the rest of the problem - if (f>0) m_S.coeffRef(f,f-1) = Scalar(0.0); - if (f == l) // One root found - { - l--; - local_iter = 0; - } - else if (f == l-1) // Two roots found - { - splitOffTwoRows(f); - l -= 2; - local_iter = 0; - } - else // No convergence yet - { - // if there's zero on diagonal of T, we can isolate an eigenvalue with Givens rotations - Index z = findSmallDiagEntry(f,l); - if (z>=f) - { - // zero found - pushDownZero(z,f,l); - } - else - { - // We are sure now that S.block(f,f, l-f+1,l-f+1) is underuced upper-Hessenberg - // and T.block(f,f, l-f+1,l-f+1) is invertible uper-triangular, which allows to - // apply a QR-like iteration to rows and columns f..l. - step(f,l, local_iter); - local_iter++; - m_global_iter++; - } - } - } - // check if we converged before reaching iterations limit - m_info = (local_iter<m_maxIters) ? Success : NoConvergence; - return *this; - } // end compute - -} // end namespace Eigen - -#endif //EIGEN_REAL_QZ diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/RealSchur.h b/third_party/eigen3/Eigen/src/Eigenvalues/RealSchur.h deleted file mode 100644 index 64d1363414..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/RealSchur.h +++ /dev/null @@ -1,529 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_REAL_SCHUR_H -#define EIGEN_REAL_SCHUR_H - -#include "./HessenbergDecomposition.h" - -namespace Eigen { - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class RealSchur - * - * \brief Performs a real Schur decomposition of a square matrix - * - * \tparam _MatrixType the type of the matrix of which we are computing the - * real Schur decomposition; this is expected to be an instantiation of the - * Matrix class template. - * - * Given a real square matrix A, this class computes the real Schur - * decomposition: \f$ A = U T U^T \f$ where U is a real orthogonal matrix and - * T is a real quasi-triangular matrix. An orthogonal matrix is a matrix whose - * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular - * matrix is a block-triangular matrix whose diagonal consists of 1-by-1 - * blocks and 2-by-2 blocks with complex eigenvalues. The eigenvalues of the - * blocks on the diagonal of T are the same as the eigenvalues of the matrix - * A, and thus the real Schur decomposition is used in EigenSolver to compute - * the eigendecomposition of a matrix. - * - * Call the function compute() to compute the real Schur decomposition of a - * given matrix. Alternatively, you can use the RealSchur(const MatrixType&, bool) - * constructor which computes the real Schur decomposition at construction - * time. Once the decomposition is computed, you can use the matrixU() and - * matrixT() functions to retrieve the matrices U and T in the decomposition. - * - * The documentation of RealSchur(const MatrixType&, bool) contains an example - * of the typical use of this class. - * - * \note The implementation is adapted from - * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain). - * Their code is based on EISPACK. - * - * \sa class ComplexSchur, class EigenSolver, class ComplexEigenSolver - */ -template<typename _MatrixType> class RealSchur -{ - public: - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar; - typedef typename MatrixType::Index Index; - - typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType; - typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType; - - /** \brief Default constructor. - * - * \param [in] size Positive integer, size of the matrix whose Schur decomposition will be computed. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via compute(). The \p size parameter is only - * used as a hint. It is not an error to give a wrong \p size, but it may - * impair performance. - * - * \sa compute() for an example. - */ - RealSchur(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime) - : m_matT(size, size), - m_matU(size, size), - m_workspaceVector(size), - m_hess(size), - m_isInitialized(false), - m_matUisUptodate(false), - m_maxIters(-1) - { } - - /** \brief Constructor; computes real Schur decomposition of given matrix. - * - * \param[in] matrix Square matrix whose Schur decomposition is to be computed. - * \param[in] computeU If true, both T and U are computed; if false, only T is computed. - * - * This constructor calls compute() to compute the Schur decomposition. - * - * Example: \include RealSchur_RealSchur_MatrixType.cpp - * Output: \verbinclude RealSchur_RealSchur_MatrixType.out - */ - RealSchur(const MatrixType& matrix, bool computeU = true) - : m_matT(matrix.rows(),matrix.cols()), - m_matU(matrix.rows(),matrix.cols()), - m_workspaceVector(matrix.rows()), - m_hess(matrix.rows()), - m_isInitialized(false), - m_matUisUptodate(false), - m_maxIters(-1) - { - compute(matrix, computeU); - } - - /** \brief Returns the orthogonal matrix in the Schur decomposition. - * - * \returns A const reference to the matrix U. - * - * \pre Either the constructor RealSchur(const MatrixType&, bool) or the - * member function compute(const MatrixType&, bool) has been called before - * to compute the Schur decomposition of a matrix, and \p computeU was set - * to true (the default value). - * - * \sa RealSchur(const MatrixType&, bool) for an example - */ - const MatrixType& matrixU() const - { - eigen_assert(m_isInitialized && "RealSchur is not initialized."); - eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the RealSchur decomposition."); - return m_matU; - } - - /** \brief Returns the quasi-triangular matrix in the Schur decomposition. - * - * \returns A const reference to the matrix T. - * - * \pre Either the constructor RealSchur(const MatrixType&, bool) or the - * member function compute(const MatrixType&, bool) has been called before - * to compute the Schur decomposition of a matrix. - * - * \sa RealSchur(const MatrixType&, bool) for an example - */ - const MatrixType& matrixT() const - { - eigen_assert(m_isInitialized && "RealSchur is not initialized."); - return m_matT; - } - - /** \brief Computes Schur decomposition of given matrix. - * - * \param[in] matrix Square matrix whose Schur decomposition is to be computed. - * \param[in] computeU If true, both T and U are computed; if false, only T is computed. - * \returns Reference to \c *this - * - * The Schur decomposition is computed by first reducing the matrix to - * Hessenberg form using the class HessenbergDecomposition. The Hessenberg - * matrix is then reduced to triangular form by performing Francis QR - * iterations with implicit double shift. The cost of computing the Schur - * decomposition depends on the number of iterations; as a rough guide, it - * may be taken to be \f$25n^3\f$ flops if \a computeU is true and - * \f$10n^3\f$ flops if \a computeU is false. - * - * Example: \include RealSchur_compute.cpp - * Output: \verbinclude RealSchur_compute.out - * - * \sa compute(const MatrixType&, bool, Index) - */ - RealSchur& compute(const MatrixType& matrix, bool computeU = true); - - /** \brief Computes Schur decomposition of a Hessenberg matrix H = Z T Z^T - * \param[in] matrixH Matrix in Hessenberg form H - * \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T - * \param computeU Computes the matriX U of the Schur vectors - * \return Reference to \c *this - * - * This routine assumes that the matrix is already reduced in Hessenberg form matrixH - * using either the class HessenbergDecomposition or another mean. - * It computes the upper quasi-triangular matrix T of the Schur decomposition of H - * When computeU is true, this routine computes the matrix U such that - * A = U T U^T = (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix - * - * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix - * is not available, the user should give an identity matrix (Q.setIdentity()) - * - * \sa compute(const MatrixType&, bool) - */ - template<typename HessMatrixType, typename OrthMatrixType> - RealSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU); - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, \c NoConvergence otherwise. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "RealSchur is not initialized."); - return m_info; - } - - /** \brief Sets the maximum number of iterations allowed. - * - * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size - * of the matrix. - */ - RealSchur& setMaxIterations(Index maxIters) - { - m_maxIters = maxIters; - return *this; - } - - /** \brief Returns the maximum number of iterations. */ - Index getMaxIterations() - { - return m_maxIters; - } - - /** \brief Maximum number of iterations per row. - * - * If not otherwise specified, the maximum number of iterations is this number times the size of the - * matrix. It is currently set to 40. - */ - static const int m_maxIterationsPerRow = 40; - - private: - - MatrixType m_matT; - MatrixType m_matU; - ColumnVectorType m_workspaceVector; - HessenbergDecomposition<MatrixType> m_hess; - ComputationInfo m_info; - bool m_isInitialized; - bool m_matUisUptodate; - Index m_maxIters; - - typedef Matrix<Scalar,3,1> Vector3s; - - Scalar computeNormOfT(); - Index findSmallSubdiagEntry(Index iu, const Scalar& norm); - void splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift); - void computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo); - void initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector); - void performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector, Scalar* workspace); -}; - - -template<typename MatrixType> -RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const MatrixType& matrix, bool computeU) -{ - eigen_assert(matrix.cols() == matrix.rows()); - Index maxIters = m_maxIters; - if (maxIters == -1) - maxIters = m_maxIterationsPerRow * matrix.rows(); - - // Step 1. Reduce to Hessenberg form - m_hess.compute(matrix); - - // Step 2. Reduce to real Schur form - computeFromHessenberg(m_hess.matrixH(), m_hess.matrixQ(), computeU); - - return *this; -} -template<typename MatrixType> -template<typename HessMatrixType, typename OrthMatrixType> -RealSchur<MatrixType>& RealSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU) -{ - m_matT = matrixH; - if(computeU) - m_matU = matrixQ; - - Index maxIters = m_maxIters; - if (maxIters == -1) - maxIters = m_maxIterationsPerRow * matrixH.rows(); - m_workspaceVector.resize(m_matT.cols()); - Scalar* workspace = &m_workspaceVector.coeffRef(0); - - // The matrix m_matT is divided in three parts. - // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero. - // Rows il,...,iu is the part we are working on (the active window). - // Rows iu+1,...,end are already brought in triangular form. - Index iu = m_matT.cols() - 1; - Index iter = 0; // iteration count for current eigenvalue - Index totalIter = 0; // iteration count for whole matrix - Scalar exshift(0); // sum of exceptional shifts - Scalar norm = computeNormOfT(); - - if(norm!=0) - { - while (iu >= 0) - { - Index il = findSmallSubdiagEntry(iu, norm); - - // Check for convergence - if (il == iu) // One root found - { - m_matT.coeffRef(iu,iu) = m_matT.coeff(iu,iu) + exshift; - if (iu > 0) - m_matT.coeffRef(iu, iu-1) = Scalar(0); - iu--; - iter = 0; - } - else if (il == iu-1) // Two roots found - { - splitOffTwoRows(iu, computeU, exshift); - iu -= 2; - iter = 0; - } - else // No convergence yet - { - // The firstHouseholderVector vector has to be initialized to something to get rid of a silly GCC warning (-O1 -Wall -DNDEBUG ) - Vector3s firstHouseholderVector(0,0,0), shiftInfo; - computeShift(iu, iter, exshift, shiftInfo); - iter = iter + 1; - totalIter = totalIter + 1; - if (totalIter > maxIters) break; - Index im; - initFrancisQRStep(il, iu, shiftInfo, im, firstHouseholderVector); - performFrancisQRStep(il, im, iu, computeU, firstHouseholderVector, workspace); - } - } - } - if(totalIter <= maxIters) - m_info = Success; - else - m_info = NoConvergence; - - m_isInitialized = true; - m_matUisUptodate = computeU; - return *this; -} - -/** \internal Computes and returns vector L1 norm of T */ -template<typename MatrixType> -inline typename MatrixType::Scalar RealSchur<MatrixType>::computeNormOfT() -{ - const Index size = m_matT.cols(); - // FIXME to be efficient the following would requires a triangular reduxion code - // Scalar norm = m_matT.upper().cwiseAbs().sum() - // + m_matT.bottomLeftCorner(size-1,size-1).diagonal().cwiseAbs().sum(); - Scalar norm(0); - for (Index j = 0; j < size; ++j) - norm += m_matT.col(j).segment(0, (std::min)(size,j+2)).cwiseAbs().sum(); - return norm; -} - -/** \internal Look for single small sub-diagonal element and returns its index */ -template<typename MatrixType> -inline typename MatrixType::Index RealSchur<MatrixType>::findSmallSubdiagEntry(Index iu, const Scalar& norm) -{ - using std::abs; - Index res = iu; - while (res > 0) - { - Scalar s = abs(m_matT.coeff(res-1,res-1)) + abs(m_matT.coeff(res,res)); - if (s == 0.0) - s = norm; - if (abs(m_matT.coeff(res,res-1)) < NumTraits<Scalar>::epsilon() * s) - break; - res--; - } - return res; -} - -/** \internal Update T given that rows iu-1 and iu decouple from the rest. */ -template<typename MatrixType> -inline void RealSchur<MatrixType>::splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift) -{ - using std::sqrt; - using std::abs; - const Index size = m_matT.cols(); - - // The eigenvalues of the 2x2 matrix [a b; c d] are - // trace +/- sqrt(discr/4) where discr = tr^2 - 4*det, tr = a + d, det = ad - bc - Scalar p = Scalar(0.5) * (m_matT.coeff(iu-1,iu-1) - m_matT.coeff(iu,iu)); - Scalar q = p * p + m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu); // q = tr^2 / 4 - det = discr/4 - m_matT.coeffRef(iu,iu) += exshift; - m_matT.coeffRef(iu-1,iu-1) += exshift; - - if (q >= Scalar(0)) // Two real eigenvalues - { - Scalar z = sqrt(abs(q)); - JacobiRotation<Scalar> rot; - if (p >= Scalar(0)) - rot.makeGivens(p + z, m_matT.coeff(iu, iu-1)); - else - rot.makeGivens(p - z, m_matT.coeff(iu, iu-1)); - - m_matT.rightCols(size-iu+1).applyOnTheLeft(iu-1, iu, rot.adjoint()); - m_matT.topRows(iu+1).applyOnTheRight(iu-1, iu, rot); - m_matT.coeffRef(iu, iu-1) = Scalar(0); - if (computeU) - m_matU.applyOnTheRight(iu-1, iu, rot); - } - - if (iu > 1) - m_matT.coeffRef(iu-1, iu-2) = Scalar(0); -} - -/** \internal Form shift in shiftInfo, and update exshift if an exceptional shift is performed. */ -template<typename MatrixType> -inline void RealSchur<MatrixType>::computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo) -{ - using std::sqrt; - using std::abs; - shiftInfo.coeffRef(0) = m_matT.coeff(iu,iu); - shiftInfo.coeffRef(1) = m_matT.coeff(iu-1,iu-1); - shiftInfo.coeffRef(2) = m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu); - - // Wilkinson's original ad hoc shift - if (iter == 10) - { - exshift += shiftInfo.coeff(0); - for (Index i = 0; i <= iu; ++i) - m_matT.coeffRef(i,i) -= shiftInfo.coeff(0); - Scalar s = abs(m_matT.coeff(iu,iu-1)) + abs(m_matT.coeff(iu-1,iu-2)); - shiftInfo.coeffRef(0) = Scalar(0.75) * s; - shiftInfo.coeffRef(1) = Scalar(0.75) * s; - shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s; - } - - // MATLAB's new ad hoc shift - if (iter == 30) - { - Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0); - s = s * s + shiftInfo.coeff(2); - if (s > Scalar(0)) - { - s = sqrt(s); - if (shiftInfo.coeff(1) < shiftInfo.coeff(0)) - s = -s; - s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0); - s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s; - exshift += s; - for (Index i = 0; i <= iu; ++i) - m_matT.coeffRef(i,i) -= s; - shiftInfo.setConstant(Scalar(0.964)); - } - } -} - -/** \internal Compute index im at which Francis QR step starts and the first Householder vector. */ -template<typename MatrixType> -inline void RealSchur<MatrixType>::initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector) -{ - using std::abs; - Vector3s& v = firstHouseholderVector; // alias to save typing - - for (im = iu-2; im >= il; --im) - { - const Scalar Tmm = m_matT.coeff(im,im); - const Scalar r = shiftInfo.coeff(0) - Tmm; - const Scalar s = shiftInfo.coeff(1) - Tmm; - v.coeffRef(0) = (r * s - shiftInfo.coeff(2)) / m_matT.coeff(im+1,im) + m_matT.coeff(im,im+1); - v.coeffRef(1) = m_matT.coeff(im+1,im+1) - Tmm - r - s; - v.coeffRef(2) = m_matT.coeff(im+2,im+1); - if (im == il) { - break; - } - const Scalar lhs = m_matT.coeff(im,im-1) * (abs(v.coeff(1)) + abs(v.coeff(2))); - const Scalar rhs = v.coeff(0) * (abs(m_matT.coeff(im-1,im-1)) + abs(Tmm) + abs(m_matT.coeff(im+1,im+1))); - if (abs(lhs) < NumTraits<Scalar>::epsilon() * rhs) - { - break; - } - } -} - -/** \internal Perform a Francis QR step involving rows il:iu and columns im:iu. */ -template<typename MatrixType> -inline void RealSchur<MatrixType>::performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector, Scalar* workspace) -{ - eigen_assert(im >= il); - eigen_assert(im <= iu-2); - - const Index size = m_matT.cols(); - - for (Index k = im; k <= iu-2; ++k) - { - bool firstIteration = (k == im); - - Vector3s v; - if (firstIteration) - v = firstHouseholderVector; - else - v = m_matT.template block<3,1>(k,k-1); - - Scalar tau, beta; - Matrix<Scalar, 2, 1> ess; - v.makeHouseholder(ess, tau, beta); - - if (beta != Scalar(0)) // if v is not zero - { - if (firstIteration && k > il) - m_matT.coeffRef(k,k-1) = -m_matT.coeff(k,k-1); - else if (!firstIteration) - m_matT.coeffRef(k,k-1) = beta; - - // These Householder transformations form the O(n^3) part of the algorithm - m_matT.block(k, k, 3, size-k).applyHouseholderOnTheLeft(ess, tau, workspace); - m_matT.block(0, k, (std::min)(iu,k+3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace); - if (computeU) - m_matU.block(0, k, size, 3).applyHouseholderOnTheRight(ess, tau, workspace); - } - } - - Matrix<Scalar, 2, 1> v = m_matT.template block<2,1>(iu-1, iu-2); - Scalar tau, beta; - Matrix<Scalar, 1, 1> ess; - v.makeHouseholder(ess, tau, beta); - - if (beta != Scalar(0)) // if v is not zero - { - m_matT.coeffRef(iu-1, iu-2) = beta; - m_matT.block(iu-1, iu-1, 2, size-iu+1).applyHouseholderOnTheLeft(ess, tau, workspace); - m_matT.block(0, iu-1, iu+1, 2).applyHouseholderOnTheRight(ess, tau, workspace); - if (computeU) - m_matU.block(0, iu-1, size, 2).applyHouseholderOnTheRight(ess, tau, workspace); - } - - // clean up pollution due to round-off errors - for (Index i = im+2; i <= iu; ++i) - { - m_matT.coeffRef(i,i-2) = Scalar(0); - if (i > im+2) - m_matT.coeffRef(i,i-3) = Scalar(0); - } -} - -} // end namespace Eigen - -#endif // EIGEN_REAL_SCHUR_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/RealSchur_MKL.h b/third_party/eigen3/Eigen/src/Eigenvalues/RealSchur_MKL.h deleted file mode 100644 index ad97364602..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/RealSchur_MKL.h +++ /dev/null @@ -1,83 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Real Schur needed to real unsymmetrical eigenvalues/eigenvectors. - ******************************************************************************** -*/ - -#ifndef EIGEN_REAL_SCHUR_MKL_H -#define EIGEN_REAL_SCHUR_MKL_H - -#include "Eigen/src/Core/util/MKL_support.h" - -namespace Eigen { - -/** \internal Specialization for the data types supported by MKL */ - -#define EIGEN_MKL_SCHUR_REAL(EIGTYPE, MKLTYPE, MKLPREFIX, MKLPREFIX_U, EIGCOLROW, MKLCOLROW) \ -template<> inline \ -RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \ -RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, bool computeU) \ -{ \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType; \ - typedef MatrixType::Scalar Scalar; \ - typedef MatrixType::RealScalar RealScalar; \ -\ - eigen_assert(matrix.cols() == matrix.rows()); \ -\ - lapack_int n = matrix.cols(), sdim, info; \ - lapack_int lda = matrix.outerStride(); \ - lapack_int matrix_order = MKLCOLROW; \ - char jobvs, sort='N'; \ - LAPACK_##MKLPREFIX_U##_SELECT2 select = 0; \ - jobvs = (computeU) ? 'V' : 'N'; \ - m_matU.resize(n, n); \ - lapack_int ldvs = m_matU.outerStride(); \ - m_matT = matrix; \ - Matrix<EIGTYPE, Dynamic, Dynamic> wr, wi; \ - wr.resize(n, 1); wi.resize(n, 1); \ - info = LAPACKE_##MKLPREFIX##gees( matrix_order, jobvs, sort, select, n, (MKLTYPE*)m_matT.data(), lda, &sdim, (MKLTYPE*)wr.data(), (MKLTYPE*)wi.data(), (MKLTYPE*)m_matU.data(), ldvs ); \ - if(info == 0) \ - m_info = Success; \ - else \ - m_info = NoConvergence; \ -\ - m_isInitialized = true; \ - m_matUisUptodate = computeU; \ - return *this; \ -\ -} - -EIGEN_MKL_SCHUR_REAL(double, double, d, D, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_SCHUR_REAL(float, float, s, S, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_SCHUR_REAL(double, double, d, D, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_SCHUR_REAL(float, float, s, S, RowMajor, LAPACK_ROW_MAJOR) - -} // end namespace Eigen - -#endif // EIGEN_REAL_SCHUR_MKL_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/third_party/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h deleted file mode 100644 index d97d905273..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h +++ /dev/null @@ -1,884 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SELFADJOINTEIGENSOLVER_H -#define EIGEN_SELFADJOINTEIGENSOLVER_H - -#include "./Tridiagonalization.h" - -namespace Eigen { - -template<typename _MatrixType> -class GeneralizedSelfAdjointEigenSolver; - -namespace internal { -template<typename SolverType,int Size,bool IsComplex> struct direct_selfadjoint_eigenvalues; -template<typename MatrixType, typename DiagType, typename SubDiagType> -ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag, const typename MatrixType::Index maxIterations, bool computeEigenvectors, MatrixType& eivec); -} - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class SelfAdjointEigenSolver - * - * \brief Computes eigenvalues and eigenvectors of selfadjoint matrices - * - * \tparam _MatrixType the type of the matrix of which we are computing the - * eigendecomposition; this is expected to be an instantiation of the Matrix - * class template. - * - * A matrix \f$ A \f$ is selfadjoint if it equals its adjoint. For real - * matrices, this means that the matrix is symmetric: it equals its - * transpose. This class computes the eigenvalues and eigenvectors of a - * selfadjoint matrix. These are the scalars \f$ \lambda \f$ and vectors - * \f$ v \f$ such that \f$ Av = \lambda v \f$. The eigenvalues of a - * selfadjoint matrix are always real. If \f$ D \f$ is a diagonal matrix with - * the eigenvalues on the diagonal, and \f$ V \f$ is a matrix with the - * eigenvectors as its columns, then \f$ A = V D V^{-1} \f$ (for selfadjoint - * matrices, the matrix \f$ V \f$ is always invertible). This is called the - * eigendecomposition. - * - * The algorithm exploits the fact that the matrix is selfadjoint, making it - * faster and more accurate than the general purpose eigenvalue algorithms - * implemented in EigenSolver and ComplexEigenSolver. - * - * Only the \b lower \b triangular \b part of the input matrix is referenced. - * - * Call the function compute() to compute the eigenvalues and eigenvectors of - * a given matrix. Alternatively, you can use the - * SelfAdjointEigenSolver(const MatrixType&, int) constructor which computes - * the eigenvalues and eigenvectors at construction time. Once the eigenvalue - * and eigenvectors are computed, they can be retrieved with the eigenvalues() - * and eigenvectors() functions. - * - * The documentation for SelfAdjointEigenSolver(const MatrixType&, int) - * contains an example of the typical use of this class. - * - * To solve the \em generalized eigenvalue problem \f$ Av = \lambda Bv \f$ and - * the likes, see the class GeneralizedSelfAdjointEigenSolver. - * - * \sa MatrixBase::eigenvalues(), class EigenSolver, class ComplexEigenSolver - */ -template<typename _MatrixType> class SelfAdjointEigenSolver -{ - public: - - typedef _MatrixType MatrixType; - enum { - Size = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - - /** \brief Scalar type for matrices of type \p _MatrixType. */ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - - /** \brief Real scalar type for \p _MatrixType. - * - * This is just \c Scalar if #Scalar is real (e.g., \c float or - * \c double), and the type of the real part of \c Scalar if #Scalar is - * complex. - */ - typedef typename NumTraits<Scalar>::Real RealScalar; - - friend struct internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver,Size,NumTraits<Scalar>::IsComplex>; - - /** \brief Type for vector of eigenvalues as returned by eigenvalues(). - * - * This is a column vector with entries of type #RealScalar. - * The length of the vector is the size of \p _MatrixType. - */ - typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType; - typedef Tridiagonalization<MatrixType> TridiagonalizationType; - typedef typename TridiagonalizationType::SubDiagonalType SubDiagonalType; - - /** \brief Default constructor for fixed-size matrices. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via compute(). This constructor - * can only be used if \p _MatrixType is a fixed-size matrix; use - * SelfAdjointEigenSolver(Index) for dynamic-size matrices. - * - * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver.cpp - * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver.out - */ - EIGEN_DEVICE_FUNC - SelfAdjointEigenSolver() - : m_eivec(), - m_eivalues(), - m_subdiag(), - m_isInitialized(false) - { } - - /** \brief Constructor, pre-allocates memory for dynamic-size matrices. - * - * \param [in] size Positive integer, size of the matrix whose - * eigenvalues and eigenvectors will be computed. - * - * This constructor is useful for dynamic-size matrices, when the user - * intends to perform decompositions via compute(). The \p size - * parameter is only used as a hint. It is not an error to give a wrong - * \p size, but it may impair performance. - * - * \sa compute() for an example - */ - EIGEN_DEVICE_FUNC - SelfAdjointEigenSolver(Index size) - : m_eivec(size, size), - m_eivalues(size), - m_subdiag(size > 1 ? size - 1 : 1), - m_isInitialized(false) - {} - - /** \brief Constructor; computes eigendecomposition of given matrix. - * - * \param[in] matrix Selfadjoint matrix whose eigendecomposition is to - * be computed. Only the lower triangular part of the matrix is referenced. - * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly. - * - * This constructor calls compute(const MatrixType&, int) to compute the - * eigenvalues of the matrix \p matrix. The eigenvectors are computed if - * \p options equals #ComputeEigenvectors. - * - * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.cpp - * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.out - * - * \sa compute(const MatrixType&, int) - */ - EIGEN_DEVICE_FUNC - SelfAdjointEigenSolver(const MatrixType& matrix, int options = ComputeEigenvectors) - : m_eivec(matrix.rows(), matrix.cols()), - m_eivalues(matrix.cols()), - m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1), - m_isInitialized(false) - { - compute(matrix, options); - } - - /** \brief Computes eigendecomposition of given matrix. - * - * \param[in] matrix Selfadjoint matrix whose eigendecomposition is to - * be computed. Only the lower triangular part of the matrix is referenced. - * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly. - * \returns Reference to \c *this - * - * This function computes the eigenvalues of \p matrix. The eigenvalues() - * function can be used to retrieve them. If \p options equals #ComputeEigenvectors, - * then the eigenvectors are also computed and can be retrieved by - * calling eigenvectors(). - * - * This implementation uses a symmetric QR algorithm. The matrix is first - * reduced to tridiagonal form using the Tridiagonalization class. The - * tridiagonal matrix is then brought to diagonal form with implicit - * symmetric QR steps with Wilkinson shift. Details can be found in - * Section 8.3 of Golub \& Van Loan, <i>%Matrix Computations</i>. - * - * The cost of the computation is about \f$ 9n^3 \f$ if the eigenvectors - * are required and \f$ 4n^3/3 \f$ if they are not required. - * - * This method reuses the memory in the SelfAdjointEigenSolver object that - * was allocated when the object was constructed, if the size of the - * matrix does not change. - * - * Example: \include SelfAdjointEigenSolver_compute_MatrixType.cpp - * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType.out - * - * \sa SelfAdjointEigenSolver(const MatrixType&, int) - */ - EIGEN_DEVICE_FUNC - SelfAdjointEigenSolver& compute(const MatrixType& matrix, int options = ComputeEigenvectors); - - /** \brief Computes eigendecomposition of given matrix using a direct algorithm - * - * This is a variant of compute(const MatrixType&, int options) which - * directly solves the underlying polynomial equation. - * - * Currently only 3x3 matrices for which the sizes are known at compile time are supported (e.g., Matrix3d). - * - * This method is usually significantly faster than the QR algorithm - * but it might also be less accurate. It is also worth noting that - * for 3x3 matrices it involves trigonometric operations which are - * not necessarily available for all scalar types. - * - * \sa compute(const MatrixType&, int options) - */ - EIGEN_DEVICE_FUNC - SelfAdjointEigenSolver& computeDirect(const MatrixType& matrix, int options = ComputeEigenvectors); - - /** - *\brief Computes the eigen decomposition from a tridiagonal symmetric matrix - * - * \param[in] diag The vector containing the diagonal of the matrix. - * \param[in] subdiag The subdiagonal of the matrix. - * \returns Reference to \c *this - * - * This function assumes that the matrix has been reduced to tridiagonal form. - * - * \sa compute(const MatrixType&, int) for more information - */ - SelfAdjointEigenSolver& computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag , int options=ComputeEigenvectors); - - /** \brief Returns the eigenvectors of given matrix. - * - * \returns A const reference to the matrix whose columns are the eigenvectors. - * - * \pre The eigenvectors have been computed before. - * - * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding - * to eigenvalue number \f$ k \f$ as returned by eigenvalues(). The - * eigenvectors are normalized to have (Euclidean) norm equal to one. If - * this object was used to solve the eigenproblem for the selfadjoint - * matrix \f$ A \f$, then the matrix returned by this function is the - * matrix \f$ V \f$ in the eigendecomposition \f$ A = V D V^{-1} \f$. - * - * Example: \include SelfAdjointEigenSolver_eigenvectors.cpp - * Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out - * - * \sa eigenvalues() - */ - EIGEN_DEVICE_FUNC - const MatrixType& eigenvectors() const - { - eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized."); - eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues."); - return m_eivec; - } - - /** \brief Returns the eigenvalues of given matrix. - * - * \returns A const reference to the column vector containing the eigenvalues. - * - * \pre The eigenvalues have been computed before. - * - * The eigenvalues are repeated according to their algebraic multiplicity, - * so there are as many eigenvalues as rows in the matrix. The eigenvalues - * are sorted in increasing order. - * - * Example: \include SelfAdjointEigenSolver_eigenvalues.cpp - * Output: \verbinclude SelfAdjointEigenSolver_eigenvalues.out - * - * \sa eigenvectors(), MatrixBase::eigenvalues() - */ - EIGEN_DEVICE_FUNC - const RealVectorType& eigenvalues() const - { - eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized."); - return m_eivalues; - } - - /** \brief Computes the positive-definite square root of the matrix. - * - * \returns the positive-definite square root of the matrix - * - * \pre The eigenvalues and eigenvectors of a positive-definite matrix - * have been computed before. - * - * The square root of a positive-definite matrix \f$ A \f$ is the - * positive-definite matrix whose square equals \f$ A \f$. This function - * uses the eigendecomposition \f$ A = V D V^{-1} \f$ to compute the - * square root as \f$ A^{1/2} = V D^{1/2} V^{-1} \f$. - * - * Example: \include SelfAdjointEigenSolver_operatorSqrt.cpp - * Output: \verbinclude SelfAdjointEigenSolver_operatorSqrt.out - * - * \sa operatorInverseSqrt(), - * \ref MatrixFunctions_Module "MatrixFunctions Module" - */ - EIGEN_DEVICE_FUNC - MatrixType operatorSqrt() const - { - eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized."); - eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues."); - return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint(); - } - - /** \brief Computes the inverse square root of the matrix. - * - * \returns the inverse positive-definite square root of the matrix - * - * \pre The eigenvalues and eigenvectors of a positive-definite matrix - * have been computed before. - * - * This function uses the eigendecomposition \f$ A = V D V^{-1} \f$ to - * compute the inverse square root as \f$ V D^{-1/2} V^{-1} \f$. This is - * cheaper than first computing the square root with operatorSqrt() and - * then its inverse with MatrixBase::inverse(). - * - * Example: \include SelfAdjointEigenSolver_operatorInverseSqrt.cpp - * Output: \verbinclude SelfAdjointEigenSolver_operatorInverseSqrt.out - * - * \sa operatorSqrt(), MatrixBase::inverse(), - * \ref MatrixFunctions_Module "MatrixFunctions Module" - */ - EIGEN_DEVICE_FUNC - MatrixType operatorInverseSqrt() const - { - eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized."); - eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues."); - return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint(); - } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, \c NoConvergence otherwise. - */ - EIGEN_DEVICE_FUNC - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized."); - return m_info; - } - - /** \brief Maximum number of iterations. - * - * The algorithm terminates if it does not converge within m_maxIterations * n iterations, where n - * denotes the size of the matrix. This value is currently set to 30 (copied from LAPACK). - */ - static const int m_maxIterations = 30; - - #ifdef EIGEN2_SUPPORT - EIGEN_DEVICE_FUNC - SelfAdjointEigenSolver(const MatrixType& matrix, bool computeEigenvectors) - : m_eivec(matrix.rows(), matrix.cols()), - m_eivalues(matrix.cols()), - m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1), - m_isInitialized(false) - { - compute(matrix, computeEigenvectors); - } - - EIGEN_DEVICE_FUNC - SelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true) - : m_eivec(matA.cols(), matA.cols()), - m_eivalues(matA.cols()), - m_subdiag(matA.cols() > 1 ? matA.cols() - 1 : 1), - m_isInitialized(false) - { - static_cast<GeneralizedSelfAdjointEigenSolver<MatrixType>*>(this)->compute(matA, matB, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly); - } - - EIGEN_DEVICE_FUNC - void compute(const MatrixType& matrix, bool computeEigenvectors) - { - compute(matrix, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly); - } - - EIGEN_DEVICE_FUNC - void compute(const MatrixType& matA, const MatrixType& matB, bool computeEigenvectors = true) - { - compute(matA, matB, computeEigenvectors ? ComputeEigenvectors : EigenvaluesOnly); - } - #endif // EIGEN2_SUPPORT - - protected: - MatrixType m_eivec; - RealVectorType m_eivalues; - typename TridiagonalizationType::SubDiagonalType m_subdiag; - ComputationInfo m_info; - bool m_isInitialized; - bool m_eigenvectorsOk; -}; - -/** \internal - * - * \eigenvalues_module \ingroup Eigenvalues_Module - * - * Performs a QR step on a tridiagonal symmetric matrix represented as a - * pair of two vectors \a diag and \a subdiag. - * - * \param matA the input selfadjoint matrix - * \param hCoeffs returned Householder coefficients - * - * For compilation efficiency reasons, this procedure does not use eigen expression - * for its arguments. - * - * Implemented from Golub's "Matrix Computations", algorithm 8.3.2: - * "implicit symmetric QR step with Wilkinson shift" - */ -namespace internal { -template<int StorageOrder,typename RealScalar, typename Scalar, typename Index> -EIGEN_DEVICE_FUNC -static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end, Scalar* matrixQ, Index n); -} - -template<typename MatrixType> -EIGEN_DEVICE_FUNC -SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType> -::compute(const MatrixType& matrix, int options) -{ - using std::abs; - eigen_assert(matrix.cols() == matrix.rows()); - eigen_assert((options&~(EigVecMask|GenEigMask))==0 - && (options&EigVecMask)!=EigVecMask - && "invalid option parameter"); - bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; - Index n = matrix.cols(); - m_eivalues.resize(n,1); - - if(n==1) - { - m_eivalues.coeffRef(0,0) = numext::real(matrix.coeff(0,0)); - if(computeEigenvectors) - m_eivec.setOnes(n,n); - m_info = Success; - m_isInitialized = true; - m_eigenvectorsOk = computeEigenvectors; - return *this; - } - - // declare some aliases - RealVectorType& diag = m_eivalues; - MatrixType& mat = m_eivec; - - // map the matrix coefficients to [-1:1] to avoid over- and underflow. - mat = matrix.template triangularView<Lower>(); - RealScalar scale = mat.cwiseAbs().maxCoeff(); - if(scale==RealScalar(0)) scale = RealScalar(1); - mat.template triangularView<Lower>() /= scale; - m_subdiag.resize(n-1); - internal::tridiagonalization_inplace(mat, diag, m_subdiag, computeEigenvectors); - - m_info = internal::computeFromTridiagonal_impl(diag, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec); - - // scale back the eigen values - m_eivalues *= scale; - - m_isInitialized = true; - m_eigenvectorsOk = computeEigenvectors; - return *this; -} - -template<typename MatrixType> -SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType> -::computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag , int options) -{ - //TODO : Add an option to scale the values beforehand - bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; - - m_eivalues = diag; - m_subdiag = subdiag; - if (computeEigenvectors) - { - m_eivec.setIdentity(diag.size(), diag.size()); - } - m_info = computeFromTridiagonal_impl(m_eivalues, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec); - - m_isInitialized = true; - m_eigenvectorsOk = computeEigenvectors; - return *this; -} - -namespace internal { -/** - * \internal - * \brief Compute the eigendecomposition from a tridiagonal matrix - * - * \param[in,out] diag : On input, the diagonal of the matrix, on output the eigenvalues - * \param[in] subdiag : The subdiagonal part of the matrix. - * \param[in,out] : On input, the maximum number of iterations, on output, the effective number of iterations. - * \param[out] eivec : The matrix to store the eigenvectors... if needed. allocated on input - * \returns \c Success or \c NoConvergence - */ -template<typename MatrixType, typename DiagType, typename SubDiagType> -ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag, const typename MatrixType::Index maxIterations, bool computeEigenvectors, MatrixType& eivec) -{ - using std::abs; - - ComputationInfo info; - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - - Index n = diag.size(); - Index end = n-1; - Index start = 0; - Index iter = 0; // total number of iterations - - while (end>0) - { - for (Index i = start; i<end; ++i) - if (internal::isMuchSmallerThan(abs(subdiag[i]),(abs(diag[i])+abs(diag[i+1])))) - subdiag[i] = 0; - - // find the largest unreduced block - while (end>0 && subdiag[end-1]==0) - { - end--; - } - if (end<=0) - break; - - // if we spent too many iterations, we give up - iter++; - if(iter > maxIterations * n) break; - - start = end - 1; - while (start>0 && subdiag[start-1]!=0) - start--; - - internal::tridiagonal_qr_step<MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor>(diag.data(), subdiag.data(), start, end, computeEigenvectors ? eivec.data() : (Scalar*)0, n); - } - if (iter <= maxIterations * n) - info = Success; - else - info = NoConvergence; - - // Sort eigenvalues and corresponding vectors. - // TODO make the sort optional ? - // TODO use a better sort algorithm !! - if (info == Success) - { - for (Index i = 0; i < n-1; ++i) - { - Index k; - diag.segment(i,n-i).minCoeff(&k); - if (k > 0) - { - std::swap(diag[i], diag[k+i]); - if(computeEigenvectors) - eivec.col(i).swap(eivec.col(k+i)); - } - } - } - return info; -} - -template<typename SolverType,int Size,bool IsComplex> struct direct_selfadjoint_eigenvalues -{ - EIGEN_DEVICE_FUNC - static inline void run(SolverType& eig, const typename SolverType::MatrixType& A, int options) - { eig.compute(A,options); } -}; - -template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3,false> -{ - typedef typename SolverType::MatrixType MatrixType; - typedef typename SolverType::RealVectorType VectorType; - typedef typename SolverType::Scalar Scalar; - - EIGEN_DEVICE_FUNC - static inline void computeRoots(const MatrixType& m, VectorType& roots) - { - EIGEN_USING_STD_MATH(sqrt) - EIGEN_USING_STD_MATH(atan2) - EIGEN_USING_STD_MATH(cos) - EIGEN_USING_STD_MATH(sin) - const Scalar s_inv3 = Scalar(1.0)/Scalar(3.0); - const Scalar s_sqrt3 = sqrt(Scalar(3.0)); - - // The characteristic equation is x^3 - c2*x^2 + c1*x - c0 = 0. The - // eigenvalues are the roots to this equation, all guaranteed to be - // real-valued, because the matrix is symmetric. - Scalar c0 = m(0,0)*m(1,1)*m(2,2) + Scalar(2)*m(1,0)*m(2,0)*m(2,1) - m(0,0)*m(2,1)*m(2,1) - m(1,1)*m(2,0)*m(2,0) - m(2,2)*m(1,0)*m(1,0); - Scalar c1 = m(0,0)*m(1,1) - m(1,0)*m(1,0) + m(0,0)*m(2,2) - m(2,0)*m(2,0) + m(1,1)*m(2,2) - m(2,1)*m(2,1); - Scalar c2 = m(0,0) + m(1,1) + m(2,2); - - // Construct the parameters used in classifying the roots of the equation - // and in solving the equation for the roots in closed form. - Scalar c2_over_3 = c2*s_inv3; - Scalar a_over_3 = (c1 - c2*c2_over_3)*s_inv3; - if (a_over_3 > Scalar(0)) - a_over_3 = Scalar(0); - - Scalar half_b = Scalar(0.5)*(c0 + c2_over_3*(Scalar(2)*c2_over_3*c2_over_3 - c1)); - - Scalar q = half_b*half_b + a_over_3*a_over_3*a_over_3; - if (q > Scalar(0)) - q = Scalar(0); - - // Compute the eigenvalues by solving for the roots of the polynomial. - Scalar rho = sqrt(-a_over_3); - Scalar theta = atan2(sqrt(-q),half_b)*s_inv3; - Scalar cos_theta = cos(theta); - Scalar sin_theta = sin(theta); - roots(0) = c2_over_3 + Scalar(2)*rho*cos_theta; - roots(1) = c2_over_3 - rho*(cos_theta + s_sqrt3*sin_theta); - roots(2) = c2_over_3 - rho*(cos_theta - s_sqrt3*sin_theta); - - // Sort in increasing order. - if (roots(0) >= roots(1)) - numext::swap(roots(0),roots(1)); - if (roots(1) >= roots(2)) - { - numext::swap(roots(1),roots(2)); - if (roots(0) >= roots(1)) - numext::swap(roots(0),roots(1)); - } - } - - EIGEN_DEVICE_FUNC - static inline void run(SolverType& solver, const MatrixType& mat, int options) - { - using std::sqrt; - eigen_assert(mat.cols() == 3 && mat.cols() == mat.rows()); - eigen_assert((options&~(EigVecMask|GenEigMask))==0 - && (options&EigVecMask)!=EigVecMask - && "invalid option parameter"); - bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; - - MatrixType& eivecs = solver.m_eivec; - VectorType& eivals = solver.m_eivalues; - - // map the matrix coefficients to [-1:1] to avoid over- and underflow. - Scalar scale = mat.cwiseAbs().maxCoeff(); - MatrixType scaledMat = mat / scale; - - // compute the eigenvalues - computeRoots(scaledMat,eivals); - - // compute the eigen vectors - if(computeEigenvectors) - { - Scalar safeNorm2 = Eigen::NumTraits<Scalar>::epsilon(); - safeNorm2 *= safeNorm2; - if((eivals(2)-eivals(0))<=Eigen::NumTraits<Scalar>::epsilon()) - { - eivecs.setIdentity(); - } - else - { - scaledMat = scaledMat.template selfadjointView<Lower>(); - MatrixType tmp; - tmp = scaledMat; - - Scalar d0 = eivals(2) - eivals(1); - Scalar d1 = eivals(1) - eivals(0); - int k = d0 > d1 ? 2 : 0; - d0 = d0 > d1 ? d1 : d0; - - tmp.diagonal().array () -= eivals(k); - VectorType cross; - Scalar n; - n = (cross = tmp.row(0).cross(tmp.row(1))).squaredNorm(); - - if(n>safeNorm2) - eivecs.col(k) = cross / sqrt(n); - else - { - n = (cross = tmp.row(0).cross(tmp.row(2))).squaredNorm(); - - if(n>safeNorm2) - eivecs.col(k) = cross / sqrt(n); - else - { - n = (cross = tmp.row(1).cross(tmp.row(2))).squaredNorm(); - - if(n>safeNorm2) - eivecs.col(k) = cross / sqrt(n); - else - { - // the input matrix and/or the eigenvaues probably contains some inf/NaN, - // => exit - // scale back to the original size. - eivals *= scale; - - solver.m_info = NumericalIssue; - solver.m_isInitialized = true; - solver.m_eigenvectorsOk = computeEigenvectors; - return; - } - } - } - - tmp = scaledMat; - tmp.diagonal().array() -= eivals(1); - - if(d0<=Eigen::NumTraits<Scalar>::epsilon()) - eivecs.col(1) = eivecs.col(k).unitOrthogonal(); - else - { - n = (cross = eivecs.col(k).cross(tmp.row(0).normalized())).squaredNorm(); - if(n>safeNorm2) - eivecs.col(1) = cross / sqrt(n); - else - { - n = (cross = eivecs.col(k).cross(tmp.row(1))).squaredNorm(); - if(n>safeNorm2) - eivecs.col(1) = cross / sqrt(n); - else - { - n = (cross = eivecs.col(k).cross(tmp.row(2))).squaredNorm(); - if(n>safeNorm2) - eivecs.col(1) = cross / sqrt(n); - else - { - // we should never reach this point, - // if so the last two eigenvalues are likely to ve very closed to each other - eivecs.col(1) = eivecs.col(k).unitOrthogonal(); - } - } - } - - // make sure that eivecs[1] is orthogonal to eivecs[2] - Scalar d = eivecs.col(1).dot(eivecs.col(k)); - eivecs.col(1) = (eivecs.col(1) - d * eivecs.col(k)).normalized(); - } - - eivecs.col(k==2 ? 0 : 2) = eivecs.col(k).cross(eivecs.col(1)).normalized(); - } - } - // Rescale back to the original size. - eivals *= scale; - - solver.m_info = Success; - solver.m_isInitialized = true; - solver.m_eigenvectorsOk = computeEigenvectors; - } -}; - -// 2x2 direct eigenvalues decomposition, code from Hauke Heibel -template<typename SolverType> -struct direct_selfadjoint_eigenvalues<SolverType,2,false> -{ - typedef typename SolverType::MatrixType MatrixType; - typedef typename SolverType::RealVectorType VectorType; - typedef typename SolverType::Scalar Scalar; - - EIGEN_DEVICE_FUNC - static inline void computeRoots(const MatrixType& m, VectorType& roots) - { - using std::sqrt; - const Scalar t0 = Scalar(0.5) * sqrt( numext::abs2(m(0,0)-m(1,1)) + Scalar(4)*m(1,0)*m(1,0)); - const Scalar t1 = Scalar(0.5) * (m(0,0) + m(1,1)); - roots(0) = t1 - t0; - roots(1) = t1 + t0; - } - - EIGEN_DEVICE_FUNC - static inline void run(SolverType& solver, const MatrixType& mat, int options) - { - EIGEN_USING_STD_MATH(sqrt); - - eigen_assert(mat.cols() == 2 && mat.cols() == mat.rows()); - eigen_assert((options&~(EigVecMask|GenEigMask))==0 - && (options&EigVecMask)!=EigVecMask - && "invalid option parameter"); - bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; - - MatrixType& eivecs = solver.m_eivec; - VectorType& eivals = solver.m_eivalues; - - // map the matrix coefficients to [-1:1] to avoid over- and underflow. - Scalar scale = mat.cwiseAbs().maxCoeff(); - scale = numext::maxi(scale,Scalar(1)); - MatrixType scaledMat = mat / scale; - - // Compute the eigenvalues - computeRoots(scaledMat,eivals); - - // compute the eigen vectors - if(computeEigenvectors) - { - scaledMat.diagonal().array () -= eivals(1); - Scalar a2 = numext::abs2(scaledMat(0,0)); - Scalar c2 = numext::abs2(scaledMat(1,1)); - Scalar b2 = numext::abs2(scaledMat(1,0)); - if(a2>c2) - { - eivecs.col(1) << -scaledMat(1,0), scaledMat(0,0); - eivecs.col(1) /= sqrt(a2+b2); - } - else - { - eivecs.col(1) << -scaledMat(1,1), scaledMat(1,0); - eivecs.col(1) /= sqrt(c2+b2); - } - - eivecs.col(0) << eivecs.col(1).unitOrthogonal(); - } - - // Rescale back to the original size. - eivals *= scale; - - solver.m_info = Success; - solver.m_isInitialized = true; - solver.m_eigenvectorsOk = computeEigenvectors; - } -}; - -} - -template<typename MatrixType> -EIGEN_DEVICE_FUNC -SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType> -::computeDirect(const MatrixType& matrix, int options) -{ - internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver,Size,NumTraits<Scalar>::IsComplex>::run(*this,matrix,options); - return *this; -} - -namespace internal { -template<int StorageOrder,typename RealScalar, typename Scalar, typename Index> -EIGEN_DEVICE_FUNC -static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end, Scalar* matrixQ, Index n) -{ - using std::abs; - RealScalar td = (diag[end-1] - diag[end])*RealScalar(0.5); - RealScalar e = subdiag[end-1]; - // Note that thanks to scaling, e^2 or td^2 cannot overflow, however they can still - // underflow thus leading to inf/NaN values when using the following commented code: -// RealScalar e2 = numext::abs2(subdiag[end-1]); -// RealScalar mu = diag[end] - e2 / (td + (td>0 ? 1 : -1) * sqrt(td*td + e2)); - // This explain the following, somewhat more complicated, version: - RealScalar mu = diag[end]; - if(td==0) - mu -= abs(e); - else - { - RealScalar e2 = numext::abs2(subdiag[end-1]); - RealScalar h = numext::hypot(td,e); - if(e2==0) mu -= (e / (td + (td>0 ? 1 : -1))) * (e / h); - else mu -= e2 / (td + (td>0 ? h : -h)); - } - - RealScalar x = diag[start] - mu; - RealScalar z = subdiag[start]; - for (Index k = start; k < end; ++k) - { - JacobiRotation<RealScalar> rot; - rot.makeGivens(x, z); - - // do T = G' T G - RealScalar sdk = rot.s() * diag[k] + rot.c() * subdiag[k]; - RealScalar dkp1 = rot.s() * subdiag[k] + rot.c() * diag[k+1]; - - diag[k] = rot.c() * (rot.c() * diag[k] - rot.s() * subdiag[k]) - rot.s() * (rot.c() * subdiag[k] - rot.s() * diag[k+1]); - diag[k+1] = rot.s() * sdk + rot.c() * dkp1; - subdiag[k] = rot.c() * sdk - rot.s() * dkp1; - - - if (k > start) - subdiag[k - 1] = rot.c() * subdiag[k-1] - rot.s() * z; - - x = subdiag[k]; - - if (k < end - 1) - { - z = -rot.s() * subdiag[k+1]; - subdiag[k + 1] = rot.c() * subdiag[k+1]; - } - - // apply the givens rotation to the unit matrix Q = Q * G - if (matrixQ) - { - // FIXME if StorageOrder == RowMajor this operation is not very efficient - Map<Matrix<Scalar,Dynamic,Dynamic,StorageOrder> > q(matrixQ,n,n); - q.applyOnTheRight(k,k+1,rot); - } - } -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_SELFADJOINTEIGENSOLVER_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h b/third_party/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h deleted file mode 100644 index 17c0dadd23..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h +++ /dev/null @@ -1,92 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Self-adjoint eigenvalues/eigenvectors. - ******************************************************************************** -*/ - -#ifndef EIGEN_SAEIGENSOLVER_MKL_H -#define EIGEN_SAEIGENSOLVER_MKL_H - -#include "Eigen/src/Core/util/MKL_support.h" - -namespace Eigen { - -/** \internal Specialization for the data types supported by MKL */ - -#define EIGEN_MKL_EIG_SELFADJ(EIGTYPE, MKLTYPE, MKLRTYPE, MKLNAME, EIGCOLROW, MKLCOLROW ) \ -template<> inline \ -SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \ -SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, int options) \ -{ \ - eigen_assert(matrix.cols() == matrix.rows()); \ - eigen_assert((options&~(EigVecMask|GenEigMask))==0 \ - && (options&EigVecMask)!=EigVecMask \ - && "invalid option parameter"); \ - bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; \ - lapack_int n = matrix.cols(), lda, matrix_order, info; \ - m_eivalues.resize(n,1); \ - m_subdiag.resize(n-1); \ - m_eivec = matrix; \ -\ - if(n==1) \ - { \ - m_eivalues.coeffRef(0,0) = numext::real(matrix.coeff(0,0)); \ - if(computeEigenvectors) m_eivec.setOnes(n,n); \ - m_info = Success; \ - m_isInitialized = true; \ - m_eigenvectorsOk = computeEigenvectors; \ - return *this; \ - } \ -\ - lda = matrix.outerStride(); \ - matrix_order=MKLCOLROW; \ - char jobz, uplo='L'/*, range='A'*/; \ - jobz = computeEigenvectors ? 'V' : 'N'; \ -\ - info = LAPACKE_##MKLNAME( matrix_order, jobz, uplo, n, (MKLTYPE*)m_eivec.data(), lda, (MKLRTYPE*)m_eivalues.data() ); \ - m_info = (info==0) ? Success : NoConvergence; \ - m_isInitialized = true; \ - m_eigenvectorsOk = computeEigenvectors; \ - return *this; \ -} - - -EIGEN_MKL_EIG_SELFADJ(double, double, double, dsyev, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_EIG_SELFADJ(float, float, float, ssyev, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_EIG_SELFADJ(dcomplex, MKL_Complex16, double, zheev, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_EIG_SELFADJ(scomplex, MKL_Complex8, float, cheev, ColMajor, LAPACK_COL_MAJOR) - -EIGEN_MKL_EIG_SELFADJ(double, double, double, dsyev, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_EIG_SELFADJ(float, float, float, ssyev, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_EIG_SELFADJ(dcomplex, MKL_Complex16, double, zheev, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_EIG_SELFADJ(scomplex, MKL_Complex8, float, cheev, RowMajor, LAPACK_ROW_MAJOR) - -} // end namespace Eigen - -#endif // EIGEN_SAEIGENSOLVER_H diff --git a/third_party/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h b/third_party/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h deleted file mode 100644 index 192278d685..0000000000 --- a/third_party/eigen3/Eigen/src/Eigenvalues/Tridiagonalization.h +++ /dev/null @@ -1,557 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRIDIAGONALIZATION_H -#define EIGEN_TRIDIAGONALIZATION_H - -namespace Eigen { - -namespace internal { - -template<typename MatrixType> struct TridiagonalizationMatrixTReturnType; -template<typename MatrixType> -struct traits<TridiagonalizationMatrixTReturnType<MatrixType> > -{ - typedef typename MatrixType::PlainObject ReturnType; -}; - -template<typename MatrixType, typename CoeffVectorType> -void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs); -} - -/** \eigenvalues_module \ingroup Eigenvalues_Module - * - * - * \class Tridiagonalization - * - * \brief Tridiagonal decomposition of a selfadjoint matrix - * - * \tparam _MatrixType the type of the matrix of which we are computing the - * tridiagonal decomposition; this is expected to be an instantiation of the - * Matrix class template. - * - * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ A \f$ such that: - * \f$ A = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real symmetric tridiagonal matrix. - * - * A tridiagonal matrix is a matrix which has nonzero elements only on the - * main diagonal and the first diagonal below and above it. The Hessenberg - * decomposition of a selfadjoint matrix is in fact a tridiagonal - * decomposition. This class is used in SelfAdjointEigenSolver to compute the - * eigenvalues and eigenvectors of a selfadjoint matrix. - * - * Call the function compute() to compute the tridiagonal decomposition of a - * given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&) - * constructor which computes the tridiagonal Schur decomposition at - * construction time. Once the decomposition is computed, you can use the - * matrixQ() and matrixT() functions to retrieve the matrices Q and T in the - * decomposition. - * - * The documentation of Tridiagonalization(const MatrixType&) contains an - * example of the typical use of this class. - * - * \sa class HessenbergDecomposition, class SelfAdjointEigenSolver - */ -template<typename _MatrixType> class Tridiagonalization -{ - public: - - /** \brief Synonym for the template parameter \p _MatrixType. */ - typedef _MatrixType MatrixType; - - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - - enum { - Size = MatrixType::RowsAtCompileTime, - SizeMinusOne = Size == Dynamic ? Dynamic : (Size > 1 ? Size - 1 : 1), - Options = MatrixType::Options, - MaxSize = MatrixType::MaxRowsAtCompileTime, - MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : (MaxSize > 1 ? MaxSize - 1 : 1) - }; - - typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType; - typedef typename internal::plain_col_type<MatrixType, RealScalar>::type DiagonalType; - typedef Matrix<RealScalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> SubDiagonalType; - typedef typename internal::remove_all<typename MatrixType::RealReturnType>::type MatrixTypeRealView; - typedef internal::TridiagonalizationMatrixTReturnType<MatrixTypeRealView> MatrixTReturnType; - - typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, - typename internal::add_const_on_value_type<typename Diagonal<const MatrixType>::RealReturnType>::type, - const Diagonal<const MatrixType> - >::type DiagonalReturnType; - - typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, - typename internal::add_const_on_value_type<typename Diagonal< - Block<const MatrixType,SizeMinusOne,SizeMinusOne> >::RealReturnType>::type, - const Diagonal< - Block<const MatrixType,SizeMinusOne,SizeMinusOne> > - >::type SubDiagonalReturnType; - - /** \brief Return type of matrixQ() */ - typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType; - - /** \brief Default constructor. - * - * \param [in] size Positive integer, size of the matrix whose tridiagonal - * decomposition will be computed. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via compute(). The \p size parameter is only - * used as a hint. It is not an error to give a wrong \p size, but it may - * impair performance. - * - * \sa compute() for an example. - */ - Tridiagonalization(Index size = Size==Dynamic ? 2 : Size) - : m_matrix(size,size), - m_hCoeffs(size > 1 ? size-1 : 1), - m_isInitialized(false) - {} - - /** \brief Constructor; computes tridiagonal decomposition of given matrix. - * - * \param[in] matrix Selfadjoint matrix whose tridiagonal decomposition - * is to be computed. - * - * This constructor calls compute() to compute the tridiagonal decomposition. - * - * Example: \include Tridiagonalization_Tridiagonalization_MatrixType.cpp - * Output: \verbinclude Tridiagonalization_Tridiagonalization_MatrixType.out - */ - Tridiagonalization(const MatrixType& matrix) - : m_matrix(matrix), - m_hCoeffs(matrix.cols() > 1 ? matrix.cols()-1 : 1), - m_isInitialized(false) - { - internal::tridiagonalization_inplace(m_matrix, m_hCoeffs); - m_isInitialized = true; - } - - /** \brief Computes tridiagonal decomposition of given matrix. - * - * \param[in] matrix Selfadjoint matrix whose tridiagonal decomposition - * is to be computed. - * \returns Reference to \c *this - * - * The tridiagonal decomposition is computed by bringing the columns of - * the matrix successively in the required form using Householder - * reflections. The cost is \f$ 4n^3/3 \f$ flops, where \f$ n \f$ denotes - * the size of the given matrix. - * - * This method reuses of the allocated data in the Tridiagonalization - * object, if the size of the matrix does not change. - * - * Example: \include Tridiagonalization_compute.cpp - * Output: \verbinclude Tridiagonalization_compute.out - */ - Tridiagonalization& compute(const MatrixType& matrix) - { - m_matrix = matrix; - m_hCoeffs.resize(matrix.rows()-1, 1); - internal::tridiagonalization_inplace(m_matrix, m_hCoeffs); - m_isInitialized = true; - return *this; - } - - /** \brief Returns the Householder coefficients. - * - * \returns a const reference to the vector of Householder coefficients - * - * \pre Either the constructor Tridiagonalization(const MatrixType&) or - * the member function compute(const MatrixType&) has been called before - * to compute the tridiagonal decomposition of a matrix. - * - * The Householder coefficients allow the reconstruction of the matrix - * \f$ Q \f$ in the tridiagonal decomposition from the packed data. - * - * Example: \include Tridiagonalization_householderCoefficients.cpp - * Output: \verbinclude Tridiagonalization_householderCoefficients.out - * - * \sa packedMatrix(), \ref Householder_Module "Householder module" - */ - inline CoeffVectorType householderCoefficients() const - { - eigen_assert(m_isInitialized && "Tridiagonalization is not initialized."); - return m_hCoeffs; - } - - /** \brief Returns the internal representation of the decomposition - * - * \returns a const reference to a matrix with the internal representation - * of the decomposition. - * - * \pre Either the constructor Tridiagonalization(const MatrixType&) or - * the member function compute(const MatrixType&) has been called before - * to compute the tridiagonal decomposition of a matrix. - * - * The returned matrix contains the following information: - * - the strict upper triangular part is equal to the input matrix A. - * - the diagonal and lower sub-diagonal represent the real tridiagonal - * symmetric matrix T. - * - the rest of the lower part contains the Householder vectors that, - * combined with Householder coefficients returned by - * householderCoefficients(), allows to reconstruct the matrix Q as - * \f$ Q = H_{N-1} \ldots H_1 H_0 \f$. - * Here, the matrices \f$ H_i \f$ are the Householder transformations - * \f$ H_i = (I - h_i v_i v_i^T) \f$ - * where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and - * \f$ v_i \f$ is the Householder vector defined by - * \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$ - * with M the matrix returned by this function. - * - * See LAPACK for further details on this packed storage. - * - * Example: \include Tridiagonalization_packedMatrix.cpp - * Output: \verbinclude Tridiagonalization_packedMatrix.out - * - * \sa householderCoefficients() - */ - inline const MatrixType& packedMatrix() const - { - eigen_assert(m_isInitialized && "Tridiagonalization is not initialized."); - return m_matrix; - } - - /** \brief Returns the unitary matrix Q in the decomposition - * - * \returns object representing the matrix Q - * - * \pre Either the constructor Tridiagonalization(const MatrixType&) or - * the member function compute(const MatrixType&) has been called before - * to compute the tridiagonal decomposition of a matrix. - * - * This function returns a light-weight object of template class - * HouseholderSequence. You can either apply it directly to a matrix or - * you can convert it to a matrix of type #MatrixType. - * - * \sa Tridiagonalization(const MatrixType&) for an example, - * matrixT(), class HouseholderSequence - */ - HouseholderSequenceType matrixQ() const - { - eigen_assert(m_isInitialized && "Tridiagonalization is not initialized."); - return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate()) - .setLength(m_matrix.rows() - 1) - .setShift(1); - } - - /** \brief Returns an expression of the tridiagonal matrix T in the decomposition - * - * \returns expression object representing the matrix T - * - * \pre Either the constructor Tridiagonalization(const MatrixType&) or - * the member function compute(const MatrixType&) has been called before - * to compute the tridiagonal decomposition of a matrix. - * - * Currently, this function can be used to extract the matrix T from internal - * data and copy it to a dense matrix object. In most cases, it may be - * sufficient to directly use the packed matrix or the vector expressions - * returned by diagonal() and subDiagonal() instead of creating a new - * dense copy matrix with this function. - * - * \sa Tridiagonalization(const MatrixType&) for an example, - * matrixQ(), packedMatrix(), diagonal(), subDiagonal() - */ - MatrixTReturnType matrixT() const - { - eigen_assert(m_isInitialized && "Tridiagonalization is not initialized."); - return MatrixTReturnType(m_matrix.real()); - } - - /** \brief Returns the diagonal of the tridiagonal matrix T in the decomposition. - * - * \returns expression representing the diagonal of T - * - * \pre Either the constructor Tridiagonalization(const MatrixType&) or - * the member function compute(const MatrixType&) has been called before - * to compute the tridiagonal decomposition of a matrix. - * - * Example: \include Tridiagonalization_diagonal.cpp - * Output: \verbinclude Tridiagonalization_diagonal.out - * - * \sa matrixT(), subDiagonal() - */ - DiagonalReturnType diagonal() const; - - /** \brief Returns the subdiagonal of the tridiagonal matrix T in the decomposition. - * - * \returns expression representing the subdiagonal of T - * - * \pre Either the constructor Tridiagonalization(const MatrixType&) or - * the member function compute(const MatrixType&) has been called before - * to compute the tridiagonal decomposition of a matrix. - * - * \sa diagonal() for an example, matrixT() - */ - SubDiagonalReturnType subDiagonal() const; - - protected: - - MatrixType m_matrix; - CoeffVectorType m_hCoeffs; - bool m_isInitialized; -}; - -template<typename MatrixType> -typename Tridiagonalization<MatrixType>::DiagonalReturnType -Tridiagonalization<MatrixType>::diagonal() const -{ - eigen_assert(m_isInitialized && "Tridiagonalization is not initialized."); - return m_matrix.diagonal(); -} - -template<typename MatrixType> -typename Tridiagonalization<MatrixType>::SubDiagonalReturnType -Tridiagonalization<MatrixType>::subDiagonal() const -{ - eigen_assert(m_isInitialized && "Tridiagonalization is not initialized."); - Index n = m_matrix.rows(); - return Block<const MatrixType,SizeMinusOne,SizeMinusOne>(m_matrix, 1, 0, n-1,n-1).diagonal(); -} - -namespace internal { - -/** \internal - * Performs a tridiagonal decomposition of the selfadjoint matrix \a matA in-place. - * - * \param[in,out] matA On input the selfadjoint matrix. Only the \b lower triangular part is referenced. - * On output, the strict upper part is left unchanged, and the lower triangular part - * represents the T and Q matrices in packed format has detailed below. - * \param[out] hCoeffs returned Householder coefficients (see below) - * - * On output, the tridiagonal selfadjoint matrix T is stored in the diagonal - * and lower sub-diagonal of the matrix \a matA. - * The unitary matrix Q is represented in a compact way as a product of - * Householder reflectors \f$ H_i \f$ such that: - * \f$ Q = H_{N-1} \ldots H_1 H_0 \f$. - * The Householder reflectors are defined as - * \f$ H_i = (I - h_i v_i v_i^T) \f$ - * where \f$ h_i = hCoeffs[i]\f$ is the \f$ i \f$th Householder coefficient and - * \f$ v_i \f$ is the Householder vector defined by - * \f$ v_i = [ 0, \ldots, 0, 1, matA(i+2,i), \ldots, matA(N-1,i) ]^T \f$. - * - * Implemented from Golub's "Matrix Computations", algorithm 8.3.1. - * - * \sa Tridiagonalization::packedMatrix() - */ -template<typename MatrixType, typename CoeffVectorType> -void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs) -{ - using numext::conj; - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - Index n = matA.rows(); - eigen_assert(n==matA.cols()); - eigen_assert(n==hCoeffs.size()+1 || n==1); - - for (Index i = 0; i<n-1; ++i) - { - Index remainingSize = n-i-1; - RealScalar beta; - Scalar h; - matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta); - - // Apply similarity transformation to remaining columns, - // i.e., A = H A H' where H = I - h v v' and v = matA.col(i).tail(n-i-1) - matA.col(i).coeffRef(i+1) = 1; - - hCoeffs.tail(n-i-1).noalias() = (matA.bottomRightCorner(remainingSize,remainingSize).template selfadjointView<Lower>() - * (conj(h) * matA.col(i).tail(remainingSize))); - - hCoeffs.tail(n-i-1) += (conj(h)*Scalar(-0.5)*(hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) * matA.col(i).tail(n-i-1); - - matA.bottomRightCorner(remainingSize, remainingSize).template selfadjointView<Lower>() - .rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), -1); - - matA.col(i).coeffRef(i+1) = beta; - hCoeffs.coeffRef(i) = h; - } -} - -// forward declaration, implementation at the end of this file -template<typename MatrixType, - int Size=MatrixType::ColsAtCompileTime, - bool IsComplex=NumTraits<typename MatrixType::Scalar>::IsComplex> -struct tridiagonalization_inplace_selector; - -/** \brief Performs a full tridiagonalization in place - * - * \param[in,out] mat On input, the selfadjoint matrix whose tridiagonal - * decomposition is to be computed. Only the lower triangular part referenced. - * The rest is left unchanged. On output, the orthogonal matrix Q - * in the decomposition if \p extractQ is true. - * \param[out] diag The diagonal of the tridiagonal matrix T in the - * decomposition. - * \param[out] subdiag The subdiagonal of the tridiagonal matrix T in - * the decomposition. - * \param[in] extractQ If true, the orthogonal matrix Q in the - * decomposition is computed and stored in \p mat. - * - * Computes the tridiagonal decomposition of the selfadjoint matrix \p mat in place - * such that \f$ mat = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real - * symmetric tridiagonal matrix. - * - * The tridiagonal matrix T is passed to the output parameters \p diag and \p subdiag. If - * \p extractQ is true, then the orthogonal matrix Q is passed to \p mat. Otherwise the lower - * part of the matrix \p mat is destroyed. - * - * The vectors \p diag and \p subdiag are not resized. The function - * assumes that they are already of the correct size. The length of the - * vector \p diag should equal the number of rows in \p mat, and the - * length of the vector \p subdiag should be one left. - * - * This implementation contains an optimized path for 3-by-3 matrices - * which is especially useful for plane fitting. - * - * \note Currently, it requires two temporary vectors to hold the intermediate - * Householder coefficients, and to reconstruct the matrix Q from the Householder - * reflectors. - * - * Example (this uses the same matrix as the example in - * Tridiagonalization::Tridiagonalization(const MatrixType&)): - * \include Tridiagonalization_decomposeInPlace.cpp - * Output: \verbinclude Tridiagonalization_decomposeInPlace.out - * - * \sa class Tridiagonalization - */ -template<typename MatrixType, typename DiagonalType, typename SubDiagonalType> -void tridiagonalization_inplace(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, bool extractQ) -{ - eigen_assert(mat.cols()==mat.rows() && diag.size()==mat.rows() && subdiag.size()==mat.rows()-1); - tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, extractQ); -} - -/** \internal - * General full tridiagonalization - */ -template<typename MatrixType, int Size, bool IsComplex> -struct tridiagonalization_inplace_selector -{ - typedef typename Tridiagonalization<MatrixType>::CoeffVectorType CoeffVectorType; - typedef typename Tridiagonalization<MatrixType>::HouseholderSequenceType HouseholderSequenceType; - typedef typename MatrixType::Index Index; - template<typename DiagonalType, typename SubDiagonalType> - static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, bool extractQ) - { - CoeffVectorType hCoeffs(mat.cols()-1); - tridiagonalization_inplace(mat,hCoeffs); - diag = mat.diagonal().real(); - subdiag = mat.template diagonal<-1>().real(); - if(extractQ) - mat = HouseholderSequenceType(mat, hCoeffs.conjugate()) - .setLength(mat.rows() - 1) - .setShift(1); - } -}; - -/** \internal - * Specialization for 3x3 real matrices. - * Especially useful for plane fitting. - */ -template<typename MatrixType> -struct tridiagonalization_inplace_selector<MatrixType,3,false> -{ - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - - template<typename DiagonalType, typename SubDiagonalType> - static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, bool extractQ) - { - using std::sqrt; - diag[0] = mat(0,0); - RealScalar v1norm2 = numext::abs2(mat(2,0)); - if(v1norm2 == RealScalar(0)) - { - diag[1] = mat(1,1); - diag[2] = mat(2,2); - subdiag[0] = mat(1,0); - subdiag[1] = mat(2,1); - if (extractQ) - mat.setIdentity(); - } - else - { - RealScalar beta = sqrt(numext::abs2(mat(1,0)) + v1norm2); - RealScalar invBeta = RealScalar(1)/beta; - Scalar m01 = mat(1,0) * invBeta; - Scalar m02 = mat(2,0) * invBeta; - Scalar q = RealScalar(2)*m01*mat(2,1) + m02*(mat(2,2) - mat(1,1)); - diag[1] = mat(1,1) + m02*q; - diag[2] = mat(2,2) - m02*q; - subdiag[0] = beta; - subdiag[1] = mat(2,1) - m01 * q; - if (extractQ) - { - mat << 1, 0, 0, - 0, m01, m02, - 0, m02, -m01; - } - } - } -}; - -/** \internal - * Trivial specialization for 1x1 matrices - */ -template<typename MatrixType, bool IsComplex> -struct tridiagonalization_inplace_selector<MatrixType,1,IsComplex> -{ - typedef typename MatrixType::Scalar Scalar; - - template<typename DiagonalType, typename SubDiagonalType> - static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType&, bool extractQ) - { - diag(0,0) = numext::real(mat(0,0)); - if(extractQ) - mat(0,0) = Scalar(1); - } -}; - -/** \internal - * \eigenvalues_module \ingroup Eigenvalues_Module - * - * \brief Expression type for return value of Tridiagonalization::matrixT() - * - * \tparam MatrixType type of underlying dense matrix - */ -template<typename MatrixType> struct TridiagonalizationMatrixTReturnType -: public ReturnByValue<TridiagonalizationMatrixTReturnType<MatrixType> > -{ - typedef typename MatrixType::Index Index; - public: - /** \brief Constructor. - * - * \param[in] mat The underlying dense matrix - */ - TridiagonalizationMatrixTReturnType(const MatrixType& mat) : m_matrix(mat) { } - - template <typename ResultType> - inline void evalTo(ResultType& result) const - { - result.setZero(); - result.template diagonal<1>() = m_matrix.template diagonal<-1>().conjugate(); - result.diagonal() = m_matrix.diagonal(); - result.template diagonal<-1>() = m_matrix.template diagonal<-1>(); - } - - Index rows() const { return m_matrix.rows(); } - Index cols() const { return m_matrix.cols(); } - - protected: - typename MatrixType::Nested m_matrix; -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TRIDIAGONALIZATION_H diff --git a/third_party/eigen3/Eigen/src/Geometry/AlignedBox.h b/third_party/eigen3/Eigen/src/Geometry/AlignedBox.h deleted file mode 100644 index b6a2f0e24c..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/AlignedBox.h +++ /dev/null @@ -1,379 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ALIGNEDBOX_H -#define EIGEN_ALIGNEDBOX_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * - * \class AlignedBox - * - * \brief An axis aligned box - * - * \param _Scalar the type of the scalar coefficients - * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic. - * - * This class represents an axis aligned box as a pair of the minimal and maximal corners. - */ -template <typename _Scalar, int _AmbientDim> -class AlignedBox -{ -public: -EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim) - enum { AmbientDimAtCompileTime = _AmbientDim }; - typedef _Scalar Scalar; - typedef NumTraits<Scalar> ScalarTraits; - typedef DenseIndex Index; - typedef typename ScalarTraits::Real RealScalar; - typedef typename ScalarTraits::NonInteger NonInteger; - typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType; - - /** Define constants to name the corners of a 1D, 2D or 3D axis aligned bounding box */ - enum CornerType - { - /** 1D names */ - Min=0, Max=1, - - /** Added names for 2D */ - BottomLeft=0, BottomRight=1, - TopLeft=2, TopRight=3, - - /** Added names for 3D */ - BottomLeftFloor=0, BottomRightFloor=1, - TopLeftFloor=2, TopRightFloor=3, - BottomLeftCeil=4, BottomRightCeil=5, - TopLeftCeil=6, TopRightCeil=7 - }; - - - /** Default constructor initializing a null box. */ - inline AlignedBox() - { if (AmbientDimAtCompileTime!=Dynamic) setEmpty(); } - - /** Constructs a null box with \a _dim the dimension of the ambient space. */ - inline explicit AlignedBox(Index _dim) : m_min(_dim), m_max(_dim) - { setEmpty(); } - - /** Constructs a box with extremities \a _min and \a _max. */ - template<typename OtherVectorType1, typename OtherVectorType2> - inline AlignedBox(const OtherVectorType1& _min, const OtherVectorType2& _max) : m_min(_min), m_max(_max) {} - - /** Constructs a box containing a single point \a p. */ - template<typename Derived> - inline explicit AlignedBox(const MatrixBase<Derived>& a_p) - { - typename internal::nested<Derived,2>::type p(a_p.derived()); - m_min = p; - m_max = p; - } - - ~AlignedBox() {} - - /** \returns the dimension in which the box holds */ - inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size() : Index(AmbientDimAtCompileTime); } - - /** \deprecated use isEmpty */ - inline bool isNull() const { return isEmpty(); } - - /** \deprecated use setEmpty */ - inline void setNull() { setEmpty(); } - - /** \returns true if the box is empty. */ - inline bool isEmpty() const { return (m_min.array() > m_max.array()).any(); } - - /** Makes \c *this an empty box. */ - inline void setEmpty() - { - m_min.setConstant( ScalarTraits::highest() ); - m_max.setConstant( ScalarTraits::lowest() ); - } - - /** \returns the minimal corner */ - inline const VectorType& (min)() const { return m_min; } - /** \returns a non const reference to the minimal corner */ - inline VectorType& (min)() { return m_min; } - /** \returns the maximal corner */ - inline const VectorType& (max)() const { return m_max; } - /** \returns a non const reference to the maximal corner */ - inline VectorType& (max)() { return m_max; } - - /** \returns the center of the box */ - inline const CwiseUnaryOp<internal::scalar_quotient1_op<Scalar>, - const CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const VectorType, const VectorType> > - center() const - { return (m_min+m_max)/2; } - - /** \returns the lengths of the sides of the bounding box. - * Note that this function does not get the same - * result for integral or floating scalar types: see - */ - inline const CwiseBinaryOp< internal::scalar_difference_op<Scalar>, const VectorType, const VectorType> sizes() const - { return m_max - m_min; } - - /** \returns the volume of the bounding box */ - inline Scalar volume() const - { return sizes().prod(); } - - /** \returns an expression for the bounding box diagonal vector - * if the length of the diagonal is needed: diagonal().norm() - * will provide it. - */ - inline CwiseBinaryOp< internal::scalar_difference_op<Scalar>, const VectorType, const VectorType> diagonal() const - { return sizes(); } - - /** \returns the vertex of the bounding box at the corner defined by - * the corner-id corner. It works only for a 1D, 2D or 3D bounding box. - * For 1D bounding boxes corners are named by 2 enum constants: - * BottomLeft and BottomRight. - * For 2D bounding boxes, corners are named by 4 enum constants: - * BottomLeft, BottomRight, TopLeft, TopRight. - * For 3D bounding boxes, the following names are added: - * BottomLeftCeil, BottomRightCeil, TopLeftCeil, TopRightCeil. - */ - inline VectorType corner(CornerType corner) const - { - EIGEN_STATIC_ASSERT(_AmbientDim <= 3, THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE); - - VectorType res; - - Index mult = 1; - for(Index d=0; d<dim(); ++d) - { - if( mult & corner ) res[d] = m_max[d]; - else res[d] = m_min[d]; - mult *= 2; - } - return res; - } - - /** \returns a random point inside the bounding box sampled with - * a uniform distribution */ - inline VectorType sample() const - { - VectorType r; - for(Index d=0; d<dim(); ++d) - { - if(!ScalarTraits::IsInteger) - { - r[d] = m_min[d] + (m_max[d]-m_min[d]) - * internal::random<Scalar>(Scalar(0), Scalar(1)); - } - else - r[d] = internal::random(m_min[d], m_max[d]); - } - return r; - } - - /** \returns true if the point \a p is inside the box \c *this. */ - template<typename Derived> - inline bool contains(const MatrixBase<Derived>& a_p) const - { - typename internal::nested<Derived,2>::type p(a_p.derived()); - return (m_min.array()<=p.array()).all() && (p.array()<=m_max.array()).all(); - } - - /** \returns true if the box \a b is entirely inside the box \c *this. */ - inline bool contains(const AlignedBox& b) const - { return (m_min.array()<=(b.min)().array()).all() && ((b.max)().array()<=m_max.array()).all(); } - - /** \returns true if the box \a b is intersecting the box \c *this. */ - inline bool intersects(const AlignedBox& b) const - { return (m_min.array()<=(b.max)().array()).all() && ((b.min)().array()<=m_max.array()).all(); } - - /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */ - template<typename Derived> - inline AlignedBox& extend(const MatrixBase<Derived>& a_p) - { - typename internal::nested<Derived,2>::type p(a_p.derived()); - m_min = m_min.cwiseMin(p); - m_max = m_max.cwiseMax(p); - return *this; - } - - /** Extends \c *this such that it contains the box \a b and returns a reference to \c *this. */ - inline AlignedBox& extend(const AlignedBox& b) - { - m_min = m_min.cwiseMin(b.m_min); - m_max = m_max.cwiseMax(b.m_max); - return *this; - } - - /** Clamps \c *this by the box \a b and returns a reference to \c *this. */ - inline AlignedBox& clamp(const AlignedBox& b) - { - m_min = m_min.cwiseMax(b.m_min); - m_max = m_max.cwiseMin(b.m_max); - return *this; - } - - /** Returns an AlignedBox that is the intersection of \a b and \c *this */ - inline AlignedBox intersection(const AlignedBox& b) const - {return AlignedBox(m_min.cwiseMax(b.m_min), m_max.cwiseMin(b.m_max)); } - - /** Returns an AlignedBox that is the union of \a b and \c *this */ - inline AlignedBox merged(const AlignedBox& b) const - { return AlignedBox(m_min.cwiseMin(b.m_min), m_max.cwiseMax(b.m_max)); } - - /** Translate \c *this by the vector \a t and returns a reference to \c *this. */ - template<typename Derived> - inline AlignedBox& translate(const MatrixBase<Derived>& a_t) - { - const typename internal::nested<Derived,2>::type t(a_t.derived()); - m_min += t; - m_max += t; - return *this; - } - - /** \returns the squared distance between the point \a p and the box \c *this, - * and zero if \a p is inside the box. - * \sa exteriorDistance() - */ - template<typename Derived> - inline Scalar squaredExteriorDistance(const MatrixBase<Derived>& a_p) const; - - /** \returns the squared distance between the boxes \a b and \c *this, - * and zero if the boxes intersect. - * \sa exteriorDistance() - */ - inline Scalar squaredExteriorDistance(const AlignedBox& b) const; - - /** \returns the distance between the point \a p and the box \c *this, - * and zero if \a p is inside the box. - * \sa squaredExteriorDistance() - */ - template<typename Derived> - inline NonInteger exteriorDistance(const MatrixBase<Derived>& p) const - { using std::sqrt; return sqrt(NonInteger(squaredExteriorDistance(p))); } - - /** \returns the distance between the boxes \a b and \c *this, - * and zero if the boxes intersect. - * \sa squaredExteriorDistance() - */ - inline NonInteger exteriorDistance(const AlignedBox& b) const - { using std::sqrt; return sqrt(NonInteger(squaredExteriorDistance(b))); } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<AlignedBox, - AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type cast() const - { - return typename internal::cast_return_type<AlignedBox, - AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type(*this); - } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other) - { - m_min = (other.min)().template cast<Scalar>(); - m_max = (other.max)().template cast<Scalar>(); - } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const AlignedBox& other, const RealScalar& prec = ScalarTraits::dummy_precision()) const - { return m_min.isApprox(other.m_min, prec) && m_max.isApprox(other.m_max, prec); } - -protected: - - VectorType m_min, m_max; -}; - - - -template<typename Scalar,int AmbientDim> -template<typename Derived> -inline Scalar AlignedBox<Scalar,AmbientDim>::squaredExteriorDistance(const MatrixBase<Derived>& a_p) const -{ - typename internal::nested<Derived,2*AmbientDim>::type p(a_p.derived()); - Scalar dist2(0); - Scalar aux; - for (Index k=0; k<dim(); ++k) - { - if( m_min[k] > p[k] ) - { - aux = m_min[k] - p[k]; - dist2 += aux*aux; - } - else if( p[k] > m_max[k] ) - { - aux = p[k] - m_max[k]; - dist2 += aux*aux; - } - } - return dist2; -} - -template<typename Scalar,int AmbientDim> -inline Scalar AlignedBox<Scalar,AmbientDim>::squaredExteriorDistance(const AlignedBox& b) const -{ - Scalar dist2(0); - Scalar aux; - for (Index k=0; k<dim(); ++k) - { - if( m_min[k] > b.m_max[k] ) - { - aux = m_min[k] - b.m_max[k]; - dist2 += aux*aux; - } - else if( b.m_min[k] > m_max[k] ) - { - aux = b.m_min[k] - m_max[k]; - dist2 += aux*aux; - } - } - return dist2; -} - -/** \defgroup alignedboxtypedefs Global aligned box typedefs - * - * \ingroup Geometry_Module - * - * Eigen defines several typedef shortcuts for most common aligned box types. - * - * The general patterns are the following: - * - * \c AlignedBoxSizeType where \c Size can be \c 1, \c 2,\c 3,\c 4 for fixed size boxes or \c X for dynamic size, - * and where \c Type can be \c i for integer, \c f for float, \c d for double. - * - * For example, \c AlignedBox3d is a fixed-size 3x3 aligned box type of doubles, and \c AlignedBoxXf is a dynamic-size aligned box of floats. - * - * \sa class AlignedBox - */ - -#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix) \ -/** \ingroup alignedboxtypedefs */ \ -typedef AlignedBox<Type, Size> AlignedBox##SizeSuffix##TypeSuffix; - -#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 1, 1) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \ -EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X) - -EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int, i) -EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float, f) -EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double, d) - -#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES -#undef EIGEN_MAKE_TYPEDEFS - -} // end namespace Eigen - -#endif // EIGEN_ALIGNEDBOX_H diff --git a/third_party/eigen3/Eigen/src/Geometry/AngleAxis.h b/third_party/eigen3/Eigen/src/Geometry/AngleAxis.h deleted file mode 100644 index 636712c2b9..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/AngleAxis.h +++ /dev/null @@ -1,233 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ANGLEAXIS_H -#define EIGEN_ANGLEAXIS_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class AngleAxis - * - * \brief Represents a 3D rotation as a rotation angle around an arbitrary 3D axis - * - * \param _Scalar the scalar type, i.e., the type of the coefficients. - * - * \warning When setting up an AngleAxis object, the axis vector \b must \b be \b normalized. - * - * The following two typedefs are provided for convenience: - * \li \c AngleAxisf for \c float - * \li \c AngleAxisd for \c double - * - * Combined with MatrixBase::Unit{X,Y,Z}, AngleAxis can be used to easily - * mimic Euler-angles. Here is an example: - * \include AngleAxis_mimic_euler.cpp - * Output: \verbinclude AngleAxis_mimic_euler.out - * - * \note This class is not aimed to be used to store a rotation transformation, - * but rather to make easier the creation of other rotation (Quaternion, rotation Matrix) - * and transformation objects. - * - * \sa class Quaternion, class Transform, MatrixBase::UnitX() - */ - -namespace internal { -template<typename _Scalar> struct traits<AngleAxis<_Scalar> > -{ - typedef _Scalar Scalar; -}; -} - -template<typename _Scalar> -class AngleAxis : public RotationBase<AngleAxis<_Scalar>,3> -{ - typedef RotationBase<AngleAxis<_Scalar>,3> Base; - -public: - - using Base::operator*; - - enum { Dim = 3 }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - typedef Matrix<Scalar,3,3> Matrix3; - typedef Matrix<Scalar,3,1> Vector3; - typedef Quaternion<Scalar> QuaternionType; - -protected: - - Vector3 m_axis; - Scalar m_angle; - -public: - - /** Default constructor without initialization. */ - AngleAxis() {} - /** Constructs and initialize the angle-axis rotation from an \a angle in radian - * and an \a axis which \b must \b be \b normalized. - * - * \warning If the \a axis vector is not normalized, then the angle-axis object - * represents an invalid rotation. */ - template<typename Derived> - inline AngleAxis(const Scalar& angle, const MatrixBase<Derived>& axis) : m_axis(axis), m_angle(angle) {} - /** Constructs and initialize the angle-axis rotation from a quaternion \a q. - * This function implicitly normalizes the quaternion \a q. - */ - template<typename QuatDerived> inline explicit AngleAxis(const QuaternionBase<QuatDerived>& q) { *this = q; } - /** Constructs and initialize the angle-axis rotation from a 3x3 rotation matrix. */ - template<typename Derived> - inline explicit AngleAxis(const MatrixBase<Derived>& m) { *this = m; } - - Scalar angle() const { return m_angle; } - Scalar& angle() { return m_angle; } - - const Vector3& axis() const { return m_axis; } - Vector3& axis() { return m_axis; } - - /** Concatenates two rotations */ - inline QuaternionType operator* (const AngleAxis& other) const - { return QuaternionType(*this) * QuaternionType(other); } - - /** Concatenates two rotations */ - inline QuaternionType operator* (const QuaternionType& other) const - { return QuaternionType(*this) * other; } - - /** Concatenates two rotations */ - friend inline QuaternionType operator* (const QuaternionType& a, const AngleAxis& b) - { return a * QuaternionType(b); } - - /** \returns the inverse rotation, i.e., an angle-axis with opposite rotation angle */ - AngleAxis inverse() const - { return AngleAxis(-m_angle, m_axis); } - - template<class QuatDerived> - AngleAxis& operator=(const QuaternionBase<QuatDerived>& q); - template<typename Derived> - AngleAxis& operator=(const MatrixBase<Derived>& m); - - template<typename Derived> - AngleAxis& fromRotationMatrix(const MatrixBase<Derived>& m); - Matrix3 toRotationMatrix(void) const; - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type cast() const - { return typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other) - { - m_axis = other.axis().template cast<Scalar>(); - m_angle = Scalar(other.angle()); - } - - static inline const AngleAxis Identity() { return AngleAxis(0, Vector3::UnitX()); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const AngleAxis& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const - { return m_axis.isApprox(other.m_axis, prec) && internal::isApprox(m_angle,other.m_angle, prec); } -}; - -/** \ingroup Geometry_Module - * single precision angle-axis type */ -typedef AngleAxis<float> AngleAxisf; -/** \ingroup Geometry_Module - * double precision angle-axis type */ -typedef AngleAxis<double> AngleAxisd; - -/** Set \c *this from a \b unit quaternion. - * The resulting axis is normalized. - * - * This function implicitly normalizes the quaternion \a q. - */ -template<typename Scalar> -template<typename QuatDerived> -AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionBase<QuatDerived>& q) -{ - using std::atan2; - Scalar n = q.vec().norm(); - if(n<NumTraits<Scalar>::epsilon()) - n = q.vec().stableNorm(); - if (n > Scalar(0)) - { - m_angle = Scalar(2)*atan2(n, q.w()); - m_axis = q.vec() / n; - } - else - { - m_angle = 0; - m_axis << 1, 0, 0; - } - return *this; -} - -/** Set \c *this from a 3x3 rotation matrix \a mat. - */ -template<typename Scalar> -template<typename Derived> -AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const MatrixBase<Derived>& mat) -{ - // Since a direct conversion would not be really faster, - // let's use the robust Quaternion implementation: - return *this = QuaternionType(mat); -} - -/** -* \brief Sets \c *this from a 3x3 rotation matrix. -**/ -template<typename Scalar> -template<typename Derived> -AngleAxis<Scalar>& AngleAxis<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat) -{ - return *this = QuaternionType(mat); -} - -/** Constructs and \returns an equivalent 3x3 rotation matrix. - */ -template<typename Scalar> -typename AngleAxis<Scalar>::Matrix3 -AngleAxis<Scalar>::toRotationMatrix(void) const -{ - using std::sin; - using std::cos; - Matrix3 res; - Vector3 sin_axis = sin(m_angle) * m_axis; - Scalar c = cos(m_angle); - Vector3 cos1_axis = (Scalar(1)-c) * m_axis; - - Scalar tmp; - tmp = cos1_axis.x() * m_axis.y(); - res.coeffRef(0,1) = tmp - sin_axis.z(); - res.coeffRef(1,0) = tmp + sin_axis.z(); - - tmp = cos1_axis.x() * m_axis.z(); - res.coeffRef(0,2) = tmp + sin_axis.y(); - res.coeffRef(2,0) = tmp - sin_axis.y(); - - tmp = cos1_axis.y() * m_axis.z(); - res.coeffRef(1,2) = tmp - sin_axis.x(); - res.coeffRef(2,1) = tmp + sin_axis.x(); - - res.diagonal() = (cos1_axis.cwiseProduct(m_axis)).array() + c; - - return res; -} - -} // end namespace Eigen - -#endif // EIGEN_ANGLEAXIS_H diff --git a/third_party/eigen3/Eigen/src/Geometry/EulerAngles.h b/third_party/eigen3/Eigen/src/Geometry/EulerAngles.h deleted file mode 100644 index 82802fb43c..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/EulerAngles.h +++ /dev/null @@ -1,104 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_EULERANGLES_H -#define EIGEN_EULERANGLES_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * - * \returns the Euler-angles of the rotation matrix \c *this using the convention defined by the triplet (\a a0,\a a1,\a a2) - * - * Each of the three parameters \a a0,\a a1,\a a2 represents the respective rotation axis as an integer in {0,1,2}. - * For instance, in: - * \code Vector3f ea = mat.eulerAngles(2, 0, 2); \endcode - * "2" represents the z axis and "0" the x axis, etc. The returned angles are such that - * we have the following equality: - * \code - * mat == AngleAxisf(ea[0], Vector3f::UnitZ()) - * * AngleAxisf(ea[1], Vector3f::UnitX()) - * * AngleAxisf(ea[2], Vector3f::UnitZ()); \endcode - * This corresponds to the right-multiply conventions (with right hand side frames). - * - * The returned angles are in the ranges [0:pi]x[-pi:pi]x[-pi:pi]. - * - * \sa class AngleAxis - */ -template<typename Derived> -inline Matrix<typename MatrixBase<Derived>::Scalar,3,1> -MatrixBase<Derived>::eulerAngles(Index a0, Index a1, Index a2) const -{ - using std::atan2; - using std::sin; - using std::cos; - /* Implemented from Graphics Gems IV */ - EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived,3,3) - - Matrix<Scalar,3,1> res; - typedef Matrix<typename Derived::Scalar,2,1> Vector2; - - const Index odd = ((a0+1)%3 == a1) ? 0 : 1; - const Index i = a0; - const Index j = (a0 + 1 + odd)%3; - const Index k = (a0 + 2 - odd)%3; - - if (a0==a2) - { - res[0] = atan2(coeff(j,i), coeff(k,i)); - if((odd && res[0]<Scalar(0)) || ((!odd) && res[0]>Scalar(0))) - { - res[0] = (res[0] > Scalar(0)) ? res[0] - Scalar(M_PI) : res[0] + Scalar(M_PI); - Scalar s2 = Vector2(coeff(j,i), coeff(k,i)).norm(); - res[1] = -atan2(s2, coeff(i,i)); - } - else - { - Scalar s2 = Vector2(coeff(j,i), coeff(k,i)).norm(); - res[1] = atan2(s2, coeff(i,i)); - } - - // With a=(0,1,0), we have i=0; j=1; k=2, and after computing the first two angles, - // we can compute their respective rotation, and apply its inverse to M. Since the result must - // be a rotation around x, we have: - // - // c2 s1.s2 c1.s2 1 0 0 - // 0 c1 -s1 * M = 0 c3 s3 - // -s2 s1.c2 c1.c2 0 -s3 c3 - // - // Thus: m11.c1 - m21.s1 = c3 & m12.c1 - m22.s1 = s3 - - Scalar s1 = sin(res[0]); - Scalar c1 = cos(res[0]); - res[2] = atan2(c1*coeff(j,k)-s1*coeff(k,k), c1*coeff(j,j) - s1 * coeff(k,j)); - } - else - { - res[0] = atan2(coeff(j,k), coeff(k,k)); - Scalar c2 = Vector2(coeff(i,i), coeff(i,j)).norm(); - if((odd && res[0]<Scalar(0)) || ((!odd) && res[0]>Scalar(0))) { - res[0] = (res[0] > Scalar(0)) ? res[0] - Scalar(M_PI) : res[0] + Scalar(M_PI); - res[1] = atan2(-coeff(i,k), -c2); - } - else - res[1] = atan2(-coeff(i,k), c2); - Scalar s1 = sin(res[0]); - Scalar c1 = cos(res[0]); - res[2] = atan2(s1*coeff(k,i)-c1*coeff(j,i), c1*coeff(j,j) - s1 * coeff(k,j)); - } - if (!odd) - res = -res; - - return res; -} - -} // end namespace Eigen - -#endif // EIGEN_EULERANGLES_H diff --git a/third_party/eigen3/Eigen/src/Geometry/Homogeneous.h b/third_party/eigen3/Eigen/src/Geometry/Homogeneous.h deleted file mode 100644 index 00e71d190c..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/Homogeneous.h +++ /dev/null @@ -1,307 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_HOMOGENEOUS_H -#define EIGEN_HOMOGENEOUS_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Homogeneous - * - * \brief Expression of one (or a set of) homogeneous vector(s) - * - * \param MatrixType the type of the object in which we are making homogeneous - * - * This class represents an expression of one (or a set of) homogeneous vector(s). - * It is the return type of MatrixBase::homogeneous() and most of the time - * this is the only way it is used. - * - * \sa MatrixBase::homogeneous() - */ - -namespace internal { - -template<typename MatrixType,int Direction> -struct traits<Homogeneous<MatrixType,Direction> > - : traits<MatrixType> -{ - typedef typename traits<MatrixType>::StorageKind StorageKind; - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested; - enum { - RowsPlusOne = (MatrixType::RowsAtCompileTime != Dynamic) ? - int(MatrixType::RowsAtCompileTime) + 1 : Dynamic, - ColsPlusOne = (MatrixType::ColsAtCompileTime != Dynamic) ? - int(MatrixType::ColsAtCompileTime) + 1 : Dynamic, - RowsAtCompileTime = Direction==Vertical ? RowsPlusOne : MatrixType::RowsAtCompileTime, - ColsAtCompileTime = Direction==Horizontal ? ColsPlusOne : MatrixType::ColsAtCompileTime, - MaxRowsAtCompileTime = RowsAtCompileTime, - MaxColsAtCompileTime = ColsAtCompileTime, - TmpFlags = _MatrixTypeNested::Flags & HereditaryBits, - Flags = ColsAtCompileTime==1 ? (TmpFlags & ~RowMajorBit) - : RowsAtCompileTime==1 ? (TmpFlags | RowMajorBit) - : TmpFlags, - CoeffReadCost = _MatrixTypeNested::CoeffReadCost - }; -}; - -template<typename MatrixType,typename Lhs> struct homogeneous_left_product_impl; -template<typename MatrixType,typename Rhs> struct homogeneous_right_product_impl; - -} // end namespace internal - -template<typename MatrixType,int _Direction> class Homogeneous - : internal::no_assignment_operator, public MatrixBase<Homogeneous<MatrixType,_Direction> > -{ - public: - - enum { Direction = _Direction }; - - typedef MatrixBase<Homogeneous> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(Homogeneous) - - inline Homogeneous(const MatrixType& matrix) - : m_matrix(matrix) - {} - - inline Index rows() const { return m_matrix.rows() + (int(Direction)==Vertical ? 1 : 0); } - inline Index cols() const { return m_matrix.cols() + (int(Direction)==Horizontal ? 1 : 0); } - - inline Scalar coeff(Index row, Index col) const - { - if( (int(Direction)==Vertical && row==m_matrix.rows()) - || (int(Direction)==Horizontal && col==m_matrix.cols())) - return 1; - return m_matrix.coeff(row, col); - } - - template<typename Rhs> - inline const internal::homogeneous_right_product_impl<Homogeneous,Rhs> - operator* (const MatrixBase<Rhs>& rhs) const - { - eigen_assert(int(Direction)==Horizontal); - return internal::homogeneous_right_product_impl<Homogeneous,Rhs>(m_matrix,rhs.derived()); - } - - template<typename Lhs> friend - inline const internal::homogeneous_left_product_impl<Homogeneous,Lhs> - operator* (const MatrixBase<Lhs>& lhs, const Homogeneous& rhs) - { - eigen_assert(int(Direction)==Vertical); - return internal::homogeneous_left_product_impl<Homogeneous,Lhs>(lhs.derived(),rhs.m_matrix); - } - - template<typename Scalar, int Dim, int Mode, int Options> friend - inline const internal::homogeneous_left_product_impl<Homogeneous,Transform<Scalar,Dim,Mode,Options> > - operator* (const Transform<Scalar,Dim,Mode,Options>& lhs, const Homogeneous& rhs) - { - eigen_assert(int(Direction)==Vertical); - return internal::homogeneous_left_product_impl<Homogeneous,Transform<Scalar,Dim,Mode,Options> >(lhs,rhs.m_matrix); - } - - protected: - typename MatrixType::Nested m_matrix; -}; - -/** \geometry_module - * - * \return an expression of the equivalent homogeneous vector - * - * \only_for_vectors - * - * Example: \include MatrixBase_homogeneous.cpp - * Output: \verbinclude MatrixBase_homogeneous.out - * - * \sa class Homogeneous - */ -template<typename Derived> -inline typename MatrixBase<Derived>::HomogeneousReturnType -MatrixBase<Derived>::homogeneous() const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived); - return derived(); -} - -/** \geometry_module - * - * \returns a matrix expression of homogeneous column (or row) vectors - * - * Example: \include VectorwiseOp_homogeneous.cpp - * Output: \verbinclude VectorwiseOp_homogeneous.out - * - * \sa MatrixBase::homogeneous() */ -template<typename ExpressionType, int Direction> -inline Homogeneous<ExpressionType,Direction> -VectorwiseOp<ExpressionType,Direction>::homogeneous() const -{ - return _expression(); -} - -/** \geometry_module - * - * \returns an expression of the homogeneous normalized vector of \c *this - * - * Example: \include MatrixBase_hnormalized.cpp - * Output: \verbinclude MatrixBase_hnormalized.out - * - * \sa VectorwiseOp::hnormalized() */ -template<typename Derived> -inline const typename MatrixBase<Derived>::HNormalizedReturnType -MatrixBase<Derived>::hnormalized() const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived); - return ConstStartMinusOne(derived(),0,0, - ColsAtCompileTime==1?size()-1:1, - ColsAtCompileTime==1?1:size()-1) / coeff(size()-1); -} - -/** \geometry_module - * - * \returns an expression of the homogeneous normalized vector of \c *this - * - * Example: \include DirectionWise_hnormalized.cpp - * Output: \verbinclude DirectionWise_hnormalized.out - * - * \sa MatrixBase::hnormalized() */ -template<typename ExpressionType, int Direction> -inline const typename VectorwiseOp<ExpressionType,Direction>::HNormalizedReturnType -VectorwiseOp<ExpressionType,Direction>::hnormalized() const -{ - return HNormalized_Block(_expression(),0,0, - Direction==Vertical ? _expression().rows()-1 : _expression().rows(), - Direction==Horizontal ? _expression().cols()-1 : _expression().cols()).cwiseQuotient( - Replicate<HNormalized_Factors, - Direction==Vertical ? HNormalized_SizeMinusOne : 1, - Direction==Horizontal ? HNormalized_SizeMinusOne : 1> - (HNormalized_Factors(_expression(), - Direction==Vertical ? _expression().rows()-1:0, - Direction==Horizontal ? _expression().cols()-1:0, - Direction==Vertical ? 1 : _expression().rows(), - Direction==Horizontal ? 1 : _expression().cols()), - Direction==Vertical ? _expression().rows()-1 : 1, - Direction==Horizontal ? _expression().cols()-1 : 1)); -} - -namespace internal { - -template<typename MatrixOrTransformType> -struct take_matrix_for_product -{ - typedef MatrixOrTransformType type; - static const type& run(const type &x) { return x; } -}; - -template<typename Scalar, int Dim, int Mode,int Options> -struct take_matrix_for_product<Transform<Scalar, Dim, Mode, Options> > -{ - typedef Transform<Scalar, Dim, Mode, Options> TransformType; - typedef typename internal::add_const<typename TransformType::ConstAffinePart>::type type; - static type run (const TransformType& x) { return x.affine(); } -}; - -template<typename Scalar, int Dim, int Options> -struct take_matrix_for_product<Transform<Scalar, Dim, Projective, Options> > -{ - typedef Transform<Scalar, Dim, Projective, Options> TransformType; - typedef typename TransformType::MatrixType type; - static const type& run (const TransformType& x) { return x.matrix(); } -}; - -template<typename MatrixType,typename Lhs> -struct traits<homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> > -{ - typedef typename take_matrix_for_product<Lhs>::type LhsMatrixType; - typedef typename remove_all<MatrixType>::type MatrixTypeCleaned; - typedef typename remove_all<LhsMatrixType>::type LhsMatrixTypeCleaned; - typedef typename make_proper_matrix_type< - typename traits<MatrixTypeCleaned>::Scalar, - LhsMatrixTypeCleaned::RowsAtCompileTime, - MatrixTypeCleaned::ColsAtCompileTime, - MatrixTypeCleaned::PlainObject::Options, - LhsMatrixTypeCleaned::MaxRowsAtCompileTime, - MatrixTypeCleaned::MaxColsAtCompileTime>::type ReturnType; -}; - -template<typename MatrixType,typename Lhs> -struct homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> - : public ReturnByValue<homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> > -{ - typedef typename traits<homogeneous_left_product_impl>::LhsMatrixType LhsMatrixType; - typedef typename remove_all<LhsMatrixType>::type LhsMatrixTypeCleaned; - typedef typename remove_all<typename LhsMatrixTypeCleaned::Nested>::type LhsMatrixTypeNested; - typedef typename MatrixType::Index Index; - homogeneous_left_product_impl(const Lhs& lhs, const MatrixType& rhs) - : m_lhs(take_matrix_for_product<Lhs>::run(lhs)), - m_rhs(rhs) - {} - - inline Index rows() const { return m_lhs.rows(); } - inline Index cols() const { return m_rhs.cols(); } - - template<typename Dest> void evalTo(Dest& dst) const - { - // FIXME investigate how to allow lazy evaluation of this product when possible - dst = Block<const LhsMatrixTypeNested, - LhsMatrixTypeNested::RowsAtCompileTime, - LhsMatrixTypeNested::ColsAtCompileTime==Dynamic?Dynamic:LhsMatrixTypeNested::ColsAtCompileTime-1> - (m_lhs,0,0,m_lhs.rows(),m_lhs.cols()-1) * m_rhs; - dst += m_lhs.col(m_lhs.cols()-1).rowwise() - .template replicate<MatrixType::ColsAtCompileTime>(m_rhs.cols()); - } - - typename LhsMatrixTypeCleaned::Nested m_lhs; - typename MatrixType::Nested m_rhs; -}; - -template<typename MatrixType,typename Rhs> -struct traits<homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> > -{ - typedef typename make_proper_matrix_type<typename traits<MatrixType>::Scalar, - MatrixType::RowsAtCompileTime, - Rhs::ColsAtCompileTime, - MatrixType::PlainObject::Options, - MatrixType::MaxRowsAtCompileTime, - Rhs::MaxColsAtCompileTime>::type ReturnType; -}; - -template<typename MatrixType,typename Rhs> -struct homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> - : public ReturnByValue<homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> > -{ - typedef typename remove_all<typename Rhs::Nested>::type RhsNested; - typedef typename MatrixType::Index Index; - homogeneous_right_product_impl(const MatrixType& lhs, const Rhs& rhs) - : m_lhs(lhs), m_rhs(rhs) - {} - - inline Index rows() const { return m_lhs.rows(); } - inline Index cols() const { return m_rhs.cols(); } - - template<typename Dest> void evalTo(Dest& dst) const - { - // FIXME investigate how to allow lazy evaluation of this product when possible - dst = m_lhs * Block<const RhsNested, - RhsNested::RowsAtCompileTime==Dynamic?Dynamic:RhsNested::RowsAtCompileTime-1, - RhsNested::ColsAtCompileTime> - (m_rhs,0,0,m_rhs.rows()-1,m_rhs.cols()); - dst += m_rhs.row(m_rhs.rows()-1).colwise() - .template replicate<MatrixType::RowsAtCompileTime>(m_lhs.rows()); - } - - typename MatrixType::Nested m_lhs; - typename Rhs::Nested m_rhs; -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_HOMOGENEOUS_H diff --git a/third_party/eigen3/Eigen/src/Geometry/Hyperplane.h b/third_party/eigen3/Eigen/src/Geometry/Hyperplane.h deleted file mode 100644 index aeff43fefa..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/Hyperplane.h +++ /dev/null @@ -1,270 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_HYPERPLANE_H -#define EIGEN_HYPERPLANE_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Hyperplane - * - * \brief A hyperplane - * - * A hyperplane is an affine subspace of dimension n-1 in a space of dimension n. - * For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane. - * - * \param _Scalar the scalar type, i.e., the type of the coefficients - * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic. - * Notice that the dimension of the hyperplane is _AmbientDim-1. - * - * This class represents an hyperplane as the zero set of the implicit equation - * \f$ n \cdot x + d = 0 \f$ where \f$ n \f$ is a unit normal vector of the plane (linear part) - * and \f$ d \f$ is the distance (offset) to the origin. - */ -template <typename _Scalar, int _AmbientDim, int _Options> -class Hyperplane -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==Dynamic ? Dynamic : _AmbientDim+1) - enum { - AmbientDimAtCompileTime = _AmbientDim, - Options = _Options - }; - typedef _Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef DenseIndex Index; - typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType; - typedef Matrix<Scalar,Index(AmbientDimAtCompileTime)==Dynamic - ? Dynamic - : Index(AmbientDimAtCompileTime)+1,1,Options> Coefficients; - typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType; - typedef const Block<const Coefficients,AmbientDimAtCompileTime,1> ConstNormalReturnType; - - /** Default constructor without initialization */ - inline Hyperplane() {} - - template<int OtherOptions> - Hyperplane(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other) - : m_coeffs(other.coeffs()) - {} - - /** Constructs a dynamic-size hyperplane with \a _dim the dimension - * of the ambient space */ - inline explicit Hyperplane(Index _dim) : m_coeffs(_dim+1) {} - - /** Construct a plane from its normal \a n and a point \a e onto the plane. - * \warning the vector normal is assumed to be normalized. - */ - inline Hyperplane(const VectorType& n, const VectorType& e) - : m_coeffs(n.size()+1) - { - normal() = n; - offset() = -n.dot(e); - } - - /** Constructs a plane from its normal \a n and distance to the origin \a d - * such that the algebraic equation of the plane is \f$ n \cdot x + d = 0 \f$. - * \warning the vector normal is assumed to be normalized. - */ - inline Hyperplane(const VectorType& n, const Scalar& d) - : m_coeffs(n.size()+1) - { - normal() = n; - offset() = d; - } - - /** Constructs a hyperplane passing through the two points. If the dimension of the ambient space - * is greater than 2, then there isn't uniqueness, so an arbitrary choice is made. - */ - static inline Hyperplane Through(const VectorType& p0, const VectorType& p1) - { - Hyperplane result(p0.size()); - result.normal() = (p1 - p0).unitOrthogonal(); - result.offset() = -p0.dot(result.normal()); - return result; - } - - /** Constructs a hyperplane passing through the three points. The dimension of the ambient space - * is required to be exactly 3. - */ - static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2) - { - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3) - Hyperplane result(p0.size()); - result.normal() = (p2 - p0).cross(p1 - p0).normalized(); - result.offset() = -p0.dot(result.normal()); - return result; - } - - /** Constructs a hyperplane passing through the parametrized line \a parametrized. - * If the dimension of the ambient space is greater than 2, then there isn't uniqueness, - * so an arbitrary choice is made. - */ - // FIXME to be consitent with the rest this could be implemented as a static Through function ?? - explicit Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized) - { - normal() = parametrized.direction().unitOrthogonal(); - offset() = -parametrized.origin().dot(normal()); - } - - ~Hyperplane() {} - - /** \returns the dimension in which the plane holds */ - inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_coeffs.size()-1 : Index(AmbientDimAtCompileTime); } - - /** normalizes \c *this */ - void normalize(void) - { - m_coeffs /= normal().norm(); - } - - /** \returns the signed distance between the plane \c *this and a point \a p. - * \sa absDistance() - */ - inline Scalar signedDistance(const VectorType& p) const { return normal().dot(p) + offset(); } - - /** \returns the absolute distance between the plane \c *this and a point \a p. - * \sa signedDistance() - */ - inline Scalar absDistance(const VectorType& p) const { using std::abs; return abs(signedDistance(p)); } - - /** \returns the projection of a point \a p onto the plane \c *this. - */ - inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); } - - /** \returns a constant reference to the unit normal vector of the plane, which corresponds - * to the linear part of the implicit equation. - */ - inline ConstNormalReturnType normal() const { return ConstNormalReturnType(m_coeffs,0,0,dim(),1); } - - /** \returns a non-constant reference to the unit normal vector of the plane, which corresponds - * to the linear part of the implicit equation. - */ - inline NormalReturnType normal() { return NormalReturnType(m_coeffs,0,0,dim(),1); } - - /** \returns the distance to the origin, which is also the "constant term" of the implicit equation - * \warning the vector normal is assumed to be normalized. - */ - inline const Scalar& offset() const { return m_coeffs.coeff(dim()); } - - /** \returns a non-constant reference to the distance to the origin, which is also the constant part - * of the implicit equation */ - inline Scalar& offset() { return m_coeffs(dim()); } - - /** \returns a constant reference to the coefficients c_i of the plane equation: - * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$ - */ - inline const Coefficients& coeffs() const { return m_coeffs; } - - /** \returns a non-constant reference to the coefficients c_i of the plane equation: - * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$ - */ - inline Coefficients& coeffs() { return m_coeffs; } - - /** \returns the intersection of *this with \a other. - * - * \warning The ambient space must be a plane, i.e. have dimension 2, so that \c *this and \a other are lines. - * - * \note If \a other is approximately parallel to *this, this method will return any point on *this. - */ - VectorType intersection(const Hyperplane& other) const - { - using std::abs; - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2) - Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0); - // since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests - // whether the two lines are approximately parallel. - if(internal::isMuchSmallerThan(det, Scalar(1))) - { // special case where the two lines are approximately parallel. Pick any point on the first line. - if(abs(coeffs().coeff(1))>abs(coeffs().coeff(0))) - return VectorType(coeffs().coeff(1), -coeffs().coeff(2)/coeffs().coeff(1)-coeffs().coeff(0)); - else - return VectorType(-coeffs().coeff(2)/coeffs().coeff(0)-coeffs().coeff(1), coeffs().coeff(0)); - } - else - { // general case - Scalar invdet = Scalar(1) / det; - return VectorType(invdet*(coeffs().coeff(1)*other.coeffs().coeff(2)-other.coeffs().coeff(1)*coeffs().coeff(2)), - invdet*(other.coeffs().coeff(0)*coeffs().coeff(2)-coeffs().coeff(0)*other.coeffs().coeff(2))); - } - } - - /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this. - * - * \param mat the Dim x Dim transformation matrix - * \param traits specifies whether the matrix \a mat represents an #Isometry - * or a more generic #Affine transformation. The default is #Affine. - */ - template<typename XprType> - inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine) - { - if (traits==Affine) - normal() = mat.inverse().transpose() * normal(); - else if (traits==Isometry) - normal() = mat * normal(); - else - { - eigen_assert(0 && "invalid traits value in Hyperplane::transform()"); - } - return *this; - } - - /** Applies the transformation \a t to \c *this and returns a reference to \c *this. - * - * \param t the transformation of dimension Dim - * \param traits specifies whether the transformation \a t represents an #Isometry - * or a more generic #Affine transformation. The default is #Affine. - * Other kind of transformations are not supported. - */ - template<int TrOptions> - inline Hyperplane& transform(const Transform<Scalar,AmbientDimAtCompileTime,Affine,TrOptions>& t, - TransformTraits traits = Affine) - { - transform(t.linear(), traits); - offset() -= normal().dot(t.translation()); - return *this; - } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Hyperplane, - Hyperplane<NewScalarType,AmbientDimAtCompileTime,Options> >::type cast() const - { - return typename internal::cast_return_type<Hyperplane, - Hyperplane<NewScalarType,AmbientDimAtCompileTime,Options> >::type(*this); - } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType,int OtherOptions> - inline explicit Hyperplane(const Hyperplane<OtherScalarType,AmbientDimAtCompileTime,OtherOptions>& other) - { m_coeffs = other.coeffs().template cast<Scalar>(); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - template<int OtherOptions> - bool isApprox(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const - { return m_coeffs.isApprox(other.m_coeffs, prec); } - -protected: - - Coefficients m_coeffs; -}; - -} // end namespace Eigen - -#endif // EIGEN_HYPERPLANE_H diff --git a/third_party/eigen3/Eigen/src/Geometry/OrthoMethods.h b/third_party/eigen3/Eigen/src/Geometry/OrthoMethods.h deleted file mode 100644 index 26be3ee5b9..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/OrthoMethods.h +++ /dev/null @@ -1,221 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ORTHOMETHODS_H -#define EIGEN_ORTHOMETHODS_H - -namespace Eigen { - -/** \geometry_module - * - * \returns the cross product of \c *this and \a other - * - * Here is a very good explanation of cross-product: http://xkcd.com/199/ - * \sa MatrixBase::cross3() - */ -template<typename Derived> -template<typename OtherDerived> -inline typename MatrixBase<Derived>::template cross_product_return_type<OtherDerived>::type -MatrixBase<Derived>::cross(const MatrixBase<OtherDerived>& other) const -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,3) - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3) - - // Note that there is no need for an expression here since the compiler - // optimize such a small temporary very well (even within a complex expression) - typename internal::nested<Derived,2>::type lhs(derived()); - typename internal::nested<OtherDerived,2>::type rhs(other.derived()); - return typename cross_product_return_type<OtherDerived>::type( - numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)), - numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)), - numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)) - ); -} - -namespace internal { - -template< int Arch,typename VectorLhs,typename VectorRhs, - typename Scalar = typename VectorLhs::Scalar, - bool Vectorizable = bool((VectorLhs::Flags&VectorRhs::Flags)&PacketAccessBit)> -struct cross3_impl { - static inline typename internal::plain_matrix_type<VectorLhs>::type - run(const VectorLhs& lhs, const VectorRhs& rhs) - { - return typename internal::plain_matrix_type<VectorLhs>::type( - numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)), - numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)), - numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)), - 0 - ); - } -}; - -} - -/** \geometry_module - * - * \returns the cross product of \c *this and \a other using only the x, y, and z coefficients - * - * The size of \c *this and \a other must be four. This function is especially useful - * when using 4D vectors instead of 3D ones to get advantage of SSE/AltiVec vectorization. - * - * \sa MatrixBase::cross() - */ -template<typename Derived> -template<typename OtherDerived> -inline typename MatrixBase<Derived>::PlainObject -MatrixBase<Derived>::cross3(const MatrixBase<OtherDerived>& other) const -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,4) - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,4) - - typedef typename internal::nested<Derived,2>::type DerivedNested; - typedef typename internal::nested<OtherDerived,2>::type OtherDerivedNested; - DerivedNested lhs(derived()); - OtherDerivedNested rhs(other.derived()); - - return internal::cross3_impl<Architecture::Target, - typename internal::remove_all<DerivedNested>::type, - typename internal::remove_all<OtherDerivedNested>::type>::run(lhs,rhs); -} - -/** \returns a matrix expression of the cross product of each column or row - * of the referenced expression with the \a other vector. - * - * The referenced matrix must have one dimension equal to 3. - * The result matrix has the same dimensions than the referenced one. - * - * \geometry_module - * - * \sa MatrixBase::cross() */ -template<typename ExpressionType, int Direction> -template<typename OtherDerived> -const typename VectorwiseOp<ExpressionType,Direction>::CrossReturnType -VectorwiseOp<ExpressionType,Direction>::cross(const MatrixBase<OtherDerived>& other) const -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3) - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - - CrossReturnType res(_expression().rows(),_expression().cols()); - if(Direction==Vertical) - { - eigen_assert(CrossReturnType::RowsAtCompileTime==3 && "the matrix must have exactly 3 rows"); - res.row(0) = (_expression().row(1) * other.coeff(2) - _expression().row(2) * other.coeff(1)).conjugate(); - res.row(1) = (_expression().row(2) * other.coeff(0) - _expression().row(0) * other.coeff(2)).conjugate(); - res.row(2) = (_expression().row(0) * other.coeff(1) - _expression().row(1) * other.coeff(0)).conjugate(); - } - else - { - eigen_assert(CrossReturnType::ColsAtCompileTime==3 && "the matrix must have exactly 3 columns"); - res.col(0) = (_expression().col(1) * other.coeff(2) - _expression().col(2) * other.coeff(1)).conjugate(); - res.col(1) = (_expression().col(2) * other.coeff(0) - _expression().col(0) * other.coeff(2)).conjugate(); - res.col(2) = (_expression().col(0) * other.coeff(1) - _expression().col(1) * other.coeff(0)).conjugate(); - } - return res; -} - -namespace internal { - -template<typename Derived, int Size = Derived::SizeAtCompileTime> -struct unitOrthogonal_selector -{ - typedef typename plain_matrix_type<Derived>::type VectorType; - typedef typename traits<Derived>::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename Derived::Index Index; - typedef Matrix<Scalar,2,1> Vector2; - EIGEN_DEVICE_FUNC - static inline VectorType run(const Derived& src) - { - VectorType perp = VectorType::Zero(src.size()); - Index maxi = 0; - Index sndi = 0; - src.cwiseAbs().maxCoeff(&maxi); - if (maxi==0) - sndi = 1; - RealScalar invnm = RealScalar(1)/(Vector2() << src.coeff(sndi),src.coeff(maxi)).finished().norm(); - perp.coeffRef(maxi) = -numext::conj(src.coeff(sndi)) * invnm; - perp.coeffRef(sndi) = numext::conj(src.coeff(maxi)) * invnm; - - return perp; - } -}; - -template<typename Derived> -struct unitOrthogonal_selector<Derived,3> -{ - typedef typename plain_matrix_type<Derived>::type VectorType; - typedef typename traits<Derived>::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - EIGEN_DEVICE_FUNC - static inline VectorType run(const Derived& src) - { - VectorType perp; - /* Let us compute the crossed product of *this with a vector - * that is not too close to being colinear to *this. - */ - - /* unless the x and y coords are both close to zero, we can - * simply take ( -y, x, 0 ) and normalize it. - */ - if((!isMuchSmallerThan(src.x(), src.z())) - || (!isMuchSmallerThan(src.y(), src.z()))) - { - RealScalar invnm = RealScalar(1)/src.template head<2>().norm(); - perp.coeffRef(0) = -numext::conj(src.y())*invnm; - perp.coeffRef(1) = numext::conj(src.x())*invnm; - perp.coeffRef(2) = 0; - } - /* if both x and y are close to zero, then the vector is close - * to the z-axis, so it's far from colinear to the x-axis for instance. - * So we take the crossed product with (1,0,0) and normalize it. - */ - else - { - RealScalar invnm = RealScalar(1)/src.template tail<2>().norm(); - perp.coeffRef(0) = 0; - perp.coeffRef(1) = -numext::conj(src.z())*invnm; - perp.coeffRef(2) = numext::conj(src.y())*invnm; - } - - return perp; - } -}; - -template<typename Derived> -struct unitOrthogonal_selector<Derived,2> -{ - typedef typename plain_matrix_type<Derived>::type VectorType; - EIGEN_DEVICE_FUNC - static inline VectorType run(const Derived& src) - { return VectorType(-numext::conj(src.y()), numext::conj(src.x())).normalized(); } -}; - -} // end namespace internal - -/** \returns a unit vector which is orthogonal to \c *this - * - * The size of \c *this must be at least 2. If the size is exactly 2, - * then the returned vector is a counter clock wise rotation of \c *this, i.e., (-y,x).normalized(). - * - * \sa cross() - */ -template<typename Derived> -typename MatrixBase<Derived>::PlainObject -MatrixBase<Derived>::unitOrthogonal() const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return internal::unitOrthogonal_selector<Derived>::run(derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_ORTHOMETHODS_H diff --git a/third_party/eigen3/Eigen/src/Geometry/ParametrizedLine.h b/third_party/eigen3/Eigen/src/Geometry/ParametrizedLine.h deleted file mode 100644 index 77fa228e6a..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/ParametrizedLine.h +++ /dev/null @@ -1,195 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PARAMETRIZEDLINE_H -#define EIGEN_PARAMETRIZEDLINE_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class ParametrizedLine - * - * \brief A parametrized line - * - * A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit - * direction vector \f$ \mathbf{d} \f$ such that the line corresponds to - * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ t \in \mathbf{R} \f$. - * - * \param _Scalar the scalar type, i.e., the type of the coefficients - * \param _AmbientDim the dimension of the ambient space, can be a compile time value or Dynamic. - */ -template <typename _Scalar, int _AmbientDim, int _Options> -class ParametrizedLine -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim) - enum { - AmbientDimAtCompileTime = _AmbientDim, - Options = _Options - }; - typedef _Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef DenseIndex Index; - typedef Matrix<Scalar,AmbientDimAtCompileTime,1,Options> VectorType; - - /** Default constructor without initialization */ - inline ParametrizedLine() {} - - template<int OtherOptions> - ParametrizedLine(const ParametrizedLine<Scalar,AmbientDimAtCompileTime,OtherOptions>& other) - : m_origin(other.origin()), m_direction(other.direction()) - {} - - /** Constructs a dynamic-size line with \a _dim the dimension - * of the ambient space */ - inline explicit ParametrizedLine(Index _dim) : m_origin(_dim), m_direction(_dim) {} - - /** Initializes a parametrized line of direction \a direction and origin \a origin. - * \warning the vector direction is assumed to be normalized. - */ - ParametrizedLine(const VectorType& origin, const VectorType& direction) - : m_origin(origin), m_direction(direction) {} - - template <int OtherOptions> - explicit ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane); - - /** Constructs a parametrized line going from \a p0 to \a p1. */ - static inline ParametrizedLine Through(const VectorType& p0, const VectorType& p1) - { return ParametrizedLine(p0, (p1-p0).normalized()); } - - ~ParametrizedLine() {} - - /** \returns the dimension in which the line holds */ - inline Index dim() const { return m_direction.size(); } - - const VectorType& origin() const { return m_origin; } - VectorType& origin() { return m_origin; } - - const VectorType& direction() const { return m_direction; } - VectorType& direction() { return m_direction; } - - /** \returns the squared distance of a point \a p to its projection onto the line \c *this. - * \sa distance() - */ - RealScalar squaredDistance(const VectorType& p) const - { - VectorType diff = p - origin(); - return (diff - direction().dot(diff) * direction()).squaredNorm(); - } - /** \returns the distance of a point \a p to its projection onto the line \c *this. - * \sa squaredDistance() - */ - RealScalar distance(const VectorType& p) const { using std::sqrt; return sqrt(squaredDistance(p)); } - - /** \returns the projection of a point \a p onto the line \c *this. */ - VectorType projection(const VectorType& p) const - { return origin() + direction().dot(p-origin()) * direction(); } - - VectorType pointAt(const Scalar& t) const; - - template <int OtherOptions> - Scalar intersectionParameter(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const; - - template <int OtherOptions> - Scalar intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const; - - template <int OtherOptions> - VectorType intersectionPoint(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const; - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<ParametrizedLine, - ParametrizedLine<NewScalarType,AmbientDimAtCompileTime,Options> >::type cast() const - { - return typename internal::cast_return_type<ParametrizedLine, - ParametrizedLine<NewScalarType,AmbientDimAtCompileTime,Options> >::type(*this); - } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType,int OtherOptions> - inline explicit ParametrizedLine(const ParametrizedLine<OtherScalarType,AmbientDimAtCompileTime,OtherOptions>& other) - { - m_origin = other.origin().template cast<Scalar>(); - m_direction = other.direction().template cast<Scalar>(); - } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const ParametrizedLine& other, typename NumTraits<Scalar>::Real prec = NumTraits<Scalar>::dummy_precision()) const - { return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec); } - -protected: - - VectorType m_origin, m_direction; -}; - -/** Constructs a parametrized line from a 2D hyperplane - * - * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line - */ -template <typename _Scalar, int _AmbientDim, int _Options> -template <int OtherOptions> -inline ParametrizedLine<_Scalar, _AmbientDim,_Options>::ParametrizedLine(const Hyperplane<_Scalar, _AmbientDim,OtherOptions>& hyperplane) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2) - direction() = hyperplane.normal().unitOrthogonal(); - origin() = -hyperplane.normal()*hyperplane.offset(); -} - -/** \returns the point at \a t along this line - */ -template <typename _Scalar, int _AmbientDim, int _Options> -inline typename ParametrizedLine<_Scalar, _AmbientDim,_Options>::VectorType -ParametrizedLine<_Scalar, _AmbientDim,_Options>::pointAt(const _Scalar& t) const -{ - return origin() + (direction()*t); -} - -/** \returns the parameter value of the intersection between \c *this and the given \a hyperplane - */ -template <typename _Scalar, int _AmbientDim, int _Options> -template <int OtherOptions> -inline _Scalar ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersectionParameter(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const -{ - return -(hyperplane.offset()+hyperplane.normal().dot(origin())) - / hyperplane.normal().dot(direction()); -} - - -/** \deprecated use intersectionParameter() - * \returns the parameter value of the intersection between \c *this and the given \a hyperplane - */ -template <typename _Scalar, int _AmbientDim, int _Options> -template <int OtherOptions> -inline _Scalar ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersection(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const -{ - return intersectionParameter(hyperplane); -} - -/** \returns the point of the intersection between \c *this and the given hyperplane - */ -template <typename _Scalar, int _AmbientDim, int _Options> -template <int OtherOptions> -inline typename ParametrizedLine<_Scalar, _AmbientDim,_Options>::VectorType -ParametrizedLine<_Scalar, _AmbientDim,_Options>::intersectionPoint(const Hyperplane<_Scalar, _AmbientDim, OtherOptions>& hyperplane) const -{ - return pointAt(intersectionParameter(hyperplane)); -} - -} // end namespace Eigen - -#endif // EIGEN_PARAMETRIZEDLINE_H diff --git a/third_party/eigen3/Eigen/src/Geometry/Quaternion.h b/third_party/eigen3/Eigen/src/Geometry/Quaternion.h deleted file mode 100644 index 8524befddf..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/Quaternion.h +++ /dev/null @@ -1,778 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Mathieu Gautier <mathieu.gautier@cea.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_QUATERNION_H -#define EIGEN_QUATERNION_H -namespace Eigen { - - -/*************************************************************************** -* Definition of QuaternionBase<Derived> -* The implementation is at the end of the file -***************************************************************************/ - -namespace internal { -template<typename Other, - int OtherRows=Other::RowsAtCompileTime, - int OtherCols=Other::ColsAtCompileTime> -struct quaternionbase_assign_impl; -} - -/** \geometry_module \ingroup Geometry_Module - * \class QuaternionBase - * \brief Base class for quaternion expressions - * \tparam Derived derived type (CRTP) - * \sa class Quaternion - */ -template<class Derived> -class QuaternionBase : public RotationBase<Derived, 3> -{ - public: - typedef RotationBase<Derived, 3> Base; - - using Base::operator*; - using Base::derived; - - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename internal::traits<Derived>::Coefficients Coefficients; - enum { - Flags = Eigen::internal::traits<Derived>::Flags - }; - - // typedef typename Matrix<Scalar,4,1> Coefficients; - /** the type of a 3D vector */ - typedef Matrix<Scalar,3,1> Vector3; - /** the equivalent rotation matrix type */ - typedef Matrix<Scalar,3,3> Matrix3; - /** the equivalent angle-axis type */ - typedef AngleAxis<Scalar> AngleAxisType; - - - - /** \returns the \c x coefficient */ - inline Scalar x() const { return this->derived().coeffs().coeff(0); } - /** \returns the \c y coefficient */ - inline Scalar y() const { return this->derived().coeffs().coeff(1); } - /** \returns the \c z coefficient */ - inline Scalar z() const { return this->derived().coeffs().coeff(2); } - /** \returns the \c w coefficient */ - inline Scalar w() const { return this->derived().coeffs().coeff(3); } - - /** \returns a reference to the \c x coefficient */ - inline Scalar& x() { return this->derived().coeffs().coeffRef(0); } - /** \returns a reference to the \c y coefficient */ - inline Scalar& y() { return this->derived().coeffs().coeffRef(1); } - /** \returns a reference to the \c z coefficient */ - inline Scalar& z() { return this->derived().coeffs().coeffRef(2); } - /** \returns a reference to the \c w coefficient */ - inline Scalar& w() { return this->derived().coeffs().coeffRef(3); } - - /** \returns a read-only vector expression of the imaginary part (x,y,z) */ - inline const VectorBlock<const Coefficients,3> vec() const { return coeffs().template head<3>(); } - - /** \returns a vector expression of the imaginary part (x,y,z) */ - inline VectorBlock<Coefficients,3> vec() { return coeffs().template head<3>(); } - - /** \returns a read-only vector expression of the coefficients (x,y,z,w) */ - inline const typename internal::traits<Derived>::Coefficients& coeffs() const { return derived().coeffs(); } - - /** \returns a vector expression of the coefficients (x,y,z,w) */ - inline typename internal::traits<Derived>::Coefficients& coeffs() { return derived().coeffs(); } - - EIGEN_STRONG_INLINE QuaternionBase<Derived>& operator=(const QuaternionBase<Derived>& other); - template<class OtherDerived> EIGEN_STRONG_INLINE Derived& operator=(const QuaternionBase<OtherDerived>& other); - -// disabled this copy operator as it is giving very strange compilation errors when compiling -// test_stdvector with GCC 4.4.2. This looks like a GCC bug though, so feel free to re-enable it if it's -// useful; however notice that we already have the templated operator= above and e.g. in MatrixBase -// we didn't have to add, in addition to templated operator=, such a non-templated copy operator. -// Derived& operator=(const QuaternionBase& other) -// { return operator=<Derived>(other); } - - Derived& operator=(const AngleAxisType& aa); - template<class OtherDerived> Derived& operator=(const MatrixBase<OtherDerived>& m); - - /** \returns a quaternion representing an identity rotation - * \sa MatrixBase::Identity() - */ - static inline Quaternion<Scalar> Identity() { return Quaternion<Scalar>(1, 0, 0, 0); } - - /** \sa QuaternionBase::Identity(), MatrixBase::setIdentity() - */ - inline QuaternionBase& setIdentity() { coeffs() << 0, 0, 0, 1; return *this; } - - /** \returns the squared norm of the quaternion's coefficients - * \sa QuaternionBase::norm(), MatrixBase::squaredNorm() - */ - inline Scalar squaredNorm() const { return coeffs().squaredNorm(); } - - /** \returns the norm of the quaternion's coefficients - * \sa QuaternionBase::squaredNorm(), MatrixBase::norm() - */ - inline Scalar norm() const { return coeffs().norm(); } - - /** Normalizes the quaternion \c *this - * \sa normalized(), MatrixBase::normalize() */ - inline void normalize() { coeffs().normalize(); } - /** \returns a normalized copy of \c *this - * \sa normalize(), MatrixBase::normalized() */ - inline Quaternion<Scalar> normalized() const { return Quaternion<Scalar>(coeffs().normalized()); } - - /** \returns the dot product of \c *this and \a other - * Geometrically speaking, the dot product of two unit quaternions - * corresponds to the cosine of half the angle between the two rotations. - * \sa angularDistance() - */ - template<class OtherDerived> inline Scalar dot(const QuaternionBase<OtherDerived>& other) const { return coeffs().dot(other.coeffs()); } - - template<class OtherDerived> Scalar angularDistance(const QuaternionBase<OtherDerived>& other) const; - - /** \returns an equivalent 3x3 rotation matrix */ - Matrix3 toRotationMatrix() const; - - /** \returns the quaternion which transform \a a into \a b through a rotation */ - template<typename Derived1, typename Derived2> - Derived& setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b); - - template<class OtherDerived> EIGEN_STRONG_INLINE Quaternion<Scalar> operator* (const QuaternionBase<OtherDerived>& q) const; - template<class OtherDerived> EIGEN_STRONG_INLINE Derived& operator*= (const QuaternionBase<OtherDerived>& q); - - /** \returns the quaternion describing the inverse rotation */ - Quaternion<Scalar> inverse() const; - - /** \returns the conjugated quaternion */ - Quaternion<Scalar> conjugate() const; - - template<class OtherDerived> Quaternion<Scalar> slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const; - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - template<class OtherDerived> - bool isApprox(const QuaternionBase<OtherDerived>& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const - { return coeffs().isApprox(other.coeffs(), prec); } - - /** return the result vector of \a v through the rotation*/ - EIGEN_STRONG_INLINE Vector3 _transformVector(Vector3 v) const; - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Derived,Quaternion<NewScalarType> >::type cast() const - { - return typename internal::cast_return_type<Derived,Quaternion<NewScalarType> >::type(derived()); - } - -#ifdef EIGEN_QUATERNIONBASE_PLUGIN -# include EIGEN_QUATERNIONBASE_PLUGIN -#endif -}; - -/*************************************************************************** -* Definition/implementation of Quaternion<Scalar> -***************************************************************************/ - -/** \geometry_module \ingroup Geometry_Module - * - * \class Quaternion - * - * \brief The quaternion class used to represent 3D orientations and rotations - * - * \tparam _Scalar the scalar type, i.e., the type of the coefficients - * \tparam _Options controls the memory alignment of the coefficients. Can be \# AutoAlign or \# DontAlign. Default is AutoAlign. - * - * This class represents a quaternion \f$ w+xi+yj+zk \f$ that is a convenient representation of - * orientations and rotations of objects in three dimensions. Compared to other representations - * like Euler angles or 3x3 matrices, quaternions offer the following advantages: - * \li \b compact storage (4 scalars) - * \li \b efficient to compose (28 flops), - * \li \b stable spherical interpolation - * - * The following two typedefs are provided for convenience: - * \li \c Quaternionf for \c float - * \li \c Quaterniond for \c double - * - * \warning Operations interpreting the quaternion as rotation have undefined behavior if the quaternion is not normalized. - * - * \sa class AngleAxis, class Transform - */ - -namespace internal { -template<typename _Scalar,int _Options> -struct traits<Quaternion<_Scalar,_Options> > -{ - typedef Quaternion<_Scalar,_Options> PlainObject; - typedef _Scalar Scalar; - typedef Matrix<_Scalar,4,1,_Options> Coefficients; - enum{ - IsAligned = internal::traits<Coefficients>::Flags & AlignedBit, - Flags = IsAligned ? (AlignedBit | LvalueBit) : LvalueBit - }; -}; -} - -template<typename _Scalar, int _Options> -class Quaternion : public QuaternionBase<Quaternion<_Scalar,_Options> > -{ -public: - typedef QuaternionBase<Quaternion<_Scalar,_Options> > Base; - enum { IsAligned = internal::traits<Quaternion>::IsAligned }; - - typedef _Scalar Scalar; - - EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Quaternion) - using Base::operator*=; - - typedef typename internal::traits<Quaternion>::Coefficients Coefficients; - typedef typename Base::AngleAxisType AngleAxisType; - - /** Default constructor leaving the quaternion uninitialized. */ - inline Quaternion() {} - - /** Constructs and initializes the quaternion \f$ w+xi+yj+zk \f$ from - * its four coefficients \a w, \a x, \a y and \a z. - * - * \warning Note the order of the arguments: the real \a w coefficient first, - * while internally the coefficients are stored in the following order: - * [\c x, \c y, \c z, \c w] - */ - inline Quaternion(const Scalar& w, const Scalar& x, const Scalar& y, const Scalar& z) : m_coeffs(x, y, z, w){} - - /** Constructs and initialize a quaternion from the array data */ - inline Quaternion(const Scalar* data) : m_coeffs(data) {} - - /** Copy constructor */ - template<class Derived> EIGEN_STRONG_INLINE Quaternion(const QuaternionBase<Derived>& other) { this->Base::operator=(other); } - - /** Constructs and initializes a quaternion from the angle-axis \a aa */ - explicit inline Quaternion(const AngleAxisType& aa) { *this = aa; } - - /** Constructs and initializes a quaternion from either: - * - a rotation matrix expression, - * - a 4D vector expression representing quaternion coefficients. - */ - template<typename Derived> - explicit inline Quaternion(const MatrixBase<Derived>& other) { *this = other; } - - /** Explicit copy constructor with scalar conversion */ - template<typename OtherScalar, int OtherOptions> - explicit inline Quaternion(const Quaternion<OtherScalar, OtherOptions>& other) - { m_coeffs = other.coeffs().template cast<Scalar>(); } - - template<typename Derived1, typename Derived2> - static Quaternion FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b); - - inline Coefficients& coeffs() { return m_coeffs;} - inline const Coefficients& coeffs() const { return m_coeffs;} - - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(IsAligned) - -protected: - Coefficients m_coeffs; - -#ifndef EIGEN_PARSED_BY_DOXYGEN - static EIGEN_STRONG_INLINE void _check_template_params() - { - EIGEN_STATIC_ASSERT( (_Options & DontAlign) == _Options, - INVALID_MATRIX_TEMPLATE_PARAMETERS) - } -#endif -}; - -/** \ingroup Geometry_Module - * single precision quaternion type */ -typedef Quaternion<float> Quaternionf; -/** \ingroup Geometry_Module - * double precision quaternion type */ -typedef Quaternion<double> Quaterniond; - -/*************************************************************************** -* Specialization of Map<Quaternion<Scalar>> -***************************************************************************/ - -namespace internal { - template<typename _Scalar, int _Options> - struct traits<Map<Quaternion<_Scalar>, _Options> > : traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> > - { - typedef Map<Matrix<_Scalar,4,1>, _Options> Coefficients; - }; -} - -namespace internal { - template<typename _Scalar, int _Options> - struct traits<Map<const Quaternion<_Scalar>, _Options> > : traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> > - { - typedef Map<const Matrix<_Scalar,4,1>, _Options> Coefficients; - typedef traits<Quaternion<_Scalar, (int(_Options)&Aligned)==Aligned ? AutoAlign : DontAlign> > TraitsBase; - enum { - Flags = TraitsBase::Flags & ~LvalueBit - }; - }; -} - -/** \ingroup Geometry_Module - * \brief Quaternion expression mapping a constant memory buffer - * - * \tparam _Scalar the type of the Quaternion coefficients - * \tparam _Options see class Map - * - * This is a specialization of class Map for Quaternion. This class allows to view - * a 4 scalar memory buffer as an Eigen's Quaternion object. - * - * \sa class Map, class Quaternion, class QuaternionBase - */ -template<typename _Scalar, int _Options> -class Map<const Quaternion<_Scalar>, _Options > - : public QuaternionBase<Map<const Quaternion<_Scalar>, _Options> > -{ - public: - typedef QuaternionBase<Map<const Quaternion<_Scalar>, _Options> > Base; - - typedef _Scalar Scalar; - typedef typename internal::traits<Map>::Coefficients Coefficients; - EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Map) - using Base::operator*=; - - /** Constructs a Mapped Quaternion object from the pointer \a coeffs - * - * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order: - * \code *coeffs == {x, y, z, w} \endcode - * - * If the template parameter _Options is set to #Aligned, then the pointer coeffs must be aligned. */ - EIGEN_STRONG_INLINE Map(const Scalar* coeffs) : m_coeffs(coeffs) {} - - inline const Coefficients& coeffs() const { return m_coeffs;} - - protected: - const Coefficients m_coeffs; -}; - -/** \ingroup Geometry_Module - * \brief Expression of a quaternion from a memory buffer - * - * \tparam _Scalar the type of the Quaternion coefficients - * \tparam _Options see class Map - * - * This is a specialization of class Map for Quaternion. This class allows to view - * a 4 scalar memory buffer as an Eigen's Quaternion object. - * - * \sa class Map, class Quaternion, class QuaternionBase - */ -template<typename _Scalar, int _Options> -class Map<Quaternion<_Scalar>, _Options > - : public QuaternionBase<Map<Quaternion<_Scalar>, _Options> > -{ - public: - typedef QuaternionBase<Map<Quaternion<_Scalar>, _Options> > Base; - - typedef _Scalar Scalar; - typedef typename internal::traits<Map>::Coefficients Coefficients; - EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Map) - using Base::operator*=; - - /** Constructs a Mapped Quaternion object from the pointer \a coeffs - * - * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order: - * \code *coeffs == {x, y, z, w} \endcode - * - * If the template parameter _Options is set to #Aligned, then the pointer coeffs must be aligned. */ - EIGEN_STRONG_INLINE Map(Scalar* coeffs) : m_coeffs(coeffs) {} - - inline Coefficients& coeffs() { return m_coeffs; } - inline const Coefficients& coeffs() const { return m_coeffs; } - - protected: - Coefficients m_coeffs; -}; - -/** \ingroup Geometry_Module - * Map an unaligned array of single precision scalars as a quaternion */ -typedef Map<Quaternion<float>, 0> QuaternionMapf; -/** \ingroup Geometry_Module - * Map an unaligned array of double precision scalars as a quaternion */ -typedef Map<Quaternion<double>, 0> QuaternionMapd; -/** \ingroup Geometry_Module - * Map a 16-byte aligned array of single precision scalars as a quaternion */ -typedef Map<Quaternion<float>, Aligned> QuaternionMapAlignedf; -/** \ingroup Geometry_Module - * Map a 16-byte aligned array of double precision scalars as a quaternion */ -typedef Map<Quaternion<double>, Aligned> QuaternionMapAlignedd; - -/*************************************************************************** -* Implementation of QuaternionBase methods -***************************************************************************/ - -// Generic Quaternion * Quaternion product -// This product can be specialized for a given architecture via the Arch template argument. -namespace internal { -template<int Arch, class Derived1, class Derived2, typename Scalar, int _Options> struct quat_product -{ - static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived1>& a, const QuaternionBase<Derived2>& b){ - return Quaternion<Scalar> - ( - a.w() * b.w() - a.x() * b.x() - a.y() * b.y() - a.z() * b.z(), - a.w() * b.x() + a.x() * b.w() + a.y() * b.z() - a.z() * b.y(), - a.w() * b.y() + a.y() * b.w() + a.z() * b.x() - a.x() * b.z(), - a.w() * b.z() + a.z() * b.w() + a.x() * b.y() - a.y() * b.x() - ); - } -}; -} - -/** \returns the concatenation of two rotations as a quaternion-quaternion product */ -template <class Derived> -template <class OtherDerived> -EIGEN_STRONG_INLINE Quaternion<typename internal::traits<Derived>::Scalar> -QuaternionBase<Derived>::operator* (const QuaternionBase<OtherDerived>& other) const -{ - EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - return internal::quat_product<Architecture::Target, Derived, OtherDerived, - typename internal::traits<Derived>::Scalar, - internal::traits<Derived>::IsAligned && internal::traits<OtherDerived>::IsAligned>::run(*this, other); -} - -/** \sa operator*(Quaternion) */ -template <class Derived> -template <class OtherDerived> -EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator*= (const QuaternionBase<OtherDerived>& other) -{ - derived() = derived() * other.derived(); - return derived(); -} - -/** Rotation of a vector by a quaternion. - * \remarks If the quaternion is used to rotate several points (>1) - * then it is much more efficient to first convert it to a 3x3 Matrix. - * Comparison of the operation cost for n transformations: - * - Quaternion2: 30n - * - Via a Matrix3: 24 + 15n - */ -template <class Derived> -EIGEN_STRONG_INLINE typename QuaternionBase<Derived>::Vector3 -QuaternionBase<Derived>::_transformVector(Vector3 v) const -{ - // Note that this algorithm comes from the optimization by hand - // of the conversion to a Matrix followed by a Matrix/Vector product. - // It appears to be much faster than the common algorithm found - // in the literature (30 versus 39 flops). It also requires two - // Vector3 as temporaries. - Vector3 uv = this->vec().cross(v); - uv += uv; - return v + this->w() * uv + this->vec().cross(uv); -} - -template<class Derived> -EIGEN_STRONG_INLINE QuaternionBase<Derived>& QuaternionBase<Derived>::operator=(const QuaternionBase<Derived>& other) -{ - coeffs() = other.coeffs(); - return derived(); -} - -template<class Derived> -template<class OtherDerived> -EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const QuaternionBase<OtherDerived>& other) -{ - coeffs() = other.coeffs(); - return derived(); -} - -/** Set \c *this from an angle-axis \a aa and returns a reference to \c *this - */ -template<class Derived> -EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const AngleAxisType& aa) -{ - using std::cos; - using std::sin; - Scalar ha = Scalar(0.5)*aa.angle(); // Scalar(0.5) to suppress precision loss warnings - this->w() = cos(ha); - this->vec() = sin(ha) * aa.axis(); - return derived(); -} - -/** Set \c *this from the expression \a xpr: - * - if \a xpr is a 4x1 vector, then \a xpr is assumed to be a quaternion - * - if \a xpr is a 3x3 matrix, then \a xpr is assumed to be rotation matrix - * and \a xpr is converted to a quaternion - */ - -template<class Derived> -template<class MatrixDerived> -inline Derived& QuaternionBase<Derived>::operator=(const MatrixBase<MatrixDerived>& xpr) -{ - EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename MatrixDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - internal::quaternionbase_assign_impl<MatrixDerived>::run(*this, xpr.derived()); - return derived(); -} - -/** Convert the quaternion to a 3x3 rotation matrix. The quaternion is required to - * be normalized, otherwise the result is undefined. - */ -template<class Derived> -inline typename QuaternionBase<Derived>::Matrix3 -QuaternionBase<Derived>::toRotationMatrix(void) const -{ - // NOTE if inlined, then gcc 4.2 and 4.4 get rid of the temporary (not gcc 4.3 !!) - // if not inlined then the cost of the return by value is huge ~ +35%, - // however, not inlining this function is an order of magnitude slower, so - // it has to be inlined, and so the return by value is not an issue - Matrix3 res; - - const Scalar tx = Scalar(2)*this->x(); - const Scalar ty = Scalar(2)*this->y(); - const Scalar tz = Scalar(2)*this->z(); - const Scalar twx = tx*this->w(); - const Scalar twy = ty*this->w(); - const Scalar twz = tz*this->w(); - const Scalar txx = tx*this->x(); - const Scalar txy = ty*this->x(); - const Scalar txz = tz*this->x(); - const Scalar tyy = ty*this->y(); - const Scalar tyz = tz*this->y(); - const Scalar tzz = tz*this->z(); - - res.coeffRef(0,0) = Scalar(1)-(tyy+tzz); - res.coeffRef(0,1) = txy-twz; - res.coeffRef(0,2) = txz+twy; - res.coeffRef(1,0) = txy+twz; - res.coeffRef(1,1) = Scalar(1)-(txx+tzz); - res.coeffRef(1,2) = tyz-twx; - res.coeffRef(2,0) = txz-twy; - res.coeffRef(2,1) = tyz+twx; - res.coeffRef(2,2) = Scalar(1)-(txx+tyy); - - return res; -} - -/** Sets \c *this to be a quaternion representing a rotation between - * the two arbitrary vectors \a a and \a b. In other words, the built - * rotation represent a rotation sending the line of direction \a a - * to the line of direction \a b, both lines passing through the origin. - * - * \returns a reference to \c *this. - * - * Note that the two input vectors do \b not have to be normalized, and - * do not need to have the same norm. - */ -template<class Derived> -template<typename Derived1, typename Derived2> -inline Derived& QuaternionBase<Derived>::setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b) -{ - using std::sqrt; - Vector3 v0 = a.normalized(); - Vector3 v1 = b.normalized(); - Scalar c = v1.dot(v0); - - // if dot == -1, vectors are nearly opposites - // => accurately compute the rotation axis by computing the - // intersection of the two planes. This is done by solving: - // x^T v0 = 0 - // x^T v1 = 0 - // under the constraint: - // ||x|| = 1 - // which yields a singular value problem - if (c < Scalar(-1)+NumTraits<Scalar>::dummy_precision()) - { - c = numext::maxi(c,Scalar(-1)); - Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose(); - JacobiSVD<Matrix<Scalar,2,3> > svd(m, ComputeFullV); - Vector3 axis = svd.matrixV().col(2); - - Scalar w2 = (Scalar(1)+c)*Scalar(0.5); - this->w() = sqrt(w2); - this->vec() = axis * sqrt(Scalar(1) - w2); - return derived(); - } - Vector3 axis = v0.cross(v1); - Scalar s = sqrt((Scalar(1)+c)*Scalar(2)); - Scalar invs = Scalar(1)/s; - this->vec() = axis * invs; - this->w() = s * Scalar(0.5); - - return derived(); -} - - -/** Returns a quaternion representing a rotation between - * the two arbitrary vectors \a a and \a b. In other words, the built - * rotation represent a rotation sending the line of direction \a a - * to the line of direction \a b, both lines passing through the origin. - * - * \returns resulting quaternion - * - * Note that the two input vectors do \b not have to be normalized, and - * do not need to have the same norm. - */ -template<typename Scalar, int Options> -template<typename Derived1, typename Derived2> -Quaternion<Scalar,Options> Quaternion<Scalar,Options>::FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b) -{ - Quaternion quat; - quat.setFromTwoVectors(a, b); - return quat; -} - - -/** \returns the multiplicative inverse of \c *this - * Note that in most cases, i.e., if you simply want the opposite rotation, - * and/or the quaternion is normalized, then it is enough to use the conjugate. - * - * \sa QuaternionBase::conjugate() - */ -template <class Derived> -inline Quaternion<typename internal::traits<Derived>::Scalar> QuaternionBase<Derived>::inverse() const -{ - // FIXME should this function be called multiplicativeInverse and conjugate() be called inverse() or opposite() ?? - Scalar n2 = this->squaredNorm(); - if (n2 > 0) - return Quaternion<Scalar>(conjugate().coeffs() / n2); - else - { - // return an invalid result to flag the error - return Quaternion<Scalar>(Coefficients::Zero()); - } -} - -/** \returns the conjugate of the \c *this which is equal to the multiplicative inverse - * if the quaternion is normalized. - * The conjugate of a quaternion represents the opposite rotation. - * - * \sa Quaternion2::inverse() - */ -template <class Derived> -inline Quaternion<typename internal::traits<Derived>::Scalar> -QuaternionBase<Derived>::conjugate() const -{ - return Quaternion<Scalar>(this->w(),-this->x(),-this->y(),-this->z()); -} - -/** \returns the angle (in radian) between two rotations - * \sa dot() - */ -template <class Derived> -template <class OtherDerived> -inline typename internal::traits<Derived>::Scalar -QuaternionBase<Derived>::angularDistance(const QuaternionBase<OtherDerived>& other) const -{ - using std::acos; - using std::abs; - Scalar d = abs(this->dot(other)); - if (d>=Scalar(1)) - return Scalar(0); - return Scalar(2) * acos(d); -} - - - -/** \returns the spherical linear interpolation between the two quaternions - * \c *this and \a other at the parameter \a t in [0;1]. - * - * This represents an interpolation for a constant motion between \c *this and \a other, - * see also http://en.wikipedia.org/wiki/Slerp. - */ -template <class Derived> -template <class OtherDerived> -Quaternion<typename internal::traits<Derived>::Scalar> -QuaternionBase<Derived>::slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const -{ - using std::acos; - using std::sin; - using std::abs; - static const Scalar one = Scalar(1) - NumTraits<Scalar>::epsilon(); - Scalar d = this->dot(other); - Scalar absD = abs(d); - - Scalar scale0; - Scalar scale1; - - if(absD>=one) - { - scale0 = Scalar(1) - t; - scale1 = t; - } - else - { - // theta is the angle between the 2 quaternions - Scalar theta = acos(absD); - Scalar sinTheta = sin(theta); - - scale0 = sin( ( Scalar(1) - t ) * theta) / sinTheta; - scale1 = sin( ( t * theta) ) / sinTheta; - } - if(d<0) scale1 = -scale1; - - return Quaternion<Scalar>(scale0 * coeffs() + scale1 * other.coeffs()); -} - -namespace internal { - -// set from a rotation matrix -template<typename Other> -struct quaternionbase_assign_impl<Other,3,3> -{ - typedef typename Other::Scalar Scalar; - typedef DenseIndex Index; - template<class Derived> static inline void run(QuaternionBase<Derived>& q, const Other& mat) - { - using std::sqrt; - // This algorithm comes from "Quaternion Calculus and Fast Animation", - // Ken Shoemake, 1987 SIGGRAPH course notes - Scalar t = mat.trace(); - if (t > Scalar(0)) - { - t = sqrt(t + Scalar(1.0)); - q.w() = Scalar(0.5)*t; - t = Scalar(0.5)/t; - q.x() = (mat.coeff(2,1) - mat.coeff(1,2)) * t; - q.y() = (mat.coeff(0,2) - mat.coeff(2,0)) * t; - q.z() = (mat.coeff(1,0) - mat.coeff(0,1)) * t; - } - else - { - DenseIndex i = 0; - if (mat.coeff(1,1) > mat.coeff(0,0)) - i = 1; - if (mat.coeff(2,2) > mat.coeff(i,i)) - i = 2; - DenseIndex j = (i+1)%3; - DenseIndex k = (j+1)%3; - - t = sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + Scalar(1.0)); - q.coeffs().coeffRef(i) = Scalar(0.5) * t; - t = Scalar(0.5)/t; - q.w() = (mat.coeff(k,j)-mat.coeff(j,k))*t; - q.coeffs().coeffRef(j) = (mat.coeff(j,i)+mat.coeff(i,j))*t; - q.coeffs().coeffRef(k) = (mat.coeff(k,i)+mat.coeff(i,k))*t; - } - } -}; - -// set from a vector of coefficients assumed to be a quaternion -template<typename Other> -struct quaternionbase_assign_impl<Other,4,1> -{ - typedef typename Other::Scalar Scalar; - template<class Derived> static inline void run(QuaternionBase<Derived>& q, const Other& vec) - { - q.coeffs() = vec; - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_QUATERNION_H diff --git a/third_party/eigen3/Eigen/src/Geometry/Rotation2D.h b/third_party/eigen3/Eigen/src/Geometry/Rotation2D.h deleted file mode 100644 index 1cac343a5e..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/Rotation2D.h +++ /dev/null @@ -1,157 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ROTATION2D_H -#define EIGEN_ROTATION2D_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Rotation2D - * - * \brief Represents a rotation/orientation in a 2 dimensional space. - * - * \param _Scalar the scalar type, i.e., the type of the coefficients - * - * This class is equivalent to a single scalar representing a counter clock wise rotation - * as a single angle in radian. It provides some additional features such as the automatic - * conversion from/to a 2x2 rotation matrix. Moreover this class aims to provide a similar - * interface to Quaternion in order to facilitate the writing of generic algorithms - * dealing with rotations. - * - * \sa class Quaternion, class Transform - */ - -namespace internal { - -template<typename _Scalar> struct traits<Rotation2D<_Scalar> > -{ - typedef _Scalar Scalar; -}; -} // end namespace internal - -template<typename _Scalar> -class Rotation2D : public RotationBase<Rotation2D<_Scalar>,2> -{ - typedef RotationBase<Rotation2D<_Scalar>,2> Base; - -public: - - using Base::operator*; - - enum { Dim = 2 }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - typedef Matrix<Scalar,2,1> Vector2; - typedef Matrix<Scalar,2,2> Matrix2; - -protected: - - Scalar m_angle; - -public: - - /** Construct a 2D counter clock wise rotation from the angle \a a in radian. */ - inline Rotation2D(const Scalar& a) : m_angle(a) {} - - /** \returns the rotation angle */ - inline Scalar angle() const { return m_angle; } - - /** \returns a read-write reference to the rotation angle */ - inline Scalar& angle() { return m_angle; } - - /** \returns the inverse rotation */ - inline Rotation2D inverse() const { return -m_angle; } - - /** Concatenates two rotations */ - inline Rotation2D operator*(const Rotation2D& other) const - { return m_angle + other.m_angle; } - - /** Concatenates two rotations */ - inline Rotation2D& operator*=(const Rotation2D& other) - { m_angle += other.m_angle; return *this; } - - /** Applies the rotation to a 2D vector */ - Vector2 operator* (const Vector2& vec) const - { return toRotationMatrix() * vec; } - - template<typename Derived> - Rotation2D& fromRotationMatrix(const MatrixBase<Derived>& m); - Matrix2 toRotationMatrix(void) const; - - /** \returns the spherical interpolation between \c *this and \a other using - * parameter \a t. It is in fact equivalent to a linear interpolation. - */ - inline Rotation2D slerp(const Scalar& t, const Rotation2D& other) const - { return m_angle * (1-t) + other.angle() * t; } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type cast() const - { return typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other) - { - m_angle = Scalar(other.angle()); - } - - static inline Rotation2D Identity() { return Rotation2D(0); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Rotation2D& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const - { return internal::isApprox(m_angle,other.m_angle, prec); } -}; - -/** \ingroup Geometry_Module - * single precision 2D rotation type */ -typedef Rotation2D<float> Rotation2Df; -/** \ingroup Geometry_Module - * double precision 2D rotation type */ -typedef Rotation2D<double> Rotation2Dd; - -/** Set \c *this from a 2x2 rotation matrix \a mat. - * In other words, this function extract the rotation angle - * from the rotation matrix. - */ -template<typename Scalar> -template<typename Derived> -Rotation2D<Scalar>& Rotation2D<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat) -{ - using std::atan2; - EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime==2 && Derived::ColsAtCompileTime==2,YOU_MADE_A_PROGRAMMING_MISTAKE) - m_angle = atan2(mat.coeff(1,0), mat.coeff(0,0)); - return *this; -} - -/** Constructs and \returns an equivalent 2x2 rotation matrix. - */ -template<typename Scalar> -typename Rotation2D<Scalar>::Matrix2 -Rotation2D<Scalar>::toRotationMatrix(void) const -{ - using std::sin; - using std::cos; - Scalar sinA = sin(m_angle); - Scalar cosA = cos(m_angle); - return (Matrix2() << cosA, -sinA, sinA, cosA).finished(); -} - -} // end namespace Eigen - -#endif // EIGEN_ROTATION2D_H diff --git a/third_party/eigen3/Eigen/src/Geometry/RotationBase.h b/third_party/eigen3/Eigen/src/Geometry/RotationBase.h deleted file mode 100644 index b88661de6b..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/RotationBase.h +++ /dev/null @@ -1,206 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ROTATIONBASE_H -#define EIGEN_ROTATIONBASE_H - -namespace Eigen { - -// forward declaration -namespace internal { -template<typename RotationDerived, typename MatrixType, bool IsVector=MatrixType::IsVectorAtCompileTime> -struct rotation_base_generic_product_selector; -} - -/** \class RotationBase - * - * \brief Common base class for compact rotation representations - * - * \param Derived is the derived type, i.e., a rotation type - * \param _Dim the dimension of the space - */ -template<typename Derived, int _Dim> -class RotationBase -{ - public: - enum { Dim = _Dim }; - /** the scalar type of the coefficients */ - typedef typename internal::traits<Derived>::Scalar Scalar; - - /** corresponding linear transformation matrix type */ - typedef Matrix<Scalar,Dim,Dim> RotationMatrixType; - typedef Matrix<Scalar,Dim,1> VectorType; - - public: - inline const Derived& derived() const { return *static_cast<const Derived*>(this); } - inline Derived& derived() { return *static_cast<Derived*>(this); } - - /** \returns an equivalent rotation matrix */ - inline RotationMatrixType toRotationMatrix() const { return derived().toRotationMatrix(); } - - /** \returns an equivalent rotation matrix - * This function is added to be conform with the Transform class' naming scheme. - */ - inline RotationMatrixType matrix() const { return derived().toRotationMatrix(); } - - /** \returns the inverse rotation */ - inline Derived inverse() const { return derived().inverse(); } - - /** \returns the concatenation of the rotation \c *this with a translation \a t */ - inline Transform<Scalar,Dim,Isometry> operator*(const Translation<Scalar,Dim>& t) const - { return Transform<Scalar,Dim,Isometry>(*this) * t; } - - /** \returns the concatenation of the rotation \c *this with a uniform scaling \a s */ - inline RotationMatrixType operator*(const UniformScaling<Scalar>& s) const - { return toRotationMatrix() * s.factor(); } - - /** \returns the concatenation of the rotation \c *this with a generic expression \a e - * \a e can be: - * - a DimxDim linear transformation matrix - * - a DimxDim diagonal matrix (axis aligned scaling) - * - a vector of size Dim - */ - template<typename OtherDerived> - EIGEN_STRONG_INLINE typename internal::rotation_base_generic_product_selector<Derived,OtherDerived,OtherDerived::IsVectorAtCompileTime>::ReturnType - operator*(const EigenBase<OtherDerived>& e) const - { return internal::rotation_base_generic_product_selector<Derived,OtherDerived>::run(derived(), e.derived()); } - - /** \returns the concatenation of a linear transformation \a l with the rotation \a r */ - template<typename OtherDerived> friend - inline RotationMatrixType operator*(const EigenBase<OtherDerived>& l, const Derived& r) - { return l.derived() * r.toRotationMatrix(); } - - /** \returns the concatenation of a scaling \a l with the rotation \a r */ - friend inline Transform<Scalar,Dim,Affine> operator*(const DiagonalMatrix<Scalar,Dim>& l, const Derived& r) - { - Transform<Scalar,Dim,Affine> res(r); - res.linear().applyOnTheLeft(l); - return res; - } - - /** \returns the concatenation of the rotation \c *this with a transformation \a t */ - template<int Mode, int Options> - inline Transform<Scalar,Dim,Mode> operator*(const Transform<Scalar,Dim,Mode,Options>& t) const - { return toRotationMatrix() * t; } - - template<typename OtherVectorType> - inline VectorType _transformVector(const OtherVectorType& v) const - { return toRotationMatrix() * v; } -}; - -namespace internal { - -// implementation of the generic product rotation * matrix -template<typename RotationDerived, typename MatrixType> -struct rotation_base_generic_product_selector<RotationDerived,MatrixType,false> -{ - enum { Dim = RotationDerived::Dim }; - typedef Matrix<typename RotationDerived::Scalar,Dim,Dim> ReturnType; - static inline ReturnType run(const RotationDerived& r, const MatrixType& m) - { return r.toRotationMatrix() * m; } -}; - -template<typename RotationDerived, typename Scalar, int Dim, int MaxDim> -struct rotation_base_generic_product_selector< RotationDerived, DiagonalMatrix<Scalar,Dim,MaxDim>, false > -{ - typedef Transform<Scalar,Dim,Affine> ReturnType; - static inline ReturnType run(const RotationDerived& r, const DiagonalMatrix<Scalar,Dim,MaxDim>& m) - { - ReturnType res(r); - res.linear() *= m; - return res; - } -}; - -template<typename RotationDerived,typename OtherVectorType> -struct rotation_base_generic_product_selector<RotationDerived,OtherVectorType,true> -{ - enum { Dim = RotationDerived::Dim }; - typedef Matrix<typename RotationDerived::Scalar,Dim,1> ReturnType; - static EIGEN_STRONG_INLINE ReturnType run(const RotationDerived& r, const OtherVectorType& v) - { - return r._transformVector(v); - } -}; - -} // end namespace internal - -/** \geometry_module - * - * \brief Constructs a Dim x Dim rotation matrix from the rotation \a r - */ -template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols> -template<typename OtherDerived> -Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols> -::Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r) -{ - EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim)) - *this = r.toRotationMatrix(); -} - -/** \geometry_module - * - * \brief Set a Dim x Dim rotation matrix from the rotation \a r - */ -template<typename _Scalar, int _Rows, int _Cols, int _Storage, int _MaxRows, int _MaxCols> -template<typename OtherDerived> -Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols>& -Matrix<_Scalar, _Rows, _Cols, _Storage, _MaxRows, _MaxCols> -::operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r) -{ - EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim)) - return *this = r.toRotationMatrix(); -} - -namespace internal { - -/** \internal - * - * Helper function to return an arbitrary rotation object to a rotation matrix. - * - * \param Scalar the numeric type of the matrix coefficients - * \param Dim the dimension of the current space - * - * It returns a Dim x Dim fixed size matrix. - * - * Default specializations are provided for: - * - any scalar type (2D), - * - any matrix expression, - * - any type based on RotationBase (e.g., Quaternion, AngleAxis, Rotation2D) - * - * Currently toRotationMatrix is only used by Transform. - * - * \sa class Transform, class Rotation2D, class Quaternion, class AngleAxis - */ -template<typename Scalar, int Dim> -static inline Matrix<Scalar,2,2> toRotationMatrix(const Scalar& s) -{ - EIGEN_STATIC_ASSERT(Dim==2,YOU_MADE_A_PROGRAMMING_MISTAKE) - return Rotation2D<Scalar>(s).toRotationMatrix(); -} - -template<typename Scalar, int Dim, typename OtherDerived> -static inline Matrix<Scalar,Dim,Dim> toRotationMatrix(const RotationBase<OtherDerived,Dim>& r) -{ - return r.toRotationMatrix(); -} - -template<typename Scalar, int Dim, typename OtherDerived> -static inline const MatrixBase<OtherDerived>& toRotationMatrix(const MatrixBase<OtherDerived>& mat) -{ - EIGEN_STATIC_ASSERT(OtherDerived::RowsAtCompileTime==Dim && OtherDerived::ColsAtCompileTime==Dim, - YOU_MADE_A_PROGRAMMING_MISTAKE) - return mat; -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_ROTATIONBASE_H diff --git a/third_party/eigen3/Eigen/src/Geometry/Scaling.h b/third_party/eigen3/Eigen/src/Geometry/Scaling.h deleted file mode 100644 index 023fba2eec..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/Scaling.h +++ /dev/null @@ -1,166 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SCALING_H -#define EIGEN_SCALING_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Scaling - * - * \brief Represents a generic uniform scaling transformation - * - * \param _Scalar the scalar type, i.e., the type of the coefficients. - * - * This class represent a uniform scaling transformation. It is the return - * type of Scaling(Scalar), and most of the time this is the only way it - * is used. In particular, this class is not aimed to be used to store a scaling transformation, - * but rather to make easier the constructions and updates of Transform objects. - * - * To represent an axis aligned scaling, use the DiagonalMatrix class. - * - * \sa Scaling(), class DiagonalMatrix, MatrixBase::asDiagonal(), class Translation, class Transform - */ -template<typename _Scalar> -class UniformScaling -{ -public: - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - -protected: - - Scalar m_factor; - -public: - - /** Default constructor without initialization. */ - UniformScaling() {} - /** Constructs and initialize a uniform scaling transformation */ - explicit inline UniformScaling(const Scalar& s) : m_factor(s) {} - - inline const Scalar& factor() const { return m_factor; } - inline Scalar& factor() { return m_factor; } - - /** Concatenates two uniform scaling */ - inline UniformScaling operator* (const UniformScaling& other) const - { return UniformScaling(m_factor * other.factor()); } - - /** Concatenates a uniform scaling and a translation */ - template<int Dim> - inline Transform<Scalar,Dim,Affine> operator* (const Translation<Scalar,Dim>& t) const; - - /** Concatenates a uniform scaling and an affine transformation */ - template<int Dim, int Mode, int Options> - inline Transform<Scalar,Dim,(int(Mode)==int(Isometry)?Affine:Mode)> operator* (const Transform<Scalar,Dim, Mode, Options>& t) const - { - Transform<Scalar,Dim,(int(Mode)==int(Isometry)?Affine:Mode)> res = t; - res.prescale(factor()); - return res; - } - - /** Concatenates a uniform scaling and a linear transformation matrix */ - // TODO returns an expression - template<typename Derived> - inline typename internal::plain_matrix_type<Derived>::type operator* (const MatrixBase<Derived>& other) const - { return other * m_factor; } - - template<typename Derived,int Dim> - inline Matrix<Scalar,Dim,Dim> operator*(const RotationBase<Derived,Dim>& r) const - { return r.toRotationMatrix() * m_factor; } - - /** \returns the inverse scaling */ - inline UniformScaling inverse() const - { return UniformScaling(Scalar(1)/m_factor); } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline UniformScaling<NewScalarType> cast() const - { return UniformScaling<NewScalarType>(NewScalarType(m_factor)); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit UniformScaling(const UniformScaling<OtherScalarType>& other) - { m_factor = Scalar(other.factor()); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const UniformScaling& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const - { return internal::isApprox(m_factor, other.factor(), prec); } - -}; - -/** Concatenates a linear transformation matrix and a uniform scaling */ -// NOTE this operator is defiend in MatrixBase and not as a friend function -// of UniformScaling to fix an internal crash of Intel's ICC -template<typename Derived> typename MatrixBase<Derived>::ScalarMultipleReturnType -MatrixBase<Derived>::operator*(const UniformScaling<Scalar>& s) const -{ return derived() * s.factor(); } - -/** Constructs a uniform scaling from scale factor \a s */ -static inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); } -/** Constructs a uniform scaling from scale factor \a s */ -static inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); } -/** Constructs a uniform scaling from scale factor \a s */ -template<typename RealScalar> -static inline UniformScaling<std::complex<RealScalar> > Scaling(const std::complex<RealScalar>& s) -{ return UniformScaling<std::complex<RealScalar> >(s); } - -/** Constructs a 2D axis aligned scaling */ -template<typename Scalar> -static inline DiagonalMatrix<Scalar,2> Scaling(const Scalar& sx, const Scalar& sy) -{ return DiagonalMatrix<Scalar,2>(sx, sy); } -/** Constructs a 3D axis aligned scaling */ -template<typename Scalar> -static inline DiagonalMatrix<Scalar,3> Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz) -{ return DiagonalMatrix<Scalar,3>(sx, sy, sz); } - -/** Constructs an axis aligned scaling expression from vector expression \a coeffs - * This is an alias for coeffs.asDiagonal() - */ -template<typename Derived> -static inline const DiagonalWrapper<const Derived> Scaling(const MatrixBase<Derived>& coeffs) -{ return coeffs.asDiagonal(); } - -/** \addtogroup Geometry_Module */ -//@{ -/** \deprecated */ -typedef DiagonalMatrix<float, 2> AlignedScaling2f; -/** \deprecated */ -typedef DiagonalMatrix<double,2> AlignedScaling2d; -/** \deprecated */ -typedef DiagonalMatrix<float, 3> AlignedScaling3f; -/** \deprecated */ -typedef DiagonalMatrix<double,3> AlignedScaling3d; -//@} - -template<typename Scalar> -template<int Dim> -inline Transform<Scalar,Dim,Affine> -UniformScaling<Scalar>::operator* (const Translation<Scalar,Dim>& t) const -{ - Transform<Scalar,Dim,Affine> res; - res.matrix().setZero(); - res.linear().diagonal().fill(factor()); - res.translation() = factor() * t.vector(); - res(Dim,Dim) = Scalar(1); - return res; -} - -} // end namespace Eigen - -#endif // EIGEN_SCALING_H diff --git a/third_party/eigen3/Eigen/src/Geometry/Transform.h b/third_party/eigen3/Eigen/src/Geometry/Transform.h deleted file mode 100644 index b44c0324b0..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/Transform.h +++ /dev/null @@ -1,1444 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRANSFORM_H -#define EIGEN_TRANSFORM_H - -namespace Eigen { - -namespace internal { - -template<typename Transform> -struct transform_traits -{ - enum - { - Dim = Transform::Dim, - HDim = Transform::HDim, - Mode = Transform::Mode, - IsProjective = (int(Mode)==int(Projective)) - }; -}; - -template< typename TransformType, - typename MatrixType, - int Case = transform_traits<TransformType>::IsProjective ? 0 - : int(MatrixType::RowsAtCompileTime) == int(transform_traits<TransformType>::HDim) ? 1 - : 2> -struct transform_right_product_impl; - -template< typename Other, - int Mode, - int Options, - int Dim, - int HDim, - int OtherRows=Other::RowsAtCompileTime, - int OtherCols=Other::ColsAtCompileTime> -struct transform_left_product_impl; - -template< typename Lhs, - typename Rhs, - bool AnyProjective = - transform_traits<Lhs>::IsProjective || - transform_traits<Rhs>::IsProjective> -struct transform_transform_product_impl; - -template< typename Other, - int Mode, - int Options, - int Dim, - int HDim, - int OtherRows=Other::RowsAtCompileTime, - int OtherCols=Other::ColsAtCompileTime> -struct transform_construct_from_matrix; - -template<typename TransformType> struct transform_take_affine_part; - -} // end namespace internal - -/** \geometry_module \ingroup Geometry_Module - * - * \class Transform - * - * \brief Represents an homogeneous transformation in a N dimensional space - * - * \tparam _Scalar the scalar type, i.e., the type of the coefficients - * \tparam _Dim the dimension of the space - * \tparam _Mode the type of the transformation. Can be: - * - #Affine: the transformation is stored as a (Dim+1)^2 matrix, - * where the last row is assumed to be [0 ... 0 1]. - * - #AffineCompact: the transformation is stored as a (Dim)x(Dim+1) matrix. - * - #Projective: the transformation is stored as a (Dim+1)^2 matrix - * without any assumption. - * \tparam _Options has the same meaning as in class Matrix. It allows to specify DontAlign and/or RowMajor. - * These Options are passed directly to the underlying matrix type. - * - * The homography is internally represented and stored by a matrix which - * is available through the matrix() method. To understand the behavior of - * this class you have to think a Transform object as its internal - * matrix representation. The chosen convention is right multiply: - * - * \code v' = T * v \endcode - * - * Therefore, an affine transformation matrix M is shaped like this: - * - * \f$ \left( \begin{array}{cc} - * linear & translation\\ - * 0 ... 0 & 1 - * \end{array} \right) \f$ - * - * Note that for a projective transformation the last row can be anything, - * and then the interpretation of different parts might be sightly different. - * - * However, unlike a plain matrix, the Transform class provides many features - * simplifying both its assembly and usage. In particular, it can be composed - * with any other transformations (Transform,Translation,RotationBase,Matrix) - * and can be directly used to transform implicit homogeneous vectors. All these - * operations are handled via the operator*. For the composition of transformations, - * its principle consists to first convert the right/left hand sides of the product - * to a compatible (Dim+1)^2 matrix and then perform a pure matrix product. - * Of course, internally, operator* tries to perform the minimal number of operations - * according to the nature of each terms. Likewise, when applying the transform - * to non homogeneous vectors, the latters are automatically promoted to homogeneous - * one before doing the matrix product. The convertions to homogeneous representations - * are performed as follow: - * - * \b Translation t (Dim)x(1): - * \f$ \left( \begin{array}{cc} - * I & t \\ - * 0\,...\,0 & 1 - * \end{array} \right) \f$ - * - * \b Rotation R (Dim)x(Dim): - * \f$ \left( \begin{array}{cc} - * R & 0\\ - * 0\,...\,0 & 1 - * \end{array} \right) \f$ - * - * \b Linear \b Matrix L (Dim)x(Dim): - * \f$ \left( \begin{array}{cc} - * L & 0\\ - * 0\,...\,0 & 1 - * \end{array} \right) \f$ - * - * \b Affine \b Matrix A (Dim)x(Dim+1): - * \f$ \left( \begin{array}{c} - * A\\ - * 0\,...\,0\,1 - * \end{array} \right) \f$ - * - * \b Column \b vector v (Dim)x(1): - * \f$ \left( \begin{array}{c} - * v\\ - * 1 - * \end{array} \right) \f$ - * - * \b Set \b of \b column \b vectors V1...Vn (Dim)x(n): - * \f$ \left( \begin{array}{ccc} - * v_1 & ... & v_n\\ - * 1 & ... & 1 - * \end{array} \right) \f$ - * - * The concatenation of a Transform object with any kind of other transformation - * always returns a Transform object. - * - * A little exception to the "as pure matrix product" rule is the case of the - * transformation of non homogeneous vectors by an affine transformation. In - * that case the last matrix row can be ignored, and the product returns non - * homogeneous vectors. - * - * Since, for instance, a Dim x Dim matrix is interpreted as a linear transformation, - * it is not possible to directly transform Dim vectors stored in a Dim x Dim matrix. - * The solution is either to use a Dim x Dynamic matrix or explicitly request a - * vector transformation by making the vector homogeneous: - * \code - * m' = T * m.colwise().homogeneous(); - * \endcode - * Note that there is zero overhead. - * - * Conversion methods from/to Qt's QMatrix and QTransform are available if the - * preprocessor token EIGEN_QT_SUPPORT is defined. - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_TRANSFORM_PLUGIN. - * - * \sa class Matrix, class Quaternion - */ -template<typename _Scalar, int _Dim, int _Mode, int _Options> -class Transform -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim==Dynamic ? Dynamic : (_Dim+1)*(_Dim+1)) - enum { - Mode = _Mode, - Options = _Options, - Dim = _Dim, ///< space dimension in which the transformation holds - HDim = _Dim+1, ///< size of a respective homogeneous vector - Rows = int(Mode)==(AffineCompact) ? Dim : HDim - }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - typedef DenseIndex Index; - /** type of the matrix used to represent the transformation */ - typedef typename internal::make_proper_matrix_type<Scalar,Rows,HDim,Options>::type MatrixType; - /** constified MatrixType */ - typedef const MatrixType ConstMatrixType; - /** type of the matrix used to represent the linear part of the transformation */ - typedef Matrix<Scalar,Dim,Dim,Options> LinearMatrixType; - /** type of read/write reference to the linear part of the transformation */ - typedef Block<MatrixType,Dim,Dim,int(Mode)==(AffineCompact) && (Options&RowMajor)==0> LinearPart; - /** type of read reference to the linear part of the transformation */ - typedef const Block<ConstMatrixType,Dim,Dim,int(Mode)==(AffineCompact) && (Options&RowMajor)==0> ConstLinearPart; - /** type of read/write reference to the affine part of the transformation */ - typedef typename internal::conditional<int(Mode)==int(AffineCompact), - MatrixType&, - Block<MatrixType,Dim,HDim> >::type AffinePart; - /** type of read reference to the affine part of the transformation */ - typedef typename internal::conditional<int(Mode)==int(AffineCompact), - const MatrixType&, - const Block<const MatrixType,Dim,HDim> >::type ConstAffinePart; - /** type of a vector */ - typedef Matrix<Scalar,Dim,1> VectorType; - /** type of a read/write reference to the translation part of the rotation */ - typedef Block<MatrixType,Dim,1,!(internal::traits<MatrixType>::Flags & RowMajorBit)> TranslationPart; - /** type of a read reference to the translation part of the rotation */ - typedef const Block<ConstMatrixType,Dim,1,!(internal::traits<MatrixType>::Flags & RowMajorBit)> ConstTranslationPart; - /** corresponding translation type */ - typedef Translation<Scalar,Dim> TranslationType; - - // this intermediate enum is needed to avoid an ICE with gcc 3.4 and 4.0 - enum { TransformTimeDiagonalMode = ((Mode==int(Isometry))?Affine:int(Mode)) }; - /** The return type of the product between a diagonal matrix and a transform */ - typedef Transform<Scalar,Dim,TransformTimeDiagonalMode> TransformTimeDiagonalReturnType; - -protected: - - MatrixType m_matrix; - -public: - - /** Default constructor without initialization of the meaningful coefficients. - * If Mode==Affine, then the last row is set to [0 ... 0 1] */ - inline Transform() - { - check_template_params(); - if (int(Mode)==Affine) - makeAffine(); - } - - inline Transform(const Transform& other) - { - check_template_params(); - m_matrix = other.m_matrix; - } - - inline explicit Transform(const TranslationType& t) - { - check_template_params(); - *this = t; - } - inline explicit Transform(const UniformScaling<Scalar>& s) - { - check_template_params(); - *this = s; - } - template<typename Derived> - inline explicit Transform(const RotationBase<Derived, Dim>& r) - { - check_template_params(); - *this = r; - } - - inline Transform& operator=(const Transform& other) - { m_matrix = other.m_matrix; return *this; } - - typedef internal::transform_take_affine_part<Transform> take_affine_part; - - /** Constructs and initializes a transformation from a Dim^2 or a (Dim+1)^2 matrix. */ - template<typename OtherDerived> - inline explicit Transform(const EigenBase<OtherDerived>& other) - { - EIGEN_STATIC_ASSERT((internal::is_same<Scalar,typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY); - - check_template_params(); - internal::transform_construct_from_matrix<OtherDerived,Mode,Options,Dim,HDim>::run(this, other.derived()); - } - - /** Set \c *this from a Dim^2 or (Dim+1)^2 matrix. */ - template<typename OtherDerived> - inline Transform& operator=(const EigenBase<OtherDerived>& other) - { - EIGEN_STATIC_ASSERT((internal::is_same<Scalar,typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY); - - internal::transform_construct_from_matrix<OtherDerived,Mode,Options,Dim,HDim>::run(this, other.derived()); - return *this; - } - - template<int OtherOptions> - inline Transform(const Transform<Scalar,Dim,Mode,OtherOptions>& other) - { - check_template_params(); - // only the options change, we can directly copy the matrices - m_matrix = other.matrix(); - } - - template<int OtherMode,int OtherOptions> - inline Transform(const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) - { - check_template_params(); - // prevent conversions as: - // Affine | AffineCompact | Isometry = Projective - EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(OtherMode==int(Projective), Mode==int(Projective)), - YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION) - - // prevent conversions as: - // Isometry = Affine | AffineCompact - EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(OtherMode==int(Affine)||OtherMode==int(AffineCompact), Mode!=int(Isometry)), - YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION) - - enum { ModeIsAffineCompact = Mode == int(AffineCompact), - OtherModeIsAffineCompact = OtherMode == int(AffineCompact) - }; - - if(ModeIsAffineCompact == OtherModeIsAffineCompact) - { - // We need the block expression because the code is compiled for all - // combinations of transformations and will trigger a compile time error - // if one tries to assign the matrices directly - m_matrix.template block<Dim,Dim+1>(0,0) = other.matrix().template block<Dim,Dim+1>(0,0); - makeAffine(); - } - else if(OtherModeIsAffineCompact) - { - typedef typename Transform<Scalar,Dim,OtherMode,OtherOptions>::MatrixType OtherMatrixType; - internal::transform_construct_from_matrix<OtherMatrixType,Mode,Options,Dim,HDim>::run(this, other.matrix()); - } - else - { - // here we know that Mode == AffineCompact and OtherMode != AffineCompact. - // if OtherMode were Projective, the static assert above would already have caught it. - // So the only possibility is that OtherMode == Affine - linear() = other.linear(); - translation() = other.translation(); - } - } - - template<typename OtherDerived> - Transform(const ReturnByValue<OtherDerived>& other) - { - check_template_params(); - other.evalTo(*this); - } - - template<typename OtherDerived> - Transform& operator=(const ReturnByValue<OtherDerived>& other) - { - other.evalTo(*this); - return *this; - } - - #ifdef EIGEN_QT_SUPPORT - inline Transform(const QMatrix& other); - inline Transform& operator=(const QMatrix& other); - inline QMatrix toQMatrix(void) const; - inline Transform(const QTransform& other); - inline Transform& operator=(const QTransform& other); - inline QTransform toQTransform(void) const; - #endif - - /** shortcut for m_matrix(row,col); - * \sa MatrixBase::operator(Index,Index) const */ - inline Scalar operator() (Index row, Index col) const { return m_matrix(row,col); } - /** shortcut for m_matrix(row,col); - * \sa MatrixBase::operator(Index,Index) */ - inline Scalar& operator() (Index row, Index col) { return m_matrix(row,col); } - - /** \returns a read-only expression of the transformation matrix */ - inline const MatrixType& matrix() const { return m_matrix; } - /** \returns a writable expression of the transformation matrix */ - inline MatrixType& matrix() { return m_matrix; } - - /** \returns a read-only expression of the linear part of the transformation */ - inline ConstLinearPart linear() const { return ConstLinearPart(m_matrix,0,0); } - /** \returns a writable expression of the linear part of the transformation */ - inline LinearPart linear() { return LinearPart(m_matrix,0,0); } - - /** \returns a read-only expression of the Dim x HDim affine part of the transformation */ - inline ConstAffinePart affine() const { return take_affine_part::run(m_matrix); } - /** \returns a writable expression of the Dim x HDim affine part of the transformation */ - inline AffinePart affine() { return take_affine_part::run(m_matrix); } - - /** \returns a read-only expression of the translation vector of the transformation */ - inline ConstTranslationPart translation() const { return ConstTranslationPart(m_matrix,0,Dim); } - /** \returns a writable expression of the translation vector of the transformation */ - inline TranslationPart translation() { return TranslationPart(m_matrix,0,Dim); } - - /** \returns an expression of the product between the transform \c *this and a matrix expression \a other - * - * The right hand side \a other might be either: - * \li a vector of size Dim, - * \li an homogeneous vector of size Dim+1, - * \li a set of vectors of size Dim x Dynamic, - * \li a set of homogeneous vectors of size Dim+1 x Dynamic, - * \li a linear transformation matrix of size Dim x Dim, - * \li an affine transformation matrix of size Dim x Dim+1, - * \li a transformation matrix of size Dim+1 x Dim+1. - */ - // note: this function is defined here because some compilers cannot find the respective declaration - template<typename OtherDerived> - EIGEN_STRONG_INLINE const typename internal::transform_right_product_impl<Transform, OtherDerived>::ResultType - operator * (const EigenBase<OtherDerived> &other) const - { return internal::transform_right_product_impl<Transform, OtherDerived>::run(*this,other.derived()); } - - /** \returns the product expression of a transformation matrix \a a times a transform \a b - * - * The left hand side \a other might be either: - * \li a linear transformation matrix of size Dim x Dim, - * \li an affine transformation matrix of size Dim x Dim+1, - * \li a general transformation matrix of size Dim+1 x Dim+1. - */ - template<typename OtherDerived> friend - inline const typename internal::transform_left_product_impl<OtherDerived,Mode,Options,_Dim,_Dim+1>::ResultType - operator * (const EigenBase<OtherDerived> &a, const Transform &b) - { return internal::transform_left_product_impl<OtherDerived,Mode,Options,Dim,HDim>::run(a.derived(),b); } - - /** \returns The product expression of a transform \a a times a diagonal matrix \a b - * - * The rhs diagonal matrix is interpreted as an affine scaling transformation. The - * product results in a Transform of the same type (mode) as the lhs only if the lhs - * mode is no isometry. In that case, the returned transform is an affinity. - */ - template<typename DiagonalDerived> - inline const TransformTimeDiagonalReturnType - operator * (const DiagonalBase<DiagonalDerived> &b) const - { - TransformTimeDiagonalReturnType res(*this); - res.linear() *= b; - return res; - } - - /** \returns The product expression of a diagonal matrix \a a times a transform \a b - * - * The lhs diagonal matrix is interpreted as an affine scaling transformation. The - * product results in a Transform of the same type (mode) as the lhs only if the lhs - * mode is no isometry. In that case, the returned transform is an affinity. - */ - template<typename DiagonalDerived> - friend inline TransformTimeDiagonalReturnType - operator * (const DiagonalBase<DiagonalDerived> &a, const Transform &b) - { - TransformTimeDiagonalReturnType res; - res.linear().noalias() = a*b.linear(); - res.translation().noalias() = a*b.translation(); - if (Mode!=int(AffineCompact)) - res.matrix().row(Dim) = b.matrix().row(Dim); - return res; - } - - template<typename OtherDerived> - inline Transform& operator*=(const EigenBase<OtherDerived>& other) { return *this = *this * other; } - - /** Concatenates two transformations */ - inline const Transform operator * (const Transform& other) const - { - return internal::transform_transform_product_impl<Transform,Transform>::run(*this,other); - } - - #if EIGEN_COMP_ICC -private: - // this intermediate structure permits to workaround a bug in ICC 11: - // error: template instantiation resulted in unexpected function type of "Eigen::Transform<double, 3, 32, 0> - // (const Eigen::Transform<double, 3, 2, 0> &) const" - // (the meaning of a name may have changed since the template declaration -- the type of the template is: - // "Eigen::internal::transform_transform_product_impl<Eigen::Transform<double, 3, 32, 0>, - // Eigen::Transform<double, 3, Mode, Options>, <expression>>::ResultType (const Eigen::Transform<double, 3, Mode, Options> &) const") - // - template<int OtherMode,int OtherOptions> struct icc_11_workaround - { - typedef internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> > ProductType; - typedef typename ProductType::ResultType ResultType; - }; - -public: - /** Concatenates two different transformations */ - template<int OtherMode,int OtherOptions> - inline typename icc_11_workaround<OtherMode,OtherOptions>::ResultType - operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const - { - typedef typename icc_11_workaround<OtherMode,OtherOptions>::ProductType ProductType; - return ProductType::run(*this,other); - } - #else - /** Concatenates two different transformations */ - template<int OtherMode,int OtherOptions> - inline typename internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::ResultType - operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const - { - return internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::run(*this,other); - } - #endif - - /** \sa MatrixBase::setIdentity() */ - void setIdentity() { m_matrix.setIdentity(); } - - /** - * \brief Returns an identity transformation. - * \todo In the future this function should be returning a Transform expression. - */ - static const Transform Identity() - { - return Transform(MatrixType::Identity()); - } - - template<typename OtherDerived> - inline Transform& scale(const MatrixBase<OtherDerived> &other); - - template<typename OtherDerived> - inline Transform& prescale(const MatrixBase<OtherDerived> &other); - - inline Transform& scale(const Scalar& s); - inline Transform& prescale(const Scalar& s); - - template<typename OtherDerived> - inline Transform& translate(const MatrixBase<OtherDerived> &other); - - template<typename OtherDerived> - inline Transform& pretranslate(const MatrixBase<OtherDerived> &other); - - template<typename RotationType> - inline Transform& rotate(const RotationType& rotation); - - template<typename RotationType> - inline Transform& prerotate(const RotationType& rotation); - - Transform& shear(const Scalar& sx, const Scalar& sy); - Transform& preshear(const Scalar& sx, const Scalar& sy); - - inline Transform& operator=(const TranslationType& t); - inline Transform& operator*=(const TranslationType& t) { return translate(t.vector()); } - inline Transform operator*(const TranslationType& t) const; - - inline Transform& operator=(const UniformScaling<Scalar>& t); - inline Transform& operator*=(const UniformScaling<Scalar>& s) { return scale(s.factor()); } - inline TransformTimeDiagonalReturnType operator*(const UniformScaling<Scalar>& s) const - { - TransformTimeDiagonalReturnType res = *this; - res.scale(s.factor()); - return res; - } - - inline Transform& operator*=(const DiagonalMatrix<Scalar,Dim>& s) { linear() *= s; return *this; } - - template<typename Derived> - inline Transform& operator=(const RotationBase<Derived,Dim>& r); - template<typename Derived> - inline Transform& operator*=(const RotationBase<Derived,Dim>& r) { return rotate(r.toRotationMatrix()); } - template<typename Derived> - inline Transform operator*(const RotationBase<Derived,Dim>& r) const; - - const LinearMatrixType rotation() const; - template<typename RotationMatrixType, typename ScalingMatrixType> - void computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const; - template<typename ScalingMatrixType, typename RotationMatrixType> - void computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const; - - template<typename PositionDerived, typename OrientationType, typename ScaleDerived> - Transform& fromPositionOrientationScale(const MatrixBase<PositionDerived> &position, - const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale); - - inline Transform inverse(TransformTraits traits = (TransformTraits)Mode) const; - - /** \returns a const pointer to the column major internal matrix */ - const Scalar* data() const { return m_matrix.data(); } - /** \returns a non-const pointer to the column major internal matrix */ - Scalar* data() { return m_matrix.data(); } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim,Mode,Options> >::type cast() const - { return typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim,Mode,Options> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Transform(const Transform<OtherScalarType,Dim,Mode,Options>& other) - { - check_template_params(); - m_matrix = other.matrix().template cast<Scalar>(); - } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Transform& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const - { return m_matrix.isApprox(other.m_matrix, prec); } - - /** Sets the last row to [0 ... 0 1] - */ - void makeAffine() - { - if(int(Mode)!=int(AffineCompact)) - { - matrix().template block<1,Dim>(Dim,0).setZero(); - matrix().coeffRef(Dim,Dim) = Scalar(1); - } - } - - /** \internal - * \returns the Dim x Dim linear part if the transformation is affine, - * and the HDim x Dim part for projective transformations. - */ - inline Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,Dim> linearExt() - { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,Dim>(0,0); } - /** \internal - * \returns the Dim x Dim linear part if the transformation is affine, - * and the HDim x Dim part for projective transformations. - */ - inline const Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,Dim> linearExt() const - { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,Dim>(0,0); } - - /** \internal - * \returns the translation part if the transformation is affine, - * and the last column for projective transformations. - */ - inline Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,1> translationExt() - { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,1>(0,Dim); } - /** \internal - * \returns the translation part if the transformation is affine, - * and the last column for projective transformations. - */ - inline const Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,1> translationExt() const - { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,1>(0,Dim); } - - - #ifdef EIGEN_TRANSFORM_PLUGIN - #include EIGEN_TRANSFORM_PLUGIN - #endif - -protected: - #ifndef EIGEN_PARSED_BY_DOXYGEN - static EIGEN_STRONG_INLINE void check_template_params() - { - EIGEN_STATIC_ASSERT((Options & (DontAlign|RowMajor)) == Options, INVALID_MATRIX_TEMPLATE_PARAMETERS) - } - #endif - -}; - -/** \ingroup Geometry_Module */ -typedef Transform<float,2,Isometry> Isometry2f; -/** \ingroup Geometry_Module */ -typedef Transform<float,3,Isometry> Isometry3f; -/** \ingroup Geometry_Module */ -typedef Transform<double,2,Isometry> Isometry2d; -/** \ingroup Geometry_Module */ -typedef Transform<double,3,Isometry> Isometry3d; - -/** \ingroup Geometry_Module */ -typedef Transform<float,2,Affine> Affine2f; -/** \ingroup Geometry_Module */ -typedef Transform<float,3,Affine> Affine3f; -/** \ingroup Geometry_Module */ -typedef Transform<double,2,Affine> Affine2d; -/** \ingroup Geometry_Module */ -typedef Transform<double,3,Affine> Affine3d; - -/** \ingroup Geometry_Module */ -typedef Transform<float,2,AffineCompact> AffineCompact2f; -/** \ingroup Geometry_Module */ -typedef Transform<float,3,AffineCompact> AffineCompact3f; -/** \ingroup Geometry_Module */ -typedef Transform<double,2,AffineCompact> AffineCompact2d; -/** \ingroup Geometry_Module */ -typedef Transform<double,3,AffineCompact> AffineCompact3d; - -/** \ingroup Geometry_Module */ -typedef Transform<float,2,Projective> Projective2f; -/** \ingroup Geometry_Module */ -typedef Transform<float,3,Projective> Projective3f; -/** \ingroup Geometry_Module */ -typedef Transform<double,2,Projective> Projective2d; -/** \ingroup Geometry_Module */ -typedef Transform<double,3,Projective> Projective3d; - -/************************** -*** Optional QT support *** -**************************/ - -#ifdef EIGEN_QT_SUPPORT -/** Initializes \c *this from a QMatrix assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim, int Mode,int Options> -Transform<Scalar,Dim,Mode,Options>::Transform(const QMatrix& other) -{ - check_template_params(); - *this = other; -} - -/** Set \c *this from a QMatrix assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim, int Mode,int Options> -Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const QMatrix& other) -{ - EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - if (Mode == int(AffineCompact)) - m_matrix << other.m11(), other.m21(), other.dx(), - other.m12(), other.m22(), other.dy(); - else - m_matrix << other.m11(), other.m21(), other.dx(), - other.m12(), other.m22(), other.dy(), - 0, 0, 1; - return *this; -} - -/** \returns a QMatrix from \c *this assuming the dimension is 2. - * - * \warning this conversion might loss data if \c *this is not affine - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim, int Mode, int Options> -QMatrix Transform<Scalar,Dim,Mode,Options>::toQMatrix(void) const -{ - check_template_params(); - EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - return QMatrix(m_matrix.coeff(0,0), m_matrix.coeff(1,0), - m_matrix.coeff(0,1), m_matrix.coeff(1,1), - m_matrix.coeff(0,2), m_matrix.coeff(1,2)); -} - -/** Initializes \c *this from a QTransform assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim, int Mode,int Options> -Transform<Scalar,Dim,Mode,Options>::Transform(const QTransform& other) -{ - check_template_params(); - *this = other; -} - -/** Set \c *this from a QTransform assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim, int Mode, int Options> -Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const QTransform& other) -{ - check_template_params(); - EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - if (Mode == int(AffineCompact)) - m_matrix << other.m11(), other.m21(), other.dx(), - other.m12(), other.m22(), other.dy(); - else - m_matrix << other.m11(), other.m21(), other.dx(), - other.m12(), other.m22(), other.dy(), - other.m13(), other.m23(), other.m33(); - return *this; -} - -/** \returns a QTransform from \c *this assuming the dimension is 2. - * - * This function is available only if the token EIGEN_QT_SUPPORT is defined. - */ -template<typename Scalar, int Dim, int Mode, int Options> -QTransform Transform<Scalar,Dim,Mode,Options>::toQTransform(void) const -{ - EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - if (Mode == int(AffineCompact)) - return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0), - m_matrix.coeff(0,1), m_matrix.coeff(1,1), - m_matrix.coeff(0,2), m_matrix.coeff(1,2)); - else - return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0), m_matrix.coeff(2,0), - m_matrix.coeff(0,1), m_matrix.coeff(1,1), m_matrix.coeff(2,1), - m_matrix.coeff(0,2), m_matrix.coeff(1,2), m_matrix.coeff(2,2)); -} -#endif - -/********************* -*** Procedural API *** -*********************/ - -/** Applies on the right the non uniform scale transformation represented - * by the vector \a other to \c *this and returns a reference to \c *this. - * \sa prescale() - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename OtherDerived> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::scale(const MatrixBase<OtherDerived> &other) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim)) - EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS) - linearExt().noalias() = (linearExt() * other.asDiagonal()); - return *this; -} - -/** Applies on the right a uniform scale of a factor \a c to \c *this - * and returns a reference to \c *this. - * \sa prescale(Scalar) - */ -template<typename Scalar, int Dim, int Mode, int Options> -inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::scale(const Scalar& s) -{ - EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS) - linearExt() *= s; - return *this; -} - -/** Applies on the left the non uniform scale transformation represented - * by the vector \a other to \c *this and returns a reference to \c *this. - * \sa scale() - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename OtherDerived> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::prescale(const MatrixBase<OtherDerived> &other) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim)) - EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS) - m_matrix.template block<Dim,HDim>(0,0).noalias() = (other.asDiagonal() * m_matrix.template block<Dim,HDim>(0,0)); - return *this; -} - -/** Applies on the left a uniform scale of a factor \a c to \c *this - * and returns a reference to \c *this. - * \sa scale(Scalar) - */ -template<typename Scalar, int Dim, int Mode, int Options> -inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::prescale(const Scalar& s) -{ - EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS) - m_matrix.template topRows<Dim>() *= s; - return *this; -} - -/** Applies on the right the translation matrix represented by the vector \a other - * to \c *this and returns a reference to \c *this. - * \sa pretranslate() - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename OtherDerived> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::translate(const MatrixBase<OtherDerived> &other) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim)) - translationExt() += linearExt() * other; - return *this; -} - -/** Applies on the left the translation matrix represented by the vector \a other - * to \c *this and returns a reference to \c *this. - * \sa translate() - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename OtherDerived> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::pretranslate(const MatrixBase<OtherDerived> &other) -{ - EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim)) - if(int(Mode)==int(Projective)) - affine() += other * m_matrix.row(Dim); - else - translation() += other; - return *this; -} - -/** Applies on the right the rotation represented by the rotation \a rotation - * to \c *this and returns a reference to \c *this. - * - * The template parameter \a RotationType is the type of the rotation which - * must be known by internal::toRotationMatrix<>. - * - * Natively supported types includes: - * - any scalar (2D), - * - a Dim x Dim matrix expression, - * - a Quaternion (3D), - * - a AngleAxis (3D) - * - * This mechanism is easily extendable to support user types such as Euler angles, - * or a pair of Quaternion for 4D rotations. - * - * \sa rotate(Scalar), class Quaternion, class AngleAxis, prerotate(RotationType) - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename RotationType> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::rotate(const RotationType& rotation) -{ - linearExt() *= internal::toRotationMatrix<Scalar,Dim>(rotation); - return *this; -} - -/** Applies on the left the rotation represented by the rotation \a rotation - * to \c *this and returns a reference to \c *this. - * - * See rotate() for further details. - * - * \sa rotate() - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename RotationType> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::prerotate(const RotationType& rotation) -{ - m_matrix.template block<Dim,HDim>(0,0) = internal::toRotationMatrix<Scalar,Dim>(rotation) - * m_matrix.template block<Dim,HDim>(0,0); - return *this; -} - -/** Applies on the right the shear transformation represented - * by the vector \a other to \c *this and returns a reference to \c *this. - * \warning 2D only. - * \sa preshear() - */ -template<typename Scalar, int Dim, int Mode, int Options> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::shear(const Scalar& sx, const Scalar& sy) -{ - EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS) - VectorType tmp = linear().col(0)*sy + linear().col(1); - linear() << linear().col(0) + linear().col(1)*sx, tmp; - return *this; -} - -/** Applies on the left the shear transformation represented - * by the vector \a other to \c *this and returns a reference to \c *this. - * \warning 2D only. - * \sa shear() - */ -template<typename Scalar, int Dim, int Mode, int Options> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::preshear(const Scalar& sx, const Scalar& sy) -{ - EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE) - EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS) - m_matrix.template block<Dim,HDim>(0,0) = LinearMatrixType(1, sx, sy, 1) * m_matrix.template block<Dim,HDim>(0,0); - return *this; -} - -/****************************************************** -*** Scaling, Translation and Rotation compatibility *** -******************************************************/ - -template<typename Scalar, int Dim, int Mode, int Options> -inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const TranslationType& t) -{ - linear().setIdentity(); - translation() = t.vector(); - makeAffine(); - return *this; -} - -template<typename Scalar, int Dim, int Mode, int Options> -inline Transform<Scalar,Dim,Mode,Options> Transform<Scalar,Dim,Mode,Options>::operator*(const TranslationType& t) const -{ - Transform res = *this; - res.translate(t.vector()); - return res; -} - -template<typename Scalar, int Dim, int Mode, int Options> -inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const UniformScaling<Scalar>& s) -{ - m_matrix.setZero(); - linear().diagonal().fill(s.factor()); - makeAffine(); - return *this; -} - -template<typename Scalar, int Dim, int Mode, int Options> -template<typename Derived> -inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const RotationBase<Derived,Dim>& r) -{ - linear() = internal::toRotationMatrix<Scalar,Dim>(r); - translation().setZero(); - makeAffine(); - return *this; -} - -template<typename Scalar, int Dim, int Mode, int Options> -template<typename Derived> -inline Transform<Scalar,Dim,Mode,Options> Transform<Scalar,Dim,Mode,Options>::operator*(const RotationBase<Derived,Dim>& r) const -{ - Transform res = *this; - res.rotate(r.derived()); - return res; -} - -/************************ -*** Special functions *** -************************/ - -/** \returns the rotation part of the transformation - * - * - * \svd_module - * - * \sa computeRotationScaling(), computeScalingRotation(), class SVD - */ -template<typename Scalar, int Dim, int Mode, int Options> -const typename Transform<Scalar,Dim,Mode,Options>::LinearMatrixType -Transform<Scalar,Dim,Mode,Options>::rotation() const -{ - LinearMatrixType result; - computeRotationScaling(&result, (LinearMatrixType*)0); - return result; -} - - -/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being - * not necessarily positive. - * - * If either pointer is zero, the corresponding computation is skipped. - * - * - * - * \svd_module - * - * \sa computeScalingRotation(), rotation(), class SVD - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename RotationMatrixType, typename ScalingMatrixType> -void Transform<Scalar,Dim,Mode,Options>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const -{ - JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV); - - Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant(); // so x has absolute value 1 - VectorType sv(svd.singularValues()); - sv.coeffRef(0) *= x; - if(scaling) scaling->lazyAssign(svd.matrixV() * sv.asDiagonal() * svd.matrixV().adjoint()); - if(rotation) - { - LinearMatrixType m(svd.matrixU()); - m.col(0) /= x; - rotation->lazyAssign(m * svd.matrixV().adjoint()); - } -} - -/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being - * not necessarily positive. - * - * If either pointer is zero, the corresponding computation is skipped. - * - * - * - * \svd_module - * - * \sa computeRotationScaling(), rotation(), class SVD - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename ScalingMatrixType, typename RotationMatrixType> -void Transform<Scalar,Dim,Mode,Options>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const -{ - JacobiSVD<LinearMatrixType> svd(linear(), ComputeFullU | ComputeFullV); - - Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant(); // so x has absolute value 1 - VectorType sv(svd.singularValues()); - sv.coeffRef(0) *= x; - if(scaling) scaling->lazyAssign(svd.matrixU() * sv.asDiagonal() * svd.matrixU().adjoint()); - if(rotation) - { - LinearMatrixType m(svd.matrixU()); - m.col(0) /= x; - rotation->lazyAssign(m * svd.matrixV().adjoint()); - } -} - -/** Convenient method to set \c *this from a position, orientation and scale - * of a 3D object. - */ -template<typename Scalar, int Dim, int Mode, int Options> -template<typename PositionDerived, typename OrientationType, typename ScaleDerived> -Transform<Scalar,Dim,Mode,Options>& -Transform<Scalar,Dim,Mode,Options>::fromPositionOrientationScale(const MatrixBase<PositionDerived> &position, - const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale) -{ - linear() = internal::toRotationMatrix<Scalar,Dim>(orientation); - linear() *= scale.asDiagonal(); - translation() = position; - makeAffine(); - return *this; -} - -namespace internal { - -// selector needed to avoid taking the inverse of a 3x4 matrix -template<typename TransformType, int Mode=TransformType::Mode> -struct projective_transform_inverse -{ - static inline void run(const TransformType&, TransformType&) - {} -}; - -template<typename TransformType> -struct projective_transform_inverse<TransformType, Projective> -{ - static inline void run(const TransformType& m, TransformType& res) - { - res.matrix() = m.matrix().inverse(); - } -}; - -} // end namespace internal - - -/** - * - * \returns the inverse transformation according to some given knowledge - * on \c *this. - * - * \param hint allows to optimize the inversion process when the transformation - * is known to be not a general transformation (optional). The possible values are: - * - #Projective if the transformation is not necessarily affine, i.e., if the - * last row is not guaranteed to be [0 ... 0 1] - * - #Affine if the last row can be assumed to be [0 ... 0 1] - * - #Isometry if the transformation is only a concatenations of translations - * and rotations. - * The default is the template class parameter \c Mode. - * - * \warning unless \a traits is always set to NoShear or NoScaling, this function - * requires the generic inverse method of MatrixBase defined in the LU module. If - * you forget to include this module, then you will get hard to debug linking errors. - * - * \sa MatrixBase::inverse() - */ -template<typename Scalar, int Dim, int Mode, int Options> -Transform<Scalar,Dim,Mode,Options> -Transform<Scalar,Dim,Mode,Options>::inverse(TransformTraits hint) const -{ - Transform res; - if (hint == Projective) - { - internal::projective_transform_inverse<Transform>::run(*this, res); - } - else - { - if (hint == Isometry) - { - res.matrix().template topLeftCorner<Dim,Dim>() = linear().transpose(); - } - else if(hint&Affine) - { - res.matrix().template topLeftCorner<Dim,Dim>() = linear().inverse(); - } - else - { - eigen_assert(false && "Invalid transform traits in Transform::Inverse"); - } - // translation and remaining parts - res.matrix().template topRightCorner<Dim,1>() - = - res.matrix().template topLeftCorner<Dim,Dim>() * translation(); - res.makeAffine(); // we do need this, because in the beginning res is uninitialized - } - return res; -} - -namespace internal { - -/***************************************************** -*** Specializations of take affine part *** -*****************************************************/ - -template<typename TransformType> struct transform_take_affine_part { - typedef typename TransformType::MatrixType MatrixType; - typedef typename TransformType::AffinePart AffinePart; - typedef typename TransformType::ConstAffinePart ConstAffinePart; - static inline AffinePart run(MatrixType& m) - { return m.template block<TransformType::Dim,TransformType::HDim>(0,0); } - static inline ConstAffinePart run(const MatrixType& m) - { return m.template block<TransformType::Dim,TransformType::HDim>(0,0); } -}; - -template<typename Scalar, int Dim, int Options> -struct transform_take_affine_part<Transform<Scalar,Dim,AffineCompact, Options> > { - typedef typename Transform<Scalar,Dim,AffineCompact,Options>::MatrixType MatrixType; - static inline MatrixType& run(MatrixType& m) { return m; } - static inline const MatrixType& run(const MatrixType& m) { return m; } -}; - -/***************************************************** -*** Specializations of construct from matrix *** -*****************************************************/ - -template<typename Other, int Mode, int Options, int Dim, int HDim> -struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, Dim,Dim> -{ - static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other) - { - transform->linear() = other; - transform->translation().setZero(); - transform->makeAffine(); - } -}; - -template<typename Other, int Mode, int Options, int Dim, int HDim> -struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, Dim,HDim> -{ - static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other) - { - transform->affine() = other; - transform->makeAffine(); - } -}; - -template<typename Other, int Mode, int Options, int Dim, int HDim> -struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, HDim,HDim> -{ - static inline void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other) - { transform->matrix() = other; } -}; - -template<typename Other, int Options, int Dim, int HDim> -struct transform_construct_from_matrix<Other, AffineCompact,Options,Dim,HDim, HDim,HDim> -{ - static inline void run(Transform<typename Other::Scalar,Dim,AffineCompact,Options> *transform, const Other& other) - { transform->matrix() = other.template block<Dim,HDim>(0,0); } -}; - -/********************************************************** -*** Specializations of operator* with rhs EigenBase *** -**********************************************************/ - -template<int LhsMode,int RhsMode> -struct transform_product_result -{ - enum - { - Mode = - (LhsMode == (int)Projective || RhsMode == (int)Projective ) ? Projective : - (LhsMode == (int)Affine || RhsMode == (int)Affine ) ? Affine : - (LhsMode == (int)AffineCompact || RhsMode == (int)AffineCompact ) ? AffineCompact : - (LhsMode == (int)Isometry || RhsMode == (int)Isometry ) ? Isometry : Projective - }; -}; - -template< typename TransformType, typename MatrixType > -struct transform_right_product_impl< TransformType, MatrixType, 0 > -{ - typedef typename MatrixType::PlainObject ResultType; - - static EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other) - { - return T.matrix() * other; - } -}; - -template< typename TransformType, typename MatrixType > -struct transform_right_product_impl< TransformType, MatrixType, 1 > -{ - enum { - Dim = TransformType::Dim, - HDim = TransformType::HDim, - OtherRows = MatrixType::RowsAtCompileTime, - OtherCols = MatrixType::ColsAtCompileTime - }; - - typedef typename MatrixType::PlainObject ResultType; - - static EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other) - { - EIGEN_STATIC_ASSERT(OtherRows==HDim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES); - - typedef Block<ResultType, Dim, OtherCols, int(MatrixType::RowsAtCompileTime)==Dim> TopLeftLhs; - - ResultType res(other.rows(),other.cols()); - TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() = T.affine() * other; - res.row(OtherRows-1) = other.row(OtherRows-1); - - return res; - } -}; - -template< typename TransformType, typename MatrixType > -struct transform_right_product_impl< TransformType, MatrixType, 2 > -{ - enum { - Dim = TransformType::Dim, - HDim = TransformType::HDim, - OtherRows = MatrixType::RowsAtCompileTime, - OtherCols = MatrixType::ColsAtCompileTime - }; - - typedef typename MatrixType::PlainObject ResultType; - - static EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other) - { - EIGEN_STATIC_ASSERT(OtherRows==Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES); - - typedef Block<ResultType, Dim, OtherCols, true> TopLeftLhs; - ResultType res(Replicate<typename TransformType::ConstTranslationPart, 1, OtherCols>(T.translation(),1,other.cols())); - TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() += T.linear() * other; - - return res; - } -}; - -/********************************************************** -*** Specializations of operator* with lhs EigenBase *** -**********************************************************/ - -// generic HDim x HDim matrix * T => Projective -template<typename Other,int Mode, int Options, int Dim, int HDim> -struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, HDim,HDim> -{ - typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType; - typedef typename TransformType::MatrixType MatrixType; - typedef Transform<typename Other::Scalar,Dim,Projective,Options> ResultType; - static ResultType run(const Other& other,const TransformType& tr) - { return ResultType(other * tr.matrix()); } -}; - -// generic HDim x HDim matrix * AffineCompact => Projective -template<typename Other, int Options, int Dim, int HDim> -struct transform_left_product_impl<Other,AffineCompact,Options,Dim,HDim, HDim,HDim> -{ - typedef Transform<typename Other::Scalar,Dim,AffineCompact,Options> TransformType; - typedef typename TransformType::MatrixType MatrixType; - typedef Transform<typename Other::Scalar,Dim,Projective,Options> ResultType; - static ResultType run(const Other& other,const TransformType& tr) - { - ResultType res; - res.matrix().noalias() = other.template block<HDim,Dim>(0,0) * tr.matrix(); - res.matrix().col(Dim) += other.col(Dim); - return res; - } -}; - -// affine matrix * T -template<typename Other,int Mode, int Options, int Dim, int HDim> -struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, Dim,HDim> -{ - typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType; - typedef typename TransformType::MatrixType MatrixType; - typedef TransformType ResultType; - static ResultType run(const Other& other,const TransformType& tr) - { - ResultType res; - res.affine().noalias() = other * tr.matrix(); - res.matrix().row(Dim) = tr.matrix().row(Dim); - return res; - } -}; - -// affine matrix * AffineCompact -template<typename Other, int Options, int Dim, int HDim> -struct transform_left_product_impl<Other,AffineCompact,Options,Dim,HDim, Dim,HDim> -{ - typedef Transform<typename Other::Scalar,Dim,AffineCompact,Options> TransformType; - typedef typename TransformType::MatrixType MatrixType; - typedef TransformType ResultType; - static ResultType run(const Other& other,const TransformType& tr) - { - ResultType res; - res.matrix().noalias() = other.template block<Dim,Dim>(0,0) * tr.matrix(); - res.translation() += other.col(Dim); - return res; - } -}; - -// linear matrix * T -template<typename Other,int Mode, int Options, int Dim, int HDim> -struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, Dim,Dim> -{ - typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType; - typedef typename TransformType::MatrixType MatrixType; - typedef TransformType ResultType; - static ResultType run(const Other& other, const TransformType& tr) - { - TransformType res; - if(Mode!=int(AffineCompact)) - res.matrix().row(Dim) = tr.matrix().row(Dim); - res.matrix().template topRows<Dim>().noalias() - = other * tr.matrix().template topRows<Dim>(); - return res; - } -}; - -/********************************************************** -*** Specializations of operator* with another Transform *** -**********************************************************/ - -template<typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions> -struct transform_transform_product_impl<Transform<Scalar,Dim,LhsMode,LhsOptions>,Transform<Scalar,Dim,RhsMode,RhsOptions>,false > -{ - enum { ResultMode = transform_product_result<LhsMode,RhsMode>::Mode }; - typedef Transform<Scalar,Dim,LhsMode,LhsOptions> Lhs; - typedef Transform<Scalar,Dim,RhsMode,RhsOptions> Rhs; - typedef Transform<Scalar,Dim,ResultMode,LhsOptions> ResultType; - static ResultType run(const Lhs& lhs, const Rhs& rhs) - { - ResultType res; - res.linear() = lhs.linear() * rhs.linear(); - res.translation() = lhs.linear() * rhs.translation() + lhs.translation(); - res.makeAffine(); - return res; - } -}; - -template<typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions> -struct transform_transform_product_impl<Transform<Scalar,Dim,LhsMode,LhsOptions>,Transform<Scalar,Dim,RhsMode,RhsOptions>,true > -{ - typedef Transform<Scalar,Dim,LhsMode,LhsOptions> Lhs; - typedef Transform<Scalar,Dim,RhsMode,RhsOptions> Rhs; - typedef Transform<Scalar,Dim,Projective> ResultType; - static ResultType run(const Lhs& lhs, const Rhs& rhs) - { - return ResultType( lhs.matrix() * rhs.matrix() ); - } -}; - -template<typename Scalar, int Dim, int LhsOptions, int RhsOptions> -struct transform_transform_product_impl<Transform<Scalar,Dim,AffineCompact,LhsOptions>,Transform<Scalar,Dim,Projective,RhsOptions>,true > -{ - typedef Transform<Scalar,Dim,AffineCompact,LhsOptions> Lhs; - typedef Transform<Scalar,Dim,Projective,RhsOptions> Rhs; - typedef Transform<Scalar,Dim,Projective> ResultType; - static ResultType run(const Lhs& lhs, const Rhs& rhs) - { - ResultType res; - res.matrix().template topRows<Dim>() = lhs.matrix() * rhs.matrix(); - res.matrix().row(Dim) = rhs.matrix().row(Dim); - return res; - } -}; - -template<typename Scalar, int Dim, int LhsOptions, int RhsOptions> -struct transform_transform_product_impl<Transform<Scalar,Dim,Projective,LhsOptions>,Transform<Scalar,Dim,AffineCompact,RhsOptions>,true > -{ - typedef Transform<Scalar,Dim,Projective,LhsOptions> Lhs; - typedef Transform<Scalar,Dim,AffineCompact,RhsOptions> Rhs; - typedef Transform<Scalar,Dim,Projective> ResultType; - static ResultType run(const Lhs& lhs, const Rhs& rhs) - { - ResultType res(lhs.matrix().template leftCols<Dim>() * rhs.matrix()); - res.matrix().col(Dim) += lhs.matrix().col(Dim); - return res; - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_TRANSFORM_H diff --git a/third_party/eigen3/Eigen/src/Geometry/Translation.h b/third_party/eigen3/Eigen/src/Geometry/Translation.h deleted file mode 100644 index 7fda179cc3..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/Translation.h +++ /dev/null @@ -1,206 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_TRANSLATION_H -#define EIGEN_TRANSLATION_H - -namespace Eigen { - -/** \geometry_module \ingroup Geometry_Module - * - * \class Translation - * - * \brief Represents a translation transformation - * - * \param _Scalar the scalar type, i.e., the type of the coefficients. - * \param _Dim the dimension of the space, can be a compile time value or Dynamic - * - * \note This class is not aimed to be used to store a translation transformation, - * but rather to make easier the constructions and updates of Transform objects. - * - * \sa class Scaling, class Transform - */ -template<typename _Scalar, int _Dim> -class Translation -{ -public: - EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_Dim) - /** dimension of the space */ - enum { Dim = _Dim }; - /** the scalar type of the coefficients */ - typedef _Scalar Scalar; - /** corresponding vector type */ - typedef Matrix<Scalar,Dim,1> VectorType; - /** corresponding linear transformation matrix type */ - typedef Matrix<Scalar,Dim,Dim> LinearMatrixType; - /** corresponding affine transformation type */ - typedef Transform<Scalar,Dim,Affine> AffineTransformType; - /** corresponding isometric transformation type */ - typedef Transform<Scalar,Dim,Isometry> IsometryTransformType; - -protected: - - VectorType m_coeffs; - -public: - - /** Default constructor without initialization. */ - Translation() {} - /** */ - inline Translation(const Scalar& sx, const Scalar& sy) - { - eigen_assert(Dim==2); - m_coeffs.x() = sx; - m_coeffs.y() = sy; - } - /** */ - inline Translation(const Scalar& sx, const Scalar& sy, const Scalar& sz) - { - eigen_assert(Dim==3); - m_coeffs.x() = sx; - m_coeffs.y() = sy; - m_coeffs.z() = sz; - } - /** Constructs and initialize the translation transformation from a vector of translation coefficients */ - explicit inline Translation(const VectorType& vector) : m_coeffs(vector) {} - - /** \brief Retruns the x-translation by value. **/ - inline Scalar x() const { return m_coeffs.x(); } - /** \brief Retruns the y-translation by value. **/ - inline Scalar y() const { return m_coeffs.y(); } - /** \brief Retruns the z-translation by value. **/ - inline Scalar z() const { return m_coeffs.z(); } - - /** \brief Retruns the x-translation as a reference. **/ - inline Scalar& x() { return m_coeffs.x(); } - /** \brief Retruns the y-translation as a reference. **/ - inline Scalar& y() { return m_coeffs.y(); } - /** \brief Retruns the z-translation as a reference. **/ - inline Scalar& z() { return m_coeffs.z(); } - - const VectorType& vector() const { return m_coeffs; } - VectorType& vector() { return m_coeffs; } - - const VectorType& translation() const { return m_coeffs; } - VectorType& translation() { return m_coeffs; } - - /** Concatenates two translation */ - inline Translation operator* (const Translation& other) const - { return Translation(m_coeffs + other.m_coeffs); } - - /** Concatenates a translation and a uniform scaling */ - inline AffineTransformType operator* (const UniformScaling<Scalar>& other) const; - - /** Concatenates a translation and a linear transformation */ - template<typename OtherDerived> - inline AffineTransformType operator* (const EigenBase<OtherDerived>& linear) const; - - /** Concatenates a translation and a rotation */ - template<typename Derived> - inline IsometryTransformType operator*(const RotationBase<Derived,Dim>& r) const - { return *this * IsometryTransformType(r); } - - /** \returns the concatenation of a linear transformation \a l with the translation \a t */ - // its a nightmare to define a templated friend function outside its declaration - template<typename OtherDerived> friend - inline AffineTransformType operator*(const EigenBase<OtherDerived>& linear, const Translation& t) - { - AffineTransformType res; - res.matrix().setZero(); - res.linear() = linear.derived(); - res.translation() = linear.derived() * t.m_coeffs; - res.matrix().row(Dim).setZero(); - res(Dim,Dim) = Scalar(1); - return res; - } - - /** Concatenates a translation and a transformation */ - template<int Mode, int Options> - inline Transform<Scalar,Dim,Mode> operator* (const Transform<Scalar,Dim,Mode,Options>& t) const - { - Transform<Scalar,Dim,Mode> res = t; - res.pretranslate(m_coeffs); - return res; - } - - /** Applies translation to vector */ - inline VectorType operator* (const VectorType& other) const - { return m_coeffs + other; } - - /** \returns the inverse translation (opposite) */ - Translation inverse() const { return Translation(-m_coeffs); } - - Translation& operator=(const Translation& other) - { - m_coeffs = other.m_coeffs; - return *this; - } - - static const Translation Identity() { return Translation(VectorType::Zero()); } - - /** \returns \c *this with scalar type casted to \a NewScalarType - * - * Note that if \a NewScalarType is equal to the current scalar type of \c *this - * then this function smartly returns a const reference to \c *this. - */ - template<typename NewScalarType> - inline typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type cast() const - { return typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type(*this); } - - /** Copy constructor with scalar type conversion */ - template<typename OtherScalarType> - inline explicit Translation(const Translation<OtherScalarType,Dim>& other) - { m_coeffs = other.vector().template cast<Scalar>(); } - - /** \returns \c true if \c *this is approximately equal to \a other, within the precision - * determined by \a prec. - * - * \sa MatrixBase::isApprox() */ - bool isApprox(const Translation& other, typename NumTraits<Scalar>::Real prec = NumTraits<Scalar>::dummy_precision()) const - { return m_coeffs.isApprox(other.m_coeffs, prec); } - -}; - -/** \addtogroup Geometry_Module */ -//@{ -typedef Translation<float, 2> Translation2f; -typedef Translation<double,2> Translation2d; -typedef Translation<float, 3> Translation3f; -typedef Translation<double,3> Translation3d; -//@} - -template<typename Scalar, int Dim> -inline typename Translation<Scalar,Dim>::AffineTransformType -Translation<Scalar,Dim>::operator* (const UniformScaling<Scalar>& other) const -{ - AffineTransformType res; - res.matrix().setZero(); - res.linear().diagonal().fill(other.factor()); - res.translation() = m_coeffs; - res(Dim,Dim) = Scalar(1); - return res; -} - -template<typename Scalar, int Dim> -template<typename OtherDerived> -inline typename Translation<Scalar,Dim>::AffineTransformType -Translation<Scalar,Dim>::operator* (const EigenBase<OtherDerived>& linear) const -{ - AffineTransformType res; - res.matrix().setZero(); - res.linear() = linear.derived(); - res.translation() = m_coeffs; - res.matrix().row(Dim).setZero(); - res(Dim,Dim) = Scalar(1); - return res; -} - -} // end namespace Eigen - -#endif // EIGEN_TRANSLATION_H diff --git a/third_party/eigen3/Eigen/src/Geometry/Umeyama.h b/third_party/eigen3/Eigen/src/Geometry/Umeyama.h deleted file mode 100644 index 5e20662f80..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/Umeyama.h +++ /dev/null @@ -1,177 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_UMEYAMA_H -#define EIGEN_UMEYAMA_H - -// This file requires the user to include -// * Eigen/Core -// * Eigen/LU -// * Eigen/SVD -// * Eigen/Array - -namespace Eigen { - -#ifndef EIGEN_PARSED_BY_DOXYGEN - -// These helpers are required since it allows to use mixed types as parameters -// for the Umeyama. The problem with mixed parameters is that the return type -// cannot trivially be deduced when float and double types are mixed. -namespace internal { - -// Compile time return type deduction for different MatrixBase types. -// Different means here different alignment and parameters but the same underlying -// real scalar type. -template<typename MatrixType, typename OtherMatrixType> -struct umeyama_transform_matrix_type -{ - enum { - MinRowsAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(MatrixType::RowsAtCompileTime, OtherMatrixType::RowsAtCompileTime), - - // When possible we want to choose some small fixed size value since the result - // is likely to fit on the stack. So here, EIGEN_SIZE_MIN_PREFER_DYNAMIC is not what we want. - HomogeneousDimension = int(MinRowsAtCompileTime) == Dynamic ? Dynamic : int(MinRowsAtCompileTime)+1 - }; - - typedef Matrix<typename traits<MatrixType>::Scalar, - HomogeneousDimension, - HomogeneousDimension, - AutoAlign | (traits<MatrixType>::Flags & RowMajorBit ? RowMajor : ColMajor), - HomogeneousDimension, - HomogeneousDimension - > type; -}; - -} - -#endif - -/** -* \geometry_module \ingroup Geometry_Module -* -* \brief Returns the transformation between two point sets. -* -* The algorithm is based on: -* "Least-squares estimation of transformation parameters between two point patterns", -* Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573 -* -* It estimates parameters \f$ c, \mathbf{R}, \f$ and \f$ \mathbf{t} \f$ such that -* \f{align*} -* \frac{1}{n} \sum_{i=1}^n \vert\vert y_i - (c\mathbf{R}x_i + \mathbf{t}) \vert\vert_2^2 -* \f} -* is minimized. -* -* The algorithm is based on the analysis of the covariance matrix -* \f$ \Sigma_{\mathbf{x}\mathbf{y}} \in \mathbb{R}^{d \times d} \f$ -* of the input point sets \f$ \mathbf{x} \f$ and \f$ \mathbf{y} \f$ where -* \f$d\f$ is corresponding to the dimension (which is typically small). -* The analysis is involving the SVD having a complexity of \f$O(d^3)\f$ -* though the actual computational effort lies in the covariance -* matrix computation which has an asymptotic lower bound of \f$O(dm)\f$ when -* the input point sets have dimension \f$d \times m\f$. -* -* Currently the method is working only for floating point matrices. -* -* \todo Should the return type of umeyama() become a Transform? -* -* \param src Source points \f$ \mathbf{x} = \left( x_1, \hdots, x_n \right) \f$. -* \param dst Destination points \f$ \mathbf{y} = \left( y_1, \hdots, y_n \right) \f$. -* \param with_scaling Sets \f$ c=1 \f$ when <code>false</code> is passed. -* \return The homogeneous transformation -* \f{align*} -* T = \begin{bmatrix} c\mathbf{R} & \mathbf{t} \\ \mathbf{0} & 1 \end{bmatrix} -* \f} -* minimizing the resudiual above. This transformation is always returned as an -* Eigen::Matrix. -*/ -template <typename Derived, typename OtherDerived> -typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type -umeyama(const MatrixBase<Derived>& src, const MatrixBase<OtherDerived>& dst, bool with_scaling = true) -{ - typedef typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type TransformationMatrixType; - typedef typename internal::traits<TransformationMatrixType>::Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef typename Derived::Index Index; - - EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL) - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename internal::traits<OtherDerived>::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - - enum { Dimension = EIGEN_SIZE_MIN_PREFER_DYNAMIC(Derived::RowsAtCompileTime, OtherDerived::RowsAtCompileTime) }; - - typedef Matrix<Scalar, Dimension, 1> VectorType; - typedef Matrix<Scalar, Dimension, Dimension> MatrixType; - typedef typename internal::plain_matrix_type_row_major<Derived>::type RowMajorMatrixType; - - const Index m = src.rows(); // dimension - const Index n = src.cols(); // number of measurements - - // required for demeaning ... - const RealScalar one_over_n = RealScalar(1) / static_cast<RealScalar>(n); - - // computation of mean - const VectorType src_mean = src.rowwise().sum() * one_over_n; - const VectorType dst_mean = dst.rowwise().sum() * one_over_n; - - // demeaning of src and dst points - const RowMajorMatrixType src_demean = src.colwise() - src_mean; - const RowMajorMatrixType dst_demean = dst.colwise() - dst_mean; - - // Eq. (36)-(37) - const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n; - - // Eq. (38) - const MatrixType sigma = one_over_n * dst_demean * src_demean.transpose(); - - JacobiSVD<MatrixType> svd(sigma, ComputeFullU | ComputeFullV); - - // Initialize the resulting transformation with an identity matrix... - TransformationMatrixType Rt = TransformationMatrixType::Identity(m+1,m+1); - - // Eq. (39) - VectorType S = VectorType::Ones(m); - if (sigma.determinant()<Scalar(0)) S(m-1) = Scalar(-1); - - // Eq. (40) and (43) - const VectorType& d = svd.singularValues(); - Index rank = 0; for (Index i=0; i<m; ++i) if (!internal::isMuchSmallerThan(d.coeff(i),d.coeff(0))) ++rank; - if (rank == m-1) { - if ( svd.matrixU().determinant() * svd.matrixV().determinant() > Scalar(0) ) { - Rt.block(0,0,m,m).noalias() = svd.matrixU()*svd.matrixV().transpose(); - } else { - const Scalar s = S(m-1); S(m-1) = Scalar(-1); - Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose(); - S(m-1) = s; - } - } else { - Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose(); - } - - if (with_scaling) - { - // Eq. (42) - const Scalar c = Scalar(1)/src_var * svd.singularValues().dot(S); - - // Eq. (41) - Rt.col(m).head(m) = dst_mean; - Rt.col(m).head(m).noalias() -= c*Rt.topLeftCorner(m,m)*src_mean; - Rt.block(0,0,m,m) *= c; - } - else - { - Rt.col(m).head(m) = dst_mean; - Rt.col(m).head(m).noalias() -= Rt.topLeftCorner(m,m)*src_mean; - } - - return Rt; -} - -} // end namespace Eigen - -#endif // EIGEN_UMEYAMA_H diff --git a/third_party/eigen3/Eigen/src/Geometry/arch/Geometry_SSE.h b/third_party/eigen3/Eigen/src/Geometry/arch/Geometry_SSE.h deleted file mode 100644 index 3d8284f2d0..0000000000 --- a/third_party/eigen3/Eigen/src/Geometry/arch/Geometry_SSE.h +++ /dev/null @@ -1,115 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Rohit Garg <rpg.314@gmail.com> -// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_GEOMETRY_SSE_H -#define EIGEN_GEOMETRY_SSE_H - -namespace Eigen { - -namespace internal { - -template<class Derived, class OtherDerived> -struct quat_product<Architecture::SSE, Derived, OtherDerived, float, Aligned> -{ - static inline Quaternion<float> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b) - { - const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0,0,0,0x80000000)); - Quaternion<float> res; - __m128 a = _a.coeffs().template packet<Aligned>(0); - __m128 b = _b.coeffs().template packet<Aligned>(0); - __m128 flip1 = _mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a,1,2,0,2), - vec4f_swizzle1(b,2,0,1,2)),mask); - __m128 flip2 = _mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a,3,3,3,1), - vec4f_swizzle1(b,0,1,2,1)),mask); - pstore(&res.x(), - _mm_add_ps(_mm_sub_ps(_mm_mul_ps(a,vec4f_swizzle1(b,3,3,3,3)), - _mm_mul_ps(vec4f_swizzle1(a,2,0,1,0), - vec4f_swizzle1(b,1,2,0,0))), - _mm_add_ps(flip1,flip2))); - return res; - } -}; - -template<typename VectorLhs,typename VectorRhs> -struct cross3_impl<Architecture::SSE,VectorLhs,VectorRhs,float,true> -{ - static inline typename plain_matrix_type<VectorLhs>::type - run(const VectorLhs& lhs, const VectorRhs& rhs) - { - __m128 a = lhs.template packet<VectorLhs::Flags&AlignedBit ? Aligned : Unaligned>(0); - __m128 b = rhs.template packet<VectorRhs::Flags&AlignedBit ? Aligned : Unaligned>(0); - __m128 mul1=_mm_mul_ps(vec4f_swizzle1(a,1,2,0,3),vec4f_swizzle1(b,2,0,1,3)); - __m128 mul2=_mm_mul_ps(vec4f_swizzle1(a,2,0,1,3),vec4f_swizzle1(b,1,2,0,3)); - typename plain_matrix_type<VectorLhs>::type res; - pstore(&res.x(),_mm_sub_ps(mul1,mul2)); - return res; - } -}; - - - - -template<class Derived, class OtherDerived> -struct quat_product<Architecture::SSE, Derived, OtherDerived, double, Aligned> -{ - static inline Quaternion<double> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b) - { - const Packet2d mask = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0)); - - Quaternion<double> res; - - const double* a = _a.coeffs().data(); - Packet2d b_xy = _b.coeffs().template packet<Aligned>(0); - Packet2d b_zw = _b.coeffs().template packet<Aligned>(2); - Packet2d a_xx = pset1<Packet2d>(a[0]); - Packet2d a_yy = pset1<Packet2d>(a[1]); - Packet2d a_zz = pset1<Packet2d>(a[2]); - Packet2d a_ww = pset1<Packet2d>(a[3]); - - // two temporaries: - Packet2d t1, t2; - - /* - * t1 = ww*xy + yy*zw - * t2 = zz*xy - xx*zw - * res.xy = t1 +/- swap(t2) - */ - t1 = padd(pmul(a_ww, b_xy), pmul(a_yy, b_zw)); - t2 = psub(pmul(a_zz, b_xy), pmul(a_xx, b_zw)); -#ifdef EIGEN_VECTORIZE_SSE3 - EIGEN_UNUSED_VARIABLE(mask) - pstore(&res.x(), _mm_addsub_pd(t1, preverse(t2))); -#else - pstore(&res.x(), padd(t1, pxor(mask,preverse(t2)))); -#endif - - /* - * t1 = ww*zw - yy*xy - * t2 = zz*zw + xx*xy - * res.zw = t1 -/+ swap(t2) = swap( swap(t1) +/- t2) - */ - t1 = psub(pmul(a_ww, b_zw), pmul(a_yy, b_xy)); - t2 = padd(pmul(a_zz, b_zw), pmul(a_xx, b_xy)); -#ifdef EIGEN_VECTORIZE_SSE3 - EIGEN_UNUSED_VARIABLE(mask) - pstore(&res.z(), preverse(_mm_addsub_pd(preverse(t1), t2))); -#else - pstore(&res.z(), psub(t1, pxor(mask,preverse(t2)))); -#endif - - return res; -} -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_GEOMETRY_SSE_H diff --git a/third_party/eigen3/Eigen/src/Householder/BlockHouseholder.h b/third_party/eigen3/Eigen/src/Householder/BlockHouseholder.h deleted file mode 100644 index 60dbea5f56..0000000000 --- a/third_party/eigen3/Eigen/src/Householder/BlockHouseholder.h +++ /dev/null @@ -1,68 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Vincent Lejeune -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BLOCK_HOUSEHOLDER_H -#define EIGEN_BLOCK_HOUSEHOLDER_H - -// This file contains some helper function to deal with block householder reflectors - -namespace Eigen { - -namespace internal { - -/** \internal */ -template<typename TriangularFactorType,typename VectorsType,typename CoeffsType> -void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors, const CoeffsType& hCoeffs) -{ - typedef typename TriangularFactorType::Index Index; - typedef typename VectorsType::Scalar Scalar; - const Index nbVecs = vectors.cols(); - eigen_assert(triFactor.rows() == nbVecs && triFactor.cols() == nbVecs && vectors.rows()>=nbVecs); - - for(Index i = 0; i < nbVecs; i++) - { - Index rs = vectors.rows() - i; - Scalar Vii = vectors(i,i); - vectors.const_cast_derived().coeffRef(i,i) = Scalar(1); - triFactor.col(i).head(i).noalias() = -hCoeffs(i) * vectors.block(i, 0, rs, i).adjoint() - * vectors.col(i).tail(rs); - vectors.const_cast_derived().coeffRef(i, i) = Vii; - // FIXME add .noalias() once the triangular product can work inplace - triFactor.col(i).head(i) = triFactor.block(0,0,i,i).template triangularView<Upper>() - * triFactor.col(i).head(i); - triFactor(i,i) = hCoeffs(i); - } -} - -/** \internal */ -template<typename MatrixType,typename VectorsType,typename CoeffsType> -void apply_block_householder_on_the_left(MatrixType& mat, const VectorsType& vectors, const CoeffsType& hCoeffs) -{ - typedef typename MatrixType::Index Index; - enum { TFactorSize = MatrixType::ColsAtCompileTime }; - Index nbVecs = vectors.cols(); - Matrix<typename MatrixType::Scalar, TFactorSize, TFactorSize, ColMajor> T(nbVecs,nbVecs); - make_block_householder_triangular_factor(T, vectors, hCoeffs); - - const TriangularView<const VectorsType, UnitLower>& V(vectors); - - // A -= V T V^* A - Matrix<typename MatrixType::Scalar,VectorsType::ColsAtCompileTime,MatrixType::ColsAtCompileTime,0, - VectorsType::MaxColsAtCompileTime,MatrixType::MaxColsAtCompileTime> tmp = V.adjoint() * mat; - // FIXME add .noalias() once the triangular product can work inplace - tmp = T.template triangularView<Upper>().adjoint() * tmp; - mat.noalias() -= V * tmp; -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_BLOCK_HOUSEHOLDER_H diff --git a/third_party/eigen3/Eigen/src/Householder/Householder.h b/third_party/eigen3/Eigen/src/Householder/Householder.h deleted file mode 100644 index 32112af9bf..0000000000 --- a/third_party/eigen3/Eigen/src/Householder/Householder.h +++ /dev/null @@ -1,171 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_HOUSEHOLDER_H -#define EIGEN_HOUSEHOLDER_H - -namespace Eigen { - -namespace internal { -template<int n> struct decrement_size -{ - enum { - ret = n==Dynamic ? n : n-1 - }; -}; -} - -/** Computes the elementary reflector H such that: - * \f$ H *this = [ beta 0 ... 0]^T \f$ - * where the transformation H is: - * \f$ H = I - tau v v^*\f$ - * and the vector v is: - * \f$ v^T = [1 essential^T] \f$ - * - * The essential part of the vector \c v is stored in *this. - * - * On output: - * \param tau the scaling factor of the Householder transformation - * \param beta the result of H * \c *this - * - * \sa MatrixBase::makeHouseholder(), MatrixBase::applyHouseholderOnTheLeft(), - * MatrixBase::applyHouseholderOnTheRight() - */ -template<typename Derived> -void MatrixBase<Derived>::makeHouseholderInPlace(Scalar& tau, RealScalar& beta) -{ - VectorBlock<Derived, internal::decrement_size<Base::SizeAtCompileTime>::ret> essentialPart(derived(), 1, size()-1); - makeHouseholder(essentialPart, tau, beta); -} - -/** Computes the elementary reflector H such that: - * \f$ H *this = [ beta 0 ... 0]^T \f$ - * where the transformation H is: - * \f$ H = I - tau v v^*\f$ - * and the vector v is: - * \f$ v^T = [1 essential^T] \f$ - * - * On output: - * \param essential the essential part of the vector \c v - * \param tau the scaling factor of the Householder transformation - * \param beta the result of H * \c *this - * - * \sa MatrixBase::makeHouseholderInPlace(), MatrixBase::applyHouseholderOnTheLeft(), - * MatrixBase::applyHouseholderOnTheRight() - */ -template<typename Derived> -template<typename EssentialPart> -void MatrixBase<Derived>::makeHouseholder( - EssentialPart& essential, - Scalar& tau, - RealScalar& beta) const -{ - using std::sqrt; - using numext::conj; - - EIGEN_STATIC_ASSERT_VECTOR_ONLY(EssentialPart) - VectorBlock<const Derived, EssentialPart::SizeAtCompileTime> tail(derived(), 1, size()-1); - - RealScalar tailSqNorm = size()==1 ? RealScalar(0) : tail.squaredNorm(); - Scalar c0 = coeff(0); - - if(tailSqNorm == RealScalar(0) && numext::imag(c0)==RealScalar(0)) - { - tau = RealScalar(0); - beta = numext::real(c0); - essential.setZero(); - } - else - { - beta = sqrt(numext::abs2(c0) + tailSqNorm); - if (numext::real(c0)>=RealScalar(0)) - beta = -beta; - essential = tail / (c0 - beta); - tau = conj((beta - c0) / beta); - } -} - -/** Apply the elementary reflector H given by - * \f$ H = I - tau v v^*\f$ - * with - * \f$ v^T = [1 essential^T] \f$ - * from the left to a vector or matrix. - * - * On input: - * \param essential the essential part of the vector \c v - * \param tau the scaling factor of the Householder transformation - * \param workspace a pointer to working space with at least - * this->cols() * essential.size() entries - * - * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(), - * MatrixBase::applyHouseholderOnTheRight() - */ -template<typename Derived> -template<typename EssentialPart> -void MatrixBase<Derived>::applyHouseholderOnTheLeft( - const EssentialPart& essential, - const Scalar& tau, - Scalar* workspace) -{ - if(rows() == 1) - { - *this *= Scalar(1)-tau; - } - else - { - Map<typename internal::plain_row_type<PlainObject>::type> tmp(workspace,cols()); - Block<Derived, EssentialPart::SizeAtCompileTime, Derived::ColsAtCompileTime> bottom(derived(), 1, 0, rows()-1, cols()); - tmp.noalias() = essential.adjoint() * bottom; - tmp += this->row(0); - this->row(0) -= tau * tmp; - bottom.noalias() -= tau * essential * tmp; - } -} - -/** Apply the elementary reflector H given by - * \f$ H = I - tau v v^*\f$ - * with - * \f$ v^T = [1 essential^T] \f$ - * from the right to a vector or matrix. - * - * On input: - * \param essential the essential part of the vector \c v - * \param tau the scaling factor of the Householder transformation - * \param workspace a pointer to working space with at least - * this->cols() * essential.size() entries - * - * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(), - * MatrixBase::applyHouseholderOnTheLeft() - */ -template<typename Derived> -template<typename EssentialPart> -void MatrixBase<Derived>::applyHouseholderOnTheRight( - const EssentialPart& essential, - const Scalar& tau, - Scalar* workspace) -{ - if(cols() == 1) - { - *this *= Scalar(1)-tau; - } - else - { - Map<typename internal::plain_col_type<PlainObject>::type> tmp(workspace,rows()); - Block<Derived, Derived::RowsAtCompileTime, EssentialPart::SizeAtCompileTime> right(derived(), 0, 1, rows(), cols()-1); - tmp.noalias() = right * essential.conjugate(); - tmp += this->col(0); - this->col(0) -= tau * tmp; - right.noalias() -= tau * tmp * essential.transpose(); - } -} - -} // end namespace Eigen - -#endif // EIGEN_HOUSEHOLDER_H diff --git a/third_party/eigen3/Eigen/src/Householder/HouseholderSequence.h b/third_party/eigen3/Eigen/src/Householder/HouseholderSequence.h deleted file mode 100644 index d800ca1fa4..0000000000 --- a/third_party/eigen3/Eigen/src/Householder/HouseholderSequence.h +++ /dev/null @@ -1,441 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_HOUSEHOLDER_SEQUENCE_H -#define EIGEN_HOUSEHOLDER_SEQUENCE_H - -namespace Eigen { - -/** \ingroup Householder_Module - * \householder_module - * \class HouseholderSequence - * \brief Sequence of Householder reflections acting on subspaces with decreasing size - * \tparam VectorsType type of matrix containing the Householder vectors - * \tparam CoeffsType type of vector containing the Householder coefficients - * \tparam Side either OnTheLeft (the default) or OnTheRight - * - * This class represents a product sequence of Householder reflections where the first Householder reflection - * acts on the whole space, the second Householder reflection leaves the one-dimensional subspace spanned by - * the first unit vector invariant, the third Householder reflection leaves the two-dimensional subspace - * spanned by the first two unit vectors invariant, and so on up to the last reflection which leaves all but - * one dimensions invariant and acts only on the last dimension. Such sequences of Householder reflections - * are used in several algorithms to zero out certain parts of a matrix. Indeed, the methods - * HessenbergDecomposition::matrixQ(), Tridiagonalization::matrixQ(), HouseholderQR::householderQ(), - * and ColPivHouseholderQR::householderQ() all return a %HouseholderSequence. - * - * More precisely, the class %HouseholderSequence represents an \f$ n \times n \f$ matrix \f$ H \f$ of the - * form \f$ H = \prod_{i=0}^{n-1} H_i \f$ where the i-th Householder reflection is \f$ H_i = I - h_i v_i - * v_i^* \f$. The i-th Householder coefficient \f$ h_i \f$ is a scalar and the i-th Householder vector \f$ - * v_i \f$ is a vector of the form - * \f[ - * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ]. - * \f] - * The last \f$ n-i \f$ entries of \f$ v_i \f$ are called the essential part of the Householder vector. - * - * Typical usages are listed below, where H is a HouseholderSequence: - * \code - * A.applyOnTheRight(H); // A = A * H - * A.applyOnTheLeft(H); // A = H * A - * A.applyOnTheRight(H.adjoint()); // A = A * H^* - * A.applyOnTheLeft(H.adjoint()); // A = H^* * A - * MatrixXd Q = H; // conversion to a dense matrix - * \endcode - * In addition to the adjoint, you can also apply the inverse (=adjoint), the transpose, and the conjugate operators. - * - * See the documentation for HouseholderSequence(const VectorsType&, const CoeffsType&) for an example. - * - * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() - */ - -namespace internal { - -template<typename VectorsType, typename CoeffsType, int Side> -struct traits<HouseholderSequence<VectorsType,CoeffsType,Side> > -{ - typedef typename VectorsType::Scalar Scalar; - typedef typename VectorsType::Index Index; - typedef typename VectorsType::StorageKind StorageKind; - enum { - RowsAtCompileTime = Side==OnTheLeft ? traits<VectorsType>::RowsAtCompileTime - : traits<VectorsType>::ColsAtCompileTime, - ColsAtCompileTime = RowsAtCompileTime, - MaxRowsAtCompileTime = Side==OnTheLeft ? traits<VectorsType>::MaxRowsAtCompileTime - : traits<VectorsType>::MaxColsAtCompileTime, - MaxColsAtCompileTime = MaxRowsAtCompileTime, - Flags = 0 - }; -}; - -template<typename VectorsType, typename CoeffsType, int Side> -struct hseq_side_dependent_impl -{ - typedef Block<const VectorsType, Dynamic, 1> EssentialVectorType; - typedef HouseholderSequence<VectorsType, CoeffsType, OnTheLeft> HouseholderSequenceType; - typedef typename VectorsType::Index Index; - static inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k) - { - Index start = k+1+h.m_shift; - return Block<const VectorsType,Dynamic,1>(h.m_vectors, start, k, h.rows()-start, 1); - } -}; - -template<typename VectorsType, typename CoeffsType> -struct hseq_side_dependent_impl<VectorsType, CoeffsType, OnTheRight> -{ - typedef Transpose<Block<const VectorsType, 1, Dynamic> > EssentialVectorType; - typedef HouseholderSequence<VectorsType, CoeffsType, OnTheRight> HouseholderSequenceType; - typedef typename VectorsType::Index Index; - static inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k) - { - Index start = k+1+h.m_shift; - return Block<const VectorsType,1,Dynamic>(h.m_vectors, k, start, 1, h.rows()-start).transpose(); - } -}; - -template<typename OtherScalarType, typename MatrixType> struct matrix_type_times_scalar_type -{ - typedef typename scalar_product_traits<OtherScalarType, typename MatrixType::Scalar>::ReturnType - ResultScalar; - typedef Matrix<ResultScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime, - 0, MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime> Type; -}; - -} // end namespace internal - -template<typename VectorsType, typename CoeffsType, int Side> class HouseholderSequence - : public EigenBase<HouseholderSequence<VectorsType,CoeffsType,Side> > -{ - typedef typename internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::EssentialVectorType EssentialVectorType; - - public: - enum { - RowsAtCompileTime = internal::traits<HouseholderSequence>::RowsAtCompileTime, - ColsAtCompileTime = internal::traits<HouseholderSequence>::ColsAtCompileTime, - MaxRowsAtCompileTime = internal::traits<HouseholderSequence>::MaxRowsAtCompileTime, - MaxColsAtCompileTime = internal::traits<HouseholderSequence>::MaxColsAtCompileTime - }; - typedef typename internal::traits<HouseholderSequence>::Scalar Scalar; - typedef typename VectorsType::Index Index; - - typedef HouseholderSequence< - typename internal::conditional<NumTraits<Scalar>::IsComplex, - typename internal::remove_all<typename VectorsType::ConjugateReturnType>::type, - VectorsType>::type, - typename internal::conditional<NumTraits<Scalar>::IsComplex, - typename internal::remove_all<typename CoeffsType::ConjugateReturnType>::type, - CoeffsType>::type, - Side - > ConjugateReturnType; - - /** \brief Constructor. - * \param[in] v %Matrix containing the essential parts of the Householder vectors - * \param[in] h Vector containing the Householder coefficients - * - * Constructs the Householder sequence with coefficients given by \p h and vectors given by \p v. The - * i-th Householder coefficient \f$ h_i \f$ is given by \p h(i) and the essential part of the i-th - * Householder vector \f$ v_i \f$ is given by \p v(k,i) with \p k > \p i (the subdiagonal part of the - * i-th column). If \p v has fewer columns than rows, then the Householder sequence contains as many - * Householder reflections as there are columns. - * - * \note The %HouseholderSequence object stores \p v and \p h by reference. - * - * Example: \include HouseholderSequence_HouseholderSequence.cpp - * Output: \verbinclude HouseholderSequence_HouseholderSequence.out - * - * \sa setLength(), setShift() - */ - HouseholderSequence(const VectorsType& v, const CoeffsType& h) - : m_vectors(v), m_coeffs(h), m_trans(false), m_length(v.diagonalSize()), - m_shift(0) - { - } - - /** \brief Copy constructor. */ - HouseholderSequence(const HouseholderSequence& other) - : m_vectors(other.m_vectors), - m_coeffs(other.m_coeffs), - m_trans(other.m_trans), - m_length(other.m_length), - m_shift(other.m_shift) - { - } - - /** \brief Number of rows of transformation viewed as a matrix. - * \returns Number of rows - * \details This equals the dimension of the space that the transformation acts on. - */ - Index rows() const { return Side==OnTheLeft ? m_vectors.rows() : m_vectors.cols(); } - - /** \brief Number of columns of transformation viewed as a matrix. - * \returns Number of columns - * \details This equals the dimension of the space that the transformation acts on. - */ - Index cols() const { return rows(); } - - /** \brief Essential part of a Householder vector. - * \param[in] k Index of Householder reflection - * \returns Vector containing non-trivial entries of k-th Householder vector - * - * This function returns the essential part of the Householder vector \f$ v_i \f$. This is a vector of - * length \f$ n-i \f$ containing the last \f$ n-i \f$ entries of the vector - * \f[ - * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ]. - * \f] - * The index \f$ i \f$ equals \p k + shift(), corresponding to the k-th column of the matrix \p v - * passed to the constructor. - * - * \sa setShift(), shift() - */ - const EssentialVectorType essentialVector(Index k) const - { - eigen_assert(k >= 0 && k < m_length); - return internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::essentialVector(*this, k); - } - - /** \brief %Transpose of the Householder sequence. */ - HouseholderSequence transpose() const - { - return HouseholderSequence(*this).setTrans(!m_trans); - } - - /** \brief Complex conjugate of the Householder sequence. */ - ConjugateReturnType conjugate() const - { - return ConjugateReturnType(m_vectors.conjugate(), m_coeffs.conjugate()) - .setTrans(m_trans) - .setLength(m_length) - .setShift(m_shift); - } - - /** \brief Adjoint (conjugate transpose) of the Householder sequence. */ - ConjugateReturnType adjoint() const - { - return conjugate().setTrans(!m_trans); - } - - /** \brief Inverse of the Householder sequence (equals the adjoint). */ - ConjugateReturnType inverse() const { return adjoint(); } - - /** \internal */ - template<typename DestType> inline void evalTo(DestType& dst) const - { - Matrix<Scalar, DestType::RowsAtCompileTime, 1, - AutoAlign|ColMajor, DestType::MaxRowsAtCompileTime, 1> workspace(rows()); - evalTo(dst, workspace); - } - - /** \internal */ - template<typename Dest, typename Workspace> - void evalTo(Dest& dst, Workspace& workspace) const - { - workspace.resize(rows()); - Index vecs = m_length; - if( internal::is_same<typename internal::remove_all<VectorsType>::type,Dest>::value - && internal::extract_data(dst) == internal::extract_data(m_vectors)) - { - // in-place - dst.diagonal().setOnes(); - dst.template triangularView<StrictlyUpper>().setZero(); - for(Index k = vecs-1; k >= 0; --k) - { - Index cornerSize = rows() - k - m_shift; - if(m_trans) - dst.bottomRightCorner(cornerSize, cornerSize) - .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data()); - else - dst.bottomRightCorner(cornerSize, cornerSize) - .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data()); - - // clear the off diagonal vector - dst.col(k).tail(rows()-k-1).setZero(); - } - // clear the remaining columns if needed - for(Index k = 0; k<cols()-vecs ; ++k) - dst.col(k).tail(rows()-k-1).setZero(); - } - else - { - dst.setIdentity(rows(), rows()); - for(Index k = vecs-1; k >= 0; --k) - { - Index cornerSize = rows() - k - m_shift; - if(m_trans) - dst.bottomRightCorner(cornerSize, cornerSize) - .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), &workspace.coeffRef(0)); - else - dst.bottomRightCorner(cornerSize, cornerSize) - .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), &workspace.coeffRef(0)); - } - } - } - - /** \internal */ - template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const - { - Matrix<Scalar,1,Dest::RowsAtCompileTime,RowMajor,1,Dest::MaxRowsAtCompileTime> workspace(dst.rows()); - applyThisOnTheRight(dst, workspace); - } - - /** \internal */ - template<typename Dest, typename Workspace> - inline void applyThisOnTheRight(Dest& dst, Workspace& workspace) const - { - workspace.resize(dst.rows()); - for(Index k = 0; k < m_length; ++k) - { - Index actual_k = m_trans ? m_length-k-1 : k; - dst.rightCols(rows()-m_shift-actual_k) - .applyHouseholderOnTheRight(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data()); - } - } - - /** \internal */ - template<typename Dest> inline void applyThisOnTheLeft(Dest& dst) const - { - Matrix<Scalar,1,Dest::ColsAtCompileTime,RowMajor,1,Dest::MaxColsAtCompileTime> workspace(dst.cols()); - applyThisOnTheLeft(dst, workspace); - } - - /** \internal */ - template<typename Dest, typename Workspace> - inline void applyThisOnTheLeft(Dest& dst, Workspace& workspace) const - { - workspace.resize(dst.cols()); - for(Index k = 0; k < m_length; ++k) - { - Index actual_k = m_trans ? k : m_length-k-1; - dst.bottomRows(rows()-m_shift-actual_k) - .applyHouseholderOnTheLeft(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data()); - } - } - - /** \brief Computes the product of a Householder sequence with a matrix. - * \param[in] other %Matrix being multiplied. - * \returns Expression object representing the product. - * - * This function computes \f$ HM \f$ where \f$ H \f$ is the Householder sequence represented by \p *this - * and \f$ M \f$ is the matrix \p other. - */ - template<typename OtherDerived> - typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type operator*(const MatrixBase<OtherDerived>& other) const - { - typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type - res(other.template cast<typename internal::matrix_type_times_scalar_type<Scalar,OtherDerived>::ResultScalar>()); - applyThisOnTheLeft(res); - return res; - } - - template<typename _VectorsType, typename _CoeffsType, int _Side> friend struct internal::hseq_side_dependent_impl; - - /** \brief Sets the length of the Householder sequence. - * \param [in] length New value for the length. - * - * By default, the length \f$ n \f$ of the Householder sequence \f$ H = H_0 H_1 \ldots H_{n-1} \f$ is set - * to the number of columns of the matrix \p v passed to the constructor, or the number of rows if that - * is smaller. After this function is called, the length equals \p length. - * - * \sa length() - */ - HouseholderSequence& setLength(Index length) - { - m_length = length; - return *this; - } - - /** \brief Sets the shift of the Householder sequence. - * \param [in] shift New value for the shift. - * - * By default, a %HouseholderSequence object represents \f$ H = H_0 H_1 \ldots H_{n-1} \f$ and the i-th - * column of the matrix \p v passed to the constructor corresponds to the i-th Householder - * reflection. After this function is called, the object represents \f$ H = H_{\mathrm{shift}} - * H_{\mathrm{shift}+1} \ldots H_{n-1} \f$ and the i-th column of \p v corresponds to the (shift+i)-th - * Householder reflection. - * - * \sa shift() - */ - HouseholderSequence& setShift(Index shift) - { - m_shift = shift; - return *this; - } - - Index length() const { return m_length; } /**< \brief Returns the length of the Householder sequence. */ - Index shift() const { return m_shift; } /**< \brief Returns the shift of the Householder sequence. */ - - /* Necessary for .adjoint() and .conjugate() */ - template <typename VectorsType2, typename CoeffsType2, int Side2> friend class HouseholderSequence; - - protected: - - /** \brief Sets the transpose flag. - * \param [in] trans New value of the transpose flag. - * - * By default, the transpose flag is not set. If the transpose flag is set, then this object represents - * \f$ H^T = H_{n-1}^T \ldots H_1^T H_0^T \f$ instead of \f$ H = H_0 H_1 \ldots H_{n-1} \f$. - * - * \sa trans() - */ - HouseholderSequence& setTrans(bool trans) - { - m_trans = trans; - return *this; - } - - bool trans() const { return m_trans; } /**< \brief Returns the transpose flag. */ - - typename VectorsType::Nested m_vectors; - typename CoeffsType::Nested m_coeffs; - bool m_trans; - Index m_length; - Index m_shift; -}; - -/** \brief Computes the product of a matrix with a Householder sequence. - * \param[in] other %Matrix being multiplied. - * \param[in] h %HouseholderSequence being multiplied. - * \returns Expression object representing the product. - * - * This function computes \f$ MH \f$ where \f$ M \f$ is the matrix \p other and \f$ H \f$ is the - * Householder sequence represented by \p h. - */ -template<typename OtherDerived, typename VectorsType, typename CoeffsType, int Side> -typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type operator*(const MatrixBase<OtherDerived>& other, const HouseholderSequence<VectorsType,CoeffsType,Side>& h) -{ - typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type - res(other.template cast<typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::ResultScalar>()); - h.applyThisOnTheRight(res); - return res; -} - -/** \ingroup Householder_Module \householder_module - * \brief Convenience function for constructing a Householder sequence. - * \returns A HouseholderSequence constructed from the specified arguments. - */ -template<typename VectorsType, typename CoeffsType> -HouseholderSequence<VectorsType,CoeffsType> householderSequence(const VectorsType& v, const CoeffsType& h) -{ - return HouseholderSequence<VectorsType,CoeffsType,OnTheLeft>(v, h); -} - -/** \ingroup Householder_Module \householder_module - * \brief Convenience function for constructing a Householder sequence. - * \returns A HouseholderSequence constructed from the specified arguments. - * \details This function differs from householderSequence() in that the template argument \p OnTheSide of - * the constructed HouseholderSequence is set to OnTheRight, instead of the default OnTheLeft. - */ -template<typename VectorsType, typename CoeffsType> -HouseholderSequence<VectorsType,CoeffsType,OnTheRight> rightHouseholderSequence(const VectorsType& v, const CoeffsType& h) -{ - return HouseholderSequence<VectorsType,CoeffsType,OnTheRight>(v, h); -} - -} // end namespace Eigen - -#endif // EIGEN_HOUSEHOLDER_SEQUENCE_H diff --git a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/third_party/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h deleted file mode 100644 index 1f3c060d02..0000000000 --- a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h +++ /dev/null @@ -1,149 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BASIC_PRECONDITIONERS_H -#define EIGEN_BASIC_PRECONDITIONERS_H - -namespace Eigen { - -/** \ingroup IterativeLinearSolvers_Module - * \brief A preconditioner based on the digonal entries - * - * This class allows to approximately solve for A.x = b problems assuming A is a diagonal matrix. - * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for: - * \code - * A.diagonal().asDiagonal() . x = b - * \endcode - * - * \tparam _Scalar the type of the scalar. - * - * This preconditioner is suitable for both selfadjoint and general problems. - * The diagonal entries are pre-inverted and stored into a dense vector. - * - * \note A variant that has yet to be implemented would attempt to preserve the norm of each column. - * - */ -template <typename _Scalar> -class DiagonalPreconditioner -{ - typedef _Scalar Scalar; - typedef Matrix<Scalar,Dynamic,1> Vector; - typedef typename Vector::Index Index; - - public: - // this typedef is only to export the scalar type and compile-time dimensions to solve_retval - typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType; - - DiagonalPreconditioner() : m_isInitialized(false) {} - - template<typename MatType> - DiagonalPreconditioner(const MatType& mat) : m_invdiag(mat.cols()) - { - compute(mat); - } - - Index rows() const { return m_invdiag.size(); } - Index cols() const { return m_invdiag.size(); } - - template<typename MatType> - DiagonalPreconditioner& analyzePattern(const MatType& ) - { - return *this; - } - - template<typename MatType> - DiagonalPreconditioner& factorize(const MatType& mat) - { - m_invdiag.resize(mat.cols()); - for(int j=0; j<mat.outerSize(); ++j) - { - typename MatType::InnerIterator it(mat,j); - while(it && it.index()!=j) ++it; - if(it && it.index()==j && it.value()!=Scalar(0)) - m_invdiag(j) = Scalar(1)/it.value(); - else - m_invdiag(j) = Scalar(1); - } - m_isInitialized = true; - return *this; - } - - template<typename MatType> - DiagonalPreconditioner& compute(const MatType& mat) - { - return factorize(mat); - } - - template<typename Rhs, typename Dest> - void _solve(const Rhs& b, Dest& x) const - { - x = m_invdiag.array() * b.array() ; - } - - template<typename Rhs> inline const internal::solve_retval<DiagonalPreconditioner, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "DiagonalPreconditioner is not initialized."); - eigen_assert(m_invdiag.size()==b.rows() - && "DiagonalPreconditioner::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<DiagonalPreconditioner, Rhs>(*this, b.derived()); - } - - protected: - Vector m_invdiag; - bool m_isInitialized; -}; - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<DiagonalPreconditioner<_MatrixType>, Rhs> - : solve_retval_base<DiagonalPreconditioner<_MatrixType>, Rhs> -{ - typedef DiagonalPreconditioner<_MatrixType> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -} - -/** \ingroup IterativeLinearSolvers_Module - * \brief A naive preconditioner which approximates any matrix as the identity matrix - * - * \sa class DiagonalPreconditioner - */ -class IdentityPreconditioner -{ - public: - - IdentityPreconditioner() {} - - template<typename MatrixType> - IdentityPreconditioner(const MatrixType& ) {} - - template<typename MatrixType> - IdentityPreconditioner& analyzePattern(const MatrixType& ) { return *this; } - - template<typename MatrixType> - IdentityPreconditioner& factorize(const MatrixType& ) { return *this; } - - template<typename MatrixType> - IdentityPreconditioner& compute(const MatrixType& ) { return *this; } - - template<typename Rhs> - inline const Rhs& solve(const Rhs& b) const { return b; } -}; - -} // end namespace Eigen - -#endif // EIGEN_BASIC_PRECONDITIONERS_H diff --git a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/third_party/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h deleted file mode 100644 index 7a46b51fa6..0000000000 --- a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h +++ /dev/null @@ -1,254 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BICGSTAB_H -#define EIGEN_BICGSTAB_H - -namespace Eigen { - -namespace internal { - -/** \internal Low-level bi conjugate gradient stabilized algorithm - * \param mat The matrix A - * \param rhs The right hand side vector b - * \param x On input and initial solution, on output the computed solution. - * \param precond A preconditioner being able to efficiently solve for an - * approximation of Ax=b (regardless of b) - * \param iters On input the max number of iteration, on output the number of performed iterations. - * \param tol_error On input the tolerance error, on output an estimation of the relative error. - * \return false in the case of numerical issue, for example a break down of BiCGSTAB. - */ -template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner> -bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x, - const Preconditioner& precond, int& iters, - typename Dest::RealScalar& tol_error) -{ - using std::sqrt; - using std::abs; - typedef typename Dest::RealScalar RealScalar; - typedef typename Dest::Scalar Scalar; - typedef Matrix<Scalar,Dynamic,1> VectorType; - RealScalar tol = tol_error; - int maxIters = iters; - - int n = mat.cols(); - x = precond.solve(x); - VectorType r = rhs - mat * x; - VectorType r0 = r; - - RealScalar r0_sqnorm = r0.squaredNorm(); - RealScalar rhs_sqnorm = rhs.squaredNorm(); - if(rhs_sqnorm == 0) - { - x.setZero(); - return true; - } - Scalar rho = 1; - Scalar alpha = 1; - Scalar w = 1; - - VectorType v = VectorType::Zero(n), p = VectorType::Zero(n); - VectorType y(n), z(n); - VectorType kt(n), ks(n); - - VectorType s(n), t(n); - - RealScalar tol2 = tol*tol; - int i = 0; - int restarts = 0; - - while ( r.squaredNorm()/rhs_sqnorm > tol2 && i<maxIters ) - { - Scalar rho_old = rho; - - rho = r0.dot(r); - if (internal::isMuchSmallerThan(rho,r0_sqnorm)) - { - // The new residual vector became too orthogonal to the arbitrarily choosen direction r0 - // Let's restart with a new r0: - r0 = r; - rho = r0_sqnorm = r.squaredNorm(); - if(restarts++ == 0) - i = 0; - } - Scalar beta = (rho/rho_old) * (alpha / w); - p = r + beta * (p - w * v); - - y = precond.solve(p); - - v.noalias() = mat * y; - - alpha = rho / r0.dot(v); - s = r - alpha * v; - - z = precond.solve(s); - t.noalias() = mat * z; - - RealScalar tmp = t.squaredNorm(); - if(tmp>RealScalar(0)) - w = t.dot(s) / tmp; - else - w = Scalar(0); - x += alpha * y + w * z; - r = s - w * t; - ++i; - } - tol_error = sqrt(r.squaredNorm()/rhs_sqnorm); - iters = i; - return true; -} - -} - -template< typename _MatrixType, - typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> > -class BiCGSTAB; - -namespace internal { - -template< typename _MatrixType, typename _Preconditioner> -struct traits<BiCGSTAB<_MatrixType,_Preconditioner> > -{ - typedef _MatrixType MatrixType; - typedef _Preconditioner Preconditioner; -}; - -} - -/** \ingroup IterativeLinearSolvers_Module - * \brief A bi conjugate gradient stabilized solver for sparse square problems - * - * This class allows to solve for A.x = b sparse linear problems using a bi conjugate gradient - * stabilized algorithm. The vectors x and b can be either dense or sparse. - * - * \tparam _MatrixType the type of the sparse matrix A, can be a dense or a sparse matrix. - * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner - * - * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations() - * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations - * and NumTraits<Scalar>::epsilon() for the tolerance. - * - * This class can be used as the direct solver classes. Here is a typical usage example: - * \include BiCGSTAB_simple.cpp - * - * By default the iterations start with x=0 as an initial guess of the solution. - * One can control the start using the solveWithGuess() method. Here is a step by - * step execution example starting with a random guess and printing the evolution - * of the estimated error: - * \include BiCGSTAB_step_by_step.cpp - * Note that such a step by step excution is slightly slower. - * - * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner - */ -template< typename _MatrixType, typename _Preconditioner> -class BiCGSTAB : public IterativeSolverBase<BiCGSTAB<_MatrixType,_Preconditioner> > -{ - typedef IterativeSolverBase<BiCGSTAB> Base; - using Base::mp_matrix; - using Base::m_error; - using Base::m_iterations; - using Base::m_info; - using Base::m_isInitialized; -public: - typedef _MatrixType MatrixType; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - typedef typename MatrixType::RealScalar RealScalar; - typedef _Preconditioner Preconditioner; - -public: - - /** Default constructor. */ - BiCGSTAB() : Base() {} - - /** Initialize the solver with matrix \a A for further \c Ax=b solving. - * - * This constructor is a shortcut for the default constructor followed - * by a call to compute(). - * - * \warning this class stores a reference to the matrix A as well as some - * precomputed values that depend on it. Therefore, if \a A is changed - * this class becomes invalid. Call compute() to update it with the new - * matrix A, or modify a copy of A. - */ - BiCGSTAB(const MatrixType& A) : Base(A) {} - - ~BiCGSTAB() {} - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A - * \a x0 as an initial solution. - * - * \sa compute() - */ - template<typename Rhs,typename Guess> - inline const internal::solve_retval_with_guess<BiCGSTAB, Rhs, Guess> - solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const - { - eigen_assert(m_isInitialized && "BiCGSTAB is not initialized."); - eigen_assert(Base::rows()==b.rows() - && "BiCGSTAB::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval_with_guess - <BiCGSTAB, Rhs, Guess>(*this, b.derived(), x0); - } - - /** \internal */ - template<typename Rhs,typename Dest> - void _solveWithGuess(const Rhs& b, Dest& x) const - { - bool failed = false; - for(int j=0; j<b.cols(); ++j) - { - m_iterations = Base::maxIterations(); - m_error = Base::m_tolerance; - - typename Dest::ColXpr xj(x,j); - if(!internal::bicgstab(*mp_matrix, b.col(j), xj, Base::m_preconditioner, m_iterations, m_error)) - failed = true; - } - m_info = failed ? NumericalIssue - : m_error <= Base::m_tolerance ? Success - : NoConvergence; - m_isInitialized = true; - } - - /** \internal */ - template<typename Rhs,typename Dest> - void _solve(const Rhs& b, Dest& x) const - { -// x.setZero(); - x = b; - _solveWithGuess(b,x); - } - -protected: - -}; - - -namespace internal { - - template<typename _MatrixType, typename _Preconditioner, typename Rhs> -struct solve_retval<BiCGSTAB<_MatrixType, _Preconditioner>, Rhs> - : solve_retval_base<BiCGSTAB<_MatrixType, _Preconditioner>, Rhs> -{ - typedef BiCGSTAB<_MatrixType, _Preconditioner> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_BICGSTAB_H diff --git a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/third_party/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h deleted file mode 100644 index 3ce5179409..0000000000 --- a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h +++ /dev/null @@ -1,265 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CONJUGATE_GRADIENT_H -#define EIGEN_CONJUGATE_GRADIENT_H - -namespace Eigen { - -namespace internal { - -/** \internal Low-level conjugate gradient algorithm - * \param mat The matrix A - * \param rhs The right hand side vector b - * \param x On input and initial solution, on output the computed solution. - * \param precond A preconditioner being able to efficiently solve for an - * approximation of Ax=b (regardless of b) - * \param iters On input the max number of iteration, on output the number of performed iterations. - * \param tol_error On input the tolerance error, on output an estimation of the relative error. - */ -template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner> -EIGEN_DONT_INLINE -void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x, - const Preconditioner& precond, int& iters, - typename Dest::RealScalar& tol_error) -{ - using std::sqrt; - using std::abs; - typedef typename Dest::RealScalar RealScalar; - typedef typename Dest::Scalar Scalar; - typedef Matrix<Scalar,Dynamic,1> VectorType; - - RealScalar tol = tol_error; - int maxIters = iters; - - int n = mat.cols(); - - VectorType residual = rhs - mat * x; //initial residual - - RealScalar rhsNorm2 = rhs.squaredNorm(); - if(rhsNorm2 == 0) - { - x.setZero(); - iters = 0; - tol_error = 0; - return; - } - RealScalar threshold = tol*tol*rhsNorm2; - RealScalar residualNorm2 = residual.squaredNorm(); - if (residualNorm2 < threshold) - { - iters = 0; - tol_error = sqrt(residualNorm2 / rhsNorm2); - return; - } - - VectorType p(n); - p = precond.solve(residual); //initial search direction - - VectorType z(n), tmp(n); - RealScalar absNew = numext::real(residual.dot(p)); // the square of the absolute value of r scaled by invM - int i = 0; - while(i < maxIters) - { - tmp.noalias() = mat * p; // the bottleneck of the algorithm - - Scalar alpha = absNew / p.dot(tmp); // the amount we travel on dir - x += alpha * p; // update solution - residual -= alpha * tmp; // update residue - - residualNorm2 = residual.squaredNorm(); - if(residualNorm2 < threshold) - break; - - z = precond.solve(residual); // approximately solve for "A z = residual" - - RealScalar absOld = absNew; - absNew = numext::real(residual.dot(z)); // update the absolute value of r - RealScalar beta = absNew / absOld; // calculate the Gram-Schmidt value used to create the new search direction - p = z + beta * p; // update search direction - i++; - } - tol_error = sqrt(residualNorm2 / rhsNorm2); - iters = i; -} - -} - -template< typename _MatrixType, int _UpLo=Lower, - typename _Preconditioner = DiagonalPreconditioner<typename _MatrixType::Scalar> > -class ConjugateGradient; - -namespace internal { - -template< typename _MatrixType, int _UpLo, typename _Preconditioner> -struct traits<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> > -{ - typedef _MatrixType MatrixType; - typedef _Preconditioner Preconditioner; -}; - -} - -/** \ingroup IterativeLinearSolvers_Module - * \brief A conjugate gradient solver for sparse (or dense) self-adjoint problems - * - * This class allows to solve for A.x = b linear problems using an iterative conjugate gradient algorithm. - * The matrix A must be selfadjoint. The matrix A and the vectors x and b can be either dense or sparse. - * - * \tparam _MatrixType the type of the matrix A, can be a dense or a sparse matrix. - * \tparam _UpLo the triangular part that will be used for the computations. It can be Lower - * or Upper. Default is Lower. - * \tparam _Preconditioner the type of the preconditioner. Default is DiagonalPreconditioner - * - * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations() - * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations - * and NumTraits<Scalar>::epsilon() for the tolerance. - * - * This class can be used as the direct solver classes. Here is a typical usage example: - * \code - * int n = 10000; - * VectorXd x(n), b(n); - * SparseMatrix<double> A(n,n); - * // fill A and b - * ConjugateGradient<SparseMatrix<double> > cg; - * cg.compute(A); - * x = cg.solve(b); - * std::cout << "#iterations: " << cg.iterations() << std::endl; - * std::cout << "estimated error: " << cg.error() << std::endl; - * // update b, and solve again - * x = cg.solve(b); - * \endcode - * - * By default the iterations start with x=0 as an initial guess of the solution. - * One can control the start using the solveWithGuess() method. Here is a step by - * step execution example starting with a random guess and printing the evolution - * of the estimated error: - * * \code - * x = VectorXd::Random(n); - * cg.setMaxIterations(1); - * int i = 0; - * do { - * x = cg.solveWithGuess(b,x); - * std::cout << i << " : " << cg.error() << std::endl; - * ++i; - * } while (cg.info()!=Success && i<100); - * \endcode - * Note that such a step by step excution is slightly slower. - * - * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner - */ -template< typename _MatrixType, int _UpLo, typename _Preconditioner> -class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> > -{ - typedef IterativeSolverBase<ConjugateGradient> Base; - using Base::mp_matrix; - using Base::m_error; - using Base::m_iterations; - using Base::m_info; - using Base::m_isInitialized; -public: - typedef _MatrixType MatrixType; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - typedef typename MatrixType::RealScalar RealScalar; - typedef _Preconditioner Preconditioner; - - enum { - UpLo = _UpLo - }; - -public: - - /** Default constructor. */ - ConjugateGradient() : Base() {} - - /** Initialize the solver with matrix \a A for further \c Ax=b solving. - * - * This constructor is a shortcut for the default constructor followed - * by a call to compute(). - * - * \warning this class stores a reference to the matrix A as well as some - * precomputed values that depend on it. Therefore, if \a A is changed - * this class becomes invalid. Call compute() to update it with the new - * matrix A, or modify a copy of A. - */ - ConjugateGradient(const MatrixType& A) : Base(A) {} - - ~ConjugateGradient() {} - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A - * \a x0 as an initial solution. - * - * \sa compute() - */ - template<typename Rhs,typename Guess> - inline const internal::solve_retval_with_guess<ConjugateGradient, Rhs, Guess> - solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const - { - eigen_assert(m_isInitialized && "ConjugateGradient is not initialized."); - eigen_assert(Base::rows()==b.rows() - && "ConjugateGradient::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval_with_guess - <ConjugateGradient, Rhs, Guess>(*this, b.derived(), x0); - } - - /** \internal */ - template<typename Rhs,typename Dest> - void _solveWithGuess(const Rhs& b, Dest& x) const - { - m_iterations = Base::maxIterations(); - m_error = Base::m_tolerance; - - for(int j=0; j<b.cols(); ++j) - { - m_iterations = Base::maxIterations(); - m_error = Base::m_tolerance; - - typename Dest::ColXpr xj(x,j); - internal::conjugate_gradient(mp_matrix->template selfadjointView<UpLo>(), b.col(j), xj, - Base::m_preconditioner, m_iterations, m_error); - } - - m_isInitialized = true; - m_info = m_error <= Base::m_tolerance ? Success : NoConvergence; - } - - /** \internal */ - template<typename Rhs,typename Dest> - void _solve(const Rhs& b, Dest& x) const - { - x.setOnes(); - _solveWithGuess(b,x); - } - -protected: - -}; - - -namespace internal { - -template<typename _MatrixType, int _UpLo, typename _Preconditioner, typename Rhs> -struct solve_retval<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner>, Rhs> - : solve_retval_base<ConjugateGradient<_MatrixType,_UpLo,_Preconditioner>, Rhs> -{ - typedef ConjugateGradient<_MatrixType,_UpLo,_Preconditioner> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_CONJUGATE_GRADIENT_H diff --git a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/third_party/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h deleted file mode 100644 index b55afc1363..0000000000 --- a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h +++ /dev/null @@ -1,467 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_INCOMPLETE_LUT_H -#define EIGEN_INCOMPLETE_LUT_H - - -namespace Eigen { - -namespace internal { - -/** \internal - * Compute a quick-sort split of a vector - * On output, the vector row is permuted such that its elements satisfy - * abs(row(i)) >= abs(row(ncut)) if i<ncut - * abs(row(i)) <= abs(row(ncut)) if i>ncut - * \param row The vector of values - * \param ind The array of index for the elements in @p row - * \param ncut The number of largest elements to keep - **/ -template <typename VectorV, typename VectorI, typename Index> -Index QuickSplit(VectorV &row, VectorI &ind, Index ncut) -{ - typedef typename VectorV::RealScalar RealScalar; - using std::swap; - using std::abs; - Index mid; - Index n = row.size(); /* length of the vector */ - Index first, last ; - - ncut--; /* to fit the zero-based indices */ - first = 0; - last = n-1; - if (ncut < first || ncut > last ) return 0; - - do { - mid = first; - RealScalar abskey = abs(row(mid)); - for (Index j = first + 1; j <= last; j++) { - if ( abs(row(j)) > abskey) { - ++mid; - swap(row(mid), row(j)); - swap(ind(mid), ind(j)); - } - } - /* Interchange for the pivot element */ - swap(row(mid), row(first)); - swap(ind(mid), ind(first)); - - if (mid > ncut) last = mid - 1; - else if (mid < ncut ) first = mid + 1; - } while (mid != ncut ); - - return 0; /* mid is equal to ncut */ -} - -}// end namespace internal - -/** \ingroup IterativeLinearSolvers_Module - * \class IncompleteLUT - * \brief Incomplete LU factorization with dual-threshold strategy - * - * During the numerical factorization, two dropping rules are used : - * 1) any element whose magnitude is less than some tolerance is dropped. - * This tolerance is obtained by multiplying the input tolerance @p droptol - * by the average magnitude of all the original elements in the current row. - * 2) After the elimination of the row, only the @p fill largest elements in - * the L part and the @p fill largest elements in the U part are kept - * (in addition to the diagonal element ). Note that @p fill is computed from - * the input parameter @p fillfactor which is used the ratio to control the fill_in - * relatively to the initial number of nonzero elements. - * - * The two extreme cases are when @p droptol=0 (to keep all the @p fill*2 largest elements) - * and when @p fill=n/2 with @p droptol being different to zero. - * - * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization, - * Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994. - * - * NOTE : The following implementation is derived from the ILUT implementation - * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota - * released under the terms of the GNU LGPL: - * http://www-users.cs.umn.edu/~saad/software/SPARSKIT/README - * However, Yousef Saad gave us permission to relicense his ILUT code to MPL2. - * See the Eigen mailing list archive, thread: ILUT, date: July 8, 2012: - * http://listengine.tuxfamily.org/lists.tuxfamily.org/eigen/2012/07/msg00064.html - * alternatively, on GMANE: - * http://comments.gmane.org/gmane.comp.lib.eigen/3302 - */ -template <typename _Scalar> -class IncompleteLUT : internal::noncopyable -{ - typedef _Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - typedef Matrix<Scalar,Dynamic,1> Vector; - typedef SparseMatrix<Scalar,RowMajor> FactorType; - typedef SparseMatrix<Scalar,ColMajor> PermutType; - typedef typename FactorType::Index Index; - - public: - typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType; - - IncompleteLUT() - : m_droptol(NumTraits<Scalar>::dummy_precision()), m_fillfactor(10), - m_analysisIsOk(false), m_factorizationIsOk(false), m_isInitialized(false) - {} - - template<typename MatrixType> - IncompleteLUT(const MatrixType& mat, const RealScalar& droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10) - : m_droptol(droptol),m_fillfactor(fillfactor), - m_analysisIsOk(false),m_factorizationIsOk(false),m_isInitialized(false) - { - eigen_assert(fillfactor != 0); - compute(mat); - } - - Index rows() const { return m_lu.rows(); } - - Index cols() const { return m_lu.cols(); } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the matrix.appears to be negative. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "IncompleteLUT is not initialized."); - return m_info; - } - - template<typename MatrixType> - void analyzePattern(const MatrixType& amat); - - template<typename MatrixType> - void factorize(const MatrixType& amat); - - /** - * Compute an incomplete LU factorization with dual threshold on the matrix mat - * No pivoting is done in this version - * - **/ - template<typename MatrixType> - IncompleteLUT<Scalar>& compute(const MatrixType& amat) - { - analyzePattern(amat); - factorize(amat); - m_isInitialized = m_factorizationIsOk; - return *this; - } - - void setDroptol(const RealScalar& droptol); - void setFillfactor(int fillfactor); - - template<typename Rhs, typename Dest> - void _solve(const Rhs& b, Dest& x) const - { - x = m_Pinv * b; - x = m_lu.template triangularView<UnitLower>().solve(x); - x = m_lu.template triangularView<Upper>().solve(x); - x = m_P * x; - } - - template<typename Rhs> inline const internal::solve_retval<IncompleteLUT, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "IncompleteLUT is not initialized."); - eigen_assert(cols()==b.rows() - && "IncompleteLUT::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<IncompleteLUT, Rhs>(*this, b.derived()); - } - -protected: - - /** keeps off-diagonal entries; drops diagonal entries */ - struct keep_diag { - inline bool operator() (const Index& row, const Index& col, const Scalar&) const - { - return row!=col; - } - }; - -protected: - - FactorType m_lu; - RealScalar m_droptol; - int m_fillfactor; - bool m_analysisIsOk; - bool m_factorizationIsOk; - bool m_isInitialized; - ComputationInfo m_info; - PermutationMatrix<Dynamic,Dynamic,Index> m_P; // Fill-reducing permutation - PermutationMatrix<Dynamic,Dynamic,Index> m_Pinv; // Inverse permutation -}; - -/** - * Set control parameter droptol - * \param droptol Drop any element whose magnitude is less than this tolerance - **/ -template<typename Scalar> -void IncompleteLUT<Scalar>::setDroptol(const RealScalar& droptol) -{ - this->m_droptol = droptol; -} - -/** - * Set control parameter fillfactor - * \param fillfactor This is used to compute the number @p fill_in of largest elements to keep on each row. - **/ -template<typename Scalar> -void IncompleteLUT<Scalar>::setFillfactor(int fillfactor) -{ - this->m_fillfactor = fillfactor; -} - -template <typename Scalar> -template<typename _MatrixType> -void IncompleteLUT<Scalar>::analyzePattern(const _MatrixType& amat) -{ - // Compute the Fill-reducing permutation - SparseMatrix<Scalar,ColMajor, Index> mat1 = amat; - SparseMatrix<Scalar,ColMajor, Index> mat2 = amat.transpose(); - // Symmetrize the pattern - // FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice. - // on the other hand for a really non-symmetric pattern, mat2*mat1 should be prefered... - SparseMatrix<Scalar,ColMajor, Index> AtA = mat2 + mat1; - AtA.prune(keep_diag()); - internal::minimum_degree_ordering<Scalar, Index>(AtA, m_P); // Then compute the AMD ordering... - - m_Pinv = m_P.inverse(); // ... and the inverse permutation - - m_analysisIsOk = true; -} - -template <typename Scalar> -template<typename _MatrixType> -void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat) -{ - using std::sqrt; - using std::swap; - using std::abs; - - eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix"); - Index n = amat.cols(); // Size of the matrix - m_lu.resize(n,n); - // Declare Working vectors and variables - Vector u(n) ; // real values of the row -- maximum size is n -- - VectorXi ju(n); // column position of the values in u -- maximum size is n - VectorXi jr(n); // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1 - - // Apply the fill-reducing permutation - eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); - SparseMatrix<Scalar,RowMajor, Index> mat; - mat = amat.twistedBy(m_Pinv); - - // Initialization - jr.fill(-1); - ju.fill(0); - u.fill(0); - - // number of largest elements to keep in each row: - Index fill_in = static_cast<Index> (amat.nonZeros()*m_fillfactor)/n+1; - if (fill_in > n) fill_in = n; - - // number of largest nonzero elements to keep in the L and the U part of the current row: - Index nnzL = fill_in/2; - Index nnzU = nnzL; - m_lu.reserve(n * (nnzL + nnzU + 1)); - - // global loop over the rows of the sparse matrix - for (Index ii = 0; ii < n; ii++) - { - // 1 - copy the lower and the upper part of the row i of mat in the working vector u - - Index sizeu = 1; // number of nonzero elements in the upper part of the current row - Index sizel = 0; // number of nonzero elements in the lower part of the current row - ju(ii) = ii; - u(ii) = 0; - jr(ii) = ii; - RealScalar rownorm = 0; - - typename FactorType::InnerIterator j_it(mat, ii); // Iterate through the current row ii - for (; j_it; ++j_it) - { - Index k = j_it.index(); - if (k < ii) - { - // copy the lower part - ju(sizel) = k; - u(sizel) = j_it.value(); - jr(k) = sizel; - ++sizel; - } - else if (k == ii) - { - u(ii) = j_it.value(); - } - else - { - // copy the upper part - Index jpos = ii + sizeu; - ju(jpos) = k; - u(jpos) = j_it.value(); - jr(k) = jpos; - ++sizeu; - } - rownorm += numext::abs2(j_it.value()); - } - - // 2 - detect possible zero row - if(rownorm==0) - { - m_info = NumericalIssue; - return; - } - // Take the 2-norm of the current row as a relative tolerance - rownorm = sqrt(rownorm); - - // 3 - eliminate the previous nonzero rows - Index jj = 0; - Index len = 0; - while (jj < sizel) - { - // In order to eliminate in the correct order, - // we must select first the smallest column index among ju(jj:sizel) - Index k; - Index minrow = ju.segment(jj,sizel-jj).minCoeff(&k); // k is relative to the segment - k += jj; - if (minrow != ju(jj)) - { - // swap the two locations - Index j = ju(jj); - swap(ju(jj), ju(k)); - jr(minrow) = jj; jr(j) = k; - swap(u(jj), u(k)); - } - // Reset this location - jr(minrow) = -1; - - // Start elimination - typename FactorType::InnerIterator ki_it(m_lu, minrow); - while (ki_it && ki_it.index() < minrow) ++ki_it; - eigen_internal_assert(ki_it && ki_it.col()==minrow); - Scalar fact = u(jj) / ki_it.value(); - - // drop too small elements - if(abs(fact) <= m_droptol) - { - jj++; - continue; - } - - // linear combination of the current row ii and the row minrow - ++ki_it; - for (; ki_it; ++ki_it) - { - Scalar prod = fact * ki_it.value(); - Index j = ki_it.index(); - Index jpos = jr(j); - if (jpos == -1) // fill-in element - { - Index newpos; - if (j >= ii) // dealing with the upper part - { - newpos = ii + sizeu; - sizeu++; - eigen_internal_assert(sizeu<=n); - } - else // dealing with the lower part - { - newpos = sizel; - sizel++; - eigen_internal_assert(sizel<=ii); - } - ju(newpos) = j; - u(newpos) = -prod; - jr(j) = newpos; - } - else - u(jpos) -= prod; - } - // store the pivot element - u(len) = fact; - ju(len) = minrow; - ++len; - - jj++; - } // end of the elimination on the row ii - - // reset the upper part of the pointer jr to zero - for(Index k = 0; k <sizeu; k++) jr(ju(ii+k)) = -1; - - // 4 - partially sort and insert the elements in the m_lu matrix - - // sort the L-part of the row - sizel = len; - len = (std::min)(sizel, nnzL); - typename Vector::SegmentReturnType ul(u.segment(0, sizel)); - typename VectorXi::SegmentReturnType jul(ju.segment(0, sizel)); - internal::QuickSplit(ul, jul, len); - - // store the largest m_fill elements of the L part - m_lu.startVec(ii); - for(Index k = 0; k < len; k++) - m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k); - - // store the diagonal element - // apply a shifting rule to avoid zero pivots (we are doing an incomplete factorization) - if (u(ii) == Scalar(0)) - u(ii) = sqrt(m_droptol) * rownorm; - m_lu.insertBackByOuterInnerUnordered(ii, ii) = u(ii); - - // sort the U-part of the row - // apply the dropping rule first - len = 0; - for(Index k = 1; k < sizeu; k++) - { - if(abs(u(ii+k)) > m_droptol * rownorm ) - { - ++len; - u(ii + len) = u(ii + k); - ju(ii + len) = ju(ii + k); - } - } - sizeu = len + 1; // +1 to take into account the diagonal element - len = (std::min)(sizeu, nnzU); - typename Vector::SegmentReturnType uu(u.segment(ii+1, sizeu-1)); - typename VectorXi::SegmentReturnType juu(ju.segment(ii+1, sizeu-1)); - internal::QuickSplit(uu, juu, len); - - // store the largest elements of the U part - for(Index k = ii + 1; k < ii + len; k++) - m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k); - } - - m_lu.finalize(); - m_lu.makeCompressed(); - - m_factorizationIsOk = true; - m_info = Success; -} - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<IncompleteLUT<_MatrixType>, Rhs> - : solve_retval_base<IncompleteLUT<_MatrixType>, Rhs> -{ - typedef IncompleteLUT<_MatrixType> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_INCOMPLETE_LUT_H diff --git a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/third_party/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h deleted file mode 100644 index 2036922d69..0000000000 --- a/third_party/eigen3/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h +++ /dev/null @@ -1,254 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ITERATIVE_SOLVER_BASE_H -#define EIGEN_ITERATIVE_SOLVER_BASE_H - -namespace Eigen { - -/** \ingroup IterativeLinearSolvers_Module - * \brief Base class for linear iterative solvers - * - * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner - */ -template< typename Derived> -class IterativeSolverBase : internal::noncopyable -{ -public: - typedef typename internal::traits<Derived>::MatrixType MatrixType; - typedef typename internal::traits<Derived>::Preconditioner Preconditioner; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - typedef typename MatrixType::RealScalar RealScalar; - -public: - - Derived& derived() { return *static_cast<Derived*>(this); } - const Derived& derived() const { return *static_cast<const Derived*>(this); } - - /** Default constructor. */ - IterativeSolverBase() - : mp_matrix(0) - { - init(); - } - - /** Initialize the solver with matrix \a A for further \c Ax=b solving. - * - * This constructor is a shortcut for the default constructor followed - * by a call to compute(). - * - * \warning this class stores a reference to the matrix A as well as some - * precomputed values that depend on it. Therefore, if \a A is changed - * this class becomes invalid. Call compute() to update it with the new - * matrix A, or modify a copy of A. - */ - IterativeSolverBase(const MatrixType& A) - { - init(); - compute(A); - } - - ~IterativeSolverBase() {} - - /** Initializes the iterative solver for the sparcity pattern of the matrix \a A for further solving \c Ax=b problems. - * - * Currently, this function mostly call analyzePattern on the preconditioner. In the future - * we might, for instance, implement column reodering for faster matrix vector products. - */ - Derived& analyzePattern(const MatrixType& A) - { - m_preconditioner.analyzePattern(A); - m_isInitialized = true; - m_analysisIsOk = true; - m_info = Success; - return derived(); - } - - /** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b problems. - * - * Currently, this function mostly call factorize on the preconditioner. - * - * \warning this class stores a reference to the matrix A as well as some - * precomputed values that depend on it. Therefore, if \a A is changed - * this class becomes invalid. Call compute() to update it with the new - * matrix A, or modify a copy of A. - */ - Derived& factorize(const MatrixType& A) - { - eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); - mp_matrix = &A; - m_preconditioner.factorize(A); - m_factorizationIsOk = true; - m_info = Success; - return derived(); - } - - /** Initializes the iterative solver with the matrix \a A for further solving \c Ax=b problems. - * - * Currently, this function mostly initialized/compute the preconditioner. In the future - * we might, for instance, implement column reodering for faster matrix vector products. - * - * \warning this class stores a reference to the matrix A as well as some - * precomputed values that depend on it. Therefore, if \a A is changed - * this class becomes invalid. Call compute() to update it with the new - * matrix A, or modify a copy of A. - */ - Derived& compute(const MatrixType& A) - { - mp_matrix = &A; - m_preconditioner.compute(A); - m_isInitialized = true; - m_analysisIsOk = true; - m_factorizationIsOk = true; - m_info = Success; - return derived(); - } - - /** \internal */ - Index rows() const { return mp_matrix ? mp_matrix->rows() : 0; } - /** \internal */ - Index cols() const { return mp_matrix ? mp_matrix->cols() : 0; } - - /** \returns the tolerance threshold used by the stopping criteria */ - RealScalar tolerance() const { return m_tolerance; } - - /** Sets the tolerance threshold used by the stopping criteria */ - Derived& setTolerance(const RealScalar& tolerance) - { - m_tolerance = tolerance; - return derived(); - } - - /** \returns a read-write reference to the preconditioner for custom configuration. */ - Preconditioner& preconditioner() { return m_preconditioner; } - - /** \returns a read-only reference to the preconditioner. */ - const Preconditioner& preconditioner() const { return m_preconditioner; } - - /** \returns the max number of iterations */ - int maxIterations() const - { - return (mp_matrix && m_maxIterations<0) ? mp_matrix->cols() : m_maxIterations; - } - - /** Sets the max number of iterations */ - Derived& setMaxIterations(int maxIters) - { - m_maxIterations = maxIters; - return derived(); - } - - /** \returns the number of iterations performed during the last solve */ - int iterations() const - { - eigen_assert(m_isInitialized && "ConjugateGradient is not initialized."); - return m_iterations; - } - - /** \returns the tolerance error reached during the last solve */ - RealScalar error() const - { - eigen_assert(m_isInitialized && "ConjugateGradient is not initialized."); - return m_error; - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> inline const internal::solve_retval<Derived, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized."); - eigen_assert(rows()==b.rows() - && "IterativeSolverBase::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<Derived, Rhs>(derived(), b.derived()); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::sparse_solve_retval<IterativeSolverBase, Rhs> - solve(const SparseMatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized."); - eigen_assert(rows()==b.rows() - && "IterativeSolverBase::solve(): invalid number of rows of the right hand side matrix b"); - return internal::sparse_solve_retval<IterativeSolverBase, Rhs>(*this, b.derived()); - } - - /** \returns Success if the iterations converged, and NoConvergence otherwise. */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized."); - return m_info; - } - - /** \internal */ - template<typename Rhs, typename DestScalar, int DestOptions, typename DestIndex> - void _solve_sparse(const Rhs& b, SparseMatrix<DestScalar,DestOptions,DestIndex> &dest) const - { - eigen_assert(rows()==b.rows()); - - int rhsCols = b.cols(); - int size = b.rows(); - Eigen::Matrix<DestScalar,Dynamic,1> tb(size); - Eigen::Matrix<DestScalar,Dynamic,1> tx(size); - for(int k=0; k<rhsCols; ++k) - { - tb = b.col(k); - tx = derived().solve(tb); - dest.col(k) = tx.sparseView(0); - } - } - -protected: - void init() - { - m_isInitialized = false; - m_analysisIsOk = false; - m_factorizationIsOk = false; - m_maxIterations = -1; - m_tolerance = NumTraits<Scalar>::epsilon(); - } - const MatrixType* mp_matrix; - Preconditioner m_preconditioner; - - int m_maxIterations; - RealScalar m_tolerance; - - mutable RealScalar m_error; - mutable int m_iterations; - mutable ComputationInfo m_info; - mutable bool m_isInitialized, m_analysisIsOk, m_factorizationIsOk; -}; - -namespace internal { - -template<typename Derived, typename Rhs> -struct sparse_solve_retval<IterativeSolverBase<Derived>, Rhs> - : sparse_solve_retval_base<IterativeSolverBase<Derived>, Rhs> -{ - typedef IterativeSolverBase<Derived> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec().derived()._solve_sparse(rhs(),dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_ITERATIVE_SOLVER_BASE_H diff --git a/third_party/eigen3/Eigen/src/Jacobi/Jacobi.h b/third_party/eigen3/Eigen/src/Jacobi/Jacobi.h deleted file mode 100644 index 956f72d570..0000000000 --- a/third_party/eigen3/Eigen/src/Jacobi/Jacobi.h +++ /dev/null @@ -1,433 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_JACOBI_H -#define EIGEN_JACOBI_H - -namespace Eigen { - -/** \ingroup Jacobi_Module - * \jacobi_module - * \class JacobiRotation - * \brief Rotation given by a cosine-sine pair. - * - * This class represents a Jacobi or Givens rotation. - * This is a 2D rotation in the plane \c J of angle \f$ \theta \f$ defined by - * its cosine \c c and sine \c s as follow: - * \f$ J = \left ( \begin{array}{cc} c & \overline s \\ -s & \overline c \end{array} \right ) \f$ - * - * You can apply the respective counter-clockwise rotation to a column vector \c v by - * applying its adjoint on the left: \f$ v = J^* v \f$ that translates to the following Eigen code: - * \code - * v.applyOnTheLeft(J.adjoint()); - * \endcode - * - * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() - */ -template<typename Scalar> class JacobiRotation -{ - public: - typedef typename NumTraits<Scalar>::Real RealScalar; - - /** Default constructor without any initialization. */ - JacobiRotation() {} - - /** Construct a planar rotation from a cosine-sine pair (\a c, \c s). */ - JacobiRotation(const Scalar& c, const Scalar& s) : m_c(c), m_s(s) {} - - Scalar& c() { return m_c; } - Scalar c() const { return m_c; } - Scalar& s() { return m_s; } - Scalar s() const { return m_s; } - - /** Concatenates two planar rotation */ - JacobiRotation operator*(const JacobiRotation& other) - { - using numext::conj; - return JacobiRotation(m_c * other.m_c - conj(m_s) * other.m_s, - conj(m_c * conj(other.m_s) + conj(m_s) * conj(other.m_c))); - } - - /** Returns the transposed transformation */ - JacobiRotation transpose() const { using numext::conj; return JacobiRotation(m_c, -conj(m_s)); } - - /** Returns the adjoint transformation */ - JacobiRotation adjoint() const { using numext::conj; return JacobiRotation(conj(m_c), -m_s); } - - template<typename Derived> - bool makeJacobi(const MatrixBase<Derived>&, typename Derived::Index p, typename Derived::Index q); - bool makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z); - - void makeGivens(const Scalar& p, const Scalar& q, Scalar* z=0); - - protected: - void makeGivens(const Scalar& p, const Scalar& q, Scalar* z, internal::true_type); - void makeGivens(const Scalar& p, const Scalar& q, Scalar* z, internal::false_type); - - Scalar m_c, m_s; -}; - -/** Makes \c *this as a Jacobi rotation \a J such that applying \a J on both the right and left sides of the selfadjoint 2x2 matrix - * \f$ B = \left ( \begin{array}{cc} x & y \\ \overline y & z \end{array} \right )\f$ yields a diagonal matrix \f$ A = J^* B J \f$ - * - * \sa MatrixBase::makeJacobi(const MatrixBase<Derived>&, Index, Index), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() - */ -template<typename Scalar> -bool JacobiRotation<Scalar>::makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z) -{ - using std::sqrt; - using std::abs; - typedef typename NumTraits<Scalar>::Real RealScalar; - if(y == Scalar(0)) - { - m_c = Scalar(1); - m_s = Scalar(0); - return false; - } - else - { - RealScalar tau = (x-z)/(RealScalar(2)*abs(y)); - RealScalar w = sqrt(numext::abs2(tau) + RealScalar(1)); - RealScalar t; - if(tau>RealScalar(0)) - { - t = RealScalar(1) / (tau + w); - } - else - { - t = RealScalar(1) / (tau - w); - } - RealScalar sign_t = t > RealScalar(0) ? RealScalar(1) : RealScalar(-1); - RealScalar n = RealScalar(1) / sqrt(numext::abs2(t)+RealScalar(1)); - m_s = - sign_t * (numext::conj(y) / abs(y)) * abs(t) * n; - m_c = n; - return true; - } -} - -/** Makes \c *this as a Jacobi rotation \c J such that applying \a J on both the right and left sides of the 2x2 selfadjoint matrix - * \f$ B = \left ( \begin{array}{cc} \text{this}_{pp} & \text{this}_{pq} \\ (\text{this}_{pq})^* & \text{this}_{qq} \end{array} \right )\f$ yields - * a diagonal matrix \f$ A = J^* B J \f$ - * - * Example: \include Jacobi_makeJacobi.cpp - * Output: \verbinclude Jacobi_makeJacobi.out - * - * \sa JacobiRotation::makeJacobi(RealScalar, Scalar, RealScalar), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() - */ -template<typename Scalar> -template<typename Derived> -inline bool JacobiRotation<Scalar>::makeJacobi(const MatrixBase<Derived>& m, typename Derived::Index p, typename Derived::Index q) -{ - return makeJacobi(numext::real(m.coeff(p,p)), m.coeff(p,q), numext::real(m.coeff(q,q))); -} - -/** Makes \c *this as a Givens rotation \c G such that applying \f$ G^* \f$ to the left of the vector - * \f$ V = \left ( \begin{array}{c} p \\ q \end{array} \right )\f$ yields: - * \f$ G^* V = \left ( \begin{array}{c} r \\ 0 \end{array} \right )\f$. - * - * The value of \a z is returned if \a z is not null (the default is null). - * Also note that G is built such that the cosine is always real. - * - * Example: \include Jacobi_makeGivens.cpp - * Output: \verbinclude Jacobi_makeGivens.out - * - * This function implements the continuous Givens rotation generation algorithm - * found in Anderson (2000), Discontinuous Plane Rotations and the Symmetric Eigenvalue Problem. - * LAPACK Working Note 150, University of Tennessee, UT-CS-00-454, December 4, 2000. - * - * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() - */ -template<typename Scalar> -void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* z) -{ - makeGivens(p, q, z, typename internal::conditional<NumTraits<Scalar>::IsComplex, internal::true_type, internal::false_type>::type()); -} - - -// specialization for complexes -template<typename Scalar> -void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type) -{ - using std::sqrt; - using std::abs; - using numext::conj; - - if(q==Scalar(0)) - { - m_c = numext::real(p)<0 ? Scalar(-1) : Scalar(1); - m_s = 0; - if(r) *r = m_c * p; - } - else if(p==Scalar(0)) - { - m_c = 0; - m_s = -q/abs(q); - if(r) *r = abs(q); - } - else - { - RealScalar p1 = numext::norm1(p); - RealScalar q1 = numext::norm1(q); - if(p1>=q1) - { - Scalar ps = p / p1; - RealScalar p2 = numext::abs2(ps); - Scalar qs = q / p1; - RealScalar q2 = numext::abs2(qs); - - RealScalar u = sqrt(RealScalar(1) + q2/p2); - if(numext::real(p)<RealScalar(0)) - u = -u; - - m_c = Scalar(1)/u; - m_s = -qs*conj(ps)*(m_c/p2); - if(r) *r = p * u; - } - else - { - Scalar ps = p / q1; - RealScalar p2 = numext::abs2(ps); - Scalar qs = q / q1; - RealScalar q2 = numext::abs2(qs); - - RealScalar u = q1 * sqrt(p2 + q2); - if(numext::real(p)<RealScalar(0)) - u = -u; - - p1 = abs(p); - ps = p/p1; - m_c = p1/u; - m_s = -conj(ps) * (q/u); - if(r) *r = ps * u; - } - } -} - -// specialization for reals -template<typename Scalar> -void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type) -{ - using std::sqrt; - using std::abs; - if(q==Scalar(0)) - { - m_c = p<Scalar(0) ? Scalar(-1) : Scalar(1); - m_s = Scalar(0); - if(r) *r = abs(p); - } - else if(p==Scalar(0)) - { - m_c = Scalar(0); - m_s = q<Scalar(0) ? Scalar(1) : Scalar(-1); - if(r) *r = abs(q); - } - else if(abs(p) > abs(q)) - { - Scalar t = q/p; - Scalar u = sqrt(Scalar(1) + numext::abs2(t)); - if(p<Scalar(0)) - u = -u; - m_c = Scalar(1)/u; - m_s = -t * m_c; - if(r) *r = p * u; - } - else - { - Scalar t = p/q; - Scalar u = sqrt(Scalar(1) + numext::abs2(t)); - if(q<Scalar(0)) - u = -u; - m_s = -Scalar(1)/u; - m_c = -t * m_s; - if(r) *r = q * u; - } - -} - -/**************************************************************************************** -* Implementation of MatrixBase methods -****************************************************************************************/ - -/** \jacobi_module - * Applies the clock wise 2D rotation \a j to the set of 2D vectors of cordinates \a x and \a y: - * \f$ \left ( \begin{array}{cc} x \\ y \end{array} \right ) = J \left ( \begin{array}{cc} x \\ y \end{array} \right ) \f$ - * - * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight() - */ -namespace internal { -template<typename VectorX, typename VectorY, typename OtherScalar> -void apply_rotation_in_the_plane(VectorX& _x, VectorY& _y, const JacobiRotation<OtherScalar>& j); -} - -/** \jacobi_module - * Applies the rotation in the plane \a j to the rows \a p and \a q of \c *this, i.e., it computes B = J * B, - * with \f$ B = \left ( \begin{array}{cc} \text{*this.row}(p) \\ \text{*this.row}(q) \end{array} \right ) \f$. - * - * \sa class JacobiRotation, MatrixBase::applyOnTheRight(), internal::apply_rotation_in_the_plane() - */ -template<typename Derived> -template<typename OtherScalar> -inline void MatrixBase<Derived>::applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j) -{ - RowXpr x(this->row(p)); - RowXpr y(this->row(q)); - internal::apply_rotation_in_the_plane(x, y, j); -} - -/** \ingroup Jacobi_Module - * Applies the rotation in the plane \a j to the columns \a p and \a q of \c *this, i.e., it computes B = B * J - * with \f$ B = \left ( \begin{array}{cc} \text{*this.col}(p) & \text{*this.col}(q) \end{array} \right ) \f$. - * - * \sa class JacobiRotation, MatrixBase::applyOnTheLeft(), internal::apply_rotation_in_the_plane() - */ -template<typename Derived> -template<typename OtherScalar> -inline void MatrixBase<Derived>::applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j) -{ - ColXpr x(this->col(p)); - ColXpr y(this->col(q)); - internal::apply_rotation_in_the_plane(x, y, j.transpose()); -} - -namespace internal { -template<typename VectorX, typename VectorY, typename OtherScalar> -void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(VectorX& _x, VectorY& _y, const JacobiRotation<OtherScalar>& j) -{ - typedef typename VectorX::Index Index; - typedef typename VectorX::Scalar Scalar; - enum { PacketSize = packet_traits<Scalar>::size }; - typedef typename packet_traits<Scalar>::type Packet; - eigen_assert(_x.size() == _y.size()); - Index size = _x.size(); - Index incrx = _x.innerStride(); - Index incry = _y.innerStride(); - - Scalar* EIGEN_RESTRICT x = &_x.coeffRef(0); - Scalar* EIGEN_RESTRICT y = &_y.coeffRef(0); - - OtherScalar c = j.c(); - OtherScalar s = j.s(); - if (c==OtherScalar(1) && s==OtherScalar(0)) - return; - - /*** dynamic-size vectorized paths ***/ - - if(VectorX::SizeAtCompileTime == Dynamic && - (VectorX::Flags & VectorY::Flags & PacketAccessBit) && - ((incrx==1 && incry==1) || PacketSize == 1)) - { - // both vectors are sequentially stored in memory => vectorization - enum { Peeling = 2 }; - - Index alignedStart = internal::first_aligned(y, size); - Index alignedEnd = alignedStart + ((size-alignedStart)/PacketSize)*PacketSize; - - const Packet pc = pset1<Packet>(c); - const Packet ps = pset1<Packet>(s); - conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex,false> pcj; - - for(Index i=0; i<alignedStart; ++i) - { - Scalar xi = x[i]; - Scalar yi = y[i]; - x[i] = c * xi + numext::conj(s) * yi; - y[i] = -s * xi + numext::conj(c) * yi; - } - - Scalar* EIGEN_RESTRICT px = x + alignedStart; - Scalar* EIGEN_RESTRICT py = y + alignedStart; - - if(internal::first_aligned(x, size)==alignedStart) - { - for(Index i=alignedStart; i<alignedEnd; i+=PacketSize) - { - Packet xi = pload<Packet>(px); - Packet yi = pload<Packet>(py); - pstore(px, padd(pmul(pc,xi),pcj.pmul(ps,yi))); - pstore(py, psub(pcj.pmul(pc,yi),pmul(ps,xi))); - px += PacketSize; - py += PacketSize; - } - } - else - { - Index peelingEnd = alignedStart + ((size-alignedStart)/(Peeling*PacketSize))*(Peeling*PacketSize); - for(Index i=alignedStart; i<peelingEnd; i+=Peeling*PacketSize) - { - Packet xi = ploadu<Packet>(px); - Packet xi1 = ploadu<Packet>(px+PacketSize); - Packet yi = pload <Packet>(py); - Packet yi1 = pload <Packet>(py+PacketSize); - pstoreu(px, padd(pmul(pc,xi),pcj.pmul(ps,yi))); - pstoreu(px+PacketSize, padd(pmul(pc,xi1),pcj.pmul(ps,yi1))); - pstore (py, psub(pcj.pmul(pc,yi),pmul(ps,xi))); - pstore (py+PacketSize, psub(pcj.pmul(pc,yi1),pmul(ps,xi1))); - px += Peeling*PacketSize; - py += Peeling*PacketSize; - } - if(alignedEnd!=peelingEnd) - { - Packet xi = ploadu<Packet>(x+peelingEnd); - Packet yi = pload <Packet>(y+peelingEnd); - pstoreu(x+peelingEnd, padd(pmul(pc,xi),pcj.pmul(ps,yi))); - pstore (y+peelingEnd, psub(pcj.pmul(pc,yi),pmul(ps,xi))); - } - } - - for(Index i=alignedEnd; i<size; ++i) - { - Scalar xi = x[i]; - Scalar yi = y[i]; - x[i] = c * xi + numext::conj(s) * yi; - y[i] = -s * xi + numext::conj(c) * yi; - } - } - - /*** fixed-size vectorized path ***/ - else if(VectorX::SizeAtCompileTime != Dynamic && - (VectorX::Flags & VectorY::Flags & PacketAccessBit) && - (VectorX::Flags & VectorY::Flags & AlignedBit)) - { - const Packet pc = pset1<Packet>(c); - const Packet ps = pset1<Packet>(s); - conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex,false> pcj; - Scalar* EIGEN_RESTRICT px = x; - Scalar* EIGEN_RESTRICT py = y; - for(Index i=0; i<size; i+=PacketSize) - { - Packet xi = pload<Packet>(px); - Packet yi = pload<Packet>(py); - pstore(px, padd(pmul(pc,xi),pcj.pmul(ps,yi))); - pstore(py, psub(pcj.pmul(pc,yi),pmul(ps,xi))); - px += PacketSize; - py += PacketSize; - } - } - - /*** non-vectorized path ***/ - else - { - for(Index i=0; i<size; ++i) - { - Scalar xi = *x; - Scalar yi = *y; - *x = c * xi + numext::conj(s) * yi; - *y = -s * xi + numext::conj(c) * yi; - x += incrx; - y += incry; - } - } -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_JACOBI_H diff --git a/third_party/eigen3/Eigen/src/LU/Determinant.h b/third_party/eigen3/Eigen/src/LU/Determinant.h deleted file mode 100644 index bb8e78a8a8..0000000000 --- a/third_party/eigen3/Eigen/src/LU/Determinant.h +++ /dev/null @@ -1,101 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_DETERMINANT_H -#define EIGEN_DETERMINANT_H - -namespace Eigen { - -namespace internal { - -template<typename Derived> -inline const typename Derived::Scalar bruteforce_det3_helper -(const MatrixBase<Derived>& matrix, int a, int b, int c) -{ - return matrix.coeff(0,a) - * (matrix.coeff(1,b) * matrix.coeff(2,c) - matrix.coeff(1,c) * matrix.coeff(2,b)); -} - -template<typename Derived> -const typename Derived::Scalar bruteforce_det4_helper -(const MatrixBase<Derived>& matrix, int j, int k, int m, int n) -{ - return (matrix.coeff(j,0) * matrix.coeff(k,1) - matrix.coeff(k,0) * matrix.coeff(j,1)) - * (matrix.coeff(m,2) * matrix.coeff(n,3) - matrix.coeff(n,2) * matrix.coeff(m,3)); -} - -template<typename Derived, - int DeterminantType = Derived::RowsAtCompileTime -> struct determinant_impl -{ - static inline typename traits<Derived>::Scalar run(const Derived& m) - { - if(Derived::ColsAtCompileTime==Dynamic && m.rows()==0) - return typename traits<Derived>::Scalar(1); - return m.partialPivLu().determinant(); - } -}; - -template<typename Derived> struct determinant_impl<Derived, 1> -{ - static inline typename traits<Derived>::Scalar run(const Derived& m) - { - return m.coeff(0,0); - } -}; - -template<typename Derived> struct determinant_impl<Derived, 2> -{ - static inline typename traits<Derived>::Scalar run(const Derived& m) - { - return m.coeff(0,0) * m.coeff(1,1) - m.coeff(1,0) * m.coeff(0,1); - } -}; - -template<typename Derived> struct determinant_impl<Derived, 3> -{ - static inline typename traits<Derived>::Scalar run(const Derived& m) - { - return bruteforce_det3_helper(m,0,1,2) - - bruteforce_det3_helper(m,1,0,2) - + bruteforce_det3_helper(m,2,0,1); - } -}; - -template<typename Derived> struct determinant_impl<Derived, 4> -{ - static typename traits<Derived>::Scalar run(const Derived& m) - { - // trick by Martin Costabel to compute 4x4 det with only 30 muls - return bruteforce_det4_helper(m,0,1,2,3) - - bruteforce_det4_helper(m,0,2,1,3) - + bruteforce_det4_helper(m,0,3,1,2) - + bruteforce_det4_helper(m,1,2,0,3) - - bruteforce_det4_helper(m,1,3,0,2) - + bruteforce_det4_helper(m,2,3,0,1); - } -}; - -} // end namespace internal - -/** \lu_module - * - * \returns the determinant of this matrix - */ -template<typename Derived> -inline typename internal::traits<Derived>::Scalar MatrixBase<Derived>::determinant() const -{ - eigen_assert(rows() == cols()); - typedef typename internal::nested<Derived,Base::RowsAtCompileTime>::type Nested; - return internal::determinant_impl<typename internal::remove_all<Nested>::type>::run(derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_DETERMINANT_H diff --git a/third_party/eigen3/Eigen/src/LU/FullPivLU.h b/third_party/eigen3/Eigen/src/LU/FullPivLU.h deleted file mode 100644 index 971b9da1d4..0000000000 --- a/third_party/eigen3/Eigen/src/LU/FullPivLU.h +++ /dev/null @@ -1,745 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_LU_H -#define EIGEN_LU_H - -namespace Eigen { - -/** \ingroup LU_Module - * - * \class FullPivLU - * - * \brief LU decomposition of a matrix with complete pivoting, and related features - * - * \param MatrixType the type of the matrix of which we are computing the LU decomposition - * - * This class represents a LU decomposition of any matrix, with complete pivoting: the matrix A is - * decomposed as \f$ A = P^{-1} L U Q^{-1} \f$ where L is unit-lower-triangular, U is - * upper-triangular, and P and Q are permutation matrices. This is a rank-revealing LU - * decomposition. The eigenvalues (diagonal coefficients) of U are sorted in such a way that any - * zeros are at the end. - * - * This decomposition provides the generic approach to solving systems of linear equations, computing - * the rank, invertibility, inverse, kernel, and determinant. - * - * This LU decomposition is very stable and well tested with large matrices. However there are use cases where the SVD - * decomposition is inherently more stable and/or flexible. For example, when computing the kernel of a matrix, - * working with the SVD allows to select the smallest singular values of the matrix, something that - * the LU decomposition doesn't see. - * - * The data of the LU decomposition can be directly accessed through the methods matrixLU(), - * permutationP(), permutationQ(). - * - * As an exemple, here is how the original matrix can be retrieved: - * \include class_FullPivLU.cpp - * Output: \verbinclude class_FullPivLU.out - * - * \sa MatrixBase::fullPivLu(), MatrixBase::determinant(), MatrixBase::inverse() - */ -template<typename _MatrixType> class FullPivLU -{ - public: - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef typename internal::traits<MatrixType>::StorageKind StorageKind; - typedef typename MatrixType::Index Index; - typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType; - typedef typename internal::plain_col_type<MatrixType, Index>::type IntColVectorType; - typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationQType; - typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationPType; - - /** - * \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via LU::compute(const MatrixType&). - */ - FullPivLU(); - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa FullPivLU() - */ - FullPivLU(Index rows, Index cols); - - /** Constructor. - * - * \param matrix the matrix of which to compute the LU decomposition. - * It is required to be nonzero. - */ - FullPivLU(const MatrixType& matrix); - - /** Computes the LU decomposition of the given matrix. - * - * \param matrix the matrix of which to compute the LU decomposition. - * It is required to be nonzero. - * - * \returns a reference to *this - */ - FullPivLU& compute(const MatrixType& matrix); - - /** \returns the LU decomposition matrix: the upper-triangular part is U, the - * unit-lower-triangular part is L (at least for square matrices; in the non-square - * case, special care is needed, see the documentation of class FullPivLU). - * - * \sa matrixL(), matrixU() - */ - inline const MatrixType& matrixLU() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return m_lu; - } - - /** \returns the number of nonzero pivots in the LU decomposition. - * Here nonzero is meant in the exact sense, not in a fuzzy sense. - * So that notion isn't really intrinsically interesting, but it is - * still useful when implementing algorithms. - * - * \sa rank() - */ - inline Index nonzeroPivots() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return m_nonzero_pivots; - } - - /** \returns the absolute value of the biggest pivot, i.e. the biggest - * diagonal coefficient of U. - */ - RealScalar maxPivot() const { return m_maxpivot; } - - /** \returns the permutation matrix P - * - * \sa permutationQ() - */ - inline const PermutationPType& permutationP() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return m_p; - } - - /** \returns the permutation matrix Q - * - * \sa permutationP() - */ - inline const PermutationQType& permutationQ() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return m_q; - } - - /** \returns the kernel of the matrix, also called its null-space. The columns of the returned matrix - * will form a basis of the kernel. - * - * \note If the kernel has dimension zero, then the returned matrix is a column-vector filled with zeros. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - * - * Example: \include FullPivLU_kernel.cpp - * Output: \verbinclude FullPivLU_kernel.out - * - * \sa image() - */ - inline const internal::kernel_retval<FullPivLU> kernel() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return internal::kernel_retval<FullPivLU>(*this); - } - - /** \returns the image of the matrix, also called its column-space. The columns of the returned matrix - * will form a basis of the kernel. - * - * \param originalMatrix the original matrix, of which *this is the LU decomposition. - * The reason why it is needed to pass it here, is that this allows - * a large optimization, as otherwise this method would need to reconstruct it - * from the LU decomposition. - * - * \note If the image has dimension zero, then the returned matrix is a column-vector filled with zeros. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - * - * Example: \include FullPivLU_image.cpp - * Output: \verbinclude FullPivLU_image.out - * - * \sa kernel() - */ - inline const internal::image_retval<FullPivLU> - image(const MatrixType& originalMatrix) const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return internal::image_retval<FullPivLU>(*this, originalMatrix); - } - - /** \return a solution x to the equation Ax=b, where A is the matrix of which - * *this is the LU decomposition. - * - * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix, - * the only requirement in order for the equation to make sense is that - * b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition. - * - * \returns a solution. - * - * \note_about_checking_solutions - * - * \note_about_arbitrary_choice_of_solution - * \note_about_using_kernel_to_study_multiple_solutions - * - * Example: \include FullPivLU_solve.cpp - * Output: \verbinclude FullPivLU_solve.out - * - * \sa TriangularView::solve(), kernel(), inverse() - */ - template<typename Rhs> - inline const internal::solve_retval<FullPivLU, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return internal::solve_retval<FullPivLU, Rhs>(*this, b.derived()); - } - - /** \returns the determinant of the matrix of which - * *this is the LU decomposition. It has only linear complexity - * (that is, O(n) where n is the dimension of the square matrix) - * as the LU decomposition has already been computed. - * - * \note This is only for square matrices. - * - * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers - * optimized paths. - * - * \warning a determinant can be very big or small, so for matrices - * of large enough dimension, there is a risk of overflow/underflow. - * - * \sa MatrixBase::determinant() - */ - typename internal::traits<MatrixType>::Scalar determinant() const; - - /** Allows to prescribe a threshold to be used by certain methods, such as rank(), - * who need to determine when pivots are to be considered nonzero. This is not used for the - * LU decomposition itself. - * - * When it needs to get the threshold value, Eigen calls threshold(). By default, this - * uses a formula to automatically determine a reasonable threshold. - * Once you have called the present method setThreshold(const RealScalar&), - * your value is used instead. - * - * \param threshold The new value to use as the threshold. - * - * A pivot will be considered nonzero if its absolute value is strictly greater than - * \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$ - * where maxpivot is the biggest pivot. - * - * If you want to come back to the default behavior, call setThreshold(Default_t) - */ - FullPivLU& setThreshold(const RealScalar& threshold) - { - m_usePrescribedThreshold = true; - m_prescribedThreshold = threshold; - return *this; - } - - /** Allows to come back to the default behavior, letting Eigen use its default formula for - * determining the threshold. - * - * You should pass the special object Eigen::Default as parameter here. - * \code lu.setThreshold(Eigen::Default); \endcode - * - * See the documentation of setThreshold(const RealScalar&). - */ - FullPivLU& setThreshold(Default_t) - { - m_usePrescribedThreshold = false; - return *this; - } - - /** Returns the threshold that will be used by certain methods such as rank(). - * - * See the documentation of setThreshold(const RealScalar&). - */ - RealScalar threshold() const - { - eigen_assert(m_isInitialized || m_usePrescribedThreshold); - return m_usePrescribedThreshold ? m_prescribedThreshold - // this formula comes from experimenting (see "LU precision tuning" thread on the list) - // and turns out to be identical to Higham's formula used already in LDLt. - : NumTraits<Scalar>::epsilon() * m_lu.diagonalSize(); - } - - /** \returns the rank of the matrix of which *this is the LU decomposition. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline Index rank() const - { - using std::abs; - eigen_assert(m_isInitialized && "LU is not initialized."); - RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold(); - Index result = 0; - for(Index i = 0; i < m_nonzero_pivots; ++i) - result += (abs(m_lu.coeff(i,i)) > premultiplied_threshold); - return result; - } - - /** \returns the dimension of the kernel of the matrix of which *this is the LU decomposition. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline Index dimensionOfKernel() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return cols() - rank(); - } - - /** \returns true if the matrix of which *this is the LU decomposition represents an injective - * linear map, i.e. has trivial kernel; false otherwise. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isInjective() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return rank() == cols(); - } - - /** \returns true if the matrix of which *this is the LU decomposition represents a surjective - * linear map; false otherwise. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isSurjective() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return rank() == rows(); - } - - /** \returns true if the matrix of which *this is the LU decomposition is invertible. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isInvertible() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return isInjective() && (m_lu.rows() == m_lu.cols()); - } - - /** \returns the inverse of the matrix of which *this is the LU decomposition. - * - * \note If this matrix is not invertible, the returned matrix has undefined coefficients. - * Use isInvertible() to first determine whether this matrix is invertible. - * - * \sa MatrixBase::inverse() - */ - inline const internal::solve_retval<FullPivLU,typename MatrixType::IdentityReturnType> inverse() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!"); - return internal::solve_retval<FullPivLU,typename MatrixType::IdentityReturnType> - (*this, MatrixType::Identity(m_lu.rows(), m_lu.cols())); - } - - MatrixType reconstructedMatrix() const; - - inline Index rows() const { return m_lu.rows(); } - inline Index cols() const { return m_lu.cols(); } - - protected: - MatrixType m_lu; - PermutationPType m_p; - PermutationQType m_q; - IntColVectorType m_rowsTranspositions; - IntRowVectorType m_colsTranspositions; - Index m_det_pq, m_nonzero_pivots; - RealScalar m_maxpivot, m_prescribedThreshold; - bool m_isInitialized, m_usePrescribedThreshold; -}; - -template<typename MatrixType> -FullPivLU<MatrixType>::FullPivLU() - : m_isInitialized(false), m_usePrescribedThreshold(false) -{ -} - -template<typename MatrixType> -FullPivLU<MatrixType>::FullPivLU(Index rows, Index cols) - : m_lu(rows, cols), - m_p(rows), - m_q(cols), - m_rowsTranspositions(rows), - m_colsTranspositions(cols), - m_isInitialized(false), - m_usePrescribedThreshold(false) -{ -} - -template<typename MatrixType> -FullPivLU<MatrixType>::FullPivLU(const MatrixType& matrix) - : m_lu(matrix.rows(), matrix.cols()), - m_p(matrix.rows()), - m_q(matrix.cols()), - m_rowsTranspositions(matrix.rows()), - m_colsTranspositions(matrix.cols()), - m_isInitialized(false), - m_usePrescribedThreshold(false) -{ - compute(matrix); -} - -template<typename MatrixType> -FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix) -{ - // the permutations are stored as int indices, so just to be sure: - eigen_assert(matrix.rows()<=NumTraits<int>::highest() && matrix.cols()<=NumTraits<int>::highest()); - - m_isInitialized = true; - m_lu = matrix; - - const Index size = matrix.diagonalSize(); - const Index rows = matrix.rows(); - const Index cols = matrix.cols(); - - // will store the transpositions, before we accumulate them at the end. - // can't accumulate on-the-fly because that will be done in reverse order for the rows. - m_rowsTranspositions.resize(matrix.rows()); - m_colsTranspositions.resize(matrix.cols()); - Index number_of_transpositions = 0; // number of NONTRIVIAL transpositions, i.e. m_rowsTranspositions[i]!=i - - m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case) - m_maxpivot = RealScalar(0); - - for(Index k = 0; k < size; ++k) - { - // First, we need to find the pivot. - - // biggest coefficient in the remaining bottom-right corner (starting at row k, col k) - Index row_of_biggest_in_corner, col_of_biggest_in_corner; - RealScalar biggest_in_corner; - biggest_in_corner = m_lu.bottomRightCorner(rows-k, cols-k) - .cwiseAbs() - .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner); - row_of_biggest_in_corner += k; // correct the values! since they were computed in the corner, - col_of_biggest_in_corner += k; // need to add k to them. - - if(biggest_in_corner==RealScalar(0)) - { - // before exiting, make sure to initialize the still uninitialized transpositions - // in a sane state without destroying what we already have. - m_nonzero_pivots = k; - for(Index i = k; i < size; ++i) - { - m_rowsTranspositions.coeffRef(i) = i; - m_colsTranspositions.coeffRef(i) = i; - } - break; - } - - if(biggest_in_corner > m_maxpivot) m_maxpivot = biggest_in_corner; - - // Now that we've found the pivot, we need to apply the row/col swaps to - // bring it to the location (k,k). - - m_rowsTranspositions.coeffRef(k) = row_of_biggest_in_corner; - m_colsTranspositions.coeffRef(k) = col_of_biggest_in_corner; - if(k != row_of_biggest_in_corner) { - m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner)); - ++number_of_transpositions; - } - if(k != col_of_biggest_in_corner) { - m_lu.col(k).swap(m_lu.col(col_of_biggest_in_corner)); - ++number_of_transpositions; - } - - // Now that the pivot is at the right location, we update the remaining - // bottom-right corner by Gaussian elimination. - - if(k<rows-1) - m_lu.col(k).tail(rows-k-1) /= m_lu.coeff(k,k); - if(k<size-1) - m_lu.block(k+1,k+1,rows-k-1,cols-k-1).noalias() -= m_lu.col(k).tail(rows-k-1) * m_lu.row(k).tail(cols-k-1); - } - - // the main loop is over, we still have to accumulate the transpositions to find the - // permutations P and Q - - m_p.setIdentity(rows); - for(Index k = size-1; k >= 0; --k) - m_p.applyTranspositionOnTheRight(k, m_rowsTranspositions.coeff(k)); - - m_q.setIdentity(cols); - for(Index k = 0; k < size; ++k) - m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k)); - - m_det_pq = (number_of_transpositions%2) ? -1 : 1; - return *this; -} - -template<typename MatrixType> -typename internal::traits<MatrixType>::Scalar FullPivLU<MatrixType>::determinant() const -{ - eigen_assert(m_isInitialized && "LU is not initialized."); - eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the determinant of a non-square matrix!"); - return Scalar(m_det_pq) * Scalar(m_lu.diagonal().prod()); -} - -/** \returns the matrix represented by the decomposition, - * i.e., it returns the product: \f$ P^{-1} L U Q^{-1} \f$. - * This function is provided for debug purposes. */ -template<typename MatrixType> -MatrixType FullPivLU<MatrixType>::reconstructedMatrix() const -{ - eigen_assert(m_isInitialized && "LU is not initialized."); - const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols()); - // LU - MatrixType res(m_lu.rows(),m_lu.cols()); - // FIXME the .toDenseMatrix() should not be needed... - res = m_lu.leftCols(smalldim) - .template triangularView<UnitLower>().toDenseMatrix() - * m_lu.topRows(smalldim) - .template triangularView<Upper>().toDenseMatrix(); - - // P^{-1}(LU) - res = m_p.inverse() * res; - - // (P^{-1}LU)Q^{-1} - res = res * m_q.inverse(); - - return res; -} - -/********* Implementation of kernel() **************************************************/ - -namespace internal { -template<typename _MatrixType> -struct kernel_retval<FullPivLU<_MatrixType> > - : kernel_retval_base<FullPivLU<_MatrixType> > -{ - EIGEN_MAKE_KERNEL_HELPERS(FullPivLU<_MatrixType>) - - enum { MaxSmallDimAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED( - MatrixType::MaxColsAtCompileTime, - MatrixType::MaxRowsAtCompileTime) - }; - - template<typename Dest> void evalTo(Dest& dst) const - { - using std::abs; - const Index cols = dec().matrixLU().cols(), dimker = cols - rank(); - if(dimker == 0) - { - // The Kernel is just {0}, so it doesn't have a basis properly speaking, but let's - // avoid crashing/asserting as that depends on floating point calculations. Let's - // just return a single column vector filled with zeros. - dst.setZero(); - return; - } - - /* Let us use the following lemma: - * - * Lemma: If the matrix A has the LU decomposition PAQ = LU, - * then Ker A = Q(Ker U). - * - * Proof: trivial: just keep in mind that P, Q, L are invertible. - */ - - /* Thus, all we need to do is to compute Ker U, and then apply Q. - * - * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end. - * Thus, the diagonal of U ends with exactly - * dimKer zero's. Let us use that to construct dimKer linearly - * independent vectors in Ker U. - */ - - Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank()); - RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold(); - Index p = 0; - for(Index i = 0; i < dec().nonzeroPivots(); ++i) - if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold) - pivots.coeffRef(p++) = i; - eigen_internal_assert(p == rank()); - - // we construct a temporaty trapezoid matrix m, by taking the U matrix and - // permuting the rows and cols to bring the nonnegligible pivots to the top of - // the main diagonal. We need that to be able to apply our triangular solvers. - // FIXME when we get triangularView-for-rectangular-matrices, this can be simplified - Matrix<typename MatrixType::Scalar, Dynamic, Dynamic, MatrixType::Options, - MaxSmallDimAtCompileTime, MatrixType::MaxColsAtCompileTime> - m(dec().matrixLU().block(0, 0, rank(), cols)); - for(Index i = 0; i < rank(); ++i) - { - if(i) m.row(i).head(i).setZero(); - m.row(i).tail(cols-i) = dec().matrixLU().row(pivots.coeff(i)).tail(cols-i); - } - m.block(0, 0, rank(), rank()); - m.block(0, 0, rank(), rank()).template triangularView<StrictlyLower>().setZero(); - for(Index i = 0; i < rank(); ++i) - m.col(i).swap(m.col(pivots.coeff(i))); - - // ok, we have our trapezoid matrix, we can apply the triangular solver. - // notice that the math behind this suggests that we should apply this to the - // negative of the RHS, but for performance we just put the negative sign elsewhere, see below. - m.topLeftCorner(rank(), rank()) - .template triangularView<Upper>().solveInPlace( - m.topRightCorner(rank(), dimker) - ); - - // now we must undo the column permutation that we had applied! - for(Index i = rank()-1; i >= 0; --i) - m.col(i).swap(m.col(pivots.coeff(i))); - - // see the negative sign in the next line, that's what we were talking about above. - for(Index i = 0; i < rank(); ++i) dst.row(dec().permutationQ().indices().coeff(i)) = -m.row(i).tail(dimker); - for(Index i = rank(); i < cols; ++i) dst.row(dec().permutationQ().indices().coeff(i)).setZero(); - for(Index k = 0; k < dimker; ++k) dst.coeffRef(dec().permutationQ().indices().coeff(rank()+k), k) = Scalar(1); - } -}; - -/***** Implementation of image() *****************************************************/ - -template<typename _MatrixType> -struct image_retval<FullPivLU<_MatrixType> > - : image_retval_base<FullPivLU<_MatrixType> > -{ - EIGEN_MAKE_IMAGE_HELPERS(FullPivLU<_MatrixType>) - - enum { MaxSmallDimAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED( - MatrixType::MaxColsAtCompileTime, - MatrixType::MaxRowsAtCompileTime) - }; - - template<typename Dest> void evalTo(Dest& dst) const - { - using std::abs; - if(rank() == 0) - { - // The Image is just {0}, so it doesn't have a basis properly speaking, but let's - // avoid crashing/asserting as that depends on floating point calculations. Let's - // just return a single column vector filled with zeros. - dst.setZero(); - return; - } - - Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank()); - RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold(); - Index p = 0; - for(Index i = 0; i < dec().nonzeroPivots(); ++i) - if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold) - pivots.coeffRef(p++) = i; - eigen_internal_assert(p == rank()); - - for(Index i = 0; i < rank(); ++i) - dst.col(i) = originalMatrix().col(dec().permutationQ().indices().coeff(pivots.coeff(i))); - } -}; - -/***** Implementation of solve() *****************************************************/ - -template<typename _MatrixType, typename Rhs> -struct solve_retval<FullPivLU<_MatrixType>, Rhs> - : solve_retval_base<FullPivLU<_MatrixType>, Rhs> -{ - EIGEN_MAKE_SOLVE_HELPERS(FullPivLU<_MatrixType>,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1}. - * So we proceed as follows: - * Step 1: compute c = P * rhs. - * Step 2: replace c by the solution x to Lx = c. Exists because L is invertible. - * Step 3: replace c by the solution x to Ux = c. May or may not exist. - * Step 4: result = Q * c; - */ - - const Index rows = dec().rows(), cols = dec().cols(), - nonzero_pivots = dec().nonzeroPivots(); - eigen_assert(rhs().rows() == rows); - const Index smalldim = (std::min)(rows, cols); - - if(nonzero_pivots == 0) - { - dst.setZero(); - return; - } - - typename Rhs::PlainObject c(rhs().rows(), rhs().cols()); - - // Step 1 - c = dec().permutationP() * rhs(); - - // Step 2 - dec().matrixLU() - .topLeftCorner(smalldim,smalldim) - .template triangularView<UnitLower>() - .solveInPlace(c.topRows(smalldim)); - if(rows>cols) - { - c.bottomRows(rows-cols) - -= dec().matrixLU().bottomRows(rows-cols) - * c.topRows(cols); - } - - // Step 3 - dec().matrixLU() - .topLeftCorner(nonzero_pivots, nonzero_pivots) - .template triangularView<Upper>() - .solveInPlace(c.topRows(nonzero_pivots)); - - // Step 4 - for(Index i = 0; i < nonzero_pivots; ++i) - dst.row(dec().permutationQ().indices().coeff(i)) = c.row(i); - for(Index i = nonzero_pivots; i < dec().matrixLU().cols(); ++i) - dst.row(dec().permutationQ().indices().coeff(i)).setZero(); - } -}; - -} // end namespace internal - -/******* MatrixBase methods *****************************************************************/ - -/** \lu_module - * - * \return the full-pivoting LU decomposition of \c *this. - * - * \sa class FullPivLU - */ -#ifndef __CUDACC__ -template<typename Derived> -inline const FullPivLU<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::fullPivLu() const -{ - return FullPivLU<PlainObject>(eval()); -} -#endif - -} // end namespace Eigen - -#endif // EIGEN_LU_H diff --git a/third_party/eigen3/Eigen/src/LU/Inverse.h b/third_party/eigen3/Eigen/src/LU/Inverse.h deleted file mode 100644 index 8d1364e0a9..0000000000 --- a/third_party/eigen3/Eigen/src/LU/Inverse.h +++ /dev/null @@ -1,417 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_INVERSE_H -#define EIGEN_INVERSE_H - -namespace Eigen { - -namespace internal { - -/********************************** -*** General case implementation *** -**********************************/ - -template<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime> -struct compute_inverse -{ - EIGEN_DEVICE_FUNC - static inline void run(const MatrixType& matrix, ResultType& result) - { - result = matrix.partialPivLu().inverse(); - } -}; - -template<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime> -struct compute_inverse_and_det_with_check { /* nothing! general case not supported. */ }; - -/**************************** -*** Size 1 implementation *** -****************************/ - -template<typename MatrixType, typename ResultType> -struct compute_inverse<MatrixType, ResultType, 1> -{ - EIGEN_DEVICE_FUNC - static inline void run(const MatrixType& matrix, ResultType& result) - { - typedef typename MatrixType::Scalar Scalar; - result.coeffRef(0,0) = Scalar(1) / matrix.coeff(0,0); - } -}; - -template<typename MatrixType, typename ResultType> -struct compute_inverse_and_det_with_check<MatrixType, ResultType, 1> -{ - EIGEN_DEVICE_FUNC - static inline void run( - const MatrixType& matrix, - const typename MatrixType::RealScalar& absDeterminantThreshold, - ResultType& result, - typename ResultType::Scalar& determinant, - bool& invertible - ) - { - using std::abs; - determinant = matrix.coeff(0,0); - invertible = abs(determinant) > absDeterminantThreshold; - if(invertible) result.coeffRef(0,0) = typename ResultType::Scalar(1) / determinant; - } -}; - -/**************************** -*** Size 2 implementation *** -****************************/ - -template<typename MatrixType, typename ResultType> -EIGEN_DEVICE_FUNC -inline void compute_inverse_size2_helper( - const MatrixType& matrix, const typename ResultType::Scalar& invdet, - ResultType& result) -{ - result.coeffRef(0,0) = matrix.coeff(1,1) * invdet; - result.coeffRef(1,0) = -matrix.coeff(1,0) * invdet; - result.coeffRef(0,1) = -matrix.coeff(0,1) * invdet; - result.coeffRef(1,1) = matrix.coeff(0,0) * invdet; -} - -template<typename MatrixType, typename ResultType> -struct compute_inverse<MatrixType, ResultType, 2> -{ - EIGEN_DEVICE_FUNC - static inline void run(const MatrixType& matrix, ResultType& result) - { - typedef typename ResultType::Scalar Scalar; - const Scalar invdet = typename MatrixType::Scalar(1) / matrix.determinant(); - compute_inverse_size2_helper(matrix, invdet, result); - } -}; - -template<typename MatrixType, typename ResultType> -struct compute_inverse_and_det_with_check<MatrixType, ResultType, 2> -{ - EIGEN_DEVICE_FUNC - static inline void run( - const MatrixType& matrix, - const typename MatrixType::RealScalar& absDeterminantThreshold, - ResultType& inverse, - typename ResultType::Scalar& determinant, - bool& invertible - ) - { - using std::abs; - typedef typename ResultType::Scalar Scalar; - determinant = matrix.determinant(); - invertible = abs(determinant) > absDeterminantThreshold; - if(!invertible) return; - const Scalar invdet = Scalar(1) / determinant; - compute_inverse_size2_helper(matrix, invdet, inverse); - } -}; - -/**************************** -*** Size 3 implementation *** -****************************/ - -template<typename MatrixType, int i, int j> -EIGEN_DEVICE_FUNC -inline typename MatrixType::Scalar cofactor_3x3(const MatrixType& m) -{ - enum { - i1 = (i+1) % 3, - i2 = (i+2) % 3, - j1 = (j+1) % 3, - j2 = (j+2) % 3 - }; - return m.coeff(i1, j1) * m.coeff(i2, j2) - - m.coeff(i1, j2) * m.coeff(i2, j1); -} - -template<typename MatrixType, typename ResultType> -EIGEN_DEVICE_FUNC -inline void compute_inverse_size3_helper( - const MatrixType& matrix, - const typename ResultType::Scalar& invdet, - const Matrix<typename ResultType::Scalar,3,1>& cofactors_col0, - ResultType& result) -{ - result.row(0) = cofactors_col0 * invdet; - result.coeffRef(1,0) = cofactor_3x3<MatrixType,0,1>(matrix) * invdet; - result.coeffRef(1,1) = cofactor_3x3<MatrixType,1,1>(matrix) * invdet; - result.coeffRef(1,2) = cofactor_3x3<MatrixType,2,1>(matrix) * invdet; - result.coeffRef(2,0) = cofactor_3x3<MatrixType,0,2>(matrix) * invdet; - result.coeffRef(2,1) = cofactor_3x3<MatrixType,1,2>(matrix) * invdet; - result.coeffRef(2,2) = cofactor_3x3<MatrixType,2,2>(matrix) * invdet; -} - -template<typename MatrixType, typename ResultType> -struct compute_inverse<MatrixType, ResultType, 3> -{ - EIGEN_DEVICE_FUNC - static inline void run(const MatrixType& matrix, ResultType& result) - { - typedef typename ResultType::Scalar Scalar; - Matrix<typename MatrixType::Scalar,3,1> cofactors_col0; - cofactors_col0.coeffRef(0) = cofactor_3x3<MatrixType,0,0>(matrix); - cofactors_col0.coeffRef(1) = cofactor_3x3<MatrixType,1,0>(matrix); - cofactors_col0.coeffRef(2) = cofactor_3x3<MatrixType,2,0>(matrix); - const Scalar det = (cofactors_col0.cwiseProduct(matrix.col(0))).sum(); - const Scalar invdet = Scalar(1) / det; - compute_inverse_size3_helper(matrix, invdet, cofactors_col0, result); - } -}; - -template<typename MatrixType, typename ResultType> -struct compute_inverse_and_det_with_check<MatrixType, ResultType, 3> -{ - EIGEN_DEVICE_FUNC - static inline void run( - const MatrixType& matrix, - const typename MatrixType::RealScalar& absDeterminantThreshold, - ResultType& inverse, - typename ResultType::Scalar& determinant, - bool& invertible - ) - { - using std::abs; - typedef typename ResultType::Scalar Scalar; - Matrix<Scalar,3,1> cofactors_col0; - cofactors_col0.coeffRef(0) = cofactor_3x3<MatrixType,0,0>(matrix); - cofactors_col0.coeffRef(1) = cofactor_3x3<MatrixType,1,0>(matrix); - cofactors_col0.coeffRef(2) = cofactor_3x3<MatrixType,2,0>(matrix); - determinant = (cofactors_col0.cwiseProduct(matrix.col(0))).sum(); - invertible = abs(determinant) > absDeterminantThreshold; - if(!invertible) return; - const Scalar invdet = Scalar(1) / determinant; - compute_inverse_size3_helper(matrix, invdet, cofactors_col0, inverse); - } -}; - -/**************************** -*** Size 4 implementation *** -****************************/ - -template<typename Derived> -EIGEN_DEVICE_FUNC -inline const typename Derived::Scalar general_det3_helper -(const MatrixBase<Derived>& matrix, int i1, int i2, int i3, int j1, int j2, int j3) -{ - return matrix.coeff(i1,j1) - * (matrix.coeff(i2,j2) * matrix.coeff(i3,j3) - matrix.coeff(i2,j3) * matrix.coeff(i3,j2)); -} - -template<typename MatrixType, int i, int j> -EIGEN_DEVICE_FUNC -inline typename MatrixType::Scalar cofactor_4x4(const MatrixType& matrix) -{ - enum { - i1 = (i+1) % 4, - i2 = (i+2) % 4, - i3 = (i+3) % 4, - j1 = (j+1) % 4, - j2 = (j+2) % 4, - j3 = (j+3) % 4 - }; - return general_det3_helper(matrix, i1, i2, i3, j1, j2, j3) - + general_det3_helper(matrix, i2, i3, i1, j1, j2, j3) - + general_det3_helper(matrix, i3, i1, i2, j1, j2, j3); -} - -template<int Arch, typename Scalar, typename MatrixType, typename ResultType> -struct compute_inverse_size4 -{ - EIGEN_DEVICE_FUNC - static void run(const MatrixType& matrix, ResultType& result) - { - result.coeffRef(0,0) = cofactor_4x4<MatrixType,0,0>(matrix); - result.coeffRef(1,0) = -cofactor_4x4<MatrixType,0,1>(matrix); - result.coeffRef(2,0) = cofactor_4x4<MatrixType,0,2>(matrix); - result.coeffRef(3,0) = -cofactor_4x4<MatrixType,0,3>(matrix); - result.coeffRef(0,2) = cofactor_4x4<MatrixType,2,0>(matrix); - result.coeffRef(1,2) = -cofactor_4x4<MatrixType,2,1>(matrix); - result.coeffRef(2,2) = cofactor_4x4<MatrixType,2,2>(matrix); - result.coeffRef(3,2) = -cofactor_4x4<MatrixType,2,3>(matrix); - result.coeffRef(0,1) = -cofactor_4x4<MatrixType,1,0>(matrix); - result.coeffRef(1,1) = cofactor_4x4<MatrixType,1,1>(matrix); - result.coeffRef(2,1) = -cofactor_4x4<MatrixType,1,2>(matrix); - result.coeffRef(3,1) = cofactor_4x4<MatrixType,1,3>(matrix); - result.coeffRef(0,3) = -cofactor_4x4<MatrixType,3,0>(matrix); - result.coeffRef(1,3) = cofactor_4x4<MatrixType,3,1>(matrix); - result.coeffRef(2,3) = -cofactor_4x4<MatrixType,3,2>(matrix); - result.coeffRef(3,3) = cofactor_4x4<MatrixType,3,3>(matrix); - result /= (matrix.col(0).cwiseProduct(result.row(0).transpose())).sum(); - } -}; - -template<typename MatrixType, typename ResultType> -struct compute_inverse<MatrixType, ResultType, 4> - : compute_inverse_size4<Architecture::Target, typename MatrixType::Scalar, - MatrixType, ResultType> -{ -}; - -template<typename MatrixType, typename ResultType> -struct compute_inverse_and_det_with_check<MatrixType, ResultType, 4> -{ - EIGEN_DEVICE_FUNC - static inline void run( - const MatrixType& matrix, - const typename MatrixType::RealScalar& absDeterminantThreshold, - ResultType& inverse, - typename ResultType::Scalar& determinant, - bool& invertible - ) - { - using std::abs; - determinant = matrix.determinant(); - invertible = abs(determinant) > absDeterminantThreshold; - if(invertible) compute_inverse<MatrixType, ResultType>::run(matrix, inverse); - } -}; - -/************************* -*** MatrixBase methods *** -*************************/ - -template<typename MatrixType> -struct traits<inverse_impl<MatrixType> > -{ - typedef typename MatrixType::PlainObject ReturnType; -}; - -template<typename MatrixType> -struct inverse_impl : public ReturnByValue<inverse_impl<MatrixType> > -{ - typedef typename MatrixType::Index Index; - typedef typename internal::eval<MatrixType>::type MatrixTypeNested; - typedef typename remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned; - MatrixTypeNested m_matrix; - - EIGEN_DEVICE_FUNC - inverse_impl(const MatrixType& matrix) - : m_matrix(matrix) - {} - - EIGEN_DEVICE_FUNC inline Index rows() const { return m_matrix.rows(); } - EIGEN_DEVICE_FUNC inline Index cols() const { return m_matrix.cols(); } - - template<typename Dest> - EIGEN_DEVICE_FUNC - inline void evalTo(Dest& dst) const - { - const int Size = EIGEN_PLAIN_ENUM_MIN(MatrixType::ColsAtCompileTime,Dest::ColsAtCompileTime); - EIGEN_ONLY_USED_FOR_DEBUG(Size); - eigen_assert(( (Size<=1) || (Size>4) || (extract_data(m_matrix)!=extract_data(dst))) - && "Aliasing problem detected in inverse(), you need to do inverse().eval() here."); - - compute_inverse<MatrixTypeNestedCleaned, Dest>::run(m_matrix, dst); - } -}; - -} // end namespace internal - -/** \lu_module - * - * \returns the matrix inverse of this matrix. - * - * For small fixed sizes up to 4x4, this method uses cofactors. - * In the general case, this method uses class PartialPivLU. - * - * \note This matrix must be invertible, otherwise the result is undefined. If you need an - * invertibility check, do the following: - * \li for fixed sizes up to 4x4, use computeInverseAndDetWithCheck(). - * \li for the general case, use class FullPivLU. - * - * Example: \include MatrixBase_inverse.cpp - * Output: \verbinclude MatrixBase_inverse.out - * - * \sa computeInverseAndDetWithCheck() - */ -template<typename Derived> -inline const internal::inverse_impl<Derived> MatrixBase<Derived>::inverse() const -{ - EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsInteger,THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES) - eigen_assert(rows() == cols()); - return internal::inverse_impl<Derived>(derived()); -} - -/** \lu_module - * - * Computation of matrix inverse and determinant, with invertibility check. - * - * This is only for fixed-size square matrices of size up to 4x4. - * - * \param inverse Reference to the matrix in which to store the inverse. - * \param determinant Reference to the variable in which to store the determinant. - * \param invertible Reference to the bool variable in which to store whether the matrix is invertible. - * \param absDeterminantThreshold Optional parameter controlling the invertibility check. - * The matrix will be declared invertible if the absolute value of its - * determinant is greater than this threshold. - * - * Example: \include MatrixBase_computeInverseAndDetWithCheck.cpp - * Output: \verbinclude MatrixBase_computeInverseAndDetWithCheck.out - * - * \sa inverse(), computeInverseWithCheck() - */ -template<typename Derived> -template<typename ResultType> -inline void MatrixBase<Derived>::computeInverseAndDetWithCheck( - ResultType& inverse, - typename ResultType::Scalar& determinant, - bool& invertible, - const RealScalar& absDeterminantThreshold - ) const -{ - // i'd love to put some static assertions there, but SFINAE means that they have no effect... - eigen_assert(rows() == cols()); - // for 2x2, it's worth giving a chance to avoid evaluating. - // for larger sizes, evaluating has negligible cost and limits code size. - typedef typename internal::conditional< - RowsAtCompileTime == 2, - typename internal::remove_all<typename internal::nested<Derived, 2>::type>::type, - PlainObject - >::type MatrixType; - internal::compute_inverse_and_det_with_check<MatrixType, ResultType>::run - (derived(), absDeterminantThreshold, inverse, determinant, invertible); -} - -/** \lu_module - * - * Computation of matrix inverse, with invertibility check. - * - * This is only for fixed-size square matrices of size up to 4x4. - * - * \param inverse Reference to the matrix in which to store the inverse. - * \param invertible Reference to the bool variable in which to store whether the matrix is invertible. - * \param absDeterminantThreshold Optional parameter controlling the invertibility check. - * The matrix will be declared invertible if the absolute value of its - * determinant is greater than this threshold. - * - * Example: \include MatrixBase_computeInverseWithCheck.cpp - * Output: \verbinclude MatrixBase_computeInverseWithCheck.out - * - * \sa inverse(), computeInverseAndDetWithCheck() - */ -template<typename Derived> -template<typename ResultType> -inline void MatrixBase<Derived>::computeInverseWithCheck( - ResultType& inverse, - bool& invertible, - const RealScalar& absDeterminantThreshold - ) const -{ - RealScalar determinant; - // i'd love to put some static assertions there, but SFINAE means that they have no effect... - eigen_assert(rows() == cols()); - computeInverseAndDetWithCheck(inverse,determinant,invertible,absDeterminantThreshold); -} - -} // end namespace Eigen - -#endif // EIGEN_INVERSE_H diff --git a/third_party/eigen3/Eigen/src/LU/PartialPivLU.h b/third_party/eigen3/Eigen/src/LU/PartialPivLU.h deleted file mode 100644 index 1d389ecac7..0000000000 --- a/third_party/eigen3/Eigen/src/LU/PartialPivLU.h +++ /dev/null @@ -1,506 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PARTIALLU_H -#define EIGEN_PARTIALLU_H - -namespace Eigen { - -/** \ingroup LU_Module - * - * \class PartialPivLU - * - * \brief LU decomposition of a matrix with partial pivoting, and related features - * - * \param MatrixType the type of the matrix of which we are computing the LU decomposition - * - * This class represents a LU decomposition of a \b square \b invertible matrix, with partial pivoting: the matrix A - * is decomposed as A = PLU where L is unit-lower-triangular, U is upper-triangular, and P - * is a permutation matrix. - * - * Typically, partial pivoting LU decomposition is only considered numerically stable for square invertible - * matrices. Thus LAPACK's dgesv and dgesvx require the matrix to be square and invertible. The present class - * does the same. It will assert that the matrix is square, but it won't (actually it can't) check that the - * matrix is invertible: it is your task to check that you only use this decomposition on invertible matrices. - * - * The guaranteed safe alternative, working for all matrices, is the full pivoting LU decomposition, provided - * by class FullPivLU. - * - * This is \b not a rank-revealing LU decomposition. Many features are intentionally absent from this class, - * such as rank computation. If you need these features, use class FullPivLU. - * - * This LU decomposition is suitable to invert invertible matrices. It is what MatrixBase::inverse() uses - * in the general case. - * On the other hand, it is \b not suitable to determine whether a given matrix is invertible. - * - * The data of the LU decomposition can be directly accessed through the methods matrixLU(), permutationP(). - * - * \sa MatrixBase::partialPivLu(), MatrixBase::determinant(), MatrixBase::inverse(), MatrixBase::computeInverse(), class FullPivLU - */ -template<typename _MatrixType> class PartialPivLU -{ - public: - - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef typename internal::traits<MatrixType>::StorageKind StorageKind; - typedef typename MatrixType::Index Index; - typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType; - typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType; - - - /** - * \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via PartialPivLU::compute(const MatrixType&). - */ - PartialPivLU(); - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa PartialPivLU() - */ - PartialPivLU(Index size); - - /** Constructor. - * - * \param matrix the matrix of which to compute the LU decomposition. - * - * \warning The matrix should have full rank (e.g. if it's square, it should be invertible). - * If you need to deal with non-full rank, use class FullPivLU instead. - */ - PartialPivLU(const MatrixType& matrix); - - PartialPivLU& compute(const MatrixType& matrix); - - /** \returns the LU decomposition matrix: the upper-triangular part is U, the - * unit-lower-triangular part is L (at least for square matrices; in the non-square - * case, special care is needed, see the documentation of class FullPivLU). - * - * \sa matrixL(), matrixU() - */ - inline const MatrixType& matrixLU() const - { - eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); - return m_lu; - } - - /** \returns the permutation matrix P. - */ - inline const PermutationType& permutationP() const - { - eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); - return m_p; - } - - /** This method returns the solution x to the equation Ax=b, where A is the matrix of which - * *this is the LU decomposition. - * - * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix, - * the only requirement in order for the equation to make sense is that - * b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition. - * - * \returns the solution. - * - * Example: \include PartialPivLU_solve.cpp - * Output: \verbinclude PartialPivLU_solve.out - * - * Since this PartialPivLU class assumes anyway that the matrix A is invertible, the solution - * theoretically exists and is unique regardless of b. - * - * \sa TriangularView::solve(), inverse(), computeInverse() - */ - template<typename Rhs> - inline const internal::solve_retval<PartialPivLU, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); - return internal::solve_retval<PartialPivLU, Rhs>(*this, b.derived()); - } - - /** \returns the inverse of the matrix of which *this is the LU decomposition. - * - * \warning The matrix being decomposed here is assumed to be invertible. If you need to check for - * invertibility, use class FullPivLU instead. - * - * \sa MatrixBase::inverse(), LU::inverse() - */ - inline const internal::solve_retval<PartialPivLU,typename MatrixType::IdentityReturnType> inverse() const - { - eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); - return internal::solve_retval<PartialPivLU,typename MatrixType::IdentityReturnType> - (*this, MatrixType::Identity(m_lu.rows(), m_lu.cols())); - } - - /** \returns the determinant of the matrix of which - * *this is the LU decomposition. It has only linear complexity - * (that is, O(n) where n is the dimension of the square matrix) - * as the LU decomposition has already been computed. - * - * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers - * optimized paths. - * - * \warning a determinant can be very big or small, so for matrices - * of large enough dimension, there is a risk of overflow/underflow. - * - * \sa MatrixBase::determinant() - */ - typename internal::traits<MatrixType>::Scalar determinant() const; - - MatrixType reconstructedMatrix() const; - - inline Index rows() const { return m_lu.rows(); } - inline Index cols() const { return m_lu.cols(); } - - protected: - MatrixType m_lu; - PermutationType m_p; - TranspositionType m_rowsTranspositions; - Index m_det_p; - bool m_isInitialized; -}; - -template<typename MatrixType> -PartialPivLU<MatrixType>::PartialPivLU() - : m_lu(), - m_p(), - m_rowsTranspositions(), - m_det_p(0), - m_isInitialized(false) -{ -} - -template<typename MatrixType> -PartialPivLU<MatrixType>::PartialPivLU(Index size) - : m_lu(size, size), - m_p(size), - m_rowsTranspositions(size), - m_det_p(0), - m_isInitialized(false) -{ -} - -template<typename MatrixType> -PartialPivLU<MatrixType>::PartialPivLU(const MatrixType& matrix) - : m_lu(matrix.rows(), matrix.rows()), - m_p(matrix.rows()), - m_rowsTranspositions(matrix.rows()), - m_det_p(0), - m_isInitialized(false) -{ - compute(matrix); -} - -namespace internal { - -/** \internal This is the blocked version of fullpivlu_unblocked() */ -template<typename Scalar, int StorageOrder, typename PivIndex> -struct partial_lu_impl -{ - // FIXME add a stride to Map, so that the following mapping becomes easier, - // another option would be to create an expression being able to automatically - // warp any Map, Matrix, and Block expressions as a unique type, but since that's exactly - // a Map + stride, why not adding a stride to Map, and convenient ctors from a Matrix, - // and Block. - typedef Map<Matrix<Scalar, Dynamic, Dynamic, StorageOrder> > MapLU; - typedef Block<MapLU, Dynamic, Dynamic> MatrixType; - typedef Block<MatrixType,Dynamic,Dynamic> BlockType; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - - /** \internal performs the LU decomposition in-place of the matrix \a lu - * using an unblocked algorithm. - * - * In addition, this function returns the row transpositions in the - * vector \a row_transpositions which must have a size equal to the number - * of columns of the matrix \a lu, and an integer \a nb_transpositions - * which returns the actual number of transpositions. - * - * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise. - */ - static Index unblocked_lu(MatrixType& lu, PivIndex* row_transpositions, PivIndex& nb_transpositions) - { - const Index rows = lu.rows(); - const Index cols = lu.cols(); - const Index size = (std::min)(rows,cols); - nb_transpositions = 0; - Index first_zero_pivot = -1; - for(Index k = 0; k < size; ++k) - { - Index rrows = rows-k-1; - Index rcols = cols-k-1; - - Index row_of_biggest_in_col; - RealScalar biggest_in_corner - = lu.col(k).tail(rows-k).cwiseAbs().maxCoeff(&row_of_biggest_in_col); - row_of_biggest_in_col += k; - - row_transpositions[k] = PivIndex(row_of_biggest_in_col); - - if(biggest_in_corner != RealScalar(0)) - { - if(k != row_of_biggest_in_col) - { - lu.row(k).swap(lu.row(row_of_biggest_in_col)); - ++nb_transpositions; - } - - // FIXME shall we introduce a safe quotient expression in cas 1/lu.coeff(k,k) - // overflow but not the actual quotient? - lu.col(k).tail(rrows) /= lu.coeff(k,k); - } - else if(first_zero_pivot==-1) - { - // the pivot is exactly zero, we record the index of the first pivot which is exactly 0, - // and continue the factorization such we still have A = PLU - first_zero_pivot = k; - } - - if(k<rows-1) - lu.bottomRightCorner(rrows,rcols).noalias() -= lu.col(k).tail(rrows) * lu.row(k).tail(rcols); - } - return first_zero_pivot; - } - - /** \internal performs the LU decomposition in-place of the matrix represented - * by the variables \a rows, \a cols, \a lu_data, and \a lu_stride using a - * recursive, blocked algorithm. - * - * In addition, this function returns the row transpositions in the - * vector \a row_transpositions which must have a size equal to the number - * of columns of the matrix \a lu, and an integer \a nb_transpositions - * which returns the actual number of transpositions. - * - * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise. - * - * \note This very low level interface using pointers, etc. is to: - * 1 - reduce the number of instanciations to the strict minimum - * 2 - avoid infinite recursion of the instanciations with Block<Block<Block<...> > > - */ - static Index blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, PivIndex* row_transpositions, PivIndex& nb_transpositions, Index maxBlockSize=256) - { - MapLU lu1(lu_data,StorageOrder==RowMajor?rows:luStride,StorageOrder==RowMajor?luStride:cols); - MatrixType lu(lu1,0,0,rows,cols); - - const Index size = (std::min)(rows,cols); - - // if the matrix is too small, no blocking: - if(size<=16) - { - return unblocked_lu(lu, row_transpositions, nb_transpositions); - } - - // automatically adjust the number of subdivisions to the size - // of the matrix so that there is enough sub blocks: - Index blockSize; - { - blockSize = size/8; - blockSize = (blockSize/16)*16; - blockSize = (std::min)((std::max)(blockSize,Index(8)), maxBlockSize); - } - - nb_transpositions = 0; - Index first_zero_pivot = -1; - for(Index k = 0; k < size; k+=blockSize) - { - Index bs = (std::min)(size-k,blockSize); // actual size of the block - Index trows = rows - k - bs; // trailing rows - Index tsize = size - k - bs; // trailing size - - // partition the matrix: - // A00 | A01 | A02 - // lu = A_0 | A_1 | A_2 = A10 | A11 | A12 - // A20 | A21 | A22 - BlockType A_0(lu,0,0,rows,k); - BlockType A_2(lu,0,k+bs,rows,tsize); - BlockType A11(lu,k,k,bs,bs); - BlockType A12(lu,k,k+bs,bs,tsize); - BlockType A21(lu,k+bs,k,trows,bs); - BlockType A22(lu,k+bs,k+bs,trows,tsize); - - PivIndex nb_transpositions_in_panel; - // recursively call the blocked LU algorithm on [A11^T A21^T]^T - // with a very small blocking size: - Index ret = blocked_lu(trows+bs, bs, &lu.coeffRef(k,k), luStride, - row_transpositions+k, nb_transpositions_in_panel, 16); - if(ret>=0 && first_zero_pivot==-1) - first_zero_pivot = k+ret; - - nb_transpositions += nb_transpositions_in_panel; - // update permutations and apply them to A_0 - for(Index i=k; i<k+bs; ++i) - { - Index piv = (row_transpositions[i] += k); - A_0.row(i).swap(A_0.row(piv)); - } - - if(trows) - { - // apply permutations to A_2 - for(Index i=k;i<k+bs; ++i) - A_2.row(i).swap(A_2.row(row_transpositions[i])); - - // A12 = A11^-1 A12 - A11.template triangularView<UnitLower>().solveInPlace(A12); - - A22.noalias() -= A21 * A12; - } - } - return first_zero_pivot; - } -}; - -/** \internal performs the LU decomposition with partial pivoting in-place. - */ -template<typename MatrixType, typename TranspositionType> -void partial_lu_inplace(MatrixType& lu, TranspositionType& row_transpositions, typename TranspositionType::Index& nb_transpositions) -{ - eigen_assert(lu.cols() == row_transpositions.size()); - eigen_assert((&row_transpositions.coeffRef(1)-&row_transpositions.coeffRef(0)) == 1); - - partial_lu_impl - <typename MatrixType::Scalar, MatrixType::Flags&RowMajorBit?RowMajor:ColMajor, typename TranspositionType::Index> - ::blocked_lu(lu.rows(), lu.cols(), &lu.coeffRef(0,0), lu.outerStride(), &row_transpositions.coeffRef(0), nb_transpositions); -} - -} // end namespace internal - -template<typename MatrixType> -PartialPivLU<MatrixType>& PartialPivLU<MatrixType>::compute(const MatrixType& matrix) -{ - // the row permutation is stored as int indices, so just to be sure: - eigen_assert(matrix.rows()<NumTraits<int>::highest()); - - m_lu = matrix; - - eigen_assert(matrix.rows() == matrix.cols() && "PartialPivLU is only for square (and moreover invertible) matrices"); - const Index size = matrix.rows(); - - m_rowsTranspositions.resize(size); - - typename TranspositionType::Index nb_transpositions; - internal::partial_lu_inplace(m_lu, m_rowsTranspositions, nb_transpositions); - m_det_p = (nb_transpositions%2) ? -1 : 1; - - m_p = m_rowsTranspositions; - - m_isInitialized = true; - return *this; -} - -template<typename MatrixType> -typename internal::traits<MatrixType>::Scalar PartialPivLU<MatrixType>::determinant() const -{ - eigen_assert(m_isInitialized && "PartialPivLU is not initialized."); - return Scalar(m_det_p) * m_lu.diagonal().prod(); -} - -/** \returns the matrix represented by the decomposition, - * i.e., it returns the product: P^{-1} L U. - * This function is provided for debug purpose. */ -template<typename MatrixType> -MatrixType PartialPivLU<MatrixType>::reconstructedMatrix() const -{ - eigen_assert(m_isInitialized && "LU is not initialized."); - // LU - MatrixType res = m_lu.template triangularView<UnitLower>().toDenseMatrix() - * m_lu.template triangularView<Upper>(); - - // P^{-1}(LU) - res = m_p.inverse() * res; - - return res; -} - -/***** Implementation of solve() *****************************************************/ - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<PartialPivLU<_MatrixType>, Rhs> - : solve_retval_base<PartialPivLU<_MatrixType>, Rhs> -{ - EIGEN_MAKE_SOLVE_HELPERS(PartialPivLU<_MatrixType>,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - /* The decomposition PA = LU can be rewritten as A = P^{-1} L U. - * So we proceed as follows: - * Step 1: compute c = Pb. - * Step 2: replace c by the solution x to Lx = c. - * Step 3: replace c by the solution x to Ux = c. - */ - - eigen_assert(rhs().rows() == dec().matrixLU().rows()); - - // Step 1 - dst = dec().permutationP() * rhs(); - - // Step 2 - dec().matrixLU().template triangularView<UnitLower>().solveInPlace(dst); - - // Step 3 - dec().matrixLU().template triangularView<Upper>().solveInPlace(dst); - } -}; - -} // end namespace internal - -/******** MatrixBase methods *******/ - -/** \lu_module - * - * \return the partial-pivoting LU decomposition of \c *this. - * - * \sa class PartialPivLU - */ -#ifndef __CUDACC__ -template<typename Derived> -inline const PartialPivLU<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::partialPivLu() const -{ - return PartialPivLU<PlainObject>(eval()); -} -#endif - -#if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS -/** \lu_module - * - * Synonym of partialPivLu(). - * - * \return the partial-pivoting LU decomposition of \c *this. - * - * \sa class PartialPivLU - */ -#ifndef __CUDACC__ -template<typename Derived> -inline const PartialPivLU<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::lu() const -{ - return PartialPivLU<PlainObject>(eval()); -} -#endif - -#endif - -} // end namespace Eigen - -#endif // EIGEN_PARTIALLU_H diff --git a/third_party/eigen3/Eigen/src/LU/PartialPivLU_MKL.h b/third_party/eigen3/Eigen/src/LU/PartialPivLU_MKL.h deleted file mode 100644 index 9035953c82..0000000000 --- a/third_party/eigen3/Eigen/src/LU/PartialPivLU_MKL.h +++ /dev/null @@ -1,85 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * LU decomposition with partial pivoting based on LAPACKE_?getrf function. - ******************************************************************************** -*/ - -#ifndef EIGEN_PARTIALLU_LAPACK_H -#define EIGEN_PARTIALLU_LAPACK_H - -#include "Eigen/src/Core/util/MKL_support.h" - -namespace Eigen { - -namespace internal { - -/** \internal Specialization for the data types supported by MKL */ - -#define EIGEN_MKL_LU_PARTPIV(EIGTYPE, MKLTYPE, MKLPREFIX) \ -template<int StorageOrder> \ -struct partial_lu_impl<EIGTYPE, StorageOrder, lapack_int> \ -{ \ - /* \internal performs the LU decomposition in-place of the matrix represented */ \ - static lapack_int blocked_lu(lapack_int rows, lapack_int cols, EIGTYPE* lu_data, lapack_int luStride, lapack_int* row_transpositions, lapack_int& nb_transpositions, lapack_int maxBlockSize=256) \ - { \ - EIGEN_UNUSED_VARIABLE(maxBlockSize);\ - lapack_int matrix_order, first_zero_pivot; \ - lapack_int m, n, lda, *ipiv, info; \ - EIGTYPE* a; \ -/* Set up parameters for ?getrf */ \ - matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \ - lda = luStride; \ - a = lu_data; \ - ipiv = row_transpositions; \ - m = rows; \ - n = cols; \ - nb_transpositions = 0; \ -\ - info = LAPACKE_##MKLPREFIX##getrf( matrix_order, m, n, (MKLTYPE*)a, lda, ipiv ); \ -\ - for(int i=0;i<m;i++) { ipiv[i]--; if (ipiv[i]!=i) nb_transpositions++; } \ -\ - eigen_assert(info >= 0); \ -/* something should be done with nb_transpositions */ \ -\ - first_zero_pivot = info; \ - return first_zero_pivot; \ - } \ -}; - -EIGEN_MKL_LU_PARTPIV(double, double, d) -EIGEN_MKL_LU_PARTPIV(float, float, s) -EIGEN_MKL_LU_PARTPIV(dcomplex, MKL_Complex16, z) -EIGEN_MKL_LU_PARTPIV(scomplex, MKL_Complex8, c) - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PARTIALLU_LAPACK_H diff --git a/third_party/eigen3/Eigen/src/LU/arch/Inverse_SSE.h b/third_party/eigen3/Eigen/src/LU/arch/Inverse_SSE.h deleted file mode 100644 index 60b7a23763..0000000000 --- a/third_party/eigen3/Eigen/src/LU/arch/Inverse_SSE.h +++ /dev/null @@ -1,329 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2001 Intel Corporation -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// The SSE code for the 4x4 float and double matrix inverse in this file -// comes from the following Intel's library: -// http://software.intel.com/en-us/articles/optimized-matrix-library-for-use-with-the-intel-pentiumr-4-processors-sse2-instructions/ -// -// Here is the respective copyright and license statement: -// -// Copyright (c) 2001 Intel Corporation. -// -// Permition is granted to use, copy, distribute and prepare derivative works -// of this library for any purpose and without fee, provided, that the above -// copyright notice and this statement appear in all copies. -// Intel makes no representations about the suitability of this software for -// any purpose, and specifically disclaims all warranties. -// See LEGAL.TXT for all the legal information. - -#ifndef EIGEN_INVERSE_SSE_H -#define EIGEN_INVERSE_SSE_H - -namespace Eigen { - -namespace internal { - -template<typename MatrixType, typename ResultType> -struct compute_inverse_size4<Architecture::SSE, float, MatrixType, ResultType> -{ - enum { - MatrixAlignment = bool(MatrixType::Flags&AlignedBit), - ResultAlignment = bool(ResultType::Flags&AlignedBit), - StorageOrdersMatch = (MatrixType::Flags&RowMajorBit) == (ResultType::Flags&RowMajorBit) - }; - - static void run(const MatrixType& matrix, ResultType& result) - { - EIGEN_ALIGN16 const unsigned int _Sign_PNNP[4] = { 0x00000000, 0x80000000, 0x80000000, 0x00000000 }; - - // Load the full matrix into registers - __m128 _L1 = matrix.template packet<MatrixAlignment>( 0); - __m128 _L2 = matrix.template packet<MatrixAlignment>( 4); - __m128 _L3 = matrix.template packet<MatrixAlignment>( 8); - __m128 _L4 = matrix.template packet<MatrixAlignment>(12); - - // The inverse is calculated using "Divide and Conquer" technique. The - // original matrix is divide into four 2x2 sub-matrices. Since each - // register holds four matrix element, the smaller matrices are - // represented as a registers. Hence we get a better locality of the - // calculations. - - __m128 A, B, C, D; // the four sub-matrices - if(!StorageOrdersMatch) - { - A = _mm_unpacklo_ps(_L1, _L2); - B = _mm_unpacklo_ps(_L3, _L4); - C = _mm_unpackhi_ps(_L1, _L2); - D = _mm_unpackhi_ps(_L3, _L4); - } - else - { - A = _mm_movelh_ps(_L1, _L2); - B = _mm_movehl_ps(_L2, _L1); - C = _mm_movelh_ps(_L3, _L4); - D = _mm_movehl_ps(_L4, _L3); - } - - __m128 iA, iB, iC, iD, // partial inverse of the sub-matrices - DC, AB; - __m128 dA, dB, dC, dD; // determinant of the sub-matrices - __m128 det, d, d1, d2; - __m128 rd; // reciprocal of the determinant - - // AB = A# * B - AB = _mm_mul_ps(_mm_shuffle_ps(A,A,0x0F), B); - AB = _mm_sub_ps(AB,_mm_mul_ps(_mm_shuffle_ps(A,A,0xA5), _mm_shuffle_ps(B,B,0x4E))); - // DC = D# * C - DC = _mm_mul_ps(_mm_shuffle_ps(D,D,0x0F), C); - DC = _mm_sub_ps(DC,_mm_mul_ps(_mm_shuffle_ps(D,D,0xA5), _mm_shuffle_ps(C,C,0x4E))); - - // dA = |A| - dA = _mm_mul_ps(_mm_shuffle_ps(A, A, 0x5F),A); - dA = _mm_sub_ss(dA, _mm_movehl_ps(dA,dA)); - // dB = |B| - dB = _mm_mul_ps(_mm_shuffle_ps(B, B, 0x5F),B); - dB = _mm_sub_ss(dB, _mm_movehl_ps(dB,dB)); - - // dC = |C| - dC = _mm_mul_ps(_mm_shuffle_ps(C, C, 0x5F),C); - dC = _mm_sub_ss(dC, _mm_movehl_ps(dC,dC)); - // dD = |D| - dD = _mm_mul_ps(_mm_shuffle_ps(D, D, 0x5F),D); - dD = _mm_sub_ss(dD, _mm_movehl_ps(dD,dD)); - - // d = trace(AB*DC) = trace(A#*B*D#*C) - d = _mm_mul_ps(_mm_shuffle_ps(DC,DC,0xD8),AB); - - // iD = C*A#*B - iD = _mm_mul_ps(_mm_shuffle_ps(C,C,0xA0), _mm_movelh_ps(AB,AB)); - iD = _mm_add_ps(iD,_mm_mul_ps(_mm_shuffle_ps(C,C,0xF5), _mm_movehl_ps(AB,AB))); - // iA = B*D#*C - iA = _mm_mul_ps(_mm_shuffle_ps(B,B,0xA0), _mm_movelh_ps(DC,DC)); - iA = _mm_add_ps(iA,_mm_mul_ps(_mm_shuffle_ps(B,B,0xF5), _mm_movehl_ps(DC,DC))); - - // d = trace(AB*DC) = trace(A#*B*D#*C) [continue] - d = _mm_add_ps(d, _mm_movehl_ps(d, d)); - d = _mm_add_ss(d, _mm_shuffle_ps(d, d, 1)); - d1 = _mm_mul_ss(dA,dD); - d2 = _mm_mul_ss(dB,dC); - - // iD = D*|A| - C*A#*B - iD = _mm_sub_ps(_mm_mul_ps(D,_mm_shuffle_ps(dA,dA,0)), iD); - - // iA = A*|D| - B*D#*C; - iA = _mm_sub_ps(_mm_mul_ps(A,_mm_shuffle_ps(dD,dD,0)), iA); - - // det = |A|*|D| + |B|*|C| - trace(A#*B*D#*C) - det = _mm_sub_ss(_mm_add_ss(d1,d2),d); - rd = _mm_div_ss(_mm_set_ss(1.0f), det); - -// #ifdef ZERO_SINGULAR -// rd = _mm_and_ps(_mm_cmpneq_ss(det,_mm_setzero_ps()), rd); -// #endif - - // iB = D * (A#B)# = D*B#*A - iB = _mm_mul_ps(D, _mm_shuffle_ps(AB,AB,0x33)); - iB = _mm_sub_ps(iB, _mm_mul_ps(_mm_shuffle_ps(D,D,0xB1), _mm_shuffle_ps(AB,AB,0x66))); - // iC = A * (D#C)# = A*C#*D - iC = _mm_mul_ps(A, _mm_shuffle_ps(DC,DC,0x33)); - iC = _mm_sub_ps(iC, _mm_mul_ps(_mm_shuffle_ps(A,A,0xB1), _mm_shuffle_ps(DC,DC,0x66))); - - rd = _mm_shuffle_ps(rd,rd,0); - rd = _mm_xor_ps(rd, _mm_load_ps((float*)_Sign_PNNP)); - - // iB = C*|B| - D*B#*A - iB = _mm_sub_ps(_mm_mul_ps(C,_mm_shuffle_ps(dB,dB,0)), iB); - - // iC = B*|C| - A*C#*D; - iC = _mm_sub_ps(_mm_mul_ps(B,_mm_shuffle_ps(dC,dC,0)), iC); - - // iX = iX / det - iA = _mm_mul_ps(rd,iA); - iB = _mm_mul_ps(rd,iB); - iC = _mm_mul_ps(rd,iC); - iD = _mm_mul_ps(rd,iD); - - result.template writePacket<ResultAlignment>( 0, _mm_shuffle_ps(iA,iB,0x77)); - result.template writePacket<ResultAlignment>( 4, _mm_shuffle_ps(iA,iB,0x22)); - result.template writePacket<ResultAlignment>( 8, _mm_shuffle_ps(iC,iD,0x77)); - result.template writePacket<ResultAlignment>(12, _mm_shuffle_ps(iC,iD,0x22)); - } - -}; - -template<typename MatrixType, typename ResultType> -struct compute_inverse_size4<Architecture::SSE, double, MatrixType, ResultType> -{ - enum { - MatrixAlignment = bool(MatrixType::Flags&AlignedBit), - ResultAlignment = bool(ResultType::Flags&AlignedBit), - StorageOrdersMatch = (MatrixType::Flags&RowMajorBit) == (ResultType::Flags&RowMajorBit) - }; - static void run(const MatrixType& matrix, ResultType& result) - { - const __m128d _Sign_NP = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0)); - const __m128d _Sign_PN = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0)); - - // The inverse is calculated using "Divide and Conquer" technique. The - // original matrix is divide into four 2x2 sub-matrices. Since each - // register of the matrix holds two element, the smaller matrices are - // consisted of two registers. Hence we get a better locality of the - // calculations. - - // the four sub-matrices - __m128d A1, A2, B1, B2, C1, C2, D1, D2; - - if(StorageOrdersMatch) - { - A1 = matrix.template packet<MatrixAlignment>( 0); B1 = matrix.template packet<MatrixAlignment>( 2); - A2 = matrix.template packet<MatrixAlignment>( 4); B2 = matrix.template packet<MatrixAlignment>( 6); - C1 = matrix.template packet<MatrixAlignment>( 8); D1 = matrix.template packet<MatrixAlignment>(10); - C2 = matrix.template packet<MatrixAlignment>(12); D2 = matrix.template packet<MatrixAlignment>(14); - } - else - { - __m128d tmp; - A1 = matrix.template packet<MatrixAlignment>( 0); C1 = matrix.template packet<MatrixAlignment>( 2); - A2 = matrix.template packet<MatrixAlignment>( 4); C2 = matrix.template packet<MatrixAlignment>( 6); - tmp = A1; - A1 = _mm_unpacklo_pd(A1,A2); - A2 = _mm_unpackhi_pd(tmp,A2); - tmp = C1; - C1 = _mm_unpacklo_pd(C1,C2); - C2 = _mm_unpackhi_pd(tmp,C2); - - B1 = matrix.template packet<MatrixAlignment>( 8); D1 = matrix.template packet<MatrixAlignment>(10); - B2 = matrix.template packet<MatrixAlignment>(12); D2 = matrix.template packet<MatrixAlignment>(14); - tmp = B1; - B1 = _mm_unpacklo_pd(B1,B2); - B2 = _mm_unpackhi_pd(tmp,B2); - tmp = D1; - D1 = _mm_unpacklo_pd(D1,D2); - D2 = _mm_unpackhi_pd(tmp,D2); - } - - __m128d iA1, iA2, iB1, iB2, iC1, iC2, iD1, iD2, // partial invese of the sub-matrices - DC1, DC2, AB1, AB2; - __m128d dA, dB, dC, dD; // determinant of the sub-matrices - __m128d det, d1, d2, rd; - - // dA = |A| - dA = _mm_shuffle_pd(A2, A2, 1); - dA = _mm_mul_pd(A1, dA); - dA = _mm_sub_sd(dA, _mm_shuffle_pd(dA,dA,3)); - // dB = |B| - dB = _mm_shuffle_pd(B2, B2, 1); - dB = _mm_mul_pd(B1, dB); - dB = _mm_sub_sd(dB, _mm_shuffle_pd(dB,dB,3)); - - // AB = A# * B - AB1 = _mm_mul_pd(B1, _mm_shuffle_pd(A2,A2,3)); - AB2 = _mm_mul_pd(B2, _mm_shuffle_pd(A1,A1,0)); - AB1 = _mm_sub_pd(AB1, _mm_mul_pd(B2, _mm_shuffle_pd(A1,A1,3))); - AB2 = _mm_sub_pd(AB2, _mm_mul_pd(B1, _mm_shuffle_pd(A2,A2,0))); - - // dC = |C| - dC = _mm_shuffle_pd(C2, C2, 1); - dC = _mm_mul_pd(C1, dC); - dC = _mm_sub_sd(dC, _mm_shuffle_pd(dC,dC,3)); - // dD = |D| - dD = _mm_shuffle_pd(D2, D2, 1); - dD = _mm_mul_pd(D1, dD); - dD = _mm_sub_sd(dD, _mm_shuffle_pd(dD,dD,3)); - - // DC = D# * C - DC1 = _mm_mul_pd(C1, _mm_shuffle_pd(D2,D2,3)); - DC2 = _mm_mul_pd(C2, _mm_shuffle_pd(D1,D1,0)); - DC1 = _mm_sub_pd(DC1, _mm_mul_pd(C2, _mm_shuffle_pd(D1,D1,3))); - DC2 = _mm_sub_pd(DC2, _mm_mul_pd(C1, _mm_shuffle_pd(D2,D2,0))); - - // rd = trace(AB*DC) = trace(A#*B*D#*C) - d1 = _mm_mul_pd(AB1, _mm_shuffle_pd(DC1, DC2, 0)); - d2 = _mm_mul_pd(AB2, _mm_shuffle_pd(DC1, DC2, 3)); - rd = _mm_add_pd(d1, d2); - rd = _mm_add_sd(rd, _mm_shuffle_pd(rd, rd,3)); - - // iD = C*A#*B - iD1 = _mm_mul_pd(AB1, _mm_shuffle_pd(C1,C1,0)); - iD2 = _mm_mul_pd(AB1, _mm_shuffle_pd(C2,C2,0)); - iD1 = _mm_add_pd(iD1, _mm_mul_pd(AB2, _mm_shuffle_pd(C1,C1,3))); - iD2 = _mm_add_pd(iD2, _mm_mul_pd(AB2, _mm_shuffle_pd(C2,C2,3))); - - // iA = B*D#*C - iA1 = _mm_mul_pd(DC1, _mm_shuffle_pd(B1,B1,0)); - iA2 = _mm_mul_pd(DC1, _mm_shuffle_pd(B2,B2,0)); - iA1 = _mm_add_pd(iA1, _mm_mul_pd(DC2, _mm_shuffle_pd(B1,B1,3))); - iA2 = _mm_add_pd(iA2, _mm_mul_pd(DC2, _mm_shuffle_pd(B2,B2,3))); - - // iD = D*|A| - C*A#*B - dA = _mm_shuffle_pd(dA,dA,0); - iD1 = _mm_sub_pd(_mm_mul_pd(D1, dA), iD1); - iD2 = _mm_sub_pd(_mm_mul_pd(D2, dA), iD2); - - // iA = A*|D| - B*D#*C; - dD = _mm_shuffle_pd(dD,dD,0); - iA1 = _mm_sub_pd(_mm_mul_pd(A1, dD), iA1); - iA2 = _mm_sub_pd(_mm_mul_pd(A2, dD), iA2); - - d1 = _mm_mul_sd(dA, dD); - d2 = _mm_mul_sd(dB, dC); - - // iB = D * (A#B)# = D*B#*A - iB1 = _mm_mul_pd(D1, _mm_shuffle_pd(AB2,AB1,1)); - iB2 = _mm_mul_pd(D2, _mm_shuffle_pd(AB2,AB1,1)); - iB1 = _mm_sub_pd(iB1, _mm_mul_pd(_mm_shuffle_pd(D1,D1,1), _mm_shuffle_pd(AB2,AB1,2))); - iB2 = _mm_sub_pd(iB2, _mm_mul_pd(_mm_shuffle_pd(D2,D2,1), _mm_shuffle_pd(AB2,AB1,2))); - - // det = |A|*|D| + |B|*|C| - trace(A#*B*D#*C) - det = _mm_add_sd(d1, d2); - det = _mm_sub_sd(det, rd); - - // iC = A * (D#C)# = A*C#*D - iC1 = _mm_mul_pd(A1, _mm_shuffle_pd(DC2,DC1,1)); - iC2 = _mm_mul_pd(A2, _mm_shuffle_pd(DC2,DC1,1)); - iC1 = _mm_sub_pd(iC1, _mm_mul_pd(_mm_shuffle_pd(A1,A1,1), _mm_shuffle_pd(DC2,DC1,2))); - iC2 = _mm_sub_pd(iC2, _mm_mul_pd(_mm_shuffle_pd(A2,A2,1), _mm_shuffle_pd(DC2,DC1,2))); - - rd = _mm_div_sd(_mm_set_sd(1.0), det); -// #ifdef ZERO_SINGULAR -// rd = _mm_and_pd(_mm_cmpneq_sd(det,_mm_setzero_pd()), rd); -// #endif - rd = _mm_shuffle_pd(rd,rd,0); - - // iB = C*|B| - D*B#*A - dB = _mm_shuffle_pd(dB,dB,0); - iB1 = _mm_sub_pd(_mm_mul_pd(C1, dB), iB1); - iB2 = _mm_sub_pd(_mm_mul_pd(C2, dB), iB2); - - d1 = _mm_xor_pd(rd, _Sign_PN); - d2 = _mm_xor_pd(rd, _Sign_NP); - - // iC = B*|C| - A*C#*D; - dC = _mm_shuffle_pd(dC,dC,0); - iC1 = _mm_sub_pd(_mm_mul_pd(B1, dC), iC1); - iC2 = _mm_sub_pd(_mm_mul_pd(B2, dC), iC2); - - result.template writePacket<ResultAlignment>( 0, _mm_mul_pd(_mm_shuffle_pd(iA2, iA1, 3), d1)); // iA# / det - result.template writePacket<ResultAlignment>( 4, _mm_mul_pd(_mm_shuffle_pd(iA2, iA1, 0), d2)); - result.template writePacket<ResultAlignment>( 2, _mm_mul_pd(_mm_shuffle_pd(iB2, iB1, 3), d1)); // iB# / det - result.template writePacket<ResultAlignment>( 6, _mm_mul_pd(_mm_shuffle_pd(iB2, iB1, 0), d2)); - result.template writePacket<ResultAlignment>( 8, _mm_mul_pd(_mm_shuffle_pd(iC2, iC1, 3), d1)); // iC# / det - result.template writePacket<ResultAlignment>(12, _mm_mul_pd(_mm_shuffle_pd(iC2, iC1, 0), d2)); - result.template writePacket<ResultAlignment>(10, _mm_mul_pd(_mm_shuffle_pd(iD2, iD1, 3), d1)); // iD# / det - result.template writePacket<ResultAlignment>(14, _mm_mul_pd(_mm_shuffle_pd(iD2, iD1, 0), d2)); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_INVERSE_SSE_H diff --git a/third_party/eigen3/Eigen/src/MetisSupport/MetisSupport.h b/third_party/eigen3/Eigen/src/MetisSupport/MetisSupport.h deleted file mode 100644 index f2bbef20c8..0000000000 --- a/third_party/eigen3/Eigen/src/MetisSupport/MetisSupport.h +++ /dev/null @@ -1,137 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef METIS_SUPPORT_H -#define METIS_SUPPORT_H - -namespace Eigen { -/** - * Get the fill-reducing ordering from the METIS package - * - * If A is the original matrix and Ap is the permuted matrix, - * the fill-reducing permutation is defined as follows : - * Row (column) i of A is the matperm(i) row (column) of Ap. - * WARNING: As computed by METIS, this corresponds to the vector iperm (instead of perm) - */ -template <typename Index> -class MetisOrdering -{ -public: - typedef PermutationMatrix<Dynamic,Dynamic,Index> PermutationType; - typedef Matrix<Index,Dynamic,1> IndexVector; - - template <typename MatrixType> - void get_symmetrized_graph(const MatrixType& A) - { - Index m = A.cols(); - eigen_assert((A.rows() == A.cols()) && "ONLY FOR SQUARED MATRICES"); - // Get the transpose of the input matrix - MatrixType At = A.transpose(); - // Get the number of nonzeros elements in each row/col of At+A - Index TotNz = 0; - IndexVector visited(m); - visited.setConstant(-1); - for (int j = 0; j < m; j++) - { - // Compute the union structure of of A(j,:) and At(j,:) - visited(j) = j; // Do not include the diagonal element - // Get the nonzeros in row/column j of A - for (typename MatrixType::InnerIterator it(A, j); it; ++it) - { - Index idx = it.index(); // Get the row index (for column major) or column index (for row major) - if (visited(idx) != j ) - { - visited(idx) = j; - ++TotNz; - } - } - //Get the nonzeros in row/column j of At - for (typename MatrixType::InnerIterator it(At, j); it; ++it) - { - Index idx = it.index(); - if(visited(idx) != j) - { - visited(idx) = j; - ++TotNz; - } - } - } - // Reserve place for A + At - m_indexPtr.resize(m+1); - m_innerIndices.resize(TotNz); - - // Now compute the real adjacency list of each column/row - visited.setConstant(-1); - Index CurNz = 0; - for (int j = 0; j < m; j++) - { - m_indexPtr(j) = CurNz; - - visited(j) = j; // Do not include the diagonal element - // Add the pattern of row/column j of A to A+At - for (typename MatrixType::InnerIterator it(A,j); it; ++it) - { - Index idx = it.index(); // Get the row index (for column major) or column index (for row major) - if (visited(idx) != j ) - { - visited(idx) = j; - m_innerIndices(CurNz) = idx; - CurNz++; - } - } - //Add the pattern of row/column j of At to A+At - for (typename MatrixType::InnerIterator it(At, j); it; ++it) - { - Index idx = it.index(); - if(visited(idx) != j) - { - visited(idx) = j; - m_innerIndices(CurNz) = idx; - ++CurNz; - } - } - } - m_indexPtr(m) = CurNz; - } - - template <typename MatrixType> - void operator() (const MatrixType& A, PermutationType& matperm) - { - Index m = A.cols(); - IndexVector perm(m),iperm(m); - // First, symmetrize the matrix graph. - get_symmetrized_graph(A); - int output_error; - - // Call the fill-reducing routine from METIS - output_error = METIS_NodeND(&m, m_indexPtr.data(), m_innerIndices.data(), NULL, NULL, perm.data(), iperm.data()); - - if(output_error != METIS_OK) - { - //FIXME The ordering interface should define a class of possible errors - std::cerr << "ERROR WHILE CALLING THE METIS PACKAGE \n"; - return; - } - - // Get the fill-reducing permutation - //NOTE: If Ap is the permuted matrix then perm and iperm vectors are defined as follows - // Row (column) i of Ap is the perm(i) row(column) of A, and row (column) i of A is the iperm(i) row(column) of Ap - - matperm.resize(m); - for (int j = 0; j < m; j++) - matperm.indices()(iperm(j)) = j; - - } - - protected: - IndexVector m_indexPtr; // Pointer to the adjacenccy list of each row/column - IndexVector m_innerIndices; // Adjacency list -}; - -}// end namespace eigen -#endif diff --git a/third_party/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h b/third_party/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h deleted file mode 100644 index 44548f6607..0000000000 --- a/third_party/eigen3/Eigen/src/OrderingMethods/Eigen_Colamd.h +++ /dev/null @@ -1,1850 +0,0 @@ -// // This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// This file is modified from the colamd/symamd library. The copyright is below - -// The authors of the code itself are Stefan I. Larimore and Timothy A. -// Davis (davis@cise.ufl.edu), University of Florida. The algorithm was -// developed in collaboration with John Gilbert, Xerox PARC, and Esmond -// Ng, Oak Ridge National Laboratory. -// -// Date: -// -// September 8, 2003. Version 2.3. -// -// Acknowledgements: -// -// This work was supported by the National Science Foundation, under -// grants DMS-9504974 and DMS-9803599. -// -// Notice: -// -// Copyright (c) 1998-2003 by the University of Florida. -// All Rights Reserved. -// -// THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY -// EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. -// -// Permission is hereby granted to use, copy, modify, and/or distribute -// this program, provided that the Copyright, this License, and the -// Availability of the original version is retained on all copies and made -// accessible to the end-user of any code or package that includes COLAMD -// or any modified version of COLAMD. -// -// Availability: -// -// The colamd/symamd library is available at -// -// http://www.cise.ufl.edu/research/sparse/colamd/ - -// This is the http://www.cise.ufl.edu/research/sparse/colamd/colamd.h -// file. It is required by the colamd.c, colamdmex.c, and symamdmex.c -// files, and by any C code that calls the routines whose prototypes are -// listed below, or that uses the colamd/symamd definitions listed below. - -#ifndef EIGEN_COLAMD_H -#define EIGEN_COLAMD_H - -namespace internal { -/* Ensure that debugging is turned off: */ -#ifndef COLAMD_NDEBUG -#define COLAMD_NDEBUG -#endif /* NDEBUG */ -/* ========================================================================== */ -/* === Knob and statistics definitions ====================================== */ -/* ========================================================================== */ - -/* size of the knobs [ ] array. Only knobs [0..1] are currently used. */ -#define COLAMD_KNOBS 20 - -/* number of output statistics. Only stats [0..6] are currently used. */ -#define COLAMD_STATS 20 - -/* knobs [0] and stats [0]: dense row knob and output statistic. */ -#define COLAMD_DENSE_ROW 0 - -/* knobs [1] and stats [1]: dense column knob and output statistic. */ -#define COLAMD_DENSE_COL 1 - -/* stats [2]: memory defragmentation count output statistic */ -#define COLAMD_DEFRAG_COUNT 2 - -/* stats [3]: colamd status: zero OK, > 0 warning or notice, < 0 error */ -#define COLAMD_STATUS 3 - -/* stats [4..6]: error info, or info on jumbled columns */ -#define COLAMD_INFO1 4 -#define COLAMD_INFO2 5 -#define COLAMD_INFO3 6 - -/* error codes returned in stats [3]: */ -#define COLAMD_OK (0) -#define COLAMD_OK_BUT_JUMBLED (1) -#define COLAMD_ERROR_A_not_present (-1) -#define COLAMD_ERROR_p_not_present (-2) -#define COLAMD_ERROR_nrow_negative (-3) -#define COLAMD_ERROR_ncol_negative (-4) -#define COLAMD_ERROR_nnz_negative (-5) -#define COLAMD_ERROR_p0_nonzero (-6) -#define COLAMD_ERROR_A_too_small (-7) -#define COLAMD_ERROR_col_length_negative (-8) -#define COLAMD_ERROR_row_index_out_of_bounds (-9) -#define COLAMD_ERROR_out_of_memory (-10) -#define COLAMD_ERROR_internal_error (-999) - -/* ========================================================================== */ -/* === Definitions ========================================================== */ -/* ========================================================================== */ - -#define COLAMD_MAX(a,b) (((a) > (b)) ? (a) : (b)) -#define COLAMD_MIN(a,b) (((a) < (b)) ? (a) : (b)) - -#define ONES_COMPLEMENT(r) (-(r)-1) - -/* -------------------------------------------------------------------------- */ - -#define COLAMD_EMPTY (-1) - -/* Row and column status */ -#define ALIVE (0) -#define DEAD (-1) - -/* Column status */ -#define DEAD_PRINCIPAL (-1) -#define DEAD_NON_PRINCIPAL (-2) - -/* Macros for row and column status update and checking. */ -#define ROW_IS_DEAD(r) ROW_IS_MARKED_DEAD (Row[r].shared2.mark) -#define ROW_IS_MARKED_DEAD(row_mark) (row_mark < ALIVE) -#define ROW_IS_ALIVE(r) (Row [r].shared2.mark >= ALIVE) -#define COL_IS_DEAD(c) (Col [c].start < ALIVE) -#define COL_IS_ALIVE(c) (Col [c].start >= ALIVE) -#define COL_IS_DEAD_PRINCIPAL(c) (Col [c].start == DEAD_PRINCIPAL) -#define KILL_ROW(r) { Row [r].shared2.mark = DEAD ; } -#define KILL_PRINCIPAL_COL(c) { Col [c].start = DEAD_PRINCIPAL ; } -#define KILL_NON_PRINCIPAL_COL(c) { Col [c].start = DEAD_NON_PRINCIPAL ; } - -/* ========================================================================== */ -/* === Colamd reporting mechanism =========================================== */ -/* ========================================================================== */ - -// == Row and Column structures == -template <typename Index> -struct colamd_col -{ - Index start ; /* index for A of first row in this column, or DEAD */ - /* if column is dead */ - Index length ; /* number of rows in this column */ - union - { - Index thickness ; /* number of original columns represented by this */ - /* col, if the column is alive */ - Index parent ; /* parent in parent tree super-column structure, if */ - /* the column is dead */ - } shared1 ; - union - { - Index score ; /* the score used to maintain heap, if col is alive */ - Index order ; /* pivot ordering of this column, if col is dead */ - } shared2 ; - union - { - Index headhash ; /* head of a hash bucket, if col is at the head of */ - /* a degree list */ - Index hash ; /* hash value, if col is not in a degree list */ - Index prev ; /* previous column in degree list, if col is in a */ - /* degree list (but not at the head of a degree list) */ - } shared3 ; - union - { - Index degree_next ; /* next column, if col is in a degree list */ - Index hash_next ; /* next column, if col is in a hash list */ - } shared4 ; - -}; - -template <typename Index> -struct Colamd_Row -{ - Index start ; /* index for A of first col in this row */ - Index length ; /* number of principal columns in this row */ - union - { - Index degree ; /* number of principal & non-principal columns in row */ - Index p ; /* used as a row pointer in init_rows_cols () */ - } shared1 ; - union - { - Index mark ; /* for computing set differences and marking dead rows*/ - Index first_column ;/* first column in row (used in garbage collection) */ - } shared2 ; - -}; - -/* ========================================================================== */ -/* === Colamd recommended memory size ======================================= */ -/* ========================================================================== */ - -/* - The recommended length Alen of the array A passed to colamd is given by - the COLAMD_RECOMMENDED (nnz, n_row, n_col) macro. It returns -1 if any - argument is negative. 2*nnz space is required for the row and column - indices of the matrix. colamd_c (n_col) + colamd_r (n_row) space is - required for the Col and Row arrays, respectively, which are internal to - colamd. An additional n_col space is the minimal amount of "elbow room", - and nnz/5 more space is recommended for run time efficiency. - - This macro is not needed when using symamd. - - Explicit typecast to Index added Sept. 23, 2002, COLAMD version 2.2, to avoid - gcc -pedantic warning messages. -*/ -template <typename Index> -inline Index colamd_c(Index n_col) -{ return Index( ((n_col) + 1) * sizeof (colamd_col<Index>) / sizeof (Index) ) ; } - -template <typename Index> -inline Index colamd_r(Index n_row) -{ return Index(((n_row) + 1) * sizeof (Colamd_Row<Index>) / sizeof (Index)); } - -// Prototypes of non-user callable routines -template <typename Index> -static Index init_rows_cols (Index n_row, Index n_col, Colamd_Row<Index> Row [], colamd_col<Index> col [], Index A [], Index p [], Index stats[COLAMD_STATS] ); - -template <typename Index> -static void init_scoring (Index n_row, Index n_col, Colamd_Row<Index> Row [], colamd_col<Index> Col [], Index A [], Index head [], double knobs[COLAMD_KNOBS], Index *p_n_row2, Index *p_n_col2, Index *p_max_deg); - -template <typename Index> -static Index find_ordering (Index n_row, Index n_col, Index Alen, Colamd_Row<Index> Row [], colamd_col<Index> Col [], Index A [], Index head [], Index n_col2, Index max_deg, Index pfree); - -template <typename Index> -static void order_children (Index n_col, colamd_col<Index> Col [], Index p []); - -template <typename Index> -static void detect_super_cols (colamd_col<Index> Col [], Index A [], Index head [], Index row_start, Index row_length ) ; - -template <typename Index> -static Index garbage_collection (Index n_row, Index n_col, Colamd_Row<Index> Row [], colamd_col<Index> Col [], Index A [], Index *pfree) ; - -template <typename Index> -static inline Index clear_mark (Index n_row, Colamd_Row<Index> Row [] ) ; - -/* === No debugging ========================================================= */ - -#define COLAMD_DEBUG0(params) ; -#define COLAMD_DEBUG1(params) ; -#define COLAMD_DEBUG2(params) ; -#define COLAMD_DEBUG3(params) ; -#define COLAMD_DEBUG4(params) ; - -#define COLAMD_ASSERT(expression) ((void) 0) - - -/** - * \brief Returns the recommended value of Alen - * - * Returns recommended value of Alen for use by colamd. - * Returns -1 if any input argument is negative. - * The use of this routine or macro is optional. - * Note that the macro uses its arguments more than once, - * so be careful for side effects, if you pass expressions as arguments to COLAMD_RECOMMENDED. - * - * \param nnz nonzeros in A - * \param n_row number of rows in A - * \param n_col number of columns in A - * \return recommended value of Alen for use by colamd - */ -template <typename Index> -inline Index colamd_recommended ( Index nnz, Index n_row, Index n_col) -{ - if ((nnz) < 0 || (n_row) < 0 || (n_col) < 0) - return (-1); - else - return (2 * (nnz) + colamd_c (n_col) + colamd_r (n_row) + (n_col) + ((nnz) / 5)); -} - -/** - * \brief set default parameters The use of this routine is optional. - * - * Colamd: rows with more than (knobs [COLAMD_DENSE_ROW] * n_col) - * entries are removed prior to ordering. Columns with more than - * (knobs [COLAMD_DENSE_COL] * n_row) entries are removed prior to - * ordering, and placed last in the output column ordering. - * - * COLAMD_DENSE_ROW and COLAMD_DENSE_COL are defined as 0 and 1, - * respectively, in colamd.h. Default values of these two knobs - * are both 0.5. Currently, only knobs [0] and knobs [1] are - * used, but future versions may use more knobs. If so, they will - * be properly set to their defaults by the future version of - * colamd_set_defaults, so that the code that calls colamd will - * not need to change, assuming that you either use - * colamd_set_defaults, or pass a (double *) NULL pointer as the - * knobs array to colamd or symamd. - * - * \param knobs parameter settings for colamd - */ - -static inline void colamd_set_defaults(double knobs[COLAMD_KNOBS]) -{ - /* === Local variables ================================================== */ - - int i ; - - if (!knobs) - { - return ; /* no knobs to initialize */ - } - for (i = 0 ; i < COLAMD_KNOBS ; i++) - { - knobs [i] = 0 ; - } - knobs [COLAMD_DENSE_ROW] = 0.5 ; /* ignore rows over 50% dense */ - knobs [COLAMD_DENSE_COL] = 0.5 ; /* ignore columns over 50% dense */ -} - -/** - * \brief Computes a column ordering using the column approximate minimum degree ordering - * - * Computes a column ordering (Q) of A such that P(AQ)=LU or - * (AQ)'AQ=LL' have less fill-in and require fewer floating point - * operations than factorizing the unpermuted matrix A or A'A, - * respectively. - * - * - * \param n_row number of rows in A - * \param n_col number of columns in A - * \param Alen, size of the array A - * \param A row indices of the matrix, of size ALen - * \param p column pointers of A, of size n_col+1 - * \param knobs parameter settings for colamd - * \param stats colamd output statistics and error codes - */ -template <typename Index> -static bool colamd(Index n_row, Index n_col, Index Alen, Index *A, Index *p, double knobs[COLAMD_KNOBS], Index stats[COLAMD_STATS]) -{ - /* === Local variables ================================================== */ - - Index i ; /* loop index */ - Index nnz ; /* nonzeros in A */ - Index Row_size ; /* size of Row [], in integers */ - Index Col_size ; /* size of Col [], in integers */ - Index need ; /* minimum required length of A */ - Colamd_Row<Index> *Row ; /* pointer into A of Row [0..n_row] array */ - colamd_col<Index> *Col ; /* pointer into A of Col [0..n_col] array */ - Index n_col2 ; /* number of non-dense, non-empty columns */ - Index n_row2 ; /* number of non-dense, non-empty rows */ - Index ngarbage ; /* number of garbage collections performed */ - Index max_deg ; /* maximum row degree */ - double default_knobs [COLAMD_KNOBS] ; /* default knobs array */ - - - /* === Check the input arguments ======================================== */ - - if (!stats) - { - COLAMD_DEBUG0 (("colamd: stats not present\n")) ; - return (false) ; - } - for (i = 0 ; i < COLAMD_STATS ; i++) - { - stats [i] = 0 ; - } - stats [COLAMD_STATUS] = COLAMD_OK ; - stats [COLAMD_INFO1] = -1 ; - stats [COLAMD_INFO2] = -1 ; - - if (!A) /* A is not present */ - { - stats [COLAMD_STATUS] = COLAMD_ERROR_A_not_present ; - COLAMD_DEBUG0 (("colamd: A not present\n")) ; - return (false) ; - } - - if (!p) /* p is not present */ - { - stats [COLAMD_STATUS] = COLAMD_ERROR_p_not_present ; - COLAMD_DEBUG0 (("colamd: p not present\n")) ; - return (false) ; - } - - if (n_row < 0) /* n_row must be >= 0 */ - { - stats [COLAMD_STATUS] = COLAMD_ERROR_nrow_negative ; - stats [COLAMD_INFO1] = n_row ; - COLAMD_DEBUG0 (("colamd: nrow negative %d\n", n_row)) ; - return (false) ; - } - - if (n_col < 0) /* n_col must be >= 0 */ - { - stats [COLAMD_STATUS] = COLAMD_ERROR_ncol_negative ; - stats [COLAMD_INFO1] = n_col ; - COLAMD_DEBUG0 (("colamd: ncol negative %d\n", n_col)) ; - return (false) ; - } - - nnz = p [n_col] ; - if (nnz < 0) /* nnz must be >= 0 */ - { - stats [COLAMD_STATUS] = COLAMD_ERROR_nnz_negative ; - stats [COLAMD_INFO1] = nnz ; - COLAMD_DEBUG0 (("colamd: number of entries negative %d\n", nnz)) ; - return (false) ; - } - - if (p [0] != 0) - { - stats [COLAMD_STATUS] = COLAMD_ERROR_p0_nonzero ; - stats [COLAMD_INFO1] = p [0] ; - COLAMD_DEBUG0 (("colamd: p[0] not zero %d\n", p [0])) ; - return (false) ; - } - - /* === If no knobs, set default knobs =================================== */ - - if (!knobs) - { - colamd_set_defaults (default_knobs) ; - knobs = default_knobs ; - } - - /* === Allocate the Row and Col arrays from array A ===================== */ - - Col_size = colamd_c (n_col) ; - Row_size = colamd_r (n_row) ; - need = 2*nnz + n_col + Col_size + Row_size ; - - if (need > Alen) - { - /* not enough space in array A to perform the ordering */ - stats [COLAMD_STATUS] = COLAMD_ERROR_A_too_small ; - stats [COLAMD_INFO1] = need ; - stats [COLAMD_INFO2] = Alen ; - COLAMD_DEBUG0 (("colamd: Need Alen >= %d, given only Alen = %d\n", need,Alen)); - return (false) ; - } - - Alen -= Col_size + Row_size ; - Col = (colamd_col<Index> *) &A [Alen] ; - Row = (Colamd_Row<Index> *) &A [Alen + Col_size] ; - - /* === Construct the row and column data structures ===================== */ - - if (!Eigen::internal::init_rows_cols (n_row, n_col, Row, Col, A, p, stats)) - { - /* input matrix is invalid */ - COLAMD_DEBUG0 (("colamd: Matrix invalid\n")) ; - return (false) ; - } - - /* === Initialize scores, kill dense rows/columns ======================= */ - - Eigen::internal::init_scoring (n_row, n_col, Row, Col, A, p, knobs, - &n_row2, &n_col2, &max_deg) ; - - /* === Order the supercolumns =========================================== */ - - ngarbage = Eigen::internal::find_ordering (n_row, n_col, Alen, Row, Col, A, p, - n_col2, max_deg, 2*nnz) ; - - /* === Order the non-principal columns ================================== */ - - Eigen::internal::order_children (n_col, Col, p) ; - - /* === Return statistics in stats ======================================= */ - - stats [COLAMD_DENSE_ROW] = n_row - n_row2 ; - stats [COLAMD_DENSE_COL] = n_col - n_col2 ; - stats [COLAMD_DEFRAG_COUNT] = ngarbage ; - COLAMD_DEBUG0 (("colamd: done.\n")) ; - return (true) ; -} - -/* ========================================================================== */ -/* === NON-USER-CALLABLE ROUTINES: ========================================== */ -/* ========================================================================== */ - -/* There are no user-callable routines beyond this point in the file */ - - -/* ========================================================================== */ -/* === init_rows_cols ======================================================= */ -/* ========================================================================== */ - -/* - Takes the column form of the matrix in A and creates the row form of the - matrix. Also, row and column attributes are stored in the Col and Row - structs. If the columns are un-sorted or contain duplicate row indices, - this routine will also sort and remove duplicate row indices from the - column form of the matrix. Returns false if the matrix is invalid, - true otherwise. Not user-callable. -*/ -template <typename Index> -static Index init_rows_cols /* returns true if OK, or false otherwise */ - ( - /* === Parameters ======================================================= */ - - Index n_row, /* number of rows of A */ - Index n_col, /* number of columns of A */ - Colamd_Row<Index> Row [], /* of size n_row+1 */ - colamd_col<Index> Col [], /* of size n_col+1 */ - Index A [], /* row indices of A, of size Alen */ - Index p [], /* pointers to columns in A, of size n_col+1 */ - Index stats [COLAMD_STATS] /* colamd statistics */ - ) -{ - /* === Local variables ================================================== */ - - Index col ; /* a column index */ - Index row ; /* a row index */ - Index *cp ; /* a column pointer */ - Index *cp_end ; /* a pointer to the end of a column */ - Index *rp ; /* a row pointer */ - Index *rp_end ; /* a pointer to the end of a row */ - Index last_row ; /* previous row */ - - /* === Initialize columns, and check column pointers ==================== */ - - for (col = 0 ; col < n_col ; col++) - { - Col [col].start = p [col] ; - Col [col].length = p [col+1] - p [col] ; - - if (Col [col].length < 0) - { - /* column pointers must be non-decreasing */ - stats [COLAMD_STATUS] = COLAMD_ERROR_col_length_negative ; - stats [COLAMD_INFO1] = col ; - stats [COLAMD_INFO2] = Col [col].length ; - COLAMD_DEBUG0 (("colamd: col %d length %d < 0\n", col, Col [col].length)) ; - return (false) ; - } - - Col [col].shared1.thickness = 1 ; - Col [col].shared2.score = 0 ; - Col [col].shared3.prev = COLAMD_EMPTY ; - Col [col].shared4.degree_next = COLAMD_EMPTY ; - } - - /* p [0..n_col] no longer needed, used as "head" in subsequent routines */ - - /* === Scan columns, compute row degrees, and check row indices ========= */ - - stats [COLAMD_INFO3] = 0 ; /* number of duplicate or unsorted row indices*/ - - for (row = 0 ; row < n_row ; row++) - { - Row [row].length = 0 ; - Row [row].shared2.mark = -1 ; - } - - for (col = 0 ; col < n_col ; col++) - { - last_row = -1 ; - - cp = &A [p [col]] ; - cp_end = &A [p [col+1]] ; - - while (cp < cp_end) - { - row = *cp++ ; - - /* make sure row indices within range */ - if (row < 0 || row >= n_row) - { - stats [COLAMD_STATUS] = COLAMD_ERROR_row_index_out_of_bounds ; - stats [COLAMD_INFO1] = col ; - stats [COLAMD_INFO2] = row ; - stats [COLAMD_INFO3] = n_row ; - COLAMD_DEBUG0 (("colamd: row %d col %d out of bounds\n", row, col)) ; - return (false) ; - } - - if (row <= last_row || Row [row].shared2.mark == col) - { - /* row index are unsorted or repeated (or both), thus col */ - /* is jumbled. This is a notice, not an error condition. */ - stats [COLAMD_STATUS] = COLAMD_OK_BUT_JUMBLED ; - stats [COLAMD_INFO1] = col ; - stats [COLAMD_INFO2] = row ; - (stats [COLAMD_INFO3]) ++ ; - COLAMD_DEBUG1 (("colamd: row %d col %d unsorted/duplicate\n",row,col)); - } - - if (Row [row].shared2.mark != col) - { - Row [row].length++ ; - } - else - { - /* this is a repeated entry in the column, */ - /* it will be removed */ - Col [col].length-- ; - } - - /* mark the row as having been seen in this column */ - Row [row].shared2.mark = col ; - - last_row = row ; - } - } - - /* === Compute row pointers ============================================= */ - - /* row form of the matrix starts directly after the column */ - /* form of matrix in A */ - Row [0].start = p [n_col] ; - Row [0].shared1.p = Row [0].start ; - Row [0].shared2.mark = -1 ; - for (row = 1 ; row < n_row ; row++) - { - Row [row].start = Row [row-1].start + Row [row-1].length ; - Row [row].shared1.p = Row [row].start ; - Row [row].shared2.mark = -1 ; - } - - /* === Create row form ================================================== */ - - if (stats [COLAMD_STATUS] == COLAMD_OK_BUT_JUMBLED) - { - /* if cols jumbled, watch for repeated row indices */ - for (col = 0 ; col < n_col ; col++) - { - cp = &A [p [col]] ; - cp_end = &A [p [col+1]] ; - while (cp < cp_end) - { - row = *cp++ ; - if (Row [row].shared2.mark != col) - { - A [(Row [row].shared1.p)++] = col ; - Row [row].shared2.mark = col ; - } - } - } - } - else - { - /* if cols not jumbled, we don't need the mark (this is faster) */ - for (col = 0 ; col < n_col ; col++) - { - cp = &A [p [col]] ; - cp_end = &A [p [col+1]] ; - while (cp < cp_end) - { - A [(Row [*cp++].shared1.p)++] = col ; - } - } - } - - /* === Clear the row marks and set row degrees ========================== */ - - for (row = 0 ; row < n_row ; row++) - { - Row [row].shared2.mark = 0 ; - Row [row].shared1.degree = Row [row].length ; - } - - /* === See if we need to re-create columns ============================== */ - - if (stats [COLAMD_STATUS] == COLAMD_OK_BUT_JUMBLED) - { - COLAMD_DEBUG0 (("colamd: reconstructing column form, matrix jumbled\n")) ; - - - /* === Compute col pointers ========================================= */ - - /* col form of the matrix starts at A [0]. */ - /* Note, we may have a gap between the col form and the row */ - /* form if there were duplicate entries, if so, it will be */ - /* removed upon the first garbage collection */ - Col [0].start = 0 ; - p [0] = Col [0].start ; - for (col = 1 ; col < n_col ; col++) - { - /* note that the lengths here are for pruned columns, i.e. */ - /* no duplicate row indices will exist for these columns */ - Col [col].start = Col [col-1].start + Col [col-1].length ; - p [col] = Col [col].start ; - } - - /* === Re-create col form =========================================== */ - - for (row = 0 ; row < n_row ; row++) - { - rp = &A [Row [row].start] ; - rp_end = rp + Row [row].length ; - while (rp < rp_end) - { - A [(p [*rp++])++] = row ; - } - } - } - - /* === Done. Matrix is not (or no longer) jumbled ====================== */ - - return (true) ; -} - - -/* ========================================================================== */ -/* === init_scoring ========================================================= */ -/* ========================================================================== */ - -/* - Kills dense or empty columns and rows, calculates an initial score for - each column, and places all columns in the degree lists. Not user-callable. -*/ -template <typename Index> -static void init_scoring - ( - /* === Parameters ======================================================= */ - - Index n_row, /* number of rows of A */ - Index n_col, /* number of columns of A */ - Colamd_Row<Index> Row [], /* of size n_row+1 */ - colamd_col<Index> Col [], /* of size n_col+1 */ - Index A [], /* column form and row form of A */ - Index head [], /* of size n_col+1 */ - double knobs [COLAMD_KNOBS],/* parameters */ - Index *p_n_row2, /* number of non-dense, non-empty rows */ - Index *p_n_col2, /* number of non-dense, non-empty columns */ - Index *p_max_deg /* maximum row degree */ - ) -{ - /* === Local variables ================================================== */ - - Index c ; /* a column index */ - Index r, row ; /* a row index */ - Index *cp ; /* a column pointer */ - Index deg ; /* degree of a row or column */ - Index *cp_end ; /* a pointer to the end of a column */ - Index *new_cp ; /* new column pointer */ - Index col_length ; /* length of pruned column */ - Index score ; /* current column score */ - Index n_col2 ; /* number of non-dense, non-empty columns */ - Index n_row2 ; /* number of non-dense, non-empty rows */ - Index dense_row_count ; /* remove rows with more entries than this */ - Index dense_col_count ; /* remove cols with more entries than this */ - Index min_score ; /* smallest column score */ - Index max_deg ; /* maximum row degree */ - Index next_col ; /* Used to add to degree list.*/ - - - /* === Extract knobs ==================================================== */ - - dense_row_count = COLAMD_MAX (0, COLAMD_MIN (knobs [COLAMD_DENSE_ROW] * n_col, n_col)) ; - dense_col_count = COLAMD_MAX (0, COLAMD_MIN (knobs [COLAMD_DENSE_COL] * n_row, n_row)) ; - COLAMD_DEBUG1 (("colamd: densecount: %d %d\n", dense_row_count, dense_col_count)) ; - max_deg = 0 ; - n_col2 = n_col ; - n_row2 = n_row ; - - /* === Kill empty columns =============================================== */ - - /* Put the empty columns at the end in their natural order, so that LU */ - /* factorization can proceed as far as possible. */ - for (c = n_col-1 ; c >= 0 ; c--) - { - deg = Col [c].length ; - if (deg == 0) - { - /* this is a empty column, kill and order it last */ - Col [c].shared2.order = --n_col2 ; - KILL_PRINCIPAL_COL (c) ; - } - } - COLAMD_DEBUG1 (("colamd: null columns killed: %d\n", n_col - n_col2)) ; - - /* === Kill dense columns =============================================== */ - - /* Put the dense columns at the end, in their natural order */ - for (c = n_col-1 ; c >= 0 ; c--) - { - /* skip any dead columns */ - if (COL_IS_DEAD (c)) - { - continue ; - } - deg = Col [c].length ; - if (deg > dense_col_count) - { - /* this is a dense column, kill and order it last */ - Col [c].shared2.order = --n_col2 ; - /* decrement the row degrees */ - cp = &A [Col [c].start] ; - cp_end = cp + Col [c].length ; - while (cp < cp_end) - { - Row [*cp++].shared1.degree-- ; - } - KILL_PRINCIPAL_COL (c) ; - } - } - COLAMD_DEBUG1 (("colamd: Dense and null columns killed: %d\n", n_col - n_col2)) ; - - /* === Kill dense and empty rows ======================================== */ - - for (r = 0 ; r < n_row ; r++) - { - deg = Row [r].shared1.degree ; - COLAMD_ASSERT (deg >= 0 && deg <= n_col) ; - if (deg > dense_row_count || deg == 0) - { - /* kill a dense or empty row */ - KILL_ROW (r) ; - --n_row2 ; - } - else - { - /* keep track of max degree of remaining rows */ - max_deg = COLAMD_MAX (max_deg, deg) ; - } - } - COLAMD_DEBUG1 (("colamd: Dense and null rows killed: %d\n", n_row - n_row2)) ; - - /* === Compute initial column scores ==================================== */ - - /* At this point the row degrees are accurate. They reflect the number */ - /* of "live" (non-dense) columns in each row. No empty rows exist. */ - /* Some "live" columns may contain only dead rows, however. These are */ - /* pruned in the code below. */ - - /* now find the initial matlab score for each column */ - for (c = n_col-1 ; c >= 0 ; c--) - { - /* skip dead column */ - if (COL_IS_DEAD (c)) - { - continue ; - } - score = 0 ; - cp = &A [Col [c].start] ; - new_cp = cp ; - cp_end = cp + Col [c].length ; - while (cp < cp_end) - { - /* get a row */ - row = *cp++ ; - /* skip if dead */ - if (ROW_IS_DEAD (row)) - { - continue ; - } - /* compact the column */ - *new_cp++ = row ; - /* add row's external degree */ - score += Row [row].shared1.degree - 1 ; - /* guard against integer overflow */ - score = COLAMD_MIN (score, n_col) ; - } - /* determine pruned column length */ - col_length = (Index) (new_cp - &A [Col [c].start]) ; - if (col_length == 0) - { - /* a newly-made null column (all rows in this col are "dense" */ - /* and have already been killed) */ - COLAMD_DEBUG2 (("Newly null killed: %d\n", c)) ; - Col [c].shared2.order = --n_col2 ; - KILL_PRINCIPAL_COL (c) ; - } - else - { - /* set column length and set score */ - COLAMD_ASSERT (score >= 0) ; - COLAMD_ASSERT (score <= n_col) ; - Col [c].length = col_length ; - Col [c].shared2.score = score ; - } - } - COLAMD_DEBUG1 (("colamd: Dense, null, and newly-null columns killed: %d\n", - n_col-n_col2)) ; - - /* At this point, all empty rows and columns are dead. All live columns */ - /* are "clean" (containing no dead rows) and simplicial (no supercolumns */ - /* yet). Rows may contain dead columns, but all live rows contain at */ - /* least one live column. */ - - /* === Initialize degree lists ========================================== */ - - - /* clear the hash buckets */ - for (c = 0 ; c <= n_col ; c++) - { - head [c] = COLAMD_EMPTY ; - } - min_score = n_col ; - /* place in reverse order, so low column indices are at the front */ - /* of the lists. This is to encourage natural tie-breaking */ - for (c = n_col-1 ; c >= 0 ; c--) - { - /* only add principal columns to degree lists */ - if (COL_IS_ALIVE (c)) - { - COLAMD_DEBUG4 (("place %d score %d minscore %d ncol %d\n", - c, Col [c].shared2.score, min_score, n_col)) ; - - /* === Add columns score to DList =============================== */ - - score = Col [c].shared2.score ; - - COLAMD_ASSERT (min_score >= 0) ; - COLAMD_ASSERT (min_score <= n_col) ; - COLAMD_ASSERT (score >= 0) ; - COLAMD_ASSERT (score <= n_col) ; - COLAMD_ASSERT (head [score] >= COLAMD_EMPTY) ; - - /* now add this column to dList at proper score location */ - next_col = head [score] ; - Col [c].shared3.prev = COLAMD_EMPTY ; - Col [c].shared4.degree_next = next_col ; - - /* if there already was a column with the same score, set its */ - /* previous pointer to this new column */ - if (next_col != COLAMD_EMPTY) - { - Col [next_col].shared3.prev = c ; - } - head [score] = c ; - - /* see if this score is less than current min */ - min_score = COLAMD_MIN (min_score, score) ; - - - } - } - - - /* === Return number of remaining columns, and max row degree =========== */ - - *p_n_col2 = n_col2 ; - *p_n_row2 = n_row2 ; - *p_max_deg = max_deg ; -} - - -/* ========================================================================== */ -/* === find_ordering ======================================================== */ -/* ========================================================================== */ - -/* - Order the principal columns of the supercolumn form of the matrix - (no supercolumns on input). Uses a minimum approximate column minimum - degree ordering method. Not user-callable. -*/ -template <typename Index> -static Index find_ordering /* return the number of garbage collections */ - ( - /* === Parameters ======================================================= */ - - Index n_row, /* number of rows of A */ - Index n_col, /* number of columns of A */ - Index Alen, /* size of A, 2*nnz + n_col or larger */ - Colamd_Row<Index> Row [], /* of size n_row+1 */ - colamd_col<Index> Col [], /* of size n_col+1 */ - Index A [], /* column form and row form of A */ - Index head [], /* of size n_col+1 */ - Index n_col2, /* Remaining columns to order */ - Index max_deg, /* Maximum row degree */ - Index pfree /* index of first free slot (2*nnz on entry) */ - ) -{ - /* === Local variables ================================================== */ - - Index k ; /* current pivot ordering step */ - Index pivot_col ; /* current pivot column */ - Index *cp ; /* a column pointer */ - Index *rp ; /* a row pointer */ - Index pivot_row ; /* current pivot row */ - Index *new_cp ; /* modified column pointer */ - Index *new_rp ; /* modified row pointer */ - Index pivot_row_start ; /* pointer to start of pivot row */ - Index pivot_row_degree ; /* number of columns in pivot row */ - Index pivot_row_length ; /* number of supercolumns in pivot row */ - Index pivot_col_score ; /* score of pivot column */ - Index needed_memory ; /* free space needed for pivot row */ - Index *cp_end ; /* pointer to the end of a column */ - Index *rp_end ; /* pointer to the end of a row */ - Index row ; /* a row index */ - Index col ; /* a column index */ - Index max_score ; /* maximum possible score */ - Index cur_score ; /* score of current column */ - unsigned int hash ; /* hash value for supernode detection */ - Index head_column ; /* head of hash bucket */ - Index first_col ; /* first column in hash bucket */ - Index tag_mark ; /* marker value for mark array */ - Index row_mark ; /* Row [row].shared2.mark */ - Index set_difference ; /* set difference size of row with pivot row */ - Index min_score ; /* smallest column score */ - Index col_thickness ; /* "thickness" (no. of columns in a supercol) */ - Index max_mark ; /* maximum value of tag_mark */ - Index pivot_col_thickness ; /* number of columns represented by pivot col */ - Index prev_col ; /* Used by Dlist operations. */ - Index next_col ; /* Used by Dlist operations. */ - Index ngarbage ; /* number of garbage collections performed */ - - - /* === Initialization and clear mark ==================================== */ - - max_mark = INT_MAX - n_col ; /* INT_MAX defined in <limits.h> */ - tag_mark = Eigen::internal::clear_mark (n_row, Row) ; - min_score = 0 ; - ngarbage = 0 ; - COLAMD_DEBUG1 (("colamd: Ordering, n_col2=%d\n", n_col2)) ; - - /* === Order the columns ================================================ */ - - for (k = 0 ; k < n_col2 ; /* 'k' is incremented below */) - { - - /* === Select pivot column, and order it ============================ */ - - /* make sure degree list isn't empty */ - COLAMD_ASSERT (min_score >= 0) ; - COLAMD_ASSERT (min_score <= n_col) ; - COLAMD_ASSERT (head [min_score] >= COLAMD_EMPTY) ; - - /* get pivot column from head of minimum degree list */ - while (head [min_score] == COLAMD_EMPTY && min_score < n_col) - { - min_score++ ; - } - pivot_col = head [min_score] ; - COLAMD_ASSERT (pivot_col >= 0 && pivot_col <= n_col) ; - next_col = Col [pivot_col].shared4.degree_next ; - head [min_score] = next_col ; - if (next_col != COLAMD_EMPTY) - { - Col [next_col].shared3.prev = COLAMD_EMPTY ; - } - - COLAMD_ASSERT (COL_IS_ALIVE (pivot_col)) ; - COLAMD_DEBUG3 (("Pivot col: %d\n", pivot_col)) ; - - /* remember score for defrag check */ - pivot_col_score = Col [pivot_col].shared2.score ; - - /* the pivot column is the kth column in the pivot order */ - Col [pivot_col].shared2.order = k ; - - /* increment order count by column thickness */ - pivot_col_thickness = Col [pivot_col].shared1.thickness ; - k += pivot_col_thickness ; - COLAMD_ASSERT (pivot_col_thickness > 0) ; - - /* === Garbage_collection, if necessary ============================= */ - - needed_memory = COLAMD_MIN (pivot_col_score, n_col - k) ; - if (pfree + needed_memory >= Alen) - { - pfree = Eigen::internal::garbage_collection (n_row, n_col, Row, Col, A, &A [pfree]) ; - ngarbage++ ; - /* after garbage collection we will have enough */ - COLAMD_ASSERT (pfree + needed_memory < Alen) ; - /* garbage collection has wiped out the Row[].shared2.mark array */ - tag_mark = Eigen::internal::clear_mark (n_row, Row) ; - - } - - /* === Compute pivot row pattern ==================================== */ - - /* get starting location for this new merged row */ - pivot_row_start = pfree ; - - /* initialize new row counts to zero */ - pivot_row_degree = 0 ; - - /* tag pivot column as having been visited so it isn't included */ - /* in merged pivot row */ - Col [pivot_col].shared1.thickness = -pivot_col_thickness ; - - /* pivot row is the union of all rows in the pivot column pattern */ - cp = &A [Col [pivot_col].start] ; - cp_end = cp + Col [pivot_col].length ; - while (cp < cp_end) - { - /* get a row */ - row = *cp++ ; - COLAMD_DEBUG4 (("Pivot col pattern %d %d\n", ROW_IS_ALIVE (row), row)) ; - /* skip if row is dead */ - if (ROW_IS_DEAD (row)) - { - continue ; - } - rp = &A [Row [row].start] ; - rp_end = rp + Row [row].length ; - while (rp < rp_end) - { - /* get a column */ - col = *rp++ ; - /* add the column, if alive and untagged */ - col_thickness = Col [col].shared1.thickness ; - if (col_thickness > 0 && COL_IS_ALIVE (col)) - { - /* tag column in pivot row */ - Col [col].shared1.thickness = -col_thickness ; - COLAMD_ASSERT (pfree < Alen) ; - /* place column in pivot row */ - A [pfree++] = col ; - pivot_row_degree += col_thickness ; - } - } - } - - /* clear tag on pivot column */ - Col [pivot_col].shared1.thickness = pivot_col_thickness ; - max_deg = COLAMD_MAX (max_deg, pivot_row_degree) ; - - - /* === Kill all rows used to construct pivot row ==================== */ - - /* also kill pivot row, temporarily */ - cp = &A [Col [pivot_col].start] ; - cp_end = cp + Col [pivot_col].length ; - while (cp < cp_end) - { - /* may be killing an already dead row */ - row = *cp++ ; - COLAMD_DEBUG3 (("Kill row in pivot col: %d\n", row)) ; - KILL_ROW (row) ; - } - - /* === Select a row index to use as the new pivot row =============== */ - - pivot_row_length = pfree - pivot_row_start ; - if (pivot_row_length > 0) - { - /* pick the "pivot" row arbitrarily (first row in col) */ - pivot_row = A [Col [pivot_col].start] ; - COLAMD_DEBUG3 (("Pivotal row is %d\n", pivot_row)) ; - } - else - { - /* there is no pivot row, since it is of zero length */ - pivot_row = COLAMD_EMPTY ; - COLAMD_ASSERT (pivot_row_length == 0) ; - } - COLAMD_ASSERT (Col [pivot_col].length > 0 || pivot_row_length == 0) ; - - /* === Approximate degree computation =============================== */ - - /* Here begins the computation of the approximate degree. The column */ - /* score is the sum of the pivot row "length", plus the size of the */ - /* set differences of each row in the column minus the pattern of the */ - /* pivot row itself. The column ("thickness") itself is also */ - /* excluded from the column score (we thus use an approximate */ - /* external degree). */ - - /* The time taken by the following code (compute set differences, and */ - /* add them up) is proportional to the size of the data structure */ - /* being scanned - that is, the sum of the sizes of each column in */ - /* the pivot row. Thus, the amortized time to compute a column score */ - /* is proportional to the size of that column (where size, in this */ - /* context, is the column "length", or the number of row indices */ - /* in that column). The number of row indices in a column is */ - /* monotonically non-decreasing, from the length of the original */ - /* column on input to colamd. */ - - /* === Compute set differences ====================================== */ - - COLAMD_DEBUG3 (("** Computing set differences phase. **\n")) ; - - /* pivot row is currently dead - it will be revived later. */ - - COLAMD_DEBUG3 (("Pivot row: ")) ; - /* for each column in pivot row */ - rp = &A [pivot_row_start] ; - rp_end = rp + pivot_row_length ; - while (rp < rp_end) - { - col = *rp++ ; - COLAMD_ASSERT (COL_IS_ALIVE (col) && col != pivot_col) ; - COLAMD_DEBUG3 (("Col: %d\n", col)) ; - - /* clear tags used to construct pivot row pattern */ - col_thickness = -Col [col].shared1.thickness ; - COLAMD_ASSERT (col_thickness > 0) ; - Col [col].shared1.thickness = col_thickness ; - - /* === Remove column from degree list =========================== */ - - cur_score = Col [col].shared2.score ; - prev_col = Col [col].shared3.prev ; - next_col = Col [col].shared4.degree_next ; - COLAMD_ASSERT (cur_score >= 0) ; - COLAMD_ASSERT (cur_score <= n_col) ; - COLAMD_ASSERT (cur_score >= COLAMD_EMPTY) ; - if (prev_col == COLAMD_EMPTY) - { - head [cur_score] = next_col ; - } - else - { - Col [prev_col].shared4.degree_next = next_col ; - } - if (next_col != COLAMD_EMPTY) - { - Col [next_col].shared3.prev = prev_col ; - } - - /* === Scan the column ========================================== */ - - cp = &A [Col [col].start] ; - cp_end = cp + Col [col].length ; - while (cp < cp_end) - { - /* get a row */ - row = *cp++ ; - row_mark = Row [row].shared2.mark ; - /* skip if dead */ - if (ROW_IS_MARKED_DEAD (row_mark)) - { - continue ; - } - COLAMD_ASSERT (row != pivot_row) ; - set_difference = row_mark - tag_mark ; - /* check if the row has been seen yet */ - if (set_difference < 0) - { - COLAMD_ASSERT (Row [row].shared1.degree <= max_deg) ; - set_difference = Row [row].shared1.degree ; - } - /* subtract column thickness from this row's set difference */ - set_difference -= col_thickness ; - COLAMD_ASSERT (set_difference >= 0) ; - /* absorb this row if the set difference becomes zero */ - if (set_difference == 0) - { - COLAMD_DEBUG3 (("aggressive absorption. Row: %d\n", row)) ; - KILL_ROW (row) ; - } - else - { - /* save the new mark */ - Row [row].shared2.mark = set_difference + tag_mark ; - } - } - } - - - /* === Add up set differences for each column ======================= */ - - COLAMD_DEBUG3 (("** Adding set differences phase. **\n")) ; - - /* for each column in pivot row */ - rp = &A [pivot_row_start] ; - rp_end = rp + pivot_row_length ; - while (rp < rp_end) - { - /* get a column */ - col = *rp++ ; - COLAMD_ASSERT (COL_IS_ALIVE (col) && col != pivot_col) ; - hash = 0 ; - cur_score = 0 ; - cp = &A [Col [col].start] ; - /* compact the column */ - new_cp = cp ; - cp_end = cp + Col [col].length ; - - COLAMD_DEBUG4 (("Adding set diffs for Col: %d.\n", col)) ; - - while (cp < cp_end) - { - /* get a row */ - row = *cp++ ; - COLAMD_ASSERT(row >= 0 && row < n_row) ; - row_mark = Row [row].shared2.mark ; - /* skip if dead */ - if (ROW_IS_MARKED_DEAD (row_mark)) - { - continue ; - } - COLAMD_ASSERT (row_mark > tag_mark) ; - /* compact the column */ - *new_cp++ = row ; - /* compute hash function */ - hash += row ; - /* add set difference */ - cur_score += row_mark - tag_mark ; - /* integer overflow... */ - cur_score = COLAMD_MIN (cur_score, n_col) ; - } - - /* recompute the column's length */ - Col [col].length = (Index) (new_cp - &A [Col [col].start]) ; - - /* === Further mass elimination ================================= */ - - if (Col [col].length == 0) - { - COLAMD_DEBUG4 (("further mass elimination. Col: %d\n", col)) ; - /* nothing left but the pivot row in this column */ - KILL_PRINCIPAL_COL (col) ; - pivot_row_degree -= Col [col].shared1.thickness ; - COLAMD_ASSERT (pivot_row_degree >= 0) ; - /* order it */ - Col [col].shared2.order = k ; - /* increment order count by column thickness */ - k += Col [col].shared1.thickness ; - } - else - { - /* === Prepare for supercolumn detection ==================== */ - - COLAMD_DEBUG4 (("Preparing supercol detection for Col: %d.\n", col)) ; - - /* save score so far */ - Col [col].shared2.score = cur_score ; - - /* add column to hash table, for supercolumn detection */ - hash %= n_col + 1 ; - - COLAMD_DEBUG4 ((" Hash = %d, n_col = %d.\n", hash, n_col)) ; - COLAMD_ASSERT (hash <= n_col) ; - - head_column = head [hash] ; - if (head_column > COLAMD_EMPTY) - { - /* degree list "hash" is non-empty, use prev (shared3) of */ - /* first column in degree list as head of hash bucket */ - first_col = Col [head_column].shared3.headhash ; - Col [head_column].shared3.headhash = col ; - } - else - { - /* degree list "hash" is empty, use head as hash bucket */ - first_col = - (head_column + 2) ; - head [hash] = - (col + 2) ; - } - Col [col].shared4.hash_next = first_col ; - - /* save hash function in Col [col].shared3.hash */ - Col [col].shared3.hash = (Index) hash ; - COLAMD_ASSERT (COL_IS_ALIVE (col)) ; - } - } - - /* The approximate external column degree is now computed. */ - - /* === Supercolumn detection ======================================== */ - - COLAMD_DEBUG3 (("** Supercolumn detection phase. **\n")) ; - - Eigen::internal::detect_super_cols (Col, A, head, pivot_row_start, pivot_row_length) ; - - /* === Kill the pivotal column ====================================== */ - - KILL_PRINCIPAL_COL (pivot_col) ; - - /* === Clear mark =================================================== */ - - tag_mark += (max_deg + 1) ; - if (tag_mark >= max_mark) - { - COLAMD_DEBUG2 (("clearing tag_mark\n")) ; - tag_mark = Eigen::internal::clear_mark (n_row, Row) ; - } - - /* === Finalize the new pivot row, and column scores ================ */ - - COLAMD_DEBUG3 (("** Finalize scores phase. **\n")) ; - - /* for each column in pivot row */ - rp = &A [pivot_row_start] ; - /* compact the pivot row */ - new_rp = rp ; - rp_end = rp + pivot_row_length ; - while (rp < rp_end) - { - col = *rp++ ; - /* skip dead columns */ - if (COL_IS_DEAD (col)) - { - continue ; - } - *new_rp++ = col ; - /* add new pivot row to column */ - A [Col [col].start + (Col [col].length++)] = pivot_row ; - - /* retrieve score so far and add on pivot row's degree. */ - /* (we wait until here for this in case the pivot */ - /* row's degree was reduced due to mass elimination). */ - cur_score = Col [col].shared2.score + pivot_row_degree ; - - /* calculate the max possible score as the number of */ - /* external columns minus the 'k' value minus the */ - /* columns thickness */ - max_score = n_col - k - Col [col].shared1.thickness ; - - /* make the score the external degree of the union-of-rows */ - cur_score -= Col [col].shared1.thickness ; - - /* make sure score is less or equal than the max score */ - cur_score = COLAMD_MIN (cur_score, max_score) ; - COLAMD_ASSERT (cur_score >= 0) ; - - /* store updated score */ - Col [col].shared2.score = cur_score ; - - /* === Place column back in degree list ========================= */ - - COLAMD_ASSERT (min_score >= 0) ; - COLAMD_ASSERT (min_score <= n_col) ; - COLAMD_ASSERT (cur_score >= 0) ; - COLAMD_ASSERT (cur_score <= n_col) ; - COLAMD_ASSERT (head [cur_score] >= COLAMD_EMPTY) ; - next_col = head [cur_score] ; - Col [col].shared4.degree_next = next_col ; - Col [col].shared3.prev = COLAMD_EMPTY ; - if (next_col != COLAMD_EMPTY) - { - Col [next_col].shared3.prev = col ; - } - head [cur_score] = col ; - - /* see if this score is less than current min */ - min_score = COLAMD_MIN (min_score, cur_score) ; - - } - - /* === Resurrect the new pivot row ================================== */ - - if (pivot_row_degree > 0) - { - /* update pivot row length to reflect any cols that were killed */ - /* during super-col detection and mass elimination */ - Row [pivot_row].start = pivot_row_start ; - Row [pivot_row].length = (Index) (new_rp - &A[pivot_row_start]) ; - Row [pivot_row].shared1.degree = pivot_row_degree ; - Row [pivot_row].shared2.mark = 0 ; - /* pivot row is no longer dead */ - } - } - - /* === All principal columns have now been ordered ====================== */ - - return (ngarbage) ; -} - - -/* ========================================================================== */ -/* === order_children ======================================================= */ -/* ========================================================================== */ - -/* - The find_ordering routine has ordered all of the principal columns (the - representatives of the supercolumns). The non-principal columns have not - yet been ordered. This routine orders those columns by walking up the - parent tree (a column is a child of the column which absorbed it). The - final permutation vector is then placed in p [0 ... n_col-1], with p [0] - being the first column, and p [n_col-1] being the last. It doesn't look - like it at first glance, but be assured that this routine takes time linear - in the number of columns. Although not immediately obvious, the time - taken by this routine is O (n_col), that is, linear in the number of - columns. Not user-callable. -*/ -template <typename Index> -static inline void order_children -( - /* === Parameters ======================================================= */ - - Index n_col, /* number of columns of A */ - colamd_col<Index> Col [], /* of size n_col+1 */ - Index p [] /* p [0 ... n_col-1] is the column permutation*/ - ) -{ - /* === Local variables ================================================== */ - - Index i ; /* loop counter for all columns */ - Index c ; /* column index */ - Index parent ; /* index of column's parent */ - Index order ; /* column's order */ - - /* === Order each non-principal column ================================== */ - - for (i = 0 ; i < n_col ; i++) - { - /* find an un-ordered non-principal column */ - COLAMD_ASSERT (COL_IS_DEAD (i)) ; - if (!COL_IS_DEAD_PRINCIPAL (i) && Col [i].shared2.order == COLAMD_EMPTY) - { - parent = i ; - /* once found, find its principal parent */ - do - { - parent = Col [parent].shared1.parent ; - } while (!COL_IS_DEAD_PRINCIPAL (parent)) ; - - /* now, order all un-ordered non-principal columns along path */ - /* to this parent. collapse tree at the same time */ - c = i ; - /* get order of parent */ - order = Col [parent].shared2.order ; - - do - { - COLAMD_ASSERT (Col [c].shared2.order == COLAMD_EMPTY) ; - - /* order this column */ - Col [c].shared2.order = order++ ; - /* collaps tree */ - Col [c].shared1.parent = parent ; - - /* get immediate parent of this column */ - c = Col [c].shared1.parent ; - - /* continue until we hit an ordered column. There are */ - /* guarranteed not to be anymore unordered columns */ - /* above an ordered column */ - } while (Col [c].shared2.order == COLAMD_EMPTY) ; - - /* re-order the super_col parent to largest order for this group */ - Col [parent].shared2.order = order ; - } - } - - /* === Generate the permutation ========================================= */ - - for (c = 0 ; c < n_col ; c++) - { - p [Col [c].shared2.order] = c ; - } -} - - -/* ========================================================================== */ -/* === detect_super_cols ==================================================== */ -/* ========================================================================== */ - -/* - Detects supercolumns by finding matches between columns in the hash buckets. - Check amongst columns in the set A [row_start ... row_start + row_length-1]. - The columns under consideration are currently *not* in the degree lists, - and have already been placed in the hash buckets. - - The hash bucket for columns whose hash function is equal to h is stored - as follows: - - if head [h] is >= 0, then head [h] contains a degree list, so: - - head [h] is the first column in degree bucket h. - Col [head [h]].headhash gives the first column in hash bucket h. - - otherwise, the degree list is empty, and: - - -(head [h] + 2) is the first column in hash bucket h. - - For a column c in a hash bucket, Col [c].shared3.prev is NOT a "previous - column" pointer. Col [c].shared3.hash is used instead as the hash number - for that column. The value of Col [c].shared4.hash_next is the next column - in the same hash bucket. - - Assuming no, or "few" hash collisions, the time taken by this routine is - linear in the sum of the sizes (lengths) of each column whose score has - just been computed in the approximate degree computation. - Not user-callable. -*/ -template <typename Index> -static void detect_super_cols -( - /* === Parameters ======================================================= */ - - colamd_col<Index> Col [], /* of size n_col+1 */ - Index A [], /* row indices of A */ - Index head [], /* head of degree lists and hash buckets */ - Index row_start, /* pointer to set of columns to check */ - Index row_length /* number of columns to check */ -) -{ - /* === Local variables ================================================== */ - - Index hash ; /* hash value for a column */ - Index *rp ; /* pointer to a row */ - Index c ; /* a column index */ - Index super_c ; /* column index of the column to absorb into */ - Index *cp1 ; /* column pointer for column super_c */ - Index *cp2 ; /* column pointer for column c */ - Index length ; /* length of column super_c */ - Index prev_c ; /* column preceding c in hash bucket */ - Index i ; /* loop counter */ - Index *rp_end ; /* pointer to the end of the row */ - Index col ; /* a column index in the row to check */ - Index head_column ; /* first column in hash bucket or degree list */ - Index first_col ; /* first column in hash bucket */ - - /* === Consider each column in the row ================================== */ - - rp = &A [row_start] ; - rp_end = rp + row_length ; - while (rp < rp_end) - { - col = *rp++ ; - if (COL_IS_DEAD (col)) - { - continue ; - } - - /* get hash number for this column */ - hash = Col [col].shared3.hash ; - COLAMD_ASSERT (hash <= n_col) ; - - /* === Get the first column in this hash bucket ===================== */ - - head_column = head [hash] ; - if (head_column > COLAMD_EMPTY) - { - first_col = Col [head_column].shared3.headhash ; - } - else - { - first_col = - (head_column + 2) ; - } - - /* === Consider each column in the hash bucket ====================== */ - - for (super_c = first_col ; super_c != COLAMD_EMPTY ; - super_c = Col [super_c].shared4.hash_next) - { - COLAMD_ASSERT (COL_IS_ALIVE (super_c)) ; - COLAMD_ASSERT (Col [super_c].shared3.hash == hash) ; - length = Col [super_c].length ; - - /* prev_c is the column preceding column c in the hash bucket */ - prev_c = super_c ; - - /* === Compare super_c with all columns after it ================ */ - - for (c = Col [super_c].shared4.hash_next ; - c != COLAMD_EMPTY ; c = Col [c].shared4.hash_next) - { - COLAMD_ASSERT (c != super_c) ; - COLAMD_ASSERT (COL_IS_ALIVE (c)) ; - COLAMD_ASSERT (Col [c].shared3.hash == hash) ; - - /* not identical if lengths or scores are different */ - if (Col [c].length != length || - Col [c].shared2.score != Col [super_c].shared2.score) - { - prev_c = c ; - continue ; - } - - /* compare the two columns */ - cp1 = &A [Col [super_c].start] ; - cp2 = &A [Col [c].start] ; - - for (i = 0 ; i < length ; i++) - { - /* the columns are "clean" (no dead rows) */ - COLAMD_ASSERT (ROW_IS_ALIVE (*cp1)) ; - COLAMD_ASSERT (ROW_IS_ALIVE (*cp2)) ; - /* row indices will same order for both supercols, */ - /* no gather scatter nessasary */ - if (*cp1++ != *cp2++) - { - break ; - } - } - - /* the two columns are different if the for-loop "broke" */ - if (i != length) - { - prev_c = c ; - continue ; - } - - /* === Got it! two columns are identical =================== */ - - COLAMD_ASSERT (Col [c].shared2.score == Col [super_c].shared2.score) ; - - Col [super_c].shared1.thickness += Col [c].shared1.thickness ; - Col [c].shared1.parent = super_c ; - KILL_NON_PRINCIPAL_COL (c) ; - /* order c later, in order_children() */ - Col [c].shared2.order = COLAMD_EMPTY ; - /* remove c from hash bucket */ - Col [prev_c].shared4.hash_next = Col [c].shared4.hash_next ; - } - } - - /* === Empty this hash bucket ======================================= */ - - if (head_column > COLAMD_EMPTY) - { - /* corresponding degree list "hash" is not empty */ - Col [head_column].shared3.headhash = COLAMD_EMPTY ; - } - else - { - /* corresponding degree list "hash" is empty */ - head [hash] = COLAMD_EMPTY ; - } - } -} - - -/* ========================================================================== */ -/* === garbage_collection =================================================== */ -/* ========================================================================== */ - -/* - Defragments and compacts columns and rows in the workspace A. Used when - all avaliable memory has been used while performing row merging. Returns - the index of the first free position in A, after garbage collection. The - time taken by this routine is linear is the size of the array A, which is - itself linear in the number of nonzeros in the input matrix. - Not user-callable. -*/ -template <typename Index> -static Index garbage_collection /* returns the new value of pfree */ - ( - /* === Parameters ======================================================= */ - - Index n_row, /* number of rows */ - Index n_col, /* number of columns */ - Colamd_Row<Index> Row [], /* row info */ - colamd_col<Index> Col [], /* column info */ - Index A [], /* A [0 ... Alen-1] holds the matrix */ - Index *pfree /* &A [0] ... pfree is in use */ - ) -{ - /* === Local variables ================================================== */ - - Index *psrc ; /* source pointer */ - Index *pdest ; /* destination pointer */ - Index j ; /* counter */ - Index r ; /* a row index */ - Index c ; /* a column index */ - Index length ; /* length of a row or column */ - - /* === Defragment the columns =========================================== */ - - pdest = &A[0] ; - for (c = 0 ; c < n_col ; c++) - { - if (COL_IS_ALIVE (c)) - { - psrc = &A [Col [c].start] ; - - /* move and compact the column */ - COLAMD_ASSERT (pdest <= psrc) ; - Col [c].start = (Index) (pdest - &A [0]) ; - length = Col [c].length ; - for (j = 0 ; j < length ; j++) - { - r = *psrc++ ; - if (ROW_IS_ALIVE (r)) - { - *pdest++ = r ; - } - } - Col [c].length = (Index) (pdest - &A [Col [c].start]) ; - } - } - - /* === Prepare to defragment the rows =================================== */ - - for (r = 0 ; r < n_row ; r++) - { - if (ROW_IS_ALIVE (r)) - { - if (Row [r].length == 0) - { - /* this row is of zero length. cannot compact it, so kill it */ - COLAMD_DEBUG3 (("Defrag row kill\n")) ; - KILL_ROW (r) ; - } - else - { - /* save first column index in Row [r].shared2.first_column */ - psrc = &A [Row [r].start] ; - Row [r].shared2.first_column = *psrc ; - COLAMD_ASSERT (ROW_IS_ALIVE (r)) ; - /* flag the start of the row with the one's complement of row */ - *psrc = ONES_COMPLEMENT (r) ; - - } - } - } - - /* === Defragment the rows ============================================== */ - - psrc = pdest ; - while (psrc < pfree) - { - /* find a negative number ... the start of a row */ - if (*psrc++ < 0) - { - psrc-- ; - /* get the row index */ - r = ONES_COMPLEMENT (*psrc) ; - COLAMD_ASSERT (r >= 0 && r < n_row) ; - /* restore first column index */ - *psrc = Row [r].shared2.first_column ; - COLAMD_ASSERT (ROW_IS_ALIVE (r)) ; - - /* move and compact the row */ - COLAMD_ASSERT (pdest <= psrc) ; - Row [r].start = (Index) (pdest - &A [0]) ; - length = Row [r].length ; - for (j = 0 ; j < length ; j++) - { - c = *psrc++ ; - if (COL_IS_ALIVE (c)) - { - *pdest++ = c ; - } - } - Row [r].length = (Index) (pdest - &A [Row [r].start]) ; - - } - } - /* ensure we found all the rows */ - COLAMD_ASSERT (debug_rows == 0) ; - - /* === Return the new value of pfree ==================================== */ - - return ((Index) (pdest - &A [0])) ; -} - - -/* ========================================================================== */ -/* === clear_mark =========================================================== */ -/* ========================================================================== */ - -/* - Clears the Row [].shared2.mark array, and returns the new tag_mark. - Return value is the new tag_mark. Not user-callable. -*/ -template <typename Index> -static inline Index clear_mark /* return the new value for tag_mark */ - ( - /* === Parameters ======================================================= */ - - Index n_row, /* number of rows in A */ - Colamd_Row<Index> Row [] /* Row [0 ... n_row-1].shared2.mark is set to zero */ - ) -{ - /* === Local variables ================================================== */ - - Index r ; - - for (r = 0 ; r < n_row ; r++) - { - if (ROW_IS_ALIVE (r)) - { - Row [r].shared2.mark = 0 ; - } - } - return (1) ; -} - - -} // namespace internal -#endif diff --git a/third_party/eigen3/Eigen/src/OrderingMethods/Ordering.h b/third_party/eigen3/Eigen/src/OrderingMethods/Ordering.h deleted file mode 100644 index 4e06097849..0000000000 --- a/third_party/eigen3/Eigen/src/OrderingMethods/Ordering.h +++ /dev/null @@ -1,154 +0,0 @@ - -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_ORDERING_H -#define EIGEN_ORDERING_H - -namespace Eigen { - -#include "Eigen_Colamd.h" - -namespace internal { - -/** \internal - * \ingroup OrderingMethods_Module - * \returns the symmetric pattern A^T+A from the input matrix A. - * FIXME: The values should not be considered here - */ -template<typename MatrixType> -void ordering_helper_at_plus_a(const MatrixType& mat, MatrixType& symmat) -{ - MatrixType C; - C = mat.transpose(); // NOTE: Could be costly - for (int i = 0; i < C.rows(); i++) - { - for (typename MatrixType::InnerIterator it(C, i); it; ++it) - it.valueRef() = 0.0; - } - symmat = C + mat; -} - -} - -#ifndef EIGEN_MPL2_ONLY - -/** \ingroup OrderingMethods_Module - * \class AMDOrdering - * - * Functor computing the \em approximate \em minimum \em degree ordering - * If the matrix is not structurally symmetric, an ordering of A^T+A is computed - * \tparam Index The type of indices of the matrix - * \sa COLAMDOrdering - */ -template <typename Index> -class AMDOrdering -{ - public: - typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; - - /** Compute the permutation vector from a sparse matrix - * This routine is much faster if the input matrix is column-major - */ - template <typename MatrixType> - void operator()(const MatrixType& mat, PermutationType& perm) - { - // Compute the symmetric pattern - SparseMatrix<typename MatrixType::Scalar, ColMajor, Index> symm; - internal::ordering_helper_at_plus_a(mat,symm); - - // Call the AMD routine - //m_mat.prune(keep_diag()); - internal::minimum_degree_ordering(symm, perm); - } - - /** Compute the permutation with a selfadjoint matrix */ - template <typename SrcType, unsigned int SrcUpLo> - void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm) - { - SparseMatrix<typename SrcType::Scalar, ColMajor, Index> C; C = mat; - - // Call the AMD routine - // m_mat.prune(keep_diag()); //Remove the diagonal elements - internal::minimum_degree_ordering(C, perm); - } -}; - -#endif // EIGEN_MPL2_ONLY - -/** \ingroup OrderingMethods_Module - * \class NaturalOrdering - * - * Functor computing the natural ordering (identity) - * - * \note Returns an empty permutation matrix - * \tparam Index The type of indices of the matrix - */ -template <typename Index> -class NaturalOrdering -{ - public: - typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; - - /** Compute the permutation vector from a column-major sparse matrix */ - template <typename MatrixType> - void operator()(const MatrixType& /*mat*/, PermutationType& perm) - { - perm.resize(0); - } - -}; - -/** \ingroup OrderingMethods_Module - * \class COLAMDOrdering - * - * Functor computing the \em column \em approximate \em minimum \em degree ordering - * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()). - */ -template<typename Index> -class COLAMDOrdering -{ - public: - typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; - typedef Matrix<Index, Dynamic, 1> IndexVector; - - /** Compute the permutation vector \a perm form the sparse matrix \a mat - * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()). - */ - template <typename MatrixType> - void operator() (const MatrixType& mat, PermutationType& perm) - { - eigen_assert(mat.isCompressed() && "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to COLAMDOrdering"); - - Index m = mat.rows(); - Index n = mat.cols(); - Index nnz = mat.nonZeros(); - // Get the recommended value of Alen to be used by colamd - Index Alen = internal::colamd_recommended(nnz, m, n); - // Set the default parameters - double knobs [COLAMD_KNOBS]; - Index stats [COLAMD_STATS]; - internal::colamd_set_defaults(knobs); - - Index info; - IndexVector p(n+1), A(Alen); - for(Index i=0; i <= n; i++) p(i) = mat.outerIndexPtr()[i]; - for(Index i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i]; - // Call Colamd routine to compute the ordering - info = internal::colamd(m, n, Alen, A.data(), p.data(), knobs, stats); - eigen_assert( info && "COLAMD failed " ); - - perm.resize(n); - for (Index i = 0; i < n; i++) perm.indices()(p(i)) = i; - } -}; - -} // end namespace Eigen - -#endif diff --git a/third_party/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h b/third_party/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h deleted file mode 100644 index 8a546dc2ff..0000000000 --- a/third_party/eigen3/Eigen/src/PaStiXSupport/PaStiXSupport.h +++ /dev/null @@ -1,729 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_PASTIXSUPPORT_H -#define EIGEN_PASTIXSUPPORT_H - -namespace Eigen { - -#if defined(DCOMPLEX) - #define PASTIX_COMPLEX COMPLEX - #define PASTIX_DCOMPLEX DCOMPLEX -#else - #define PASTIX_COMPLEX std::complex<float> - #define PASTIX_DCOMPLEX std::complex<double> -#endif - -/** \ingroup PaStiXSupport_Module - * \brief Interface to the PaStix solver - * - * This class is used to solve the linear systems A.X = B via the PaStix library. - * The matrix can be either real or complex, symmetric or not. - * - * \sa TutorialSparseDirectSolvers - */ -template<typename _MatrixType, bool IsStrSym = false> class PastixLU; -template<typename _MatrixType, int Options> class PastixLLT; -template<typename _MatrixType, int Options> class PastixLDLT; - -namespace internal -{ - - template<class Pastix> struct pastix_traits; - - template<typename _MatrixType> - struct pastix_traits< PastixLU<_MatrixType> > - { - typedef _MatrixType MatrixType; - typedef typename _MatrixType::Scalar Scalar; - typedef typename _MatrixType::RealScalar RealScalar; - typedef typename _MatrixType::Index Index; - }; - - template<typename _MatrixType, int Options> - struct pastix_traits< PastixLLT<_MatrixType,Options> > - { - typedef _MatrixType MatrixType; - typedef typename _MatrixType::Scalar Scalar; - typedef typename _MatrixType::RealScalar RealScalar; - typedef typename _MatrixType::Index Index; - }; - - template<typename _MatrixType, int Options> - struct pastix_traits< PastixLDLT<_MatrixType,Options> > - { - typedef _MatrixType MatrixType; - typedef typename _MatrixType::Scalar Scalar; - typedef typename _MatrixType::RealScalar RealScalar; - typedef typename _MatrixType::Index Index; - }; - - void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm) - { - if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; } - if (nbrhs == 0) {x = NULL; nbrhs=1;} - s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); - } - - void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm) - { - if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; } - if (nbrhs == 0) {x = NULL; nbrhs=1;} - d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); - } - - void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm) - { - if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; } - if (nbrhs == 0) {x = NULL; nbrhs=1;} - c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_COMPLEX*>(x), nbrhs, iparm, dparm); - } - - void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm) - { - if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; } - if (nbrhs == 0) {x = NULL; nbrhs=1;} - z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_DCOMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_DCOMPLEX*>(x), nbrhs, iparm, dparm); - } - - // Convert the matrix to Fortran-style Numbering - template <typename MatrixType> - void c_to_fortran_numbering (MatrixType& mat) - { - if ( !(mat.outerIndexPtr()[0]) ) - { - int i; - for(i = 0; i <= mat.rows(); ++i) - ++mat.outerIndexPtr()[i]; - for(i = 0; i < mat.nonZeros(); ++i) - ++mat.innerIndexPtr()[i]; - } - } - - // Convert to C-style Numbering - template <typename MatrixType> - void fortran_to_c_numbering (MatrixType& mat) - { - // Check the Numbering - if ( mat.outerIndexPtr()[0] == 1 ) - { // Convert to C-style numbering - int i; - for(i = 0; i <= mat.rows(); ++i) - --mat.outerIndexPtr()[i]; - for(i = 0; i < mat.nonZeros(); ++i) - --mat.innerIndexPtr()[i]; - } - } -} - -// This is the base class to interface with PaStiX functions. -// Users should not used this class directly. -template <class Derived> -class PastixBase : internal::noncopyable -{ - public: - typedef typename internal::pastix_traits<Derived>::MatrixType _MatrixType; - typedef _MatrixType MatrixType; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef Matrix<Scalar,Dynamic,1> Vector; - typedef SparseMatrix<Scalar, ColMajor> ColSpMatrix; - - public: - - PastixBase() : m_initisOk(false), m_analysisIsOk(false), m_factorizationIsOk(false), m_isInitialized(false), m_pastixdata(0), m_size(0) - { - init(); - } - - ~PastixBase() - { - clean(); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::solve_retval<PastixBase, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "Pastix solver is not initialized."); - eigen_assert(rows()==b.rows() - && "PastixBase::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<PastixBase, Rhs>(*this, b.derived()); - } - - template<typename Rhs,typename Dest> - bool _solve (const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const; - - Derived& derived() - { - return *static_cast<Derived*>(this); - } - const Derived& derived() const - { - return *static_cast<const Derived*>(this); - } - - /** Returns a reference to the integer vector IPARM of PaStiX parameters - * to modify the default parameters. - * The statistics related to the different phases of factorization and solve are saved here as well - * \sa analyzePattern() factorize() - */ - Array<Index,IPARM_SIZE,1>& iparm() - { - return m_iparm; - } - - /** Return a reference to a particular index parameter of the IPARM vector - * \sa iparm() - */ - - int& iparm(int idxparam) - { - return m_iparm(idxparam); - } - - /** Returns a reference to the double vector DPARM of PaStiX parameters - * The statistics related to the different phases of factorization and solve are saved here as well - * \sa analyzePattern() factorize() - */ - Array<RealScalar,IPARM_SIZE,1>& dparm() - { - return m_dparm; - } - - - /** Return a reference to a particular index parameter of the DPARM vector - * \sa dparm() - */ - double& dparm(int idxparam) - { - return m_dparm(idxparam); - } - - inline Index cols() const { return m_size; } - inline Index rows() const { return m_size; } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the PaStiX reports a problem - * \c InvalidInput if the input matrix is invalid - * - * \sa iparm() - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_info; - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::sparse_solve_retval<PastixBase, Rhs> - solve(const SparseMatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "Pastix LU, LLT or LDLT is not initialized."); - eigen_assert(rows()==b.rows() - && "PastixBase::solve(): invalid number of rows of the right hand side matrix b"); - return internal::sparse_solve_retval<PastixBase, Rhs>(*this, b.derived()); - } - - protected: - - // Initialize the Pastix data structure, check the matrix - void init(); - - // Compute the ordering and the symbolic factorization - void analyzePattern(ColSpMatrix& mat); - - // Compute the numerical factorization - void factorize(ColSpMatrix& mat); - - // Free all the data allocated by Pastix - void clean() - { - eigen_assert(m_initisOk && "The Pastix structure should be allocated first"); - m_iparm(IPARM_START_TASK) = API_TASK_CLEAN; - m_iparm(IPARM_END_TASK) = API_TASK_CLEAN; - internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0, - m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data()); - } - - void compute(ColSpMatrix& mat); - - int m_initisOk; - int m_analysisIsOk; - int m_factorizationIsOk; - bool m_isInitialized; - mutable ComputationInfo m_info; - mutable pastix_data_t *m_pastixdata; // Data structure for pastix - mutable int m_comm; // The MPI communicator identifier - mutable Matrix<int,IPARM_SIZE,1> m_iparm; // integer vector for the input parameters - mutable Matrix<double,DPARM_SIZE,1> m_dparm; // Scalar vector for the input parameters - mutable Matrix<Index,Dynamic,1> m_perm; // Permutation vector - mutable Matrix<Index,Dynamic,1> m_invp; // Inverse permutation vector - mutable int m_size; // Size of the matrix -}; - - /** Initialize the PaStiX data structure. - *A first call to this function fills iparm and dparm with the default PaStiX parameters - * \sa iparm() dparm() - */ -template <class Derived> -void PastixBase<Derived>::init() -{ - m_size = 0; - m_iparm.setZero(IPARM_SIZE); - m_dparm.setZero(DPARM_SIZE); - - m_iparm(IPARM_MODIFY_PARAMETER) = API_NO; - pastix(&m_pastixdata, MPI_COMM_WORLD, - 0, 0, 0, 0, - 0, 0, 0, 1, m_iparm.data(), m_dparm.data()); - - m_iparm[IPARM_MATRIX_VERIFICATION] = API_NO; - m_iparm[IPARM_VERBOSE] = 2; - m_iparm[IPARM_ORDERING] = API_ORDER_SCOTCH; - m_iparm[IPARM_INCOMPLETE] = API_NO; - m_iparm[IPARM_OOC_LIMIT] = 2000; - m_iparm[IPARM_RHS_MAKING] = API_RHS_B; - m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO; - - m_iparm(IPARM_START_TASK) = API_TASK_INIT; - m_iparm(IPARM_END_TASK) = API_TASK_INIT; - internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0, - 0, 0, 0, 0, m_iparm.data(), m_dparm.data()); - - // Check the returned error - if(m_iparm(IPARM_ERROR_NUMBER)) { - m_info = InvalidInput; - m_initisOk = false; - } - else { - m_info = Success; - m_initisOk = true; - } -} - -template <class Derived> -void PastixBase<Derived>::compute(ColSpMatrix& mat) -{ - eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared"); - - analyzePattern(mat); - factorize(mat); - - m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO; - m_isInitialized = m_factorizationIsOk; -} - - -template <class Derived> -void PastixBase<Derived>::analyzePattern(ColSpMatrix& mat) -{ - eigen_assert(m_initisOk && "The initialization of PaSTiX failed"); - - // clean previous calls - if(m_size>0) - clean(); - - m_size = mat.rows(); - m_perm.resize(m_size); - m_invp.resize(m_size); - - m_iparm(IPARM_START_TASK) = API_TASK_ORDERING; - m_iparm(IPARM_END_TASK) = API_TASK_ANALYSE; - internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(), - mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data()); - - // Check the returned error - if(m_iparm(IPARM_ERROR_NUMBER)) - { - m_info = NumericalIssue; - m_analysisIsOk = false; - } - else - { - m_info = Success; - m_analysisIsOk = true; - } -} - -template <class Derived> -void PastixBase<Derived>::factorize(ColSpMatrix& mat) -{ -// if(&m_cpyMat != &mat) m_cpyMat = mat; - eigen_assert(m_analysisIsOk && "The analysis phase should be called before the factorization phase"); - m_iparm(IPARM_START_TASK) = API_TASK_NUMFACT; - m_iparm(IPARM_END_TASK) = API_TASK_NUMFACT; - m_size = mat.rows(); - - internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(), - mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data()); - - // Check the returned error - if(m_iparm(IPARM_ERROR_NUMBER)) - { - m_info = NumericalIssue; - m_factorizationIsOk = false; - m_isInitialized = false; - } - else - { - m_info = Success; - m_factorizationIsOk = true; - m_isInitialized = true; - } -} - -/* Solve the system */ -template<typename Base> -template<typename Rhs,typename Dest> -bool PastixBase<Base>::_solve (const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const -{ - eigen_assert(m_isInitialized && "The matrix should be factorized first"); - EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0, - THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); - int rhs = 1; - - x = b; /* on return, x is overwritten by the computed solution */ - - for (int i = 0; i < b.cols(); i++){ - m_iparm[IPARM_START_TASK] = API_TASK_SOLVE; - m_iparm[IPARM_END_TASK] = API_TASK_REFINE; - - internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, x.rows(), 0, 0, 0, - m_perm.data(), m_invp.data(), &x(0, i), rhs, m_iparm.data(), m_dparm.data()); - } - - // Check the returned error - m_info = m_iparm(IPARM_ERROR_NUMBER)==0 ? Success : NumericalIssue; - - return m_iparm(IPARM_ERROR_NUMBER)==0; -} - -/** \ingroup PaStiXSupport_Module - * \class PastixLU - * \brief Sparse direct LU solver based on PaStiX library - * - * This class is used to solve the linear systems A.X = B with a supernodal LU - * factorization in the PaStiX library. The matrix A should be squared and nonsingular - * PaStiX requires that the matrix A has a symmetric structural pattern. - * This interface can symmetrize the input matrix otherwise. - * The vectors or matrices X and B can be either dense or sparse. - * - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam IsStrSym Indicates if the input matrix has a symmetric pattern, default is false - * NOTE : Note that if the analysis and factorization phase are called separately, - * the input matrix will be symmetrized at each call, hence it is advised to - * symmetrize the matrix in a end-user program and set \p IsStrSym to true - * - * \sa \ref TutorialSparseDirectSolvers - * - */ -template<typename _MatrixType, bool IsStrSym> -class PastixLU : public PastixBase< PastixLU<_MatrixType> > -{ - public: - typedef _MatrixType MatrixType; - typedef PastixBase<PastixLU<MatrixType> > Base; - typedef typename Base::ColSpMatrix ColSpMatrix; - typedef typename MatrixType::Index Index; - - public: - PastixLU() : Base() - { - init(); - } - - PastixLU(const MatrixType& matrix):Base() - { - init(); - compute(matrix); - } - /** Compute the LU supernodal factorization of \p matrix. - * iparm and dparm can be used to tune the PaStiX parameters. - * see the PaStiX user's manual - * \sa analyzePattern() factorize() - */ - void compute (const MatrixType& matrix) - { - m_structureIsUptodate = false; - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::compute(temp); - } - /** Compute the LU symbolic factorization of \p matrix using its sparsity pattern. - * Several ordering methods can be used at this step. See the PaStiX user's manual. - * The result of this operation can be used with successive matrices having the same pattern as \p matrix - * \sa factorize() - */ - void analyzePattern(const MatrixType& matrix) - { - m_structureIsUptodate = false; - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::analyzePattern(temp); - } - - /** Compute the LU supernodal factorization of \p matrix - * WARNING The matrix \p matrix should have the same structural pattern - * as the same used in the analysis phase. - * \sa analyzePattern() - */ - void factorize(const MatrixType& matrix) - { - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::factorize(temp); - } - protected: - - void init() - { - m_structureIsUptodate = false; - m_iparm(IPARM_SYM) = API_SYM_NO; - m_iparm(IPARM_FACTORIZATION) = API_FACT_LU; - } - - void grabMatrix(const MatrixType& matrix, ColSpMatrix& out) - { - if(IsStrSym) - out = matrix; - else - { - if(!m_structureIsUptodate) - { - // update the transposed structure - m_transposedStructure = matrix.transpose(); - - // Set the elements of the matrix to zero - for (Index j=0; j<m_transposedStructure.outerSize(); ++j) - for(typename ColSpMatrix::InnerIterator it(m_transposedStructure, j); it; ++it) - it.valueRef() = 0.0; - - m_structureIsUptodate = true; - } - - out = m_transposedStructure + matrix; - } - internal::c_to_fortran_numbering(out); - } - - using Base::m_iparm; - using Base::m_dparm; - - ColSpMatrix m_transposedStructure; - bool m_structureIsUptodate; -}; - -/** \ingroup PaStiXSupport_Module - * \class PastixLLT - * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library - * - * This class is used to solve the linear systems A.X = B via a LL^T supernodal Cholesky factorization - * available in the PaStiX library. The matrix A should be symmetric and positive definite - * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX - * The vectors or matrices X and B can be either dense or sparse - * - * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename _MatrixType, int _UpLo> -class PastixLLT : public PastixBase< PastixLLT<_MatrixType, _UpLo> > -{ - public: - typedef _MatrixType MatrixType; - typedef PastixBase<PastixLLT<MatrixType, _UpLo> > Base; - typedef typename Base::ColSpMatrix ColSpMatrix; - - public: - enum { UpLo = _UpLo }; - PastixLLT() : Base() - { - init(); - } - - PastixLLT(const MatrixType& matrix):Base() - { - init(); - compute(matrix); - } - - /** Compute the L factor of the LL^T supernodal factorization of \p matrix - * \sa analyzePattern() factorize() - */ - void compute (const MatrixType& matrix) - { - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::compute(temp); - } - - /** Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern - * The result of this operation can be used with successive matrices having the same pattern as \p matrix - * \sa factorize() - */ - void analyzePattern(const MatrixType& matrix) - { - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::analyzePattern(temp); - } - /** Compute the LL^T supernodal numerical factorization of \p matrix - * \sa analyzePattern() - */ - void factorize(const MatrixType& matrix) - { - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::factorize(temp); - } - protected: - using Base::m_iparm; - - void init() - { - m_iparm(IPARM_SYM) = API_SYM_YES; - m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT; - } - - void grabMatrix(const MatrixType& matrix, ColSpMatrix& out) - { - // Pastix supports only lower, column-major matrices - out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>(); - internal::c_to_fortran_numbering(out); - } -}; - -/** \ingroup PaStiXSupport_Module - * \class PastixLDLT - * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library - * - * This class is used to solve the linear systems A.X = B via a LDL^T supernodal Cholesky factorization - * available in the PaStiX library. The matrix A should be symmetric and positive definite - * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX - * The vectors or matrices X and B can be either dense or sparse - * - * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename _MatrixType, int _UpLo> -class PastixLDLT : public PastixBase< PastixLDLT<_MatrixType, _UpLo> > -{ - public: - typedef _MatrixType MatrixType; - typedef PastixBase<PastixLDLT<MatrixType, _UpLo> > Base; - typedef typename Base::ColSpMatrix ColSpMatrix; - - public: - enum { UpLo = _UpLo }; - PastixLDLT():Base() - { - init(); - } - - PastixLDLT(const MatrixType& matrix):Base() - { - init(); - compute(matrix); - } - - /** Compute the L and D factors of the LDL^T factorization of \p matrix - * \sa analyzePattern() factorize() - */ - void compute (const MatrixType& matrix) - { - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::compute(temp); - } - - /** Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern - * The result of this operation can be used with successive matrices having the same pattern as \p matrix - * \sa factorize() - */ - void analyzePattern(const MatrixType& matrix) - { - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::analyzePattern(temp); - } - /** Compute the LDL^T supernodal numerical factorization of \p matrix - * - */ - void factorize(const MatrixType& matrix) - { - ColSpMatrix temp; - grabMatrix(matrix, temp); - Base::factorize(temp); - } - - protected: - using Base::m_iparm; - - void init() - { - m_iparm(IPARM_SYM) = API_SYM_YES; - m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT; - } - - void grabMatrix(const MatrixType& matrix, ColSpMatrix& out) - { - // Pastix supports only lower, column-major matrices - out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>(); - internal::c_to_fortran_numbering(out); - } -}; - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<PastixBase<_MatrixType>, Rhs> - : solve_retval_base<PastixBase<_MatrixType>, Rhs> -{ - typedef PastixBase<_MatrixType> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -template<typename _MatrixType, typename Rhs> -struct sparse_solve_retval<PastixBase<_MatrixType>, Rhs> - : sparse_solve_retval_base<PastixBase<_MatrixType>, Rhs> -{ - typedef PastixBase<_MatrixType> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - this->defaultEvalTo(dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif diff --git a/third_party/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h b/third_party/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h deleted file mode 100644 index b6571069e4..0000000000 --- a/third_party/eigen3/Eigen/src/PardisoSupport/PardisoSupport.h +++ /dev/null @@ -1,581 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL PARDISO - ******************************************************************************** -*/ - -#ifndef EIGEN_PARDISOSUPPORT_H -#define EIGEN_PARDISOSUPPORT_H - -namespace Eigen { - -template<typename _MatrixType> class PardisoLU; -template<typename _MatrixType, int Options=Upper> class PardisoLLT; -template<typename _MatrixType, int Options=Upper> class PardisoLDLT; - -namespace internal -{ - template<typename Index> - struct pardiso_run_selector - { - static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a, - Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x) - { - Index error = 0; - ::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error); - return error; - } - }; - template<> - struct pardiso_run_selector<long long int> - { - typedef long long int Index; - static Index run( _MKL_DSS_HANDLE_t pt, Index maxfct, Index mnum, Index type, Index phase, Index n, void *a, - Index *ia, Index *ja, Index *perm, Index nrhs, Index *iparm, Index msglvl, void *b, void *x) - { - Index error = 0; - ::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error); - return error; - } - }; - - template<class Pardiso> struct pardiso_traits; - - template<typename _MatrixType> - struct pardiso_traits< PardisoLU<_MatrixType> > - { - typedef _MatrixType MatrixType; - typedef typename _MatrixType::Scalar Scalar; - typedef typename _MatrixType::RealScalar RealScalar; - typedef typename _MatrixType::Index Index; - }; - - template<typename _MatrixType, int Options> - struct pardiso_traits< PardisoLLT<_MatrixType, Options> > - { - typedef _MatrixType MatrixType; - typedef typename _MatrixType::Scalar Scalar; - typedef typename _MatrixType::RealScalar RealScalar; - typedef typename _MatrixType::Index Index; - }; - - template<typename _MatrixType, int Options> - struct pardiso_traits< PardisoLDLT<_MatrixType, Options> > - { - typedef _MatrixType MatrixType; - typedef typename _MatrixType::Scalar Scalar; - typedef typename _MatrixType::RealScalar RealScalar; - typedef typename _MatrixType::Index Index; - }; - -} - -template<class Derived> -class PardisoImpl : internal::noncopyable -{ - typedef internal::pardiso_traits<Derived> Traits; - public: - typedef typename Traits::MatrixType MatrixType; - typedef typename Traits::Scalar Scalar; - typedef typename Traits::RealScalar RealScalar; - typedef typename Traits::Index Index; - typedef SparseMatrix<Scalar,RowMajor,Index> SparseMatrixType; - typedef Matrix<Scalar,Dynamic,1> VectorType; - typedef Matrix<Index, 1, MatrixType::ColsAtCompileTime> IntRowVectorType; - typedef Matrix<Index, MatrixType::RowsAtCompileTime, 1> IntColVectorType; - typedef Array<Index,64,1,DontAlign> ParameterType; - enum { - ScalarIsComplex = NumTraits<Scalar>::IsComplex - }; - - PardisoImpl() - { - eigen_assert((sizeof(Index) >= sizeof(_INTEGER_t) && sizeof(Index) <= 8) && "Non-supported index type"); - m_iparm.setZero(); - m_msglvl = 0; // No output - m_initialized = false; - } - - ~PardisoImpl() - { - pardisoRelease(); - } - - inline Index cols() const { return m_size; } - inline Index rows() const { return m_size; } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the matrix appears to be negative. - */ - ComputationInfo info() const - { - eigen_assert(m_initialized && "Decomposition is not initialized."); - return m_info; - } - - /** \warning for advanced usage only. - * \returns a reference to the parameter array controlling PARDISO. - * See the PARDISO manual to know how to use it. */ - ParameterType& pardisoParameterArray() - { - return m_iparm; - } - - /** Performs a symbolic decomposition on the sparcity of \a matrix. - * - * This function is particularly useful when solving for several problems having the same structure. - * - * \sa factorize() - */ - Derived& analyzePattern(const MatrixType& matrix); - - /** Performs a numeric decomposition of \a matrix - * - * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed. - * - * \sa analyzePattern() - */ - Derived& factorize(const MatrixType& matrix); - - Derived& compute(const MatrixType& matrix); - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::solve_retval<PardisoImpl, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_initialized && "Pardiso solver is not initialized."); - eigen_assert(rows()==b.rows() - && "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<PardisoImpl, Rhs>(*this, b.derived()); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::sparse_solve_retval<PardisoImpl, Rhs> - solve(const SparseMatrixBase<Rhs>& b) const - { - eigen_assert(m_initialized && "Pardiso solver is not initialized."); - eigen_assert(rows()==b.rows() - && "PardisoImpl::solve(): invalid number of rows of the right hand side matrix b"); - return internal::sparse_solve_retval<PardisoImpl, Rhs>(*this, b.derived()); - } - - Derived& derived() - { - return *static_cast<Derived*>(this); - } - const Derived& derived() const - { - return *static_cast<const Derived*>(this); - } - - template<typename BDerived, typename XDerived> - bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const; - - protected: - void pardisoRelease() - { - if(m_initialized) // Factorization ran at least once - { - internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, -1, m_size, 0, 0, 0, m_perm.data(), 0, - m_iparm.data(), m_msglvl, 0, 0); - } - } - - void pardisoInit(int type) - { - m_type = type; - bool symmetric = std::abs(m_type) < 10; - m_iparm[0] = 1; // No solver default - m_iparm[1] = 3; // use Metis for the ordering - m_iparm[2] = 1; // Numbers of processors, value of OMP_NUM_THREADS - m_iparm[3] = 0; // No iterative-direct algorithm - m_iparm[4] = 0; // No user fill-in reducing permutation - m_iparm[5] = 0; // Write solution into x - m_iparm[6] = 0; // Not in use - m_iparm[7] = 2; // Max numbers of iterative refinement steps - m_iparm[8] = 0; // Not in use - m_iparm[9] = 13; // Perturb the pivot elements with 1E-13 - m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS - m_iparm[11] = 0; // Not in use - m_iparm[12] = symmetric ? 0 : 1; // Maximum weighted matching algorithm is switched-off (default for symmetric). - // Try m_iparm[12] = 1 in case of inappropriate accuracy - m_iparm[13] = 0; // Output: Number of perturbed pivots - m_iparm[14] = 0; // Not in use - m_iparm[15] = 0; // Not in use - m_iparm[16] = 0; // Not in use - m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU - m_iparm[18] = -1; // Output: Mflops for LU factorization - m_iparm[19] = 0; // Output: Numbers of CG Iterations - - m_iparm[20] = 0; // 1x1 pivoting - m_iparm[26] = 0; // No matrix checker - m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0; - m_iparm[34] = 1; // C indexing - m_iparm[59] = 1; // Automatic switch between In-Core and Out-of-Core modes - } - - protected: - // cached data to reduce reallocation, etc. - - void manageErrorCode(Index error) - { - switch(error) - { - case 0: - m_info = Success; - break; - case -4: - case -7: - m_info = NumericalIssue; - break; - default: - m_info = InvalidInput; - } - } - - mutable SparseMatrixType m_matrix; - ComputationInfo m_info; - bool m_initialized, m_analysisIsOk, m_factorizationIsOk; - Index m_type, m_msglvl; - mutable void *m_pt[64]; - mutable ParameterType m_iparm; - mutable IntColVectorType m_perm; - Index m_size; - -}; - -template<class Derived> -Derived& PardisoImpl<Derived>::compute(const MatrixType& a) -{ - m_size = a.rows(); - eigen_assert(a.rows() == a.cols()); - - pardisoRelease(); - memset(m_pt, 0, sizeof(m_pt)); - m_perm.setZero(m_size); - derived().getMatrix(a); - - Index error; - error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 12, m_size, - m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), - m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); - - manageErrorCode(error); - m_analysisIsOk = true; - m_factorizationIsOk = true; - m_initialized = true; - return derived(); -} - -template<class Derived> -Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a) -{ - m_size = a.rows(); - eigen_assert(m_size == a.cols()); - - pardisoRelease(); - memset(m_pt, 0, sizeof(m_pt)); - m_perm.setZero(m_size); - derived().getMatrix(a); - - Index error; - error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 11, m_size, - m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), - m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); - - manageErrorCode(error); - m_analysisIsOk = true; - m_factorizationIsOk = false; - m_initialized = true; - return derived(); -} - -template<class Derived> -Derived& PardisoImpl<Derived>::factorize(const MatrixType& a) -{ - eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); - eigen_assert(m_size == a.rows() && m_size == a.cols()); - - derived().getMatrix(a); - - Index error; - error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 22, m_size, - m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), - m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL); - - manageErrorCode(error); - m_factorizationIsOk = true; - return derived(); -} - -template<class Base> -template<typename BDerived,typename XDerived> -bool PardisoImpl<Base>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const -{ - if(m_iparm[0] == 0) // Factorization was not computed - return false; - - //Index n = m_matrix.rows(); - Index nrhs = Index(b.cols()); - eigen_assert(m_size==b.rows()); - eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported"); - eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported"); - eigen_assert(((nrhs == 1) || b.outerStride() == b.rows())); - - -// switch (transposed) { -// case SvNoTrans : m_iparm[11] = 0 ; break; -// case SvTranspose : m_iparm[11] = 2 ; break; -// case SvAdjoint : m_iparm[11] = 1 ; break; -// default: -// //std::cerr << "Eigen: transposition option \"" << transposed << "\" not supported by the PARDISO backend\n"; -// m_iparm[11] = 0; -// } - - Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data()); - Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp; - - // Pardiso cannot solve in-place - if(rhs_ptr == x.derived().data()) - { - tmp = b; - rhs_ptr = tmp.data(); - } - - Index error; - error = internal::pardiso_run_selector<Index>::run(m_pt, 1, 1, m_type, 33, m_size, - m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(), - m_perm.data(), nrhs, m_iparm.data(), m_msglvl, - rhs_ptr, x.derived().data()); - - return error==0; -} - - -/** \ingroup PardisoSupport_Module - * \class PardisoLU - * \brief A sparse direct LU factorization and solver based on the PARDISO library - * - * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization - * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible. - * The vectors or matrices X and B can be either dense or sparse. - * - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename MatrixType> -class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> > -{ - protected: - typedef PardisoImpl< PardisoLU<MatrixType> > Base; - typedef typename Base::Scalar Scalar; - typedef typename Base::RealScalar RealScalar; - using Base::pardisoInit; - using Base::m_matrix; - friend class PardisoImpl< PardisoLU<MatrixType> >; - - public: - - using Base::compute; - using Base::solve; - - PardisoLU() - : Base() - { - pardisoInit(Base::ScalarIsComplex ? 13 : 11); - } - - PardisoLU(const MatrixType& matrix) - : Base() - { - pardisoInit(Base::ScalarIsComplex ? 13 : 11); - compute(matrix); - } - protected: - void getMatrix(const MatrixType& matrix) - { - m_matrix = matrix; - } -}; - -/** \ingroup PardisoSupport_Module - * \class PardisoLLT - * \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library - * - * This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization - * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite. - * The vectors or matrices X and B can be either dense or sparse. - * - * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used. - * Upper|Lower can be used to tell both triangular parts can be used as input. - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename MatrixType, int _UpLo> -class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,_UpLo> > -{ - protected: - typedef PardisoImpl< PardisoLLT<MatrixType,_UpLo> > Base; - typedef typename Base::Scalar Scalar; - typedef typename Base::Index Index; - typedef typename Base::RealScalar RealScalar; - using Base::pardisoInit; - using Base::m_matrix; - friend class PardisoImpl< PardisoLLT<MatrixType,_UpLo> >; - - public: - - enum { UpLo = _UpLo }; - using Base::compute; - using Base::solve; - - PardisoLLT() - : Base() - { - pardisoInit(Base::ScalarIsComplex ? 4 : 2); - } - - PardisoLLT(const MatrixType& matrix) - : Base() - { - pardisoInit(Base::ScalarIsComplex ? 4 : 2); - compute(matrix); - } - - protected: - - void getMatrix(const MatrixType& matrix) - { - // PARDISO supports only upper, row-major matrices - PermutationMatrix<Dynamic,Dynamic,Index> p_null; - m_matrix.resize(matrix.rows(), matrix.cols()); - m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null); - } -}; - -/** \ingroup PardisoSupport_Module - * \class PardisoLDLT - * \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library - * - * This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization - * using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite. - * For complex matrices, A can also be symmetric only, see the \a Options template parameter. - * The vectors or matrices X and B can be either dense or sparse. - * - * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used. - * Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix. - * Upper|Lower can be used to tell both triangular parts can be used as input. - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename MatrixType, int Options> -class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> > -{ - protected: - typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base; - typedef typename Base::Scalar Scalar; - typedef typename Base::Index Index; - typedef typename Base::RealScalar RealScalar; - using Base::pardisoInit; - using Base::m_matrix; - friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >; - - public: - - using Base::compute; - using Base::solve; - enum { UpLo = Options&(Upper|Lower) }; - - PardisoLDLT() - : Base() - { - pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2); - } - - PardisoLDLT(const MatrixType& matrix) - : Base() - { - pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2); - compute(matrix); - } - - void getMatrix(const MatrixType& matrix) - { - // PARDISO supports only upper, row-major matrices - PermutationMatrix<Dynamic,Dynamic,Index> p_null; - m_matrix.resize(matrix.rows(), matrix.cols()); - m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null); - } -}; - -namespace internal { - -template<typename _Derived, typename Rhs> -struct solve_retval<PardisoImpl<_Derived>, Rhs> - : solve_retval_base<PardisoImpl<_Derived>, Rhs> -{ - typedef PardisoImpl<_Derived> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -template<typename Derived, typename Rhs> -struct sparse_solve_retval<PardisoImpl<Derived>, Rhs> - : sparse_solve_retval_base<PardisoImpl<Derived>, Rhs> -{ - typedef PardisoImpl<Derived> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - this->defaultEvalTo(dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_PARDISOSUPPORT_H diff --git a/third_party/eigen3/Eigen/src/QR/ColPivHouseholderQR.h b/third_party/eigen3/Eigen/src/QR/ColPivHouseholderQR.h deleted file mode 100644 index 4824880f51..0000000000 --- a/third_party/eigen3/Eigen/src/QR/ColPivHouseholderQR.h +++ /dev/null @@ -1,582 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_H -#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_H - -namespace Eigen { - -/** \ingroup QR_Module - * - * \class ColPivHouseholderQR - * - * \brief Householder rank-revealing QR decomposition of a matrix with column-pivoting - * - * \param MatrixType the type of the matrix of which we are computing the QR decomposition - * - * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b Q and \b R - * such that - * \f[ - * \mathbf{A} \, \mathbf{P} = \mathbf{Q} \, \mathbf{R} - * \f] - * by using Householder transformations. Here, \b P is a permutation matrix, \b Q a unitary matrix and \b R an - * upper triangular matrix. - * - * This decomposition performs column pivoting in order to be rank-revealing and improve - * numerical stability. It is slower than HouseholderQR, and faster than FullPivHouseholderQR. - * - * \sa MatrixBase::colPivHouseholderQr() - */ -template<typename _MatrixType> class ColPivHouseholderQR -{ - public: - - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, Options, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType; - typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType; - typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType; - typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType; - typedef typename internal::plain_row_type<MatrixType>::type RowVectorType; - typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType; - typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType; - - private: - - typedef typename PermutationType::Index PermIndexType; - - public: - - /** - * \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via ColPivHouseholderQR::compute(const MatrixType&). - */ - ColPivHouseholderQR() - : m_qr(), - m_hCoeffs(), - m_colsPermutation(), - m_colsTranspositions(), - m_temp(), - m_colSqNorms(), - m_isInitialized(false), - m_usePrescribedThreshold(false) {} - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa ColPivHouseholderQR() - */ - ColPivHouseholderQR(Index rows, Index cols) - : m_qr(rows, cols), - m_hCoeffs((std::min)(rows,cols)), - m_colsPermutation(PermIndexType(cols)), - m_colsTranspositions(cols), - m_temp(cols), - m_colSqNorms(cols), - m_isInitialized(false), - m_usePrescribedThreshold(false) {} - - /** \brief Constructs a QR factorization from a given matrix - * - * This constructor computes the QR factorization of the matrix \a matrix by calling - * the method compute(). It is a short cut for: - * - * \code - * ColPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols()); - * qr.compute(matrix); - * \endcode - * - * \sa compute() - */ - ColPivHouseholderQR(const MatrixType& matrix) - : m_qr(matrix.rows(), matrix.cols()), - m_hCoeffs((std::min)(matrix.rows(),matrix.cols())), - m_colsPermutation(PermIndexType(matrix.cols())), - m_colsTranspositions(matrix.cols()), - m_temp(matrix.cols()), - m_colSqNorms(matrix.cols()), - m_isInitialized(false), - m_usePrescribedThreshold(false) - { - compute(matrix); - } - - /** This method finds a solution x to the equation Ax=b, where A is the matrix of which - * *this is the QR decomposition, if any exists. - * - * \param b the right-hand-side of the equation to solve. - * - * \returns a solution. - * - * \note The case where b is a matrix is not yet implemented. Also, this - * code is space inefficient. - * - * \note_about_checking_solutions - * - * \note_about_arbitrary_choice_of_solution - * - * Example: \include ColPivHouseholderQR_solve.cpp - * Output: \verbinclude ColPivHouseholderQR_solve.out - */ - template<typename Rhs> - inline const internal::solve_retval<ColPivHouseholderQR, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return internal::solve_retval<ColPivHouseholderQR, Rhs>(*this, b.derived()); - } - - HouseholderSequenceType householderQ(void) const; - HouseholderSequenceType matrixQ(void) const - { - return householderQ(); - } - - /** \returns a reference to the matrix where the Householder QR decomposition is stored - */ - const MatrixType& matrixQR() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return m_qr; - } - - /** \returns a reference to the matrix where the result Householder QR is stored - * \warning The strict lower part of this matrix contains internal values. - * Only the upper triangular part should be referenced. To get it, use - * \code matrixR().template triangularView<Upper>() \endcode - * For rank-deficient matrices, use - * \code - * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>() - * \endcode - */ - const MatrixType& matrixR() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return m_qr; - } - - ColPivHouseholderQR& compute(const MatrixType& matrix); - - /** \returns a const reference to the column permutation matrix */ - const PermutationType& colsPermutation() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return m_colsPermutation; - } - - /** \returns the absolute value of the determinant of the matrix of which - * *this is the QR decomposition. It has only linear complexity - * (that is, O(n) where n is the dimension of the square matrix) - * as the QR decomposition has already been computed. - * - * \note This is only for square matrices. - * - * \warning a determinant can be very big or small, so for matrices - * of large enough dimension, there is a risk of overflow/underflow. - * One way to work around that is to use logAbsDeterminant() instead. - * - * \sa logAbsDeterminant(), MatrixBase::determinant() - */ - typename MatrixType::RealScalar absDeterminant() const; - - /** \returns the natural log of the absolute value of the determinant of the matrix of which - * *this is the QR decomposition. It has only linear complexity - * (that is, O(n) where n is the dimension of the square matrix) - * as the QR decomposition has already been computed. - * - * \note This is only for square matrices. - * - * \note This method is useful to work around the risk of overflow/underflow that's inherent - * to determinant computation. - * - * \sa absDeterminant(), MatrixBase::determinant() - */ - typename MatrixType::RealScalar logAbsDeterminant() const; - - /** \returns the rank of the matrix of which *this is the QR decomposition. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline Index rank() const - { - using std::abs; - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold(); - Index result = 0; - for(Index i = 0; i < m_nonzero_pivots; ++i) - result += (abs(m_qr.coeff(i,i)) > premultiplied_threshold); - return result; - } - - /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline Index dimensionOfKernel() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return cols() - rank(); - } - - /** \returns true if the matrix of which *this is the QR decomposition represents an injective - * linear map, i.e. has trivial kernel; false otherwise. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isInjective() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return rank() == cols(); - } - - /** \returns true if the matrix of which *this is the QR decomposition represents a surjective - * linear map; false otherwise. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isSurjective() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return rank() == rows(); - } - - /** \returns true if the matrix of which *this is the QR decomposition is invertible. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isInvertible() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return isInjective() && isSurjective(); - } - - /** \returns the inverse of the matrix of which *this is the QR decomposition. - * - * \note If this matrix is not invertible, the returned matrix has undefined coefficients. - * Use isInvertible() to first determine whether this matrix is invertible. - */ - inline const - internal::solve_retval<ColPivHouseholderQR, typename MatrixType::IdentityReturnType> - inverse() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return internal::solve_retval<ColPivHouseholderQR,typename MatrixType::IdentityReturnType> - (*this, MatrixType::Identity(m_qr.rows(), m_qr.cols())); - } - - inline Index rows() const { return m_qr.rows(); } - inline Index cols() const { return m_qr.cols(); } - - /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q. - * - * For advanced uses only. - */ - const HCoeffsType& hCoeffs() const { return m_hCoeffs; } - - /** Allows to prescribe a threshold to be used by certain methods, such as rank(), - * who need to determine when pivots are to be considered nonzero. This is not used for the - * QR decomposition itself. - * - * When it needs to get the threshold value, Eigen calls threshold(). By default, this - * uses a formula to automatically determine a reasonable threshold. - * Once you have called the present method setThreshold(const RealScalar&), - * your value is used instead. - * - * \param threshold The new value to use as the threshold. - * - * A pivot will be considered nonzero if its absolute value is strictly greater than - * \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$ - * where maxpivot is the biggest pivot. - * - * If you want to come back to the default behavior, call setThreshold(Default_t) - */ - ColPivHouseholderQR& setThreshold(const RealScalar& threshold) - { - m_usePrescribedThreshold = true; - m_prescribedThreshold = threshold; - return *this; - } - - /** Allows to come back to the default behavior, letting Eigen use its default formula for - * determining the threshold. - * - * You should pass the special object Eigen::Default as parameter here. - * \code qr.setThreshold(Eigen::Default); \endcode - * - * See the documentation of setThreshold(const RealScalar&). - */ - ColPivHouseholderQR& setThreshold(Default_t) - { - m_usePrescribedThreshold = false; - return *this; - } - - /** Returns the threshold that will be used by certain methods such as rank(). - * - * See the documentation of setThreshold(const RealScalar&). - */ - RealScalar threshold() const - { - eigen_assert(m_isInitialized || m_usePrescribedThreshold); - return m_usePrescribedThreshold ? m_prescribedThreshold - // this formula comes from experimenting (see "LU precision tuning" thread on the list) - // and turns out to be identical to Higham's formula used already in LDLt. - : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize()); - } - - /** \returns the number of nonzero pivots in the QR decomposition. - * Here nonzero is meant in the exact sense, not in a fuzzy sense. - * So that notion isn't really intrinsically interesting, but it is - * still useful when implementing algorithms. - * - * \sa rank() - */ - inline Index nonzeroPivots() const - { - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return m_nonzero_pivots; - } - - /** \returns the absolute value of the biggest pivot, i.e. the biggest - * diagonal coefficient of R. - */ - RealScalar maxPivot() const { return m_maxpivot; } - - /** \brief Reports whether the QR factorization was succesful. - * - * \note This function always returns \c Success. It is provided for compatibility - * with other factorization routines. - * \returns \c Success - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return Success; - } - - protected: - MatrixType m_qr; - HCoeffsType m_hCoeffs; - PermutationType m_colsPermutation; - IntRowVectorType m_colsTranspositions; - RowVectorType m_temp; - RealRowVectorType m_colSqNorms; - bool m_isInitialized, m_usePrescribedThreshold; - RealScalar m_prescribedThreshold, m_maxpivot; - Index m_nonzero_pivots; - Index m_det_pq; -}; - -template<typename MatrixType> -typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::absDeterminant() const -{ - using std::abs; - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!"); - return abs(m_qr.diagonal().prod()); -} - -template<typename MatrixType> -typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::logAbsDeterminant() const -{ - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!"); - return m_qr.diagonal().cwiseAbs().array().log().sum(); -} - -/** Performs the QR factorization of the given matrix \a matrix. The result of - * the factorization is stored into \c *this, and a reference to \c *this - * is returned. - * - * \sa class ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&) - */ -template<typename MatrixType> -ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix) -{ - using std::abs; - Index rows = matrix.rows(); - Index cols = matrix.cols(); - Index size = matrix.diagonalSize(); - - // the column permutation is stored as int indices, so just to be sure: - eigen_assert(cols<=NumTraits<int>::highest()); - - m_qr = matrix; - m_hCoeffs.resize(size); - - m_temp.resize(cols); - - m_colsTranspositions.resize(matrix.cols()); - Index number_of_transpositions = 0; - - m_colSqNorms.resize(cols); - for(Index k = 0; k < cols; ++k) - m_colSqNorms.coeffRef(k) = m_qr.col(k).squaredNorm(); - - RealScalar threshold_helper = m_colSqNorms.maxCoeff() * numext::abs2(NumTraits<Scalar>::epsilon()) / RealScalar(rows); - - m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case) - m_maxpivot = RealScalar(0); - - for(Index k = 0; k < size; ++k) - { - // first, we look up in our table m_colSqNorms which column has the biggest squared norm - Index biggest_col_index; - RealScalar biggest_col_sq_norm = m_colSqNorms.tail(cols-k).maxCoeff(&biggest_col_index); - biggest_col_index += k; - - // since our table m_colSqNorms accumulates imprecision at every step, we must now recompute - // the actual squared norm of the selected column. - // Note that not doing so does result in solve() sometimes returning inf/nan values - // when running the unit test with 1000 repetitions. - biggest_col_sq_norm = m_qr.col(biggest_col_index).tail(rows-k).squaredNorm(); - - // we store that back into our table: it can't hurt to correct our table. - m_colSqNorms.coeffRef(biggest_col_index) = biggest_col_sq_norm; - - // if the current biggest column is smaller than epsilon times the initial biggest column, - // terminate to avoid generating nan/inf values. - // Note that here, if we test instead for "biggest == 0", we get a failure every 1000 (or so) - // repetitions of the unit test, with the result of solve() filled with large values of the order - // of 1/(size*epsilon). - if(biggest_col_sq_norm < threshold_helper * RealScalar(rows-k)) - { - m_nonzero_pivots = k; - m_hCoeffs.tail(size-k).setZero(); - m_qr.bottomRightCorner(rows-k,cols-k) - .template triangularView<StrictlyLower>() - .setZero(); - break; - } - - // apply the transposition to the columns - m_colsTranspositions.coeffRef(k) = biggest_col_index; - if(k != biggest_col_index) { - m_qr.col(k).swap(m_qr.col(biggest_col_index)); - std::swap(m_colSqNorms.coeffRef(k), m_colSqNorms.coeffRef(biggest_col_index)); - ++number_of_transpositions; - } - - // generate the householder vector, store it below the diagonal - RealScalar beta; - m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta); - - // apply the householder transformation to the diagonal coefficient - m_qr.coeffRef(k,k) = beta; - - // remember the maximum absolute value of diagonal coefficients - if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta); - - // apply the householder transformation - m_qr.bottomRightCorner(rows-k, cols-k-1) - .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1)); - - // update our table of squared norms of the columns - m_colSqNorms.tail(cols-k-1) -= m_qr.row(k).tail(cols-k-1).cwiseAbs2(); - } - - m_colsPermutation.setIdentity(PermIndexType(cols)); - for(PermIndexType k = 0; k < m_nonzero_pivots; ++k) - m_colsPermutation.applyTranspositionOnTheRight(k, PermIndexType(m_colsTranspositions.coeff(k))); - - m_det_pq = (number_of_transpositions%2) ? -1 : 1; - m_isInitialized = true; - - return *this; -} - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<ColPivHouseholderQR<_MatrixType>, Rhs> - : solve_retval_base<ColPivHouseholderQR<_MatrixType>, Rhs> -{ - EIGEN_MAKE_SOLVE_HELPERS(ColPivHouseholderQR<_MatrixType>,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - eigen_assert(rhs().rows() == dec().rows()); - - const Index cols = dec().cols(), - nonzero_pivots = dec().nonzeroPivots(); - - if(nonzero_pivots == 0) - { - dst.setZero(); - return; - } - - typename Rhs::PlainObject c(rhs()); - - // Note that the matrix Q = H_0^* H_1^*... so its inverse is Q^* = (H_0 H_1 ...)^T - c.applyOnTheLeft(householderSequence(dec().matrixQR(), dec().hCoeffs()) - .setLength(dec().nonzeroPivots()) - .transpose() - ); - - dec().matrixR() - .topLeftCorner(nonzero_pivots, nonzero_pivots) - .template triangularView<Upper>() - .solveInPlace(c.topRows(nonzero_pivots)); - - for(Index i = 0; i < nonzero_pivots; ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i); - for(Index i = nonzero_pivots; i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero(); - } -}; - -} // end namespace internal - -/** \returns the matrix Q as a sequence of householder transformations */ -template<typename MatrixType> -typename ColPivHouseholderQR<MatrixType>::HouseholderSequenceType ColPivHouseholderQR<MatrixType> - ::householderQ() const -{ - eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized."); - return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate()).setLength(m_nonzero_pivots); -} - -#ifndef __CUDACC__ -/** \return the column-pivoting Householder QR decomposition of \c *this. - * - * \sa class ColPivHouseholderQR - */ -template<typename Derived> -const ColPivHouseholderQR<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::colPivHouseholderQr() const -{ - return ColPivHouseholderQR<PlainObject>(eval()); -} -#endif // __CUDACC__ - -} // end namespace Eigen - -#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_H diff --git a/third_party/eigen3/Eigen/src/QR/ColPivHouseholderQR_MKL.h b/third_party/eigen3/Eigen/src/QR/ColPivHouseholderQR_MKL.h deleted file mode 100644 index b5b1983265..0000000000 --- a/third_party/eigen3/Eigen/src/QR/ColPivHouseholderQR_MKL.h +++ /dev/null @@ -1,99 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Householder QR decomposition of a matrix with column pivoting based on - * LAPACKE_?geqp3 function. - ******************************************************************************** -*/ - -#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H -#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H - -#include "Eigen/src/Core/util/MKL_support.h" - -namespace Eigen { - -/** \internal Specialization for the data types supported by MKL */ - -#define EIGEN_MKL_QR_COLPIV(EIGTYPE, MKLTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \ -template<> inline \ -ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >& \ -ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >::compute( \ - const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix) \ -\ -{ \ - using std::abs; \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \ - typedef MatrixType::Scalar Scalar; \ - typedef MatrixType::RealScalar RealScalar; \ - Index rows = matrix.rows();\ - Index cols = matrix.cols();\ - Index size = matrix.diagonalSize();\ -\ - m_qr = matrix;\ - m_hCoeffs.resize(size);\ -\ - m_colsTranspositions.resize(cols);\ - /*Index number_of_transpositions = 0;*/ \ -\ - m_nonzero_pivots = 0; \ - m_maxpivot = RealScalar(0);\ - m_colsPermutation.resize(cols); \ - m_colsPermutation.indices().setZero(); \ -\ - lapack_int lda = m_qr.outerStride(), i; \ - lapack_int matrix_order = MKLCOLROW; \ - LAPACKE_##MKLPREFIX##geqp3( matrix_order, rows, cols, (MKLTYPE*)m_qr.data(), lda, (lapack_int*)m_colsPermutation.indices().data(), (MKLTYPE*)m_hCoeffs.data()); \ - m_isInitialized = true; \ - m_maxpivot=m_qr.diagonal().cwiseAbs().maxCoeff(); \ - m_hCoeffs.adjointInPlace(); \ - RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold(); \ - lapack_int *perm = m_colsPermutation.indices().data(); \ - for(i=0;i<size;i++) { \ - m_nonzero_pivots += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);\ - } \ - for(i=0;i<cols;i++) perm[i]--;\ -\ - /*m_det_pq = (number_of_transpositions%2) ? -1 : 1; // TODO: It's not needed now; fix upon availability in Eigen */ \ -\ - return *this; \ -} - -EIGEN_MKL_QR_COLPIV(double, double, d, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_QR_COLPIV(float, float, s, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_QR_COLPIV(dcomplex, MKL_Complex16, z, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_QR_COLPIV(scomplex, MKL_Complex8, c, ColMajor, LAPACK_COL_MAJOR) - -EIGEN_MKL_QR_COLPIV(double, double, d, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_QR_COLPIV(float, float, s, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_QR_COLPIV(dcomplex, MKL_Complex16, z, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_QR_COLPIV(scomplex, MKL_Complex8, c, RowMajor, LAPACK_ROW_MAJOR) - -} // end namespace Eigen - -#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_MKL_H diff --git a/third_party/eigen3/Eigen/src/QR/FullPivHouseholderQR.h b/third_party/eigen3/Eigen/src/QR/FullPivHouseholderQR.h deleted file mode 100644 index a7b0fc16f3..0000000000 --- a/third_party/eigen3/Eigen/src/QR/FullPivHouseholderQR.h +++ /dev/null @@ -1,616 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H -#define EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H - -namespace Eigen { - -namespace internal { - -template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType; - -template<typename MatrixType> -struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType> > -{ - typedef typename MatrixType::PlainObject ReturnType; -}; - -} - -/** \ingroup QR_Module - * - * \class FullPivHouseholderQR - * - * \brief Householder rank-revealing QR decomposition of a matrix with full pivoting - * - * \param MatrixType the type of the matrix of which we are computing the QR decomposition - * - * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b P', \b Q and \b R - * such that - * \f[ - * \mathbf{P} \, \mathbf{A} \, \mathbf{P}' = \mathbf{Q} \, \mathbf{R} - * \f] - * by using Householder transformations. Here, \b P and \b P' are permutation matrices, \b Q a unitary matrix - * and \b R an upper triangular matrix. - * - * This decomposition performs a very prudent full pivoting in order to be rank-revealing and achieve optimal - * numerical stability. The trade-off is that it is slower than HouseholderQR and ColPivHouseholderQR. - * - * \sa MatrixBase::fullPivHouseholderQr() - */ -template<typename _MatrixType> class FullPivHouseholderQR -{ - public: - - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType> MatrixQReturnType; - typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType; - typedef Matrix<Index, 1, - EIGEN_SIZE_MIN_PREFER_DYNAMIC(ColsAtCompileTime,RowsAtCompileTime), RowMajor, 1, - EIGEN_SIZE_MIN_PREFER_FIXED(MaxColsAtCompileTime,MaxRowsAtCompileTime)> IntDiagSizeVectorType; - typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType; - typedef typename internal::plain_row_type<MatrixType>::type RowVectorType; - typedef typename internal::plain_col_type<MatrixType>::type ColVectorType; - - /** \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via FullPivHouseholderQR::compute(const MatrixType&). - */ - FullPivHouseholderQR() - : m_qr(), - m_hCoeffs(), - m_rows_transpositions(), - m_cols_transpositions(), - m_cols_permutation(), - m_temp(), - m_isInitialized(false), - m_usePrescribedThreshold(false) {} - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa FullPivHouseholderQR() - */ - FullPivHouseholderQR(Index rows, Index cols) - : m_qr(rows, cols), - m_hCoeffs((std::min)(rows,cols)), - m_rows_transpositions((std::min)(rows,cols)), - m_cols_transpositions((std::min)(rows,cols)), - m_cols_permutation(cols), - m_temp(cols), - m_isInitialized(false), - m_usePrescribedThreshold(false) {} - - /** \brief Constructs a QR factorization from a given matrix - * - * This constructor computes the QR factorization of the matrix \a matrix by calling - * the method compute(). It is a short cut for: - * - * \code - * FullPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols()); - * qr.compute(matrix); - * \endcode - * - * \sa compute() - */ - FullPivHouseholderQR(const MatrixType& matrix) - : m_qr(matrix.rows(), matrix.cols()), - m_hCoeffs((std::min)(matrix.rows(), matrix.cols())), - m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())), - m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())), - m_cols_permutation(matrix.cols()), - m_temp(matrix.cols()), - m_isInitialized(false), - m_usePrescribedThreshold(false) - { - compute(matrix); - } - - /** This method finds a solution x to the equation Ax=b, where A is the matrix of which - * \c *this is the QR decomposition. - * - * \param b the right-hand-side of the equation to solve. - * - * \returns the exact or least-square solution if the rank is greater or equal to the number of columns of A, - * and an arbitrary solution otherwise. - * - * \note The case where b is a matrix is not yet implemented. Also, this - * code is space inefficient. - * - * \note_about_checking_solutions - * - * \note_about_arbitrary_choice_of_solution - * - * Example: \include FullPivHouseholderQR_solve.cpp - * Output: \verbinclude FullPivHouseholderQR_solve.out - */ - template<typename Rhs> - inline const internal::solve_retval<FullPivHouseholderQR, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return internal::solve_retval<FullPivHouseholderQR, Rhs>(*this, b.derived()); - } - - /** \returns Expression object representing the matrix Q - */ - MatrixQReturnType matrixQ(void) const; - - /** \returns a reference to the matrix where the Householder QR decomposition is stored - */ - const MatrixType& matrixQR() const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return m_qr; - } - - FullPivHouseholderQR& compute(const MatrixType& matrix); - - /** \returns a const reference to the column permutation matrix */ - const PermutationType& colsPermutation() const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return m_cols_permutation; - } - - /** \returns a const reference to the vector of indices representing the rows transpositions */ - const IntDiagSizeVectorType& rowsTranspositions() const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return m_rows_transpositions; - } - - /** \returns the absolute value of the determinant of the matrix of which - * *this is the QR decomposition. It has only linear complexity - * (that is, O(n) where n is the dimension of the square matrix) - * as the QR decomposition has already been computed. - * - * \note This is only for square matrices. - * - * \warning a determinant can be very big or small, so for matrices - * of large enough dimension, there is a risk of overflow/underflow. - * One way to work around that is to use logAbsDeterminant() instead. - * - * \sa logAbsDeterminant(), MatrixBase::determinant() - */ - typename MatrixType::RealScalar absDeterminant() const; - - /** \returns the natural log of the absolute value of the determinant of the matrix of which - * *this is the QR decomposition. It has only linear complexity - * (that is, O(n) where n is the dimension of the square matrix) - * as the QR decomposition has already been computed. - * - * \note This is only for square matrices. - * - * \note This method is useful to work around the risk of overflow/underflow that's inherent - * to determinant computation. - * - * \sa absDeterminant(), MatrixBase::determinant() - */ - typename MatrixType::RealScalar logAbsDeterminant() const; - - /** \returns the rank of the matrix of which *this is the QR decomposition. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline Index rank() const - { - using std::abs; - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold(); - Index result = 0; - for(Index i = 0; i < m_nonzero_pivots; ++i) - result += (abs(m_qr.coeff(i,i)) > premultiplied_threshold); - return result; - } - - /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline Index dimensionOfKernel() const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return cols() - rank(); - } - - /** \returns true if the matrix of which *this is the QR decomposition represents an injective - * linear map, i.e. has trivial kernel; false otherwise. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isInjective() const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return rank() == cols(); - } - - /** \returns true if the matrix of which *this is the QR decomposition represents a surjective - * linear map; false otherwise. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isSurjective() const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return rank() == rows(); - } - - /** \returns true if the matrix of which *this is the QR decomposition is invertible. - * - * \note This method has to determine which pivots should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline bool isInvertible() const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return isInjective() && isSurjective(); - } - - /** \returns the inverse of the matrix of which *this is the QR decomposition. - * - * \note If this matrix is not invertible, the returned matrix has undefined coefficients. - * Use isInvertible() to first determine whether this matrix is invertible. - */ inline const - internal::solve_retval<FullPivHouseholderQR, typename MatrixType::IdentityReturnType> - inverse() const - { - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return internal::solve_retval<FullPivHouseholderQR,typename MatrixType::IdentityReturnType> - (*this, MatrixType::Identity(m_qr.rows(), m_qr.cols())); - } - - inline Index rows() const { return m_qr.rows(); } - inline Index cols() const { return m_qr.cols(); } - - /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q. - * - * For advanced uses only. - */ - const HCoeffsType& hCoeffs() const { return m_hCoeffs; } - - /** Allows to prescribe a threshold to be used by certain methods, such as rank(), - * who need to determine when pivots are to be considered nonzero. This is not used for the - * QR decomposition itself. - * - * When it needs to get the threshold value, Eigen calls threshold(). By default, this - * uses a formula to automatically determine a reasonable threshold. - * Once you have called the present method setThreshold(const RealScalar&), - * your value is used instead. - * - * \param threshold The new value to use as the threshold. - * - * A pivot will be considered nonzero if its absolute value is strictly greater than - * \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$ - * where maxpivot is the biggest pivot. - * - * If you want to come back to the default behavior, call setThreshold(Default_t) - */ - FullPivHouseholderQR& setThreshold(const RealScalar& threshold) - { - m_usePrescribedThreshold = true; - m_prescribedThreshold = threshold; - return *this; - } - - /** Allows to come back to the default behavior, letting Eigen use its default formula for - * determining the threshold. - * - * You should pass the special object Eigen::Default as parameter here. - * \code qr.setThreshold(Eigen::Default); \endcode - * - * See the documentation of setThreshold(const RealScalar&). - */ - FullPivHouseholderQR& setThreshold(Default_t) - { - m_usePrescribedThreshold = false; - return *this; - } - - /** Returns the threshold that will be used by certain methods such as rank(). - * - * See the documentation of setThreshold(const RealScalar&). - */ - RealScalar threshold() const - { - eigen_assert(m_isInitialized || m_usePrescribedThreshold); - return m_usePrescribedThreshold ? m_prescribedThreshold - // this formula comes from experimenting (see "LU precision tuning" thread on the list) - // and turns out to be identical to Higham's formula used already in LDLt. - : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize()); - } - - /** \returns the number of nonzero pivots in the QR decomposition. - * Here nonzero is meant in the exact sense, not in a fuzzy sense. - * So that notion isn't really intrinsically interesting, but it is - * still useful when implementing algorithms. - * - * \sa rank() - */ - inline Index nonzeroPivots() const - { - eigen_assert(m_isInitialized && "LU is not initialized."); - return m_nonzero_pivots; - } - - /** \returns the absolute value of the biggest pivot, i.e. the biggest - * diagonal coefficient of U. - */ - RealScalar maxPivot() const { return m_maxpivot; } - - protected: - MatrixType m_qr; - HCoeffsType m_hCoeffs; - IntDiagSizeVectorType m_rows_transpositions; - IntDiagSizeVectorType m_cols_transpositions; - PermutationType m_cols_permutation; - RowVectorType m_temp; - bool m_isInitialized, m_usePrescribedThreshold; - RealScalar m_prescribedThreshold, m_maxpivot; - Index m_nonzero_pivots; - RealScalar m_precision; - Index m_det_pq; -}; - -template<typename MatrixType> -typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::absDeterminant() const -{ - using std::abs; - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!"); - return abs(m_qr.diagonal().prod()); -} - -template<typename MatrixType> -typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::logAbsDeterminant() const -{ - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!"); - return m_qr.diagonal().cwiseAbs().array().log().sum(); -} - -/** Performs the QR factorization of the given matrix \a matrix. The result of - * the factorization is stored into \c *this, and a reference to \c *this - * is returned. - * - * \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&) - */ -template<typename MatrixType> -FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix) -{ - using std::abs; - Index rows = matrix.rows(); - Index cols = matrix.cols(); - Index size = (std::min)(rows,cols); - - m_qr = matrix; - m_hCoeffs.resize(size); - - m_temp.resize(cols); - - m_precision = NumTraits<Scalar>::epsilon() * RealScalar(size); - - m_rows_transpositions.resize(size); - m_cols_transpositions.resize(size); - Index number_of_transpositions = 0; - - RealScalar biggest(0); - - m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case) - m_maxpivot = RealScalar(0); - - for (Index k = 0; k < size; ++k) - { - Index row_of_biggest_in_corner, col_of_biggest_in_corner; - RealScalar biggest_in_corner; - - biggest_in_corner = m_qr.bottomRightCorner(rows-k, cols-k) - .cwiseAbs() - .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner); - row_of_biggest_in_corner += k; - col_of_biggest_in_corner += k; - if(k==0) biggest = biggest_in_corner; - - // if the corner is negligible, then we have less than full rank, and we can finish early - if(internal::isMuchSmallerThan(biggest_in_corner, biggest, m_precision)) - { - m_nonzero_pivots = k; - for(Index i = k; i < size; i++) - { - m_rows_transpositions.coeffRef(i) = i; - m_cols_transpositions.coeffRef(i) = i; - m_hCoeffs.coeffRef(i) = Scalar(0); - } - break; - } - - m_rows_transpositions.coeffRef(k) = row_of_biggest_in_corner; - m_cols_transpositions.coeffRef(k) = col_of_biggest_in_corner; - if(k != row_of_biggest_in_corner) { - m_qr.row(k).tail(cols-k).swap(m_qr.row(row_of_biggest_in_corner).tail(cols-k)); - ++number_of_transpositions; - } - if(k != col_of_biggest_in_corner) { - m_qr.col(k).swap(m_qr.col(col_of_biggest_in_corner)); - ++number_of_transpositions; - } - - RealScalar beta; - m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta); - m_qr.coeffRef(k,k) = beta; - - // remember the maximum absolute value of diagonal coefficients - if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta); - - m_qr.bottomRightCorner(rows-k, cols-k-1) - .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1)); - } - - m_cols_permutation.setIdentity(cols); - for(Index k = 0; k < size; ++k) - m_cols_permutation.applyTranspositionOnTheRight(k, m_cols_transpositions.coeff(k)); - - m_det_pq = (number_of_transpositions%2) ? -1 : 1; - m_isInitialized = true; - - return *this; -} - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<FullPivHouseholderQR<_MatrixType>, Rhs> - : solve_retval_base<FullPivHouseholderQR<_MatrixType>, Rhs> -{ - EIGEN_MAKE_SOLVE_HELPERS(FullPivHouseholderQR<_MatrixType>,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - const Index rows = dec().rows(), cols = dec().cols(); - eigen_assert(rhs().rows() == rows); - - // FIXME introduce nonzeroPivots() and use it here. and more generally, - // make the same improvements in this dec as in FullPivLU. - if(dec().rank()==0) - { - dst.setZero(); - return; - } - - typename Rhs::PlainObject c(rhs()); - - Matrix<Scalar,1,Rhs::ColsAtCompileTime> temp(rhs().cols()); - for (Index k = 0; k < dec().rank(); ++k) - { - Index remainingSize = rows-k; - c.row(k).swap(c.row(dec().rowsTranspositions().coeff(k))); - c.bottomRightCorner(remainingSize, rhs().cols()) - .applyHouseholderOnTheLeft(dec().matrixQR().col(k).tail(remainingSize-1), - dec().hCoeffs().coeff(k), &temp.coeffRef(0)); - } - - dec().matrixQR() - .topLeftCorner(dec().rank(), dec().rank()) - .template triangularView<Upper>() - .solveInPlace(c.topRows(dec().rank())); - - for(Index i = 0; i < dec().rank(); ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i); - for(Index i = dec().rank(); i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero(); - } -}; - -/** \ingroup QR_Module - * - * \brief Expression type for return value of FullPivHouseholderQR::matrixQ() - * - * \tparam MatrixType type of underlying dense matrix - */ -template<typename MatrixType> struct FullPivHouseholderQRMatrixQReturnType - : public ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> > -{ -public: - typedef typename MatrixType::Index Index; - typedef typename FullPivHouseholderQR<MatrixType>::IntDiagSizeVectorType IntDiagSizeVectorType; - typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType; - typedef Matrix<typename MatrixType::Scalar, 1, MatrixType::RowsAtCompileTime, RowMajor, 1, - MatrixType::MaxRowsAtCompileTime> WorkVectorType; - - FullPivHouseholderQRMatrixQReturnType(const MatrixType& qr, - const HCoeffsType& hCoeffs, - const IntDiagSizeVectorType& rowsTranspositions) - : m_qr(qr), - m_hCoeffs(hCoeffs), - m_rowsTranspositions(rowsTranspositions) - {} - - template <typename ResultType> - void evalTo(ResultType& result) const - { - const Index rows = m_qr.rows(); - WorkVectorType workspace(rows); - evalTo(result, workspace); - } - - template <typename ResultType> - void evalTo(ResultType& result, WorkVectorType& workspace) const - { - using numext::conj; - // compute the product H'_0 H'_1 ... H'_n-1, - // where H_k is the k-th Householder transformation I - h_k v_k v_k' - // and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...] - const Index rows = m_qr.rows(); - const Index cols = m_qr.cols(); - const Index size = (std::min)(rows, cols); - workspace.resize(rows); - result.setIdentity(rows, rows); - for (Index k = size-1; k >= 0; k--) - { - result.block(k, k, rows-k, rows-k) - .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), conj(m_hCoeffs.coeff(k)), &workspace.coeffRef(k)); - result.row(k).swap(result.row(m_rowsTranspositions.coeff(k))); - } - } - - Index rows() const { return m_qr.rows(); } - Index cols() const { return m_qr.rows(); } - -protected: - typename MatrixType::Nested m_qr; - typename HCoeffsType::Nested m_hCoeffs; - typename IntDiagSizeVectorType::Nested m_rowsTranspositions; -}; - -} // end namespace internal - -template<typename MatrixType> -inline typename FullPivHouseholderQR<MatrixType>::MatrixQReturnType FullPivHouseholderQR<MatrixType>::matrixQ() const -{ - eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized."); - return MatrixQReturnType(m_qr, m_hCoeffs, m_rows_transpositions); -} - -#ifndef __CUDACC__ -/** \return the full-pivoting Householder QR decomposition of \c *this. - * - * \sa class FullPivHouseholderQR - */ -template<typename Derived> -const FullPivHouseholderQR<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::fullPivHouseholderQr() const -{ - return FullPivHouseholderQR<PlainObject>(eval()); -} -#endif // __CUDACC__ - -} // end namespace Eigen - -#endif // EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H diff --git a/third_party/eigen3/Eigen/src/QR/HouseholderQR.h b/third_party/eigen3/Eigen/src/QR/HouseholderQR.h deleted file mode 100644 index 352dbf3f0e..0000000000 --- a/third_party/eigen3/Eigen/src/QR/HouseholderQR.h +++ /dev/null @@ -1,382 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// Copyright (C) 2010 Vincent Lejeune -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_QR_H -#define EIGEN_QR_H - -namespace Eigen { - -/** \ingroup QR_Module - * - * - * \class HouseholderQR - * - * \brief Householder QR decomposition of a matrix - * - * \param MatrixType the type of the matrix of which we are computing the QR decomposition - * - * This class performs a QR decomposition of a matrix \b A into matrices \b Q and \b R - * such that - * \f[ - * \mathbf{A} = \mathbf{Q} \, \mathbf{R} - * \f] - * by using Householder transformations. Here, \b Q a unitary matrix and \b R an upper triangular matrix. - * The result is stored in a compact way compatible with LAPACK. - * - * Note that no pivoting is performed. This is \b not a rank-revealing decomposition. - * If you want that feature, use FullPivHouseholderQR or ColPivHouseholderQR instead. - * - * This Householder QR decomposition is faster, but less numerically stable and less feature-full than - * FullPivHouseholderQR or ColPivHouseholderQR. - * - * \sa MatrixBase::householderQr() - */ -template<typename _MatrixType> class HouseholderQR -{ - public: - - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - Options = MatrixType::Options, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime - }; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, (MatrixType::Flags&RowMajorBit) ? RowMajor : ColMajor, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType; - typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType; - typedef typename internal::plain_row_type<MatrixType>::type RowVectorType; - typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType; - - /** - * \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via HouseholderQR::compute(const MatrixType&). - */ - HouseholderQR() : m_qr(), m_hCoeffs(), m_temp(), m_isInitialized(false) {} - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem \a size. - * \sa HouseholderQR() - */ - HouseholderQR(Index rows, Index cols) - : m_qr(rows, cols), - m_hCoeffs((std::min)(rows,cols)), - m_temp(cols), - m_isInitialized(false) {} - - /** \brief Constructs a QR factorization from a given matrix - * - * This constructor computes the QR factorization of the matrix \a matrix by calling - * the method compute(). It is a short cut for: - * - * \code - * HouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols()); - * qr.compute(matrix); - * \endcode - * - * \sa compute() - */ - HouseholderQR(const MatrixType& matrix) - : m_qr(matrix.rows(), matrix.cols()), - m_hCoeffs((std::min)(matrix.rows(),matrix.cols())), - m_temp(matrix.cols()), - m_isInitialized(false) - { - compute(matrix); - } - - /** This method finds a solution x to the equation Ax=b, where A is the matrix of which - * *this is the QR decomposition, if any exists. - * - * \param b the right-hand-side of the equation to solve. - * - * \returns a solution. - * - * \note The case where b is a matrix is not yet implemented. Also, this - * code is space inefficient. - * - * \note_about_checking_solutions - * - * \note_about_arbitrary_choice_of_solution - * - * Example: \include HouseholderQR_solve.cpp - * Output: \verbinclude HouseholderQR_solve.out - */ - template<typename Rhs> - inline const internal::solve_retval<HouseholderQR, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "HouseholderQR is not initialized."); - return internal::solve_retval<HouseholderQR, Rhs>(*this, b.derived()); - } - - /** This method returns an expression of the unitary matrix Q as a sequence of Householder transformations. - * - * The returned expression can directly be used to perform matrix products. It can also be assigned to a dense Matrix object. - * Here is an example showing how to recover the full or thin matrix Q, as well as how to perform matrix products using operator*: - * - * Example: \include HouseholderQR_householderQ.cpp - * Output: \verbinclude HouseholderQR_householderQ.out - */ - HouseholderSequenceType householderQ() const - { - eigen_assert(m_isInitialized && "HouseholderQR is not initialized."); - return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate()); - } - - /** \returns a reference to the matrix where the Householder QR decomposition is stored - * in a LAPACK-compatible way. - */ - const MatrixType& matrixQR() const - { - eigen_assert(m_isInitialized && "HouseholderQR is not initialized."); - return m_qr; - } - - HouseholderQR& compute(const MatrixType& matrix); - - /** \returns the absolute value of the determinant of the matrix of which - * *this is the QR decomposition. It has only linear complexity - * (that is, O(n) where n is the dimension of the square matrix) - * as the QR decomposition has already been computed. - * - * \note This is only for square matrices. - * - * \warning a determinant can be very big or small, so for matrices - * of large enough dimension, there is a risk of overflow/underflow. - * One way to work around that is to use logAbsDeterminant() instead. - * - * \sa logAbsDeterminant(), MatrixBase::determinant() - */ - typename MatrixType::RealScalar absDeterminant() const; - - /** \returns the natural log of the absolute value of the determinant of the matrix of which - * *this is the QR decomposition. It has only linear complexity - * (that is, O(n) where n is the dimension of the square matrix) - * as the QR decomposition has already been computed. - * - * \note This is only for square matrices. - * - * \note This method is useful to work around the risk of overflow/underflow that's inherent - * to determinant computation. - * - * \sa absDeterminant(), MatrixBase::determinant() - */ - typename MatrixType::RealScalar logAbsDeterminant() const; - - inline Index rows() const { return m_qr.rows(); } - inline Index cols() const { return m_qr.cols(); } - - /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q. - * - * For advanced uses only. - */ - const HCoeffsType& hCoeffs() const { return m_hCoeffs; } - - protected: - MatrixType m_qr; - HCoeffsType m_hCoeffs; - RowVectorType m_temp; - bool m_isInitialized; -}; - -template<typename MatrixType> -typename MatrixType::RealScalar HouseholderQR<MatrixType>::absDeterminant() const -{ - using std::abs; - eigen_assert(m_isInitialized && "HouseholderQR is not initialized."); - eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!"); - return abs(m_qr.diagonal().prod()); -} - -template<typename MatrixType> -typename MatrixType::RealScalar HouseholderQR<MatrixType>::logAbsDeterminant() const -{ - eigen_assert(m_isInitialized && "HouseholderQR is not initialized."); - eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!"); - return m_qr.diagonal().cwiseAbs().array().log().sum(); -} - -namespace internal { - -/** \internal */ -template<typename MatrixQR, typename HCoeffs> -void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename MatrixQR::Scalar* tempData = 0) -{ - typedef typename MatrixQR::Index Index; - typedef typename MatrixQR::Scalar Scalar; - typedef typename MatrixQR::RealScalar RealScalar; - Index rows = mat.rows(); - Index cols = mat.cols(); - Index size = (std::min)(rows,cols); - - eigen_assert(hCoeffs.size() == size); - - typedef Matrix<Scalar,MatrixQR::ColsAtCompileTime,1> TempType; - TempType tempVector; - if(tempData==0) - { - tempVector.resize(cols); - tempData = tempVector.data(); - } - - for(Index k = 0; k < size; ++k) - { - Index remainingRows = rows - k; - Index remainingCols = cols - k - 1; - - RealScalar beta; - mat.col(k).tail(remainingRows).makeHouseholderInPlace(hCoeffs.coeffRef(k), beta); - mat.coeffRef(k,k) = beta; - - // apply H to remaining part of m_qr from the left - mat.bottomRightCorner(remainingRows, remainingCols) - .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), hCoeffs.coeffRef(k), tempData+k+1); - } -} - -/** \internal */ -template<typename MatrixQR, typename HCoeffs, - typename MatrixQRScalar = typename MatrixQR::Scalar, - bool InnerStrideIsOne = (MatrixQR::InnerStrideAtCompileTime == 1 && HCoeffs::InnerStrideAtCompileTime == 1)> -struct householder_qr_inplace_blocked -{ - // This is specialized for MKL-supported Scalar types in HouseholderQR_MKL.h - static void run(MatrixQR& mat, HCoeffs& hCoeffs, - typename MatrixQR::Index maxBlockSize=32, - typename MatrixQR::Scalar* tempData = 0) - { - typedef typename MatrixQR::Index Index; - typedef typename MatrixQR::Scalar Scalar; - typedef Block<MatrixQR,Dynamic,Dynamic> BlockType; - - Index rows = mat.rows(); - Index cols = mat.cols(); - Index size = (std::min)(rows, cols); - - typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixQR::MaxColsAtCompileTime,1> TempType; - TempType tempVector; - if(tempData==0) - { - tempVector.resize(cols); - tempData = tempVector.data(); - } - - Index blockSize = (std::min)(maxBlockSize,size); - - Index k = 0; - for (k = 0; k < size; k += blockSize) - { - Index bs = (std::min)(size-k,blockSize); // actual size of the block - Index tcols = cols - k - bs; // trailing columns - Index brows = rows-k; // rows of the block - - // partition the matrix: - // A00 | A01 | A02 - // mat = A10 | A11 | A12 - // A20 | A21 | A22 - // and performs the qr dec of [A11^T A12^T]^T - // and update [A21^T A22^T]^T using level 3 operations. - // Finally, the algorithm continue on A22 - - BlockType A11_21 = mat.block(k,k,brows,bs); - Block<HCoeffs,Dynamic,1> hCoeffsSegment = hCoeffs.segment(k,bs); - - householder_qr_inplace_unblocked(A11_21, hCoeffsSegment, tempData); - - if(tcols) - { - BlockType A21_22 = mat.block(k,k+bs,brows,tcols); - apply_block_householder_on_the_left(A21_22,A11_21,hCoeffsSegment.adjoint()); - } - } - } -}; - -template<typename _MatrixType, typename Rhs> -struct solve_retval<HouseholderQR<_MatrixType>, Rhs> - : solve_retval_base<HouseholderQR<_MatrixType>, Rhs> -{ - EIGEN_MAKE_SOLVE_HELPERS(HouseholderQR<_MatrixType>,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - const Index rows = dec().rows(), cols = dec().cols(); - const Index rank = (std::min)(rows, cols); - eigen_assert(rhs().rows() == rows); - - typename Rhs::PlainObject c(rhs()); - - // Note that the matrix Q = H_0^* H_1^*... so its inverse is Q^* = (H_0 H_1 ...)^T - c.applyOnTheLeft(householderSequence( - dec().matrixQR().leftCols(rank), - dec().hCoeffs().head(rank)).transpose() - ); - - dec().matrixQR() - .topLeftCorner(rank, rank) - .template triangularView<Upper>() - .solveInPlace(c.topRows(rank)); - - dst.topRows(rank) = c.topRows(rank); - dst.bottomRows(cols-rank).setZero(); - } -}; - -} // end namespace internal - -/** Performs the QR factorization of the given matrix \a matrix. The result of - * the factorization is stored into \c *this, and a reference to \c *this - * is returned. - * - * \sa class HouseholderQR, HouseholderQR(const MatrixType&) - */ -template<typename MatrixType> -HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType& matrix) -{ - Index rows = matrix.rows(); - Index cols = matrix.cols(); - Index size = (std::min)(rows,cols); - - m_qr = matrix; - m_hCoeffs.resize(size); - - m_temp.resize(cols); - - internal::householder_qr_inplace_blocked<MatrixType, HCoeffsType>::run(m_qr, m_hCoeffs, 48, m_temp.data()); - - m_isInitialized = true; - return *this; -} - -#ifndef __CUDACC__ -/** \return the Householder QR decomposition of \c *this. - * - * \sa class HouseholderQR - */ -template<typename Derived> -const HouseholderQR<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::householderQr() const -{ - return HouseholderQR<PlainObject>(eval()); -} -#endif // __CUDACC__ - -} // end namespace Eigen - -#endif // EIGEN_QR_H diff --git a/third_party/eigen3/Eigen/src/QR/HouseholderQR_MKL.h b/third_party/eigen3/Eigen/src/QR/HouseholderQR_MKL.h deleted file mode 100644 index 8a3a7e4063..0000000000 --- a/third_party/eigen3/Eigen/src/QR/HouseholderQR_MKL.h +++ /dev/null @@ -1,71 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Householder QR decomposition of a matrix w/o pivoting based on - * LAPACKE_?geqrf function. - ******************************************************************************** -*/ - -#ifndef EIGEN_QR_MKL_H -#define EIGEN_QR_MKL_H - -#include "../Core/util/MKL_support.h" - -namespace Eigen { - -namespace internal { - -/** \internal Specialization for the data types supported by MKL */ - -#define EIGEN_MKL_QR_NOPIV(EIGTYPE, MKLTYPE, MKLPREFIX) \ -template<typename MatrixQR, typename HCoeffs> \ -struct householder_qr_inplace_blocked<MatrixQR, HCoeffs, EIGTYPE, true> \ -{ \ - static void run(MatrixQR& mat, HCoeffs& hCoeffs, \ - typename MatrixQR::Index = 32, \ - typename MatrixQR::Scalar* = 0) \ - { \ - lapack_int m = (lapack_int) mat.rows(); \ - lapack_int n = (lapack_int) mat.cols(); \ - lapack_int lda = (lapack_int) mat.outerStride(); \ - lapack_int matrix_order = (MatrixQR::IsRowMajor) ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR; \ - LAPACKE_##MKLPREFIX##geqrf( matrix_order, m, n, (MKLTYPE*)mat.data(), lda, (MKLTYPE*)hCoeffs.data()); \ - hCoeffs.adjointInPlace(); \ - } \ -}; - -EIGEN_MKL_QR_NOPIV(double, double, d) -EIGEN_MKL_QR_NOPIV(float, float, s) -EIGEN_MKL_QR_NOPIV(dcomplex, MKL_Complex16, z) -EIGEN_MKL_QR_NOPIV(scomplex, MKL_Complex8, c) - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_QR_MKL_H diff --git a/third_party/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/third_party/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h deleted file mode 100644 index a2cc2a9e26..0000000000 --- a/third_party/eigen3/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h +++ /dev/null @@ -1,314 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SUITESPARSEQRSUPPORT_H -#define EIGEN_SUITESPARSEQRSUPPORT_H - -namespace Eigen { - - template<typename MatrixType> class SPQR; - template<typename SPQRType> struct SPQRMatrixQReturnType; - template<typename SPQRType> struct SPQRMatrixQTransposeReturnType; - template <typename SPQRType, typename Derived> struct SPQR_QProduct; - namespace internal { - template <typename SPQRType> struct traits<SPQRMatrixQReturnType<SPQRType> > - { - typedef typename SPQRType::MatrixType ReturnType; - }; - template <typename SPQRType> struct traits<SPQRMatrixQTransposeReturnType<SPQRType> > - { - typedef typename SPQRType::MatrixType ReturnType; - }; - template <typename SPQRType, typename Derived> struct traits<SPQR_QProduct<SPQRType, Derived> > - { - typedef typename Derived::PlainObject ReturnType; - }; - } // End namespace internal - -/** - * \ingroup SPQRSupport_Module - * \class SPQR - * \brief Sparse QR factorization based on SuiteSparseQR library - * - * This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition - * of sparse matrices. The result is then used to solve linear leasts_square systems. - * Clearly, a QR factorization is returned such that A*P = Q*R where : - * - * P is the column permutation. Use colsPermutation() to get it. - * - * Q is the orthogonal matrix represented as Householder reflectors. - * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose. - * You can then apply it to a vector. - * - * R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix. - * NOTE : The Index type of R is always UF_long. You can get it with SPQR::Index - * - * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<> - * NOTE - * - */ -template<typename _MatrixType> -class SPQR -{ - public: - typedef typename _MatrixType::Scalar Scalar; - typedef typename _MatrixType::RealScalar RealScalar; - typedef UF_long Index ; - typedef SparseMatrix<Scalar, ColMajor, Index> MatrixType; - typedef PermutationMatrix<Dynamic, Dynamic> PermutationType; - public: - SPQR() - : m_isInitialized(false), - m_ordering(SPQR_ORDERING_DEFAULT), - m_allow_tol(SPQR_DEFAULT_TOL), - m_tolerance (NumTraits<Scalar>::epsilon()) - { - cholmod_l_start(&m_cc); - } - - SPQR(const _MatrixType& matrix) - : m_isInitialized(false), - m_ordering(SPQR_ORDERING_DEFAULT), - m_allow_tol(SPQR_DEFAULT_TOL), - m_tolerance (NumTraits<Scalar>::epsilon()) - { - cholmod_l_start(&m_cc); - compute(matrix); - } - - ~SPQR() - { - SPQR_free(); - cholmod_l_finish(&m_cc); - } - void SPQR_free() - { - cholmod_l_free_sparse(&m_H, &m_cc); - cholmod_l_free_sparse(&m_cR, &m_cc); - cholmod_l_free_dense(&m_HTau, &m_cc); - std::free(m_E); - std::free(m_HPinv); - } - - void compute(const _MatrixType& matrix) - { - if(m_isInitialized) SPQR_free(); - - MatrixType mat(matrix); - cholmod_sparse A; - A = viewAsCholmod(mat); - Index col = matrix.cols(); - m_rank = SuiteSparseQR<Scalar>(m_ordering, m_tolerance, col, &A, - &m_cR, &m_E, &m_H, &m_HPinv, &m_HTau, &m_cc); - - if (!m_cR) - { - m_info = NumericalIssue; - m_isInitialized = false; - return; - } - m_info = Success; - m_isInitialized = true; - m_isRUpToDate = false; - } - /** - * Get the number of rows of the input matrix and the Q matrix - */ - inline Index rows() const {return m_H->nrow; } - - /** - * Get the number of columns of the input matrix. - */ - inline Index cols() const { return m_cR->ncol; } - - /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::solve_retval<SPQR, Rhs> solve(const MatrixBase<Rhs>& B) const - { - eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()"); - eigen_assert(this->rows()==B.rows() - && "SPQR::solve(): invalid number of rows of the right hand side matrix B"); - return internal::solve_retval<SPQR, Rhs>(*this, B.derived()); - } - - template<typename Rhs, typename Dest> - void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const - { - eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()"); - eigen_assert(b.cols()==1 && "This method is for vectors only"); - - //Compute Q^T * b - typename Dest::PlainObject y; - y = matrixQ().transpose() * b; - // Solves with the triangular matrix R - Index rk = this->rank(); - y.topRows(rk) = this->matrixR().topLeftCorner(rk, rk).template triangularView<Upper>().solve(y.topRows(rk)); - y.bottomRows(cols()-rk).setZero(); - // Apply the column permutation - dest.topRows(cols()) = colsPermutation() * y.topRows(cols()); - - m_info = Success; - } - - /** \returns the sparse triangular factor R. It is a sparse matrix - */ - const MatrixType matrixR() const - { - eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()"); - if(!m_isRUpToDate) { - m_R = viewAsEigen<Scalar,ColMajor, typename MatrixType::Index>(*m_cR); - m_isRUpToDate = true; - } - return m_R; - } - /// Get an expression of the matrix Q - SPQRMatrixQReturnType<SPQR> matrixQ() const - { - return SPQRMatrixQReturnType<SPQR>(*this); - } - /// Get the permutation that was applied to columns of A - PermutationType colsPermutation() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - Index n = m_cR->ncol; - PermutationType colsPerm(n); - for(Index j = 0; j <n; j++) colsPerm.indices()(j) = m_E[j]; - return colsPerm; - - } - /** - * Gets the rank of the matrix. - * It should be equal to matrixQR().cols if the matrix is full-rank - */ - Index rank() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_cc.SPQR_istat[4]; - } - /// Set the fill-reducing ordering method to be used - void setSPQROrdering(int ord) { m_ordering = ord;} - /// Set the tolerance tol to treat columns with 2-norm < =tol as zero - void setPivotThreshold(const RealScalar& tol) { m_tolerance = tol; } - - /** \returns a pointer to the SPQR workspace */ - cholmod_common *cholmodCommon() const { return &m_cc; } - - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the sparse QR can not be computed - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_info; - } - protected: - bool m_isInitialized; - bool m_analysisIsOk; - bool m_factorizationIsOk; - mutable bool m_isRUpToDate; - mutable ComputationInfo m_info; - int m_ordering; // Ordering method to use, see SPQR's manual - int m_allow_tol; // Allow to use some tolerance during numerical factorization. - RealScalar m_tolerance; // treat columns with 2-norm below this tolerance as zero - mutable cholmod_sparse *m_cR; // The sparse R factor in cholmod format - mutable MatrixType m_R; // The sparse matrix R in Eigen format - mutable Index *m_E; // The permutation applied to columns - mutable cholmod_sparse *m_H; //The householder vectors - mutable Index *m_HPinv; // The row permutation of H - mutable cholmod_dense *m_HTau; // The Householder coefficients - mutable Index m_rank; // The rank of the matrix - mutable cholmod_common m_cc; // Workspace and parameters - template<typename ,typename > friend struct SPQR_QProduct; -}; - -template <typename SPQRType, typename Derived> -struct SPQR_QProduct : ReturnByValue<SPQR_QProduct<SPQRType,Derived> > -{ - typedef typename SPQRType::Scalar Scalar; - typedef typename SPQRType::Index Index; - //Define the constructor to get reference to argument types - SPQR_QProduct(const SPQRType& spqr, const Derived& other, bool transpose) : m_spqr(spqr),m_other(other),m_transpose(transpose) {} - - inline Index rows() const { return m_transpose ? m_spqr.rows() : m_spqr.cols(); } - inline Index cols() const { return m_other.cols(); } - // Assign to a vector - template<typename ResType> - void evalTo(ResType& res) const - { - cholmod_dense y_cd; - cholmod_dense *x_cd; - int method = m_transpose ? SPQR_QTX : SPQR_QX; - cholmod_common *cc = m_spqr.cholmodCommon(); - y_cd = viewAsCholmod(m_other.const_cast_derived()); - x_cd = SuiteSparseQR_qmult<Scalar>(method, m_spqr.m_H, m_spqr.m_HTau, m_spqr.m_HPinv, &y_cd, cc); - res = Matrix<Scalar,ResType::RowsAtCompileTime,ResType::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x), x_cd->nrow, x_cd->ncol); - cholmod_l_free_dense(&x_cd, cc); - } - const SPQRType& m_spqr; - const Derived& m_other; - bool m_transpose; - -}; -template<typename SPQRType> -struct SPQRMatrixQReturnType{ - - SPQRMatrixQReturnType(const SPQRType& spqr) : m_spqr(spqr) {} - template<typename Derived> - SPQR_QProduct<SPQRType, Derived> operator*(const MatrixBase<Derived>& other) - { - return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(),false); - } - SPQRMatrixQTransposeReturnType<SPQRType> adjoint() const - { - return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr); - } - // To use for operations with the transpose of Q - SPQRMatrixQTransposeReturnType<SPQRType> transpose() const - { - return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr); - } - const SPQRType& m_spqr; -}; - -template<typename SPQRType> -struct SPQRMatrixQTransposeReturnType{ - SPQRMatrixQTransposeReturnType(const SPQRType& spqr) : m_spqr(spqr) {} - template<typename Derived> - SPQR_QProduct<SPQRType,Derived> operator*(const MatrixBase<Derived>& other) - { - return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(), true); - } - const SPQRType& m_spqr; -}; - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<SPQR<_MatrixType>, Rhs> - : solve_retval_base<SPQR<_MatrixType>, Rhs> -{ - typedef SPQR<_MatrixType> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -} // end namespace internal - -}// End namespace Eigen -#endif diff --git a/third_party/eigen3/Eigen/src/SVD/JacobiSVD.h b/third_party/eigen3/Eigen/src/SVD/JacobiSVD.h deleted file mode 100644 index d17d3a667d..0000000000 --- a/third_party/eigen3/Eigen/src/SVD/JacobiSVD.h +++ /dev/null @@ -1,960 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_JACOBISVD_H -#define EIGEN_JACOBISVD_H - -namespace Eigen { - -namespace internal { -// forward declaration (needed by ICC) -// the empty body is required by MSVC -template<typename MatrixType, int QRPreconditioner, - bool IsComplex = NumTraits<typename MatrixType::Scalar>::IsComplex> -struct svd_precondition_2x2_block_to_be_real {}; - -/*** QR preconditioners (R-SVD) - *** - *** Their role is to reduce the problem of computing the SVD to the case of a square matrix. - *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for - *** JacobiSVD which by itself is only able to work on square matrices. - ***/ - -enum { PreconditionIfMoreColsThanRows, PreconditionIfMoreRowsThanCols }; - -template<typename MatrixType, int QRPreconditioner, int Case> -struct qr_preconditioner_should_do_anything -{ - enum { a = MatrixType::RowsAtCompileTime != Dynamic && - MatrixType::ColsAtCompileTime != Dynamic && - MatrixType::ColsAtCompileTime <= MatrixType::RowsAtCompileTime, - b = MatrixType::RowsAtCompileTime != Dynamic && - MatrixType::ColsAtCompileTime != Dynamic && - MatrixType::RowsAtCompileTime <= MatrixType::ColsAtCompileTime, - ret = !( (QRPreconditioner == NoQRPreconditioner) || - (Case == PreconditionIfMoreColsThanRows && bool(a)) || - (Case == PreconditionIfMoreRowsThanCols && bool(b)) ) - }; -}; - -template<typename MatrixType, int QRPreconditioner, int Case, - bool DoAnything = qr_preconditioner_should_do_anything<MatrixType, QRPreconditioner, Case>::ret -> struct qr_preconditioner_impl {}; - -template<typename MatrixType, int QRPreconditioner, int Case> -class qr_preconditioner_impl<MatrixType, QRPreconditioner, Case, false> -{ -public: - typedef typename MatrixType::Index Index; - void allocate(const JacobiSVD<MatrixType, QRPreconditioner>&) {} - bool run(JacobiSVD<MatrixType, QRPreconditioner>&, const MatrixType&) - { - return false; - } -}; - -/*** preconditioner using FullPivHouseholderQR ***/ - -template<typename MatrixType> -class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true> -{ -public: - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - enum - { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime - }; - typedef Matrix<Scalar, 1, RowsAtCompileTime, RowMajor, 1, MaxRowsAtCompileTime> WorkspaceType; - - void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd) - { - if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols()) - { - m_qr.~QRType(); - ::new (&m_qr) QRType(svd.rows(), svd.cols()); - } - if (svd.m_computeFullU) m_workspace.resize(svd.rows()); - } - - bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix) - { - if(matrix.rows() > matrix.cols()) - { - m_qr.compute(matrix); - svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>(); - if(svd.m_computeFullU) m_qr.matrixQ().evalTo(svd.m_matrixU, m_workspace); - if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation(); - return true; - } - return false; - } -private: - typedef FullPivHouseholderQR<MatrixType> QRType; - QRType m_qr; - WorkspaceType m_workspace; -}; - -template<typename MatrixType> -class qr_preconditioner_impl<MatrixType, FullPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true> -{ -public: - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - enum - { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, - Options = MatrixType::Options - }; - typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime> - TransposeTypeWithSameStorageOrder; - - void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd) - { - if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols()) - { - m_qr.~QRType(); - ::new (&m_qr) QRType(svd.cols(), svd.rows()); - } - m_adjoint.resize(svd.cols(), svd.rows()); - if (svd.m_computeFullV) m_workspace.resize(svd.cols()); - } - - bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix) - { - if(matrix.cols() > matrix.rows()) - { - m_adjoint = matrix.adjoint(); - m_qr.compute(m_adjoint); - svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint(); - if(svd.m_computeFullV) m_qr.matrixQ().evalTo(svd.m_matrixV, m_workspace); - if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation(); - return true; - } - else return false; - } -private: - typedef FullPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType; - QRType m_qr; - TransposeTypeWithSameStorageOrder m_adjoint; - typename internal::plain_row_type<MatrixType>::type m_workspace; -}; - -/*** preconditioner using ColPivHouseholderQR ***/ - -template<typename MatrixType> -class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true> -{ -public: - typedef typename MatrixType::Index Index; - - void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd) - { - if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols()) - { - m_qr.~QRType(); - ::new (&m_qr) QRType(svd.rows(), svd.cols()); - } - if (svd.m_computeFullU) m_workspace.resize(svd.rows()); - else if (svd.m_computeThinU) m_workspace.resize(svd.cols()); - } - - bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix) - { - if(matrix.rows() > matrix.cols()) - { - m_qr.compute(matrix); - svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>(); - if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace); - else if(svd.m_computeThinU) - { - svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols()); - m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace); - } - if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation(); - return true; - } - return false; - } - -private: - typedef ColPivHouseholderQR<MatrixType> QRType; - QRType m_qr; - typename internal::plain_col_type<MatrixType>::type m_workspace; -}; - -template<typename MatrixType> -class qr_preconditioner_impl<MatrixType, ColPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true> -{ -public: - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - enum - { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, - Options = MatrixType::Options - }; - - typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime> - TransposeTypeWithSameStorageOrder; - - void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd) - { - if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols()) - { - m_qr.~QRType(); - ::new (&m_qr) QRType(svd.cols(), svd.rows()); - } - if (svd.m_computeFullV) m_workspace.resize(svd.cols()); - else if (svd.m_computeThinV) m_workspace.resize(svd.rows()); - m_adjoint.resize(svd.cols(), svd.rows()); - } - - bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix) - { - if(matrix.cols() > matrix.rows()) - { - m_adjoint = matrix.adjoint(); - m_qr.compute(m_adjoint); - - svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint(); - if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace); - else if(svd.m_computeThinV) - { - svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows()); - m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace); - } - if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation(); - return true; - } - else return false; - } - -private: - typedef ColPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType; - QRType m_qr; - TransposeTypeWithSameStorageOrder m_adjoint; - typename internal::plain_row_type<MatrixType>::type m_workspace; -}; - -/*** preconditioner using HouseholderQR ***/ - -template<typename MatrixType> -class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true> -{ -public: - typedef typename MatrixType::Index Index; - - void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd) - { - if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols()) - { - m_qr.~QRType(); - ::new (&m_qr) QRType(svd.rows(), svd.cols()); - } - if (svd.m_computeFullU) m_workspace.resize(svd.rows()); - else if (svd.m_computeThinU) m_workspace.resize(svd.cols()); - } - - bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix) - { - if(matrix.rows() > matrix.cols()) - { - m_qr.compute(matrix); - svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>(); - if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace); - else if(svd.m_computeThinU) - { - svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols()); - m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace); - } - if(svd.computeV()) svd.m_matrixV.setIdentity(matrix.cols(), matrix.cols()); - return true; - } - return false; - } -private: - typedef HouseholderQR<MatrixType> QRType; - QRType m_qr; - typename internal::plain_col_type<MatrixType>::type m_workspace; -}; - -template<typename MatrixType> -class qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true> -{ -public: - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - enum - { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, - Options = MatrixType::Options - }; - - typedef Matrix<Scalar, ColsAtCompileTime, RowsAtCompileTime, Options, MaxColsAtCompileTime, MaxRowsAtCompileTime> - TransposeTypeWithSameStorageOrder; - - void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd) - { - if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols()) - { - m_qr.~QRType(); - ::new (&m_qr) QRType(svd.cols(), svd.rows()); - } - if (svd.m_computeFullV) m_workspace.resize(svd.cols()); - else if (svd.m_computeThinV) m_workspace.resize(svd.rows()); - m_adjoint.resize(svd.cols(), svd.rows()); - } - - bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix) - { - if(matrix.cols() > matrix.rows()) - { - m_adjoint = matrix.adjoint(); - m_qr.compute(m_adjoint); - - svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint(); - if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace); - else if(svd.m_computeThinV) - { - svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows()); - m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace); - } - if(svd.computeU()) svd.m_matrixU.setIdentity(matrix.rows(), matrix.rows()); - return true; - } - else return false; - } - -private: - typedef HouseholderQR<TransposeTypeWithSameStorageOrder> QRType; - QRType m_qr; - TransposeTypeWithSameStorageOrder m_adjoint; - typename internal::plain_row_type<MatrixType>::type m_workspace; -}; - -/*** 2x2 SVD implementation - *** - *** JacobiSVD consists in performing a series of 2x2 SVD subproblems - ***/ - -template<typename MatrixType, int QRPreconditioner> -struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, false> -{ - typedef JacobiSVD<MatrixType, QRPreconditioner> SVD; - typedef typename SVD::Index Index; - static void run(typename SVD::WorkMatrixType&, SVD&, Index, Index) {} -}; - -template<typename MatrixType, int QRPreconditioner> -struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, true> -{ - typedef JacobiSVD<MatrixType, QRPreconditioner> SVD; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename SVD::Index Index; - static void run(typename SVD::WorkMatrixType& work_matrix, SVD& svd, Index p, Index q) - { - using std::sqrt; - Scalar z; - JacobiRotation<Scalar> rot; - RealScalar n = sqrt(numext::abs2(work_matrix.coeff(p,p)) + numext::abs2(work_matrix.coeff(q,p))); - if(n==0) - { - z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q); - work_matrix.row(p) *= z; - if(svd.computeU()) svd.m_matrixU.col(p) *= conj(z); - if(work_matrix.coeff(q,q)!=Scalar(0)) - z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q); - else - z = Scalar(0); - work_matrix.row(q) *= z; - if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z); - } - else - { - rot.c() = conj(work_matrix.coeff(p,p)) / n; - rot.s() = work_matrix.coeff(q,p) / n; - work_matrix.applyOnTheLeft(p,q,rot); - if(svd.computeU()) svd.m_matrixU.applyOnTheRight(p,q,rot.adjoint()); - if(work_matrix.coeff(p,q) != Scalar(0)) - { - Scalar z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q); - work_matrix.col(q) *= z; - if(svd.computeV()) svd.m_matrixV.col(q) *= z; - } - if(work_matrix.coeff(q,q) != Scalar(0)) - { - z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q); - work_matrix.row(q) *= z; - if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z); - } - } - } -}; - -template<typename MatrixType, typename RealScalar, typename Index> -void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q, - JacobiRotation<RealScalar> *j_left, - JacobiRotation<RealScalar> *j_right) -{ - using std::sqrt; - using std::abs; - Matrix<RealScalar,2,2> m; - m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)), - numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q)); - JacobiRotation<RealScalar> rot1; - RealScalar t = m.coeff(0,0) + m.coeff(1,1); - RealScalar d = m.coeff(1,0) - m.coeff(0,1); - if(t == RealScalar(0)) - { - rot1.c() = RealScalar(0); - rot1.s() = d > RealScalar(0) ? RealScalar(1) : RealScalar(-1); - } - else - { - RealScalar t2d2 = numext::hypot(t,d); - rot1.c() = abs(t)/t2d2; - rot1.s() = d/t2d2; - if(t<RealScalar(0)) - rot1.s() = -rot1.s(); - } - m.applyOnTheLeft(0,1,rot1); - j_right->makeJacobi(m,0,1); - *j_left = rot1 * j_right->transpose(); -} - -} // end namespace internal - -/** \ingroup SVD_Module - * - * - * \class JacobiSVD - * - * \brief Two-sided Jacobi SVD decomposition of a rectangular matrix - * - * \param MatrixType the type of the matrix of which we are computing the SVD decomposition - * \param QRPreconditioner this optional parameter allows to specify the type of QR decomposition that will be used internally - * for the R-SVD step for non-square matrices. See discussion of possible values below. - * - * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product - * \f[ A = U S V^* \f] - * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal; - * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left - * and right \em singular \em vectors of \a A respectively. - * - * Singular values are always sorted in decreasing order. - * - * This JacobiSVD decomposition computes only the singular values by default. If you want \a U or \a V, you need to ask for them explicitly. - * - * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the - * smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual - * singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix, - * and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving. - * - * Here's an example demonstrating basic usage: - * \include JacobiSVD_basic.cpp - * Output: \verbinclude JacobiSVD_basic.out - * - * This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than - * bidiagonalizing SVD algorithms for large square matrices; however its complexity is still \f$ O(n^2p) \f$ where \a n is the smaller dimension and - * \a p is the greater dimension, meaning that it is still of the same order of complexity as the faster bidiagonalizing R-SVD algorithms. - * In particular, like any R-SVD, it takes advantage of non-squareness in that its complexity is only linear in the greater dimension. - * - * If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to - * terminate in finite (and reasonable) time. - * - * The possible values for QRPreconditioner are: - * \li ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR. - * \li FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR. - * Contrary to other QRs, it doesn't allow computing thin unitaries. - * \li HouseholderQRPreconditioner is the fastest, and less safe and accurate than the pivoting variants. It uses non-pivoting QR. - * This is very similar in safety and accuracy to the bidiagonalization process used by bidiagonalizing SVD algorithms (since bidiagonalization - * is inherently non-pivoting). However the resulting SVD is still more reliable than bidiagonalizing SVDs because the Jacobi-based iterarive - * process is more reliable than the optimized bidiagonal SVD iterations. - * \li NoQRPreconditioner allows not to use a QR preconditioner at all. This is useful if you know that you will only be computing - * JacobiSVD decompositions of square matrices. Non-square matrices require a QR preconditioner. Using this option will result in - * faster compilation and smaller executable code. It won't significantly speed up computation, since JacobiSVD is always checking - * if QR preconditioning is needed before applying it anyway. - * - * \sa MatrixBase::jacobiSvd() - */ -template<typename _MatrixType, int QRPreconditioner> class JacobiSVD -{ - public: - - typedef _MatrixType MatrixType; - typedef typename MatrixType::Scalar Scalar; - typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; - typedef typename MatrixType::Index Index; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime), - MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime, - MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime, - MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime), - MatrixOptions = MatrixType::Options - }; - - typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, - MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime> - MatrixUType; - typedef Matrix<Scalar, ColsAtCompileTime, ColsAtCompileTime, - MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime> - MatrixVType; - typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType; - typedef typename internal::plain_row_type<MatrixType>::type RowType; - typedef typename internal::plain_col_type<MatrixType>::type ColType; - typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime, - MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime> - WorkMatrixType; - - /** \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via JacobiSVD::compute(const MatrixType&). - */ - JacobiSVD() - : m_isInitialized(false), - m_isAllocated(false), - m_usePrescribedThreshold(false), - m_computationOptions(0), - m_rows(-1), m_cols(-1), m_diagSize(0) - {} - - - /** \brief Default Constructor with memory preallocation - * - * Like the default constructor but with preallocation of the internal data - * according to the specified problem size. - * \sa JacobiSVD() - */ - JacobiSVD(Index rows, Index cols, unsigned int computationOptions = 0) - : m_isInitialized(false), - m_isAllocated(false), - m_usePrescribedThreshold(false), - m_computationOptions(0), - m_rows(-1), m_cols(-1) - { - allocate(rows, cols, computationOptions); - } - - /** \brief Constructor performing the decomposition of given matrix. - * - * \param matrix the matrix to decompose - * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed. - * By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU, - * #ComputeFullV, #ComputeThinV. - * - * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not - * available with the (non-default) FullPivHouseholderQR preconditioner. - */ - JacobiSVD(const MatrixType& matrix, unsigned int computationOptions = 0) - : m_isInitialized(false), - m_isAllocated(false), - m_usePrescribedThreshold(false), - m_computationOptions(0), - m_rows(-1), m_cols(-1) - { - compute(matrix, computationOptions); - } - - /** \brief Method performing the decomposition of given matrix using custom options. - * - * \param matrix the matrix to decompose - * \param computationOptions optional parameter allowing to specify if you want full or thin U or V unitaries to be computed. - * By default, none is computed. This is a bit-field, the possible bits are #ComputeFullU, #ComputeThinU, - * #ComputeFullV, #ComputeThinV. - * - * Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not - * available with the (non-default) FullPivHouseholderQR preconditioner. - */ - JacobiSVD& compute(const MatrixType& matrix, unsigned int computationOptions); - - /** \brief Method performing the decomposition of given matrix using current options. - * - * \param matrix the matrix to decompose - * - * This method uses the current \a computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int). - */ - JacobiSVD& compute(const MatrixType& matrix) - { - return compute(matrix, m_computationOptions); - } - - /** \returns the \a U matrix. - * - * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, - * the U matrix is n-by-n if you asked for #ComputeFullU, and is n-by-m if you asked for #ComputeThinU. - * - * The \a m first columns of \a U are the left singular vectors of the matrix being decomposed. - * - * This method asserts that you asked for \a U to be computed. - */ - const MatrixUType& matrixU() const - { - eigen_assert(m_isInitialized && "JacobiSVD is not initialized."); - eigen_assert(computeU() && "This JacobiSVD decomposition didn't compute U. Did you ask for it?"); - return m_matrixU; - } - - /** \returns the \a V matrix. - * - * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, - * the V matrix is p-by-p if you asked for #ComputeFullV, and is p-by-m if you asked for ComputeThinV. - * - * The \a m first columns of \a V are the right singular vectors of the matrix being decomposed. - * - * This method asserts that you asked for \a V to be computed. - */ - const MatrixVType& matrixV() const - { - eigen_assert(m_isInitialized && "JacobiSVD is not initialized."); - eigen_assert(computeV() && "This JacobiSVD decomposition didn't compute V. Did you ask for it?"); - return m_matrixV; - } - - /** \returns the vector of singular values. - * - * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, the - * returned vector has size \a m. Singular values are always sorted in decreasing order. - */ - const SingularValuesType& singularValues() const - { - eigen_assert(m_isInitialized && "JacobiSVD is not initialized."); - return m_singularValues; - } - - /** \returns true if \a U (full or thin) is asked for in this SVD decomposition */ - inline bool computeU() const { return m_computeFullU || m_computeThinU; } - /** \returns true if \a V (full or thin) is asked for in this SVD decomposition */ - inline bool computeV() const { return m_computeFullV || m_computeThinV; } - - /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A. - * - * \param b the right-hand-side of the equation to solve. - * - * \note Solving requires both U and V to be computed. Thin U and V are enough, there is no need for full U or V. - * - * \note SVD solving is implicitly least-squares. Thus, this method serves both purposes of exact solving and least-squares solving. - * In other words, the returned solution is guaranteed to minimize the Euclidean norm \f$ \Vert A x - b \Vert \f$. - */ - template<typename Rhs> - inline const internal::solve_retval<JacobiSVD, Rhs> - solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "JacobiSVD is not initialized."); - eigen_assert(computeU() && computeV() && "JacobiSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice)."); - return internal::solve_retval<JacobiSVD, Rhs>(*this, b.derived()); - } - - /** \returns the number of singular values that are not exactly 0 */ - Index nonzeroSingularValues() const - { - eigen_assert(m_isInitialized && "JacobiSVD is not initialized."); - return m_nonzeroSingularValues; - } - - /** \returns the rank of the matrix of which \c *this is the SVD. - * - * \note This method has to determine which singular values should be considered nonzero. - * For that, it uses the threshold value that you can control by calling - * setThreshold(const RealScalar&). - */ - inline Index rank() const - { - using std::abs; - eigen_assert(m_isInitialized && "JacobiSVD is not initialized."); - if(m_singularValues.size()==0) return 0; - RealScalar premultiplied_threshold = m_singularValues.coeff(0) * threshold(); - Index i = m_nonzeroSingularValues-1; - while(i>=0 && m_singularValues.coeff(i) < premultiplied_threshold) --i; - return i+1; - } - - /** Allows to prescribe a threshold to be used by certain methods, such as rank() and solve(), - * which need to determine when singular values are to be considered nonzero. - * This is not used for the SVD decomposition itself. - * - * When it needs to get the threshold value, Eigen calls threshold(). - * The default is \c NumTraits<Scalar>::epsilon() - * - * \param threshold The new value to use as the threshold. - * - * A singular value will be considered nonzero if its value is strictly greater than - * \f$ \vert singular value \vert \leqslant threshold \times \vert max singular value \vert \f$. - * - * If you want to come back to the default behavior, call setThreshold(Default_t) - */ - JacobiSVD& setThreshold(const RealScalar& threshold) - { - m_usePrescribedThreshold = true; - m_prescribedThreshold = threshold; - return *this; - } - - /** Allows to come back to the default behavior, letting Eigen use its default formula for - * determining the threshold. - * - * You should pass the special object Eigen::Default as parameter here. - * \code svd.setThreshold(Eigen::Default); \endcode - * - * See the documentation of setThreshold(const RealScalar&). - */ - JacobiSVD& setThreshold(Default_t) - { - m_usePrescribedThreshold = false; - return *this; - } - - /** Returns the threshold that will be used by certain methods such as rank(). - * - * See the documentation of setThreshold(const RealScalar&). - */ - RealScalar threshold() const - { - eigen_assert(m_isInitialized || m_usePrescribedThreshold); - return m_usePrescribedThreshold ? m_prescribedThreshold - : (std::max<Index>)(1,m_diagSize)*NumTraits<Scalar>::epsilon(); - } - - inline Index rows() const { return m_rows; } - inline Index cols() const { return m_cols; } - - private: - void allocate(Index rows, Index cols, unsigned int computationOptions); - - protected: - MatrixUType m_matrixU; - MatrixVType m_matrixV; - SingularValuesType m_singularValues; - WorkMatrixType m_workMatrix; - bool m_isInitialized, m_isAllocated, m_usePrescribedThreshold; - bool m_computeFullU, m_computeThinU; - bool m_computeFullV, m_computeThinV; - unsigned int m_computationOptions; - Index m_nonzeroSingularValues, m_rows, m_cols, m_diagSize; - RealScalar m_prescribedThreshold; - - template<typename __MatrixType, int _QRPreconditioner, bool _IsComplex> - friend struct internal::svd_precondition_2x2_block_to_be_real; - template<typename __MatrixType, int _QRPreconditioner, int _Case, bool _DoAnything> - friend struct internal::qr_preconditioner_impl; - - internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreColsThanRows> m_qr_precond_morecols; - internal::qr_preconditioner_impl<MatrixType, QRPreconditioner, internal::PreconditionIfMoreRowsThanCols> m_qr_precond_morerows; -}; - -template<typename MatrixType, int QRPreconditioner> -void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Index rows, Index cols, unsigned int computationOptions) -{ - eigen_assert(rows >= 0 && cols >= 0); - - if (m_isAllocated && - rows == m_rows && - cols == m_cols && - computationOptions == m_computationOptions) - { - return; - } - - m_rows = rows; - m_cols = cols; - m_isInitialized = false; - m_isAllocated = true; - m_computationOptions = computationOptions; - m_computeFullU = (computationOptions & ComputeFullU) != 0; - m_computeThinU = (computationOptions & ComputeThinU) != 0; - m_computeFullV = (computationOptions & ComputeFullV) != 0; - m_computeThinV = (computationOptions & ComputeThinV) != 0; - eigen_assert(!(m_computeFullU && m_computeThinU) && "JacobiSVD: you can't ask for both full and thin U"); - eigen_assert(!(m_computeFullV && m_computeThinV) && "JacobiSVD: you can't ask for both full and thin V"); - eigen_assert(EIGEN_IMPLIES(m_computeThinU || m_computeThinV, MatrixType::ColsAtCompileTime==Dynamic) && - "JacobiSVD: thin U and V are only available when your matrix has a dynamic number of columns."); - if (QRPreconditioner == FullPivHouseholderQRPreconditioner) - { - eigen_assert(!(m_computeThinU || m_computeThinV) && - "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. " - "Use the ColPivHouseholderQR preconditioner instead."); - } - m_diagSize = (std::min)(m_rows, m_cols); - m_singularValues.resize(m_diagSize); - if(RowsAtCompileTime==Dynamic) - m_matrixU.resize(m_rows, m_computeFullU ? m_rows - : m_computeThinU ? m_diagSize - : 0); - if(ColsAtCompileTime==Dynamic) - m_matrixV.resize(m_cols, m_computeFullV ? m_cols - : m_computeThinV ? m_diagSize - : 0); - m_workMatrix.resize(m_diagSize, m_diagSize); - - if(m_cols>m_rows) m_qr_precond_morecols.allocate(*this); - if(m_rows>m_cols) m_qr_precond_morerows.allocate(*this); -} - -template<typename MatrixType, int QRPreconditioner> -JacobiSVD<MatrixType, QRPreconditioner>& -JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsigned int computationOptions) -{ - using std::abs; - allocate(matrix.rows(), matrix.cols(), computationOptions); - - // currently we stop when we reach precision 2*epsilon as the last bit of precision can require an unreasonable number of iterations, - // only worsening the precision of U and V as we accumulate more rotations - const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon(); - - // limit for very small denormal numbers to be considered zero in order to avoid infinite loops (see bug 286) - const RealScalar considerAsZero = RealScalar(2) * std::numeric_limits<RealScalar>::denorm_min(); - - /*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */ - - if(!m_qr_precond_morecols.run(*this, matrix) && !m_qr_precond_morerows.run(*this, matrix)) - { - m_workMatrix = matrix.block(0,0,m_diagSize,m_diagSize); - if(m_computeFullU) m_matrixU.setIdentity(m_rows,m_rows); - if(m_computeThinU) m_matrixU.setIdentity(m_rows,m_diagSize); - if(m_computeFullV) m_matrixV.setIdentity(m_cols,m_cols); - if(m_computeThinV) m_matrixV.setIdentity(m_cols, m_diagSize); - } - - // Scaling factor to reducover/under-flows - RealScalar scale = m_workMatrix.cwiseAbs().maxCoeff(); - if(scale==RealScalar(0)) scale = RealScalar(1); - m_workMatrix /= scale; - - /*** step 2. The main Jacobi SVD iteration. ***/ - - bool finished = false; - while(!finished) - { - finished = true; - - // do a sweep: for all index pairs (p,q), perform SVD of the corresponding 2x2 sub-matrix - - for(Index p = 1; p < m_diagSize; ++p) - { - for(Index q = 0; q < p; ++q) - { - // if this 2x2 sub-matrix is not diagonal already... - // notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't - // keep us iterating forever. Similarly, small denormal numbers are considered zero. - RealScalar threshold = numext::maxi(considerAsZero, precision * numext::maxi(abs(m_workMatrix.coeff(p,p)), - abs(m_workMatrix.coeff(q,q)))); - if(numext::maxi(abs(m_workMatrix.coeff(p,q)),abs(m_workMatrix.coeff(q,p))) > threshold) - { - finished = false; - - // perform SVD decomposition of 2x2 sub-matrix corresponding to indices p,q to make it diagonal - internal::svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner>::run(m_workMatrix, *this, p, q); - JacobiRotation<RealScalar> j_left, j_right; - internal::real_2x2_jacobi_svd(m_workMatrix, p, q, &j_left, &j_right); - - // accumulate resulting Jacobi rotations - m_workMatrix.applyOnTheLeft(p,q,j_left); - if(computeU()) m_matrixU.applyOnTheRight(p,q,j_left.transpose()); - - m_workMatrix.applyOnTheRight(p,q,j_right); - if(computeV()) m_matrixV.applyOnTheRight(p,q,j_right); - } - } - } - } - - /*** step 3. The work matrix is now diagonal, so ensure it's positive so its diagonal entries are the singular values ***/ - - for(Index i = 0; i < m_diagSize; ++i) - { - RealScalar a = abs(m_workMatrix.coeff(i,i)); - m_singularValues.coeffRef(i) = a; - if(computeU() && (a!=RealScalar(0))) m_matrixU.col(i) *= m_workMatrix.coeff(i,i)/a; - } - - m_singularValues *= scale; - - /*** step 4. Sort singular values in descending order and compute the number of nonzero singular values ***/ - - m_nonzeroSingularValues = m_diagSize; - for(Index i = 0; i < m_diagSize; i++) - { - Index pos; - RealScalar maxRemainingSingularValue = m_singularValues.tail(m_diagSize-i).maxCoeff(&pos); - if(maxRemainingSingularValue == RealScalar(0)) - { - m_nonzeroSingularValues = i; - break; - } - if(pos) - { - pos += i; - std::swap(m_singularValues.coeffRef(i), m_singularValues.coeffRef(pos)); - if(computeU()) m_matrixU.col(pos).swap(m_matrixU.col(i)); - if(computeV()) m_matrixV.col(pos).swap(m_matrixV.col(i)); - } - } - - m_isInitialized = true; - return *this; -} - -namespace internal { -template<typename _MatrixType, int QRPreconditioner, typename Rhs> -struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs> - : solve_retval_base<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs> -{ - typedef JacobiSVD<_MatrixType, QRPreconditioner> JacobiSVDType; - EIGEN_MAKE_SOLVE_HELPERS(JacobiSVDType,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - eigen_assert(rhs().rows() == dec().rows()); - - // A = U S V^* - // So A^{-1} = V S^{-1} U^* - - Matrix<Scalar, Dynamic, Rhs::ColsAtCompileTime, 0, _MatrixType::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime> tmp; - Index rank = dec().rank(); - - tmp.noalias() = dec().matrixU().leftCols(rank).adjoint() * rhs(); - tmp = dec().singularValues().head(rank).asDiagonal().inverse() * tmp; - dst = dec().matrixV().leftCols(rank) * tmp; - } -}; -} // end namespace internal - -#ifndef __CUDACC__ -/** \svd_module - * - * \return the singular value decomposition of \c *this computed by two-sided - * Jacobi transformations. - * - * \sa class JacobiSVD - */ -template<typename Derived> -JacobiSVD<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::jacobiSvd(unsigned int computationOptions) const -{ - return JacobiSVD<PlainObject>(*this, computationOptions); -} -#endif // __CUDACC__ - -} // end namespace Eigen - -#endif // EIGEN_JACOBISVD_H diff --git a/third_party/eigen3/Eigen/src/SVD/JacobiSVD_MKL.h b/third_party/eigen3/Eigen/src/SVD/JacobiSVD_MKL.h deleted file mode 100644 index decda75405..0000000000 --- a/third_party/eigen3/Eigen/src/SVD/JacobiSVD_MKL.h +++ /dev/null @@ -1,92 +0,0 @@ -/* - Copyright (c) 2011, Intel Corporation. All rights reserved. - - Redistribution and use in source and binary forms, with or without modification, - are permitted provided that the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - * Neither the name of Intel Corporation nor the names of its contributors may - be used to endorse or promote products derived from this software without - specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND - ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED - WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR - ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON - ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - ******************************************************************************** - * Content : Eigen bindings to Intel(R) MKL - * Singular Value Decomposition - SVD. - ******************************************************************************** -*/ - -#ifndef EIGEN_JACOBISVD_MKL_H -#define EIGEN_JACOBISVD_MKL_H - -#include "Eigen/src/Core/util/MKL_support.h" - -namespace Eigen { - -/** \internal Specialization for the data types supported by MKL */ - -#define EIGEN_MKL_SVD(EIGTYPE, MKLTYPE, MKLRTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \ -template<> inline \ -JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>& \ -JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) \ -{ \ - typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \ - typedef MatrixType::Scalar Scalar; \ - typedef MatrixType::RealScalar RealScalar; \ - allocate(matrix.rows(), matrix.cols(), computationOptions); \ -\ - /*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \ - m_nonzeroSingularValues = m_diagSize; \ -\ - lapack_int lda = matrix.outerStride(), ldu, ldvt; \ - lapack_int matrix_order = MKLCOLROW; \ - char jobu, jobvt; \ - MKLTYPE *u, *vt, dummy; \ - jobu = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \ - jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \ - if (computeU()) { \ - ldu = m_matrixU.outerStride(); \ - u = (MKLTYPE*)m_matrixU.data(); \ - } else { ldu=1; u=&dummy; }\ - MatrixType localV; \ - ldvt = (m_computeFullV) ? m_cols : (m_computeThinV) ? m_diagSize : 1; \ - if (computeV()) { \ - localV.resize(ldvt, m_cols); \ - vt = (MKLTYPE*)localV.data(); \ - } else { ldvt=1; vt=&dummy; }\ - Matrix<MKLRTYPE, Dynamic, Dynamic> superb; superb.resize(m_diagSize, 1); \ - MatrixType m_temp; m_temp = matrix; \ - LAPACKE_##MKLPREFIX##gesvd( matrix_order, jobu, jobvt, m_rows, m_cols, (MKLTYPE*)m_temp.data(), lda, (MKLRTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \ - if (computeV()) m_matrixV = localV.adjoint(); \ - /* for(int i=0;i<m_diagSize;i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; m_singularValues.coeffRef(i)=RealScalar(0);}*/ \ - m_isInitialized = true; \ - return *this; \ -} - -EIGEN_MKL_SVD(double, double, double, d, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_SVD(float, float, float , s, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_SVD(dcomplex, MKL_Complex16, double, z, ColMajor, LAPACK_COL_MAJOR) -EIGEN_MKL_SVD(scomplex, MKL_Complex8, float , c, ColMajor, LAPACK_COL_MAJOR) - -EIGEN_MKL_SVD(double, double, double, d, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_SVD(float, float, float , s, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_SVD(dcomplex, MKL_Complex16, double, z, RowMajor, LAPACK_ROW_MAJOR) -EIGEN_MKL_SVD(scomplex, MKL_Complex8, float , c, RowMajor, LAPACK_ROW_MAJOR) - -} // end namespace Eigen - -#endif // EIGEN_JACOBISVD_MKL_H diff --git a/third_party/eigen3/Eigen/src/SVD/UpperBidiagonalization.h b/third_party/eigen3/Eigen/src/SVD/UpperBidiagonalization.h deleted file mode 100644 index 40067682c9..0000000000 --- a/third_party/eigen3/Eigen/src/SVD/UpperBidiagonalization.h +++ /dev/null @@ -1,396 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_BIDIAGONALIZATION_H -#define EIGEN_BIDIAGONALIZATION_H - -namespace Eigen { - -namespace internal { -// UpperBidiagonalization will probably be replaced by a Bidiagonalization class, don't want to make it stable API. -// At the same time, it's useful to keep for now as it's about the only thing that is testing the BandMatrix class. - -template<typename _MatrixType> class UpperBidiagonalization -{ - public: - - typedef _MatrixType MatrixType; - enum { - RowsAtCompileTime = MatrixType::RowsAtCompileTime, - ColsAtCompileTime = MatrixType::ColsAtCompileTime, - ColsAtCompileTimeMinusOne = internal::decrement_size<ColsAtCompileTime>::ret - }; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef Matrix<Scalar, 1, ColsAtCompileTime> RowVectorType; - typedef Matrix<Scalar, RowsAtCompileTime, 1> ColVectorType; - typedef BandMatrix<RealScalar, ColsAtCompileTime, ColsAtCompileTime, 1, 0, RowMajor> BidiagonalType; - typedef Matrix<Scalar, ColsAtCompileTime, 1> DiagVectorType; - typedef Matrix<Scalar, ColsAtCompileTimeMinusOne, 1> SuperDiagVectorType; - typedef HouseholderSequence< - const MatrixType, - CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Diagonal<const MatrixType,0> > - > HouseholderUSequenceType; - typedef HouseholderSequence< - const typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type, - Diagonal<const MatrixType,1>, - OnTheRight - > HouseholderVSequenceType; - - /** - * \brief Default Constructor. - * - * The default constructor is useful in cases in which the user intends to - * perform decompositions via Bidiagonalization::compute(const MatrixType&). - */ - UpperBidiagonalization() : m_householder(), m_bidiagonal(), m_isInitialized(false) {} - - UpperBidiagonalization(const MatrixType& matrix) - : m_householder(matrix.rows(), matrix.cols()), - m_bidiagonal(matrix.cols(), matrix.cols()), - m_isInitialized(false) - { - compute(matrix); - } - - UpperBidiagonalization& compute(const MatrixType& matrix); - UpperBidiagonalization& computeUnblocked(const MatrixType& matrix); - - const MatrixType& householder() const { return m_householder; } - const BidiagonalType& bidiagonal() const { return m_bidiagonal; } - - const HouseholderUSequenceType householderU() const - { - eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized."); - return HouseholderUSequenceType(m_householder, m_householder.diagonal().conjugate()); - } - - const HouseholderVSequenceType householderV() // const here gives nasty errors and i'm lazy - { - eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized."); - return HouseholderVSequenceType(m_householder.conjugate(), m_householder.const_derived().template diagonal<1>()) - .setLength(m_householder.cols()-1) - .setShift(1); - } - - protected: - MatrixType m_householder; - BidiagonalType m_bidiagonal; - bool m_isInitialized; -}; - -// Standard upper bidiagonalization without fancy optimizations -// This version should be faster for small matrix size -template<typename MatrixType> -void upperbidiagonalization_inplace_unblocked(MatrixType& mat, - typename MatrixType::RealScalar *diagonal, - typename MatrixType::RealScalar *upper_diagonal, - typename MatrixType::Scalar* tempData = 0) -{ - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - - Index rows = mat.rows(); - Index cols = mat.cols(); - - typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixType::MaxRowsAtCompileTime,1> TempType; - TempType tempVector; - if(tempData==0) - { - tempVector.resize(rows); - tempData = tempVector.data(); - } - - for (Index k = 0; /* breaks at k==cols-1 below */ ; ++k) - { - Index remainingRows = rows - k; - Index remainingCols = cols - k - 1; - - // construct left householder transform in-place in A - mat.col(k).tail(remainingRows) - .makeHouseholderInPlace(mat.coeffRef(k,k), diagonal[k]); - // apply householder transform to remaining part of A on the left - mat.bottomRightCorner(remainingRows, remainingCols) - .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), mat.coeff(k,k), tempData); - - if(k == cols-1) break; - - // construct right householder transform in-place in mat - mat.row(k).tail(remainingCols) - .makeHouseholderInPlace(mat.coeffRef(k,k+1), upper_diagonal[k]); - // apply householder transform to remaining part of mat on the left - mat.bottomRightCorner(remainingRows-1, remainingCols) - .applyHouseholderOnTheRight(mat.row(k).tail(remainingCols-1).transpose(), mat.coeff(k,k+1), tempData); - } -} - -/** \internal - * Helper routine for the block reduction to upper bidiagonal form. - * - * Let's partition the matrix A: - * - * | A00 A01 | - * A = | | - * | A10 A11 | - * - * This function reduces to bidiagonal form the left \c rows x \a blockSize vertical panel [A00/A10] - * and the \a blockSize x \c cols horizontal panel [A00 A01] of the matrix \a A. The bottom-right block A11 - * is updated using matrix-matrix products: - * A22 -= V * Y^T - X * U^T - * where V and U contains the left and right Householder vectors. U and V are stored in A10, and A01 - * respectively, and the update matrices X and Y are computed during the reduction. - * - */ -template<typename MatrixType> -void upperbidiagonalization_blocked_helper(MatrixType& A, - typename MatrixType::RealScalar *diagonal, - typename MatrixType::RealScalar *upper_diagonal, - typename MatrixType::Index bs, - Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic> > X, - Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic> > Y) -{ - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - typedef Ref<Matrix<Scalar, Dynamic, 1> > SubColumnType; - typedef Ref<Matrix<Scalar, 1, Dynamic>, 0, InnerStride<> > SubRowType; - typedef Ref<Matrix<Scalar, Dynamic, Dynamic> > SubMatType; - - Index brows = A.rows(); - Index bcols = A.cols(); - - Scalar tau_u, tau_u_prev(0), tau_v; - - for(Index k = 0; k < bs; ++k) - { - Index remainingRows = brows - k; - Index remainingCols = bcols - k - 1; - - SubMatType X_k1( X.block(k,0, remainingRows,k) ); - SubMatType V_k1( A.block(k,0, remainingRows,k) ); - - // 1 - update the k-th column of A - SubColumnType v_k = A.col(k).tail(remainingRows); - v_k -= V_k1 * Y.row(k).head(k).adjoint(); - if(k) v_k -= X_k1 * A.col(k).head(k); - - // 2 - construct left Householder transform in-place - v_k.makeHouseholderInPlace(tau_v, diagonal[k]); - - if(k+1<bcols) - { - SubMatType Y_k ( Y.block(k+1,0, remainingCols, k+1) ); - SubMatType U_k1 ( A.block(0,k+1, k,remainingCols) ); - - // this eases the application of Householder transforAions - // A(k,k) will store tau_v later - A(k,k) = Scalar(1); - - // 3 - Compute y_k^T = tau_v * ( A^T*v_k - Y_k-1*V_k-1^T*v_k - U_k-1*X_k-1^T*v_k ) - { - SubColumnType y_k( Y.col(k).tail(remainingCols) ); - - // let's use the begining of column k of Y as a temporary vector - SubColumnType tmp( Y.col(k).head(k) ); - y_k.noalias() = A.block(k,k+1, remainingRows,remainingCols).adjoint() * v_k; // bottleneck - tmp.noalias() = V_k1.adjoint() * v_k; - y_k.noalias() -= Y_k.leftCols(k) * tmp; - tmp.noalias() = X_k1.adjoint() * v_k; - y_k.noalias() -= U_k1.adjoint() * tmp; - y_k *= numext::conj(tau_v); - } - - // 4 - update k-th row of A (it will become u_k) - SubRowType u_k( A.row(k).tail(remainingCols) ); - u_k = u_k.conjugate(); - { - u_k -= Y_k * A.row(k).head(k+1).adjoint(); - if(k) u_k -= U_k1.adjoint() * X.row(k).head(k).adjoint(); - } - - // 5 - construct right Householder transform in-placecols - u_k.makeHouseholderInPlace(tau_u, upper_diagonal[k]); - - // this eases the application of Householder transforAions - // A(k,k+1) will store tau_u later - A(k,k+1) = Scalar(1); - - // 6 - Compute x_k = tau_u * ( A*u_k - X_k-1*U_k-1^T*u_k - V_k*Y_k^T*u_k ) - { - SubColumnType x_k ( X.col(k).tail(remainingRows-1) ); - - // let's use the begining of column k of X as a temporary vectors - // note that tmp0 and tmp1 overlaps - SubColumnType tmp0 ( X.col(k).head(k) ), - tmp1 ( X.col(k).head(k+1) ); - - x_k.noalias() = A.block(k+1,k+1, remainingRows-1,remainingCols) * u_k.transpose(); // bottleneck - tmp0.noalias() = U_k1 * u_k.transpose(); - x_k.noalias() -= X_k1.bottomRows(remainingRows-1) * tmp0; - tmp1.noalias() = Y_k.adjoint() * u_k.transpose(); - x_k.noalias() -= A.block(k+1,0, remainingRows-1,k+1) * tmp1; - x_k *= numext::conj(tau_u); - tau_u = numext::conj(tau_u); - u_k = u_k.conjugate(); - } - - if(k>0) A.coeffRef(k-1,k) = tau_u_prev; - tau_u_prev = tau_u; - } - else - A.coeffRef(k-1,k) = tau_u_prev; - - A.coeffRef(k,k) = tau_v; - } - - if(bs<bcols) - A.coeffRef(bs-1,bs) = tau_u_prev; - - // update A22 - if(bcols>bs && brows>bs) - { - SubMatType A11( A.bottomRightCorner(brows-bs,bcols-bs) ); - SubMatType A10( A.block(bs,0, brows-bs,bs) ); - SubMatType A01( A.block(0,bs, bs,bcols-bs) ); - Scalar tmp = A01(bs-1,0); - A01(bs-1,0) = 1; - A11.noalias() -= A10 * Y.topLeftCorner(bcols,bs).bottomRows(bcols-bs).adjoint(); - A11.noalias() -= X.topLeftCorner(brows,bs).bottomRows(brows-bs) * A01; - A01(bs-1,0) = tmp; - } -} - -/** \internal - * - * Implementation of a block-bidiagonal reduction. - * It is based on the following paper: - * The Design of a Parallel Dense Linear Algebra Software Library: Reduction to Hessenberg, Tridiagonal, and Bidiagonal Form. - * by Jaeyoung Choi, Jack J. Dongarra, David W. Walker. (1995) - * section 3.3 - */ -template<typename MatrixType, typename BidiagType> -void upperbidiagonalization_inplace_blocked(MatrixType& A, BidiagType& bidiagonal, - typename MatrixType::Index maxBlockSize=32, - typename MatrixType::Scalar* /*tempData*/ = 0) -{ - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - typedef Block<MatrixType,Dynamic,Dynamic> BlockType; - - Index rows = A.rows(); - Index cols = A.cols(); - Index size = (std::min)(rows, cols); - - Matrix<Scalar,MatrixType::RowsAtCompileTime,Dynamic,ColMajor,MatrixType::MaxRowsAtCompileTime> X(rows,maxBlockSize); - Matrix<Scalar,MatrixType::ColsAtCompileTime,Dynamic,ColMajor,MatrixType::MaxColsAtCompileTime> Y(cols,maxBlockSize); - Index blockSize = (std::min)(maxBlockSize,size); - - Index k = 0; - for(k = 0; k < size; k += blockSize) - { - Index bs = (std::min)(size-k,blockSize); // actual size of the block - Index brows = rows - k; // rows of the block - Index bcols = cols - k; // columns of the block - - // partition the matrix A: - // - // | A00 A01 A02 | - // | | - // A = | A10 A11 A12 | - // | | - // | A20 A21 A22 | - // - // where A11 is a bs x bs diagonal block, - // and let: - // | A11 A12 | - // B = | | - // | A21 A22 | - - BlockType B = A.block(k,k,brows,bcols); - - // This stage performs the bidiagonalization of A11, A21, A12, and updating of A22. - // Finally, the algorithm continue on the updated A22. - // - // However, if B is too small, or A22 empty, then let's use an unblocked strategy - if(k+bs==cols || bcols<48) // somewhat arbitrary threshold - { - upperbidiagonalization_inplace_unblocked(B, - &(bidiagonal.template diagonal<0>().coeffRef(k)), - &(bidiagonal.template diagonal<1>().coeffRef(k)), - X.data() - ); - break; // We're done - } - else - { - upperbidiagonalization_blocked_helper<BlockType>( B, - &(bidiagonal.template diagonal<0>().coeffRef(k)), - &(bidiagonal.template diagonal<1>().coeffRef(k)), - bs, - X.topLeftCorner(brows,bs), - Y.topLeftCorner(bcols,bs) - ); - } - } -} - -template<typename _MatrixType> -UpperBidiagonalization<_MatrixType>& UpperBidiagonalization<_MatrixType>::computeUnblocked(const _MatrixType& matrix) -{ - Index rows = matrix.rows(); - Index cols = matrix.cols(); - - eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols."); - - m_householder = matrix; - - ColVectorType temp(rows); - - upperbidiagonalization_inplace_unblocked(m_householder, - &(m_bidiagonal.template diagonal<0>().coeffRef(0)), - &(m_bidiagonal.template diagonal<1>().coeffRef(0)), - temp.data()); - - m_isInitialized = true; - return *this; -} - -template<typename _MatrixType> -UpperBidiagonalization<_MatrixType>& UpperBidiagonalization<_MatrixType>::compute(const _MatrixType& matrix) -{ - Index rows = matrix.rows(); - Index cols = matrix.cols(); - - eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols."); - - m_householder = matrix; - upperbidiagonalization_inplace_blocked(m_householder, m_bidiagonal); - - m_isInitialized = true; - return *this; -} - -#if 0 -/** \return the Householder QR decomposition of \c *this. - * - * \sa class Bidiagonalization - */ -template<typename Derived> -const UpperBidiagonalization<typename MatrixBase<Derived>::PlainObject> -MatrixBase<Derived>::bidiagonalization() const -{ - return UpperBidiagonalization<PlainObject>(eval()); -} -#endif - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_BIDIAGONALIZATION_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/AmbiVector.h b/third_party/eigen3/Eigen/src/SparseCore/AmbiVector.h deleted file mode 100644 index 17fff96a78..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/AmbiVector.h +++ /dev/null @@ -1,373 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_AMBIVECTOR_H -#define EIGEN_AMBIVECTOR_H - -namespace Eigen { - -namespace internal { - -/** \internal - * Hybrid sparse/dense vector class designed for intensive read-write operations. - * - * See BasicSparseLLT and SparseProduct for usage examples. - */ -template<typename _Scalar, typename _Index> -class AmbiVector -{ - public: - typedef _Scalar Scalar; - typedef _Index Index; - typedef typename NumTraits<Scalar>::Real RealScalar; - - AmbiVector(Index size) - : m_buffer(0), m_zero(0), m_size(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1) - { - resize(size); - } - - void init(double estimatedDensity); - void init(int mode); - - Index nonZeros() const; - - /** Specifies a sub-vector to work on */ - void setBounds(Index start, Index end) { m_start = start; m_end = end; } - - void setZero(); - - void restart(); - Scalar& coeffRef(Index i); - Scalar& coeff(Index i); - - class Iterator; - - ~AmbiVector() { delete[] m_buffer; } - - void resize(Index size) - { - if (m_allocatedSize < size) - reallocate(size); - m_size = size; - } - - Index size() const { return m_size; } - - protected: - - void reallocate(Index size) - { - // if the size of the matrix is not too large, let's allocate a bit more than needed such - // that we can handle dense vector even in sparse mode. - delete[] m_buffer; - if (size<1000) - { - Index allocSize = (size * sizeof(ListEl))/sizeof(Scalar); - m_allocatedElements = (allocSize*sizeof(Scalar))/sizeof(ListEl); - m_buffer = new Scalar[allocSize]; - } - else - { - m_allocatedElements = (size*sizeof(Scalar))/sizeof(ListEl); - m_buffer = new Scalar[size]; - } - m_size = size; - m_start = 0; - m_end = m_size; - } - - void reallocateSparse() - { - Index copyElements = m_allocatedElements; - m_allocatedElements = (std::min)(Index(m_allocatedElements*1.5),m_size); - Index allocSize = m_allocatedElements * sizeof(ListEl); - allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0); - Scalar* newBuffer = new Scalar[allocSize]; - memcpy(newBuffer, m_buffer, copyElements * sizeof(ListEl)); - delete[] m_buffer; - m_buffer = newBuffer; - } - - protected: - // element type of the linked list - struct ListEl - { - Index next; - Index index; - Scalar value; - }; - - // used to store data in both mode - Scalar* m_buffer; - Scalar m_zero; - Index m_size; - Index m_start; - Index m_end; - Index m_allocatedSize; - Index m_allocatedElements; - Index m_mode; - - // linked list mode - Index m_llStart; - Index m_llCurrent; - Index m_llSize; -}; - -/** \returns the number of non zeros in the current sub vector */ -template<typename _Scalar,typename _Index> -_Index AmbiVector<_Scalar,_Index>::nonZeros() const -{ - if (m_mode==IsSparse) - return m_llSize; - else - return m_end - m_start; -} - -template<typename _Scalar,typename _Index> -void AmbiVector<_Scalar,_Index>::init(double estimatedDensity) -{ - if (estimatedDensity>0.1) - init(IsDense); - else - init(IsSparse); -} - -template<typename _Scalar,typename _Index> -void AmbiVector<_Scalar,_Index>::init(int mode) -{ - m_mode = mode; - if (m_mode==IsSparse) - { - m_llSize = 0; - m_llStart = -1; - } -} - -/** Must be called whenever we might perform a write access - * with an index smaller than the previous one. - * - * Don't worry, this function is extremely cheap. - */ -template<typename _Scalar,typename _Index> -void AmbiVector<_Scalar,_Index>::restart() -{ - m_llCurrent = m_llStart; -} - -/** Set all coefficients of current subvector to zero */ -template<typename _Scalar,typename _Index> -void AmbiVector<_Scalar,_Index>::setZero() -{ - if (m_mode==IsDense) - { - for (Index i=m_start; i<m_end; ++i) - m_buffer[i] = Scalar(0); - } - else - { - eigen_assert(m_mode==IsSparse); - m_llSize = 0; - m_llStart = -1; - } -} - -template<typename _Scalar,typename _Index> -_Scalar& AmbiVector<_Scalar,_Index>::coeffRef(_Index i) -{ - if (m_mode==IsDense) - return m_buffer[i]; - else - { - ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer); - // TODO factorize the following code to reduce code generation - eigen_assert(m_mode==IsSparse); - if (m_llSize==0) - { - // this is the first element - m_llStart = 0; - m_llCurrent = 0; - ++m_llSize; - llElements[0].value = Scalar(0); - llElements[0].index = i; - llElements[0].next = -1; - return llElements[0].value; - } - else if (i<llElements[m_llStart].index) - { - // this is going to be the new first element of the list - ListEl& el = llElements[m_llSize]; - el.value = Scalar(0); - el.index = i; - el.next = m_llStart; - m_llStart = m_llSize; - ++m_llSize; - m_llCurrent = m_llStart; - return el.value; - } - else - { - Index nextel = llElements[m_llCurrent].next; - eigen_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index"); - while (nextel >= 0 && llElements[nextel].index<=i) - { - m_llCurrent = nextel; - nextel = llElements[nextel].next; - } - - if (llElements[m_llCurrent].index==i) - { - // the coefficient already exists and we found it ! - return llElements[m_llCurrent].value; - } - else - { - if (m_llSize>=m_allocatedElements) - { - reallocateSparse(); - llElements = reinterpret_cast<ListEl*>(m_buffer); - } - eigen_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode"); - // let's insert a new coefficient - ListEl& el = llElements[m_llSize]; - el.value = Scalar(0); - el.index = i; - el.next = llElements[m_llCurrent].next; - llElements[m_llCurrent].next = m_llSize; - ++m_llSize; - return el.value; - } - } - } -} - -template<typename _Scalar,typename _Index> -_Scalar& AmbiVector<_Scalar,_Index>::coeff(_Index i) -{ - if (m_mode==IsDense) - return m_buffer[i]; - else - { - ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer); - eigen_assert(m_mode==IsSparse); - if ((m_llSize==0) || (i<llElements[m_llStart].index)) - { - return m_zero; - } - else - { - Index elid = m_llStart; - while (elid >= 0 && llElements[elid].index<i) - elid = llElements[elid].next; - - if (llElements[elid].index==i) - return llElements[m_llCurrent].value; - else - return m_zero; - } - } -} - -/** Iterator over the nonzero coefficients */ -template<typename _Scalar,typename _Index> -class AmbiVector<_Scalar,_Index>::Iterator -{ - public: - typedef _Scalar Scalar; - typedef typename NumTraits<Scalar>::Real RealScalar; - - /** Default constructor - * \param vec the vector on which we iterate - * \param epsilon the minimal value used to prune zero coefficients. - * In practice, all coefficients having a magnitude smaller than \a epsilon - * are skipped. - */ - Iterator(const AmbiVector& vec, const RealScalar& epsilon = 0) - : m_vector(vec) - { - using std::abs; - m_epsilon = epsilon; - m_isDense = m_vector.m_mode==IsDense; - if (m_isDense) - { - m_currentEl = 0; // this is to avoid a compilation warning - m_cachedValue = 0; // this is to avoid a compilation warning - m_cachedIndex = m_vector.m_start-1; - ++(*this); - } - else - { - ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer); - m_currentEl = m_vector.m_llStart; - while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<=m_epsilon) - m_currentEl = llElements[m_currentEl].next; - if (m_currentEl<0) - { - m_cachedValue = 0; // this is to avoid a compilation warning - m_cachedIndex = -1; - } - else - { - m_cachedIndex = llElements[m_currentEl].index; - m_cachedValue = llElements[m_currentEl].value; - } - } - } - - Index index() const { return m_cachedIndex; } - Scalar value() const { return m_cachedValue; } - - operator bool() const { return m_cachedIndex>=0; } - - Iterator& operator++() - { - using std::abs; - if (m_isDense) - { - do { - ++m_cachedIndex; - } while (m_cachedIndex<m_vector.m_end && abs(m_vector.m_buffer[m_cachedIndex])<m_epsilon); - if (m_cachedIndex<m_vector.m_end) - m_cachedValue = m_vector.m_buffer[m_cachedIndex]; - else - m_cachedIndex=-1; - } - else - { - ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer); - do { - m_currentEl = llElements[m_currentEl].next; - } while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<m_epsilon); - if (m_currentEl<0) - { - m_cachedIndex = -1; - } - else - { - m_cachedIndex = llElements[m_currentEl].index; - m_cachedValue = llElements[m_currentEl].value; - } - } - return *this; - } - - protected: - const AmbiVector& m_vector; // the target vector - Index m_currentEl; // the current element in sparse/linked-list mode - RealScalar m_epsilon; // epsilon used to prune zero coefficients - Index m_cachedIndex; // current coordinate - Scalar m_cachedValue; // current value - bool m_isDense; // mode of the vector -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_AMBIVECTOR_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/CompressedStorage.h b/third_party/eigen3/Eigen/src/SparseCore/CompressedStorage.h deleted file mode 100644 index ab3989ce28..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/CompressedStorage.h +++ /dev/null @@ -1,235 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_COMPRESSED_STORAGE_H -#define EIGEN_COMPRESSED_STORAGE_H - -namespace Eigen { - -namespace internal { - -/** \internal - * Stores a sparse set of values as a list of values and a list of indices. - * - */ -template<typename _Scalar,typename _Index> -class CompressedStorage -{ - public: - - typedef _Scalar Scalar; - typedef _Index Index; - - protected: - - typedef typename NumTraits<Scalar>::Real RealScalar; - - public: - - CompressedStorage() - : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0) - {} - - CompressedStorage(size_t size) - : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0) - { - resize(size); - } - - CompressedStorage(const CompressedStorage& other) - : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0) - { - *this = other; - } - - CompressedStorage& operator=(const CompressedStorage& other) - { - resize(other.size()); - internal::smart_copy(other.m_values, other.m_values + m_size, m_values); - internal::smart_copy(other.m_indices, other.m_indices + m_size, m_indices); - return *this; - } - - void swap(CompressedStorage& other) - { - std::swap(m_values, other.m_values); - std::swap(m_indices, other.m_indices); - std::swap(m_size, other.m_size); - std::swap(m_allocatedSize, other.m_allocatedSize); - } - - ~CompressedStorage() - { - delete[] m_values; - delete[] m_indices; - } - - void reserve(size_t size) - { - size_t newAllocatedSize = m_size + size; - if (newAllocatedSize > m_allocatedSize) - reallocate(newAllocatedSize); - } - - void squeeze() - { - if (m_allocatedSize>m_size) - reallocate(m_size); - } - - void resize(size_t size, float reserveSizeFactor = 0) - { - if (m_allocatedSize<size) - reallocate(size + size_t(reserveSizeFactor*size)); - m_size = size; - } - - void append(const Scalar& v, Index i) - { - Index id = static_cast<Index>(m_size); - resize(m_size+1, 1); - m_values[id] = v; - m_indices[id] = i; - } - - inline size_t size() const { return m_size; } - inline size_t allocatedSize() const { return m_allocatedSize; } - inline void clear() { m_size = 0; } - - inline Scalar& value(size_t i) { return m_values[i]; } - inline const Scalar& value(size_t i) const { return m_values[i]; } - - inline Index& index(size_t i) { return m_indices[i]; } - inline const Index& index(size_t i) const { return m_indices[i]; } - - static CompressedStorage Map(Index* indices, Scalar* values, size_t size) - { - CompressedStorage res; - res.m_indices = indices; - res.m_values = values; - res.m_allocatedSize = res.m_size = size; - return res; - } - - /** \returns the largest \c k such that for all \c j in [0,k) index[\c j]\<\a key */ - inline Index searchLowerIndex(Index key) const - { - return searchLowerIndex(0, m_size, key); - } - - /** \returns the largest \c k in [start,end) such that for all \c j in [start,k) index[\c j]\<\a key */ - inline Index searchLowerIndex(size_t start, size_t end, Index key) const - { - while(end>start) - { - size_t mid = (end+start)>>1; - if (m_indices[mid]<key) - start = mid+1; - else - end = mid; - } - return static_cast<Index>(start); - } - - /** \returns the stored value at index \a key - * If the value does not exist, then the value \a defaultValue is returned without any insertion. */ - inline Scalar at(Index key, const Scalar& defaultValue = Scalar(0)) const - { - if (m_size==0) - return defaultValue; - else if (key==m_indices[m_size-1]) - return m_values[m_size-1]; - // ^^ optimization: let's first check if it is the last coefficient - // (very common in high level algorithms) - const size_t id = searchLowerIndex(0,m_size-1,key); - return ((id<m_size) && (m_indices[id]==key)) ? m_values[id] : defaultValue; - } - - /** Like at(), but the search is performed in the range [start,end) */ - inline Scalar atInRange(size_t start, size_t end, Index key, const Scalar& defaultValue = Scalar(0)) const - { - if (start>=end) - return Scalar(0); - else if (end>start && key==m_indices[end-1]) - return m_values[end-1]; - // ^^ optimization: let's first check if it is the last coefficient - // (very common in high level algorithms) - const size_t id = searchLowerIndex(start,end-1,key); - return ((id<end) && (m_indices[id]==key)) ? m_values[id] : defaultValue; - } - - /** \returns a reference to the value at index \a key - * If the value does not exist, then the value \a defaultValue is inserted - * such that the keys are sorted. */ - inline Scalar& atWithInsertion(Index key, const Scalar& defaultValue = Scalar(0)) - { - size_t id = searchLowerIndex(0,m_size,key); - if (id>=m_size || m_indices[id]!=key) - { - resize(m_size+1,1); - for (size_t j=m_size-1; j>id; --j) - { - m_indices[j] = m_indices[j-1]; - m_values[j] = m_values[j-1]; - } - m_indices[id] = key; - m_values[id] = defaultValue; - } - return m_values[id]; - } - - void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) - { - size_t k = 0; - size_t n = size(); - for (size_t i=0; i<n; ++i) - { - if (!internal::isMuchSmallerThan(value(i), reference, epsilon)) - { - value(k) = value(i); - index(k) = index(i); - ++k; - } - } - resize(k,0); - } - - protected: - - inline void reallocate(size_t size) - { - Scalar* newValues = new Scalar[size]; - Index* newIndices = new Index[size]; - size_t copySize = (std::min)(size, m_size); - // copy - if (copySize>0) { - internal::smart_copy(m_values, m_values+copySize, newValues); - internal::smart_copy(m_indices, m_indices+copySize, newIndices); - } - // delete old stuff - delete[] m_values; - delete[] m_indices; - m_values = newValues; - m_indices = newIndices; - m_allocatedSize = size; - } - - protected: - Scalar* m_values; - Index* m_indices; - size_t m_size; - size_t m_allocatedSize; - -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_COMPRESSED_STORAGE_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/third_party/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h deleted file mode 100644 index 5c320e2d2d..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h +++ /dev/null @@ -1,245 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H -#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H - -namespace Eigen { - -namespace internal { - -template<typename Lhs, typename Rhs, typename ResultType> -static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res) -{ - typedef typename remove_all<Lhs>::type::Scalar Scalar; - typedef typename remove_all<Lhs>::type::Index Index; - - // make sure to call innerSize/outerSize since we fake the storage order. - Index rows = lhs.innerSize(); - Index cols = rhs.outerSize(); - eigen_assert(lhs.outerSize() == rhs.innerSize()); - - std::vector<bool> mask(rows,false); - Matrix<Scalar,Dynamic,1> values(rows); - Matrix<Index,Dynamic,1> indices(rows); - - // estimate the number of non zero entries - // given a rhs column containing Y non zeros, we assume that the respective Y columns - // of the lhs differs in average of one non zeros, thus the number of non zeros for - // the product of a rhs column with the lhs is X+Y where X is the average number of non zero - // per column of the lhs. - // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs) - Index estimated_nnz_prod = lhs.nonZeros() + rhs.nonZeros(); - - res.setZero(); - res.reserve(Index(estimated_nnz_prod)); - // we compute each column of the result, one after the other - for (Index j=0; j<cols; ++j) - { - - res.startVec(j); - Index nnz = 0; - for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt) - { - Scalar y = rhsIt.value(); - Index k = rhsIt.index(); - for (typename Lhs::InnerIterator lhsIt(lhs, k); lhsIt; ++lhsIt) - { - Index i = lhsIt.index(); - Scalar x = lhsIt.value(); - if(!mask[i]) - { - mask[i] = true; - values[i] = x * y; - indices[nnz] = i; - ++nnz; - } - else - values[i] += x * y; - } - } - - // unordered insertion - for(Index k=0; k<nnz; ++k) - { - Index i = indices[k]; - res.insertBackByOuterInnerUnordered(j,i) = values[i]; - mask[i] = false; - } - -#if 0 - // alternative ordered insertion code: - - Index t200 = rows/(log2(200)*1.39); - Index t = (rows*100)/139; - - // FIXME reserve nnz non zeros - // FIXME implement fast sort algorithms for very small nnz - // if the result is sparse enough => use a quick sort - // otherwise => loop through the entire vector - // In order to avoid to perform an expensive log2 when the - // result is clearly very sparse we use a linear bound up to 200. - //if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t) - //res.startVec(j); - if(true) - { - if(nnz>1) std::sort(indices.data(),indices.data()+nnz); - for(Index k=0; k<nnz; ++k) - { - Index i = indices[k]; - res.insertBackByOuterInner(j,i) = values[i]; - mask[i] = false; - } - } - else - { - // dense path - for(Index i=0; i<rows; ++i) - { - if(mask[i]) - { - mask[i] = false; - res.insertBackByOuterInner(j,i) = values[i]; - } - } - } -#endif - - } - res.finalize(); -} - - -} // end namespace internal - -namespace internal { - -template<typename Lhs, typename Rhs, typename ResultType, - int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor, - int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor, - int ResStorageOrder = (traits<ResultType>::Flags&RowMajorBit) ? RowMajor : ColMajor> -struct conservative_sparse_sparse_product_selector; - -template<typename Lhs, typename Rhs, typename ResultType> -struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor> -{ - typedef typename remove_all<Lhs>::type LhsCleaned; - typedef typename LhsCleaned::Scalar Scalar; - - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) - { - typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::Index> RowMajorMatrix; - typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> ColMajorMatrix; - ColMajorMatrix resCol(lhs.rows(),rhs.cols()); - internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol); - // sort the non zeros: - RowMajorMatrix resRow(resCol); - res = resRow; - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor> -{ - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) - { - typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::Index> RowMajorMatrix; - RowMajorMatrix rhsRow = rhs; - RowMajorMatrix resRow(lhs.rows(), rhs.cols()); - internal::conservative_sparse_sparse_product_impl<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow); - res = resRow; - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor> -{ - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) - { - typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::Index> RowMajorMatrix; - RowMajorMatrix lhsRow = lhs; - RowMajorMatrix resRow(lhs.rows(), rhs.cols()); - internal::conservative_sparse_sparse_product_impl<Rhs,RowMajorMatrix,RowMajorMatrix>(rhs, lhsRow, resRow); - res = resRow; - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor> -{ - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) - { - typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::Index> RowMajorMatrix; - RowMajorMatrix resRow(lhs.rows(), rhs.cols()); - internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow); - res = resRow; - } -}; - - -template<typename Lhs, typename Rhs, typename ResultType> -struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor> -{ - typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar; - - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) - { - typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> ColMajorMatrix; - ColMajorMatrix resCol(lhs.rows(), rhs.cols()); - internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol); - res = resCol; - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor> -{ - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) - { - typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> ColMajorMatrix; - ColMajorMatrix lhsCol = lhs; - ColMajorMatrix resCol(lhs.rows(), rhs.cols()); - internal::conservative_sparse_sparse_product_impl<ColMajorMatrix,Rhs,ColMajorMatrix>(lhsCol, rhs, resCol); - res = resCol; - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor> -{ - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) - { - typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> ColMajorMatrix; - ColMajorMatrix rhsCol = rhs; - ColMajorMatrix resCol(lhs.rows(), rhs.cols()); - internal::conservative_sparse_sparse_product_impl<Lhs,ColMajorMatrix,ColMajorMatrix>(lhs, rhsCol, resCol); - res = resCol; - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor> -{ - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) - { - typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename ResultType::Index> RowMajorMatrix; - typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> ColMajorMatrix; - RowMajorMatrix resRow(lhs.rows(),rhs.cols()); - internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow); - // sort the non zeros: - ColMajorMatrix resCol(resRow); - res = resCol; - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h b/third_party/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h deleted file mode 100644 index ab1a266a90..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/MappedSparseMatrix.h +++ /dev/null @@ -1,181 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MAPPED_SPARSEMATRIX_H -#define EIGEN_MAPPED_SPARSEMATRIX_H - -namespace Eigen { - -/** \class MappedSparseMatrix - * - * \brief Sparse matrix - * - * \param _Scalar the scalar type, i.e. the type of the coefficients - * - * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme. - * - */ -namespace internal { -template<typename _Scalar, int _Flags, typename _Index> -struct traits<MappedSparseMatrix<_Scalar, _Flags, _Index> > : traits<SparseMatrix<_Scalar, _Flags, _Index> > -{}; -} - -template<typename _Scalar, int _Flags, typename _Index> -class MappedSparseMatrix - : public SparseMatrixBase<MappedSparseMatrix<_Scalar, _Flags, _Index> > -{ - public: - EIGEN_SPARSE_PUBLIC_INTERFACE(MappedSparseMatrix) - enum { IsRowMajor = Base::IsRowMajor }; - - protected: - - Index m_outerSize; - Index m_innerSize; - Index m_nnz; - Index* m_outerIndex; - Index* m_innerIndices; - Scalar* m_values; - - public: - - inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; } - inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; } - inline Index innerSize() const { return m_innerSize; } - inline Index outerSize() const { return m_outerSize; } - - bool isCompressed() const { return true; } - - //---------------------------------------- - // direct access interface - inline const Scalar* valuePtr() const { return m_values; } - inline Scalar* valuePtr() { return m_values; } - - inline const Index* innerIndexPtr() const { return m_innerIndices; } - inline Index* innerIndexPtr() { return m_innerIndices; } - - inline const Index* outerIndexPtr() const { return m_outerIndex; } - inline Index* outerIndexPtr() { return m_outerIndex; } - //---------------------------------------- - - inline Scalar coeff(Index row, Index col) const - { - const Index outer = IsRowMajor ? row : col; - const Index inner = IsRowMajor ? col : row; - - Index start = m_outerIndex[outer]; - Index end = m_outerIndex[outer+1]; - if (start==end) - return Scalar(0); - else if (end>0 && inner==m_innerIndices[end-1]) - return m_values[end-1]; - // ^^ optimization: let's first check if it is the last coefficient - // (very common in high level algorithms) - - const Index* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end-1],inner); - const Index id = r-&m_innerIndices[0]; - return ((*r==inner) && (id<end)) ? m_values[id] : Scalar(0); - } - - inline Scalar& coeffRef(Index row, Index col) - { - const Index outer = IsRowMajor ? row : col; - const Index inner = IsRowMajor ? col : row; - - Index start = m_outerIndex[outer]; - Index end = m_outerIndex[outer+1]; - eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix"); - eigen_assert(end>start && "coeffRef cannot be called on a zero coefficient"); - Index* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end],inner); - const Index id = r-&m_innerIndices[0]; - eigen_assert((*r==inner) && (id<end) && "coeffRef cannot be called on a zero coefficient"); - return m_values[id]; - } - - class InnerIterator; - class ReverseInnerIterator; - - /** \returns the number of non zero coefficients */ - inline Index nonZeros() const { return m_nnz; } - - inline MappedSparseMatrix(Index rows, Index cols, Index nnz, Index* outerIndexPtr, Index* innerIndexPtr, Scalar* valuePtr) - : m_outerSize(IsRowMajor?rows:cols), m_innerSize(IsRowMajor?cols:rows), m_nnz(nnz), m_outerIndex(outerIndexPtr), - m_innerIndices(innerIndexPtr), m_values(valuePtr) - {} - - /** Empty destructor */ - inline ~MappedSparseMatrix() {} -}; - -template<typename Scalar, int _Flags, typename _Index> -class MappedSparseMatrix<Scalar,_Flags,_Index>::InnerIterator -{ - public: - InnerIterator(const MappedSparseMatrix& mat, Index outer) - : m_matrix(mat), - m_outer(outer), - m_id(mat.outerIndexPtr()[outer]), - m_start(m_id), - m_end(mat.outerIndexPtr()[outer+1]) - {} - - inline InnerIterator& operator++() { m_id++; return *this; } - - inline Scalar value() const { return m_matrix.valuePtr()[m_id]; } - inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_id]); } - - inline Index index() const { return m_matrix.innerIndexPtr()[m_id]; } - inline Index row() const { return IsRowMajor ? m_outer : index(); } - inline Index col() const { return IsRowMajor ? index() : m_outer; } - - inline operator bool() const { return (m_id < m_end) && (m_id>=m_start); } - - protected: - const MappedSparseMatrix& m_matrix; - const Index m_outer; - Index m_id; - const Index m_start; - const Index m_end; -}; - -template<typename Scalar, int _Flags, typename _Index> -class MappedSparseMatrix<Scalar,_Flags,_Index>::ReverseInnerIterator -{ - public: - ReverseInnerIterator(const MappedSparseMatrix& mat, Index outer) - : m_matrix(mat), - m_outer(outer), - m_id(mat.outerIndexPtr()[outer+1]), - m_start(mat.outerIndexPtr()[outer]), - m_end(m_id) - {} - - inline ReverseInnerIterator& operator--() { m_id--; return *this; } - - inline Scalar value() const { return m_matrix.valuePtr()[m_id-1]; } - inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_id-1]); } - - inline Index index() const { return m_matrix.innerIndexPtr()[m_id-1]; } - inline Index row() const { return IsRowMajor ? m_outer : index(); } - inline Index col() const { return IsRowMajor ? index() : m_outer; } - - inline operator bool() const { return (m_id <= m_end) && (m_id>m_start); } - - protected: - const MappedSparseMatrix& m_matrix; - const Index m_outer; - Index m_id; - const Index m_start; - const Index m_end; -}; - -} // end namespace Eigen - -#endif // EIGEN_MAPPED_SPARSEMATRIX_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseBlock.h b/third_party/eigen3/Eigen/src/SparseCore/SparseBlock.h deleted file mode 100644 index 3a6d8a275c..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseBlock.h +++ /dev/null @@ -1,547 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_BLOCK_H -#define EIGEN_SPARSE_BLOCK_H - -namespace Eigen { - -template<typename XprType, int BlockRows, int BlockCols> -class BlockImpl<XprType,BlockRows,BlockCols,true,Sparse> - : public SparseMatrixBase<Block<XprType,BlockRows,BlockCols,true> > -{ - typedef typename internal::remove_all<typename XprType::Nested>::type _MatrixTypeNested; - typedef Block<XprType, BlockRows, BlockCols, true> BlockType; -public: - enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor }; -protected: - enum { OuterSize = IsRowMajor ? BlockRows : BlockCols }; -public: - EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType) - - class InnerIterator: public XprType::InnerIterator - { - typedef typename BlockImpl::Index Index; - public: - inline InnerIterator(const BlockType& xpr, Index outer) - : XprType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer) - {} - inline Index row() const { return IsRowMajor ? m_outer : this->index(); } - inline Index col() const { return IsRowMajor ? this->index() : m_outer; } - protected: - Index m_outer; - }; - class ReverseInnerIterator: public XprType::ReverseInnerIterator - { - typedef typename BlockImpl::Index Index; - public: - inline ReverseInnerIterator(const BlockType& xpr, Index outer) - : XprType::ReverseInnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer) - {} - inline Index row() const { return IsRowMajor ? m_outer : this->index(); } - inline Index col() const { return IsRowMajor ? this->index() : m_outer; } - protected: - Index m_outer; - }; - - inline BlockImpl(const XprType& xpr, int i) - : m_matrix(xpr), m_outerStart(i), m_outerSize(OuterSize) - {} - - inline BlockImpl(const XprType& xpr, int startRow, int startCol, int blockRows, int blockCols) - : m_matrix(xpr), m_outerStart(IsRowMajor ? startRow : startCol), m_outerSize(IsRowMajor ? blockRows : blockCols) - {} - - EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); } - EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); } - - Index nonZeros() const - { - Index nnz = 0; - Index end = m_outerStart + m_outerSize.value(); - for(int j=m_outerStart; j<end; ++j) - for(typename XprType::InnerIterator it(m_matrix, j); it; ++it) - ++nnz; - return nnz; - } - - protected: - - typename XprType::Nested m_matrix; - Index m_outerStart; - const internal::variable_if_dynamic<Index, OuterSize> m_outerSize; - - public: - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl) -}; - - -/*************************************************************************** -* specialization for SparseMatrix -***************************************************************************/ - -namespace internal { - -template<typename SparseMatrixType, int BlockRows, int BlockCols> -class sparse_matrix_block_impl - : public SparseMatrixBase<Block<SparseMatrixType,BlockRows,BlockCols,true> > -{ - typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _MatrixTypeNested; - typedef Block<SparseMatrixType, BlockRows, BlockCols, true> BlockType; -public: - enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor }; - EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType) -protected: - enum { OuterSize = IsRowMajor ? BlockRows : BlockCols }; -public: - - class InnerIterator: public SparseMatrixType::InnerIterator - { - public: - inline InnerIterator(const BlockType& xpr, Index outer) - : SparseMatrixType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer) - {} - inline Index row() const { return IsRowMajor ? m_outer : this->index(); } - inline Index col() const { return IsRowMajor ? this->index() : m_outer; } - protected: - Index m_outer; - }; - class ReverseInnerIterator: public SparseMatrixType::ReverseInnerIterator - { - public: - inline ReverseInnerIterator(const BlockType& xpr, Index outer) - : SparseMatrixType::ReverseInnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer) - {} - inline Index row() const { return IsRowMajor ? m_outer : this->index(); } - inline Index col() const { return IsRowMajor ? this->index() : m_outer; } - protected: - Index m_outer; - }; - - inline sparse_matrix_block_impl(const SparseMatrixType& xpr, int i) - : m_matrix(xpr), m_outerStart(i), m_outerSize(OuterSize) - {} - - inline sparse_matrix_block_impl(const SparseMatrixType& xpr, int startRow, int startCol, int blockRows, int blockCols) - : m_matrix(xpr), m_outerStart(IsRowMajor ? startRow : startCol), m_outerSize(IsRowMajor ? blockRows : blockCols) - {} - - template<typename OtherDerived> - inline BlockType& operator=(const SparseMatrixBase<OtherDerived>& other) - { - typedef typename internal::remove_all<typename SparseMatrixType::Nested>::type _NestedMatrixType; - _NestedMatrixType& matrix = const_cast<_NestedMatrixType&>(m_matrix);; - // This assignment is slow if this vector set is not empty - // and/or it is not at the end of the nonzeros of the underlying matrix. - - // 1 - eval to a temporary to avoid transposition and/or aliasing issues - SparseMatrix<Scalar, IsRowMajor ? RowMajor : ColMajor, Index> tmp(other); - - // 2 - let's check whether there is enough allocated memory - Index nnz = tmp.nonZeros(); - Index start = m_outerStart==0 ? 0 : matrix.outerIndexPtr()[m_outerStart]; // starting position of the current block - Index end = m_matrix.outerIndexPtr()[m_outerStart+m_outerSize.value()]; // ending position of the current block - Index block_size = end - start; // available room in the current block - Index tail_size = m_matrix.outerIndexPtr()[m_matrix.outerSize()] - end; - - Index free_size = m_matrix.isCompressed() - ? Index(matrix.data().allocatedSize()) + block_size - : block_size; - - if(nnz>free_size) - { - // realloc manually to reduce copies - typename SparseMatrixType::Storage newdata(m_matrix.data().allocatedSize() - block_size + nnz); - - internal::smart_copy(&m_matrix.data().value(0), &m_matrix.data().value(0) + start, &newdata.value(0)); - internal::smart_copy(&m_matrix.data().index(0), &m_matrix.data().index(0) + start, &newdata.index(0)); - - internal::smart_copy(&tmp.data().value(0), &tmp.data().value(0) + nnz, &newdata.value(start)); - internal::smart_copy(&tmp.data().index(0), &tmp.data().index(0) + nnz, &newdata.index(start)); - - internal::smart_copy(&matrix.data().value(end), &matrix.data().value(end) + tail_size, &newdata.value(start+nnz)); - internal::smart_copy(&matrix.data().index(end), &matrix.data().index(end) + tail_size, &newdata.index(start+nnz)); - - newdata.resize(m_matrix.outerIndexPtr()[m_matrix.outerSize()] - block_size + nnz); - - matrix.data().swap(newdata); - } - else - { - // no need to realloc, simply copy the tail at its respective position and insert tmp - matrix.data().resize(start + nnz + tail_size); - - internal::smart_memmove(&matrix.data().value(end), &matrix.data().value(end) + tail_size, &matrix.data().value(start + nnz)); - internal::smart_memmove(&matrix.data().index(end), &matrix.data().index(end) + tail_size, &matrix.data().index(start + nnz)); - - internal::smart_copy(&tmp.data().value(0), &tmp.data().value(0) + nnz, &matrix.data().value(start)); - internal::smart_copy(&tmp.data().index(0), &tmp.data().index(0) + nnz, &matrix.data().index(start)); - } - - // update innerNonZeros - if(!m_matrix.isCompressed()) - for(Index j=0; j<m_outerSize.value(); ++j) - matrix.innerNonZeroPtr()[m_outerStart+j] = tmp.innerVector(j).nonZeros(); - - // update outer index pointers - Index p = start; - for(Index k=0; k<m_outerSize.value(); ++k) - { - matrix.outerIndexPtr()[m_outerStart+k] = p; - p += tmp.innerVector(k).nonZeros(); - } - std::ptrdiff_t offset = nnz - block_size; - for(Index k = m_outerStart + m_outerSize.value(); k<=matrix.outerSize(); ++k) - { - matrix.outerIndexPtr()[k] += offset; - } - - return derived(); - } - - inline BlockType& operator=(const BlockType& other) - { - return operator=<BlockType>(other); - } - - inline const Scalar* valuePtr() const - { return m_matrix.valuePtr() + m_matrix.outerIndexPtr()[m_outerStart]; } - inline Scalar* valuePtr() - { return m_matrix.const_cast_derived().valuePtr() + m_matrix.outerIndexPtr()[m_outerStart]; } - - inline const Index* innerIndexPtr() const - { return m_matrix.innerIndexPtr() + m_matrix.outerIndexPtr()[m_outerStart]; } - inline Index* innerIndexPtr() - { return m_matrix.const_cast_derived().innerIndexPtr() + m_matrix.outerIndexPtr()[m_outerStart]; } - - inline const Index* outerIndexPtr() const - { return m_matrix.outerIndexPtr() + m_outerStart; } - inline Index* outerIndexPtr() - { return m_matrix.const_cast_derived().outerIndexPtr() + m_outerStart; } - - Index nonZeros() const - { - if(m_matrix.isCompressed()) - return std::size_t(m_matrix.outerIndexPtr()[m_outerStart+m_outerSize.value()]) - - std::size_t(m_matrix.outerIndexPtr()[m_outerStart]); - else if(m_outerSize.value()==0) - return 0; - else - return Map<const Matrix<Index,OuterSize,1> >(m_matrix.innerNonZeroPtr()+m_outerStart, m_outerSize.value()).sum(); - } - - const Scalar& lastCoeff() const - { - EIGEN_STATIC_ASSERT_VECTOR_ONLY(sparse_matrix_block_impl); - eigen_assert(nonZeros()>0); - if(m_matrix.isCompressed()) - return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart+1]-1]; - else - return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart]+m_matrix.innerNonZeroPtr()[m_outerStart]-1]; - } - - EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); } - EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); } - - protected: - - typename SparseMatrixType::Nested m_matrix; - Index m_outerStart; - const internal::variable_if_dynamic<Index, OuterSize> m_outerSize; - -}; - -} // namespace internal - -template<typename _Scalar, int _Options, typename _Index, int BlockRows, int BlockCols> -class BlockImpl<SparseMatrix<_Scalar, _Options, _Index>,BlockRows,BlockCols,true,Sparse> - : public internal::sparse_matrix_block_impl<SparseMatrix<_Scalar, _Options, _Index>,BlockRows,BlockCols> -{ -public: - typedef SparseMatrix<_Scalar, _Options, _Index> SparseMatrixType; - typedef internal::sparse_matrix_block_impl<SparseMatrixType,BlockRows,BlockCols> Base; - inline BlockImpl(SparseMatrixType& xpr, int i) - : Base(xpr, i) - {} - - inline BlockImpl(SparseMatrixType& xpr, int startRow, int startCol, int blockRows, int blockCols) - : Base(xpr, startRow, startCol, blockRows, blockCols) - {} - - using Base::operator=; -}; - -template<typename _Scalar, int _Options, typename _Index, int BlockRows, int BlockCols> -class BlockImpl<const SparseMatrix<_Scalar, _Options, _Index>,BlockRows,BlockCols,true,Sparse> - : public internal::sparse_matrix_block_impl<const SparseMatrix<_Scalar, _Options, _Index>,BlockRows,BlockCols> -{ -public: - typedef const SparseMatrix<_Scalar, _Options, _Index> SparseMatrixType; - typedef internal::sparse_matrix_block_impl<SparseMatrixType,BlockRows,BlockCols> Base; - inline BlockImpl(SparseMatrixType& xpr, int i) - : Base(xpr, i) - {} - - inline BlockImpl(SparseMatrixType& xpr, int startRow, int startCol, int blockRows, int blockCols) - : Base(xpr, startRow, startCol, blockRows, blockCols) - {} - - using Base::operator=; -}; - -//---------- - -/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this - * is col-major (resp. row-major). - */ -template<typename Derived> -typename SparseMatrixBase<Derived>::InnerVectorReturnType SparseMatrixBase<Derived>::innerVector(Index outer) -{ return InnerVectorReturnType(derived(), outer); } - -/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this - * is col-major (resp. row-major). Read-only. - */ -template<typename Derived> -const typename SparseMatrixBase<Derived>::ConstInnerVectorReturnType SparseMatrixBase<Derived>::innerVector(Index outer) const -{ return ConstInnerVectorReturnType(derived(), outer); } - -/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this - * is col-major (resp. row-major). - */ -template<typename Derived> -Block<Derived,Dynamic,Dynamic,true> SparseMatrixBase<Derived>::innerVectors(Index outerStart, Index outerSize) -{ - return Block<Derived,Dynamic,Dynamic,true>(derived(), - IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart, - IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize); - -} - -/** \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this - * is col-major (resp. row-major). Read-only. - */ -template<typename Derived> -const Block<const Derived,Dynamic,Dynamic,true> SparseMatrixBase<Derived>::innerVectors(Index outerStart, Index outerSize) const -{ - return Block<const Derived,Dynamic,Dynamic,true>(derived(), - IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart, - IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize); - -} - -namespace internal { - -template< typename XprType, int BlockRows, int BlockCols, bool InnerPanel, - bool OuterVector = (BlockCols==1 && XprType::IsRowMajor) - | // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&". - // revert to || as soon as not needed anymore. - (BlockRows==1 && !XprType::IsRowMajor)> -class GenericSparseBlockInnerIteratorImpl; - -} - -/** Generic implementation of sparse Block expression. - * Real-only. - */ -template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> -class BlockImpl<XprType,BlockRows,BlockCols,InnerPanel,Sparse> - : public SparseMatrixBase<Block<XprType,BlockRows,BlockCols,InnerPanel> >, internal::no_assignment_operator -{ - typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType; -public: - enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor }; - EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType) - - typedef typename internal::remove_all<typename XprType::Nested>::type _MatrixTypeNested; - - /** Column or Row constructor - */ - inline BlockImpl(const XprType& xpr, int i) - : m_matrix(xpr), - m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0), - m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0), - m_blockRows(BlockRows==1 ? 1 : xpr.rows()), - m_blockCols(BlockCols==1 ? 1 : xpr.cols()) - {} - - /** Dynamic-size constructor - */ - inline BlockImpl(const XprType& xpr, int startRow, int startCol, int blockRows, int blockCols) - : m_matrix(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(blockRows), m_blockCols(blockCols) - {} - - inline int rows() const { return m_blockRows.value(); } - inline int cols() const { return m_blockCols.value(); } - - inline Scalar& coeffRef(int row, int col) - { - return m_matrix.const_cast_derived() - .coeffRef(row + m_startRow.value(), col + m_startCol.value()); - } - - inline const Scalar coeff(int row, int col) const - { - return m_matrix.coeff(row + m_startRow.value(), col + m_startCol.value()); - } - - inline Scalar& coeffRef(int index) - { - return m_matrix.const_cast_derived() - .coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), - m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); - } - - inline const Scalar coeff(int index) const - { - return m_matrix - .coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index), - m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0)); - } - - inline const _MatrixTypeNested& nestedExpression() const { return m_matrix; } - - typedef internal::GenericSparseBlockInnerIteratorImpl<XprType,BlockRows,BlockCols,InnerPanel> InnerIterator; - - class ReverseInnerIterator : public _MatrixTypeNested::ReverseInnerIterator - { - typedef typename _MatrixTypeNested::ReverseInnerIterator Base; - const BlockType& m_block; - Index m_begin; - public: - - EIGEN_STRONG_INLINE ReverseInnerIterator(const BlockType& block, Index outer) - : Base(block.derived().nestedExpression(), outer + (IsRowMajor ? block.m_startRow.value() : block.m_startCol.value())), - m_block(block), - m_begin(IsRowMajor ? block.m_startCol.value() : block.m_startRow.value()) - { - while( (Base::operator bool()) && (Base::index() >= (IsRowMajor ? m_block.m_startCol.value()+block.m_blockCols.value() : m_block.m_startRow.value()+block.m_blockRows.value())) ) - Base::operator--(); - } - - inline Index index() const { return Base::index() - (IsRowMajor ? m_block.m_startCol.value() : m_block.m_startRow.value()); } - inline Index outer() const { return Base::outer() - (IsRowMajor ? m_block.m_startRow.value() : m_block.m_startCol.value()); } - inline Index row() const { return Base::row() - m_block.m_startRow.value(); } - inline Index col() const { return Base::col() - m_block.m_startCol.value(); } - - inline operator bool() const { return Base::operator bool() && Base::index() >= m_begin; } - }; - protected: - friend class internal::GenericSparseBlockInnerIteratorImpl<XprType,BlockRows,BlockCols,InnerPanel>; - friend class ReverseInnerIterator; - - EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl) - - typename XprType::Nested m_matrix; - const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow; - const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol; - const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows; - const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols; - -}; - -namespace internal { - template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> - class GenericSparseBlockInnerIteratorImpl<XprType,BlockRows,BlockCols,InnerPanel,false> : public Block<XprType, BlockRows, BlockCols, InnerPanel>::_MatrixTypeNested::InnerIterator - { - typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType; - enum { - IsRowMajor = BlockType::IsRowMajor - }; - typedef typename BlockType::_MatrixTypeNested _MatrixTypeNested; - typedef typename BlockType::Index Index; - typedef typename _MatrixTypeNested::InnerIterator Base; - const BlockType& m_block; - Index m_end; - public: - - EIGEN_STRONG_INLINE GenericSparseBlockInnerIteratorImpl(const BlockType& block, Index outer) - : Base(block.derived().nestedExpression(), outer + (IsRowMajor ? block.m_startRow.value() : block.m_startCol.value())), - m_block(block), - m_end(IsRowMajor ? block.m_startCol.value()+block.m_blockCols.value() : block.m_startRow.value()+block.m_blockRows.value()) - { - while( (Base::operator bool()) && (Base::index() < (IsRowMajor ? m_block.m_startCol.value() : m_block.m_startRow.value())) ) - Base::operator++(); - } - - inline Index index() const { return Base::index() - (IsRowMajor ? m_block.m_startCol.value() : m_block.m_startRow.value()); } - inline Index outer() const { return Base::outer() - (IsRowMajor ? m_block.m_startRow.value() : m_block.m_startCol.value()); } - inline Index row() const { return Base::row() - m_block.m_startRow.value(); } - inline Index col() const { return Base::col() - m_block.m_startCol.value(); } - - inline operator bool() const { return Base::operator bool() && Base::index() < m_end; } - }; - - // Row vector of a column-major sparse matrix or column of a row-major one. - template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> - class GenericSparseBlockInnerIteratorImpl<XprType,BlockRows,BlockCols,InnerPanel,true> - { - typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType; - enum { - IsRowMajor = BlockType::IsRowMajor - }; - typedef typename BlockType::_MatrixTypeNested _MatrixTypeNested; - typedef typename BlockType::Index Index; - typedef typename BlockType::Scalar Scalar; - const BlockType& m_block; - Index m_outerPos; - Index m_innerIndex; - Scalar m_value; - Index m_end; - public: - - EIGEN_STRONG_INLINE GenericSparseBlockInnerIteratorImpl(const BlockType& block, Index outer = 0) - : - m_block(block), - m_outerPos( (IsRowMajor ? block.m_startCol.value() : block.m_startRow.value()) - 1), // -1 so that operator++ finds the first non-zero entry - m_innerIndex(IsRowMajor ? block.m_startRow.value() : block.m_startCol.value()), - m_end(IsRowMajor ? block.m_startCol.value()+block.m_blockCols.value() : block.m_startRow.value()+block.m_blockRows.value()) - { - EIGEN_UNUSED_VARIABLE(outer); - eigen_assert(outer==0); - - ++(*this); - } - - inline Index index() const { return m_outerPos - (IsRowMajor ? m_block.m_startCol.value() : m_block.m_startRow.value()); } - inline Index outer() const { return 0; } - inline Index row() const { return IsRowMajor ? 0 : index(); } - inline Index col() const { return IsRowMajor ? index() : 0; } - - inline Scalar value() const { return m_value; } - - inline GenericSparseBlockInnerIteratorImpl& operator++() - { - // At end already? - if (m_outerPos >= m_end) - return *this; - - // search next non-zero entry. - while(++m_outerPos<m_end) - { - typename XprType::InnerIterator it(m_block.m_matrix, m_outerPos); - // search for the key m_innerIndex in the current outer-vector - while(it && it.index() < m_innerIndex) ++it; - if(it && it.index()==m_innerIndex) - { - m_value = it.value(); - break; - } - } - return *this; - } - - inline operator bool() const { return m_outerPos < m_end; } - }; - -} // end namespace internal - - -} // end namespace Eigen - -#endif // EIGEN_SPARSE_BLOCK_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseColEtree.h b/third_party/eigen3/Eigen/src/SparseCore/SparseColEtree.h deleted file mode 100644 index f8745f4610..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseColEtree.h +++ /dev/null @@ -1,206 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -/* - - * NOTE: This file is the modified version of sp_coletree.c file in SuperLU - - * -- SuperLU routine (version 3.1) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * August 1, 2008 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ -#ifndef SPARSE_COLETREE_H -#define SPARSE_COLETREE_H - -namespace Eigen { - -namespace internal { - -/** Find the root of the tree/set containing the vertex i : Use Path halving */ -template<typename Index, typename IndexVector> -Index etree_find (Index i, IndexVector& pp) -{ - Index p = pp(i); // Parent - Index gp = pp(p); // Grand parent - while (gp != p) - { - pp(i) = gp; // Parent pointer on find path is changed to former grand parent - i = gp; - p = pp(i); - gp = pp(p); - } - return p; -} - -/** Compute the column elimination tree of a sparse matrix - * \param mat The matrix in column-major format. - * \param parent The elimination tree - * \param firstRowElt The column index of the first element in each row - * \param perm The permutation to apply to the column of \b mat - */ -template <typename MatrixType, typename IndexVector> -int coletree(const MatrixType& mat, IndexVector& parent, IndexVector& firstRowElt, typename MatrixType::Index *perm=0) -{ - typedef typename MatrixType::Index Index; - Index nc = mat.cols(); // Number of columns - Index m = mat.rows(); - Index diagSize = (std::min)(nc,m); - IndexVector root(nc); // root of subtree of etree - root.setZero(); - IndexVector pp(nc); // disjoint sets - pp.setZero(); // Initialize disjoint sets - parent.resize(mat.cols()); - //Compute first nonzero column in each row - Index row,col; - firstRowElt.resize(m); - firstRowElt.setConstant(nc); - firstRowElt.segment(0, diagSize).setLinSpaced(diagSize, 0, diagSize-1); - bool found_diag; - for (col = 0; col < nc; col++) - { - Index pcol = col; - if(perm) pcol = perm[col]; - for (typename MatrixType::InnerIterator it(mat, pcol); it; ++it) - { - row = it.row(); - firstRowElt(row) = (std::min)(firstRowElt(row), col); - } - } - /* Compute etree by Liu's algorithm for symmetric matrices, - except use (firstRowElt[r],c) in place of an edge (r,c) of A. - Thus each row clique in A'*A is replaced by a star - centered at its first vertex, which has the same fill. */ - Index rset, cset, rroot; - for (col = 0; col < nc; col++) - { - found_diag = col>=m; - pp(col) = col; - cset = col; - root(cset) = col; - parent(col) = nc; - /* The diagonal element is treated here even if it does not exist in the matrix - * hence the loop is executed once more */ - Index pcol = col; - if(perm) pcol = perm[col]; - for (typename MatrixType::InnerIterator it(mat, pcol); it||!found_diag; ++it) - { // A sequence of interleaved find and union is performed - Index i = col; - if(it) i = it.index(); - if (i == col) found_diag = true; - - row = firstRowElt(i); - if (row >= col) continue; - rset = internal::etree_find(row, pp); // Find the name of the set containing row - rroot = root(rset); - if (rroot != col) - { - parent(rroot) = col; - pp(cset) = rset; - cset = rset; - root(cset) = col; - } - } - } - return 0; -} - -/** - * Depth-first search from vertex n. No recursion. - * This routine was contributed by Cédric Doucet, CEDRAT Group, Meylan, France. -*/ -template <typename Index, typename IndexVector> -void nr_etdfs (Index n, IndexVector& parent, IndexVector& first_kid, IndexVector& next_kid, IndexVector& post, Index postnum) -{ - Index current = n, first, next; - while (postnum != n) - { - // No kid for the current node - first = first_kid(current); - - // no kid for the current node - if (first == -1) - { - // Numbering this node because it has no kid - post(current) = postnum++; - - // looking for the next kid - next = next_kid(current); - while (next == -1) - { - // No more kids : back to the parent node - current = parent(current); - // numbering the parent node - post(current) = postnum++; - - // Get the next kid - next = next_kid(current); - } - // stopping criterion - if (postnum == n+1) return; - - // Updating current node - current = next; - } - else - { - current = first; - } - } -} - - -/** - * \brief Post order a tree - * \param n the number of nodes - * \param parent Input tree - * \param post postordered tree - */ -template <typename Index, typename IndexVector> -void treePostorder(Index n, IndexVector& parent, IndexVector& post) -{ - IndexVector first_kid, next_kid; // Linked list of children - Index postnum; - // Allocate storage for working arrays and results - first_kid.resize(n+1); - next_kid.setZero(n+1); - post.setZero(n+1); - - // Set up structure describing children - Index v, dad; - first_kid.setConstant(-1); - for (v = n-1; v >= 0; v--) - { - dad = parent(v); - next_kid(v) = first_kid(dad); - first_kid(dad) = v; - } - - // Depth-first search from dummy root vertex #n - postnum = 0; - internal::nr_etdfs(n, parent, first_kid, next_kid, post, postnum); -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // SPARSE_COLETREE_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/third_party/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h deleted file mode 100644 index ec86ca933c..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseCwiseBinaryOp.h +++ /dev/null @@ -1,324 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_CWISE_BINARY_OP_H -#define EIGEN_SPARSE_CWISE_BINARY_OP_H - -namespace Eigen { - -// Here we have to handle 3 cases: -// 1 - sparse op dense -// 2 - dense op sparse -// 3 - sparse op sparse -// We also need to implement a 4th iterator for: -// 4 - dense op dense -// Finally, we also need to distinguish between the product and other operations : -// configuration returned mode -// 1 - sparse op dense product sparse -// generic dense -// 2 - dense op sparse product sparse -// generic dense -// 3 - sparse op sparse product sparse -// generic sparse -// 4 - dense op dense product dense -// generic dense - -namespace internal { - -template<> struct promote_storage_type<Dense,Sparse> -{ typedef Sparse ret; }; - -template<> struct promote_storage_type<Sparse,Dense> -{ typedef Sparse ret; }; - -template<typename BinaryOp, typename Lhs, typename Rhs, typename Derived, - typename _LhsStorageMode = typename traits<Lhs>::StorageKind, - typename _RhsStorageMode = typename traits<Rhs>::StorageKind> -class sparse_cwise_binary_op_inner_iterator_selector; - -} // end namespace internal - -template<typename BinaryOp, typename Lhs, typename Rhs> -class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse> - : public SparseMatrixBase<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > -{ - public: - class InnerIterator; - class ReverseInnerIterator; - typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived; - EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) - CwiseBinaryOpImpl() - { - typedef typename internal::traits<Lhs>::StorageKind LhsStorageKind; - typedef typename internal::traits<Rhs>::StorageKind RhsStorageKind; - EIGEN_STATIC_ASSERT(( - (!internal::is_same<LhsStorageKind,RhsStorageKind>::value) - || ((Lhs::Flags&RowMajorBit) == (Rhs::Flags&RowMajorBit))), - THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH); - } -}; - -template<typename BinaryOp, typename Lhs, typename Rhs> -class CwiseBinaryOpImpl<BinaryOp,Lhs,Rhs,Sparse>::InnerIterator - : public internal::sparse_cwise_binary_op_inner_iterator_selector<BinaryOp,Lhs,Rhs,typename CwiseBinaryOpImpl<BinaryOp,Lhs,Rhs,Sparse>::InnerIterator> -{ - public: - typedef typename Lhs::Index Index; - typedef internal::sparse_cwise_binary_op_inner_iterator_selector< - BinaryOp,Lhs,Rhs, InnerIterator> Base; - - EIGEN_STRONG_INLINE InnerIterator(const CwiseBinaryOpImpl& binOp, Index outer) - : Base(binOp.derived(),outer) - {} -}; - -/*************************************************************************** -* Implementation of inner-iterators -***************************************************************************/ - -// template<typename T> struct internal::func_is_conjunction { enum { ret = false }; }; -// template<typename T> struct internal::func_is_conjunction<internal::scalar_product_op<T> > { enum { ret = true }; }; - -// TODO generalize the internal::scalar_product_op specialization to all conjunctions if any ! - -namespace internal { - -// sparse - sparse (generic) -template<typename BinaryOp, typename Lhs, typename Rhs, typename Derived> -class sparse_cwise_binary_op_inner_iterator_selector<BinaryOp, Lhs, Rhs, Derived, Sparse, Sparse> -{ - typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> CwiseBinaryXpr; - typedef typename traits<CwiseBinaryXpr>::Scalar Scalar; - typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested; - typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested; - typedef typename _LhsNested::InnerIterator LhsIterator; - typedef typename _RhsNested::InnerIterator RhsIterator; - typedef typename Lhs::Index Index; - - public: - - EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer) - : m_lhsIter(xpr.lhs(),outer), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor()) - { - this->operator++(); - } - - EIGEN_STRONG_INLINE Derived& operator++() - { - if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index())) - { - m_id = m_lhsIter.index(); - m_value = m_functor(m_lhsIter.value(), m_rhsIter.value()); - ++m_lhsIter; - ++m_rhsIter; - } - else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index()))) - { - m_id = m_lhsIter.index(); - m_value = m_functor(m_lhsIter.value(), Scalar(0)); - ++m_lhsIter; - } - else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index()))) - { - m_id = m_rhsIter.index(); - m_value = m_functor(Scalar(0), m_rhsIter.value()); - ++m_rhsIter; - } - else - { - m_value = 0; // this is to avoid a compilation warning - m_id = -1; - } - return *static_cast<Derived*>(this); - } - - EIGEN_STRONG_INLINE Scalar value() const { return m_value; } - - EIGEN_STRONG_INLINE Index index() const { return m_id; } - EIGEN_STRONG_INLINE Index row() const { return Lhs::IsRowMajor ? m_lhsIter.row() : index(); } - EIGEN_STRONG_INLINE Index col() const { return Lhs::IsRowMajor ? index() : m_lhsIter.col(); } - - EIGEN_STRONG_INLINE operator bool() const { return m_id>=0; } - - protected: - LhsIterator m_lhsIter; - RhsIterator m_rhsIter; - const BinaryOp& m_functor; - Scalar m_value; - Index m_id; -}; - -// sparse - sparse (product) -template<typename T, typename Lhs, typename Rhs, typename Derived> -class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Sparse, Sparse> -{ - typedef scalar_product_op<T> BinaryFunc; - typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr; - typedef typename CwiseBinaryXpr::Scalar Scalar; - typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested; - typedef typename _LhsNested::InnerIterator LhsIterator; - typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested; - typedef typename _RhsNested::InnerIterator RhsIterator; - typedef typename Lhs::Index Index; - public: - - EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer) - : m_lhsIter(xpr.lhs(),outer), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor()) - { - while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index())) - { - if (m_lhsIter.index() < m_rhsIter.index()) - ++m_lhsIter; - else - ++m_rhsIter; - } - } - - EIGEN_STRONG_INLINE Derived& operator++() - { - ++m_lhsIter; - ++m_rhsIter; - while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index())) - { - if (m_lhsIter.index() < m_rhsIter.index()) - ++m_lhsIter; - else - ++m_rhsIter; - } - return *static_cast<Derived*>(this); - } - - EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_lhsIter.value(), m_rhsIter.value()); } - - EIGEN_STRONG_INLINE Index index() const { return m_lhsIter.index(); } - EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); } - EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); } - - EIGEN_STRONG_INLINE operator bool() const { return (m_lhsIter && m_rhsIter); } - - protected: - LhsIterator m_lhsIter; - RhsIterator m_rhsIter; - const BinaryFunc& m_functor; -}; - -// sparse - dense (product) -template<typename T, typename Lhs, typename Rhs, typename Derived> -class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Sparse, Dense> -{ - typedef scalar_product_op<T> BinaryFunc; - typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr; - typedef typename CwiseBinaryXpr::Scalar Scalar; - typedef typename traits<CwiseBinaryXpr>::_LhsNested _LhsNested; - typedef typename traits<CwiseBinaryXpr>::RhsNested RhsNested; - typedef typename _LhsNested::InnerIterator LhsIterator; - typedef typename Lhs::Index Index; - enum { IsRowMajor = (int(Lhs::Flags)&RowMajorBit)==RowMajorBit }; - public: - - EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer) - : m_rhs(xpr.rhs()), m_lhsIter(xpr.lhs(),outer), m_functor(xpr.functor()), m_outer(outer) - {} - - EIGEN_STRONG_INLINE Derived& operator++() - { - ++m_lhsIter; - return *static_cast<Derived*>(this); - } - - EIGEN_STRONG_INLINE Scalar value() const - { return m_functor(m_lhsIter.value(), - m_rhs.coeff(IsRowMajor?m_outer:m_lhsIter.index(),IsRowMajor?m_lhsIter.index():m_outer)); } - - EIGEN_STRONG_INLINE Index index() const { return m_lhsIter.index(); } - EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); } - EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); } - - EIGEN_STRONG_INLINE operator bool() const { return m_lhsIter; } - - protected: - RhsNested m_rhs; - LhsIterator m_lhsIter; - const BinaryFunc m_functor; - const Index m_outer; -}; - -// sparse - dense (product) -template<typename T, typename Lhs, typename Rhs, typename Derived> -class sparse_cwise_binary_op_inner_iterator_selector<scalar_product_op<T>, Lhs, Rhs, Derived, Dense, Sparse> -{ - typedef scalar_product_op<T> BinaryFunc; - typedef CwiseBinaryOp<BinaryFunc, Lhs, Rhs> CwiseBinaryXpr; - typedef typename CwiseBinaryXpr::Scalar Scalar; - typedef typename traits<CwiseBinaryXpr>::_RhsNested _RhsNested; - typedef typename _RhsNested::InnerIterator RhsIterator; - typedef typename Lhs::Index Index; - - enum { IsRowMajor = (int(Rhs::Flags)&RowMajorBit)==RowMajorBit }; - public: - - EIGEN_STRONG_INLINE sparse_cwise_binary_op_inner_iterator_selector(const CwiseBinaryXpr& xpr, Index outer) - : m_xpr(xpr), m_rhsIter(xpr.rhs(),outer), m_functor(xpr.functor()), m_outer(outer) - {} - - EIGEN_STRONG_INLINE Derived& operator++() - { - ++m_rhsIter; - return *static_cast<Derived*>(this); - } - - EIGEN_STRONG_INLINE Scalar value() const - { return m_functor(m_xpr.lhs().coeff(IsRowMajor?m_outer:m_rhsIter.index(),IsRowMajor?m_rhsIter.index():m_outer), m_rhsIter.value()); } - - EIGEN_STRONG_INLINE Index index() const { return m_rhsIter.index(); } - EIGEN_STRONG_INLINE Index row() const { return m_rhsIter.row(); } - EIGEN_STRONG_INLINE Index col() const { return m_rhsIter.col(); } - - EIGEN_STRONG_INLINE operator bool() const { return m_rhsIter; } - - protected: - const CwiseBinaryXpr& m_xpr; - RhsIterator m_rhsIter; - const BinaryFunc& m_functor; - const Index m_outer; -}; - -} // end namespace internal - -/*************************************************************************** -* Implementation of SparseMatrixBase and SparseCwise functions/operators -***************************************************************************/ - -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived & -SparseMatrixBase<Derived>::operator-=(const SparseMatrixBase<OtherDerived> &other) -{ - return derived() = derived() - other.derived(); -} - -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE Derived & -SparseMatrixBase<Derived>::operator+=(const SparseMatrixBase<OtherDerived>& other) -{ - return derived() = derived() + other.derived(); -} - -template<typename Derived> -template<typename OtherDerived> -EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE -SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived> &other) const -{ - return EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE(derived(), other.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSE_CWISE_BINARY_OP_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/third_party/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h deleted file mode 100644 index 5a50c78030..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseCwiseUnaryOp.h +++ /dev/null @@ -1,163 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_CWISE_UNARY_OP_H -#define EIGEN_SPARSE_CWISE_UNARY_OP_H - -namespace Eigen { - -template<typename UnaryOp, typename MatrixType> -class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse> - : public SparseMatrixBase<CwiseUnaryOp<UnaryOp, MatrixType> > -{ - public: - - class InnerIterator; - class ReverseInnerIterator; - - typedef CwiseUnaryOp<UnaryOp, MatrixType> Derived; - EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) - - protected: - typedef typename internal::traits<Derived>::_XprTypeNested _MatrixTypeNested; - typedef typename _MatrixTypeNested::InnerIterator MatrixTypeIterator; - typedef typename _MatrixTypeNested::ReverseInnerIterator MatrixTypeReverseIterator; -}; - -template<typename UnaryOp, typename MatrixType> -class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::InnerIterator - : public CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeIterator -{ - typedef typename CwiseUnaryOpImpl::Scalar Scalar; - typedef typename CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeIterator Base; - public: - - EIGEN_STRONG_INLINE InnerIterator(const CwiseUnaryOpImpl& unaryOp, typename CwiseUnaryOpImpl::Index outer) - : Base(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor()) - {} - - EIGEN_STRONG_INLINE InnerIterator& operator++() - { Base::operator++(); return *this; } - - EIGEN_STRONG_INLINE typename CwiseUnaryOpImpl::Scalar value() const { return m_functor(Base::value()); } - - protected: - const UnaryOp m_functor; - private: - typename CwiseUnaryOpImpl::Scalar& valueRef(); -}; - -template<typename UnaryOp, typename MatrixType> -class CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::ReverseInnerIterator - : public CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeReverseIterator -{ - typedef typename CwiseUnaryOpImpl::Scalar Scalar; - typedef typename CwiseUnaryOpImpl<UnaryOp,MatrixType,Sparse>::MatrixTypeReverseIterator Base; - public: - - EIGEN_STRONG_INLINE ReverseInnerIterator(const CwiseUnaryOpImpl& unaryOp, typename CwiseUnaryOpImpl::Index outer) - : Base(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor()) - {} - - EIGEN_STRONG_INLINE ReverseInnerIterator& operator--() - { Base::operator--(); return *this; } - - EIGEN_STRONG_INLINE typename CwiseUnaryOpImpl::Scalar value() const { return m_functor(Base::value()); } - - protected: - const UnaryOp m_functor; - private: - typename CwiseUnaryOpImpl::Scalar& valueRef(); -}; - -template<typename ViewOp, typename MatrixType> -class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse> - : public SparseMatrixBase<CwiseUnaryView<ViewOp, MatrixType> > -{ - public: - - class InnerIterator; - class ReverseInnerIterator; - - typedef CwiseUnaryView<ViewOp, MatrixType> Derived; - EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) - - protected: - typedef typename internal::traits<Derived>::_MatrixTypeNested _MatrixTypeNested; - typedef typename _MatrixTypeNested::InnerIterator MatrixTypeIterator; - typedef typename _MatrixTypeNested::ReverseInnerIterator MatrixTypeReverseIterator; -}; - -template<typename ViewOp, typename MatrixType> -class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::InnerIterator - : public CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeIterator -{ - typedef typename CwiseUnaryViewImpl::Scalar Scalar; - typedef typename CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeIterator Base; - public: - - EIGEN_STRONG_INLINE InnerIterator(const CwiseUnaryViewImpl& unaryOp, typename CwiseUnaryViewImpl::Index outer) - : Base(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor()) - {} - - EIGEN_STRONG_INLINE InnerIterator& operator++() - { Base::operator++(); return *this; } - - EIGEN_STRONG_INLINE typename CwiseUnaryViewImpl::Scalar value() const { return m_functor(Base::value()); } - EIGEN_STRONG_INLINE typename CwiseUnaryViewImpl::Scalar& valueRef() { return m_functor(Base::valueRef()); } - - protected: - const ViewOp m_functor; -}; - -template<typename ViewOp, typename MatrixType> -class CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::ReverseInnerIterator - : public CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeReverseIterator -{ - typedef typename CwiseUnaryViewImpl::Scalar Scalar; - typedef typename CwiseUnaryViewImpl<ViewOp,MatrixType,Sparse>::MatrixTypeReverseIterator Base; - public: - - EIGEN_STRONG_INLINE ReverseInnerIterator(const CwiseUnaryViewImpl& unaryOp, typename CwiseUnaryViewImpl::Index outer) - : Base(unaryOp.derived().nestedExpression(),outer), m_functor(unaryOp.derived().functor()) - {} - - EIGEN_STRONG_INLINE ReverseInnerIterator& operator--() - { Base::operator--(); return *this; } - - EIGEN_STRONG_INLINE typename CwiseUnaryViewImpl::Scalar value() const { return m_functor(Base::value()); } - EIGEN_STRONG_INLINE typename CwiseUnaryViewImpl::Scalar& valueRef() { return m_functor(Base::valueRef()); } - - protected: - const ViewOp m_functor; -}; - -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -SparseMatrixBase<Derived>::operator*=(const Scalar& other) -{ - for (Index j=0; j<outerSize(); ++j) - for (typename Derived::InnerIterator i(derived(),j); i; ++i) - i.valueRef() *= other; - return derived(); -} - -template<typename Derived> -EIGEN_STRONG_INLINE Derived& -SparseMatrixBase<Derived>::operator/=(const Scalar& other) -{ - for (Index j=0; j<outerSize(); ++j) - for (typename Derived::InnerIterator i(derived(),j); i; ++i) - i.valueRef() /= other; - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSE_CWISE_UNARY_OP_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h b/third_party/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h deleted file mode 100644 index 610833f3b0..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseDenseProduct.h +++ /dev/null @@ -1,311 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSEDENSEPRODUCT_H -#define EIGEN_SPARSEDENSEPRODUCT_H - -namespace Eigen { - -template<typename Lhs, typename Rhs, int InnerSize> struct SparseDenseProductReturnType -{ - typedef SparseTimeDenseProduct<Lhs,Rhs> Type; -}; - -template<typename Lhs, typename Rhs> struct SparseDenseProductReturnType<Lhs,Rhs,1> -{ - typedef SparseDenseOuterProduct<Lhs,Rhs,false> Type; -}; - -template<typename Lhs, typename Rhs, int InnerSize> struct DenseSparseProductReturnType -{ - typedef DenseTimeSparseProduct<Lhs,Rhs> Type; -}; - -template<typename Lhs, typename Rhs> struct DenseSparseProductReturnType<Lhs,Rhs,1> -{ - typedef SparseDenseOuterProduct<Rhs,Lhs,true> Type; -}; - -namespace internal { - -template<typename Lhs, typename Rhs, bool Tr> -struct traits<SparseDenseOuterProduct<Lhs,Rhs,Tr> > -{ - typedef Sparse StorageKind; - typedef typename scalar_product_traits<typename traits<Lhs>::Scalar, - typename traits<Rhs>::Scalar>::ReturnType Scalar; - typedef typename Lhs::Index Index; - typedef typename Lhs::Nested LhsNested; - typedef typename Rhs::Nested RhsNested; - typedef typename remove_all<LhsNested>::type _LhsNested; - typedef typename remove_all<RhsNested>::type _RhsNested; - - enum { - LhsCoeffReadCost = traits<_LhsNested>::CoeffReadCost, - RhsCoeffReadCost = traits<_RhsNested>::CoeffReadCost, - - RowsAtCompileTime = Tr ? int(traits<Rhs>::RowsAtCompileTime) : int(traits<Lhs>::RowsAtCompileTime), - ColsAtCompileTime = Tr ? int(traits<Lhs>::ColsAtCompileTime) : int(traits<Rhs>::ColsAtCompileTime), - MaxRowsAtCompileTime = Tr ? int(traits<Rhs>::MaxRowsAtCompileTime) : int(traits<Lhs>::MaxRowsAtCompileTime), - MaxColsAtCompileTime = Tr ? int(traits<Lhs>::MaxColsAtCompileTime) : int(traits<Rhs>::MaxColsAtCompileTime), - - Flags = Tr ? RowMajorBit : 0, - - CoeffReadCost = LhsCoeffReadCost + RhsCoeffReadCost + NumTraits<Scalar>::MulCost - }; -}; - -} // end namespace internal - -template<typename Lhs, typename Rhs, bool Tr> -class SparseDenseOuterProduct - : public SparseMatrixBase<SparseDenseOuterProduct<Lhs,Rhs,Tr> > -{ - public: - - typedef SparseMatrixBase<SparseDenseOuterProduct> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(SparseDenseOuterProduct) - typedef internal::traits<SparseDenseOuterProduct> Traits; - - private: - - typedef typename Traits::LhsNested LhsNested; - typedef typename Traits::RhsNested RhsNested; - typedef typename Traits::_LhsNested _LhsNested; - typedef typename Traits::_RhsNested _RhsNested; - - public: - - class InnerIterator; - - EIGEN_STRONG_INLINE SparseDenseOuterProduct(const Lhs& lhs, const Rhs& rhs) - : m_lhs(lhs), m_rhs(rhs) - { - EIGEN_STATIC_ASSERT(!Tr,YOU_MADE_A_PROGRAMMING_MISTAKE); - } - - EIGEN_STRONG_INLINE SparseDenseOuterProduct(const Rhs& rhs, const Lhs& lhs) - : m_lhs(lhs), m_rhs(rhs) - { - EIGEN_STATIC_ASSERT(Tr,YOU_MADE_A_PROGRAMMING_MISTAKE); - } - - EIGEN_STRONG_INLINE Index rows() const { return Tr ? m_rhs.rows() : m_lhs.rows(); } - EIGEN_STRONG_INLINE Index cols() const { return Tr ? m_lhs.cols() : m_rhs.cols(); } - - EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; } - EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; } - - protected: - LhsNested m_lhs; - RhsNested m_rhs; -}; - -template<typename Lhs, typename Rhs, bool Transpose> -class SparseDenseOuterProduct<Lhs,Rhs,Transpose>::InnerIterator : public _LhsNested::InnerIterator -{ - typedef typename _LhsNested::InnerIterator Base; - typedef typename SparseDenseOuterProduct::Index Index; - public: - EIGEN_STRONG_INLINE InnerIterator(const SparseDenseOuterProduct& prod, Index outer) - : Base(prod.lhs(), 0), m_outer(outer), m_factor(prod.rhs().coeff(outer)) - { - } - - inline Index outer() const { return m_outer; } - inline Index row() const { return Transpose ? Base::row() : m_outer; } - inline Index col() const { return Transpose ? m_outer : Base::row(); } - - inline Scalar value() const { return Base::value() * m_factor; } - - protected: - Index m_outer; - Scalar m_factor; -}; - -namespace internal { -template<typename Lhs, typename Rhs> -struct traits<SparseTimeDenseProduct<Lhs,Rhs> > - : traits<ProductBase<SparseTimeDenseProduct<Lhs,Rhs>, Lhs, Rhs> > -{ - typedef Dense StorageKind; - typedef MatrixXpr XprKind; -}; - -template<typename SparseLhsType, typename DenseRhsType, typename DenseResType, - typename AlphaType, - int LhsStorageOrder = ((SparseLhsType::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor, - bool ColPerCol = ((DenseRhsType::Flags&RowMajorBit)==0) || DenseRhsType::ColsAtCompileTime==1> -struct sparse_time_dense_product_impl; - -template<typename SparseLhsType, typename DenseRhsType, typename DenseResType> -struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, RowMajor, true> -{ - typedef typename internal::remove_all<SparseLhsType>::type Lhs; - typedef typename internal::remove_all<DenseRhsType>::type Rhs; - typedef typename internal::remove_all<DenseResType>::type Res; - typedef typename Lhs::Index Index; - typedef typename Lhs::InnerIterator LhsInnerIterator; - static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha) - { - for(Index c=0; c<rhs.cols(); ++c) - { - Index n = lhs.outerSize(); - for(Index j=0; j<n; ++j) - { - typename Res::Scalar tmp(0); - for(LhsInnerIterator it(lhs,j); it ;++it) - tmp += it.value() * rhs.coeff(it.index(),c); - res.coeffRef(j,c) = alpha * tmp; - } - } - } -}; - -template<typename T1, typename T2/*, int _Options, typename _StrideType*/> -struct scalar_product_traits<T1, Ref<T2/*, _Options, _StrideType*/> > -{ - enum { - Defined = 1 - }; - typedef typename CwiseUnaryOp<scalar_multiple2_op<T1, typename T2::Scalar>, T2>::PlainObject ReturnType; -}; -template<typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType> -struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, AlphaType, ColMajor, true> -{ - typedef typename internal::remove_all<SparseLhsType>::type Lhs; - typedef typename internal::remove_all<DenseRhsType>::type Rhs; - typedef typename internal::remove_all<DenseResType>::type Res; - typedef typename Lhs::InnerIterator LhsInnerIterator; - typedef typename Lhs::Index Index; - static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha) - { - for(Index c=0; c<rhs.cols(); ++c) - { - for(Index j=0; j<lhs.outerSize(); ++j) - { -// typename Res::Scalar rhs_j = alpha * rhs.coeff(j,c); - typename internal::scalar_product_traits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j,c)); - for(LhsInnerIterator it(lhs,j); it ;++it) - res.coeffRef(it.index(),c) += it.value() * rhs_j; - } - } - } -}; - -template<typename SparseLhsType, typename DenseRhsType, typename DenseResType> -struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, RowMajor, false> -{ - typedef typename internal::remove_all<SparseLhsType>::type Lhs; - typedef typename internal::remove_all<DenseRhsType>::type Rhs; - typedef typename internal::remove_all<DenseResType>::type Res; - typedef typename Lhs::InnerIterator LhsInnerIterator; - typedef typename Lhs::Index Index; - static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha) - { - for(Index j=0; j<lhs.outerSize(); ++j) - { - typename Res::RowXpr res_j(res.row(j)); - for(LhsInnerIterator it(lhs,j); it ;++it) - res_j += (alpha*it.value()) * rhs.row(it.index()); - } - } -}; - -template<typename SparseLhsType, typename DenseRhsType, typename DenseResType> -struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, ColMajor, false> -{ - typedef typename internal::remove_all<SparseLhsType>::type Lhs; - typedef typename internal::remove_all<DenseRhsType>::type Rhs; - typedef typename internal::remove_all<DenseResType>::type Res; - typedef typename Lhs::InnerIterator LhsInnerIterator; - typedef typename Lhs::Index Index; - static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha) - { - for(Index j=0; j<lhs.outerSize(); ++j) - { - typename Rhs::ConstRowXpr rhs_j(rhs.row(j)); - for(LhsInnerIterator it(lhs,j); it ;++it) - res.row(it.index()) += (alpha*it.value()) * rhs_j; - } - } -}; - -template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,typename AlphaType> -inline void sparse_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha) -{ - sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, AlphaType>::run(lhs, rhs, res, alpha); -} - -} // end namespace internal - -template<typename Lhs, typename Rhs> -class SparseTimeDenseProduct - : public ProductBase<SparseTimeDenseProduct<Lhs,Rhs>, Lhs, Rhs> -{ - public: - EIGEN_PRODUCT_PUBLIC_INTERFACE(SparseTimeDenseProduct) - - SparseTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) - {} - - template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const - { - internal::sparse_time_dense_product(m_lhs, m_rhs, dest, alpha); - } - - private: - SparseTimeDenseProduct& operator=(const SparseTimeDenseProduct&); -}; - - -// dense = dense * sparse -namespace internal { -template<typename Lhs, typename Rhs> -struct traits<DenseTimeSparseProduct<Lhs,Rhs> > - : traits<ProductBase<DenseTimeSparseProduct<Lhs,Rhs>, Lhs, Rhs> > -{ - typedef Dense StorageKind; -}; -} // end namespace internal - -template<typename Lhs, typename Rhs> -class DenseTimeSparseProduct - : public ProductBase<DenseTimeSparseProduct<Lhs,Rhs>, Lhs, Rhs> -{ - public: - EIGEN_PRODUCT_PUBLIC_INTERFACE(DenseTimeSparseProduct) - - DenseTimeSparseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) - {} - - template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const - { - Transpose<const _LhsNested> lhs_t(m_lhs); - Transpose<const _RhsNested> rhs_t(m_rhs); - Transpose<Dest> dest_t(dest); - internal::sparse_time_dense_product(rhs_t, lhs_t, dest_t, alpha); - } - - private: - DenseTimeSparseProduct& operator=(const DenseTimeSparseProduct&); -}; - -// sparse * dense -template<typename Derived> -template<typename OtherDerived> -inline const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type -SparseMatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const -{ - return typename SparseDenseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSEDENSEPRODUCT_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h b/third_party/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h deleted file mode 100644 index 1bb590e64d..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseDiagonalProduct.h +++ /dev/null @@ -1,196 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_DIAGONAL_PRODUCT_H -#define EIGEN_SPARSE_DIAGONAL_PRODUCT_H - -namespace Eigen { - -// The product of a diagonal matrix with a sparse matrix can be easily -// implemented using expression template. -// We have two consider very different cases: -// 1 - diag * row-major sparse -// => each inner vector <=> scalar * sparse vector product -// => so we can reuse CwiseUnaryOp::InnerIterator -// 2 - diag * col-major sparse -// => each inner vector <=> densevector * sparse vector cwise product -// => again, we can reuse specialization of CwiseBinaryOp::InnerIterator -// for that particular case -// The two other cases are symmetric. - -namespace internal { - -template<typename Lhs, typename Rhs> -struct traits<SparseDiagonalProduct<Lhs, Rhs> > -{ - typedef typename remove_all<Lhs>::type _Lhs; - typedef typename remove_all<Rhs>::type _Rhs; - typedef typename _Lhs::Scalar Scalar; - typedef typename promote_index_type<typename traits<Lhs>::Index, - typename traits<Rhs>::Index>::type Index; - typedef Sparse StorageKind; - typedef MatrixXpr XprKind; - enum { - RowsAtCompileTime = _Lhs::RowsAtCompileTime, - ColsAtCompileTime = _Rhs::ColsAtCompileTime, - - MaxRowsAtCompileTime = _Lhs::MaxRowsAtCompileTime, - MaxColsAtCompileTime = _Rhs::MaxColsAtCompileTime, - - SparseFlags = is_diagonal<_Lhs>::ret ? int(_Rhs::Flags) : int(_Lhs::Flags), - Flags = (SparseFlags&RowMajorBit), - CoeffReadCost = Dynamic - }; -}; - -enum {SDP_IsDiagonal, SDP_IsSparseRowMajor, SDP_IsSparseColMajor}; -template<typename Lhs, typename Rhs, typename SparseDiagonalProductType, int RhsMode, int LhsMode> -class sparse_diagonal_product_inner_iterator_selector; - -} // end namespace internal - -template<typename Lhs, typename Rhs> -class SparseDiagonalProduct - : public SparseMatrixBase<SparseDiagonalProduct<Lhs,Rhs> >, - internal::no_assignment_operator -{ - typedef typename Lhs::Nested LhsNested; - typedef typename Rhs::Nested RhsNested; - - typedef typename internal::remove_all<LhsNested>::type _LhsNested; - typedef typename internal::remove_all<RhsNested>::type _RhsNested; - - enum { - LhsMode = internal::is_diagonal<_LhsNested>::ret ? internal::SDP_IsDiagonal - : (_LhsNested::Flags&RowMajorBit) ? internal::SDP_IsSparseRowMajor : internal::SDP_IsSparseColMajor, - RhsMode = internal::is_diagonal<_RhsNested>::ret ? internal::SDP_IsDiagonal - : (_RhsNested::Flags&RowMajorBit) ? internal::SDP_IsSparseRowMajor : internal::SDP_IsSparseColMajor - }; - - public: - - EIGEN_SPARSE_PUBLIC_INTERFACE(SparseDiagonalProduct) - - typedef internal::sparse_diagonal_product_inner_iterator_selector - <_LhsNested,_RhsNested,SparseDiagonalProduct,LhsMode,RhsMode> InnerIterator; - - // We do not want ReverseInnerIterator for diagonal-sparse products, - // but this dummy declaration is needed to make diag * sparse * diag compile. - class ReverseInnerIterator; - - EIGEN_STRONG_INLINE SparseDiagonalProduct(const Lhs& lhs, const Rhs& rhs) - : m_lhs(lhs), m_rhs(rhs) - { - eigen_assert(lhs.cols() == rhs.rows() && "invalid sparse matrix * diagonal matrix product"); - } - - EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); } - EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); } - - EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; } - EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; } - - protected: - LhsNested m_lhs; - RhsNested m_rhs; -}; - -namespace internal { - -template<typename Lhs, typename Rhs, typename SparseDiagonalProductType> -class sparse_diagonal_product_inner_iterator_selector -<Lhs,Rhs,SparseDiagonalProductType,SDP_IsDiagonal,SDP_IsSparseRowMajor> - : public CwiseUnaryOp<scalar_multiple_op<typename Lhs::Scalar>,const Rhs>::InnerIterator -{ - typedef typename CwiseUnaryOp<scalar_multiple_op<typename Lhs::Scalar>,const Rhs>::InnerIterator Base; - typedef typename Lhs::Index Index; - public: - inline sparse_diagonal_product_inner_iterator_selector( - const SparseDiagonalProductType& expr, Index outer) - : Base(expr.rhs()*(expr.lhs().diagonal().coeff(outer)), outer) - {} -}; - -template<typename Lhs, typename Rhs, typename SparseDiagonalProductType> -class sparse_diagonal_product_inner_iterator_selector -<Lhs,Rhs,SparseDiagonalProductType,SDP_IsDiagonal,SDP_IsSparseColMajor> - : public CwiseBinaryOp< - scalar_product_op<typename Lhs::Scalar>, - const typename Rhs::ConstInnerVectorReturnType, - const typename Lhs::DiagonalVectorType>::InnerIterator -{ - typedef typename CwiseBinaryOp< - scalar_product_op<typename Lhs::Scalar>, - const typename Rhs::ConstInnerVectorReturnType, - const typename Lhs::DiagonalVectorType>::InnerIterator Base; - typedef typename Lhs::Index Index; - Index m_outer; - public: - inline sparse_diagonal_product_inner_iterator_selector( - const SparseDiagonalProductType& expr, Index outer) - : Base(expr.rhs().innerVector(outer) .cwiseProduct(expr.lhs().diagonal()), 0), m_outer(outer) - {} - - inline Index outer() const { return m_outer; } - inline Index col() const { return m_outer; } -}; - -template<typename Lhs, typename Rhs, typename SparseDiagonalProductType> -class sparse_diagonal_product_inner_iterator_selector -<Lhs,Rhs,SparseDiagonalProductType,SDP_IsSparseColMajor,SDP_IsDiagonal> - : public CwiseUnaryOp<scalar_multiple_op<typename Rhs::Scalar>,const Lhs>::InnerIterator -{ - typedef typename CwiseUnaryOp<scalar_multiple_op<typename Rhs::Scalar>,const Lhs>::InnerIterator Base; - typedef typename Lhs::Index Index; - public: - inline sparse_diagonal_product_inner_iterator_selector( - const SparseDiagonalProductType& expr, Index outer) - : Base(expr.lhs()*expr.rhs().diagonal().coeff(outer), outer) - {} -}; - -template<typename Lhs, typename Rhs, typename SparseDiagonalProductType> -class sparse_diagonal_product_inner_iterator_selector -<Lhs,Rhs,SparseDiagonalProductType,SDP_IsSparseRowMajor,SDP_IsDiagonal> - : public CwiseBinaryOp< - scalar_product_op<typename Rhs::Scalar>, - const typename Lhs::ConstInnerVectorReturnType, - const Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator -{ - typedef typename CwiseBinaryOp< - scalar_product_op<typename Rhs::Scalar>, - const typename Lhs::ConstInnerVectorReturnType, - const Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator Base; - typedef typename Lhs::Index Index; - Index m_outer; - public: - inline sparse_diagonal_product_inner_iterator_selector( - const SparseDiagonalProductType& expr, Index outer) - : Base(expr.lhs().innerVector(outer) .cwiseProduct(expr.rhs().diagonal().transpose()), 0), m_outer(outer) - {} - - inline Index outer() const { return m_outer; } - inline Index row() const { return m_outer; } -}; - -} // end namespace internal - -// SparseMatrixBase functions - -template<typename Derived> -template<typename OtherDerived> -const SparseDiagonalProduct<Derived,OtherDerived> -SparseMatrixBase<Derived>::operator*(const DiagonalBase<OtherDerived> &other) const -{ - return SparseDiagonalProduct<Derived,OtherDerived>(this->derived(), other.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSE_DIAGONAL_PRODUCT_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseDot.h b/third_party/eigen3/Eigen/src/SparseCore/SparseDot.h deleted file mode 100644 index db39c9aecc..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseDot.h +++ /dev/null @@ -1,101 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_DOT_H -#define EIGEN_SPARSE_DOT_H - -namespace Eigen { - -template<typename Derived> -template<typename OtherDerived> -typename internal::traits<Derived>::Scalar -SparseMatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived) - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - - eigen_assert(size() == other.size()); - eigen_assert(other.size()>0 && "you are using a non initialized vector"); - - typename Derived::InnerIterator i(derived(),0); - Scalar res(0); - while (i) - { - res += numext::conj(i.value()) * other.coeff(i.index()); - ++i; - } - return res; -} - -template<typename Derived> -template<typename OtherDerived> -typename internal::traits<Derived>::Scalar -SparseMatrixBase<Derived>::dot(const SparseMatrixBase<OtherDerived>& other) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) - EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived) - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - - eigen_assert(size() == other.size()); - - typedef typename Derived::Nested Nested; - typedef typename OtherDerived::Nested OtherNested; - typedef typename internal::remove_all<Nested>::type NestedCleaned; - typedef typename internal::remove_all<OtherNested>::type OtherNestedCleaned; - - Nested nthis(derived()); - OtherNested nother(other.derived()); - - typename NestedCleaned::InnerIterator i(nthis,0); - typename OtherNestedCleaned::InnerIterator j(nother,0); - Scalar res(0); - while (i && j) - { - if (i.index()==j.index()) - { - res += numext::conj(i.value()) * j.value(); - ++i; ++j; - } - else if (i.index()<j.index()) - ++i; - else - ++j; - } - return res; -} - -template<typename Derived> -inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real -SparseMatrixBase<Derived>::squaredNorm() const -{ - return numext::real((*this).cwiseAbs2().sum()); -} - -template<typename Derived> -inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real -SparseMatrixBase<Derived>::norm() const -{ - using std::sqrt; - return sqrt(squaredNorm()); -} - -template<typename Derived> -inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real -SparseMatrixBase<Derived>::blueNorm() const -{ - return internal::blueNorm_impl(*this); -} -} // end namespace Eigen - -#endif // EIGEN_SPARSE_DOT_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseFuzzy.h b/third_party/eigen3/Eigen/src/SparseCore/SparseFuzzy.h deleted file mode 100644 index 45f36e9eb9..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseFuzzy.h +++ /dev/null @@ -1,26 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_FUZZY_H -#define EIGEN_SPARSE_FUZZY_H - -// template<typename Derived> -// template<typename OtherDerived> -// bool SparseMatrixBase<Derived>::isApprox( -// const OtherDerived& other, -// typename NumTraits<Scalar>::Real prec -// ) const -// { -// const typename internal::nested<Derived,2>::type nested(derived()); -// const typename internal::nested<OtherDerived,2>::type otherNested(other.derived()); -// return (nested - otherNested).cwise().abs2().sum() -// <= prec * prec * (std::min)(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum()); -// } - -#endif // EIGEN_SPARSE_FUZZY_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseMatrix.h b/third_party/eigen3/Eigen/src/SparseCore/SparseMatrix.h deleted file mode 100644 index 5070c81d9f..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseMatrix.h +++ /dev/null @@ -1,1259 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSEMATRIX_H -#define EIGEN_SPARSEMATRIX_H - -namespace Eigen { - -/** \ingroup SparseCore_Module - * - * \class SparseMatrix - * - * \brief A versatible sparse matrix representation - * - * This class implements a more versatile variants of the common \em compressed row/column storage format. - * Each colmun's (resp. row) non zeros are stored as a pair of value with associated row (resp. colmiun) index. - * All the non zeros are stored in a single large buffer. Unlike the \em compressed format, there might be extra - * space inbetween the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero - * can be done with limited memory reallocation and copies. - * - * A call to the function makeCompressed() turns the matrix into the standard \em compressed format - * compatible with many library. - * - * More details on this storage sceheme are given in the \ref TutorialSparse "manual pages". - * - * \tparam _Scalar the scalar type, i.e. the type of the coefficients - * \tparam _Options Union of bit flags controlling the storage scheme. Currently the only possibility - * is ColMajor or RowMajor. The default is 0 which means column-major. - * \tparam _Index the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int. - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEMATRIX_PLUGIN. - */ - -namespace internal { -template<typename _Scalar, int _Options, typename _Index> -struct traits<SparseMatrix<_Scalar, _Options, _Index> > -{ - typedef _Scalar Scalar; - typedef _Index Index; - typedef Sparse StorageKind; - typedef MatrixXpr XprKind; - enum { - RowsAtCompileTime = Dynamic, - ColsAtCompileTime = Dynamic, - MaxRowsAtCompileTime = Dynamic, - MaxColsAtCompileTime = Dynamic, - Flags = _Options | NestByRefBit | LvalueBit, - CoeffReadCost = NumTraits<Scalar>::ReadCost, - SupportedAccessPatterns = InnerRandomAccessPattern - }; -}; - -template<typename _Scalar, int _Options, typename _Index, int DiagIndex> -struct traits<Diagonal<const SparseMatrix<_Scalar, _Options, _Index>, DiagIndex> > -{ - typedef SparseMatrix<_Scalar, _Options, _Index> MatrixType; - typedef typename nested<MatrixType>::type MatrixTypeNested; - typedef typename remove_reference<MatrixTypeNested>::type _MatrixTypeNested; - - typedef _Scalar Scalar; - typedef Dense StorageKind; - typedef _Index Index; - typedef MatrixXpr XprKind; - - enum { - RowsAtCompileTime = Dynamic, - ColsAtCompileTime = 1, - MaxRowsAtCompileTime = Dynamic, - MaxColsAtCompileTime = 1, - Flags = 0, - CoeffReadCost = _MatrixTypeNested::CoeffReadCost*10 - }; -}; - -} // end namespace internal - -template<typename _Scalar, int _Options, typename _Index> -class SparseMatrix - : public SparseMatrixBase<SparseMatrix<_Scalar, _Options, _Index> > -{ - public: - EIGEN_SPARSE_PUBLIC_INTERFACE(SparseMatrix) - EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseMatrix, +=) - EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseMatrix, -=) - - typedef MappedSparseMatrix<Scalar,Flags> Map; - using Base::IsRowMajor; - typedef internal::CompressedStorage<Scalar,Index> Storage; - enum { - Options = _Options - }; - - protected: - - typedef SparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0)> TransposedSparseMatrix; - - Index m_outerSize; - Index m_innerSize; - Index* m_outerIndex; - Index* m_innerNonZeros; // optional, if null then the data is compressed - Storage m_data; - - Eigen::Map<Matrix<Index,Dynamic,1> > innerNonZeros() { return Eigen::Map<Matrix<Index,Dynamic,1> >(m_innerNonZeros, m_innerNonZeros?m_outerSize:0); } - const Eigen::Map<const Matrix<Index,Dynamic,1> > innerNonZeros() const { return Eigen::Map<const Matrix<Index,Dynamic,1> >(m_innerNonZeros, m_innerNonZeros?m_outerSize:0); } - - public: - - /** \returns whether \c *this is in compressed form. */ - inline bool isCompressed() const { return m_innerNonZeros==0; } - - /** \returns the number of rows of the matrix */ - inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; } - /** \returns the number of columns of the matrix */ - inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; } - - /** \returns the number of rows (resp. columns) of the matrix if the storage order column major (resp. row major) */ - inline Index innerSize() const { return m_innerSize; } - /** \returns the number of columns (resp. rows) of the matrix if the storage order column major (resp. row major) */ - inline Index outerSize() const { return m_outerSize; } - - /** \returns a const pointer to the array of values. - * This function is aimed at interoperability with other libraries. - * \sa innerIndexPtr(), outerIndexPtr() */ - inline const Scalar* valuePtr() const { return &m_data.value(0); } - /** \returns a non-const pointer to the array of values. - * This function is aimed at interoperability with other libraries. - * \sa innerIndexPtr(), outerIndexPtr() */ - inline Scalar* valuePtr() { return &m_data.value(0); } - - /** \returns a const pointer to the array of inner indices. - * This function is aimed at interoperability with other libraries. - * \sa valuePtr(), outerIndexPtr() */ - inline const Index* innerIndexPtr() const { return &m_data.index(0); } - /** \returns a non-const pointer to the array of inner indices. - * This function is aimed at interoperability with other libraries. - * \sa valuePtr(), outerIndexPtr() */ - inline Index* innerIndexPtr() { return &m_data.index(0); } - - /** \returns a const pointer to the array of the starting positions of the inner vectors. - * This function is aimed at interoperability with other libraries. - * \sa valuePtr(), innerIndexPtr() */ - inline const Index* outerIndexPtr() const { return m_outerIndex; } - /** \returns a non-const pointer to the array of the starting positions of the inner vectors. - * This function is aimed at interoperability with other libraries. - * \sa valuePtr(), innerIndexPtr() */ - inline Index* outerIndexPtr() { return m_outerIndex; } - - /** \returns a const pointer to the array of the number of non zeros of the inner vectors. - * This function is aimed at interoperability with other libraries. - * \warning it returns the null pointer 0 in compressed mode */ - inline const Index* innerNonZeroPtr() const { return m_innerNonZeros; } - /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors. - * This function is aimed at interoperability with other libraries. - * \warning it returns the null pointer 0 in compressed mode */ - inline Index* innerNonZeroPtr() { return m_innerNonZeros; } - - /** \internal */ - inline Storage& data() { return m_data; } - /** \internal */ - inline const Storage& data() const { return m_data; } - - /** \returns the value of the matrix at position \a i, \a j - * This function returns Scalar(0) if the element is an explicit \em zero */ - inline Scalar coeff(Index row, Index col) const - { - eigen_assert(row>=0 && row<rows() && col>=0 && col<cols()); - - const Index outer = IsRowMajor ? row : col; - const Index inner = IsRowMajor ? col : row; - Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1]; - return m_data.atInRange(m_outerIndex[outer], end, inner); - } - - /** \returns a non-const reference to the value of the matrix at position \a i, \a j - * - * If the element does not exist then it is inserted via the insert(Index,Index) function - * which itself turns the matrix into a non compressed form if that was not the case. - * - * This is a O(log(nnz_j)) operation (binary search) plus the cost of insert(Index,Index) - * function if the element does not already exist. - */ - inline Scalar& coeffRef(Index row, Index col) - { - eigen_assert(row>=0 && row<rows() && col>=0 && col<cols()); - - const Index outer = IsRowMajor ? row : col; - const Index inner = IsRowMajor ? col : row; - - Index start = m_outerIndex[outer]; - Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1]; - eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix"); - if(end<=start) - return insert(row,col); - const Index p = m_data.searchLowerIndex(start,end-1,inner); - if((p<end) && (m_data.index(p)==inner)) - return m_data.value(p); - else - return insert(row,col); - } - - /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col. - * The non zero coefficient must \b not already exist. - * - * If the matrix \c *this is in compressed mode, then \c *this is turned into uncompressed - * mode while reserving room for 2 non zeros per inner vector. It is strongly recommended to first - * call reserve(const SizesType &) to reserve a more appropriate number of elements per - * inner vector that better match your scenario. - * - * This function performs a sorted insertion in O(1) if the elements of each inner vector are - * inserted in increasing inner index order, and in O(nnz_j) for a random insertion. - * - */ - Scalar& insert(Index row, Index col) - { - eigen_assert(row>=0 && row<rows() && col>=0 && col<cols()); - - if(isCompressed()) - { - reserve(Matrix<Index,Dynamic,1>::Constant(outerSize(), 2)); - } - return insertUncompressed(row,col); - } - - public: - - class InnerIterator; - class ReverseInnerIterator; - - /** Removes all non zeros but keep allocated memory */ - inline void setZero() - { - m_data.clear(); - memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(Index)); - if(m_innerNonZeros) - memset(m_innerNonZeros, 0, (m_outerSize)*sizeof(Index)); - } - - /** \returns the number of non zero coefficients */ - inline Index nonZeros() const - { - if(m_innerNonZeros) - return innerNonZeros().sum(); - return static_cast<Index>(m_data.size()); - } - - /** Preallocates \a reserveSize non zeros. - * - * Precondition: the matrix must be in compressed mode. */ - inline void reserve(Index reserveSize) - { - eigen_assert(isCompressed() && "This function does not make sense in non compressed mode."); - m_data.reserve(reserveSize); - } - - #ifdef EIGEN_PARSED_BY_DOXYGEN - /** Preallocates \a reserveSize[\c j] non zeros for each column (resp. row) \c j. - * - * This function turns the matrix in non-compressed mode */ - template<class SizesType> - inline void reserve(const SizesType& reserveSizes); - #else - template<class SizesType> - inline void reserve(const SizesType& reserveSizes, const typename SizesType::value_type& enableif = typename SizesType::value_type()) - { - EIGEN_UNUSED_VARIABLE(enableif); - reserveInnerVectors(reserveSizes); - } - template<class SizesType> - inline void reserve(const SizesType& reserveSizes, const typename SizesType::Scalar& enableif = - #if (!EIGEN_COMP_MSVC) || (EIGEN_COMP_MSVC>=1500) // MSVC 2005 fails to compile with this typename - typename - #endif - SizesType::Scalar()) - { - EIGEN_UNUSED_VARIABLE(enableif); - reserveInnerVectors(reserveSizes); - } - #endif // EIGEN_PARSED_BY_DOXYGEN - protected: - template<class SizesType> - inline void reserveInnerVectors(const SizesType& reserveSizes) - { - if(isCompressed()) - { - std::size_t totalReserveSize = 0; - // turn the matrix into non-compressed mode - m_innerNonZeros = static_cast<Index*>(std::malloc(m_outerSize * sizeof(Index))); - if (!m_innerNonZeros) internal::throw_std_bad_alloc(); - - // temporarily use m_innerSizes to hold the new starting points. - Index* newOuterIndex = m_innerNonZeros; - - Index count = 0; - for(Index j=0; j<m_outerSize; ++j) - { - newOuterIndex[j] = count; - count += reserveSizes[j] + (m_outerIndex[j+1]-m_outerIndex[j]); - totalReserveSize += reserveSizes[j]; - } - m_data.reserve(totalReserveSize); - Index previousOuterIndex = m_outerIndex[m_outerSize]; - for(Index j=m_outerSize-1; j>=0; --j) - { - Index innerNNZ = previousOuterIndex - m_outerIndex[j]; - for(Index i=innerNNZ-1; i>=0; --i) - { - m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i); - m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i); - } - previousOuterIndex = m_outerIndex[j]; - m_outerIndex[j] = newOuterIndex[j]; - m_innerNonZeros[j] = innerNNZ; - } - m_outerIndex[m_outerSize] = m_outerIndex[m_outerSize-1] + m_innerNonZeros[m_outerSize-1] + reserveSizes[m_outerSize-1]; - - m_data.resize(m_outerIndex[m_outerSize]); - } - else - { - Index* newOuterIndex = static_cast<Index*>(std::malloc((m_outerSize+1)*sizeof(Index))); - if (!newOuterIndex) internal::throw_std_bad_alloc(); - - Index count = 0; - for(Index j=0; j<m_outerSize; ++j) - { - newOuterIndex[j] = count; - Index alreadyReserved = (m_outerIndex[j+1]-m_outerIndex[j]) - m_innerNonZeros[j]; - Index toReserve = std::max<Index>(reserveSizes[j], alreadyReserved); - count += toReserve + m_innerNonZeros[j]; - } - newOuterIndex[m_outerSize] = count; - - m_data.resize(count); - for(Index j=m_outerSize-1; j>=0; --j) - { - Index offset = newOuterIndex[j] - m_outerIndex[j]; - if(offset>0) - { - Index innerNNZ = m_innerNonZeros[j]; - for(Index i=innerNNZ-1; i>=0; --i) - { - m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i); - m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i); - } - } - } - - std::swap(m_outerIndex, newOuterIndex); - std::free(newOuterIndex); - } - - } - public: - - //--- low level purely coherent filling --- - - /** \internal - * \returns a reference to the non zero coefficient at position \a row, \a col assuming that: - * - the nonzero does not already exist - * - the new coefficient is the last one according to the storage order - * - * Before filling a given inner vector you must call the statVec(Index) function. - * - * After an insertion session, you should call the finalize() function. - * - * \sa insert, insertBackByOuterInner, startVec */ - inline Scalar& insertBack(Index row, Index col) - { - return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row); - } - - /** \internal - * \sa insertBack, startVec */ - inline Scalar& insertBackByOuterInner(Index outer, Index inner) - { - eigen_assert(size_t(m_outerIndex[outer+1]) == m_data.size() && "Invalid ordered insertion (invalid outer index)"); - eigen_assert( (m_outerIndex[outer+1]-m_outerIndex[outer]==0 || m_data.index(m_data.size()-1)<inner) && "Invalid ordered insertion (invalid inner index)"); - Index p = m_outerIndex[outer+1]; - ++m_outerIndex[outer+1]; - m_data.append(Scalar(0), inner); - return m_data.value(p); - } - - /** \internal - * \warning use it only if you know what you are doing */ - inline Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner) - { - Index p = m_outerIndex[outer+1]; - ++m_outerIndex[outer+1]; - m_data.append(Scalar(0), inner); - return m_data.value(p); - } - - /** \internal - * \sa insertBack, insertBackByOuterInner */ - inline void startVec(Index outer) - { - eigen_assert(m_outerIndex[outer]==Index(m_data.size()) && "You must call startVec for each inner vector sequentially"); - eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially"); - m_outerIndex[outer+1] = m_outerIndex[outer]; - } - - /** \internal - * Must be called after inserting a set of non zero entries using the low level compressed API. - */ - inline void finalize() - { - if(isCompressed()) - { - Index size = static_cast<Index>(m_data.size()); - Index i = m_outerSize; - // find the last filled column - while (i>=0 && m_outerIndex[i]==0) - --i; - ++i; - while (i<=m_outerSize) - { - m_outerIndex[i] = size; - ++i; - } - } - } - - //--- - - template<typename InputIterators> - void setFromTriplets(const InputIterators& begin, const InputIterators& end); - - void sumupDuplicates(); - - //--- - - /** \internal - * same as insert(Index,Index) except that the indices are given relative to the storage order */ - Scalar& insertByOuterInner(Index j, Index i) - { - return insert(IsRowMajor ? j : i, IsRowMajor ? i : j); - } - - /** Turns the matrix into the \em compressed format. - */ - void makeCompressed() - { - if(isCompressed()) - return; - - Index oldStart = m_outerIndex[1]; - m_outerIndex[1] = m_innerNonZeros[0]; - for(Index j=1; j<m_outerSize; ++j) - { - Index nextOldStart = m_outerIndex[j+1]; - Index offset = oldStart - m_outerIndex[j]; - if(offset>0) - { - for(Index k=0; k<m_innerNonZeros[j]; ++k) - { - m_data.index(m_outerIndex[j]+k) = m_data.index(oldStart+k); - m_data.value(m_outerIndex[j]+k) = m_data.value(oldStart+k); - } - } - m_outerIndex[j+1] = m_outerIndex[j] + m_innerNonZeros[j]; - oldStart = nextOldStart; - } - std::free(m_innerNonZeros); - m_innerNonZeros = 0; - m_data.resize(m_outerIndex[m_outerSize]); - m_data.squeeze(); - } - - /** Turns the matrix into the uncompressed mode */ - void uncompress() - { - if(m_innerNonZeros != 0) - return; - m_innerNonZeros = static_cast<Index*>(std::malloc(m_outerSize * sizeof(Index))); - for (Index i = 0; i < m_outerSize; i++) - { - m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i]; - } - } - - /** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerence \a epsilon */ - void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) - { - prune(default_prunning_func(reference,epsilon)); - } - - /** Turns the matrix into compressed format, and suppresses all nonzeros which do not satisfy the predicate \a keep. - * The functor type \a KeepFunc must implement the following function: - * \code - * bool operator() (const Index& row, const Index& col, const Scalar& value) const; - * \endcode - * \sa prune(Scalar,RealScalar) - */ - template<typename KeepFunc> - void prune(const KeepFunc& keep = KeepFunc()) - { - // TODO optimize the uncompressed mode to avoid moving and allocating the data twice - // TODO also implement a unit test - makeCompressed(); - - Index k = 0; - for(Index j=0; j<m_outerSize; ++j) - { - Index previousStart = m_outerIndex[j]; - m_outerIndex[j] = k; - Index end = m_outerIndex[j+1]; - for(Index i=previousStart; i<end; ++i) - { - if(keep(IsRowMajor?j:m_data.index(i), IsRowMajor?m_data.index(i):j, m_data.value(i))) - { - m_data.value(k) = m_data.value(i); - m_data.index(k) = m_data.index(i); - ++k; - } - } - } - m_outerIndex[m_outerSize] = k; - m_data.resize(k,0); - } - - /** Resizes the matrix to a \a rows x \a cols matrix leaving old values untouched. - * \sa resizeNonZeros(Index), reserve(), setZero() - */ - void conservativeResize(Index rows, Index cols) - { - // No change - if (this->rows() == rows && this->cols() == cols) return; - - // If one dimension is null, then there is nothing to be preserved - if(rows==0 || cols==0) return resize(rows,cols); - - Index innerChange = IsRowMajor ? cols - this->cols() : rows - this->rows(); - Index outerChange = IsRowMajor ? rows - this->rows() : cols - this->cols(); - Index newInnerSize = IsRowMajor ? cols : rows; - - // Deals with inner non zeros - if (m_innerNonZeros) - { - // Resize m_innerNonZeros - Index *newInnerNonZeros = static_cast<Index*>(std::realloc(m_innerNonZeros, (m_outerSize + outerChange) * sizeof(Index))); - if (!newInnerNonZeros) internal::throw_std_bad_alloc(); - m_innerNonZeros = newInnerNonZeros; - - for(Index i=m_outerSize; i<m_outerSize+outerChange; i++) - m_innerNonZeros[i] = 0; - } - else if (innerChange < 0) - { - // Inner size decreased: allocate a new m_innerNonZeros - m_innerNonZeros = static_cast<Index*>(std::malloc((m_outerSize+outerChange+1) * sizeof(Index))); - if (!m_innerNonZeros) internal::throw_std_bad_alloc(); - for(Index i = 0; i < m_outerSize; i++) - m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i]; - } - - // Change the m_innerNonZeros in case of a decrease of inner size - if (m_innerNonZeros && innerChange < 0) - { - for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++) - { - Index &n = m_innerNonZeros[i]; - Index start = m_outerIndex[i]; - while (n > 0 && m_data.index(start+n-1) >= newInnerSize) --n; - } - } - - m_innerSize = newInnerSize; - - // Re-allocate outer index structure if necessary - if (outerChange == 0) - return; - - Index *newOuterIndex = static_cast<Index*>(std::realloc(m_outerIndex, (m_outerSize + outerChange + 1) * sizeof(Index))); - if (!newOuterIndex) internal::throw_std_bad_alloc(); - m_outerIndex = newOuterIndex; - if (outerChange > 0) - { - Index last = m_outerSize == 0 ? 0 : m_outerIndex[m_outerSize]; - for(Index i=m_outerSize; i<m_outerSize+outerChange+1; i++) - m_outerIndex[i] = last; - } - m_outerSize += outerChange; - } - - /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero. - * \sa resizeNonZeros(Index), reserve(), setZero() - */ - void resize(Index rows, Index cols) - { - const Index outerSize = IsRowMajor ? rows : cols; - m_innerSize = IsRowMajor ? cols : rows; - m_data.clear(); - if (m_outerSize != outerSize || m_outerSize==0) - { - std::free(m_outerIndex); - m_outerIndex = static_cast<Index*>(std::malloc((outerSize + 1) * sizeof(Index))); - if (!m_outerIndex) internal::throw_std_bad_alloc(); - - m_outerSize = outerSize; - } - if(m_innerNonZeros) - { - std::free(m_innerNonZeros); - m_innerNonZeros = 0; - } - memset(m_outerIndex, 0, (m_outerSize+1)*sizeof(Index)); - } - - /** \internal - * Resize the nonzero vector to \a size */ - void resizeNonZeros(Index size) - { - // TODO remove this function - m_data.resize(size); - } - - /** \returns a const expression of the diagonal coefficients */ - const Diagonal<const SparseMatrix> diagonal() const { return *this; } - - /** Default constructor yielding an empty \c 0 \c x \c 0 matrix */ - inline SparseMatrix() - : m_outerSize(-1), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) - { - check_template_parameters(); - resize(0, 0); - } - - /** Constructs a \a rows \c x \a cols empty matrix */ - inline SparseMatrix(Index rows, Index cols) - : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) - { - check_template_parameters(); - resize(rows, cols); - } - - /** Constructs a sparse matrix from the sparse expression \a other */ - template<typename OtherDerived> - inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other) - : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) - { - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - check_template_parameters(); - *this = other.derived(); - } - - /** Constructs a sparse matrix from the sparse selfadjoint view \a other */ - template<typename OtherDerived, unsigned int UpLo> - inline SparseMatrix(const SparseSelfAdjointView<OtherDerived, UpLo>& other) - : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) - { - check_template_parameters(); - *this = other; - } - - /** Copy constructor (it performs a deep copy) */ - inline SparseMatrix(const SparseMatrix& other) - : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) - { - check_template_parameters(); - *this = other.derived(); - } - - /** \brief Copy constructor with in-place evaluation */ - template<typename OtherDerived> - SparseMatrix(const ReturnByValue<OtherDerived>& other) - : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) - { - check_template_parameters(); - initAssignment(other); - other.evalTo(*this); - } - - /** Swaps the content of two sparse matrices of the same type. - * This is a fast operation that simply swaps the underlying pointers and parameters. */ - inline void swap(SparseMatrix& other) - { - //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n"); - std::swap(m_outerIndex, other.m_outerIndex); - std::swap(m_innerSize, other.m_innerSize); - std::swap(m_outerSize, other.m_outerSize); - std::swap(m_innerNonZeros, other.m_innerNonZeros); - m_data.swap(other.m_data); - } - - /** Sets *this to the identity matrix */ - inline void setIdentity() - { - eigen_assert(rows() == cols() && "ONLY FOR SQUARED MATRICES"); - this->m_data.resize(rows()); - Eigen::Map<Matrix<Index, Dynamic, 1> >(&this->m_data.index(0), rows()).setLinSpaced(0, rows()-1); - Eigen::Map<Matrix<Scalar, Dynamic, 1> >(&this->m_data.value(0), rows()).setOnes(); - Eigen::Map<Matrix<Index, Dynamic, 1> >(this->m_outerIndex, rows()+1).setLinSpaced(0, rows()); - } - inline SparseMatrix& operator=(const SparseMatrix& other) - { - if (other.isRValue()) - { - swap(other.const_cast_derived()); - } - else if(this!=&other) - { - initAssignment(other); - if(other.isCompressed()) - { - internal::smart_copy(other.m_outerIndex, other.m_outerIndex + m_outerSize + 1, m_outerIndex); - m_data = other.m_data; - } - else - { - Base::operator=(other); - } - } - return *this; - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename Lhs, typename Rhs> - inline SparseMatrix& operator=(const SparseSparseProduct<Lhs,Rhs>& product) - { return Base::operator=(product); } - - template<typename OtherDerived> - inline SparseMatrix& operator=(const ReturnByValue<OtherDerived>& other) - { - initAssignment(other); - return Base::operator=(other.derived()); - } - - template<typename OtherDerived> - inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other) - { return Base::operator=(other.derived()); } - #endif - - template<typename OtherDerived> - EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other); - - friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m) - { - EIGEN_DBG_SPARSE( - s << "Nonzero entries:\n"; - if(m.isCompressed()) - for (Index i=0; i<m.nonZeros(); ++i) - s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") "; - else - for (Index i=0; i<m.outerSize(); ++i) - { - Index p = m.m_outerIndex[i]; - Index pe = m.m_outerIndex[i]+m.m_innerNonZeros[i]; - Index k=p; - for (; k<pe; ++k) - s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") "; - for (; k<m.m_outerIndex[i+1]; ++k) - s << "(_,_) "; - } - s << std::endl; - s << std::endl; - s << "Outer pointers:\n"; - for (Index i=0; i<m.outerSize(); ++i) - s << m.m_outerIndex[i] << " "; - s << " $" << std::endl; - if(!m.isCompressed()) - { - s << "Inner non zeros:\n"; - for (Index i=0; i<m.outerSize(); ++i) - s << m.m_innerNonZeros[i] << " "; - s << " $" << std::endl; - } - s << std::endl; - ); - s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m); - return s; - } - - /** Destructor */ - inline ~SparseMatrix() - { - std::free(m_outerIndex); - std::free(m_innerNonZeros); - } - -#ifndef EIGEN_PARSED_BY_DOXYGEN - /** Overloaded for performance */ - Scalar sum() const; -#endif - -# ifdef EIGEN_SPARSEMATRIX_PLUGIN -# include EIGEN_SPARSEMATRIX_PLUGIN -# endif - -protected: - - template<typename Other> - void initAssignment(const Other& other) - { - resize(other.rows(), other.cols()); - if(m_innerNonZeros) - { - std::free(m_innerNonZeros); - m_innerNonZeros = 0; - } - } - - /** \internal - * \sa insert(Index,Index) */ - EIGEN_DONT_INLINE Scalar& insertCompressed(Index row, Index col); - - /** \internal - * A vector object that is equal to 0 everywhere but v at the position i */ - class SingletonVector - { - Index m_index; - Index m_value; - public: - typedef Index value_type; - SingletonVector(Index i, Index v) - : m_index(i), m_value(v) - {} - - Index operator[](Index i) const { return i==m_index ? m_value : 0; } - }; - - /** \internal - * \sa insert(Index,Index) */ - EIGEN_DONT_INLINE Scalar& insertUncompressed(Index row, Index col); - -public: - /** \internal - * \sa insert(Index,Index) */ - EIGEN_STRONG_INLINE Scalar& insertBackUncompressed(Index row, Index col) - { - const Index outer = IsRowMajor ? row : col; - const Index inner = IsRowMajor ? col : row; - - eigen_assert(!isCompressed()); - eigen_assert(m_innerNonZeros[outer]<=(m_outerIndex[outer+1] - m_outerIndex[outer])); - - Index p = m_outerIndex[outer] + m_innerNonZeros[outer]++; - m_data.index(p) = inner; - return (m_data.value(p) = 0); - } - -private: - static void check_template_parameters() - { - EIGEN_STATIC_ASSERT(NumTraits<Index>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE); - EIGEN_STATIC_ASSERT((Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS); - } - - struct default_prunning_func { - default_prunning_func(const Scalar& ref, const RealScalar& eps) : reference(ref), epsilon(eps) {} - inline bool operator() (const Index&, const Index&, const Scalar& value) const - { - return !internal::isMuchSmallerThan(value, reference, epsilon); - } - Scalar reference; - RealScalar epsilon; - }; -}; - -template<typename Scalar, int _Options, typename _Index> -class SparseMatrix<Scalar,_Options,_Index>::InnerIterator -{ - public: - InnerIterator(const SparseMatrix& mat, Index outer) - : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer), m_id(mat.m_outerIndex[outer]) - { - if(mat.isCompressed()) - m_end = mat.m_outerIndex[outer+1]; - else - m_end = m_id + mat.m_innerNonZeros[outer]; - } - - inline InnerIterator& operator++() { m_id++; return *this; } - - inline const Scalar& value() const { return m_values[m_id]; } - inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id]); } - - inline Index index() const { return m_indices[m_id]; } - inline Index outer() const { return m_outer; } - inline Index row() const { return IsRowMajor ? m_outer : index(); } - inline Index col() const { return IsRowMajor ? index() : m_outer; } - - inline operator bool() const { return (m_id < m_end); } - - protected: - const Scalar* m_values; - const Index* m_indices; - const Index m_outer; - Index m_id; - Index m_end; -}; - -template<typename Scalar, int _Options, typename _Index> -class SparseMatrix<Scalar,_Options,_Index>::ReverseInnerIterator -{ - public: - ReverseInnerIterator(const SparseMatrix& mat, Index outer) - : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer), m_start(mat.m_outerIndex[outer]) - { - if(mat.isCompressed()) - m_id = mat.m_outerIndex[outer+1]; - else - m_id = m_start + mat.m_innerNonZeros[outer]; - } - - inline ReverseInnerIterator& operator--() { --m_id; return *this; } - - inline const Scalar& value() const { return m_values[m_id-1]; } - inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id-1]); } - - inline Index index() const { return m_indices[m_id-1]; } - inline Index outer() const { return m_outer; } - inline Index row() const { return IsRowMajor ? m_outer : index(); } - inline Index col() const { return IsRowMajor ? index() : m_outer; } - - inline operator bool() const { return (m_id > m_start); } - - protected: - const Scalar* m_values; - const Index* m_indices; - const Index m_outer; - Index m_id; - const Index m_start; -}; - -namespace internal { - -template<typename InputIterator, typename SparseMatrixType> -void set_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat, int Options = 0) -{ - EIGEN_UNUSED_VARIABLE(Options); - enum { IsRowMajor = SparseMatrixType::IsRowMajor }; - typedef typename SparseMatrixType::Scalar Scalar; - typedef typename SparseMatrixType::Index Index; - SparseMatrix<Scalar,IsRowMajor?ColMajor:RowMajor> trMat(mat.rows(),mat.cols()); - - if(begin!=end) - { - // pass 1: count the nnz per inner-vector - Matrix<Index,Dynamic,1> wi(trMat.outerSize()); - wi.setZero(); - for(InputIterator it(begin); it!=end; ++it) - { - eigen_assert(it->row()>=0 && it->row()<mat.rows() && it->col()>=0 && it->col()<mat.cols()); - wi(IsRowMajor ? it->col() : it->row())++; - } - - // pass 2: insert all the elements into trMat - trMat.reserve(wi); - for(InputIterator it(begin); it!=end; ++it) - trMat.insertBackUncompressed(it->row(),it->col()) = it->value(); - - // pass 3: - trMat.sumupDuplicates(); - } - - // pass 4: transposed copy -> implicit sorting - mat = trMat; -} - -} - - -/** Fill the matrix \c *this with the list of \em triplets defined by the iterator range \a begin - \a end. - * - * A \em triplet is a tuple (i,j,value) defining a non-zero element. - * The input list of triplets does not have to be sorted, and can contains duplicated elements. - * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up. - * This is a \em O(n) operation, with \em n the number of triplet elements. - * The initial contents of \c *this is destroyed. - * The matrix \c *this must be properly resized beforehand using the SparseMatrix(Index,Index) constructor, - * or the resize(Index,Index) method. The sizes are not extracted from the triplet list. - * - * The \a InputIterators value_type must provide the following interface: - * \code - * Scalar value() const; // the value - * Scalar row() const; // the row index i - * Scalar col() const; // the column index j - * \endcode - * See for instance the Eigen::Triplet template class. - * - * Here is a typical usage example: - * \code - typedef Triplet<double> T; - std::vector<T> tripletList; - triplets.reserve(estimation_of_entries); - for(...) - { - // ... - tripletList.push_back(T(i,j,v_ij)); - } - SparseMatrixType m(rows,cols); - m.setFromTriplets(tripletList.begin(), tripletList.end()); - // m is ready to go! - * \endcode - * - * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define - * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather - * be explicitely stored into a std::vector for instance. - */ -template<typename Scalar, int _Options, typename _Index> -template<typename InputIterators> -void SparseMatrix<Scalar,_Options,_Index>::setFromTriplets(const InputIterators& begin, const InputIterators& end) -{ - internal::set_from_triplets(begin, end, *this); -} - -/** \internal */ -template<typename Scalar, int _Options, typename _Index> -void SparseMatrix<Scalar,_Options,_Index>::sumupDuplicates() -{ - eigen_assert(!isCompressed()); - // TODO, in practice we should be able to use m_innerNonZeros for that task - Matrix<Index,Dynamic,1> wi(innerSize()); - wi.fill(-1); - Index count = 0; - // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers - for(Index j=0; j<outerSize(); ++j) - { - Index start = count; - Index oldEnd = m_outerIndex[j]+m_innerNonZeros[j]; - for(Index k=m_outerIndex[j]; k<oldEnd; ++k) - { - Index i = m_data.index(k); - if(wi(i)>=start) - { - // we already meet this entry => accumulate it - m_data.value(wi(i)) += m_data.value(k); - } - else - { - m_data.value(count) = m_data.value(k); - m_data.index(count) = m_data.index(k); - wi(i) = count; - ++count; - } - } - m_outerIndex[j] = start; - } - m_outerIndex[m_outerSize] = count; - - // turn the matrix into compressed form - std::free(m_innerNonZeros); - m_innerNonZeros = 0; - m_data.resize(m_outerIndex[m_outerSize]); -} - -template<typename Scalar, int _Options, typename _Index> -template<typename OtherDerived> -EIGEN_DONT_INLINE SparseMatrix<Scalar,_Options,_Index>& SparseMatrix<Scalar,_Options,_Index>::operator=(const SparseMatrixBase<OtherDerived>& other) -{ - EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), - YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) - - const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit); - if (needToTranspose) - { - // two passes algorithm: - // 1 - compute the number of coeffs per dest inner vector - // 2 - do the actual copy/eval - // Since each coeff of the rhs has to be evaluated twice, let's evaluate it if needed - typedef typename internal::nested<OtherDerived,2>::type OtherCopy; - typedef typename internal::remove_all<OtherCopy>::type _OtherCopy; - OtherCopy otherCopy(other.derived()); - - SparseMatrix dest(other.rows(),other.cols()); - Eigen::Map<Matrix<Index, Dynamic, 1> > (dest.m_outerIndex,dest.outerSize()).setZero(); - - // pass 1 - // FIXME the above copy could be merged with that pass - for (Index j=0; j<otherCopy.outerSize(); ++j) - for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it) - ++dest.m_outerIndex[it.index()]; - - // prefix sum - Index count = 0; - Matrix<Index,Dynamic,1> positions(dest.outerSize()); - for (Index j=0; j<dest.outerSize(); ++j) - { - Index tmp = dest.m_outerIndex[j]; - dest.m_outerIndex[j] = count; - positions[j] = count; - count += tmp; - } - dest.m_outerIndex[dest.outerSize()] = count; - // alloc - dest.m_data.resize(count); - // pass 2 - for (Index j=0; j<otherCopy.outerSize(); ++j) - { - for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it) - { - Index pos = positions[it.index()]++; - dest.m_data.index(pos) = j; - dest.m_data.value(pos) = it.value(); - } - } - this->swap(dest); - return *this; - } - else - { - if(other.isRValue()) - initAssignment(other.derived()); - // there is no special optimization - return Base::operator=(other.derived()); - } -} - -template<typename _Scalar, int _Options, typename _Index> -EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_Index>::Scalar& SparseMatrix<_Scalar,_Options,_Index>::insertUncompressed(Index row, Index col) -{ - eigen_assert(!isCompressed()); - - const Index outer = IsRowMajor ? row : col; - const Index inner = IsRowMajor ? col : row; - - Index room = m_outerIndex[outer+1] - m_outerIndex[outer]; - Index innerNNZ = m_innerNonZeros[outer]; - if(innerNNZ>=room) - { - // this inner vector is full, we need to reallocate the whole buffer :( - reserve(SingletonVector(outer,std::max<Index>(2,innerNNZ))); - } - - Index startId = m_outerIndex[outer]; - Index p = startId + m_innerNonZeros[outer]; - while ( (p > startId) && (m_data.index(p-1) > inner) ) - { - m_data.index(p) = m_data.index(p-1); - m_data.value(p) = m_data.value(p-1); - --p; - } - eigen_assert((p<=startId || m_data.index(p-1)!=inner) && "you cannot insert an element that already exists, you must call coeffRef to this end"); - - m_innerNonZeros[outer]++; - - m_data.index(p) = inner; - return (m_data.value(p) = 0); -} - -template<typename _Scalar, int _Options, typename _Index> -EIGEN_DONT_INLINE typename SparseMatrix<_Scalar,_Options,_Index>::Scalar& SparseMatrix<_Scalar,_Options,_Index>::insertCompressed(Index row, Index col) -{ - eigen_assert(isCompressed()); - - const Index outer = IsRowMajor ? row : col; - const Index inner = IsRowMajor ? col : row; - - Index previousOuter = outer; - if (m_outerIndex[outer+1]==0) - { - // we start a new inner vector - while (previousOuter>=0 && m_outerIndex[previousOuter]==0) - { - m_outerIndex[previousOuter] = static_cast<Index>(m_data.size()); - --previousOuter; - } - m_outerIndex[outer+1] = m_outerIndex[outer]; - } - - // here we have to handle the tricky case where the outerIndex array - // starts with: [ 0 0 0 0 0 1 ...] and we are inserted in, e.g., - // the 2nd inner vector... - bool isLastVec = (!(previousOuter==-1 && m_data.size()!=0)) - && (size_t(m_outerIndex[outer+1]) == m_data.size()); - - size_t startId = m_outerIndex[outer]; - // FIXME let's make sure sizeof(long int) == sizeof(size_t) - size_t p = m_outerIndex[outer+1]; - ++m_outerIndex[outer+1]; - - float reallocRatio = 1; - if (m_data.allocatedSize()<=m_data.size()) - { - // if there is no preallocated memory, let's reserve a minimum of 32 elements - if (m_data.size()==0) - { - m_data.reserve(32); - } - else - { - // we need to reallocate the data, to reduce multiple reallocations - // we use a smart resize algorithm based on the current filling ratio - // in addition, we use float to avoid integers overflows - float nnzEstimate = float(m_outerIndex[outer])*float(m_outerSize)/float(outer+1); - reallocRatio = (nnzEstimate-float(m_data.size()))/float(m_data.size()); - // furthermore we bound the realloc ratio to: - // 1) reduce multiple minor realloc when the matrix is almost filled - // 2) avoid to allocate too much memory when the matrix is almost empty - reallocRatio = (std::min)((std::max)(reallocRatio,1.5f),8.f); - } - } - m_data.resize(m_data.size()+1,reallocRatio); - - if (!isLastVec) - { - if (previousOuter==-1) - { - // oops wrong guess. - // let's correct the outer offsets - for (Index k=0; k<=(outer+1); ++k) - m_outerIndex[k] = 0; - Index k=outer+1; - while(m_outerIndex[k]==0) - m_outerIndex[k++] = 1; - while (k<=m_outerSize && m_outerIndex[k]!=0) - m_outerIndex[k++]++; - p = 0; - --k; - k = m_outerIndex[k]-1; - while (k>0) - { - m_data.index(k) = m_data.index(k-1); - m_data.value(k) = m_data.value(k-1); - k--; - } - } - else - { - // we are not inserting into the last inner vec - // update outer indices: - Index j = outer+2; - while (j<=m_outerSize && m_outerIndex[j]!=0) - m_outerIndex[j++]++; - --j; - // shift data of last vecs: - Index k = m_outerIndex[j]-1; - while (k>=Index(p)) - { - m_data.index(k) = m_data.index(k-1); - m_data.value(k) = m_data.value(k-1); - k--; - } - } - } - - while ( (p > startId) && (m_data.index(p-1) > inner) ) - { - m_data.index(p) = m_data.index(p-1); - m_data.value(p) = m_data.value(p-1); - --p; - } - - m_data.index(p) = inner; - return (m_data.value(p) = 0); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSEMATRIX_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h b/third_party/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h deleted file mode 100644 index bbcf7fb1c6..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseMatrixBase.h +++ /dev/null @@ -1,451 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSEMATRIXBASE_H -#define EIGEN_SPARSEMATRIXBASE_H - -namespace Eigen { - -/** \ingroup SparseCore_Module - * - * \class SparseMatrixBase - * - * \brief Base class of any sparse matrices or sparse expressions - * - * \tparam Derived - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEMATRIXBASE_PLUGIN. - */ -template<typename Derived> class SparseMatrixBase : public EigenBase<Derived> -{ - public: - - typedef typename internal::traits<Derived>::Scalar Scalar; - typedef typename internal::packet_traits<Scalar>::type PacketScalar; - typedef typename internal::traits<Derived>::StorageKind StorageKind; - typedef typename internal::traits<Derived>::Index Index; - typedef typename internal::add_const_on_value_type_if_arithmetic< - typename internal::packet_traits<Scalar>::type - >::type PacketReturnType; - - typedef SparseMatrixBase StorageBaseType; - typedef EigenBase<Derived> Base; - - template<typename OtherDerived> - Derived& operator=(const EigenBase<OtherDerived> &other) - { - other.derived().evalTo(derived()); - return derived(); - } - - enum { - - RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime, - /**< The number of rows at compile-time. This is just a copy of the value provided - * by the \a Derived type. If a value is not known at compile-time, - * it is set to the \a Dynamic constant. - * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */ - - ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime, - /**< The number of columns at compile-time. This is just a copy of the value provided - * by the \a Derived type. If a value is not known at compile-time, - * it is set to the \a Dynamic constant. - * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */ - - - SizeAtCompileTime = (internal::size_at_compile_time<internal::traits<Derived>::RowsAtCompileTime, - internal::traits<Derived>::ColsAtCompileTime>::ret), - /**< This is equal to the number of coefficients, i.e. the number of - * rows times the number of columns, or to \a Dynamic if this is not - * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */ - - MaxRowsAtCompileTime = RowsAtCompileTime, - MaxColsAtCompileTime = ColsAtCompileTime, - - MaxSizeAtCompileTime = (internal::size_at_compile_time<MaxRowsAtCompileTime, - MaxColsAtCompileTime>::ret), - - IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1, - /**< This is set to true if either the number of rows or the number of - * columns is known at compile-time to be equal to 1. Indeed, in that case, - * we are dealing with a column-vector (if there is only one column) or with - * a row-vector (if there is only one row). */ - - Flags = internal::traits<Derived>::Flags, - /**< This stores expression \ref flags flags which may or may not be inherited by new expressions - * constructed from this one. See the \ref flags "list of flags". - */ - - CoeffReadCost = internal::traits<Derived>::CoeffReadCost, - /**< This is a rough measure of how expensive it is to read one coefficient from - * this expression. - */ - - IsRowMajor = Flags&RowMajorBit ? 1 : 0, - - InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime) - : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime), - - #ifndef EIGEN_PARSED_BY_DOXYGEN - _HasDirectAccess = (int(Flags)&DirectAccessBit) ? 1 : 0 // workaround sunCC - #endif - }; - - /** \internal the return type of MatrixBase::adjoint() */ - typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, - CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Eigen::Transpose<const Derived> >, - Transpose<const Derived> - >::type AdjointReturnType; - - - typedef SparseMatrix<Scalar, Flags&RowMajorBit ? RowMajor : ColMajor, Index> PlainObject; - - -#ifndef EIGEN_PARSED_BY_DOXYGEN - /** This is the "real scalar" type; if the \a Scalar type is already real numbers - * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If - * \a Scalar is \a std::complex<T> then RealScalar is \a T. - * - * \sa class NumTraits - */ - typedef typename NumTraits<Scalar>::Real RealScalar; - - /** \internal the return type of coeff() - */ - typedef typename internal::conditional<_HasDirectAccess, const Scalar&, Scalar>::type CoeffReturnType; - - /** \internal Represents a matrix with all coefficients equal to one another*/ - typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Matrix<Scalar,Dynamic,Dynamic> > ConstantReturnType; - - /** type of the equivalent square matrix */ - typedef Matrix<Scalar,EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime), - EIGEN_SIZE_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType; - - inline const Derived& derived() const { return *static_cast<const Derived*>(this); } - inline Derived& derived() { return *static_cast<Derived*>(this); } - inline Derived& const_cast_derived() const - { return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); } -#endif // not EIGEN_PARSED_BY_DOXYGEN - -#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::SparseMatrixBase -# include "../plugins/CommonCwiseUnaryOps.h" -# include "../plugins/CommonCwiseBinaryOps.h" -# include "../plugins/MatrixCwiseUnaryOps.h" -# include "../plugins/MatrixCwiseBinaryOps.h" -# include "../plugins/BlockMethods.h" -# ifdef EIGEN_SPARSEMATRIXBASE_PLUGIN -# include EIGEN_SPARSEMATRIXBASE_PLUGIN -# endif -# undef EIGEN_CURRENT_STORAGE_BASE_CLASS -#undef EIGEN_CURRENT_STORAGE_BASE_CLASS - - /** \returns the number of rows. \sa cols() */ - inline Index rows() const { return derived().rows(); } - /** \returns the number of columns. \sa rows() */ - inline Index cols() const { return derived().cols(); } - /** \returns the number of coefficients, which is \a rows()*cols(). - * \sa rows(), cols(). */ - inline Index size() const { return rows() * cols(); } - /** \returns the number of nonzero coefficients which is in practice the number - * of stored coefficients. */ - inline Index nonZeros() const { return derived().nonZeros(); } - /** \returns true if either the number of rows or the number of columns is equal to 1. - * In other words, this function returns - * \code rows()==1 || cols()==1 \endcode - * \sa rows(), cols(), IsVectorAtCompileTime. */ - inline bool isVector() const { return rows()==1 || cols()==1; } - /** \returns the size of the storage major dimension, - * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */ - Index outerSize() const { return (int(Flags)&RowMajorBit) ? this->rows() : this->cols(); } - /** \returns the size of the inner dimension according to the storage order, - * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */ - Index innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); } - - bool isRValue() const { return m_isRValue; } - Derived& markAsRValue() { m_isRValue = true; return derived(); } - - SparseMatrixBase() : m_isRValue(false) { /* TODO check flags */ } - - - template<typename OtherDerived> - Derived& operator=(const ReturnByValue<OtherDerived>& other) - { - other.evalTo(derived()); - return derived(); - } - - - template<typename OtherDerived> - inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other) - { - return assign(other.derived()); - } - - inline Derived& operator=(const Derived& other) - { -// if (other.isRValue()) -// derived().swap(other.const_cast_derived()); -// else - return assign(other.derived()); - } - - protected: - - template<typename OtherDerived> - inline Derived& assign(const OtherDerived& other) - { - const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit); - const Index outerSize = (int(OtherDerived::Flags) & RowMajorBit) ? other.rows() : other.cols(); - if ((!transpose) && other.isRValue()) - { - // eval without temporary - derived().resize(other.rows(), other.cols()); - derived().setZero(); - derived().reserve((std::max)(this->rows(),this->cols())*2); - for (Index j=0; j<outerSize; ++j) - { - derived().startVec(j); - for (typename OtherDerived::InnerIterator it(other, j); it; ++it) - { - Scalar v = it.value(); - derived().insertBackByOuterInner(j,it.index()) = v; - } - } - derived().finalize(); - } - else - { - assignGeneric(other); - } - return derived(); - } - - template<typename OtherDerived> - inline void assignGeneric(const OtherDerived& other) - { - //const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit); - eigen_assert(( ((internal::traits<Derived>::SupportedAccessPatterns&OuterRandomAccessPattern)==OuterRandomAccessPattern) || - (!((Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit)))) && - "the transpose operation is supposed to be handled in SparseMatrix::operator="); - - enum { Flip = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit) }; - - const Index outerSize = other.outerSize(); - //typedef typename internal::conditional<transpose, LinkedVectorMatrix<Scalar,Flags&RowMajorBit>, Derived>::type TempType; - // thanks to shallow copies, we always eval to a tempary - Derived temp(other.rows(), other.cols()); - - temp.reserve((std::max)(this->rows(),this->cols())*2); - for (Index j=0; j<outerSize; ++j) - { - temp.startVec(j); - for (typename OtherDerived::InnerIterator it(other.derived(), j); it; ++it) - { - Scalar v = it.value(); - temp.insertBackByOuterInner(Flip?it.index():j,Flip?j:it.index()) = v; - } - } - temp.finalize(); - - derived() = temp.markAsRValue(); - } - - public: - - template<typename Lhs, typename Rhs> - inline Derived& operator=(const SparseSparseProduct<Lhs,Rhs>& product); - - friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m) - { - typedef typename Derived::Nested Nested; - typedef typename internal::remove_all<Nested>::type NestedCleaned; - - if (Flags&RowMajorBit) - { - const Nested nm(m.derived()); - for (Index row=0; row<nm.outerSize(); ++row) - { - Index col = 0; - for (typename NestedCleaned::InnerIterator it(nm.derived(), row); it; ++it) - { - for ( ; col<it.index(); ++col) - s << "0 "; - s << it.value() << " "; - ++col; - } - for ( ; col<m.cols(); ++col) - s << "0 "; - s << std::endl; - } - } - else - { - const Nested nm(m.derived()); - if (m.cols() == 1) { - Index row = 0; - for (typename NestedCleaned::InnerIterator it(nm.derived(), 0); it; ++it) - { - for ( ; row<it.index(); ++row) - s << "0" << std::endl; - s << it.value() << std::endl; - ++row; - } - for ( ; row<m.rows(); ++row) - s << "0" << std::endl; - } - else - { - SparseMatrix<Scalar, RowMajorBit, Index> trans = m; - s << static_cast<const SparseMatrixBase<SparseMatrix<Scalar, RowMajorBit, Index> >&>(trans); - } - } - return s; - } - - template<typename OtherDerived> - Derived& operator+=(const SparseMatrixBase<OtherDerived>& other); - template<typename OtherDerived> - Derived& operator-=(const SparseMatrixBase<OtherDerived>& other); - - Derived& operator*=(const Scalar& other); - Derived& operator/=(const Scalar& other); - - #define EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE \ - CwiseBinaryOp< \ - internal::scalar_product_op< \ - typename internal::scalar_product_traits< \ - typename internal::traits<Derived>::Scalar, \ - typename internal::traits<OtherDerived>::Scalar \ - >::ReturnType \ - >, \ - const Derived, \ - const OtherDerived \ - > - - template<typename OtherDerived> - EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE - cwiseProduct(const MatrixBase<OtherDerived> &other) const; - - // sparse * sparse - template<typename OtherDerived> - const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type - operator*(const SparseMatrixBase<OtherDerived> &other) const; - - // sparse * diagonal - template<typename OtherDerived> - const SparseDiagonalProduct<Derived,OtherDerived> - operator*(const DiagonalBase<OtherDerived> &other) const; - - // diagonal * sparse - template<typename OtherDerived> friend - const SparseDiagonalProduct<OtherDerived,Derived> - operator*(const DiagonalBase<OtherDerived> &lhs, const SparseMatrixBase& rhs) - { return SparseDiagonalProduct<OtherDerived,Derived>(lhs.derived(), rhs.derived()); } - - /** dense * sparse (return a dense object unless it is an outer product) */ - template<typename OtherDerived> friend - const typename DenseSparseProductReturnType<OtherDerived,Derived>::Type - operator*(const MatrixBase<OtherDerived>& lhs, const Derived& rhs) - { return typename DenseSparseProductReturnType<OtherDerived,Derived>::Type(lhs.derived(),rhs); } - - /** sparse * dense (returns a dense object unless it is an outer product) */ - template<typename OtherDerived> - const typename SparseDenseProductReturnType<Derived,OtherDerived>::Type - operator*(const MatrixBase<OtherDerived> &other) const; - - /** \returns an expression of P H P^-1 where H is the matrix represented by \c *this */ - SparseSymmetricPermutationProduct<Derived,Upper|Lower> twistedBy(const PermutationMatrix<Dynamic,Dynamic,Index>& perm) const - { - return SparseSymmetricPermutationProduct<Derived,Upper|Lower>(derived(), perm); - } - - template<typename OtherDerived> - Derived& operator*=(const SparseMatrixBase<OtherDerived>& other); - - #ifdef EIGEN2_SUPPORT - // deprecated - template<typename OtherDerived> - typename internal::plain_matrix_type_column_major<OtherDerived>::type - solveTriangular(const MatrixBase<OtherDerived>& other) const; - - // deprecated - template<typename OtherDerived> - void solveTriangularInPlace(MatrixBase<OtherDerived>& other) const; - #endif // EIGEN2_SUPPORT - - template<int Mode> - inline const SparseTriangularView<Derived, Mode> triangularView() const; - - template<unsigned int UpLo> inline const SparseSelfAdjointView<Derived, UpLo> selfadjointView() const; - template<unsigned int UpLo> inline SparseSelfAdjointView<Derived, UpLo> selfadjointView(); - - template<typename OtherDerived> Scalar dot(const MatrixBase<OtherDerived>& other) const; - template<typename OtherDerived> Scalar dot(const SparseMatrixBase<OtherDerived>& other) const; - RealScalar squaredNorm() const; - RealScalar norm() const; - RealScalar blueNorm() const; - - Transpose<Derived> transpose() { return derived(); } - const Transpose<const Derived> transpose() const { return derived(); } - const AdjointReturnType adjoint() const { return transpose(); } - - // inner-vector - typedef Block<Derived,IsRowMajor?1:Dynamic,IsRowMajor?Dynamic:1,true> InnerVectorReturnType; - typedef Block<const Derived,IsRowMajor?1:Dynamic,IsRowMajor?Dynamic:1,true> ConstInnerVectorReturnType; - InnerVectorReturnType innerVector(Index outer); - const ConstInnerVectorReturnType innerVector(Index outer) const; - - // set of inner-vectors - Block<Derived,Dynamic,Dynamic,true> innerVectors(Index outerStart, Index outerSize); - const Block<const Derived,Dynamic,Dynamic,true> innerVectors(Index outerStart, Index outerSize) const; - - /** \internal use operator= */ - template<typename DenseDerived> - void evalTo(MatrixBase<DenseDerived>& dst) const - { - dst.setZero(); - for (Index j=0; j<outerSize(); ++j) - for (typename Derived::InnerIterator i(derived(),j); i; ++i) - dst.coeffRef(i.row(),i.col()) = i.value(); - } - - Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> toDense() const - { - return derived(); - } - - template<typename OtherDerived> - bool isApprox(const SparseMatrixBase<OtherDerived>& other, - const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const - { return toDense().isApprox(other.toDense(),prec); } - - template<typename OtherDerived> - bool isApprox(const MatrixBase<OtherDerived>& other, - const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const - { return toDense().isApprox(other,prec); } - - /** \returns the matrix or vector obtained by evaluating this expression. - * - * Notice that in the case of a plain matrix or vector (not an expression) this function just returns - * a const reference, in order to avoid a useless copy. - */ - inline const typename internal::eval<Derived>::type eval() const - { return typename internal::eval<Derived>::type(derived()); } - - Scalar sum() const; - - protected: - - bool m_isRValue; -}; - -} // end namespace Eigen - -#endif // EIGEN_SPARSEMATRIXBASE_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparsePermutation.h b/third_party/eigen3/Eigen/src/SparseCore/SparsePermutation.h deleted file mode 100644 index b85be93f6f..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparsePermutation.h +++ /dev/null @@ -1,148 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_PERMUTATION_H -#define EIGEN_SPARSE_PERMUTATION_H - -// This file implements sparse * permutation products - -namespace Eigen { - -namespace internal { - -template<typename PermutationType, typename MatrixType, int Side, bool Transposed> -struct traits<permut_sparsematrix_product_retval<PermutationType, MatrixType, Side, Transposed> > -{ - typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned; - typedef typename MatrixTypeNestedCleaned::Scalar Scalar; - typedef typename MatrixTypeNestedCleaned::Index Index; - enum { - SrcStorageOrder = MatrixTypeNestedCleaned::Flags&RowMajorBit ? RowMajor : ColMajor, - MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight - }; - - typedef typename internal::conditional<MoveOuter, - SparseMatrix<Scalar,SrcStorageOrder,Index>, - SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> >::type ReturnType; -}; - -template<typename PermutationType, typename MatrixType, int Side, bool Transposed> -struct permut_sparsematrix_product_retval - : public ReturnByValue<permut_sparsematrix_product_retval<PermutationType, MatrixType, Side, Transposed> > -{ - typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned; - typedef typename MatrixTypeNestedCleaned::Scalar Scalar; - typedef typename MatrixTypeNestedCleaned::Index Index; - - enum { - SrcStorageOrder = MatrixTypeNestedCleaned::Flags&RowMajorBit ? RowMajor : ColMajor, - MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight - }; - - permut_sparsematrix_product_retval(const PermutationType& perm, const MatrixType& matrix) - : m_permutation(perm), m_matrix(matrix) - {} - - inline int rows() const { return m_matrix.rows(); } - inline int cols() const { return m_matrix.cols(); } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - if(MoveOuter) - { - SparseMatrix<Scalar,SrcStorageOrder,Index> tmp(m_matrix.rows(), m_matrix.cols()); - Matrix<Index,Dynamic,1> sizes(m_matrix.outerSize()); - for(Index j=0; j<m_matrix.outerSize(); ++j) - { - Index jp = m_permutation.indices().coeff(j); - sizes[((Side==OnTheLeft) ^ Transposed) ? jp : j] = m_matrix.innerVector(((Side==OnTheRight) ^ Transposed) ? jp : j).size(); - } - tmp.reserve(sizes); - for(Index j=0; j<m_matrix.outerSize(); ++j) - { - Index jp = m_permutation.indices().coeff(j); - Index jsrc = ((Side==OnTheRight) ^ Transposed) ? jp : j; - Index jdst = ((Side==OnTheLeft) ^ Transposed) ? jp : j; - for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,jsrc); it; ++it) - tmp.insertByOuterInner(jdst,it.index()) = it.value(); - } - dst = tmp; - } - else - { - SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> tmp(m_matrix.rows(), m_matrix.cols()); - Matrix<Index,Dynamic,1> sizes(tmp.outerSize()); - sizes.setZero(); - PermutationMatrix<Dynamic,Dynamic,Index> perm; - if((Side==OnTheLeft) ^ Transposed) - perm = m_permutation; - else - perm = m_permutation.transpose(); - - for(Index j=0; j<m_matrix.outerSize(); ++j) - for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,j); it; ++it) - sizes[perm.indices().coeff(it.index())]++; - tmp.reserve(sizes); - for(Index j=0; j<m_matrix.outerSize(); ++j) - for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,j); it; ++it) - tmp.insertByOuterInner(perm.indices().coeff(it.index()),j) = it.value(); - dst = tmp; - } - } - - protected: - const PermutationType& m_permutation; - typename MatrixType::Nested m_matrix; -}; - -} - - - -/** \returns the matrix with the permutation applied to the columns - */ -template<typename SparseDerived, typename PermDerived> -inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, false> -operator*(const SparseMatrixBase<SparseDerived>& matrix, const PermutationBase<PermDerived>& perm) -{ - return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, false>(perm, matrix.derived()); -} - -/** \returns the matrix with the permutation applied to the rows - */ -template<typename SparseDerived, typename PermDerived> -inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, false> -operator*( const PermutationBase<PermDerived>& perm, const SparseMatrixBase<SparseDerived>& matrix) -{ - return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, false>(perm, matrix.derived()); -} - - - -/** \returns the matrix with the inverse permutation applied to the columns. - */ -template<typename SparseDerived, typename PermDerived> -inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, true> -operator*(const SparseMatrixBase<SparseDerived>& matrix, const Transpose<PermutationBase<PermDerived> >& tperm) -{ - return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, true>(tperm.nestedPermutation(), matrix.derived()); -} - -/** \returns the matrix with the inverse permutation applied to the rows. - */ -template<typename SparseDerived, typename PermDerived> -inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, true> -operator*(const Transpose<PermutationBase<PermDerived> >& tperm, const SparseMatrixBase<SparseDerived>& matrix) -{ - return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, true>(tperm.nestedPermutation(), matrix.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseProduct.h b/third_party/eigen3/Eigen/src/SparseCore/SparseProduct.h deleted file mode 100644 index cf76630700..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseProduct.h +++ /dev/null @@ -1,188 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSEPRODUCT_H -#define EIGEN_SPARSEPRODUCT_H - -namespace Eigen { - -template<typename Lhs, typename Rhs> -struct SparseSparseProductReturnType -{ - typedef typename internal::traits<Lhs>::Scalar Scalar; - typedef typename internal::traits<Lhs>::Index Index; - enum { - LhsRowMajor = internal::traits<Lhs>::Flags & RowMajorBit, - RhsRowMajor = internal::traits<Rhs>::Flags & RowMajorBit, - TransposeRhs = (!LhsRowMajor) && RhsRowMajor, - TransposeLhs = LhsRowMajor && (!RhsRowMajor) - }; - - typedef typename internal::conditional<TransposeLhs, - SparseMatrix<Scalar,0,Index>, - typename internal::nested<Lhs,Rhs::RowsAtCompileTime>::type>::type LhsNested; - - typedef typename internal::conditional<TransposeRhs, - SparseMatrix<Scalar,0,Index>, - typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type>::type RhsNested; - - typedef SparseSparseProduct<LhsNested, RhsNested> Type; -}; - -namespace internal { -template<typename LhsNested, typename RhsNested> -struct traits<SparseSparseProduct<LhsNested, RhsNested> > -{ - typedef MatrixXpr XprKind; - // clean the nested types: - typedef typename remove_all<LhsNested>::type _LhsNested; - typedef typename remove_all<RhsNested>::type _RhsNested; - typedef typename _LhsNested::Scalar Scalar; - typedef typename promote_index_type<typename traits<_LhsNested>::Index, - typename traits<_RhsNested>::Index>::type Index; - - enum { - LhsCoeffReadCost = _LhsNested::CoeffReadCost, - RhsCoeffReadCost = _RhsNested::CoeffReadCost, - LhsFlags = _LhsNested::Flags, - RhsFlags = _RhsNested::Flags, - - RowsAtCompileTime = _LhsNested::RowsAtCompileTime, - ColsAtCompileTime = _RhsNested::ColsAtCompileTime, - MaxRowsAtCompileTime = _LhsNested::MaxRowsAtCompileTime, - MaxColsAtCompileTime = _RhsNested::MaxColsAtCompileTime, - - InnerSize = EIGEN_SIZE_MIN_PREFER_FIXED(_LhsNested::ColsAtCompileTime, _RhsNested::RowsAtCompileTime), - - EvalToRowMajor = (RhsFlags & LhsFlags & RowMajorBit), - - RemovedBits = ~(EvalToRowMajor ? 0 : RowMajorBit), - - Flags = (int(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits) - | EvalBeforeAssigningBit - | EvalBeforeNestingBit, - - CoeffReadCost = Dynamic - }; - - typedef Sparse StorageKind; -}; - -} // end namespace internal - -template<typename LhsNested, typename RhsNested> -class SparseSparseProduct : internal::no_assignment_operator, - public SparseMatrixBase<SparseSparseProduct<LhsNested, RhsNested> > -{ - public: - - typedef SparseMatrixBase<SparseSparseProduct> Base; - EIGEN_DENSE_PUBLIC_INTERFACE(SparseSparseProduct) - - private: - - typedef typename internal::traits<SparseSparseProduct>::_LhsNested _LhsNested; - typedef typename internal::traits<SparseSparseProduct>::_RhsNested _RhsNested; - - public: - - template<typename Lhs, typename Rhs> - EIGEN_STRONG_INLINE SparseSparseProduct(const Lhs& lhs, const Rhs& rhs) - : m_lhs(lhs), m_rhs(rhs), m_tolerance(0), m_conservative(true) - { - init(); - } - - template<typename Lhs, typename Rhs> - EIGEN_STRONG_INLINE SparseSparseProduct(const Lhs& lhs, const Rhs& rhs, const RealScalar& tolerance) - : m_lhs(lhs), m_rhs(rhs), m_tolerance(tolerance), m_conservative(false) - { - init(); - } - - SparseSparseProduct pruned(const Scalar& reference = 0, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) const - { - using std::abs; - return SparseSparseProduct(m_lhs,m_rhs,abs(reference)*epsilon); - } - - template<typename Dest> - void evalTo(Dest& result) const - { - if(m_conservative) - internal::conservative_sparse_sparse_product_selector<_LhsNested, _RhsNested, Dest>::run(lhs(),rhs(),result); - else - internal::sparse_sparse_product_with_pruning_selector<_LhsNested, _RhsNested, Dest>::run(lhs(),rhs(),result,m_tolerance); - } - - EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); } - EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); } - - EIGEN_STRONG_INLINE const _LhsNested& lhs() const { return m_lhs; } - EIGEN_STRONG_INLINE const _RhsNested& rhs() const { return m_rhs; } - - protected: - void init() - { - eigen_assert(m_lhs.cols() == m_rhs.rows()); - - enum { - ProductIsValid = _LhsNested::ColsAtCompileTime==Dynamic - || _RhsNested::RowsAtCompileTime==Dynamic - || int(_LhsNested::ColsAtCompileTime)==int(_RhsNested::RowsAtCompileTime), - AreVectors = _LhsNested::IsVectorAtCompileTime && _RhsNested::IsVectorAtCompileTime, - SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(_LhsNested,_RhsNested) - }; - // note to the lost user: - // * for a dot product use: v1.dot(v2) - // * for a coeff-wise product use: v1.cwise()*v2 - EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), - INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) - EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), - INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) - EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) - } - - LhsNested m_lhs; - RhsNested m_rhs; - RealScalar m_tolerance; - bool m_conservative; -}; - -// sparse = sparse * sparse -template<typename Derived> -template<typename Lhs, typename Rhs> -inline Derived& SparseMatrixBase<Derived>::operator=(const SparseSparseProduct<Lhs,Rhs>& product) -{ - product.evalTo(derived()); - return derived(); -} - -/** \returns an expression of the product of two sparse matrices. - * By default a conservative product preserving the symbolic non zeros is performed. - * The automatic pruning of the small values can be achieved by calling the pruned() function - * in which case a totally different product algorithm is employed: - * \code - * C = (A*B).pruned(); // supress numerical zeros (exact) - * C = (A*B).pruned(ref); - * C = (A*B).pruned(ref,epsilon); - * \endcode - * where \c ref is a meaningful non zero reference value. - * */ -template<typename Derived> -template<typename OtherDerived> -inline const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type -SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const -{ - return typename SparseSparseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSEPRODUCT_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseRedux.h b/third_party/eigen3/Eigen/src/SparseCore/SparseRedux.h deleted file mode 100644 index f3da93a71d..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseRedux.h +++ /dev/null @@ -1,45 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSEREDUX_H -#define EIGEN_SPARSEREDUX_H - -namespace Eigen { - -template<typename Derived> -typename internal::traits<Derived>::Scalar -SparseMatrixBase<Derived>::sum() const -{ - eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix"); - Scalar res(0); - for (Index j=0; j<outerSize(); ++j) - for (typename Derived::InnerIterator iter(derived(),j); iter; ++iter) - res += iter.value(); - return res; -} - -template<typename _Scalar, int _Options, typename _Index> -typename internal::traits<SparseMatrix<_Scalar,_Options,_Index> >::Scalar -SparseMatrix<_Scalar,_Options,_Index>::sum() const -{ - eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix"); - return Matrix<Scalar,1,Dynamic>::Map(&m_data.value(0), m_data.size()).sum(); -} - -template<typename _Scalar, int _Options, typename _Index> -typename internal::traits<SparseVector<_Scalar,_Options, _Index> >::Scalar -SparseVector<_Scalar,_Options,_Index>::sum() const -{ - eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix"); - return Matrix<Scalar,1,Dynamic>::Map(&m_data.value(0), m_data.size()).sum(); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSEREDUX_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h b/third_party/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h deleted file mode 100644 index 0eda96bc47..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseSelfAdjointView.h +++ /dev/null @@ -1,507 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_SELFADJOINTVIEW_H -#define EIGEN_SPARSE_SELFADJOINTVIEW_H - -namespace Eigen { - -/** \ingroup SparseCore_Module - * \class SparseSelfAdjointView - * - * \brief Pseudo expression to manipulate a triangular sparse matrix as a selfadjoint matrix. - * - * \param MatrixType the type of the dense matrix storing the coefficients - * \param UpLo can be either \c #Lower or \c #Upper - * - * This class is an expression of a sefladjoint matrix from a triangular part of a matrix - * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView() - * and most of the time this is the only way that it is used. - * - * \sa SparseMatrixBase::selfadjointView() - */ -template<typename Lhs, typename Rhs, int UpLo> -class SparseSelfAdjointTimeDenseProduct; - -template<typename Lhs, typename Rhs, int UpLo> -class DenseTimeSparseSelfAdjointProduct; - -namespace internal { - -template<typename MatrixType, unsigned int UpLo> -struct traits<SparseSelfAdjointView<MatrixType,UpLo> > : traits<MatrixType> { -}; - -template<int SrcUpLo,int DstUpLo,typename MatrixType,int DestOrder> -void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm = 0); - -template<int UpLo,typename MatrixType,int DestOrder> -void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm = 0); - -} - -template<typename MatrixType, unsigned int UpLo> class SparseSelfAdjointView - : public EigenBase<SparseSelfAdjointView<MatrixType,UpLo> > -{ - public: - - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - typedef Matrix<Index,Dynamic,1> VectorI; - typedef typename MatrixType::Nested MatrixTypeNested; - typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested; - - inline SparseSelfAdjointView(const MatrixType& matrix) : m_matrix(matrix) - { - eigen_assert(rows()==cols() && "SelfAdjointView is only for squared matrices"); - } - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - /** \internal \returns a reference to the nested matrix */ - const _MatrixTypeNested& matrix() const { return m_matrix; } - _MatrixTypeNested& matrix() { return m_matrix.const_cast_derived(); } - - /** \returns an expression of the matrix product between a sparse self-adjoint matrix \c *this and a sparse matrix \a rhs. - * - * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product. - * Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product. - */ - template<typename OtherDerived> - SparseSparseProduct<typename OtherDerived::PlainObject, OtherDerived> - operator*(const SparseMatrixBase<OtherDerived>& rhs) const - { - return SparseSparseProduct<typename OtherDerived::PlainObject, OtherDerived>(*this, rhs.derived()); - } - - /** \returns an expression of the matrix product between a sparse matrix \a lhs and a sparse self-adjoint matrix \a rhs. - * - * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product. - * Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product. - */ - template<typename OtherDerived> friend - SparseSparseProduct<OtherDerived, typename OtherDerived::PlainObject > - operator*(const SparseMatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs) - { - return SparseSparseProduct<OtherDerived, typename OtherDerived::PlainObject>(lhs.derived(), rhs); - } - - /** Efficient sparse self-adjoint matrix times dense vector/matrix product */ - template<typename OtherDerived> - SparseSelfAdjointTimeDenseProduct<MatrixType,OtherDerived,UpLo> - operator*(const MatrixBase<OtherDerived>& rhs) const - { - return SparseSelfAdjointTimeDenseProduct<MatrixType,OtherDerived,UpLo>(m_matrix, rhs.derived()); - } - - /** Efficient dense vector/matrix times sparse self-adjoint matrix product */ - template<typename OtherDerived> friend - DenseTimeSparseSelfAdjointProduct<OtherDerived,MatrixType,UpLo> - operator*(const MatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs) - { - return DenseTimeSparseSelfAdjointProduct<OtherDerived,_MatrixTypeNested,UpLo>(lhs.derived(), rhs.m_matrix); - } - - /** Perform a symmetric rank K update of the selfadjoint matrix \c *this: - * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix. - * - * \returns a reference to \c *this - * - * To perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply - * call this function with u.adjoint(). - */ - template<typename DerivedU> - SparseSelfAdjointView& rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1)); - - /** \internal triggered by sparse_matrix = SparseSelfadjointView; */ - template<typename DestScalar,int StorageOrder> void evalTo(SparseMatrix<DestScalar,StorageOrder,Index>& _dest) const - { - internal::permute_symm_to_fullsymm<UpLo>(m_matrix, _dest); - } - - template<typename DestScalar> void evalTo(DynamicSparseMatrix<DestScalar,ColMajor,Index>& _dest) const - { - // TODO directly evaluate into _dest; - SparseMatrix<DestScalar,ColMajor,Index> tmp(_dest.rows(),_dest.cols()); - internal::permute_symm_to_fullsymm<UpLo>(m_matrix, tmp); - _dest = tmp; - } - - /** \returns an expression of P H P^-1 */ - SparseSymmetricPermutationProduct<_MatrixTypeNested,UpLo> twistedBy(const PermutationMatrix<Dynamic,Dynamic,Index>& perm) const - { - return SparseSymmetricPermutationProduct<_MatrixTypeNested,UpLo>(m_matrix, perm); - } - - template<typename SrcMatrixType,int SrcUpLo> - SparseSelfAdjointView& operator=(const SparseSymmetricPermutationProduct<SrcMatrixType,SrcUpLo>& permutedMatrix) - { - permutedMatrix.evalTo(*this); - return *this; - } - - - SparseSelfAdjointView& operator=(const SparseSelfAdjointView& src) - { - PermutationMatrix<Dynamic> pnull; - return *this = src.twistedBy(pnull); - } - - template<typename SrcMatrixType,unsigned int SrcUpLo> - SparseSelfAdjointView& operator=(const SparseSelfAdjointView<SrcMatrixType,SrcUpLo>& src) - { - PermutationMatrix<Dynamic> pnull; - return *this = src.twistedBy(pnull); - } - - - // const SparseLLT<PlainObject, UpLo> llt() const; - // const SparseLDLT<PlainObject, UpLo> ldlt() const; - - protected: - - typename MatrixType::Nested m_matrix; - mutable VectorI m_countPerRow; - mutable VectorI m_countPerCol; -}; - -/*************************************************************************** -* Implementation of SparseMatrixBase methods -***************************************************************************/ - -template<typename Derived> -template<unsigned int UpLo> -const SparseSelfAdjointView<Derived, UpLo> SparseMatrixBase<Derived>::selfadjointView() const -{ - return derived(); -} - -template<typename Derived> -template<unsigned int UpLo> -SparseSelfAdjointView<Derived, UpLo> SparseMatrixBase<Derived>::selfadjointView() -{ - return derived(); -} - -/*************************************************************************** -* Implementation of SparseSelfAdjointView methods -***************************************************************************/ - -template<typename MatrixType, unsigned int UpLo> -template<typename DerivedU> -SparseSelfAdjointView<MatrixType,UpLo>& -SparseSelfAdjointView<MatrixType,UpLo>::rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha) -{ - SparseMatrix<Scalar,MatrixType::Flags&RowMajorBit?RowMajor:ColMajor> tmp = u * u.adjoint(); - if(alpha==Scalar(0)) - m_matrix.const_cast_derived() = tmp.template triangularView<UpLo>(); - else - m_matrix.const_cast_derived() += alpha * tmp.template triangularView<UpLo>(); - - return *this; -} - -/*************************************************************************** -* Implementation of sparse self-adjoint time dense matrix -***************************************************************************/ - -namespace internal { -template<typename Lhs, typename Rhs, int UpLo> -struct traits<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo> > - : traits<ProductBase<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo>, Lhs, Rhs> > -{ - typedef Dense StorageKind; -}; -} - -template<typename Lhs, typename Rhs, int UpLo> -class SparseSelfAdjointTimeDenseProduct - : public ProductBase<SparseSelfAdjointTimeDenseProduct<Lhs,Rhs,UpLo>, Lhs, Rhs> -{ - public: - EIGEN_PRODUCT_PUBLIC_INTERFACE(SparseSelfAdjointTimeDenseProduct) - - SparseSelfAdjointTimeDenseProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) - {} - - template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const - { - EIGEN_ONLY_USED_FOR_DEBUG(alpha); - // TODO use alpha - eigen_assert(alpha==Scalar(1) && "alpha != 1 is not implemented yet, sorry"); - typedef typename internal::remove_all<Lhs>::type _Lhs; - typedef typename _Lhs::InnerIterator LhsInnerIterator; - enum { - LhsIsRowMajor = (_Lhs::Flags&RowMajorBit)==RowMajorBit, - ProcessFirstHalf = - ((UpLo&(Upper|Lower))==(Upper|Lower)) - || ( (UpLo&Upper) && !LhsIsRowMajor) - || ( (UpLo&Lower) && LhsIsRowMajor), - ProcessSecondHalf = !ProcessFirstHalf - }; - for (Index j=0; j<m_lhs.outerSize(); ++j) - { - LhsInnerIterator i(m_lhs,j); - if (ProcessSecondHalf) - { - while (i && i.index()<j) ++i; - if(i && i.index()==j) - { - dest.row(j) += i.value() * m_rhs.row(j); - ++i; - } - } - for(; (ProcessFirstHalf ? i && i.index() < j : i) ; ++i) - { - Index a = LhsIsRowMajor ? j : i.index(); - Index b = LhsIsRowMajor ? i.index() : j; - typename Lhs::Scalar v = i.value(); - dest.row(a) += (v) * m_rhs.row(b); - dest.row(b) += numext::conj(v) * m_rhs.row(a); - } - if (ProcessFirstHalf && i && (i.index()==j)) - dest.row(j) += i.value() * m_rhs.row(j); - } - } - - private: - SparseSelfAdjointTimeDenseProduct& operator=(const SparseSelfAdjointTimeDenseProduct&); -}; - -namespace internal { -template<typename Lhs, typename Rhs, int UpLo> -struct traits<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo> > - : traits<ProductBase<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo>, Lhs, Rhs> > -{}; -} - -template<typename Lhs, typename Rhs, int UpLo> -class DenseTimeSparseSelfAdjointProduct - : public ProductBase<DenseTimeSparseSelfAdjointProduct<Lhs,Rhs,UpLo>, Lhs, Rhs> -{ - public: - EIGEN_PRODUCT_PUBLIC_INTERFACE(DenseTimeSparseSelfAdjointProduct) - - DenseTimeSparseSelfAdjointProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) - {} - - template<typename Dest> void scaleAndAddTo(Dest& /*dest*/, const Scalar& /*alpha*/) const - { - // TODO - } - - private: - DenseTimeSparseSelfAdjointProduct& operator=(const DenseTimeSparseSelfAdjointProduct&); -}; - -/*************************************************************************** -* Implementation of symmetric copies and permutations -***************************************************************************/ -namespace internal { - -template<typename MatrixType, int UpLo> -struct traits<SparseSymmetricPermutationProduct<MatrixType,UpLo> > : traits<MatrixType> { -}; - -template<int UpLo,typename MatrixType,int DestOrder> -void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm) -{ - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - typedef SparseMatrix<Scalar,DestOrder,Index> Dest; - typedef Matrix<Index,Dynamic,1> VectorI; - - Dest& dest(_dest.derived()); - enum { - StorageOrderMatch = int(Dest::IsRowMajor) == int(MatrixType::IsRowMajor) - }; - - Index size = mat.rows(); - VectorI count; - count.resize(size); - count.setZero(); - dest.resize(size,size); - for(Index j = 0; j<size; ++j) - { - Index jp = perm ? perm[j] : j; - for(typename MatrixType::InnerIterator it(mat,j); it; ++it) - { - Index i = it.index(); - Index r = it.row(); - Index c = it.col(); - Index ip = perm ? perm[i] : i; - if(UpLo==(Upper|Lower)) - count[StorageOrderMatch ? jp : ip]++; - else if(r==c) - count[ip]++; - else if(( UpLo==Lower && r>c) || ( UpLo==Upper && r<c)) - { - count[ip]++; - count[jp]++; - } - } - } - Index nnz = count.sum(); - - // reserve space - dest.resizeNonZeros(nnz); - dest.outerIndexPtr()[0] = 0; - for(Index j=0; j<size; ++j) - dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j]; - for(Index j=0; j<size; ++j) - count[j] = dest.outerIndexPtr()[j]; - - // copy data - for(Index j = 0; j<size; ++j) - { - for(typename MatrixType::InnerIterator it(mat,j); it; ++it) - { - Index i = it.index(); - Index r = it.row(); - Index c = it.col(); - - Index jp = perm ? perm[j] : j; - Index ip = perm ? perm[i] : i; - - if(UpLo==(Upper|Lower)) - { - Index k = count[StorageOrderMatch ? jp : ip]++; - dest.innerIndexPtr()[k] = StorageOrderMatch ? ip : jp; - dest.valuePtr()[k] = it.value(); - } - else if(r==c) - { - Index k = count[ip]++; - dest.innerIndexPtr()[k] = ip; - dest.valuePtr()[k] = it.value(); - } - else if(( (UpLo&Lower)==Lower && r>c) || ( (UpLo&Upper)==Upper && r<c)) - { - if(!StorageOrderMatch) - std::swap(ip,jp); - Index k = count[jp]++; - dest.innerIndexPtr()[k] = ip; - dest.valuePtr()[k] = it.value(); - k = count[ip]++; - dest.innerIndexPtr()[k] = jp; - dest.valuePtr()[k] = numext::conj(it.value()); - } - } - } -} - -template<int _SrcUpLo,int _DstUpLo,typename MatrixType,int DstOrder> -void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DstOrder,typename MatrixType::Index>& _dest, const typename MatrixType::Index* perm) -{ - typedef typename MatrixType::Index Index; - typedef typename MatrixType::Scalar Scalar; - SparseMatrix<Scalar,DstOrder,Index>& dest(_dest.derived()); - typedef Matrix<Index,Dynamic,1> VectorI; - enum { - SrcOrder = MatrixType::IsRowMajor ? RowMajor : ColMajor, - StorageOrderMatch = int(SrcOrder) == int(DstOrder), - DstUpLo = DstOrder==RowMajor ? (_DstUpLo==Upper ? Lower : Upper) : _DstUpLo, - SrcUpLo = SrcOrder==RowMajor ? (_SrcUpLo==Upper ? Lower : Upper) : _SrcUpLo - }; - - Index size = mat.rows(); - VectorI count(size); - count.setZero(); - dest.resize(size,size); - for(Index j = 0; j<size; ++j) - { - Index jp = perm ? perm[j] : j; - for(typename MatrixType::InnerIterator it(mat,j); it; ++it) - { - Index i = it.index(); - if((int(SrcUpLo)==int(Lower) && i<j) || (int(SrcUpLo)==int(Upper) && i>j)) - continue; - - Index ip = perm ? perm[i] : i; - count[int(DstUpLo)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++; - } - } - dest.outerIndexPtr()[0] = 0; - for(Index j=0; j<size; ++j) - dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j]; - dest.resizeNonZeros(dest.outerIndexPtr()[size]); - for(Index j=0; j<size; ++j) - count[j] = dest.outerIndexPtr()[j]; - - for(Index j = 0; j<size; ++j) - { - - for(typename MatrixType::InnerIterator it(mat,j); it; ++it) - { - Index i = it.index(); - if((int(SrcUpLo)==int(Lower) && i<j) || (int(SrcUpLo)==int(Upper) && i>j)) - continue; - - Index jp = perm ? perm[j] : j; - Index ip = perm? perm[i] : i; - - Index k = count[int(DstUpLo)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++; - dest.innerIndexPtr()[k] = int(DstUpLo)==int(Lower) ? (std::max)(ip,jp) : (std::min)(ip,jp); - - if(!StorageOrderMatch) std::swap(ip,jp); - if( ((int(DstUpLo)==int(Lower) && ip<jp) || (int(DstUpLo)==int(Upper) && ip>jp))) - dest.valuePtr()[k] = numext::conj(it.value()); - else - dest.valuePtr()[k] = it.value(); - } - } -} - -} - -template<typename MatrixType,int UpLo> -class SparseSymmetricPermutationProduct - : public EigenBase<SparseSymmetricPermutationProduct<MatrixType,UpLo> > -{ - public: - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::Index Index; - protected: - typedef PermutationMatrix<Dynamic,Dynamic,Index> Perm; - public: - typedef Matrix<Index,Dynamic,1> VectorI; - typedef typename MatrixType::Nested MatrixTypeNested; - typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested; - - SparseSymmetricPermutationProduct(const MatrixType& mat, const Perm& perm) - : m_matrix(mat), m_perm(perm) - {} - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - template<typename DestScalar, int Options, typename DstIndex> - void evalTo(SparseMatrix<DestScalar,Options,DstIndex>& _dest) const - { -// internal::permute_symm_to_fullsymm<UpLo>(m_matrix,_dest,m_perm.indices().data()); - SparseMatrix<DestScalar,(Options&RowMajor)==RowMajor ? ColMajor : RowMajor, DstIndex> tmp; - internal::permute_symm_to_fullsymm<UpLo>(m_matrix,tmp,m_perm.indices().data()); - _dest = tmp; - } - - template<typename DestType,unsigned int DestUpLo> void evalTo(SparseSelfAdjointView<DestType,DestUpLo>& dest) const - { - internal::permute_symm_to_symm<UpLo,DestUpLo>(m_matrix,dest.matrix(),m_perm.indices().data()); - } - - protected: - MatrixTypeNested m_matrix; - const Perm& m_perm; - -}; - -} // end namespace Eigen - -#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/third_party/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h deleted file mode 100644 index fcc18f5c9c..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseSparseProductWithPruning.h +++ /dev/null @@ -1,150 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H -#define EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H - -namespace Eigen { - -namespace internal { - - -// perform a pseudo in-place sparse * sparse product assuming all matrices are col major -template<typename Lhs, typename Rhs, typename ResultType> -static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, const typename ResultType::RealScalar& tolerance) -{ - // return sparse_sparse_product_with_pruning_impl2(lhs,rhs,res); - - typedef typename remove_all<Lhs>::type::Scalar Scalar; - typedef typename remove_all<Lhs>::type::Index Index; - - // make sure to call innerSize/outerSize since we fake the storage order. - Index rows = lhs.innerSize(); - Index cols = rhs.outerSize(); - //Index size = lhs.outerSize(); - eigen_assert(lhs.outerSize() == rhs.innerSize()); - - // allocate a temporary buffer - AmbiVector<Scalar,Index> tempVector(rows); - - // estimate the number of non zero entries - // given a rhs column containing Y non zeros, we assume that the respective Y columns - // of the lhs differs in average of one non zeros, thus the number of non zeros for - // the product of a rhs column with the lhs is X+Y where X is the average number of non zero - // per column of the lhs. - // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs) - Index estimated_nnz_prod = lhs.nonZeros() + rhs.nonZeros(); - - // mimics a resizeByInnerOuter: - if(ResultType::IsRowMajor) - res.resize(cols, rows); - else - res.resize(rows, cols); - - res.reserve(estimated_nnz_prod); - double ratioColRes = double(estimated_nnz_prod)/double(lhs.rows()*rhs.cols()); - for (Index j=0; j<cols; ++j) - { - // FIXME: - //double ratioColRes = (double(rhs.innerVector(j).nonZeros()) + double(lhs.nonZeros())/double(lhs.cols()))/double(lhs.rows()); - // let's do a more accurate determination of the nnz ratio for the current column j of res - tempVector.init(ratioColRes); - tempVector.setZero(); - for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt) - { - // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index()) - tempVector.restart(); - Scalar x = rhsIt.value(); - for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt) - { - tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x; - } - } - res.startVec(j); - for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector,tolerance); it; ++it) - res.insertBackByOuterInner(j,it.index()) = it.value(); - } - res.finalize(); -} - -template<typename Lhs, typename Rhs, typename ResultType, - int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit, - int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit, - int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit> -struct sparse_sparse_product_with_pruning_selector; - -template<typename Lhs, typename Rhs, typename ResultType> -struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor> -{ - typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar; - typedef typename ResultType::RealScalar RealScalar; - - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) - { - typename remove_all<ResultType>::type _res(res.rows(), res.cols()); - internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res, tolerance); - res.swap(_res); - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor> -{ - typedef typename ResultType::RealScalar RealScalar; - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) - { - // we need a col-major matrix to hold the result - typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> SparseTemporaryType; - SparseTemporaryType _res(res.rows(), res.cols()); - internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res, tolerance); - res = _res; - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor> -{ - typedef typename ResultType::RealScalar RealScalar; - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) - { - // let's transpose the product to get a column x column product - typename remove_all<ResultType>::type _res(res.rows(), res.cols()); - internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res, tolerance); - res.swap(_res); - } -}; - -template<typename Lhs, typename Rhs, typename ResultType> -struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor> -{ - typedef typename ResultType::RealScalar RealScalar; - static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) - { - typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::Index> ColMajorMatrixLhs; - typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::Index> ColMajorMatrixRhs; - ColMajorMatrixLhs colLhs(lhs); - ColMajorMatrixRhs colRhs(rhs); - internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,ColMajorMatrixRhs,ResultType>(colLhs, colRhs, res, tolerance); - - // let's transpose the product to get a column x column product -// typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType; -// SparseTemporaryType _res(res.cols(), res.rows()); -// sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res); -// res = _res.transpose(); - } -}; - -// NOTE the 2 others cases (col row *) must never occur since they are caught -// by ProductReturnType which transforms it to (col col *) by evaluating rhs. - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseTranspose.h b/third_party/eigen3/Eigen/src/SparseCore/SparseTranspose.h deleted file mode 100644 index 7c300ee8db..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseTranspose.h +++ /dev/null @@ -1,63 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSETRANSPOSE_H -#define EIGEN_SPARSETRANSPOSE_H - -namespace Eigen { - -template<typename MatrixType> class TransposeImpl<MatrixType,Sparse> - : public SparseMatrixBase<Transpose<MatrixType> > -{ - typedef typename internal::remove_all<typename MatrixType::Nested>::type _MatrixTypeNested; - public: - - EIGEN_SPARSE_PUBLIC_INTERFACE(Transpose<MatrixType> ) - - class InnerIterator; - class ReverseInnerIterator; - - inline Index nonZeros() const { return derived().nestedExpression().nonZeros(); } -}; - -// NOTE: VC10 trigger an ICE if don't put typename TransposeImpl<MatrixType,Sparse>:: in front of Index, -// a typedef typename TransposeImpl<MatrixType,Sparse>::Index Index; -// does not fix the issue. -// An alternative is to define the nested class in the parent class itself. -template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::InnerIterator - : public _MatrixTypeNested::InnerIterator -{ - typedef typename _MatrixTypeNested::InnerIterator Base; - typedef typename TransposeImpl::Index Index; - public: - - EIGEN_STRONG_INLINE InnerIterator(const TransposeImpl& trans, typename TransposeImpl<MatrixType,Sparse>::Index outer) - : Base(trans.derived().nestedExpression(), outer) - {} - Index row() const { return Base::col(); } - Index col() const { return Base::row(); } -}; - -template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::ReverseInnerIterator - : public _MatrixTypeNested::ReverseInnerIterator -{ - typedef typename _MatrixTypeNested::ReverseInnerIterator Base; - typedef typename TransposeImpl::Index Index; - public: - - EIGEN_STRONG_INLINE ReverseInnerIterator(const TransposeImpl& xpr, typename TransposeImpl<MatrixType,Sparse>::Index outer) - : Base(xpr.derived().nestedExpression(), outer) - {} - Index row() const { return Base::col(); } - Index col() const { return Base::row(); } -}; - -} // end namespace Eigen - -#endif // EIGEN_SPARSETRANSPOSE_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseTriangularView.h b/third_party/eigen3/Eigen/src/SparseCore/SparseTriangularView.h deleted file mode 100644 index 333127b78e..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseTriangularView.h +++ /dev/null @@ -1,179 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_TRIANGULARVIEW_H -#define EIGEN_SPARSE_TRIANGULARVIEW_H - -namespace Eigen { - -namespace internal { - -template<typename MatrixType, int Mode> -struct traits<SparseTriangularView<MatrixType,Mode> > -: public traits<MatrixType> -{}; - -} // namespace internal - -template<typename MatrixType, int Mode> class SparseTriangularView - : public SparseMatrixBase<SparseTriangularView<MatrixType,Mode> > -{ - enum { SkipFirst = ((Mode&Lower) && !(MatrixType::Flags&RowMajorBit)) - || ((Mode&Upper) && (MatrixType::Flags&RowMajorBit)), - SkipLast = !SkipFirst, - SkipDiag = (Mode&ZeroDiag) ? 1 : 0, - HasUnitDiag = (Mode&UnitDiag) ? 1 : 0 - }; - - public: - - EIGEN_SPARSE_PUBLIC_INTERFACE(SparseTriangularView) - - class InnerIterator; - class ReverseInnerIterator; - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - typedef typename MatrixType::Nested MatrixTypeNested; - typedef typename internal::remove_reference<MatrixTypeNested>::type MatrixTypeNestedNonRef; - typedef typename internal::remove_all<MatrixTypeNested>::type MatrixTypeNestedCleaned; - - inline SparseTriangularView(const MatrixType& matrix) : m_matrix(matrix) {} - - /** \internal */ - inline const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; } - - template<typename OtherDerived> - typename internal::plain_matrix_type_column_major<OtherDerived>::type - solve(const MatrixBase<OtherDerived>& other) const; - - template<typename OtherDerived> void solveInPlace(MatrixBase<OtherDerived>& other) const; - template<typename OtherDerived> void solveInPlace(SparseMatrixBase<OtherDerived>& other) const; - - protected: - MatrixTypeNested m_matrix; -}; - -template<typename MatrixType, int Mode> -class SparseTriangularView<MatrixType,Mode>::InnerIterator : public MatrixTypeNestedCleaned::InnerIterator -{ - typedef typename MatrixTypeNestedCleaned::InnerIterator Base; - typedef typename SparseTriangularView::Index Index; - public: - - EIGEN_STRONG_INLINE InnerIterator(const SparseTriangularView& view, Index outer) - : Base(view.nestedExpression(), outer), m_returnOne(false) - { - if(SkipFirst) - { - while((*this) && ((HasUnitDiag||SkipDiag) ? this->index()<=outer : this->index()<outer)) - Base::operator++(); - if(HasUnitDiag) - m_returnOne = true; - } - else if(HasUnitDiag && ((!Base::operator bool()) || Base::index()>=Base::outer())) - { - if((!SkipFirst) && Base::operator bool()) - Base::operator++(); - m_returnOne = true; - } - } - - EIGEN_STRONG_INLINE InnerIterator& operator++() - { - if(HasUnitDiag && m_returnOne) - m_returnOne = false; - else - { - Base::operator++(); - if(HasUnitDiag && (!SkipFirst) && ((!Base::operator bool()) || Base::index()>=Base::outer())) - { - if((!SkipFirst) && Base::operator bool()) - Base::operator++(); - m_returnOne = true; - } - } - return *this; - } - - inline Index row() const { return (MatrixType::Flags&RowMajorBit ? Base::outer() : this->index()); } - inline Index col() const { return (MatrixType::Flags&RowMajorBit ? this->index() : Base::outer()); } - inline Index index() const - { - if(HasUnitDiag && m_returnOne) return Base::outer(); - else return Base::index(); - } - inline Scalar value() const - { - if(HasUnitDiag && m_returnOne) return Scalar(1); - else return Base::value(); - } - - EIGEN_STRONG_INLINE operator bool() const - { - if(HasUnitDiag && m_returnOne) - return true; - if(SkipFirst) return Base::operator bool(); - else - { - if (SkipDiag) return (Base::operator bool() && this->index() < this->outer()); - else return (Base::operator bool() && this->index() <= this->outer()); - } - } - protected: - bool m_returnOne; -}; - -template<typename MatrixType, int Mode> -class SparseTriangularView<MatrixType,Mode>::ReverseInnerIterator : public MatrixTypeNestedCleaned::ReverseInnerIterator -{ - typedef typename MatrixTypeNestedCleaned::ReverseInnerIterator Base; - typedef typename SparseTriangularView::Index Index; - public: - - EIGEN_STRONG_INLINE ReverseInnerIterator(const SparseTriangularView& view, Index outer) - : Base(view.nestedExpression(), outer) - { - eigen_assert((!HasUnitDiag) && "ReverseInnerIterator does not support yet triangular views with a unit diagonal"); - if(SkipLast) { - while((*this) && (SkipDiag ? this->index()>=outer : this->index()>outer)) - --(*this); - } - } - - EIGEN_STRONG_INLINE ReverseInnerIterator& operator--() - { Base::operator--(); return *this; } - - inline Index row() const { return Base::row(); } - inline Index col() const { return Base::col(); } - - EIGEN_STRONG_INLINE operator bool() const - { - if (SkipLast) return Base::operator bool() ; - else - { - if(SkipDiag) return (Base::operator bool() && this->index() > this->outer()); - else return (Base::operator bool() && this->index() >= this->outer()); - } - } -}; - -template<typename Derived> -template<int Mode> -inline const SparseTriangularView<Derived, Mode> -SparseMatrixBase<Derived>::triangularView() const -{ - return derived(); -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSE_TRIANGULARVIEW_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseUtil.h b/third_party/eigen3/Eigen/src/SparseCore/SparseUtil.h deleted file mode 100644 index 05023858b1..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseUtil.h +++ /dev/null @@ -1,171 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSEUTIL_H -#define EIGEN_SPARSEUTIL_H - -namespace Eigen { - -#ifdef NDEBUG -#define EIGEN_DBG_SPARSE(X) -#else -#define EIGEN_DBG_SPARSE(X) X -#endif - -#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \ -template<typename OtherDerived> \ -EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SparseMatrixBase<OtherDerived>& other) \ -{ \ - return Base::operator Op(other.derived()); \ -} \ -EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \ -{ \ - return Base::operator Op(other); \ -} - -#define EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \ -template<typename Other> \ -EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \ -{ \ - return Base::operator Op(scalar); \ -} - -#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \ -EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =) \ -EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, +=) \ -EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, -=) \ -EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, *=) \ -EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, /=) - -#define _EIGEN_SPARSE_PUBLIC_INTERFACE(Derived, BaseClass) \ - typedef BaseClass Base; \ - typedef typename Eigen::internal::traits<Derived >::Scalar Scalar; \ - typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \ - typedef typename Eigen::internal::nested<Derived >::type Nested; \ - typedef typename Eigen::internal::traits<Derived >::StorageKind StorageKind; \ - typedef typename Eigen::internal::traits<Derived >::Index Index; \ - enum { RowsAtCompileTime = Eigen::internal::traits<Derived >::RowsAtCompileTime, \ - ColsAtCompileTime = Eigen::internal::traits<Derived >::ColsAtCompileTime, \ - Flags = Eigen::internal::traits<Derived >::Flags, \ - CoeffReadCost = Eigen::internal::traits<Derived >::CoeffReadCost, \ - SizeAtCompileTime = Base::SizeAtCompileTime, \ - IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; \ - using Base::derived; \ - using Base::const_cast_derived; - -#define EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) \ - _EIGEN_SPARSE_PUBLIC_INTERFACE(Derived, Eigen::SparseMatrixBase<Derived >) - -const int CoherentAccessPattern = 0x1; -const int InnerRandomAccessPattern = 0x2 | CoherentAccessPattern; -const int OuterRandomAccessPattern = 0x4 | CoherentAccessPattern; -const int RandomAccessPattern = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern; - -template<typename Derived> class SparseMatrixBase; -template<typename _Scalar, int _Flags = 0, typename _Index = int> class SparseMatrix; -template<typename _Scalar, int _Flags = 0, typename _Index = int> class DynamicSparseMatrix; -template<typename _Scalar, int _Flags = 0, typename _Index = int> class SparseVector; -template<typename _Scalar, int _Flags = 0, typename _Index = int> class MappedSparseMatrix; - -template<typename MatrixType, int Mode> class SparseTriangularView; -template<typename MatrixType, unsigned int UpLo> class SparseSelfAdjointView; -template<typename Lhs, typename Rhs> class SparseDiagonalProduct; -template<typename MatrixType> class SparseView; - -template<typename Lhs, typename Rhs> class SparseSparseProduct; -template<typename Lhs, typename Rhs> class SparseTimeDenseProduct; -template<typename Lhs, typename Rhs> class DenseTimeSparseProduct; -template<typename Lhs, typename Rhs, bool Transpose> class SparseDenseOuterProduct; - -template<typename Lhs, typename Rhs> struct SparseSparseProductReturnType; -template<typename Lhs, typename Rhs, int InnerSize = internal::traits<Lhs>::ColsAtCompileTime> struct DenseSparseProductReturnType; -template<typename Lhs, typename Rhs, int InnerSize = internal::traits<Lhs>::ColsAtCompileTime> struct SparseDenseProductReturnType; -template<typename MatrixType,int UpLo> class SparseSymmetricPermutationProduct; - -namespace internal { - -template<typename T,int Rows,int Cols> struct sparse_eval; - -template<typename T> struct eval<T,Sparse> - : public sparse_eval<T, traits<T>::RowsAtCompileTime,traits<T>::ColsAtCompileTime> -{}; - -template<typename T,int Cols> struct sparse_eval<T,1,Cols> { - typedef typename traits<T>::Scalar _Scalar; - typedef typename traits<T>::Index _Index; - public: - typedef SparseVector<_Scalar, RowMajor, _Index> type; -}; - -template<typename T,int Rows> struct sparse_eval<T,Rows,1> { - typedef typename traits<T>::Scalar _Scalar; - typedef typename traits<T>::Index _Index; - public: - typedef SparseVector<_Scalar, ColMajor, _Index> type; -}; - -template<typename T,int Rows,int Cols> struct sparse_eval { - typedef typename traits<T>::Scalar _Scalar; - typedef typename traits<T>::Index _Index; - enum { _Options = ((traits<T>::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor }; - public: - typedef SparseMatrix<_Scalar, _Options, _Index> type; -}; - -template<typename T> struct sparse_eval<T,1,1> { - typedef typename traits<T>::Scalar _Scalar; - public: - typedef Matrix<_Scalar, 1, 1> type; -}; - -template<typename T> struct plain_matrix_type<T,Sparse> -{ - typedef typename traits<T>::Scalar _Scalar; - typedef typename traits<T>::Index _Index; - enum { _Options = ((traits<T>::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor }; - public: - typedef SparseMatrix<_Scalar, _Options, _Index> type; -}; - -} // end namespace internal - -/** \ingroup SparseCore_Module - * - * \class Triplet - * - * \brief A small structure to hold a non zero as a triplet (i,j,value). - * - * \sa SparseMatrix::setFromTriplets() - */ -template<typename Scalar, typename Index=typename SparseMatrix<Scalar>::Index > -class Triplet -{ -public: - Triplet() : m_row(0), m_col(0), m_value(0) {} - - Triplet(const Index& i, const Index& j, const Scalar& v = Scalar(0)) - : m_row(i), m_col(j), m_value(v) - {} - - /** \returns the row index of the element */ - const Index& row() const { return m_row; } - - /** \returns the column index of the element */ - const Index& col() const { return m_col; } - - /** \returns the value of the element */ - const Scalar& value() const { return m_value; } -protected: - Index m_row, m_col; - Scalar m_value; -}; - -} // end namespace Eigen - -#endif // EIGEN_SPARSEUTIL_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseVector.h b/third_party/eigen3/Eigen/src/SparseCore/SparseVector.h deleted file mode 100644 index 7e15c814b6..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseVector.h +++ /dev/null @@ -1,447 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSEVECTOR_H -#define EIGEN_SPARSEVECTOR_H - -namespace Eigen { - -/** \ingroup SparseCore_Module - * \class SparseVector - * - * \brief a sparse vector class - * - * \tparam _Scalar the scalar type, i.e. the type of the coefficients - * - * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme. - * - * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN. - */ - -namespace internal { -template<typename _Scalar, int _Options, typename _Index> -struct traits<SparseVector<_Scalar, _Options, _Index> > -{ - typedef _Scalar Scalar; - typedef _Index Index; - typedef Sparse StorageKind; - typedef MatrixXpr XprKind; - enum { - IsColVector = (_Options & RowMajorBit) ? 0 : 1, - - RowsAtCompileTime = IsColVector ? Dynamic : 1, - ColsAtCompileTime = IsColVector ? 1 : Dynamic, - MaxRowsAtCompileTime = RowsAtCompileTime, - MaxColsAtCompileTime = ColsAtCompileTime, - Flags = _Options | NestByRefBit | LvalueBit | (IsColVector ? 0 : RowMajorBit), - CoeffReadCost = NumTraits<Scalar>::ReadCost, - SupportedAccessPatterns = InnerRandomAccessPattern - }; -}; - -// Sparse-Vector-Assignment kinds: -enum { - SVA_RuntimeSwitch, - SVA_Inner, - SVA_Outer -}; - -template< typename Dest, typename Src, - int AssignmentKind = !bool(Src::IsVectorAtCompileTime) ? SVA_RuntimeSwitch - : Src::InnerSizeAtCompileTime==1 ? SVA_Outer - : SVA_Inner> -struct sparse_vector_assign_selector; - -} - -template<typename _Scalar, int _Options, typename _Index> -class SparseVector - : public SparseMatrixBase<SparseVector<_Scalar, _Options, _Index> > -{ - typedef SparseMatrixBase<SparseVector> SparseBase; - - public: - EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector) - EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=) - EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=) - - typedef internal::CompressedStorage<Scalar,Index> Storage; - enum { IsColVector = internal::traits<SparseVector>::IsColVector }; - - enum { - Options = _Options - }; - - EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; } - EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; } - EIGEN_STRONG_INLINE Index innerSize() const { return m_size; } - EIGEN_STRONG_INLINE Index outerSize() const { return 1; } - - EIGEN_STRONG_INLINE const Scalar* valuePtr() const { return &m_data.value(0); } - EIGEN_STRONG_INLINE Scalar* valuePtr() { return &m_data.value(0); } - - EIGEN_STRONG_INLINE const Index* innerIndexPtr() const { return &m_data.index(0); } - EIGEN_STRONG_INLINE Index* innerIndexPtr() { return &m_data.index(0); } - - /** \internal */ - inline Storage& data() { return m_data; } - /** \internal */ - inline const Storage& data() const { return m_data; } - - inline Scalar coeff(Index row, Index col) const - { - eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size)); - return coeff(IsColVector ? row : col); - } - inline Scalar coeff(Index i) const - { - eigen_assert(i>=0 && i<m_size); - return m_data.at(i); - } - - inline Scalar& coeffRef(Index row, Index col) - { - eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size)); - return coeff(IsColVector ? row : col); - } - - /** \returns a reference to the coefficient value at given index \a i - * This operation involes a log(rho*size) binary search. If the coefficient does not - * exist yet, then a sorted insertion into a sequential buffer is performed. - * - * This insertion might be very costly if the number of nonzeros above \a i is large. - */ - inline Scalar& coeffRef(Index i) - { - eigen_assert(i>=0 && i<m_size); - return m_data.atWithInsertion(i); - } - - public: - - class InnerIterator; - class ReverseInnerIterator; - - inline void setZero() { m_data.clear(); } - - /** \returns the number of non zero coefficients */ - inline Index nonZeros() const { return static_cast<Index>(m_data.size()); } - - inline void startVec(Index outer) - { - EIGEN_UNUSED_VARIABLE(outer); - eigen_assert(outer==0); - } - - inline Scalar& insertBackByOuterInner(Index outer, Index inner) - { - EIGEN_UNUSED_VARIABLE(outer); - eigen_assert(outer==0); - return insertBack(inner); - } - inline Scalar& insertBack(Index i) - { - m_data.append(0, i); - return m_data.value(m_data.size()-1); - } - - inline Scalar& insert(Index row, Index col) - { - eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size)); - - Index inner = IsColVector ? row : col; - Index outer = IsColVector ? col : row; - eigen_assert(outer==0); - return insert(inner); - } - Scalar& insert(Index i) - { - eigen_assert(i>=0 && i<m_size); - - Index startId = 0; - Index p = Index(m_data.size()) - 1; - // TODO smart realloc - m_data.resize(p+2,1); - - while ( (p >= startId) && (m_data.index(p) > i) ) - { - m_data.index(p+1) = m_data.index(p); - m_data.value(p+1) = m_data.value(p); - --p; - } - m_data.index(p+1) = i; - m_data.value(p+1) = 0; - return m_data.value(p+1); - } - - /** - */ - inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); } - - - inline void finalize() {} - - void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) - { - m_data.prune(reference,epsilon); - } - - void resize(Index rows, Index cols) - { - eigen_assert(rows==1 || cols==1); - resize(IsColVector ? rows : cols); - } - - void resize(Index newSize) - { - m_size = newSize; - m_data.clear(); - } - - void resizeNonZeros(Index size) { m_data.resize(size); } - - inline SparseVector() : m_size(0) { check_template_parameters(); resize(0); } - - inline SparseVector(Index size) : m_size(0) { check_template_parameters(); resize(size); } - - inline SparseVector(Index rows, Index cols) : m_size(0) { check_template_parameters(); resize(rows,cols); } - - template<typename OtherDerived> - inline SparseVector(const SparseMatrixBase<OtherDerived>& other) - : m_size(0) - { - check_template_parameters(); - *this = other.derived(); - } - - inline SparseVector(const SparseVector& other) - : SparseBase(other), m_size(0) - { - check_template_parameters(); - *this = other.derived(); - } - - /** Swaps the values of \c *this and \a other. - * Overloaded for performance: this version performs a \em shallow swap by swaping pointers and attributes only. - * \sa SparseMatrixBase::swap() - */ - inline void swap(SparseVector& other) - { - std::swap(m_size, other.m_size); - m_data.swap(other.m_data); - } - - inline SparseVector& operator=(const SparseVector& other) - { - if (other.isRValue()) - { - swap(other.const_cast_derived()); - } - else - { - resize(other.size()); - m_data = other.m_data; - } - return *this; - } - - template<typename OtherDerived> - inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other) - { - SparseVector tmp(other.size()); - internal::sparse_vector_assign_selector<SparseVector,OtherDerived>::run(tmp,other.derived()); - this->swap(tmp); - return *this; - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - template<typename Lhs, typename Rhs> - inline SparseVector& operator=(const SparseSparseProduct<Lhs,Rhs>& product) - { - return Base::operator=(product); - } - #endif - - friend std::ostream & operator << (std::ostream & s, const SparseVector& m) - { - for (Index i=0; i<m.nonZeros(); ++i) - s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") "; - s << std::endl; - return s; - } - - /** Destructor */ - inline ~SparseVector() {} - - /** Overloaded for performance */ - Scalar sum() const; - - public: - - /** \internal \deprecated use setZero() and reserve() */ - EIGEN_DEPRECATED void startFill(Index reserve) - { - setZero(); - m_data.reserve(reserve); - } - - /** \internal \deprecated use insertBack(Index,Index) */ - EIGEN_DEPRECATED Scalar& fill(Index r, Index c) - { - eigen_assert(r==0 || c==0); - return fill(IsColVector ? r : c); - } - - /** \internal \deprecated use insertBack(Index) */ - EIGEN_DEPRECATED Scalar& fill(Index i) - { - m_data.append(0, i); - return m_data.value(m_data.size()-1); - } - - /** \internal \deprecated use insert(Index,Index) */ - EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c) - { - eigen_assert(r==0 || c==0); - return fillrand(IsColVector ? r : c); - } - - /** \internal \deprecated use insert(Index) */ - EIGEN_DEPRECATED Scalar& fillrand(Index i) - { - return insert(i); - } - - /** \internal \deprecated use finalize() */ - EIGEN_DEPRECATED void endFill() {} - - // These two functions were here in the 3.1 release, so let's keep them in case some code rely on them. - /** \internal \deprecated use data() */ - EIGEN_DEPRECATED Storage& _data() { return m_data; } - /** \internal \deprecated use data() */ - EIGEN_DEPRECATED const Storage& _data() const { return m_data; } - -# ifdef EIGEN_SPARSEVECTOR_PLUGIN -# include EIGEN_SPARSEVECTOR_PLUGIN -# endif - -protected: - - static void check_template_parameters() - { - EIGEN_STATIC_ASSERT(NumTraits<Index>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE); - EIGEN_STATIC_ASSERT((_Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS); - } - - Storage m_data; - Index m_size; -}; - -template<typename Scalar, int _Options, typename _Index> -class SparseVector<Scalar,_Options,_Index>::InnerIterator -{ - public: - InnerIterator(const SparseVector& vec, Index outer=0) - : m_data(vec.m_data), m_id(0), m_end(static_cast<Index>(m_data.size())) - { - EIGEN_UNUSED_VARIABLE(outer); - eigen_assert(outer==0); - } - - InnerIterator(const internal::CompressedStorage<Scalar,Index>& data) - : m_data(data), m_id(0), m_end(static_cast<Index>(m_data.size())) - {} - - inline InnerIterator& operator++() { m_id++; return *this; } - - inline Scalar value() const { return m_data.value(m_id); } - inline Scalar& valueRef() { return const_cast<Scalar&>(m_data.value(m_id)); } - - inline Index index() const { return m_data.index(m_id); } - inline Index row() const { return IsColVector ? index() : 0; } - inline Index col() const { return IsColVector ? 0 : index(); } - - inline operator bool() const { return (m_id < m_end); } - - protected: - const internal::CompressedStorage<Scalar,Index>& m_data; - Index m_id; - const Index m_end; -}; - -template<typename Scalar, int _Options, typename _Index> -class SparseVector<Scalar,_Options,_Index>::ReverseInnerIterator -{ - public: - ReverseInnerIterator(const SparseVector& vec, Index outer=0) - : m_data(vec.m_data), m_id(static_cast<Index>(m_data.size())), m_start(0) - { - EIGEN_UNUSED_VARIABLE(outer); - eigen_assert(outer==0); - } - - ReverseInnerIterator(const internal::CompressedStorage<Scalar,Index>& data) - : m_data(data), m_id(static_cast<Index>(m_data.size())), m_start(0) - {} - - inline ReverseInnerIterator& operator--() { m_id--; return *this; } - - inline Scalar value() const { return m_data.value(m_id-1); } - inline Scalar& valueRef() { return const_cast<Scalar&>(m_data.value(m_id-1)); } - - inline Index index() const { return m_data.index(m_id-1); } - inline Index row() const { return IsColVector ? index() : 0; } - inline Index col() const { return IsColVector ? 0 : index(); } - - inline operator bool() const { return (m_id > m_start); } - - protected: - const internal::CompressedStorage<Scalar,Index>& m_data; - Index m_id; - const Index m_start; -}; - -namespace internal { - -template< typename Dest, typename Src> -struct sparse_vector_assign_selector<Dest,Src,SVA_Inner> { - static void run(Dest& dst, const Src& src) { - eigen_internal_assert(src.innerSize()==src.size()); - for(typename Src::InnerIterator it(src, 0); it; ++it) - dst.insert(it.index()) = it.value(); - } -}; - -template< typename Dest, typename Src> -struct sparse_vector_assign_selector<Dest,Src,SVA_Outer> { - static void run(Dest& dst, const Src& src) { - eigen_internal_assert(src.outerSize()==src.size()); - for(typename Dest::Index i=0; i<src.size(); ++i) - { - typename Src::InnerIterator it(src, i); - if(it) - dst.insert(i) = it.value(); - } - } -}; - -template< typename Dest, typename Src> -struct sparse_vector_assign_selector<Dest,Src,SVA_RuntimeSwitch> { - static void run(Dest& dst, const Src& src) { - if(src.outerSize()==1) sparse_vector_assign_selector<Dest,Src,SVA_Inner>::run(dst, src); - else sparse_vector_assign_selector<Dest,Src,SVA_Outer>::run(dst, src); - } -}; - -} - -} // end namespace Eigen - -#endif // EIGEN_SPARSEVECTOR_H diff --git a/third_party/eigen3/Eigen/src/SparseCore/SparseView.h b/third_party/eigen3/Eigen/src/SparseCore/SparseView.h deleted file mode 100644 index fd8450463f..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/SparseView.h +++ /dev/null @@ -1,99 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2010 Daniel Lowengrub <lowdanie@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSEVIEW_H -#define EIGEN_SPARSEVIEW_H - -namespace Eigen { - -namespace internal { - -template<typename MatrixType> -struct traits<SparseView<MatrixType> > : traits<MatrixType> -{ - typedef typename MatrixType::Index Index; - typedef Sparse StorageKind; - enum { - Flags = int(traits<MatrixType>::Flags) & (RowMajorBit) - }; -}; - -} // end namespace internal - -template<typename MatrixType> -class SparseView : public SparseMatrixBase<SparseView<MatrixType> > -{ - typedef typename MatrixType::Nested MatrixTypeNested; - typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested; -public: - EIGEN_SPARSE_PUBLIC_INTERFACE(SparseView) - - SparseView(const MatrixType& mat, const Scalar& m_reference = Scalar(0), - typename NumTraits<Scalar>::Real m_epsilon = NumTraits<Scalar>::dummy_precision()) : - m_matrix(mat), m_reference(m_reference), m_epsilon(m_epsilon) {} - - class InnerIterator; - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - inline Index innerSize() const { return m_matrix.innerSize(); } - inline Index outerSize() const { return m_matrix.outerSize(); } - -protected: - MatrixTypeNested m_matrix; - Scalar m_reference; - typename NumTraits<Scalar>::Real m_epsilon; -}; - -template<typename MatrixType> -class SparseView<MatrixType>::InnerIterator : public _MatrixTypeNested::InnerIterator -{ - typedef typename SparseView::Index Index; -public: - typedef typename _MatrixTypeNested::InnerIterator IterBase; - InnerIterator(const SparseView& view, Index outer) : - IterBase(view.m_matrix, outer), m_view(view) - { - incrementToNonZero(); - } - - EIGEN_STRONG_INLINE InnerIterator& operator++() - { - IterBase::operator++(); - incrementToNonZero(); - return *this; - } - - using IterBase::value; - -protected: - const SparseView& m_view; - -private: - void incrementToNonZero() - { - while((bool(*this)) && internal::isMuchSmallerThan(value(), m_view.m_reference, m_view.m_epsilon)) - { - IterBase::operator++(); - } - } -}; - -template<typename Derived> -const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& m_reference, - const typename NumTraits<Scalar>::Real& m_epsilon) const -{ - return SparseView<Derived>(derived(), m_reference, m_epsilon); -} - -} // end namespace Eigen - -#endif diff --git a/third_party/eigen3/Eigen/src/SparseCore/TriangularSolver.h b/third_party/eigen3/Eigen/src/SparseCore/TriangularSolver.h deleted file mode 100644 index cb8ad82b4f..0000000000 --- a/third_party/eigen3/Eigen/src/SparseCore/TriangularSolver.h +++ /dev/null @@ -1,334 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSETRIANGULARSOLVER_H -#define EIGEN_SPARSETRIANGULARSOLVER_H - -namespace Eigen { - -namespace internal { - -template<typename Lhs, typename Rhs, int Mode, - int UpLo = (Mode & Lower) - ? Lower - : (Mode & Upper) - ? Upper - : -1, - int StorageOrder = int(traits<Lhs>::Flags) & RowMajorBit> -struct sparse_solve_triangular_selector; - -// forward substitution, row-major -template<typename Lhs, typename Rhs, int Mode> -struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,RowMajor> -{ - typedef typename Rhs::Scalar Scalar; - static void run(const Lhs& lhs, Rhs& other) - { - for(int col=0 ; col<other.cols() ; ++col) - { - for(int i=0; i<lhs.rows(); ++i) - { - Scalar tmp = other.coeff(i,col); - Scalar lastVal(0); - int lastIndex = 0; - for(typename Lhs::InnerIterator it(lhs, i); it; ++it) - { - lastVal = it.value(); - lastIndex = it.index(); - if(lastIndex==i) - break; - tmp -= lastVal * other.coeff(lastIndex,col); - } - if (Mode & UnitDiag) - other.coeffRef(i,col) = tmp; - else - { - eigen_assert(lastIndex==i); - other.coeffRef(i,col) = tmp/lastVal; - } - } - } - } -}; - -// backward substitution, row-major -template<typename Lhs, typename Rhs, int Mode> -struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,RowMajor> -{ - typedef typename Rhs::Scalar Scalar; - static void run(const Lhs& lhs, Rhs& other) - { - for(int col=0 ; col<other.cols() ; ++col) - { - for(int i=lhs.rows()-1 ; i>=0 ; --i) - { - Scalar tmp = other.coeff(i,col); - Scalar l_ii = 0; - typename Lhs::InnerIterator it(lhs, i); - while(it && it.index()<i) - ++it; - if(!(Mode & UnitDiag)) - { - eigen_assert(it && it.index()==i); - l_ii = it.value(); - ++it; - } - else if (it && it.index() == i) - ++it; - for(; it; ++it) - { - tmp -= it.value() * other.coeff(it.index(),col); - } - - if (Mode & UnitDiag) - other.coeffRef(i,col) = tmp; - else - other.coeffRef(i,col) = tmp/l_ii; - } - } - } -}; - -// forward substitution, col-major -template<typename Lhs, typename Rhs, int Mode> -struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,ColMajor> -{ - typedef typename Rhs::Scalar Scalar; - static void run(const Lhs& lhs, Rhs& other) - { - for(int col=0 ; col<other.cols() ; ++col) - { - for(int i=0; i<lhs.cols(); ++i) - { - Scalar& tmp = other.coeffRef(i,col); - if (tmp!=Scalar(0)) // optimization when other is actually sparse - { - typename Lhs::InnerIterator it(lhs, i); - while(it && it.index()<i) - ++it; - if(!(Mode & UnitDiag)) - { - eigen_assert(it && it.index()==i); - tmp /= it.value(); - } - if (it && it.index()==i) - ++it; - for(; it; ++it) - other.coeffRef(it.index(), col) -= tmp * it.value(); - } - } - } - } -}; - -// backward substitution, col-major -template<typename Lhs, typename Rhs, int Mode> -struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,ColMajor> -{ - typedef typename Rhs::Scalar Scalar; - static void run(const Lhs& lhs, Rhs& other) - { - for(int col=0 ; col<other.cols() ; ++col) - { - for(int i=lhs.cols()-1; i>=0; --i) - { - Scalar& tmp = other.coeffRef(i,col); - if (tmp!=Scalar(0)) // optimization when other is actually sparse - { - if(!(Mode & UnitDiag)) - { - // TODO replace this by a binary search. make sure the binary search is safe for partially sorted elements - typename Lhs::ReverseInnerIterator it(lhs, i); - while(it && it.index()!=i) - --it; - eigen_assert(it && it.index()==i); - other.coeffRef(i,col) /= it.value(); - } - typename Lhs::InnerIterator it(lhs, i); - for(; it && it.index()<i; ++it) - other.coeffRef(it.index(), col) -= tmp * it.value(); - } - } - } - } -}; - -} // end namespace internal - -template<typename ExpressionType,int Mode> -template<typename OtherDerived> -void SparseTriangularView<ExpressionType,Mode>::solveInPlace(MatrixBase<OtherDerived>& other) const -{ - eigen_assert(m_matrix.cols() == m_matrix.rows() && m_matrix.cols() == other.rows()); - eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower))); - - enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit }; - - typedef typename internal::conditional<copy, - typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy; - OtherCopy otherCopy(other.derived()); - - internal::sparse_solve_triangular_selector<ExpressionType, typename internal::remove_reference<OtherCopy>::type, Mode>::run(m_matrix, otherCopy); - - if (copy) - other = otherCopy; -} - -template<typename ExpressionType,int Mode> -template<typename OtherDerived> -typename internal::plain_matrix_type_column_major<OtherDerived>::type -SparseTriangularView<ExpressionType,Mode>::solve(const MatrixBase<OtherDerived>& other) const -{ - typename internal::plain_matrix_type_column_major<OtherDerived>::type res(other); - solveInPlace(res); - return res; -} - -// pure sparse path - -namespace internal { - -template<typename Lhs, typename Rhs, int Mode, - int UpLo = (Mode & Lower) - ? Lower - : (Mode & Upper) - ? Upper - : -1, - int StorageOrder = int(Lhs::Flags) & (RowMajorBit)> -struct sparse_solve_triangular_sparse_selector; - -// forward substitution, col-major -template<typename Lhs, typename Rhs, int Mode, int UpLo> -struct sparse_solve_triangular_sparse_selector<Lhs,Rhs,Mode,UpLo,ColMajor> -{ - typedef typename Rhs::Scalar Scalar; - typedef typename promote_index_type<typename traits<Lhs>::Index, - typename traits<Rhs>::Index>::type Index; - static void run(const Lhs& lhs, Rhs& other) - { - const bool IsLower = (UpLo==Lower); - AmbiVector<Scalar,Index> tempVector(other.rows()*2); - tempVector.setBounds(0,other.rows()); - - Rhs res(other.rows(), other.cols()); - res.reserve(other.nonZeros()); - - for(int col=0 ; col<other.cols() ; ++col) - { - // FIXME estimate number of non zeros - tempVector.init(.99/*float(other.col(col).nonZeros())/float(other.rows())*/); - tempVector.setZero(); - tempVector.restart(); - for (typename Rhs::InnerIterator rhsIt(other, col); rhsIt; ++rhsIt) - { - tempVector.coeffRef(rhsIt.index()) = rhsIt.value(); - } - - for(int i=IsLower?0:lhs.cols()-1; - IsLower?i<lhs.cols():i>=0; - i+=IsLower?1:-1) - { - tempVector.restart(); - Scalar& ci = tempVector.coeffRef(i); - if (ci!=Scalar(0)) - { - // find - typename Lhs::InnerIterator it(lhs, i); - if(!(Mode & UnitDiag)) - { - if (IsLower) - { - eigen_assert(it.index()==i); - ci /= it.value(); - } - else - ci /= lhs.coeff(i,i); - } - tempVector.restart(); - if (IsLower) - { - if (it.index()==i) - ++it; - for(; it; ++it) - tempVector.coeffRef(it.index()) -= ci * it.value(); - } - else - { - for(; it && it.index()<i; ++it) - tempVector.coeffRef(it.index()) -= ci * it.value(); - } - } - } - - - int count = 0; - // FIXME compute a reference value to filter zeros - for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector/*,1e-12*/); it; ++it) - { - ++ count; -// std::cerr << "fill " << it.index() << ", " << col << "\n"; -// std::cout << it.value() << " "; - // FIXME use insertBack - res.insert(it.index(), col) = it.value(); - } -// std::cout << "tempVector.nonZeros() == " << int(count) << " / " << (other.rows()) << "\n"; - } - res.finalize(); - other = res.markAsRValue(); - } -}; - -} // end namespace internal - -template<typename ExpressionType,int Mode> -template<typename OtherDerived> -void SparseTriangularView<ExpressionType,Mode>::solveInPlace(SparseMatrixBase<OtherDerived>& other) const -{ - eigen_assert(m_matrix.cols() == m_matrix.rows() && m_matrix.cols() == other.rows()); - eigen_assert( (!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower))); - -// enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit }; - -// typedef typename internal::conditional<copy, -// typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&>::type OtherCopy; -// OtherCopy otherCopy(other.derived()); - - internal::sparse_solve_triangular_sparse_selector<ExpressionType, OtherDerived, Mode>::run(m_matrix, other.derived()); - -// if (copy) -// other = otherCopy; -} - -#ifdef EIGEN2_SUPPORT - -// deprecated stuff: - -/** \deprecated */ -template<typename Derived> -template<typename OtherDerived> -void SparseMatrixBase<Derived>::solveTriangularInPlace(MatrixBase<OtherDerived>& other) const -{ - this->template triangular<Flags&(Upper|Lower)>().solveInPlace(other); -} - -/** \deprecated */ -template<typename Derived> -template<typename OtherDerived> -typename internal::plain_matrix_type_column_major<OtherDerived>::type -SparseMatrixBase<Derived>::solveTriangular(const MatrixBase<OtherDerived>& other) const -{ - typename internal::plain_matrix_type_column_major<OtherDerived>::type res(other); - derived().solveTriangularInPlace(res); - return res; -} -#endif // EIGEN2_SUPPORT - -} // end namespace Eigen - -#endif // EIGEN_SPARSETRIANGULARSOLVER_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU.h deleted file mode 100644 index 7a9aeec2da..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU.h +++ /dev/null @@ -1,762 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -#ifndef EIGEN_SPARSE_LU_H -#define EIGEN_SPARSE_LU_H - -namespace Eigen { - -template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::Index> > class SparseLU; -template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType; -template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType; - -/** \ingroup SparseLU_Module - * \class SparseLU - * - * \brief Sparse supernodal LU factorization for general matrices - * - * This class implements the supernodal LU factorization for general matrices. - * It uses the main techniques from the sequential SuperLU package - * (http://crd-legacy.lbl.gov/~xiaoye/SuperLU/). It handles transparently real - * and complex arithmetics with single and double precision, depending on the - * scalar type of your input matrix. - * The code has been optimized to provide BLAS-3 operations during supernode-panel updates. - * It benefits directly from the built-in high-performant Eigen BLAS routines. - * Moreover, when the size of a supernode is very small, the BLAS calls are avoided to - * enable a better optimization from the compiler. For best performance, - * you should compile it with NDEBUG flag to avoid the numerous bounds checking on vectors. - * - * An important parameter of this class is the ordering method. It is used to reorder the columns - * (and eventually the rows) of the matrix to reduce the number of new elements that are created during - * numerical factorization. The cheapest method available is COLAMD. - * See \link OrderingMethods_Module the OrderingMethods module \endlink for the list of - * built-in and external ordering methods. - * - * Simple example with key steps - * \code - * VectorXd x(n), b(n); - * SparseMatrix<double, ColMajor> A; - * SparseLU<SparseMatrix<scalar, ColMajor>, COLAMDOrdering<Index> > solver; - * // fill A and b; - * // Compute the ordering permutation vector from the structural pattern of A - * solver.analyzePattern(A); - * // Compute the numerical factorization - * solver.factorize(A); - * //Use the factors to solve the linear system - * x = solver.solve(b); - * \endcode - * - * \warning The input matrix A should be in a \b compressed and \b column-major form. - * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix. - * - * \note Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix. - * For badly scaled matrices, this step can be useful to reduce the pivoting during factorization. - * If this is the case for your matrices, you can try the basic scaling method at - * "unsupported/Eigen/src/IterativeSolvers/Scaling.h" - * - * \tparam _MatrixType The type of the sparse matrix. It must be a column-major SparseMatrix<> - * \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD - * - * - * \sa \ref TutorialSparseDirectSolvers - * \sa \ref OrderingMethods_Module - */ -template <typename _MatrixType, typename _OrderingType> -class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typename _MatrixType::Index> -{ - public: - typedef _MatrixType MatrixType; - typedef _OrderingType OrderingType; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef SparseMatrix<Scalar,ColMajor,Index> NCMatrix; - typedef internal::MappedSuperNodalMatrix<Scalar, Index> SCMatrix; - typedef Matrix<Scalar,Dynamic,1> ScalarVector; - typedef Matrix<Index,Dynamic,1> IndexVector; - typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; - typedef internal::SparseLUImpl<Scalar, Index> Base; - - public: - SparseLU():m_isInitialized(true),m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1) - { - initperfvalues(); - } - SparseLU(const MatrixType& matrix):m_isInitialized(true),m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1) - { - initperfvalues(); - compute(matrix); - } - - ~SparseLU() - { - // Free all explicit dynamic pointers - } - - void analyzePattern (const MatrixType& matrix); - void factorize (const MatrixType& matrix); - void simplicialfactorize(const MatrixType& matrix); - - /** - * Compute the symbolic and numeric factorization of the input sparse matrix. - * The input matrix should be in column-major storage. - */ - void compute (const MatrixType& matrix) - { - // Analyze - analyzePattern(matrix); - //Factorize - factorize(matrix); - } - - inline Index rows() const { return m_mat.rows(); } - inline Index cols() const { return m_mat.cols(); } - /** Indicate that the pattern of the input matrix is symmetric */ - void isSymmetric(bool sym) - { - m_symmetricmode = sym; - } - - /** \returns an expression of the matrix L, internally stored as supernodes - * The only operation available with this expression is the triangular solve - * \code - * y = b; matrixL().solveInPlace(y); - * \endcode - */ - SparseLUMatrixLReturnType<SCMatrix> matrixL() const - { - return SparseLUMatrixLReturnType<SCMatrix>(m_Lstore); - } - /** \returns an expression of the matrix U, - * The only operation available with this expression is the triangular solve - * \code - * y = b; matrixU().solveInPlace(y); - * \endcode - */ - SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,Index> > matrixU() const - { - return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,Index> >(m_Lstore, m_Ustore); - } - - /** - * \returns a reference to the row matrix permutation \f$ P_r \f$ such that \f$P_r A P_c^T = L U\f$ - * \sa colsPermutation() - */ - inline const PermutationType& rowsPermutation() const - { - return m_perm_r; - } - /** - * \returns a reference to the column matrix permutation\f$ P_c^T \f$ such that \f$P_r A P_c^T = L U\f$ - * \sa rowsPermutation() - */ - inline const PermutationType& colsPermutation() const - { - return m_perm_c; - } - /** Set the threshold used for a diagonal entry to be an acceptable pivot. */ - void setPivotThreshold(const RealScalar& thresh) - { - m_diagpivotthresh = thresh; - } - - /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A. - * - * \warning the destination matrix X in X = this->solve(B) must be colmun-major. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::solve_retval<SparseLU, Rhs> solve(const MatrixBase<Rhs>& B) const - { - eigen_assert(m_factorizationIsOk && "SparseLU is not initialized."); - eigen_assert(rows()==B.rows() - && "SparseLU::solve(): invalid number of rows of the right hand side matrix B"); - return internal::solve_retval<SparseLU, Rhs>(*this, B.derived()); - } - - /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::sparse_solve_retval<SparseLU, Rhs> solve(const SparseMatrixBase<Rhs>& B) const - { - eigen_assert(m_factorizationIsOk && "SparseLU is not initialized."); - eigen_assert(rows()==B.rows() - && "SparseLU::solve(): invalid number of rows of the right hand side matrix B"); - return internal::sparse_solve_retval<SparseLU, Rhs>(*this, B.derived()); - } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the LU factorization reports a problem, zero diagonal for instance - * \c InvalidInput if the input matrix is invalid - * - * \sa iparm() - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_info; - } - - /** - * \returns A string describing the type of error - */ - std::string lastErrorMessage() const - { - return m_lastError; - } - - template<typename Rhs, typename Dest> - bool _solve(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const - { - Dest& X(X_base.derived()); - eigen_assert(m_factorizationIsOk && "The matrix should be factorized first"); - EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0, - THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES); - - // Permute the right hand side to form X = Pr*B - // on return, X is overwritten by the computed solution - X.resize(B.rows(),B.cols()); - - // this ugly const_cast_derived() helps to detect aliasing when applying the permutations - for(Index j = 0; j < B.cols(); ++j) - X.col(j) = rowsPermutation() * B.const_cast_derived().col(j); - - //Forward substitution with L - this->matrixL().solveInPlace(X); - this->matrixU().solveInPlace(X); - - // Permute back the solution - for (Index j = 0; j < B.cols(); ++j) - X.col(j) = colsPermutation().inverse() * X.col(j); - - return true; - } - - /** - * \returns the absolute value of the determinant of the matrix of which - * *this is the QR decomposition. - * - * \warning a determinant can be very big or small, so for matrices - * of large enough dimension, there is a risk of overflow/underflow. - * One way to work around that is to use logAbsDeterminant() instead. - * - * \sa logAbsDeterminant(), signDeterminant() - */ - Scalar absDeterminant() - { - using std::abs; - eigen_assert(m_factorizationIsOk && "The matrix should be factorized first."); - // Initialize with the determinant of the row matrix - Scalar det = Scalar(1.); - //Note that the diagonal blocks of U are stored in supernodes, - // which are available in the L part :) - for (Index j = 0; j < this->cols(); ++j) - { - for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it) - { - if(it.row() < j) continue; - if(it.row() == j) - { - det *= abs(it.value()); - break; - } - } - } - return det; - } - - /** \returns the natural log of the absolute value of the determinant of the matrix - * of which **this is the QR decomposition - * - * \note This method is useful to work around the risk of overflow/underflow that's - * inherent to the determinant computation. - * - * \sa absDeterminant(), signDeterminant() - */ - Scalar logAbsDeterminant() const - { - using std::log; - using std::abs; - - eigen_assert(m_factorizationIsOk && "The matrix should be factorized first."); - Scalar det = Scalar(0.); - for (Index j = 0; j < this->cols(); ++j) - { - for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it) - { - if(it.row() < j) continue; - if(it.row() == j) - { - det += log(abs(it.value())); - break; - } - } - } - return det; - } - - /** \returns A number representing the sign of the determinant - * - * \sa absDeterminant(), logAbsDeterminant() - */ - Scalar signDeterminant() - { - eigen_assert(m_factorizationIsOk && "The matrix should be factorized first."); - return Scalar(m_detPermR); - } - - protected: - // Functions - void initperfvalues() - { - m_perfv.panel_size = 1; - m_perfv.relax = 1; - m_perfv.maxsuper = 128; - m_perfv.rowblk = 16; - m_perfv.colblk = 8; - m_perfv.fillfactor = 20; - } - - // Variables - mutable ComputationInfo m_info; - bool m_isInitialized; - bool m_factorizationIsOk; - bool m_analysisIsOk; - std::string m_lastError; - NCMatrix m_mat; // The input (permuted ) matrix - SCMatrix m_Lstore; // The lower triangular matrix (supernodal) - MappedSparseMatrix<Scalar,ColMajor,Index> m_Ustore; // The upper triangular matrix - PermutationType m_perm_c; // Column permutation - PermutationType m_perm_r ; // Row permutation - IndexVector m_etree; // Column elimination tree - - typename Base::GlobalLU_t m_glu; - - // SparseLU options - bool m_symmetricmode; - // values for performance - internal::perfvalues<Index> m_perfv; - RealScalar m_diagpivotthresh; // Specifies the threshold used for a diagonal entry to be an acceptable pivot - Index m_nnzL, m_nnzU; // Nonzeros in L and U factors - Index m_detPermR; // Determinant of the coefficient matrix - private: - // Disable copy constructor - SparseLU (const SparseLU& ); - -}; // End class SparseLU - - - -// Functions needed by the anaysis phase -/** - * Compute the column permutation to minimize the fill-in - * - * - Apply this permutation to the input matrix - - * - * - Compute the column elimination tree on the permuted matrix - * - * - Postorder the elimination tree and the column permutation - * - */ -template <typename MatrixType, typename OrderingType> -void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat) -{ - - //TODO It is possible as in SuperLU to compute row and columns scaling vectors to equilibrate the matrix mat. - - OrderingType ord; - ord(mat,m_perm_c); - - // Apply the permutation to the column of the input matrix - //First copy the whole input matrix. - m_mat = mat; - if (m_perm_c.size()) { - m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This vector is filled but not subsequently used. - //Then, permute only the column pointers - const Index * outerIndexPtr; - if (mat.isCompressed()) outerIndexPtr = mat.outerIndexPtr(); - else - { - Index *outerIndexPtr_t = new Index[mat.cols()+1]; - for(Index i = 0; i <= mat.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i]; - outerIndexPtr = outerIndexPtr_t; - } - for (Index i = 0; i < mat.cols(); i++) - { - m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i]; - m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i]; - } - if(!mat.isCompressed()) delete[] outerIndexPtr; - } - // Compute the column elimination tree of the permuted matrix - IndexVector firstRowElt; - internal::coletree(m_mat, m_etree,firstRowElt); - - // In symmetric mode, do not do postorder here - if (!m_symmetricmode) { - IndexVector post, iwork; - // Post order etree - internal::treePostorder(m_mat.cols(), m_etree, post); - - - // Renumber etree in postorder - Index m = m_mat.cols(); - iwork.resize(m+1); - for (Index i = 0; i < m; ++i) iwork(post(i)) = post(m_etree(i)); - m_etree = iwork; - - // Postmultiply A*Pc by post, i.e reorder the matrix according to the postorder of the etree - PermutationType post_perm(m); - for (Index i = 0; i < m; i++) - post_perm.indices()(i) = post(i); - - // Combine the two permutations : postorder the permutation for future use - if(m_perm_c.size()) { - m_perm_c = post_perm * m_perm_c; - } - - } // end postordering - - m_analysisIsOk = true; -} - -// Functions needed by the numerical factorization phase - - -/** - * - Numerical factorization - * - Interleaved with the symbolic factorization - * On exit, info is - * - * = 0: successful factorization - * - * > 0: if info = i, and i is - * - * <= A->ncol: U(i,i) is exactly zero. The factorization has - * been completed, but the factor U is exactly singular, - * and division by zero will occur if it is used to solve a - * system of equations. - * - * > A->ncol: number of bytes allocated when memory allocation - * failure occurred, plus A->ncol. If lwork = -1, it is - * the estimated amount of space needed, plus A->ncol. - */ -template <typename MatrixType, typename OrderingType> -void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix) -{ - using internal::emptyIdxLU; - eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); - eigen_assert((matrix.rows() == matrix.cols()) && "Only for squared matrices"); - - typedef typename IndexVector::Scalar Index; - - - // Apply the column permutation computed in analyzepattern() - // m_mat = matrix * m_perm_c.inverse(); - m_mat = matrix; - if (m_perm_c.size()) - { - m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. - //Then, permute only the column pointers - const Index * outerIndexPtr; - if (matrix.isCompressed()) outerIndexPtr = matrix.outerIndexPtr(); - else - { - Index* outerIndexPtr_t = new Index[matrix.cols()+1]; - for(Index i = 0; i <= matrix.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i]; - outerIndexPtr = outerIndexPtr_t; - } - for (Index i = 0; i < matrix.cols(); i++) - { - m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i]; - m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i]; - } - if(!matrix.isCompressed()) delete[] outerIndexPtr; - } - else - { //FIXME This should not be needed if the empty permutation is handled transparently - m_perm_c.resize(matrix.cols()); - for(Index i = 0; i < matrix.cols(); ++i) m_perm_c.indices()(i) = i; - } - - Index m = m_mat.rows(); - Index n = m_mat.cols(); - Index nnz = m_mat.nonZeros(); - Index maxpanel = m_perfv.panel_size * m; - // Allocate working storage common to the factor routines - Index lwork = 0; - Index info = Base::memInit(m, n, nnz, lwork, m_perfv.fillfactor, m_perfv.panel_size, m_glu); - if (info) - { - m_lastError = "UNABLE TO ALLOCATE WORKING MEMORY\n\n" ; - m_factorizationIsOk = false; - return ; - } - - // Set up pointers for integer working arrays - IndexVector segrep(m); segrep.setZero(); - IndexVector parent(m); parent.setZero(); - IndexVector xplore(m); xplore.setZero(); - IndexVector repfnz(maxpanel); - IndexVector panel_lsub(maxpanel); - IndexVector xprune(n); xprune.setZero(); - IndexVector marker(m*internal::LUNoMarker); marker.setZero(); - - repfnz.setConstant(-1); - panel_lsub.setConstant(-1); - - // Set up pointers for scalar working arrays - ScalarVector dense; - dense.setZero(maxpanel); - ScalarVector tempv; - tempv.setZero(internal::LUnumTempV(m, m_perfv.panel_size, m_perfv.maxsuper, /*m_perfv.rowblk*/m) ); - - // Compute the inverse of perm_c - PermutationType iperm_c(m_perm_c.inverse()); - - // Identify initial relaxed snodes - IndexVector relax_end(n); - if ( m_symmetricmode == true ) - Base::heap_relax_snode(n, m_etree, m_perfv.relax, marker, relax_end); - else - Base::relax_snode(n, m_etree, m_perfv.relax, marker, relax_end); - - - m_perm_r.resize(m); - m_perm_r.indices().setConstant(-1); - marker.setConstant(-1); - m_detPermR = 1; // Record the determinant of the row permutation - - m_glu.supno(0) = emptyIdxLU; m_glu.xsup.setConstant(0); - m_glu.xsup(0) = m_glu.xlsub(0) = m_glu.xusub(0) = m_glu.xlusup(0) = Index(0); - - // Work on one 'panel' at a time. A panel is one of the following : - // (a) a relaxed supernode at the bottom of the etree, or - // (b) panel_size contiguous columns, <panel_size> defined by the user - Index jcol; - IndexVector panel_histo(n); - Index pivrow; // Pivotal row number in the original row matrix - Index nseg1; // Number of segments in U-column above panel row jcol - Index nseg; // Number of segments in each U-column - Index irep; - Index i, k, jj; - for (jcol = 0; jcol < n; ) - { - // Adjust panel size so that a panel won't overlap with the next relaxed snode. - Index panel_size = m_perfv.panel_size; // upper bound on panel width - for (k = jcol + 1; k < (std::min)(jcol+panel_size, n); k++) - { - if (relax_end(k) != emptyIdxLU) - { - panel_size = k - jcol; - break; - } - } - if (k == n) - panel_size = n - jcol; - - // Symbolic outer factorization on a panel of columns - Base::panel_dfs(m, panel_size, jcol, m_mat, m_perm_r.indices(), nseg1, dense, panel_lsub, segrep, repfnz, xprune, marker, parent, xplore, m_glu); - - // Numeric sup-panel updates in topological order - Base::panel_bmod(m, panel_size, jcol, nseg1, dense, tempv, segrep, repfnz, m_glu); - - // Sparse LU within the panel, and below the panel diagonal - for ( jj = jcol; jj< jcol + panel_size; jj++) - { - k = (jj - jcol) * m; // Column index for w-wide arrays - - nseg = nseg1; // begin after all the panel segments - //Depth-first-search for the current column - VectorBlock<IndexVector> panel_lsubk(panel_lsub, k, m); - VectorBlock<IndexVector> repfnz_k(repfnz, k, m); - info = Base::column_dfs(m, jj, m_perm_r.indices(), m_perfv.maxsuper, nseg, panel_lsubk, segrep, repfnz_k, xprune, marker, parent, xplore, m_glu); - if ( info ) - { - m_lastError = "UNABLE TO EXPAND MEMORY IN COLUMN_DFS() "; - m_info = NumericalIssue; - m_factorizationIsOk = false; - return; - } - // Numeric updates to this column - VectorBlock<ScalarVector> dense_k(dense, k, m); - VectorBlock<IndexVector> segrep_k(segrep, nseg1, m-nseg1); - info = Base::column_bmod(jj, (nseg - nseg1), dense_k, tempv, segrep_k, repfnz_k, jcol, m_glu); - if ( info ) - { - m_lastError = "UNABLE TO EXPAND MEMORY IN COLUMN_BMOD() "; - m_info = NumericalIssue; - m_factorizationIsOk = false; - return; - } - - // Copy the U-segments to ucol(*) - info = Base::copy_to_ucol(jj, nseg, segrep, repfnz_k ,m_perm_r.indices(), dense_k, m_glu); - if ( info ) - { - m_lastError = "UNABLE TO EXPAND MEMORY IN COPY_TO_UCOL() "; - m_info = NumericalIssue; - m_factorizationIsOk = false; - return; - } - - // Form the L-segment - info = Base::pivotL(jj, m_diagpivotthresh, m_perm_r.indices(), iperm_c.indices(), pivrow, m_glu); - if ( info ) - { - m_lastError = "THE MATRIX IS STRUCTURALLY SINGULAR ... ZERO COLUMN AT "; - std::ostringstream returnInfo; - returnInfo << info; - m_lastError += returnInfo.str(); - m_info = NumericalIssue; - m_factorizationIsOk = false; - return; - } - - // Update the determinant of the row permutation matrix - if (pivrow != jj) m_detPermR *= -1; - - // Prune columns (0:jj-1) using column jj - Base::pruneL(jj, m_perm_r.indices(), pivrow, nseg, segrep, repfnz_k, xprune, m_glu); - - // Reset repfnz for this column - for (i = 0; i < nseg; i++) - { - irep = segrep(i); - repfnz_k(irep) = emptyIdxLU; - } - } // end SparseLU within the panel - jcol += panel_size; // Move to the next panel - } // end for -- end elimination - - // Count the number of nonzeros in factors - Base::countnz(n, m_nnzL, m_nnzU, m_glu); - // Apply permutation to the L subscripts - Base::fixupL(n, m_perm_r.indices(), m_glu); - - // Create supernode matrix L - m_Lstore.setInfos(m, n, m_glu.lusup, m_glu.xlusup, m_glu.lsub, m_glu.xlsub, m_glu.supno, m_glu.xsup); - // Create the column major upper sparse matrix U; - new (&m_Ustore) MappedSparseMatrix<Scalar, ColMajor, Index> ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() ); - - m_info = Success; - m_factorizationIsOk = true; -} - -template<typename MappedSupernodalType> -struct SparseLUMatrixLReturnType : internal::no_assignment_operator -{ - typedef typename MappedSupernodalType::Index Index; - typedef typename MappedSupernodalType::Scalar Scalar; - SparseLUMatrixLReturnType(const MappedSupernodalType& mapL) : m_mapL(mapL) - { } - Index rows() { return m_mapL.rows(); } - Index cols() { return m_mapL.cols(); } - template<typename Dest> - void solveInPlace( MatrixBase<Dest> &X) const - { - m_mapL.solveInPlace(X); - } - const MappedSupernodalType& m_mapL; -}; - -template<typename MatrixLType, typename MatrixUType> -struct SparseLUMatrixUReturnType : internal::no_assignment_operator -{ - typedef typename MatrixLType::Index Index; - typedef typename MatrixLType::Scalar Scalar; - SparseLUMatrixUReturnType(const MatrixLType& mapL, const MatrixUType& mapU) - : m_mapL(mapL),m_mapU(mapU) - { } - Index rows() { return m_mapL.rows(); } - Index cols() { return m_mapL.cols(); } - - template<typename Dest> void solveInPlace(MatrixBase<Dest> &X) const - { - Index nrhs = X.cols(); - Index n = X.rows(); - // Backward solve with U - for (Index k = m_mapL.nsuper(); k >= 0; k--) - { - Index fsupc = m_mapL.supToCol()[k]; - Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension - Index nsupc = m_mapL.supToCol()[k+1] - fsupc; - Index luptr = m_mapL.colIndexPtr()[fsupc]; - - if (nsupc == 1) - { - for (Index j = 0; j < nrhs; j++) - { - X(fsupc, j) /= m_mapL.valuePtr()[luptr]; - } - } - else - { - Map<const Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) ); - Map< Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) ); - U = A.template triangularView<Upper>().solve(U); - } - - for (Index j = 0; j < nrhs; ++j) - { - for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++) - { - typename MatrixUType::InnerIterator it(m_mapU, jcol); - for ( ; it; ++it) - { - Index irow = it.index(); - X(irow, j) -= X(jcol, j) * it.value(); - } - } - } - } // End For U-solve - } - const MatrixLType& m_mapL; - const MatrixUType& m_mapU; -}; - -namespace internal { - -template<typename _MatrixType, typename Derived, typename Rhs> -struct solve_retval<SparseLU<_MatrixType,Derived>, Rhs> - : solve_retval_base<SparseLU<_MatrixType,Derived>, Rhs> -{ - typedef SparseLU<_MatrixType,Derived> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -template<typename _MatrixType, typename Derived, typename Rhs> -struct sparse_solve_retval<SparseLU<_MatrixType,Derived>, Rhs> - : sparse_solve_retval_base<SparseLU<_MatrixType,Derived>, Rhs> -{ - typedef SparseLU<_MatrixType,Derived> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - this->defaultEvalTo(dst); - } -}; -} // end namespace internal - -} // End namespace Eigen - -#endif diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLUImpl.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLUImpl.h deleted file mode 100644 index 14d70897df..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLUImpl.h +++ /dev/null @@ -1,64 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef SPARSELU_IMPL_H -#define SPARSELU_IMPL_H - -namespace Eigen { -namespace internal { - -/** \ingroup SparseLU_Module - * \class SparseLUImpl - * Base class for sparseLU - */ -template <typename Scalar, typename Index> -class SparseLUImpl -{ - public: - typedef Matrix<Scalar,Dynamic,1> ScalarVector; - typedef Matrix<Index,Dynamic,1> IndexVector; - typedef typename ScalarVector::RealScalar RealScalar; - typedef Ref<Matrix<Scalar,Dynamic,1> > BlockScalarVector; - typedef Ref<Matrix<Index,Dynamic,1> > BlockIndexVector; - typedef LU_GlobalLU_t<IndexVector, ScalarVector> GlobalLU_t; - typedef SparseMatrix<Scalar,ColMajor,Index> MatrixType; - - protected: - template <typename VectorType> - Index expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions); - Index memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size, GlobalLU_t& glu); - template <typename VectorType> - Index memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions); - void heap_relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end); - void relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end); - Index snode_dfs(const Index jcol, const Index kcol,const MatrixType& mat, IndexVector& xprune, IndexVector& marker, GlobalLU_t& glu); - Index snode_bmod (const Index jcol, const Index fsupc, ScalarVector& dense, GlobalLU_t& glu); - Index pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow, GlobalLU_t& glu); - template <typename Traits> - void dfs_kernel(const Index jj, IndexVector& perm_r, - Index& nseg, IndexVector& panel_lsub, IndexVector& segrep, - Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent, - IndexVector& xplore, GlobalLU_t& glu, Index& nextl_col, Index krow, Traits& traits); - void panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu); - - void panel_bmod(const Index m, const Index w, const Index jcol, const Index nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu); - Index column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg, BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu); - Index column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv, BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu); - Index copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep, BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu); - void pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg, const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu); - void countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu); - void fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu); - - template<typename , typename > - friend struct column_dfs_traits; -}; - -} // end namespace internal -} // namespace Eigen - -#endif diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h deleted file mode 100644 index 1ffa7d54e9..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Memory.h +++ /dev/null @@ -1,227 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* - - * NOTE: This file is the modified version of [s,d,c,z]memory.c files in SuperLU - - * -- SuperLU routine (version 3.1) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * August 1, 2008 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ - -#ifndef EIGEN_SPARSELU_MEMORY -#define EIGEN_SPARSELU_MEMORY - -namespace Eigen { -namespace internal { - -enum { LUNoMarker = 3 }; -enum {emptyIdxLU = -1}; -template<typename Index> -inline Index LUnumTempV(Index& m, Index& w, Index& t, Index& b) -{ - return (std::max)(m, (t+b)*w); -} - -template< typename Scalar, typename Index> -inline Index LUTempSpace(Index&m, Index& w) -{ - return (2*w + 4 + LUNoMarker) * m * sizeof(Index) + (w + 1) * m * sizeof(Scalar); -} - - - - -/** - * Expand the existing storage to accomodate more fill-ins - * \param vec Valid pointer to the vector to allocate or expand - * \param[in,out] length At input, contain the current length of the vector that is to be increased. At output, length of the newly allocated vector - * \param[in] nbElts Current number of elements in the factors - * \param keep_prev 1: use length and do not expand the vector; 0: compute new_len and expand - * \param[in,out] num_expansions Number of times the memory has been expanded - */ -template <typename Scalar, typename Index> -template <typename VectorType> -Index SparseLUImpl<Scalar,Index>::expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions) -{ - - float alpha = 1.5; // Ratio of the memory increase - Index new_len; // New size of the allocated memory - - if(num_expansions == 0 || keep_prev) - new_len = length ; // First time allocate requested - else - new_len = (std::max)(length+1,Index(alpha * length)); - - VectorType old_vec; // Temporary vector to hold the previous values - if (nbElts > 0 ) - old_vec = vec.segment(0,nbElts); - - //Allocate or expand the current vector -#ifdef EIGEN_EXCEPTIONS - try -#endif - { - vec.resize(new_len); - } -#ifdef EIGEN_EXCEPTIONS - catch(std::bad_alloc& ) -#else - if(!vec.size()) -#endif - { - if (!num_expansions) - { - // First time to allocate from LUMemInit() - // Let LUMemInit() deals with it. - return -1; - } - if (keep_prev) - { - // In this case, the memory length should not not be reduced - return new_len; - } - else - { - // Reduce the size and increase again - Index tries = 0; // Number of attempts - do - { - alpha = (alpha + 1)/2; - new_len = (std::max)(length+1,Index(alpha * length)); -#ifdef EIGEN_EXCEPTIONS - try -#endif - { - vec.resize(new_len); - } -#ifdef EIGEN_EXCEPTIONS - catch(std::bad_alloc& ) -#else - if (!vec.size()) -#endif - { - tries += 1; - if ( tries > 10) return new_len; - } - } while (!vec.size()); - } - } - //Copy the previous values to the newly allocated space - if (nbElts > 0) - vec.segment(0, nbElts) = old_vec; - - - length = new_len; - if(num_expansions) ++num_expansions; - return 0; -} - -/** - * \brief Allocate various working space for the numerical factorization phase. - * \param m number of rows of the input matrix - * \param n number of columns - * \param annz number of initial nonzeros in the matrix - * \param lwork if lwork=-1, this routine returns an estimated size of the required memory - * \param glu persistent data to facilitate multiple factors : will be deleted later ?? - * \param fillratio estimated ratio of fill in the factors - * \param panel_size Size of a panel - * \return an estimated size of the required memory if lwork = -1; otherwise, return the size of actually allocated memory when allocation failed, and 0 on success - * \note Unlike SuperLU, this routine does not support successive factorization with the same pattern and the same row permutation - */ -template <typename Scalar, typename Index> -Index SparseLUImpl<Scalar,Index>::memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size, GlobalLU_t& glu) -{ - Index& num_expansions = glu.num_expansions; //No memory expansions so far - num_expansions = 0; - glu.nzumax = glu.nzlumax = (std::min)(fillratio * annz / n, m) * n; // estimated number of nonzeros in U - glu.nzlmax = (std::max)(Index(4), fillratio) * annz / 4; // estimated nnz in L factor - // Return the estimated size to the user if necessary - Index tempSpace; - tempSpace = (2*panel_size + 4 + LUNoMarker) * m * sizeof(Index) + (panel_size + 1) * m * sizeof(Scalar); - if (lwork == emptyIdxLU) - { - Index estimated_size; - estimated_size = (5 * n + 5) * sizeof(Index) + tempSpace - + (glu.nzlmax + glu.nzumax) * sizeof(Index) + (glu.nzlumax+glu.nzumax) * sizeof(Scalar) + n; - return estimated_size; - } - - // Setup the required space - - // First allocate Integer pointers for L\U factors - glu.xsup.resize(n+1); - glu.supno.resize(n+1); - glu.xlsub.resize(n+1); - glu.xlusup.resize(n+1); - glu.xusub.resize(n+1); - - // Reserve memory for L/U factors - do - { - if( (expand<ScalarVector>(glu.lusup, glu.nzlumax, 0, 0, num_expansions)<0) - || (expand<ScalarVector>(glu.ucol, glu.nzumax, 0, 0, num_expansions)<0) - || (expand<IndexVector> (glu.lsub, glu.nzlmax, 0, 0, num_expansions)<0) - || (expand<IndexVector> (glu.usub, glu.nzumax, 0, 1, num_expansions)<0) ) - { - //Reduce the estimated size and retry - glu.nzlumax /= 2; - glu.nzumax /= 2; - glu.nzlmax /= 2; - if (glu.nzlumax < annz ) return glu.nzlumax; - } - } while (!glu.lusup.size() || !glu.ucol.size() || !glu.lsub.size() || !glu.usub.size()); - - ++num_expansions; - return 0; - -} // end LuMemInit - -/** - * \brief Expand the existing storage - * \param vec vector to expand - * \param[in,out] maxlen On input, previous size of vec (Number of elements to copy ). on output, new size - * \param nbElts current number of elements in the vector. - * \param memtype Type of the element to expand - * \param num_expansions Number of expansions - * \return 0 on success, > 0 size of the memory allocated so far - */ -template <typename Scalar, typename Index> -template <typename VectorType> -Index SparseLUImpl<Scalar,Index>::memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions) -{ - Index failed_size; - if (memtype == USUB) - failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 1, num_expansions); - else - failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 0, num_expansions); - - if (failed_size) - return failed_size; - - return 0 ; -} - -} // end namespace internal - -} // end namespace Eigen -#endif // EIGEN_SPARSELU_MEMORY diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h deleted file mode 100644 index 24d6bf1794..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Structs.h +++ /dev/null @@ -1,111 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* - * NOTE: This file comes from a partly modified version of files slu_[s,d,c,z]defs.h - * -- SuperLU routine (version 4.1) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * November, 2010 - * - * Global data structures used in LU factorization - - * - * nsuper: #supernodes = nsuper + 1, numbered [0, nsuper]. - * (xsup,supno): supno[i] is the supernode no to which i belongs; - * xsup(s) points to the beginning of the s-th supernode. - * e.g. supno 0 1 2 2 3 3 3 4 4 4 4 4 (n=12) - * xsup 0 1 2 4 7 12 - * Note: dfs will be performed on supernode rep. relative to the new - * row pivoting ordering - * - * (xlsub,lsub): lsub[*] contains the compressed subscript of - * rectangular supernodes; xlsub[j] points to the starting - * location of the j-th column in lsub[*]. Note that xlsub - * is indexed by column. - * Storage: original row subscripts - * - * During the course of sparse LU factorization, we also use - * (xlsub,lsub) for the purpose of symmetric pruning. For each - * supernode {s,s+1,...,t=s+r} with first column s and last - * column t, the subscript set - * lsub[j], j=xlsub[s], .., xlsub[s+1]-1 - * is the structure of column s (i.e. structure of this supernode). - * It is used for the storage of numerical values. - * Furthermore, - * lsub[j], j=xlsub[t], .., xlsub[t+1]-1 - * is the structure of the last column t of this supernode. - * It is for the purpose of symmetric pruning. Therefore, the - * structural subscripts can be rearranged without making physical - * interchanges among the numerical values. - * - * However, if the supernode has only one column, then we - * only keep one set of subscripts. For any subscript interchange - * performed, similar interchange must be done on the numerical - * values. - * - * The last column structures (for pruning) will be removed - * after the numercial LU factorization phase. - * - * (xlusup,lusup): lusup[*] contains the numerical values of the - * rectangular supernodes; xlusup[j] points to the starting - * location of the j-th column in storage vector lusup[*] - * Note: xlusup is indexed by column. - * Each rectangular supernode is stored by column-major - * scheme, consistent with Fortran 2-dim array storage. - * - * (xusub,ucol,usub): ucol[*] stores the numerical values of - * U-columns outside the rectangular supernodes. The row - * subscript of nonzero ucol[k] is stored in usub[k]. - * xusub[i] points to the starting location of column i in ucol. - * Storage: new row subscripts; that is subscripts of PA. - */ - -#ifndef EIGEN_LU_STRUCTS -#define EIGEN_LU_STRUCTS -namespace Eigen { -namespace internal { - -typedef enum {LUSUP, UCOL, LSUB, USUB, LLVL, ULVL} MemType; - -template <typename IndexVector, typename ScalarVector> -struct LU_GlobalLU_t { - typedef typename IndexVector::Scalar Index; - IndexVector xsup; //First supernode column ... xsup(s) points to the beginning of the s-th supernode - IndexVector supno; // Supernode number corresponding to this column (column to supernode mapping) - ScalarVector lusup; // nonzero values of L ordered by columns - IndexVector lsub; // Compressed row indices of L rectangular supernodes. - IndexVector xlusup; // pointers to the beginning of each column in lusup - IndexVector xlsub; // pointers to the beginning of each column in lsub - Index nzlmax; // Current max size of lsub - Index nzlumax; // Current max size of lusup - ScalarVector ucol; // nonzero values of U ordered by columns - IndexVector usub; // row indices of U columns in ucol - IndexVector xusub; // Pointers to the beginning of each column of U in ucol - Index nzumax; // Current max size of ucol - Index n; // Number of columns in the matrix - Index num_expansions; -}; - -// Values to set for performance -template <typename Index> -struct perfvalues { - Index panel_size; // a panel consists of at most <panel_size> consecutive columns - Index relax; // To control degree of relaxing supernodes. If the number of nodes (columns) - // in a subtree of the elimination tree is less than relax, this subtree is considered - // as one supernode regardless of the row structures of those columns - Index maxsuper; // The maximum size for a supernode in complete LU - Index rowblk; // The minimum row dimension for 2-D blocking to be used; - Index colblk; // The minimum column dimension for 2-D blocking to be used; - Index fillfactor; // The estimated fills factors for L and U, compared with A -}; - -} // end namespace internal - -} // end namespace Eigen -#endif // EIGEN_LU_STRUCTS diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h deleted file mode 100644 index ad6f2183fe..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h +++ /dev/null @@ -1,298 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSELU_SUPERNODAL_MATRIX_H -#define EIGEN_SPARSELU_SUPERNODAL_MATRIX_H - -namespace Eigen { -namespace internal { - -/** \ingroup SparseLU_Module - * \brief a class to manipulate the L supernodal factor from the SparseLU factorization - * - * This class contain the data to easily store - * and manipulate the supernodes during the factorization and solution phase of Sparse LU. - * Only the lower triangular matrix has supernodes. - * - * NOTE : This class corresponds to the SCformat structure in SuperLU - * - */ -/* TODO - * InnerIterator as for sparsematrix - * SuperInnerIterator to iterate through all supernodes - * Function for triangular solve - */ -template <typename _Scalar, typename _Index> -class MappedSuperNodalMatrix -{ - public: - typedef _Scalar Scalar; - typedef _Index Index; - typedef Matrix<Index,Dynamic,1> IndexVector; - typedef Matrix<Scalar,Dynamic,1> ScalarVector; - public: - MappedSuperNodalMatrix() - { - - } - MappedSuperNodalMatrix(Index m, Index n, ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind, - IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col ) - { - setInfos(m, n, nzval, nzval_colptr, rowind, rowind_colptr, col_to_sup, sup_to_col); - } - - ~MappedSuperNodalMatrix() - { - - } - /** - * Set appropriate pointers for the lower triangular supernodal matrix - * These infos are available at the end of the numerical factorization - * FIXME This class will be modified such that it can be use in the course - * of the factorization. - */ - void setInfos(Index m, Index n, ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind, - IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col ) - { - m_row = m; - m_col = n; - m_nzval = nzval.data(); - m_nzval_colptr = nzval_colptr.data(); - m_rowind = rowind.data(); - m_rowind_colptr = rowind_colptr.data(); - m_nsuper = col_to_sup(n); - m_col_to_sup = col_to_sup.data(); - m_sup_to_col = sup_to_col.data(); - } - - /** - * Number of rows - */ - Index rows() { return m_row; } - - /** - * Number of columns - */ - Index cols() { return m_col; } - - /** - * Return the array of nonzero values packed by column - * - * The size is nnz - */ - Scalar* valuePtr() { return m_nzval; } - - const Scalar* valuePtr() const - { - return m_nzval; - } - /** - * Return the pointers to the beginning of each column in \ref valuePtr() - */ - Index* colIndexPtr() - { - return m_nzval_colptr; - } - - const Index* colIndexPtr() const - { - return m_nzval_colptr; - } - - /** - * Return the array of compressed row indices of all supernodes - */ - Index* rowIndex() { return m_rowind; } - - const Index* rowIndex() const - { - return m_rowind; - } - - /** - * Return the location in \em rowvaluePtr() which starts each column - */ - Index* rowIndexPtr() { return m_rowind_colptr; } - - const Index* rowIndexPtr() const - { - return m_rowind_colptr; - } - - /** - * Return the array of column-to-supernode mapping - */ - Index* colToSup() { return m_col_to_sup; } - - const Index* colToSup() const - { - return m_col_to_sup; - } - /** - * Return the array of supernode-to-column mapping - */ - Index* supToCol() { return m_sup_to_col; } - - const Index* supToCol() const - { - return m_sup_to_col; - } - - /** - * Return the number of supernodes - */ - Index nsuper() const - { - return m_nsuper; - } - - class InnerIterator; - template<typename Dest> - void solveInPlace( MatrixBase<Dest>&X) const; - - - - - protected: - Index m_row; // Number of rows - Index m_col; // Number of columns - Index m_nsuper; // Number of supernodes - Scalar* m_nzval; //array of nonzero values packed by column - Index* m_nzval_colptr; //nzval_colptr[j] Stores the location in nzval[] which starts column j - Index* m_rowind; // Array of compressed row indices of rectangular supernodes - Index* m_rowind_colptr; //rowind_colptr[j] stores the location in rowind[] which starts column j - Index* m_col_to_sup; // col_to_sup[j] is the supernode number to which column j belongs - Index* m_sup_to_col; //sup_to_col[s] points to the starting column of the s-th supernode - - private : -}; - -/** - * \brief InnerIterator class to iterate over nonzero values of the current column in the supernodal matrix L - * - */ -template<typename Scalar, typename Index> -class MappedSuperNodalMatrix<Scalar,Index>::InnerIterator -{ - public: - InnerIterator(const MappedSuperNodalMatrix& mat, Index outer) - : m_matrix(mat), - m_outer(outer), - m_supno(mat.colToSup()[outer]), - m_idval(mat.colIndexPtr()[outer]), - m_startidval(m_idval), - m_endidval(mat.colIndexPtr()[outer+1]), - m_idrow(mat.rowIndexPtr()[outer]), - m_endidrow(mat.rowIndexPtr()[outer+1]) - {} - inline InnerIterator& operator++() - { - m_idval++; - m_idrow++; - return *this; - } - inline Scalar value() const { return m_matrix.valuePtr()[m_idval]; } - - inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_idval]); } - - inline Index index() const { return m_matrix.rowIndex()[m_idrow]; } - inline Index row() const { return index(); } - inline Index col() const { return m_outer; } - - inline Index supIndex() const { return m_supno; } - - inline operator bool() const - { - return ( (m_idval < m_endidval) && (m_idval >= m_startidval) - && (m_idrow < m_endidrow) ); - } - - protected: - const MappedSuperNodalMatrix& m_matrix; // Supernodal lower triangular matrix - const Index m_outer; // Current column - const Index m_supno; // Current SuperNode number - Index m_idval; // Index to browse the values in the current column - const Index m_startidval; // Start of the column value - const Index m_endidval; // End of the column value - Index m_idrow; // Index to browse the row indices - Index m_endidrow; // End index of row indices of the current column -}; - -/** - * \brief Solve with the supernode triangular matrix - * - */ -template<typename Scalar, typename Index> -template<typename Dest> -void MappedSuperNodalMatrix<Scalar,Index>::solveInPlace( MatrixBase<Dest>&X) const -{ - Index n = X.rows(); - Index nrhs = X.cols(); - const Scalar * Lval = valuePtr(); // Nonzero values - Matrix<Scalar,Dynamic,Dynamic> work(n, nrhs); // working vector - work.setZero(); - for (Index k = 0; k <= nsuper(); k ++) - { - Index fsupc = supToCol()[k]; // First column of the current supernode - Index istart = rowIndexPtr()[fsupc]; // Pointer index to the subscript of the current column - Index nsupr = rowIndexPtr()[fsupc+1] - istart; // Number of rows in the current supernode - Index nsupc = supToCol()[k+1] - fsupc; // Number of columns in the current supernode - Index nrow = nsupr - nsupc; // Number of rows in the non-diagonal part of the supernode - Index irow; //Current index row - - if (nsupc == 1 ) - { - for (Index j = 0; j < nrhs; j++) - { - InnerIterator it(*this, fsupc); - ++it; // Skip the diagonal element - for (; it; ++it) - { - irow = it.row(); - X(irow, j) -= X(fsupc, j) * it.value(); - } - } - } - else - { - // The supernode has more than one column - Index luptr = colIndexPtr()[fsupc]; - Index lda = colIndexPtr()[fsupc+1] - luptr; - - // Triangular solve - Map<const Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) ); - Map< Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) ); - U = A.template triangularView<UnitLower>().solve(U); - - // Matrix-vector product - new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > ( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) ); - work.block(0, 0, nrow, nrhs) = A * U; - - //Begin Scatter - for (Index j = 0; j < nrhs; j++) - { - Index iptr = istart + nsupc; - for (Index i = 0; i < nrow; i++) - { - irow = rowIndex()[iptr]; - X(irow, j) -= work(i, j); // Scatter operation - work(i, j) = Scalar(0); - iptr++; - } - } - } - } -} - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_SPARSELU_MATRIX_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h deleted file mode 100644 index 15352ac33a..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_Utils.h +++ /dev/null @@ -1,80 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -#ifndef EIGEN_SPARSELU_UTILS_H -#define EIGEN_SPARSELU_UTILS_H - -namespace Eigen { -namespace internal { - -/** - * \brief Count Nonzero elements in the factors - */ -template <typename Scalar, typename Index> -void SparseLUImpl<Scalar,Index>::countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu) -{ - nnzL = 0; - nnzU = (glu.xusub)(n); - Index nsuper = (glu.supno)(n); - Index jlen; - Index i, j, fsupc; - if (n <= 0 ) return; - // For each supernode - for (i = 0; i <= nsuper; i++) - { - fsupc = glu.xsup(i); - jlen = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); - - for (j = fsupc; j < glu.xsup(i+1); j++) - { - nnzL += jlen; - nnzU += j - fsupc + 1; - jlen--; - } - } -} - -/** - * \brief Fix up the data storage lsub for L-subscripts. - * - * It removes the subscripts sets for structural pruning, - * and applies permutation to the remaining subscripts - * - */ -template <typename Scalar, typename Index> -void SparseLUImpl<Scalar,Index>::fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu) -{ - Index fsupc, i, j, k, jstart; - - Index nextl = 0; - Index nsuper = (glu.supno)(n); - - // For each supernode - for (i = 0; i <= nsuper; i++) - { - fsupc = glu.xsup(i); - jstart = glu.xlsub(fsupc); - glu.xlsub(fsupc) = nextl; - for (j = jstart; j < glu.xlsub(fsupc + 1); j++) - { - glu.lsub(nextl) = perm_r(glu.lsub(j)); // Now indexed into P*A - nextl++; - } - for (k = fsupc+1; k < glu.xsup(i+1); k++) - glu.xlsub(k) = nextl; // other columns in supernode i - } - - glu.xlsub(n) = nextl; -} - -} // end namespace internal - -} // end namespace Eigen -#endif // EIGEN_SPARSELU_UTILS_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h deleted file mode 100644 index f24bd87d3e..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_column_bmod.h +++ /dev/null @@ -1,180 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* - - * NOTE: This file is the modified version of xcolumn_bmod.c file in SuperLU - - * -- SuperLU routine (version 3.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * October 15, 2003 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ -#ifndef SPARSELU_COLUMN_BMOD_H -#define SPARSELU_COLUMN_BMOD_H - -namespace Eigen { - -namespace internal { -/** - * \brief Performs numeric block updates (sup-col) in topological order - * - * \param jcol current column to update - * \param nseg Number of segments in the U part - * \param dense Store the full representation of the column - * \param tempv working array - * \param segrep segment representative ... - * \param repfnz ??? First nonzero column in each row ??? ... - * \param fpanelc First column in the current panel - * \param glu Global LU data. - * \return 0 - successful return - * > 0 - number of bytes allocated when run out of space - * - */ -template <typename Scalar, typename Index> -Index SparseLUImpl<Scalar,Index>::column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv, BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu) -{ - Index jsupno, k, ksub, krep, ksupno; - Index lptr, nrow, isub, irow, nextlu, new_next, ufirst; - Index fsupc, nsupc, nsupr, luptr, kfnz, no_zeros; - /* krep = representative of current k-th supernode - * fsupc = first supernodal column - * nsupc = number of columns in a supernode - * nsupr = number of rows in a supernode - * luptr = location of supernodal LU-block in storage - * kfnz = first nonz in the k-th supernodal segment - * no_zeros = no lf leading zeros in a supernodal U-segment - */ - - jsupno = glu.supno(jcol); - // For each nonzero supernode segment of U[*,j] in topological order - k = nseg - 1; - Index d_fsupc; // distance between the first column of the current panel and the - // first column of the current snode - Index fst_col; // First column within small LU update - Index segsize; - for (ksub = 0; ksub < nseg; ksub++) - { - krep = segrep(k); k--; - ksupno = glu.supno(krep); - if (jsupno != ksupno ) - { - // outside the rectangular supernode - fsupc = glu.xsup(ksupno); - fst_col = (std::max)(fsupc, fpanelc); - - // Distance from the current supernode to the current panel; - // d_fsupc = 0 if fsupc > fpanelc - d_fsupc = fst_col - fsupc; - - luptr = glu.xlusup(fst_col) + d_fsupc; - lptr = glu.xlsub(fsupc) + d_fsupc; - - kfnz = repfnz(krep); - kfnz = (std::max)(kfnz, fpanelc); - - segsize = krep - kfnz + 1; - nsupc = krep - fst_col + 1; - nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); - nrow = nsupr - d_fsupc - nsupc; - Index lda = glu.xlusup(fst_col+1) - glu.xlusup(fst_col); - - - // Perform a triangular solver and block update, - // then scatter the result of sup-col update to dense - no_zeros = kfnz - fst_col; - if(segsize==1) - LU_kernel_bmod<1>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); - else - LU_kernel_bmod<Dynamic>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); - } // end if jsupno - } // end for each segment - - // Process the supernodal portion of L\U[*,j] - nextlu = glu.xlusup(jcol); - fsupc = glu.xsup(jsupno); - - // copy the SPA dense into L\U[*,j] - Index mem; - new_next = nextlu + glu.xlsub(fsupc + 1) - glu.xlsub(fsupc); - Index offset = internal::first_multiple<Index>(new_next, internal::packet_traits<Scalar>::size) - new_next; - if(offset) - new_next += offset; - while (new_next > glu.nzlumax ) - { - mem = memXpand<ScalarVector>(glu.lusup, glu.nzlumax, nextlu, LUSUP, glu.num_expansions); - if (mem) return mem; - } - - for (isub = glu.xlsub(fsupc); isub < glu.xlsub(fsupc+1); isub++) - { - irow = glu.lsub(isub); - glu.lusup(nextlu) = dense(irow); - dense(irow) = Scalar(0.0); - ++nextlu; - } - - if(offset) - { - glu.lusup.segment(nextlu,offset).setZero(); - nextlu += offset; - } - glu.xlusup(jcol + 1) = nextlu; // close L\U(*,jcol); - - /* For more updates within the panel (also within the current supernode), - * should start from the first column of the panel, or the first column - * of the supernode, whichever is bigger. There are two cases: - * 1) fsupc < fpanelc, then fst_col <-- fpanelc - * 2) fsupc >= fpanelc, then fst_col <-- fsupc - */ - fst_col = (std::max)(fsupc, fpanelc); - - if (fst_col < jcol) - { - // Distance between the current supernode and the current panel - // d_fsupc = 0 if fsupc >= fpanelc - d_fsupc = fst_col - fsupc; - - lptr = glu.xlsub(fsupc) + d_fsupc; - luptr = glu.xlusup(fst_col) + d_fsupc; - nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); // leading dimension - nsupc = jcol - fst_col; // excluding jcol - nrow = nsupr - d_fsupc - nsupc; - - // points to the beginning of jcol in snode L\U(jsupno) - ufirst = glu.xlusup(jcol) + d_fsupc; - Index lda = glu.xlusup(jcol+1) - glu.xlusup(jcol); - Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(glu.lusup.data()[luptr]), nsupc, nsupc, OuterStride<>(lda) ); - VectorBlock<ScalarVector> u(glu.lusup, ufirst, nsupc); - u = A.template triangularView<UnitLower>().solve(u); - - new (&A) Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > ( &(glu.lusup.data()[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) ); - VectorBlock<ScalarVector> l(glu.lusup, ufirst+nsupc, nrow); - l.noalias() -= A * u; - - } // End if fst_col - return 0; -} - -} // end namespace internal -} // end namespace Eigen - -#endif // SPARSELU_COLUMN_BMOD_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h deleted file mode 100644 index 4c04b0e44e..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_column_dfs.h +++ /dev/null @@ -1,177 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* - - * NOTE: This file is the modified version of [s,d,c,z]column_dfs.c file in SuperLU - - * -- SuperLU routine (version 2.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * November 15, 1997 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ -#ifndef SPARSELU_COLUMN_DFS_H -#define SPARSELU_COLUMN_DFS_H - -template <typename Scalar, typename Index> class SparseLUImpl; -namespace Eigen { - -namespace internal { - -template<typename IndexVector, typename ScalarVector> -struct column_dfs_traits : no_assignment_operator -{ - typedef typename ScalarVector::Scalar Scalar; - typedef typename IndexVector::Scalar Index; - column_dfs_traits(Index jcol, Index& jsuper, typename SparseLUImpl<Scalar, Index>::GlobalLU_t& glu, SparseLUImpl<Scalar, Index>& luImpl) - : m_jcol(jcol), m_jsuper_ref(jsuper), m_glu(glu), m_luImpl(luImpl) - {} - bool update_segrep(Index /*krep*/, Index /*jj*/) - { - return true; - } - void mem_expand(IndexVector& lsub, Index& nextl, Index chmark) - { - if (nextl >= m_glu.nzlmax) - m_luImpl.memXpand(lsub, m_glu.nzlmax, nextl, LSUB, m_glu.num_expansions); - if (chmark != (m_jcol-1)) m_jsuper_ref = emptyIdxLU; - } - enum { ExpandMem = true }; - - Index m_jcol; - Index& m_jsuper_ref; - typename SparseLUImpl<Scalar, Index>::GlobalLU_t& m_glu; - SparseLUImpl<Scalar, Index>& m_luImpl; -}; - - -/** - * \brief Performs a symbolic factorization on column jcol and decide the supernode boundary - * - * A supernode representative is the last column of a supernode. - * The nonzeros in U[*,j] are segments that end at supernodes representatives. - * The routine returns a list of the supernodal representatives - * in topological order of the dfs that generates them. - * The location of the first nonzero in each supernodal segment - * (supernodal entry location) is also returned. - * - * \param m number of rows in the matrix - * \param jcol Current column - * \param perm_r Row permutation - * \param maxsuper Maximum number of column allowed in a supernode - * \param [in,out] nseg Number of segments in current U[*,j] - new segments appended - * \param lsub_col defines the rhs vector to start the dfs - * \param [in,out] segrep Segment representatives - new segments appended - * \param repfnz First nonzero location in each row - * \param xprune - * \param marker marker[i] == jj, if i was visited during dfs of current column jj; - * \param parent - * \param xplore working array - * \param glu global LU data - * \return 0 success - * > 0 number of bytes allocated when run out of space - * - */ -template <typename Scalar, typename Index> -Index SparseLUImpl<Scalar,Index>::column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg, BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu) -{ - - Index jsuper = glu.supno(jcol); - Index nextl = glu.xlsub(jcol); - VectorBlock<IndexVector> marker2(marker, 2*m, m); - - - column_dfs_traits<IndexVector, ScalarVector> traits(jcol, jsuper, glu, *this); - - // For each nonzero in A(*,jcol) do dfs - for (Index k = 0; ((k < m) ? lsub_col[k] != emptyIdxLU : false) ; k++) - { - Index krow = lsub_col(k); - lsub_col(k) = emptyIdxLU; - Index kmark = marker2(krow); - - // krow was visited before, go to the next nonz; - if (kmark == jcol) continue; - - dfs_kernel(jcol, perm_r, nseg, glu.lsub, segrep, repfnz, xprune, marker2, parent, - xplore, glu, nextl, krow, traits); - } // for each nonzero ... - - Index fsupc, jptr, jm1ptr, ito, ifrom, istop; - Index nsuper = glu.supno(jcol); - Index jcolp1 = jcol + 1; - Index jcolm1 = jcol - 1; - - // check to see if j belongs in the same supernode as j-1 - if ( jcol == 0 ) - { // Do nothing for column 0 - nsuper = glu.supno(0) = 0 ; - } - else - { - fsupc = glu.xsup(nsuper); - jptr = glu.xlsub(jcol); // Not yet compressed - jm1ptr = glu.xlsub(jcolm1); - - // Use supernodes of type T2 : see SuperLU paper - if ( (nextl-jptr != jptr-jm1ptr-1) ) jsuper = emptyIdxLU; - - // Make sure the number of columns in a supernode doesn't - // exceed threshold - if ( (jcol - fsupc) >= maxsuper) jsuper = emptyIdxLU; - - /* If jcol starts a new supernode, reclaim storage space in - * glu.lsub from previous supernode. Note we only store - * the subscript set of the first and last columns of - * a supernode. (first for num values, last for pruning) - */ - if (jsuper == emptyIdxLU) - { // starts a new supernode - if ( (fsupc < jcolm1-1) ) - { // >= 3 columns in nsuper - ito = glu.xlsub(fsupc+1); - glu.xlsub(jcolm1) = ito; - istop = ito + jptr - jm1ptr; - xprune(jcolm1) = istop; // intialize xprune(jcol-1) - glu.xlsub(jcol) = istop; - - for (ifrom = jm1ptr; ifrom < nextl; ++ifrom, ++ito) - glu.lsub(ito) = glu.lsub(ifrom); - nextl = ito; // = istop + length(jcol) - } - nsuper++; - glu.supno(jcol) = nsuper; - } // if a new supernode - } // end else: jcol > 0 - - // Tidy up the pointers before exit - glu.xsup(nsuper+1) = jcolp1; - glu.supno(jcolp1) = nsuper; - xprune(jcol) = nextl; // Intialize upper bound for pruning - glu.xlsub(jcolp1) = nextl; - - return 0; -} - -} // end namespace internal - -} // end namespace Eigen - -#endif diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h deleted file mode 100644 index 170610d9f2..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h +++ /dev/null @@ -1,106 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -/* - - * NOTE: This file is the modified version of [s,d,c,z]copy_to_ucol.c file in SuperLU - - * -- SuperLU routine (version 2.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * November 15, 1997 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ -#ifndef SPARSELU_COPY_TO_UCOL_H -#define SPARSELU_COPY_TO_UCOL_H - -namespace Eigen { -namespace internal { - -/** - * \brief Performs numeric block updates (sup-col) in topological order - * - * \param jcol current column to update - * \param nseg Number of segments in the U part - * \param segrep segment representative ... - * \param repfnz First nonzero column in each row ... - * \param perm_r Row permutation - * \param dense Store the full representation of the column - * \param glu Global LU data. - * \return 0 - successful return - * > 0 - number of bytes allocated when run out of space - * - */ -template <typename Scalar, typename Index> -Index SparseLUImpl<Scalar,Index>::copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep, BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu) -{ - Index ksub, krep, ksupno; - - Index jsupno = glu.supno(jcol); - - // For each nonzero supernode segment of U[*,j] in topological order - Index k = nseg - 1, i; - Index nextu = glu.xusub(jcol); - Index kfnz, isub, segsize; - Index new_next,irow; - Index fsupc, mem; - for (ksub = 0; ksub < nseg; ksub++) - { - krep = segrep(k); k--; - ksupno = glu.supno(krep); - if (jsupno != ksupno ) // should go into ucol(); - { - kfnz = repfnz(krep); - if (kfnz != emptyIdxLU) - { // Nonzero U-segment - fsupc = glu.xsup(ksupno); - isub = glu.xlsub(fsupc) + kfnz - fsupc; - segsize = krep - kfnz + 1; - new_next = nextu + segsize; - while (new_next > glu.nzumax) - { - mem = memXpand<ScalarVector>(glu.ucol, glu.nzumax, nextu, UCOL, glu.num_expansions); - if (mem) return mem; - mem = memXpand<IndexVector>(glu.usub, glu.nzumax, nextu, USUB, glu.num_expansions); - if (mem) return mem; - - } - - for (i = 0; i < segsize; i++) - { - irow = glu.lsub(isub); - glu.usub(nextu) = perm_r(irow); // Unlike the L part, the U part is stored in its final order - glu.ucol(nextu) = dense(irow); - dense(irow) = Scalar(0.0); - nextu++; - isub++; - } - - } // end nonzero U-segment - - } // end if jsupno - - } // end for each segment - glu.xusub(jcol + 1) = nextu; // close U(*,jcol) - return 0; -} - -} // namespace internal -} // end namespace Eigen - -#endif // SPARSELU_COPY_TO_UCOL_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h deleted file mode 100644 index 9e4e3e72b7..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_gemm_kernel.h +++ /dev/null @@ -1,279 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSELU_GEMM_KERNEL_H -#define EIGEN_SPARSELU_GEMM_KERNEL_H - -namespace Eigen { - -namespace internal { - - -/** \internal - * A general matrix-matrix product kernel optimized for the SparseLU factorization. - * - A, B, and C must be column major - * - lda and ldc must be multiples of the respective packet size - * - C must have the same alignment as A - */ -template<typename Scalar,typename Index> -EIGEN_DONT_INLINE -void sparselu_gemm(Index m, Index n, Index d, const Scalar* A, Index lda, const Scalar* B, Index ldb, Scalar* C, Index ldc) -{ - using namespace Eigen::internal; - - typedef typename packet_traits<Scalar>::type Packet; - enum { - NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS, - PacketSize = packet_traits<Scalar>::size, - PM = 8, // peeling in M - RN = 2, // register blocking - RK = NumberOfRegisters>=16 ? 4 : 2, // register blocking - BM = 4096/sizeof(Scalar), // number of rows of A-C per chunk - SM = PM*PacketSize // step along M - }; - Index d_end = (d/RK)*RK; // number of columns of A (rows of B) suitable for full register blocking - Index n_end = (n/RN)*RN; // number of columns of B-C suitable for processing RN columns at once - Index i0 = internal::first_aligned(A,m); - - eigen_internal_assert(((lda%PacketSize)==0) && ((ldc%PacketSize)==0) && (i0==internal::first_aligned(C,m))); - - // handle the non aligned rows of A and C without any optimization: - for(Index i=0; i<i0; ++i) - { - for(Index j=0; j<n; ++j) - { - Scalar c = C[i+j*ldc]; - for(Index k=0; k<d; ++k) - c += B[k+j*ldb] * A[i+k*lda]; - C[i+j*ldc] = c; - } - } - // process the remaining rows per chunk of BM rows - for(Index ib=i0; ib<m; ib+=BM) - { - Index actual_b = std::min<Index>(BM, m-ib); // actual number of rows - Index actual_b_end1 = (actual_b/SM)*SM; // actual number of rows suitable for peeling - Index actual_b_end2 = (actual_b/PacketSize)*PacketSize; // actual number of rows suitable for vectorization - - // Let's process two columns of B-C at once - for(Index j=0; j<n_end; j+=RN) - { - const Scalar* Bc0 = B+(j+0)*ldb; - const Scalar* Bc1 = B+(j+1)*ldb; - - for(Index k=0; k<d_end; k+=RK) - { - - // load and expand a RN x RK block of B - Packet b00, b10, b20, b30, b01, b11, b21, b31; - b00 = pset1<Packet>(Bc0[0]); - b10 = pset1<Packet>(Bc0[1]); - if(RK==4) b20 = pset1<Packet>(Bc0[2]); - if(RK==4) b30 = pset1<Packet>(Bc0[3]); - b01 = pset1<Packet>(Bc1[0]); - b11 = pset1<Packet>(Bc1[1]); - if(RK==4) b21 = pset1<Packet>(Bc1[2]); - if(RK==4) b31 = pset1<Packet>(Bc1[3]); - - Packet a0, a1, a2, a3, c0, c1, t0, t1; - - const Scalar* A0 = A+ib+(k+0)*lda; - const Scalar* A1 = A+ib+(k+1)*lda; - const Scalar* A2 = A+ib+(k+2)*lda; - const Scalar* A3 = A+ib+(k+3)*lda; - - Scalar* C0 = C+ib+(j+0)*ldc; - Scalar* C1 = C+ib+(j+1)*ldc; - - a0 = pload<Packet>(A0); - a1 = pload<Packet>(A1); - if(RK==4) - { - a2 = pload<Packet>(A2); - a3 = pload<Packet>(A3); - } - else - { - // workaround "may be used uninitialized in this function" warning - a2 = a3 = a0; - } - -#define KMADD(c, a, b, tmp) {tmp = b; tmp = pmul(a,tmp); c = padd(c,tmp);} -#define WORK(I) \ - c0 = pload<Packet>(C0+i+(I)*PacketSize); \ - c1 = pload<Packet>(C1+i+(I)*PacketSize); \ - KMADD(c0, a0, b00, t0) \ - KMADD(c1, a0, b01, t1) \ - a0 = pload<Packet>(A0+i+(I+1)*PacketSize); \ - KMADD(c0, a1, b10, t0) \ - KMADD(c1, a1, b11, t1) \ - a1 = pload<Packet>(A1+i+(I+1)*PacketSize); \ - if(RK==4) KMADD(c0, a2, b20, t0) \ - if(RK==4) KMADD(c1, a2, b21, t1) \ - if(RK==4) a2 = pload<Packet>(A2+i+(I+1)*PacketSize); \ - if(RK==4) KMADD(c0, a3, b30, t0) \ - if(RK==4) KMADD(c1, a3, b31, t1) \ - if(RK==4) a3 = pload<Packet>(A3+i+(I+1)*PacketSize); \ - pstore(C0+i+(I)*PacketSize, c0); \ - pstore(C1+i+(I)*PacketSize, c1) - - // process rows of A' - C' with aggressive vectorization and peeling - for(Index i=0; i<actual_b_end1; i+=PacketSize*8) - { - EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL1"); - prefetch((A0+i+(5)*PacketSize)); - prefetch((A1+i+(5)*PacketSize)); - if(RK==4) prefetch((A2+i+(5)*PacketSize)); - if(RK==4) prefetch((A3+i+(5)*PacketSize)); - WORK(0); - WORK(1); - WORK(2); - WORK(3); - WORK(4); - WORK(5); - WORK(6); - WORK(7); - } - // process the remaining rows with vectorization only - for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize) - { - WORK(0); - } -#undef WORK - // process the remaining rows without vectorization - for(Index i=actual_b_end2; i<actual_b; ++i) - { - if(RK==4) - { - C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3]; - C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1]+A2[i]*Bc1[2]+A3[i]*Bc1[3]; - } - else - { - C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]; - C1[i] += A0[i]*Bc1[0]+A1[i]*Bc1[1]; - } - } - - Bc0 += RK; - Bc1 += RK; - } // peeled loop on k - } // peeled loop on the columns j - // process the last column (we now perform a matrux-vector product) - if((n-n_end)>0) - { - const Scalar* Bc0 = B+(n-1)*ldb; - - for(Index k=0; k<d_end; k+=RK) - { - - // load and expand a 1 x RK block of B - Packet b00, b10, b20, b30; - b00 = pset1<Packet>(Bc0[0]); - b10 = pset1<Packet>(Bc0[1]); - if(RK==4) b20 = pset1<Packet>(Bc0[2]); - if(RK==4) b30 = pset1<Packet>(Bc0[3]); - - Packet a0, a1, a2, a3, c0, t0/*, t1*/; - - const Scalar* A0 = A+ib+(k+0)*lda; - const Scalar* A1 = A+ib+(k+1)*lda; - const Scalar* A2 = A+ib+(k+2)*lda; - const Scalar* A3 = A+ib+(k+3)*lda; - - Scalar* C0 = C+ib+(n_end)*ldc; - - a0 = pload<Packet>(A0); - a1 = pload<Packet>(A1); - if(RK==4) - { - a2 = pload<Packet>(A2); - a3 = pload<Packet>(A3); - } - else - { - // workaround "may be used uninitialized in this function" warning - a2 = a3 = a0; - } - -#define WORK(I) \ - c0 = pload<Packet>(C0+i+(I)*PacketSize); \ - KMADD(c0, a0, b00, t0) \ - a0 = pload<Packet>(A0+i+(I+1)*PacketSize); \ - KMADD(c0, a1, b10, t0) \ - a1 = pload<Packet>(A1+i+(I+1)*PacketSize); \ - if(RK==4) KMADD(c0, a2, b20, t0) \ - if(RK==4) a2 = pload<Packet>(A2+i+(I+1)*PacketSize); \ - if(RK==4) KMADD(c0, a3, b30, t0) \ - if(RK==4) a3 = pload<Packet>(A3+i+(I+1)*PacketSize); \ - pstore(C0+i+(I)*PacketSize, c0); - - // agressive vectorization and peeling - for(Index i=0; i<actual_b_end1; i+=PacketSize*8) - { - EIGEN_ASM_COMMENT("SPARSELU_GEMML_KERNEL2"); - WORK(0); - WORK(1); - WORK(2); - WORK(3); - WORK(4); - WORK(5); - WORK(6); - WORK(7); - } - // vectorization only - for(Index i=actual_b_end1; i<actual_b_end2; i+=PacketSize) - { - WORK(0); - } - // remaining scalars - for(Index i=actual_b_end2; i<actual_b; ++i) - { - if(RK==4) - C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]+A2[i]*Bc0[2]+A3[i]*Bc0[3]; - else - C0[i] += A0[i]*Bc0[0]+A1[i]*Bc0[1]; - } - - Bc0 += RK; -#undef WORK - } - } - - // process the last columns of A, corresponding to the last rows of B - Index rd = d-d_end; - if(rd>0) - { - for(Index j=0; j<n; ++j) - { - enum { - Alignment = PacketSize>1 ? Aligned : 0 - }; - typedef Map<Matrix<Scalar,Dynamic,1>, Alignment > MapVector; - typedef Map<const Matrix<Scalar,Dynamic,1>, Alignment > ConstMapVector; - if(rd==1) MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b); - - else if(rd==2) MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b) - + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b); - - else MapVector(C+j*ldc+ib,actual_b) += B[0+d_end+j*ldb] * ConstMapVector(A+(d_end+0)*lda+ib, actual_b) - + B[1+d_end+j*ldb] * ConstMapVector(A+(d_end+1)*lda+ib, actual_b) - + B[2+d_end+j*ldb] * ConstMapVector(A+(d_end+2)*lda+ib, actual_b); - } - } - - } // blocking on the rows of A and C -} -#undef KMADD - -} // namespace internal - -} // namespace Eigen - -#endif // EIGEN_SPARSELU_GEMM_KERNEL_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h deleted file mode 100644 index 7a4e4305aa..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h +++ /dev/null @@ -1,127 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* This file is a modified version of heap_relax_snode.c file in SuperLU - * -- SuperLU routine (version 3.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * October 15, 2003 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ - -#ifndef SPARSELU_HEAP_RELAX_SNODE_H -#define SPARSELU_HEAP_RELAX_SNODE_H - -namespace Eigen { -namespace internal { - -/** - * \brief Identify the initial relaxed supernodes - * - * This routine applied to a symmetric elimination tree. - * It assumes that the matrix has been reordered according to the postorder of the etree - * \param n The number of columns - * \param et elimination tree - * \param relax_columns Maximum number of columns allowed in a relaxed snode - * \param descendants Number of descendants of each node in the etree - * \param relax_end last column in a supernode - */ -template <typename Scalar, typename Index> -void SparseLUImpl<Scalar,Index>::heap_relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end) -{ - - // The etree may not be postordered, but its heap ordered - IndexVector post; - internal::treePostorder(n, et, post); // Post order etree - IndexVector inv_post(n+1); - Index i; - for (i = 0; i < n+1; ++i) inv_post(post(i)) = i; // inv_post = post.inverse()??? - - // Renumber etree in postorder - IndexVector iwork(n); - IndexVector et_save(n+1); - for (i = 0; i < n; ++i) - { - iwork(post(i)) = post(et(i)); - } - et_save = et; // Save the original etree - et = iwork; - - // compute the number of descendants of each node in the etree - relax_end.setConstant(emptyIdxLU); - Index j, parent; - descendants.setZero(); - for (j = 0; j < n; j++) - { - parent = et(j); - if (parent != n) // not the dummy root - descendants(parent) += descendants(j) + 1; - } - // Identify the relaxed supernodes by postorder traversal of the etree - Index snode_start; // beginning of a snode - Index k; - Index nsuper_et_post = 0; // Number of relaxed snodes in postordered etree - Index nsuper_et = 0; // Number of relaxed snodes in the original etree - Index l; - for (j = 0; j < n; ) - { - parent = et(j); - snode_start = j; - while ( parent != n && descendants(parent) < relax_columns ) - { - j = parent; - parent = et(j); - } - // Found a supernode in postordered etree, j is the last column - ++nsuper_et_post; - k = n; - for (i = snode_start; i <= j; ++i) - k = (std::min)(k, inv_post(i)); - l = inv_post(j); - if ( (l - k) == (j - snode_start) ) // Same number of columns in the snode - { - // This is also a supernode in the original etree - relax_end(k) = l; // Record last column - ++nsuper_et; - } - else - { - for (i = snode_start; i <= j; ++i) - { - l = inv_post(i); - if (descendants(i) == 0) - { - relax_end(l) = l; - ++nsuper_et; - } - } - } - j++; - // Search for a new leaf - while (descendants(j) != 0 && j < n) j++; - } // End postorder traversal of the etree - - // Recover the original etree - et = et_save; -} - -} // end namespace internal - -} // end namespace Eigen -#endif // SPARSELU_HEAP_RELAX_SNODE_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h deleted file mode 100644 index 0d0283b132..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_kernel_bmod.h +++ /dev/null @@ -1,130 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef SPARSELU_KERNEL_BMOD_H -#define SPARSELU_KERNEL_BMOD_H - -namespace Eigen { -namespace internal { - -/** - * \brief Performs numeric block updates from a given supernode to a single column - * - * \param segsize Size of the segment (and blocks ) to use for updates - * \param[in,out] dense Packed values of the original matrix - * \param tempv temporary vector to use for updates - * \param lusup array containing the supernodes - * \param lda Leading dimension in the supernode - * \param nrow Number of rows in the rectangular part of the supernode - * \param lsub compressed row subscripts of supernodes - * \param lptr pointer to the first column of the current supernode in lsub - * \param no_zeros Number of nonzeros elements before the diagonal part of the supernode - * \return 0 on success - */ -template <int SegSizeAtCompileTime> struct LU_kernel_bmod -{ - template <typename BlockScalarVector, typename ScalarVector, typename IndexVector, typename Index> - static EIGEN_DONT_INLINE void run(const int segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda, - const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros); -}; - -template <int SegSizeAtCompileTime> -template <typename BlockScalarVector, typename ScalarVector, typename IndexVector, typename Index> -EIGEN_DONT_INLINE void LU_kernel_bmod<SegSizeAtCompileTime>::run(const int segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda, - const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros) -{ - typedef typename ScalarVector::Scalar Scalar; - // First, copy U[*,j] segment from dense(*) to tempv(*) - // The result of triangular solve is in tempv[*]; - // The result of matric-vector update is in dense[*] - Index isub = lptr + no_zeros; - int i; - Index irow; - for (i = 0; i < ((SegSizeAtCompileTime==Dynamic)?segsize:SegSizeAtCompileTime); i++) - { - irow = lsub(isub); - tempv(i) = dense(irow); - ++isub; - } - // Dense triangular solve -- start effective triangle - luptr += lda * no_zeros + no_zeros; - // Form Eigen matrix and vector - Map<Matrix<Scalar,SegSizeAtCompileTime,SegSizeAtCompileTime>, 0, OuterStride<> > A( &(lusup.data()[luptr]), segsize, segsize, OuterStride<>(lda) ); - Map<Matrix<Scalar,SegSizeAtCompileTime,1> > u(tempv.data(), segsize); - - u = A.template triangularView<UnitLower>().solve(u); - - // Dense matrix-vector product y <-- B*x - luptr += segsize; - const Index PacketSize = internal::packet_traits<Scalar>::size; - Index ldl = internal::first_multiple(nrow, PacketSize); - Map<Matrix<Scalar,Dynamic,SegSizeAtCompileTime>, 0, OuterStride<> > B( &(lusup.data()[luptr]), nrow, segsize, OuterStride<>(lda) ); - Index aligned_offset = internal::first_aligned(tempv.data()+segsize, PacketSize); - Index aligned_with_B_offset = (PacketSize-internal::first_aligned(B.data(), PacketSize))%PacketSize; - Map<Matrix<Scalar,Dynamic,1>, 0, OuterStride<> > l(tempv.data()+segsize+aligned_offset+aligned_with_B_offset, nrow, OuterStride<>(ldl) ); - - l.setZero(); - internal::sparselu_gemm<Scalar>(l.rows(), l.cols(), B.cols(), B.data(), B.outerStride(), u.data(), u.outerStride(), l.data(), l.outerStride()); - - // Scatter tempv[] into SPA dense[] as a temporary storage - isub = lptr + no_zeros; - for (i = 0; i < ((SegSizeAtCompileTime==Dynamic)?segsize:SegSizeAtCompileTime); i++) - { - irow = lsub(isub++); - dense(irow) = tempv(i); - } - - // Scatter l into SPA dense[] - for (i = 0; i < nrow; i++) - { - irow = lsub(isub++); - dense(irow) -= l(i); - } -} - -template <> struct LU_kernel_bmod<1> -{ - template <typename BlockScalarVector, typename ScalarVector, typename IndexVector, typename Index> - static EIGEN_DONT_INLINE void run(const int /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr, - const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros); -}; - - -template <typename BlockScalarVector, typename ScalarVector, typename IndexVector, typename Index> -EIGEN_DONT_INLINE void LU_kernel_bmod<1>::run(const int /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr, - const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros) -{ - typedef typename ScalarVector::Scalar Scalar; - Scalar f = dense(lsub(lptr + no_zeros)); - luptr += lda * no_zeros + no_zeros + 1; - const Scalar* a(lusup.data() + luptr); - const /*typename IndexVector::Scalar*/Index* irow(lsub.data()+lptr + no_zeros + 1); - Index i = 0; - for (; i+1 < nrow; i+=2) - { - Index i0 = *(irow++); - Index i1 = *(irow++); - Scalar a0 = *(a++); - Scalar a1 = *(a++); - Scalar d0 = dense.coeff(i0); - Scalar d1 = dense.coeff(i1); - d0 -= f*a0; - d1 -= f*a1; - dense.coeffRef(i0) = d0; - dense.coeffRef(i1) = d1; - } - if(i<nrow) - dense.coeffRef(*(irow++)) -= f * *(a++); -} - -} // end namespace internal - -} // end namespace Eigen -#endif // SPARSELU_KERNEL_BMOD_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h deleted file mode 100644 index da0e0fc3c6..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_panel_bmod.h +++ /dev/null @@ -1,223 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* - - * NOTE: This file is the modified version of [s,d,c,z]panel_bmod.c file in SuperLU - - * -- SuperLU routine (version 3.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * October 15, 2003 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ -#ifndef SPARSELU_PANEL_BMOD_H -#define SPARSELU_PANEL_BMOD_H - -namespace Eigen { -namespace internal { - -/** - * \brief Performs numeric block updates (sup-panel) in topological order. - * - * Before entering this routine, the original nonzeros in the panel - * were already copied i nto the spa[m,w] - * - * \param m number of rows in the matrix - * \param w Panel size - * \param jcol Starting column of the panel - * \param nseg Number of segments in the U part - * \param dense Store the full representation of the panel - * \param tempv working array - * \param segrep segment representative... first row in the segment - * \param repfnz First nonzero rows - * \param glu Global LU data. - * - * - */ -template <typename Scalar, typename Index> -void SparseLUImpl<Scalar,Index>::panel_bmod(const Index m, const Index w, const Index jcol, - const Index nseg, ScalarVector& dense, ScalarVector& tempv, - IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu) -{ - - Index ksub,jj,nextl_col; - Index fsupc, nsupc, nsupr, nrow; - Index krep, kfnz; - Index lptr; // points to the row subscripts of a supernode - Index luptr; // ... - Index segsize,no_zeros ; - // For each nonz supernode segment of U[*,j] in topological order - Index k = nseg - 1; - const Index PacketSize = internal::packet_traits<Scalar>::size; - - for (ksub = 0; ksub < nseg; ksub++) - { // For each updating supernode - /* krep = representative of current k-th supernode - * fsupc = first supernodal column - * nsupc = number of columns in a supernode - * nsupr = number of rows in a supernode - */ - krep = segrep(k); k--; - fsupc = glu.xsup(glu.supno(krep)); - nsupc = krep - fsupc + 1; - nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); - nrow = nsupr - nsupc; - lptr = glu.xlsub(fsupc); - - // loop over the panel columns to detect the actual number of columns and rows - Index u_rows = 0; - Index u_cols = 0; - for (jj = jcol; jj < jcol + w; jj++) - { - nextl_col = (jj-jcol) * m; - VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row - - kfnz = repfnz_col(krep); - if ( kfnz == emptyIdxLU ) - continue; // skip any zero segment - - segsize = krep - kfnz + 1; - u_cols++; - u_rows = (std::max)(segsize,u_rows); - } - - if(nsupc >= 2) - { - Index ldu = internal::first_multiple<Index>(u_rows, PacketSize); - Map<Matrix<Scalar,Dynamic,Dynamic>, Aligned, OuterStride<> > U(tempv.data(), u_rows, u_cols, OuterStride<>(ldu)); - - // gather U - Index u_col = 0; - for (jj = jcol; jj < jcol + w; jj++) - { - nextl_col = (jj-jcol) * m; - VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row - VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here - - kfnz = repfnz_col(krep); - if ( kfnz == emptyIdxLU ) - continue; // skip any zero segment - - segsize = krep - kfnz + 1; - luptr = glu.xlusup(fsupc); - no_zeros = kfnz - fsupc; - - Index isub = lptr + no_zeros; - Index off = u_rows-segsize; - for (Index i = 0; i < off; i++) U(i,u_col) = 0; - for (Index i = 0; i < segsize; i++) - { - Index irow = glu.lsub(isub); - U(i+off,u_col) = dense_col(irow); - ++isub; - } - u_col++; - } - // solve U = A^-1 U - luptr = glu.xlusup(fsupc); - Index lda = glu.xlusup(fsupc+1) - glu.xlusup(fsupc); - no_zeros = (krep - u_rows + 1) - fsupc; - luptr += lda * no_zeros + no_zeros; - Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A(glu.lusup.data()+luptr, u_rows, u_rows, OuterStride<>(lda) ); - U = A.template triangularView<UnitLower>().solve(U); - - // update - luptr += u_rows; - Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > B(glu.lusup.data()+luptr, nrow, u_rows, OuterStride<>(lda) ); - eigen_assert(tempv.size()>w*ldu + nrow*w + 1); - - Index ldl = internal::first_multiple<Index>(nrow, PacketSize); - Index offset = (PacketSize-internal::first_aligned(B.data(), PacketSize)) % PacketSize; - Map<Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > L(tempv.data()+w*ldu+offset, nrow, u_cols, OuterStride<>(ldl)); - - L.setZero(); - internal::sparselu_gemm<Scalar>(L.rows(), L.cols(), B.cols(), B.data(), B.outerStride(), U.data(), U.outerStride(), L.data(), L.outerStride()); - - // scatter U and L - u_col = 0; - for (jj = jcol; jj < jcol + w; jj++) - { - nextl_col = (jj-jcol) * m; - VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row - VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here - - kfnz = repfnz_col(krep); - if ( kfnz == emptyIdxLU ) - continue; // skip any zero segment - - segsize = krep - kfnz + 1; - no_zeros = kfnz - fsupc; - Index isub = lptr + no_zeros; - - Index off = u_rows-segsize; - for (Index i = 0; i < segsize; i++) - { - Index irow = glu.lsub(isub++); - dense_col(irow) = U.coeff(i+off,u_col); - U.coeffRef(i+off,u_col) = 0; - } - - // Scatter l into SPA dense[] - for (Index i = 0; i < nrow; i++) - { - Index irow = glu.lsub(isub++); - dense_col(irow) -= L.coeff(i,u_col); - L.coeffRef(i,u_col) = 0; - } - u_col++; - } - } - else // level 2 only - { - // Sequence through each column in the panel - for (jj = jcol; jj < jcol + w; jj++) - { - nextl_col = (jj-jcol) * m; - VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row - VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here - - kfnz = repfnz_col(krep); - if ( kfnz == emptyIdxLU ) - continue; // skip any zero segment - - segsize = krep - kfnz + 1; - luptr = glu.xlusup(fsupc); - - Index lda = glu.xlusup(fsupc+1)-glu.xlusup(fsupc);// nsupr - - // Perform a trianglar solve and block update, - // then scatter the result of sup-col update to dense[] - no_zeros = kfnz - fsupc; - if(segsize==1) LU_kernel_bmod<1>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); - else if(segsize==2) LU_kernel_bmod<2>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); - else if(segsize==3) LU_kernel_bmod<3>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); - else LU_kernel_bmod<Dynamic>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); - } // End for each column in the panel - } - - } // End for each updating supernode -} // end panel bmod - -} // end namespace internal - -} // end namespace Eigen - -#endif // SPARSELU_PANEL_BMOD_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h deleted file mode 100644 index dc0054efd2..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_panel_dfs.h +++ /dev/null @@ -1,258 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* - - * NOTE: This file is the modified version of [s,d,c,z]panel_dfs.c file in SuperLU - - * -- SuperLU routine (version 2.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * November 15, 1997 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ -#ifndef SPARSELU_PANEL_DFS_H -#define SPARSELU_PANEL_DFS_H - -namespace Eigen { - -namespace internal { - -template<typename IndexVector> -struct panel_dfs_traits -{ - typedef typename IndexVector::Scalar Index; - panel_dfs_traits(Index jcol, Index* marker) - : m_jcol(jcol), m_marker(marker) - {} - bool update_segrep(Index krep, Index jj) - { - if(m_marker[krep]<m_jcol) - { - m_marker[krep] = jj; - return true; - } - return false; - } - void mem_expand(IndexVector& /*glu.lsub*/, Index /*nextl*/, Index /*chmark*/) {} - enum { ExpandMem = false }; - Index m_jcol; - Index* m_marker; -}; - - -template <typename Scalar, typename Index> -template <typename Traits> -void SparseLUImpl<Scalar,Index>::dfs_kernel(const Index jj, IndexVector& perm_r, - Index& nseg, IndexVector& panel_lsub, IndexVector& segrep, - Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent, - IndexVector& xplore, GlobalLU_t& glu, - Index& nextl_col, Index krow, Traits& traits - ) -{ - - Index kmark = marker(krow); - - // For each unmarked krow of jj - marker(krow) = jj; - Index kperm = perm_r(krow); - if (kperm == emptyIdxLU ) { - // krow is in L : place it in structure of L(*, jj) - panel_lsub(nextl_col++) = krow; // krow is indexed into A - - traits.mem_expand(panel_lsub, nextl_col, kmark); - } - else - { - // krow is in U : if its supernode-representative krep - // has been explored, update repfnz(*) - // krep = supernode representative of the current row - Index krep = glu.xsup(glu.supno(kperm)+1) - 1; - // First nonzero element in the current column: - Index myfnz = repfnz_col(krep); - - if (myfnz != emptyIdxLU ) - { - // Representative visited before - if (myfnz > kperm ) repfnz_col(krep) = kperm; - - } - else - { - // Otherwise, perform dfs starting at krep - Index oldrep = emptyIdxLU; - parent(krep) = oldrep; - repfnz_col(krep) = kperm; - Index xdfs = glu.xlsub(krep); - Index maxdfs = xprune(krep); - - Index kpar; - do - { - // For each unmarked kchild of krep - while (xdfs < maxdfs) - { - Index kchild = glu.lsub(xdfs); - xdfs++; - Index chmark = marker(kchild); - - if (chmark != jj ) - { - marker(kchild) = jj; - Index chperm = perm_r(kchild); - - if (chperm == emptyIdxLU) - { - // case kchild is in L: place it in L(*, j) - panel_lsub(nextl_col++) = kchild; - traits.mem_expand(panel_lsub, nextl_col, chmark); - } - else - { - // case kchild is in U : - // chrep = its supernode-rep. If its rep has been explored, - // update its repfnz(*) - Index chrep = glu.xsup(glu.supno(chperm)+1) - 1; - myfnz = repfnz_col(chrep); - - if (myfnz != emptyIdxLU) - { // Visited before - if (myfnz > chperm) - repfnz_col(chrep) = chperm; - } - else - { // Cont. dfs at snode-rep of kchild - xplore(krep) = xdfs; - oldrep = krep; - krep = chrep; // Go deeper down G(L) - parent(krep) = oldrep; - repfnz_col(krep) = chperm; - xdfs = glu.xlsub(krep); - maxdfs = xprune(krep); - - } // end if myfnz != -1 - } // end if chperm == -1 - - } // end if chmark !=jj - } // end while xdfs < maxdfs - - // krow has no more unexplored nbrs : - // Place snode-rep krep in postorder DFS, if this - // segment is seen for the first time. (Note that - // "repfnz(krep)" may change later.) - // Baktrack dfs to its parent - if(traits.update_segrep(krep,jj)) - //if (marker1(krep) < jcol ) - { - segrep(nseg) = krep; - ++nseg; - //marker1(krep) = jj; - } - - kpar = parent(krep); // Pop recursion, mimic recursion - if (kpar == emptyIdxLU) - break; // dfs done - krep = kpar; - xdfs = xplore(krep); - maxdfs = xprune(krep); - - } while (kpar != emptyIdxLU); // Do until empty stack - - } // end if (myfnz = -1) - - } // end if (kperm == -1) -} - -/** - * \brief Performs a symbolic factorization on a panel of columns [jcol, jcol+w) - * - * A supernode representative is the last column of a supernode. - * The nonzeros in U[*,j] are segments that end at supernodes representatives - * - * The routine returns a list of the supernodal representatives - * in topological order of the dfs that generates them. This list is - * a superset of the topological order of each individual column within - * the panel. - * The location of the first nonzero in each supernodal segment - * (supernodal entry location) is also returned. Each column has - * a separate list for this purpose. - * - * Two markers arrays are used for dfs : - * marker[i] == jj, if i was visited during dfs of current column jj; - * marker1[i] >= jcol, if i was visited by earlier columns in this panel; - * - * \param[in] m number of rows in the matrix - * \param[in] w Panel size - * \param[in] jcol Starting column of the panel - * \param[in] A Input matrix in column-major storage - * \param[in] perm_r Row permutation - * \param[out] nseg Number of U segments - * \param[out] dense Accumulate the column vectors of the panel - * \param[out] panel_lsub Subscripts of the row in the panel - * \param[out] segrep Segment representative i.e first nonzero row of each segment - * \param[out] repfnz First nonzero location in each row - * \param[out] xprune The pruned elimination tree - * \param[out] marker work vector - * \param parent The elimination tree - * \param xplore work vector - * \param glu The global data structure - * - */ - -template <typename Scalar, typename Index> -void SparseLUImpl<Scalar,Index>::panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu) -{ - Index nextl_col; // Next available position in panel_lsub[*,jj] - - // Initialize pointers - VectorBlock<IndexVector> marker1(marker, m, m); - nseg = 0; - - panel_dfs_traits<IndexVector> traits(jcol, marker1.data()); - - // For each column in the panel - for (Index jj = jcol; jj < jcol + w; jj++) - { - nextl_col = (jj - jcol) * m; - - VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero location in each row - VectorBlock<ScalarVector> dense_col(dense,nextl_col, m); // Accumulate a column vector here - - - // For each nnz in A[*, jj] do depth first search - for (typename MatrixType::InnerIterator it(A, jj); it; ++it) - { - Index krow = it.row(); - dense_col(krow) = it.value(); - - Index kmark = marker(krow); - if (kmark == jj) - continue; // krow visited before, go to the next nonzero - - dfs_kernel(jj, perm_r, nseg, panel_lsub, segrep, repfnz_col, xprune, marker, parent, - xplore, glu, nextl_col, krow, traits); - }// end for nonzeros in column jj - - } // end for column jj -} - -} // end namespace internal -} // end namespace Eigen - -#endif // SPARSELU_PANEL_DFS_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h deleted file mode 100644 index 457789c780..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_pivotL.h +++ /dev/null @@ -1,136 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* - - * NOTE: This file is the modified version of xpivotL.c file in SuperLU - - * -- SuperLU routine (version 3.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * October 15, 2003 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ -#ifndef SPARSELU_PIVOTL_H -#define SPARSELU_PIVOTL_H - -namespace Eigen { -namespace internal { - -/** - * \brief Performs the numerical pivotin on the current column of L, and the CDIV operation. - * - * Pivot policy : - * (1) Compute thresh = u * max_(i>=j) abs(A_ij); - * (2) IF user specifies pivot row k and abs(A_kj) >= thresh THEN - * pivot row = k; - * ELSE IF abs(A_jj) >= thresh THEN - * pivot row = j; - * ELSE - * pivot row = m; - * - * Note: If you absolutely want to use a given pivot order, then set u=0.0. - * - * \param jcol The current column of L - * \param diagpivotthresh diagonal pivoting threshold - * \param[in,out] perm_r Row permutation (threshold pivoting) - * \param[in] iperm_c column permutation - used to finf diagonal of Pc*A*Pc' - * \param[out] pivrow The pivot row - * \param glu Global LU data - * \return 0 if success, i > 0 if U(i,i) is exactly zero - * - */ -template <typename Scalar, typename Index> -Index SparseLUImpl<Scalar,Index>::pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow, GlobalLU_t& glu) -{ - - Index fsupc = (glu.xsup)((glu.supno)(jcol)); // First column in the supernode containing the column jcol - Index nsupc = jcol - fsupc; // Number of columns in the supernode portion, excluding jcol; nsupc >=0 - Index lptr = glu.xlsub(fsupc); // pointer to the starting location of the row subscripts for this supernode portion - Index nsupr = glu.xlsub(fsupc+1) - lptr; // Number of rows in the supernode - Index lda = glu.xlusup(fsupc+1) - glu.xlusup(fsupc); // leading dimension - Scalar* lu_sup_ptr = &(glu.lusup.data()[glu.xlusup(fsupc)]); // Start of the current supernode - Scalar* lu_col_ptr = &(glu.lusup.data()[glu.xlusup(jcol)]); // Start of jcol in the supernode - Index* lsub_ptr = &(glu.lsub.data()[lptr]); // Start of row indices of the supernode - - // Determine the largest abs numerical value for partial pivoting - Index diagind = iperm_c(jcol); // diagonal index - RealScalar pivmax = 0.0; - Index pivptr = nsupc; - Index diag = emptyIdxLU; - RealScalar rtemp; - Index isub, icol, itemp, k; - for (isub = nsupc; isub < nsupr; ++isub) { - using std::abs; - rtemp = abs(lu_col_ptr[isub]); - if (rtemp > pivmax) { - pivmax = rtemp; - pivptr = isub; - } - if (lsub_ptr[isub] == diagind) diag = isub; - } - - // Test for singularity - if ( pivmax == 0.0 ) { - pivrow = lsub_ptr[pivptr]; - perm_r(pivrow) = jcol; - return (jcol+1); - } - - RealScalar thresh = diagpivotthresh * pivmax; - - // Choose appropriate pivotal element - - { - // Test if the diagonal element can be used as a pivot (given the threshold value) - if (diag >= 0 ) - { - // Diagonal element exists - using std::abs; - rtemp = abs(lu_col_ptr[diag]); - if (rtemp != 0.0 && rtemp >= thresh) pivptr = diag; - } - pivrow = lsub_ptr[pivptr]; - } - - // Record pivot row - perm_r(pivrow) = jcol; - // Interchange row subscripts - if (pivptr != nsupc ) - { - std::swap( lsub_ptr[pivptr], lsub_ptr[nsupc] ); - // Interchange numerical values as well, for the two rows in the whole snode - // such that L is indexed the same way as A - for (icol = 0; icol <= nsupc; icol++) - { - itemp = pivptr + icol * lda; - std::swap(lu_sup_ptr[itemp], lu_sup_ptr[nsupc + icol * lda]); - } - } - // cdiv operations - Scalar temp = Scalar(1.0) / lu_col_ptr[nsupc]; - for (k = nsupc+1; k < nsupr; k++) - lu_col_ptr[k] *= temp; - return 0; -} - -} // end namespace internal -} // end namespace Eigen - -#endif // SPARSELU_PIVOTL_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h deleted file mode 100644 index 66460d1688..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_pruneL.h +++ /dev/null @@ -1,135 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* - - * NOTE: This file is the modified version of [s,d,c,z]pruneL.c file in SuperLU - - * -- SuperLU routine (version 2.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * November 15, 1997 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ -#ifndef SPARSELU_PRUNEL_H -#define SPARSELU_PRUNEL_H - -namespace Eigen { -namespace internal { - -/** - * \brief Prunes the L-structure. - * - * It prunes the L-structure of supernodes whose L-structure contains the current pivot row "pivrow" - * - * - * \param jcol The current column of L - * \param[in] perm_r Row permutation - * \param[out] pivrow The pivot row - * \param nseg Number of segments - * \param segrep - * \param repfnz - * \param[out] xprune - * \param glu Global LU data - * - */ -template <typename Scalar, typename Index> -void SparseLUImpl<Scalar,Index>::pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg, const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu) -{ - // For each supernode-rep irep in U(*,j] - Index jsupno = glu.supno(jcol); - Index i,irep,irep1; - bool movnum, do_prune = false; - Index kmin = 0, kmax = 0, minloc, maxloc,krow; - for (i = 0; i < nseg; i++) - { - irep = segrep(i); - irep1 = irep + 1; - do_prune = false; - - // Don't prune with a zero U-segment - if (repfnz(irep) == emptyIdxLU) continue; - - // If a snode overlaps with the next panel, then the U-segment - // is fragmented into two parts -- irep and irep1. We should let - // pruning occur at the rep-column in irep1s snode. - if (glu.supno(irep) == glu.supno(irep1) ) continue; // don't prune - - // If it has not been pruned & it has a nonz in row L(pivrow,i) - if (glu.supno(irep) != jsupno ) - { - if ( xprune (irep) >= glu.xlsub(irep1) ) - { - kmin = glu.xlsub(irep); - kmax = glu.xlsub(irep1) - 1; - for (krow = kmin; krow <= kmax; krow++) - { - if (glu.lsub(krow) == pivrow) - { - do_prune = true; - break; - } - } - } - - if (do_prune) - { - // do a quicksort-type partition - // movnum=true means that the num values have to be exchanged - movnum = false; - if (irep == glu.xsup(glu.supno(irep)) ) // Snode of size 1 - movnum = true; - - while (kmin <= kmax) - { - if (perm_r(glu.lsub(kmax)) == emptyIdxLU) - kmax--; - else if ( perm_r(glu.lsub(kmin)) != emptyIdxLU) - kmin++; - else - { - // kmin below pivrow (not yet pivoted), and kmax - // above pivrow: interchange the two suscripts - std::swap(glu.lsub(kmin), glu.lsub(kmax)); - - // If the supernode has only one column, then we - // only keep one set of subscripts. For any subscript - // intercnahge performed, similar interchange must be - // done on the numerical values. - if (movnum) - { - minloc = glu.xlusup(irep) + ( kmin - glu.xlsub(irep) ); - maxloc = glu.xlusup(irep) + ( kmax - glu.xlsub(irep) ); - std::swap(glu.lusup(minloc), glu.lusup(maxloc)); - } - kmin++; - kmax--; - } - } // end while - - xprune(irep) = kmin; //Pruning - } // end if do_prune - } // end pruning - } // End for each U-segment -} - -} // end namespace internal -} // end namespace Eigen - -#endif // SPARSELU_PRUNEL_H diff --git a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h b/third_party/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h deleted file mode 100644 index 58ec32e27e..0000000000 --- a/third_party/eigen3/Eigen/src/SparseLU/SparseLU_relax_snode.h +++ /dev/null @@ -1,83 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -/* This file is a modified version of heap_relax_snode.c file in SuperLU - * -- SuperLU routine (version 3.0) -- - * Univ. of California Berkeley, Xerox Palo Alto Research Center, - * and Lawrence Berkeley National Lab. - * October 15, 2003 - * - * Copyright (c) 1994 by Xerox Corporation. All rights reserved. - * - * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY - * EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. - * - * Permission is hereby granted to use or copy this program for any - * purpose, provided the above notices are retained on all copies. - * Permission to modify the code and to distribute modified code is - * granted, provided the above notices are retained, and a notice that - * the code was modified is included with the above copyright notice. - */ - -#ifndef SPARSELU_RELAX_SNODE_H -#define SPARSELU_RELAX_SNODE_H - -namespace Eigen { - -namespace internal { - -/** - * \brief Identify the initial relaxed supernodes - * - * This routine is applied to a column elimination tree. - * It assumes that the matrix has been reordered according to the postorder of the etree - * \param n the number of columns - * \param et elimination tree - * \param relax_columns Maximum number of columns allowed in a relaxed snode - * \param descendants Number of descendants of each node in the etree - * \param relax_end last column in a supernode - */ -template <typename Scalar, typename Index> -void SparseLUImpl<Scalar,Index>::relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end) -{ - - // compute the number of descendants of each node in the etree - Index j, parent; - relax_end.setConstant(emptyIdxLU); - descendants.setZero(); - for (j = 0; j < n; j++) - { - parent = et(j); - if (parent != n) // not the dummy root - descendants(parent) += descendants(j) + 1; - } - // Identify the relaxed supernodes by postorder traversal of the etree - Index snode_start; // beginning of a snode - for (j = 0; j < n; ) - { - parent = et(j); - snode_start = j; - while ( parent != n && descendants(parent) < relax_columns ) - { - j = parent; - parent = et(j); - } - // Found a supernode in postordered etree, j is the last column - relax_end(snode_start) = j; // Record last column - j++; - // Search for a new leaf - while (descendants(j) != 0 && j < n) j++; - } // End postorder traversal of the etree - -} - -} // end namespace internal - -} // end namespace Eigen -#endif diff --git a/third_party/eigen3/Eigen/src/SparseQR/SparseQR.h b/third_party/eigen3/Eigen/src/SparseQR/SparseQR.h deleted file mode 100644 index 5fb5bc2038..0000000000 --- a/third_party/eigen3/Eigen/src/SparseQR/SparseQR.h +++ /dev/null @@ -1,675 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2012-2013 Desire Nuentsa <desire.nuentsa_wakam@inria.fr> -// Copyright (C) 2012-2013 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_QR_H -#define EIGEN_SPARSE_QR_H - -namespace Eigen { - -template<typename MatrixType, typename OrderingType> class SparseQR; -template<typename SparseQRType> struct SparseQRMatrixQReturnType; -template<typename SparseQRType> struct SparseQRMatrixQTransposeReturnType; -template<typename SparseQRType, typename Derived> struct SparseQR_QProduct; -namespace internal { - template <typename SparseQRType> struct traits<SparseQRMatrixQReturnType<SparseQRType> > - { - typedef typename SparseQRType::MatrixType ReturnType; - typedef typename ReturnType::Index Index; - typedef typename ReturnType::StorageKind StorageKind; - }; - template <typename SparseQRType> struct traits<SparseQRMatrixQTransposeReturnType<SparseQRType> > - { - typedef typename SparseQRType::MatrixType ReturnType; - }; - template <typename SparseQRType, typename Derived> struct traits<SparseQR_QProduct<SparseQRType, Derived> > - { - typedef typename Derived::PlainObject ReturnType; - }; -} // End namespace internal - -/** - * \ingroup SparseQR_Module - * \class SparseQR - * \brief Sparse left-looking rank-revealing QR factorization - * - * This class implements a left-looking rank-revealing QR decomposition - * of sparse matrices. When a column has a norm less than a given tolerance - * it is implicitly permuted to the end. The QR factorization thus obtained is - * given by A*P = Q*R where R is upper triangular or trapezoidal. - * - * P is the column permutation which is the product of the fill-reducing and the - * rank-revealing permutations. Use colsPermutation() to get it. - * - * Q is the orthogonal matrix represented as products of Householder reflectors. - * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose. - * You can then apply it to a vector. - * - * R is the sparse triangular or trapezoidal matrix. The later occurs when A is rank-deficient. - * matrixR().topLeftCorner(rank(), rank()) always returns a triangular factor of full rank. - * - * \tparam _MatrixType The type of the sparse matrix A, must be a column-major SparseMatrix<> - * \tparam _OrderingType The fill-reducing ordering method. See the \link OrderingMethods_Module - * OrderingMethods \endlink module for the list of built-in and external ordering methods. - * - * \warning The input sparse matrix A must be in compressed mode (see SparseMatrix::makeCompressed()). - * - */ -template<typename _MatrixType, typename _OrderingType> -class SparseQR -{ - public: - typedef _MatrixType MatrixType; - typedef _OrderingType OrderingType; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef SparseMatrix<Scalar,ColMajor,Index> QRMatrixType; - typedef Matrix<Index, Dynamic, 1> IndexVector; - typedef Matrix<Scalar, Dynamic, 1> ScalarVector; - typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; - public: - SparseQR () : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false) - { } - - /** Construct a QR factorization of the matrix \a mat. - * - * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()). - * - * \sa compute() - */ - SparseQR(const MatrixType& mat) : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false) - { - compute(mat); - } - - /** Computes the QR factorization of the sparse matrix \a mat. - * - * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()). - * - * \sa analyzePattern(), factorize() - */ - void compute(const MatrixType& mat) - { - analyzePattern(mat); - factorize(mat); - } - void analyzePattern(const MatrixType& mat); - void factorize(const MatrixType& mat); - - /** \returns the number of rows of the represented matrix. - */ - inline Index rows() const { return m_pmat.rows(); } - - /** \returns the number of columns of the represented matrix. - */ - inline Index cols() const { return m_pmat.cols();} - - /** \returns a const reference to the \b sparse upper triangular matrix R of the QR factorization. - */ - const QRMatrixType& matrixR() const { return m_R; } - - /** \returns the number of non linearly dependent columns as determined by the pivoting threshold. - * - * \sa setPivotThreshold() - */ - Index rank() const - { - eigen_assert(m_isInitialized && "The factorization should be called first, use compute()"); - return m_nonzeropivots; - } - - /** \returns an expression of the matrix Q as products of sparse Householder reflectors. - * The common usage of this function is to apply it to a dense matrix or vector - * \code - * VectorXd B1, B2; - * // Initialize B1 - * B2 = matrixQ() * B1; - * \endcode - * - * To get a plain SparseMatrix representation of Q: - * \code - * SparseMatrix<double> Q; - * Q = SparseQR<SparseMatrix<double> >(A).matrixQ(); - * \endcode - * Internally, this call simply performs a sparse product between the matrix Q - * and a sparse identity matrix. However, due to the fact that the sparse - * reflectors are stored unsorted, two transpositions are needed to sort - * them before performing the product. - */ - SparseQRMatrixQReturnType<SparseQR> matrixQ() const - { return SparseQRMatrixQReturnType<SparseQR>(*this); } - - /** \returns a const reference to the column permutation P that was applied to A such that A*P = Q*R - * It is the combination of the fill-in reducing permutation and numerical column pivoting. - */ - const PermutationType& colsPermutation() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_outputPerm_c; - } - - /** \returns A string describing the type of error. - * This method is provided to ease debugging, not to handle errors. - */ - std::string lastErrorMessage() const { return m_lastError; } - - /** \internal */ - template<typename Rhs, typename Dest> - bool _solve(const MatrixBase<Rhs> &B, MatrixBase<Dest> &dest) const - { - eigen_assert(m_isInitialized && "The factorization should be called first, use compute()"); - eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix"); - - Index rank = this->rank(); - - // Compute Q^T * b; - typename Dest::PlainObject y, b; - y = this->matrixQ().transpose() * B; - b = y; - - // Solve with the triangular matrix R - y.resize((std::max)(cols(),Index(y.rows())),y.cols()); - y.topRows(rank) = this->matrixR().topLeftCorner(rank, rank).template triangularView<Upper>().solve(b.topRows(rank)); - y.bottomRows(y.rows()-rank).setZero(); - - // Apply the column permutation - if (m_perm_c.size()) dest.topRows(cols()) = colsPermutation() * y.topRows(cols()); - else dest = y.topRows(cols()); - - m_info = Success; - return true; - } - - - /** Sets the threshold that is used to determine linearly dependent columns during the factorization. - * - * In practice, if during the factorization the norm of the column that has to be eliminated is below - * this threshold, then the entire column is treated as zero, and it is moved at the end. - */ - void setPivotThreshold(const RealScalar& threshold) - { - m_useDefaultThreshold = false; - m_threshold = threshold; - } - - /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::solve_retval<SparseQR, Rhs> solve(const MatrixBase<Rhs>& B) const - { - eigen_assert(m_isInitialized && "The factorization should be called first, use compute()"); - eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix"); - return internal::solve_retval<SparseQR, Rhs>(*this, B.derived()); - } - template<typename Rhs> - inline const internal::sparse_solve_retval<SparseQR, Rhs> solve(const SparseMatrixBase<Rhs>& B) const - { - eigen_assert(m_isInitialized && "The factorization should be called first, use compute()"); - eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix"); - return internal::sparse_solve_retval<SparseQR, Rhs>(*this, B.derived()); - } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was successful, - * \c NumericalIssue if the QR factorization reports a numerical problem - * \c InvalidInput if the input matrix is invalid - * - * \sa iparm() - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_info; - } - - protected: - inline void sort_matrix_Q() - { - if(this->m_isQSorted) return; - // The matrix Q is sorted during the transposition - SparseMatrix<Scalar, RowMajor, Index> mQrm(this->m_Q); - this->m_Q = mQrm; - this->m_isQSorted = true; - } - - - protected: - bool m_isInitialized; - bool m_analysisIsok; - bool m_factorizationIsok; - mutable ComputationInfo m_info; - std::string m_lastError; - QRMatrixType m_pmat; // Temporary matrix - QRMatrixType m_R; // The triangular factor matrix - QRMatrixType m_Q; // The orthogonal reflectors - ScalarVector m_hcoeffs; // The Householder coefficients - PermutationType m_perm_c; // Fill-reducing Column permutation - PermutationType m_pivotperm; // The permutation for rank revealing - PermutationType m_outputPerm_c; // The final column permutation - RealScalar m_threshold; // Threshold to determine null Householder reflections - bool m_useDefaultThreshold; // Use default threshold - Index m_nonzeropivots; // Number of non zero pivots found - IndexVector m_etree; // Column elimination tree - IndexVector m_firstRowElt; // First element in each row - bool m_isQSorted; // whether Q is sorted or not - - template <typename, typename > friend struct SparseQR_QProduct; - template <typename > friend struct SparseQRMatrixQReturnType; - -}; - -/** \brief Preprocessing step of a QR factorization - * - * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()). - * - * In this step, the fill-reducing permutation is computed and applied to the columns of A - * and the column elimination tree is computed as well. Only the sparsity pattern of \a mat is exploited. - * - * \note In this step it is assumed that there is no empty row in the matrix \a mat. - */ -template <typename MatrixType, typename OrderingType> -void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat) -{ - eigen_assert(mat.isCompressed() && "SparseQR requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to SparseQR"); - // Compute the column fill reducing ordering - OrderingType ord; - ord(mat, m_perm_c); - Index n = mat.cols(); - Index m = mat.rows(); - - if (!m_perm_c.size()) - { - m_perm_c.resize(n); - m_perm_c.indices().setLinSpaced(n, 0,n-1); - } - - // Compute the column elimination tree of the permuted matrix - m_outputPerm_c = m_perm_c.inverse(); - internal::coletree(mat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data()); - - m_R.resize(n, n); - m_Q.resize(m, n); - - // Allocate space for nonzero elements : rough estimation - m_R.reserve(2*mat.nonZeros()); //FIXME Get a more accurate estimation through symbolic factorization with the etree - m_Q.reserve(2*mat.nonZeros()); - m_hcoeffs.resize(n); - m_analysisIsok = true; -} - -/** \brief Performs the numerical QR factorization of the input matrix - * - * The function SparseQR::analyzePattern(const MatrixType&) must have been called beforehand with - * a matrix having the same sparsity pattern than \a mat. - * - * \param mat The sparse column-major matrix - */ -template <typename MatrixType, typename OrderingType> -void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat) -{ - using std::abs; - using std::max; - - eigen_assert(m_analysisIsok && "analyzePattern() should be called before this step"); - Index m = mat.rows(); - Index n = mat.cols(); - IndexVector mark(m); mark.setConstant(-1); // Record the visited nodes - IndexVector Ridx(n), Qidx(m); // Store temporarily the row indexes for the current column of R and Q - Index nzcolR, nzcolQ; // Number of nonzero for the current column of R and Q - ScalarVector tval(m); // The dense vector used to compute the current column - bool found_diag; - - m_pmat = mat; - m_pmat.uncompress(); // To have the innerNonZeroPtr allocated - // Apply the fill-in reducing permutation lazily: - for (int i = 0; i < n; i++) - { - Index p = m_perm_c.size() ? m_perm_c.indices()(i) : i; - m_pmat.outerIndexPtr()[p] = mat.outerIndexPtr()[i]; - m_pmat.innerNonZeroPtr()[p] = mat.outerIndexPtr()[i+1] - mat.outerIndexPtr()[i]; - } - - /* Compute the default threshold, see : - * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing - * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3 - */ - if(m_useDefaultThreshold) - { - RealScalar max2Norm = 0.0; - for (int j = 0; j < n; j++) max2Norm = (max)(max2Norm, m_pmat.col(j).norm()); - m_threshold = 20 * (m + n) * max2Norm * NumTraits<RealScalar>::epsilon(); - } - - // Initialize the numerical permutation - m_pivotperm.setIdentity(n); - - Index nonzeroCol = 0; // Record the number of valid pivots - - // Left looking rank-revealing QR factorization: compute a column of R and Q at a time - for (Index col = 0; col < (std::min)(n,m); ++col) - { - mark.setConstant(-1); - m_R.startVec(col); - m_Q.startVec(col); - mark(nonzeroCol) = col; - Qidx(0) = nonzeroCol; - nzcolR = 0; nzcolQ = 1; - found_diag = col>=m; - tval.setZero(); - - // Symbolic factorization: find the nonzero locations of the column k of the factors R and Q, i.e., - // all the nodes (with indexes lower than rank) reachable through the column elimination tree (etree) rooted at node k. - // Note: if the diagonal entry does not exist, then its contribution must be explicitly added, - // thus the trick with found_diag that permits to do one more iteration on the diagonal element if this one has not been found. - for (typename MatrixType::InnerIterator itp(m_pmat, col); itp || !found_diag; ++itp) - { - Index curIdx = nonzeroCol ; - if(itp) curIdx = itp.row(); - if(curIdx == nonzeroCol) found_diag = true; - - // Get the nonzeros indexes of the current column of R - Index st = m_firstRowElt(curIdx); // The traversal of the etree starts here - if (st < 0 ) - { - m_lastError = "Empty row found during numerical factorization"; - m_info = InvalidInput; - return; - } - - // Traverse the etree - Index bi = nzcolR; - for (; mark(st) != col; st = m_etree(st)) - { - Ridx(nzcolR) = st; // Add this row to the list, - mark(st) = col; // and mark this row as visited - nzcolR++; - } - - // Reverse the list to get the topological ordering - Index nt = nzcolR-bi; - for(Index i = 0; i < nt/2; i++) std::swap(Ridx(bi+i), Ridx(nzcolR-i-1)); - - // Copy the current (curIdx,pcol) value of the input matrix - if(itp) tval(curIdx) = itp.value(); - else tval(curIdx) = Scalar(0); - - // Compute the pattern of Q(:,k) - if(curIdx > nonzeroCol && mark(curIdx) != col ) - { - Qidx(nzcolQ) = curIdx; // Add this row to the pattern of Q, - mark(curIdx) = col; // and mark it as visited - nzcolQ++; - } - } - - // Browse all the indexes of R(:,col) in reverse order - for (Index i = nzcolR-1; i >= 0; i--) - { - Index curIdx = m_pivotperm.indices()(Ridx(i)); - - // Apply the curIdx-th householder vector to the current column (temporarily stored into tval) - Scalar tdot(0); - - // First compute q' * tval - tdot = m_Q.col(curIdx).dot(tval); - - tdot *= m_hcoeffs(curIdx); - - // Then update tval = tval - q * tau - // FIXME: tval -= tdot * m_Q.col(curIdx) should amount to the same (need to check/add support for efficient "dense ?= sparse") - for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq) - tval(itq.row()) -= itq.value() * tdot; - - // Detect fill-in for the current column of Q - if(m_etree(Ridx(i)) == nonzeroCol) - { - for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq) - { - Index iQ = itq.row(); - if (mark(iQ) != col) - { - Qidx(nzcolQ++) = iQ; // Add this row to the pattern of Q, - mark(iQ) = col; // and mark it as visited - } - } - } - } // End update current column - - // Compute the Householder reflection that eliminate the current column - // FIXME this step should call the Householder module. - Scalar tau; - RealScalar beta; - Scalar c0 = nzcolQ ? tval(Qidx(0)) : Scalar(0); - - // First, the squared norm of Q((col+1):m, col) - RealScalar sqrNorm = 0.; - for (Index itq = 1; itq < nzcolQ; ++itq) sqrNorm += numext::abs2(tval(Qidx(itq))); - - if(sqrNorm == RealScalar(0) && numext::imag(c0) == RealScalar(0)) - { - tau = RealScalar(0); - beta = numext::real(c0); - tval(Qidx(0)) = 1; - } - else - { - using std::sqrt; - beta = sqrt(numext::abs2(c0) + sqrNorm); - if(numext::real(c0) >= RealScalar(0)) - beta = -beta; - tval(Qidx(0)) = 1; - for (Index itq = 1; itq < nzcolQ; ++itq) - tval(Qidx(itq)) /= (c0 - beta); - tau = numext::conj((beta-c0) / beta); - - } - - // Insert values in R - for (Index i = nzcolR-1; i >= 0; i--) - { - Index curIdx = Ridx(i); - if(curIdx < nonzeroCol) - { - m_R.insertBackByOuterInnerUnordered(col, curIdx) = tval(curIdx); - tval(curIdx) = Scalar(0.); - } - } - - if(abs(beta) >= m_threshold) - { - m_R.insertBackByOuterInner(col, nonzeroCol) = beta; - nonzeroCol++; - // The householder coefficient - m_hcoeffs(col) = tau; - // Record the householder reflections - for (Index itq = 0; itq < nzcolQ; ++itq) - { - Index iQ = Qidx(itq); - m_Q.insertBackByOuterInnerUnordered(col,iQ) = tval(iQ); - tval(iQ) = Scalar(0.); - } - } - else - { - // Zero pivot found: move implicitly this column to the end - m_hcoeffs(col) = Scalar(0); - for (Index j = nonzeroCol; j < n-1; j++) - std::swap(m_pivotperm.indices()(j), m_pivotperm.indices()[j+1]); - - // Recompute the column elimination tree - internal::coletree(m_pmat, m_etree, m_firstRowElt, m_pivotperm.indices().data()); - } - } - - // Finalize the column pointers of the sparse matrices R and Q - m_Q.finalize(); - m_Q.makeCompressed(); - m_R.finalize(); - m_R.makeCompressed(); - m_isQSorted = false; - - m_nonzeropivots = nonzeroCol; - - if(nonzeroCol<n) - { - // Permute the triangular factor to put the 'dead' columns to the end - MatrixType tempR(m_R); - m_R = tempR * m_pivotperm; - - // Update the column permutation - m_outputPerm_c = m_outputPerm_c * m_pivotperm; - } - - m_isInitialized = true; - m_factorizationIsok = true; - m_info = Success; -} - -namespace internal { - -template<typename _MatrixType, typename OrderingType, typename Rhs> -struct solve_retval<SparseQR<_MatrixType,OrderingType>, Rhs> - : solve_retval_base<SparseQR<_MatrixType,OrderingType>, Rhs> -{ - typedef SparseQR<_MatrixType,OrderingType> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; -template<typename _MatrixType, typename OrderingType, typename Rhs> -struct sparse_solve_retval<SparseQR<_MatrixType, OrderingType>, Rhs> - : sparse_solve_retval_base<SparseQR<_MatrixType, OrderingType>, Rhs> -{ - typedef SparseQR<_MatrixType, OrderingType> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec, Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - this->defaultEvalTo(dst); - } -}; -} // end namespace internal - -template <typename SparseQRType, typename Derived> -struct SparseQR_QProduct : ReturnByValue<SparseQR_QProduct<SparseQRType, Derived> > -{ - typedef typename SparseQRType::QRMatrixType MatrixType; - typedef typename SparseQRType::Scalar Scalar; - typedef typename SparseQRType::Index Index; - // Get the references - SparseQR_QProduct(const SparseQRType& qr, const Derived& other, bool transpose) : - m_qr(qr),m_other(other),m_transpose(transpose) {} - inline Index rows() const { return m_transpose ? m_qr.rows() : m_qr.cols(); } - inline Index cols() const { return m_other.cols(); } - - // Assign to a vector - template<typename DesType> - void evalTo(DesType& res) const - { - Index n = m_qr.cols(); - res = m_other; - if (m_transpose) - { - eigen_assert(m_qr.m_Q.rows() == m_other.rows() && "Non conforming object sizes"); - //Compute res = Q' * other column by column - for(Index j = 0; j < res.cols(); j++){ - for (Index k = 0; k < n; k++) - { - Scalar tau = Scalar(0); - tau = m_qr.m_Q.col(k).dot(res.col(j)); - if(tau==Scalar(0)) continue; - tau = tau * m_qr.m_hcoeffs(k); - res.col(j) -= tau * m_qr.m_Q.col(k); - } - } - } - else - { - eigen_assert(m_qr.m_Q.rows() == m_other.rows() && "Non conforming object sizes"); - // Compute res = Q' * other column by column - for(Index j = 0; j < res.cols(); j++) - { - for (Index k = n-1; k >=0; k--) - { - Scalar tau = Scalar(0); - tau = m_qr.m_Q.col(k).dot(res.col(j)); - if(tau==Scalar(0)) continue; - tau = tau * m_qr.m_hcoeffs(k); - res.col(j) -= tau * m_qr.m_Q.col(k); - } - } - } - } - - const SparseQRType& m_qr; - const Derived& m_other; - bool m_transpose; -}; - -template<typename SparseQRType> -struct SparseQRMatrixQReturnType : public EigenBase<SparseQRMatrixQReturnType<SparseQRType> > -{ - typedef typename SparseQRType::Index Index; - typedef typename SparseQRType::Scalar Scalar; - typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; - SparseQRMatrixQReturnType(const SparseQRType& qr) : m_qr(qr) {} - template<typename Derived> - SparseQR_QProduct<SparseQRType, Derived> operator*(const MatrixBase<Derived>& other) - { - return SparseQR_QProduct<SparseQRType,Derived>(m_qr,other.derived(),false); - } - SparseQRMatrixQTransposeReturnType<SparseQRType> adjoint() const - { - return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr); - } - inline Index rows() const { return m_qr.rows(); } - inline Index cols() const { return m_qr.cols(); } - // To use for operations with the transpose of Q - SparseQRMatrixQTransposeReturnType<SparseQRType> transpose() const - { - return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr); - } - template<typename Dest> void evalTo(MatrixBase<Dest>& dest) const - { - dest.derived() = m_qr.matrixQ() * Dest::Identity(m_qr.rows(), m_qr.rows()); - } - template<typename Dest> void evalTo(SparseMatrixBase<Dest>& dest) const - { - Dest idMat(m_qr.rows(), m_qr.rows()); - idMat.setIdentity(); - // Sort the sparse householder reflectors if needed - const_cast<SparseQRType *>(&m_qr)->sort_matrix_Q(); - dest.derived() = SparseQR_QProduct<SparseQRType, Dest>(m_qr, idMat, false); - } - - const SparseQRType& m_qr; -}; - -template<typename SparseQRType> -struct SparseQRMatrixQTransposeReturnType -{ - SparseQRMatrixQTransposeReturnType(const SparseQRType& qr) : m_qr(qr) {} - template<typename Derived> - SparseQR_QProduct<SparseQRType,Derived> operator*(const MatrixBase<Derived>& other) - { - return SparseQR_QProduct<SparseQRType,Derived>(m_qr,other.derived(), true); - } - const SparseQRType& m_qr; -}; - -} // end namespace Eigen - -#endif diff --git a/third_party/eigen3/Eigen/src/StlSupport/StdDeque.h b/third_party/eigen3/Eigen/src/StlSupport/StdDeque.h deleted file mode 100644 index 4ee8e5c10a..0000000000 --- a/third_party/eigen3/Eigen/src/StlSupport/StdDeque.h +++ /dev/null @@ -1,134 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_STDDEQUE_H -#define EIGEN_STDDEQUE_H - -#include "Eigen/src/StlSupport/details.h" - -// Define the explicit instantiation (e.g. necessary for the Intel compiler) -#if defined(__INTEL_COMPILER) || defined(__GNUC__) - #define EIGEN_EXPLICIT_STL_DEQUE_INSTANTIATION(...) template class std::deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> >; -#else - #define EIGEN_EXPLICIT_STL_DEQUE_INSTANTIATION(...) -#endif - -/** - * This section contains a convenience MACRO which allows an easy specialization of - * std::deque such that for data types with alignment issues the correct allocator - * is used automatically. - */ -#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...) \ -EIGEN_EXPLICIT_STL_DEQUE_INSTANTIATION(__VA_ARGS__) \ -namespace std \ -{ \ - template<typename _Ay> \ - class deque<__VA_ARGS__, _Ay> \ - : public deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \ - { \ - typedef deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > deque_base; \ - public: \ - typedef __VA_ARGS__ value_type; \ - typedef typename deque_base::allocator_type allocator_type; \ - typedef typename deque_base::size_type size_type; \ - typedef typename deque_base::iterator iterator; \ - explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {} \ - template<typename InputIterator> \ - deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : deque_base(first, last, a) {} \ - deque(const deque& c) : deque_base(c) {} \ - explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \ - deque(iterator start, iterator end) : deque_base(start, end) {} \ - deque& operator=(const deque& x) { \ - deque_base::operator=(x); \ - return *this; \ - } \ - }; \ -} - -// check whether we really need the std::deque specialization -#if !(defined(_GLIBCXX_DEQUE) && (!EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::deque::resize(size_type,const T&). */ - -namespace std { - -#define EIGEN_STD_DEQUE_SPECIALIZATION_BODY \ - public: \ - typedef T value_type; \ - typedef typename deque_base::allocator_type allocator_type; \ - typedef typename deque_base::size_type size_type; \ - typedef typename deque_base::iterator iterator; \ - typedef typename deque_base::const_iterator const_iterator; \ - explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {} \ - template<typename InputIterator> \ - deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \ - : deque_base(first, last, a) {} \ - deque(const deque& c) : deque_base(c) {} \ - explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \ - deque(iterator start, iterator end) : deque_base(start, end) {} \ - deque& operator=(const deque& x) { \ - deque_base::operator=(x); \ - return *this; \ - } - - template<typename T> - class deque<T,EIGEN_ALIGNED_ALLOCATOR<T> > - : public deque<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T), - Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > -{ - typedef deque<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T), - Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > deque_base; - EIGEN_STD_DEQUE_SPECIALIZATION_BODY - - void resize(size_type new_size) - { resize(new_size, T()); } - -#if defined(_DEQUE_) - // workaround MSVC std::deque implementation - void resize(size_type new_size, const value_type& x) - { - if (deque_base::size() < new_size) - deque_base::_Insert_n(deque_base::end(), new_size - deque_base::size(), x); - else if (new_size < deque_base::size()) - deque_base::erase(deque_base::begin() + new_size, deque_base::end()); - } - void push_back(const value_type& x) - { deque_base::push_back(x); } - void push_front(const value_type& x) - { deque_base::push_front(x); } - using deque_base::insert; - iterator insert(const_iterator position, const value_type& x) - { return deque_base::insert(position,x); } - void insert(const_iterator position, size_type new_size, const value_type& x) - { deque_base::insert(position, new_size, x); } -#elif defined(_GLIBCXX_DEQUE) && EIGEN_GNUC_AT_LEAST(4,2) - // workaround GCC std::deque implementation - void resize(size_type new_size, const value_type& x) - { - if (new_size < deque_base::size()) - deque_base::_M_erase_at_end(this->_M_impl._M_start + new_size); - else - deque_base::insert(deque_base::end(), new_size - deque_base::size(), x); - } -#else - // either GCC 4.1 or non-GCC - // default implementation which should always work. - void resize(size_type new_size, const value_type& x) - { - if (new_size < deque_base::size()) - deque_base::erase(deque_base::begin() + new_size, deque_base::end()); - else if (new_size > deque_base::size()) - deque_base::insert(deque_base::end(), new_size - deque_base::size(), x); - } -#endif - }; -} - -#endif // check whether specialization is actually required - -#endif // EIGEN_STDDEQUE_H diff --git a/third_party/eigen3/Eigen/src/StlSupport/StdList.h b/third_party/eigen3/Eigen/src/StlSupport/StdList.h deleted file mode 100644 index 627381ecec..0000000000 --- a/third_party/eigen3/Eigen/src/StlSupport/StdList.h +++ /dev/null @@ -1,114 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_STDLIST_H -#define EIGEN_STDLIST_H - -#include "Eigen/src/StlSupport/details.h" - -// Define the explicit instantiation (e.g. necessary for the Intel compiler) -#if defined(__INTEL_COMPILER) || defined(__GNUC__) - #define EIGEN_EXPLICIT_STL_LIST_INSTANTIATION(...) template class std::list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> >; -#else - #define EIGEN_EXPLICIT_STL_LIST_INSTANTIATION(...) -#endif - -/** - * This section contains a convenience MACRO which allows an easy specialization of - * std::list such that for data types with alignment issues the correct allocator - * is used automatically. - */ -#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...) \ -EIGEN_EXPLICIT_STL_LIST_INSTANTIATION(__VA_ARGS__) \ -namespace std \ -{ \ - template<typename _Ay> \ - class list<__VA_ARGS__, _Ay> \ - : public list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \ - { \ - typedef list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > list_base; \ - public: \ - typedef __VA_ARGS__ value_type; \ - typedef typename list_base::allocator_type allocator_type; \ - typedef typename list_base::size_type size_type; \ - typedef typename list_base::iterator iterator; \ - explicit list(const allocator_type& a = allocator_type()) : list_base(a) {} \ - template<typename InputIterator> \ - list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : list_base(first, last, a) {} \ - list(const list& c) : list_base(c) {} \ - explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \ - list(iterator start, iterator end) : list_base(start, end) {} \ - list& operator=(const list& x) { \ - list_base::operator=(x); \ - return *this; \ - } \ - }; \ -} - -// check whether we really need the std::vector specialization -#if !(defined(_GLIBCXX_VECTOR) && (!EIGEN_GNUC_AT_LEAST(4,1))) /* Note that before gcc-4.1 we already have: std::list::resize(size_type,const T&). */ - -namespace std -{ - -#define EIGEN_STD_LIST_SPECIALIZATION_BODY \ - public: \ - typedef T value_type; \ - typedef typename list_base::allocator_type allocator_type; \ - typedef typename list_base::size_type size_type; \ - typedef typename list_base::iterator iterator; \ - typedef typename list_base::const_iterator const_iterator; \ - explicit list(const allocator_type& a = allocator_type()) : list_base(a) {} \ - template<typename InputIterator> \ - list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \ - : list_base(first, last, a) {} \ - list(const list& c) : list_base(c) {} \ - explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \ - list(iterator start, iterator end) : list_base(start, end) {} \ - list& operator=(const list& x) { \ - list_base::operator=(x); \ - return *this; \ - } - - template<typename T> - class list<T,EIGEN_ALIGNED_ALLOCATOR<T> > - : public list<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T), - Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > - { - typedef list<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T), - Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > list_base; - EIGEN_STD_LIST_SPECIALIZATION_BODY - - void resize(size_type new_size) - { resize(new_size, T()); } - - void resize(size_type new_size, const value_type& x) - { - if (list_base::size() < new_size) - list_base::insert(list_base::end(), new_size - list_base::size(), x); - else - while (new_size < list_base::size()) list_base::pop_back(); - } - -#if defined(_LIST_) - // workaround MSVC std::list implementation - void push_back(const value_type& x) - { list_base::push_back(x); } - using list_base::insert; - iterator insert(const_iterator position, const value_type& x) - { return list_base::insert(position,x); } - void insert(const_iterator position, size_type new_size, const value_type& x) - { list_base::insert(position, new_size, x); } -#endif - }; -} - -#endif // check whether specialization is actually required - -#endif // EIGEN_STDLIST_H diff --git a/third_party/eigen3/Eigen/src/StlSupport/StdVector.h b/third_party/eigen3/Eigen/src/StlSupport/StdVector.h deleted file mode 100644 index 40a9abefa8..0000000000 --- a/third_party/eigen3/Eigen/src/StlSupport/StdVector.h +++ /dev/null @@ -1,126 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_STDVECTOR_H -#define EIGEN_STDVECTOR_H - -#include "Eigen/src/StlSupport/details.h" - -/** - * This section contains a convenience MACRO which allows an easy specialization of - * std::vector such that for data types with alignment issues the correct allocator - * is used automatically. - */ -#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) \ -namespace std \ -{ \ - template<> \ - class vector<__VA_ARGS__, std::allocator<__VA_ARGS__> > \ - : public vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \ - { \ - typedef vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > vector_base; \ - public: \ - typedef __VA_ARGS__ value_type; \ - typedef vector_base::allocator_type allocator_type; \ - typedef vector_base::size_type size_type; \ - typedef vector_base::iterator iterator; \ - explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {} \ - template<typename InputIterator> \ - vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : vector_base(first, last, a) {} \ - vector(const vector& c) : vector_base(c) {} \ - explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \ - vector(iterator start, iterator end) : vector_base(start, end) {} \ - vector& operator=(const vector& x) { \ - vector_base::operator=(x); \ - return *this; \ - } \ - }; \ -} - -namespace std { - -#define EIGEN_STD_VECTOR_SPECIALIZATION_BODY \ - public: \ - typedef T value_type; \ - typedef typename vector_base::allocator_type allocator_type; \ - typedef typename vector_base::size_type size_type; \ - typedef typename vector_base::iterator iterator; \ - typedef typename vector_base::const_iterator const_iterator; \ - explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {} \ - template<typename InputIterator> \ - vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) \ - : vector_base(first, last, a) {} \ - vector(const vector& c) : vector_base(c) {} \ - explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \ - vector(iterator start, iterator end) : vector_base(start, end) {} \ - vector& operator=(const vector& x) { \ - vector_base::operator=(x); \ - return *this; \ - } - - template<typename T> - class vector<T,EIGEN_ALIGNED_ALLOCATOR<T> > - : public vector<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T), - Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > -{ - typedef vector<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T), - Eigen::aligned_allocator_indirection<EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T)> > vector_base; - EIGEN_STD_VECTOR_SPECIALIZATION_BODY - - void resize(size_type new_size) - { resize(new_size, T()); } - -#if defined(_VECTOR_) - // workaround MSVC std::vector implementation - void resize(size_type new_size, const value_type& x) - { - if (vector_base::size() < new_size) - vector_base::_Insert_n(vector_base::end(), new_size - vector_base::size(), x); - else if (new_size < vector_base::size()) - vector_base::erase(vector_base::begin() + new_size, vector_base::end()); - } - void push_back(const value_type& x) - { vector_base::push_back(x); } - using vector_base::insert; - iterator insert(const_iterator position, const value_type& x) - { return vector_base::insert(position,x); } - void insert(const_iterator position, size_type new_size, const value_type& x) - { vector_base::insert(position, new_size, x); } -#elif defined(_GLIBCXX_VECTOR) && (!(EIGEN_GNUC_AT_LEAST(4,1))) - /* Note that before gcc-4.1 we already have: std::vector::resize(size_type,const T&). - * However, this specialization is still needed to make the above EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION trick to work. */ - void resize(size_type new_size, const value_type& x) - { - vector_base::resize(new_size,x); - } -#elif defined(_GLIBCXX_VECTOR) && EIGEN_GNUC_AT_LEAST(4,2) - // workaround GCC std::vector implementation - void resize(size_type new_size, const value_type& x) - { - if (new_size < vector_base::size()) - vector_base::_M_erase_at_end(this->_M_impl._M_start + new_size); - else - vector_base::insert(vector_base::end(), new_size - vector_base::size(), x); - } -#else - // either GCC 4.1 or non-GCC - // default implementation which should always work. - void resize(size_type new_size, const value_type& x) - { - if (new_size < vector_base::size()) - vector_base::erase(vector_base::begin() + new_size, vector_base::end()); - else if (new_size > vector_base::size()) - vector_base::insert(vector_base::end(), new_size - vector_base::size(), x); - } -#endif - }; -} - -#endif // EIGEN_STDVECTOR_H diff --git a/third_party/eigen3/Eigen/src/StlSupport/details.h b/third_party/eigen3/Eigen/src/StlSupport/details.h deleted file mode 100644 index e42ec024f2..0000000000 --- a/third_party/eigen3/Eigen/src/StlSupport/details.h +++ /dev/null @@ -1,84 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_STL_DETAILS_H -#define EIGEN_STL_DETAILS_H - -#ifndef EIGEN_ALIGNED_ALLOCATOR - #define EIGEN_ALIGNED_ALLOCATOR Eigen::aligned_allocator -#endif - -namespace Eigen { - - // This one is needed to prevent reimplementing the whole std::vector. - template <class T> - class aligned_allocator_indirection : public EIGEN_ALIGNED_ALLOCATOR<T> - { - public: - typedef size_t size_type; - typedef ptrdiff_t difference_type; - typedef T* pointer; - typedef const T* const_pointer; - typedef T& reference; - typedef const T& const_reference; - typedef T value_type; - - template<class U> - struct rebind - { - typedef aligned_allocator_indirection<U> other; - }; - - aligned_allocator_indirection() {} - aligned_allocator_indirection(const aligned_allocator_indirection& ) : EIGEN_ALIGNED_ALLOCATOR<T>() {} - aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<T>& ) {} - template<class U> - aligned_allocator_indirection(const aligned_allocator_indirection<U>& ) {} - template<class U> - aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<U>& ) {} - ~aligned_allocator_indirection() {} - }; - -#if EIGEN_COMP_MSVC - - // sometimes, MSVC detects, at compile time, that the argument x - // in std::vector::resize(size_t s,T x) won't be aligned and generate an error - // even if this function is never called. Whence this little wrapper. -#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) \ - typename Eigen::internal::conditional< \ - Eigen::internal::is_arithmetic<T>::value, \ - T, \ - Eigen::internal::workaround_msvc_stl_support<T> \ - >::type - - namespace internal { - template<typename T> struct workaround_msvc_stl_support : public T - { - inline workaround_msvc_stl_support() : T() {} - inline workaround_msvc_stl_support(const T& other) : T(other) {} - inline operator T& () { return *static_cast<T*>(this); } - inline operator const T& () const { return *static_cast<const T*>(this); } - template<typename OtherT> - inline T& operator=(const OtherT& other) - { T::operator=(other); return *this; } - inline workaround_msvc_stl_support& operator=(const workaround_msvc_stl_support& other) - { T::operator=(other); return *this; } - }; - } - -#else - -#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) T - -#endif - -} - -#endif // EIGEN_STL_DETAILS_H diff --git a/third_party/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h b/third_party/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h deleted file mode 100644 index bcb355760c..0000000000 --- a/third_party/eigen3/Eigen/src/SuperLUSupport/SuperLUSupport.h +++ /dev/null @@ -1,1026 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SUPERLUSUPPORT_H -#define EIGEN_SUPERLUSUPPORT_H - -namespace Eigen { - -#define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE) \ - extern "C" { \ - typedef struct { FLOATTYPE for_lu; FLOATTYPE total_needed; int expansions; } PREFIX##mem_usage_t; \ - extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *, \ - char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *, \ - void *, int, SuperMatrix *, SuperMatrix *, \ - FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, \ - PREFIX##mem_usage_t *, SuperLUStat_t *, int *); \ - } \ - inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A, \ - int *perm_c, int *perm_r, int *etree, char *equed, \ - FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L, \ - SuperMatrix *U, void *work, int lwork, \ - SuperMatrix *B, SuperMatrix *X, \ - FLOATTYPE *recip_pivot_growth, \ - FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr, \ - SuperLUStat_t *stats, int *info, KEYTYPE) { \ - PREFIX##mem_usage_t mem_usage; \ - PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L, \ - U, work, lwork, B, X, recip_pivot_growth, rcond, \ - ferr, berr, &mem_usage, stats, info); \ - return mem_usage.for_lu; /* bytes used by the factor storage */ \ - } - -DECL_GSSVX(s,float,float) -DECL_GSSVX(c,float,std::complex<float>) -DECL_GSSVX(d,double,double) -DECL_GSSVX(z,double,std::complex<double>) - -#ifdef MILU_ALPHA -#define EIGEN_SUPERLU_HAS_ILU -#endif - -#ifdef EIGEN_SUPERLU_HAS_ILU - -// similarly for the incomplete factorization using gsisx -#define DECL_GSISX(PREFIX,FLOATTYPE,KEYTYPE) \ - extern "C" { \ - extern void PREFIX##gsisx(superlu_options_t *, SuperMatrix *, int *, int *, int *, \ - char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *, \ - void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *, FLOATTYPE *, \ - PREFIX##mem_usage_t *, SuperLUStat_t *, int *); \ - } \ - inline float SuperLU_gsisx(superlu_options_t *options, SuperMatrix *A, \ - int *perm_c, int *perm_r, int *etree, char *equed, \ - FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L, \ - SuperMatrix *U, void *work, int lwork, \ - SuperMatrix *B, SuperMatrix *X, \ - FLOATTYPE *recip_pivot_growth, \ - FLOATTYPE *rcond, \ - SuperLUStat_t *stats, int *info, KEYTYPE) { \ - PREFIX##mem_usage_t mem_usage; \ - PREFIX##gsisx(options, A, perm_c, perm_r, etree, equed, R, C, L, \ - U, work, lwork, B, X, recip_pivot_growth, rcond, \ - &mem_usage, stats, info); \ - return mem_usage.for_lu; /* bytes used by the factor storage */ \ - } - -DECL_GSISX(s,float,float) -DECL_GSISX(c,float,std::complex<float>) -DECL_GSISX(d,double,double) -DECL_GSISX(z,double,std::complex<double>) - -#endif - -template<typename MatrixType> -struct SluMatrixMapHelper; - -/** \internal - * - * A wrapper class for SuperLU matrices. It supports only compressed sparse matrices - * and dense matrices. Supernodal and other fancy format are not supported by this wrapper. - * - * This wrapper class mainly aims to avoids the need of dynamic allocation of the storage structure. - */ -struct SluMatrix : SuperMatrix -{ - SluMatrix() - { - Store = &storage; - } - - SluMatrix(const SluMatrix& other) - : SuperMatrix(other) - { - Store = &storage; - storage = other.storage; - } - - SluMatrix& operator=(const SluMatrix& other) - { - SuperMatrix::operator=(static_cast<const SuperMatrix&>(other)); - Store = &storage; - storage = other.storage; - return *this; - } - - struct - { - union {int nnz;int lda;}; - void *values; - int *innerInd; - int *outerInd; - } storage; - - void setStorageType(Stype_t t) - { - Stype = t; - if (t==SLU_NC || t==SLU_NR || t==SLU_DN) - Store = &storage; - else - { - eigen_assert(false && "storage type not supported"); - Store = 0; - } - } - - template<typename Scalar> - void setScalarType() - { - if (internal::is_same<Scalar,float>::value) - Dtype = SLU_S; - else if (internal::is_same<Scalar,double>::value) - Dtype = SLU_D; - else if (internal::is_same<Scalar,std::complex<float> >::value) - Dtype = SLU_C; - else if (internal::is_same<Scalar,std::complex<double> >::value) - Dtype = SLU_Z; - else - { - eigen_assert(false && "Scalar type not supported by SuperLU"); - } - } - - template<typename MatrixType> - static SluMatrix Map(MatrixBase<MatrixType>& _mat) - { - MatrixType& mat(_mat.derived()); - eigen_assert( ((MatrixType::Flags&RowMajorBit)!=RowMajorBit) && "row-major dense matrices are not supported by SuperLU"); - SluMatrix res; - res.setStorageType(SLU_DN); - res.setScalarType<typename MatrixType::Scalar>(); - res.Mtype = SLU_GE; - - res.nrow = mat.rows(); - res.ncol = mat.cols(); - - res.storage.lda = MatrixType::IsVectorAtCompileTime ? mat.size() : mat.outerStride(); - res.storage.values = (void*)(mat.data()); - return res; - } - - template<typename MatrixType> - static SluMatrix Map(SparseMatrixBase<MatrixType>& mat) - { - SluMatrix res; - if ((MatrixType::Flags&RowMajorBit)==RowMajorBit) - { - res.setStorageType(SLU_NR); - res.nrow = mat.cols(); - res.ncol = mat.rows(); - } - else - { - res.setStorageType(SLU_NC); - res.nrow = mat.rows(); - res.ncol = mat.cols(); - } - - res.Mtype = SLU_GE; - - res.storage.nnz = mat.nonZeros(); - res.storage.values = mat.derived().valuePtr(); - res.storage.innerInd = mat.derived().innerIndexPtr(); - res.storage.outerInd = mat.derived().outerIndexPtr(); - - res.setScalarType<typename MatrixType::Scalar>(); - - // FIXME the following is not very accurate - if (MatrixType::Flags & Upper) - res.Mtype = SLU_TRU; - if (MatrixType::Flags & Lower) - res.Mtype = SLU_TRL; - - eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU"); - - return res; - } -}; - -template<typename Scalar, int Rows, int Cols, int Options, int MRows, int MCols> -struct SluMatrixMapHelper<Matrix<Scalar,Rows,Cols,Options,MRows,MCols> > -{ - typedef Matrix<Scalar,Rows,Cols,Options,MRows,MCols> MatrixType; - static void run(MatrixType& mat, SluMatrix& res) - { - eigen_assert( ((Options&RowMajor)!=RowMajor) && "row-major dense matrices is not supported by SuperLU"); - res.setStorageType(SLU_DN); - res.setScalarType<Scalar>(); - res.Mtype = SLU_GE; - - res.nrow = mat.rows(); - res.ncol = mat.cols(); - - res.storage.lda = mat.outerStride(); - res.storage.values = mat.data(); - } -}; - -template<typename Derived> -struct SluMatrixMapHelper<SparseMatrixBase<Derived> > -{ - typedef Derived MatrixType; - static void run(MatrixType& mat, SluMatrix& res) - { - if ((MatrixType::Flags&RowMajorBit)==RowMajorBit) - { - res.setStorageType(SLU_NR); - res.nrow = mat.cols(); - res.ncol = mat.rows(); - } - else - { - res.setStorageType(SLU_NC); - res.nrow = mat.rows(); - res.ncol = mat.cols(); - } - - res.Mtype = SLU_GE; - - res.storage.nnz = mat.nonZeros(); - res.storage.values = mat.valuePtr(); - res.storage.innerInd = mat.innerIndexPtr(); - res.storage.outerInd = mat.outerIndexPtr(); - - res.setScalarType<typename MatrixType::Scalar>(); - - // FIXME the following is not very accurate - if (MatrixType::Flags & Upper) - res.Mtype = SLU_TRU; - if (MatrixType::Flags & Lower) - res.Mtype = SLU_TRL; - - eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU"); - } -}; - -namespace internal { - -template<typename MatrixType> -SluMatrix asSluMatrix(MatrixType& mat) -{ - return SluMatrix::Map(mat); -} - -/** View a Super LU matrix as an Eigen expression */ -template<typename Scalar, int Flags, typename Index> -MappedSparseMatrix<Scalar,Flags,Index> map_superlu(SluMatrix& sluMat) -{ - eigen_assert((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR - || (Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC); - - Index outerSize = (Flags&RowMajor)==RowMajor ? sluMat.ncol : sluMat.nrow; - - return MappedSparseMatrix<Scalar,Flags,Index>( - sluMat.nrow, sluMat.ncol, sluMat.storage.outerInd[outerSize], - sluMat.storage.outerInd, sluMat.storage.innerInd, reinterpret_cast<Scalar*>(sluMat.storage.values) ); -} - -} // end namespace internal - -/** \ingroup SuperLUSupport_Module - * \class SuperLUBase - * \brief The base class for the direct and incomplete LU factorization of SuperLU - */ -template<typename _MatrixType, typename Derived> -class SuperLUBase : internal::noncopyable -{ - public: - typedef _MatrixType MatrixType; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef Matrix<Scalar,Dynamic,1> Vector; - typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType; - typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType; - typedef SparseMatrix<Scalar> LUMatrixType; - - public: - - SuperLUBase() {} - - ~SuperLUBase() - { - clearFactors(); - } - - Derived& derived() { return *static_cast<Derived*>(this); } - const Derived& derived() const { return *static_cast<const Derived*>(this); } - - inline Index rows() const { return m_matrix.rows(); } - inline Index cols() const { return m_matrix.cols(); } - - /** \returns a reference to the Super LU option object to configure the Super LU algorithms. */ - inline superlu_options_t& options() { return m_sluOptions; } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the matrix.appears to be negative. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_info; - } - - /** Computes the sparse Cholesky decomposition of \a matrix */ - void compute(const MatrixType& matrix) - { - derived().analyzePattern(matrix); - derived().factorize(matrix); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::solve_retval<SuperLUBase, Rhs> solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "SuperLU is not initialized."); - eigen_assert(rows()==b.rows() - && "SuperLU::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<SuperLUBase, Rhs>(*this, b.derived()); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::sparse_solve_retval<SuperLUBase, Rhs> solve(const SparseMatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "SuperLU is not initialized."); - eigen_assert(rows()==b.rows() - && "SuperLU::solve(): invalid number of rows of the right hand side matrix b"); - return internal::sparse_solve_retval<SuperLUBase, Rhs>(*this, b.derived()); - } - - /** Performs a symbolic decomposition on the sparcity of \a matrix. - * - * This function is particularly useful when solving for several problems having the same structure. - * - * \sa factorize() - */ - void analyzePattern(const MatrixType& /*matrix*/) - { - m_isInitialized = true; - m_info = Success; - m_analysisIsOk = true; - m_factorizationIsOk = false; - } - - template<typename Stream> - void dumpMemory(Stream& /*s*/) - {} - - protected: - - void initFactorization(const MatrixType& a) - { - set_default_options(&this->m_sluOptions); - - const int size = a.rows(); - m_matrix = a; - - m_sluA = internal::asSluMatrix(m_matrix); - clearFactors(); - - m_p.resize(size); - m_q.resize(size); - m_sluRscale.resize(size); - m_sluCscale.resize(size); - m_sluEtree.resize(size); - - // set empty B and X - m_sluB.setStorageType(SLU_DN); - m_sluB.setScalarType<Scalar>(); - m_sluB.Mtype = SLU_GE; - m_sluB.storage.values = 0; - m_sluB.nrow = 0; - m_sluB.ncol = 0; - m_sluB.storage.lda = size; - m_sluX = m_sluB; - - m_extractedDataAreDirty = true; - } - - void init() - { - m_info = InvalidInput; - m_isInitialized = false; - m_sluL.Store = 0; - m_sluU.Store = 0; - } - - void extractData() const; - - void clearFactors() - { - if(m_sluL.Store) - Destroy_SuperNode_Matrix(&m_sluL); - if(m_sluU.Store) - Destroy_CompCol_Matrix(&m_sluU); - - m_sluL.Store = 0; - m_sluU.Store = 0; - - memset(&m_sluL,0,sizeof m_sluL); - memset(&m_sluU,0,sizeof m_sluU); - } - - // cached data to reduce reallocation, etc. - mutable LUMatrixType m_l; - mutable LUMatrixType m_u; - mutable IntColVectorType m_p; - mutable IntRowVectorType m_q; - - mutable LUMatrixType m_matrix; // copy of the factorized matrix - mutable SluMatrix m_sluA; - mutable SuperMatrix m_sluL, m_sluU; - mutable SluMatrix m_sluB, m_sluX; - mutable SuperLUStat_t m_sluStat; - mutable superlu_options_t m_sluOptions; - mutable std::vector<int> m_sluEtree; - mutable Matrix<RealScalar,Dynamic,1> m_sluRscale, m_sluCscale; - mutable Matrix<RealScalar,Dynamic,1> m_sluFerr, m_sluBerr; - mutable char m_sluEqued; - - mutable ComputationInfo m_info; - bool m_isInitialized; - int m_factorizationIsOk; - int m_analysisIsOk; - mutable bool m_extractedDataAreDirty; - - private: - SuperLUBase(SuperLUBase& ) { } -}; - - -/** \ingroup SuperLUSupport_Module - * \class SuperLU - * \brief A sparse direct LU factorization and solver based on the SuperLU library - * - * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization - * using the SuperLU library. The sparse matrix A must be squared and invertible. The vectors or matrices - * X and B can be either dense or sparse. - * - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename _MatrixType> -class SuperLU : public SuperLUBase<_MatrixType,SuperLU<_MatrixType> > -{ - public: - typedef SuperLUBase<_MatrixType,SuperLU> Base; - typedef _MatrixType MatrixType; - typedef typename Base::Scalar Scalar; - typedef typename Base::RealScalar RealScalar; - typedef typename Base::Index Index; - typedef typename Base::IntRowVectorType IntRowVectorType; - typedef typename Base::IntColVectorType IntColVectorType; - typedef typename Base::LUMatrixType LUMatrixType; - typedef TriangularView<LUMatrixType, Lower|UnitDiag> LMatrixType; - typedef TriangularView<LUMatrixType, Upper> UMatrixType; - - public: - - SuperLU() : Base() { init(); } - - SuperLU(const MatrixType& matrix) : Base() - { - init(); - Base::compute(matrix); - } - - ~SuperLU() - { - } - - /** Performs a symbolic decomposition on the sparcity of \a matrix. - * - * This function is particularly useful when solving for several problems having the same structure. - * - * \sa factorize() - */ - void analyzePattern(const MatrixType& matrix) - { - m_info = InvalidInput; - m_isInitialized = false; - Base::analyzePattern(matrix); - } - - /** Performs a numeric decomposition of \a matrix - * - * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed. - * - * \sa analyzePattern() - */ - void factorize(const MatrixType& matrix); - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** \internal */ - template<typename Rhs,typename Dest> - void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const; - #endif // EIGEN_PARSED_BY_DOXYGEN - - inline const LMatrixType& matrixL() const - { - if (m_extractedDataAreDirty) this->extractData(); - return m_l; - } - - inline const UMatrixType& matrixU() const - { - if (m_extractedDataAreDirty) this->extractData(); - return m_u; - } - - inline const IntColVectorType& permutationP() const - { - if (m_extractedDataAreDirty) this->extractData(); - return m_p; - } - - inline const IntRowVectorType& permutationQ() const - { - if (m_extractedDataAreDirty) this->extractData(); - return m_q; - } - - Scalar determinant() const; - - protected: - - using Base::m_matrix; - using Base::m_sluOptions; - using Base::m_sluA; - using Base::m_sluB; - using Base::m_sluX; - using Base::m_p; - using Base::m_q; - using Base::m_sluEtree; - using Base::m_sluEqued; - using Base::m_sluRscale; - using Base::m_sluCscale; - using Base::m_sluL; - using Base::m_sluU; - using Base::m_sluStat; - using Base::m_sluFerr; - using Base::m_sluBerr; - using Base::m_l; - using Base::m_u; - - using Base::m_analysisIsOk; - using Base::m_factorizationIsOk; - using Base::m_extractedDataAreDirty; - using Base::m_isInitialized; - using Base::m_info; - - void init() - { - Base::init(); - - set_default_options(&this->m_sluOptions); - m_sluOptions.PrintStat = NO; - m_sluOptions.ConditionNumber = NO; - m_sluOptions.Trans = NOTRANS; - m_sluOptions.ColPerm = COLAMD; - } - - - private: - SuperLU(SuperLU& ) { } -}; - -template<typename MatrixType> -void SuperLU<MatrixType>::factorize(const MatrixType& a) -{ - eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); - if(!m_analysisIsOk) - { - m_info = InvalidInput; - return; - } - - this->initFactorization(a); - - m_sluOptions.ColPerm = COLAMD; - int info = 0; - RealScalar recip_pivot_growth, rcond; - RealScalar ferr, berr; - - StatInit(&m_sluStat); - SuperLU_gssvx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0], - &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0], - &m_sluL, &m_sluU, - NULL, 0, - &m_sluB, &m_sluX, - &recip_pivot_growth, &rcond, - &ferr, &berr, - &m_sluStat, &info, Scalar()); - StatFree(&m_sluStat); - - m_extractedDataAreDirty = true; - - // FIXME how to better check for errors ??? - m_info = info == 0 ? Success : NumericalIssue; - m_factorizationIsOk = true; -} - -template<typename MatrixType> -template<typename Rhs,typename Dest> -void SuperLU<MatrixType>::_solve(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const -{ - eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()"); - - const int size = m_matrix.rows(); - const int rhsCols = b.cols(); - eigen_assert(size==b.rows()); - - m_sluOptions.Trans = NOTRANS; - m_sluOptions.Fact = FACTORED; - m_sluOptions.IterRefine = NOREFINE; - - - m_sluFerr.resize(rhsCols); - m_sluBerr.resize(rhsCols); - m_sluB = SluMatrix::Map(b.const_cast_derived()); - m_sluX = SluMatrix::Map(x.derived()); - - typename Rhs::PlainObject b_cpy; - if(m_sluEqued!='N') - { - b_cpy = b; - m_sluB = SluMatrix::Map(b_cpy.const_cast_derived()); - } - - StatInit(&m_sluStat); - int info = 0; - RealScalar recip_pivot_growth, rcond; - SuperLU_gssvx(&m_sluOptions, &m_sluA, - m_q.data(), m_p.data(), - &m_sluEtree[0], &m_sluEqued, - &m_sluRscale[0], &m_sluCscale[0], - &m_sluL, &m_sluU, - NULL, 0, - &m_sluB, &m_sluX, - &recip_pivot_growth, &rcond, - &m_sluFerr[0], &m_sluBerr[0], - &m_sluStat, &info, Scalar()); - StatFree(&m_sluStat); - m_info = info==0 ? Success : NumericalIssue; -} - -// the code of this extractData() function has been adapted from the SuperLU's Matlab support code, -// -// Copyright (c) 1994 by Xerox Corporation. All rights reserved. -// -// THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY -// EXPRESSED OR IMPLIED. ANY USE IS AT YOUR OWN RISK. -// -template<typename MatrixType, typename Derived> -void SuperLUBase<MatrixType,Derived>::extractData() const -{ - eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for extracting factors, you must first call either compute() or analyzePattern()/factorize()"); - if (m_extractedDataAreDirty) - { - int upper; - int fsupc, istart, nsupr; - int lastl = 0, lastu = 0; - SCformat *Lstore = static_cast<SCformat*>(m_sluL.Store); - NCformat *Ustore = static_cast<NCformat*>(m_sluU.Store); - Scalar *SNptr; - - const int size = m_matrix.rows(); - m_l.resize(size,size); - m_l.resizeNonZeros(Lstore->nnz); - m_u.resize(size,size); - m_u.resizeNonZeros(Ustore->nnz); - - int* Lcol = m_l.outerIndexPtr(); - int* Lrow = m_l.innerIndexPtr(); - Scalar* Lval = m_l.valuePtr(); - - int* Ucol = m_u.outerIndexPtr(); - int* Urow = m_u.innerIndexPtr(); - Scalar* Uval = m_u.valuePtr(); - - Ucol[0] = 0; - Ucol[0] = 0; - - /* for each supernode */ - for (int k = 0; k <= Lstore->nsuper; ++k) - { - fsupc = L_FST_SUPC(k); - istart = L_SUB_START(fsupc); - nsupr = L_SUB_START(fsupc+1) - istart; - upper = 1; - - /* for each column in the supernode */ - for (int j = fsupc; j < L_FST_SUPC(k+1); ++j) - { - SNptr = &((Scalar*)Lstore->nzval)[L_NZ_START(j)]; - - /* Extract U */ - for (int i = U_NZ_START(j); i < U_NZ_START(j+1); ++i) - { - Uval[lastu] = ((Scalar*)Ustore->nzval)[i]; - /* Matlab doesn't like explicit zero. */ - if (Uval[lastu] != 0.0) - Urow[lastu++] = U_SUB(i); - } - for (int i = 0; i < upper; ++i) - { - /* upper triangle in the supernode */ - Uval[lastu] = SNptr[i]; - /* Matlab doesn't like explicit zero. */ - if (Uval[lastu] != 0.0) - Urow[lastu++] = L_SUB(istart+i); - } - Ucol[j+1] = lastu; - - /* Extract L */ - Lval[lastl] = 1.0; /* unit diagonal */ - Lrow[lastl++] = L_SUB(istart + upper - 1); - for (int i = upper; i < nsupr; ++i) - { - Lval[lastl] = SNptr[i]; - /* Matlab doesn't like explicit zero. */ - if (Lval[lastl] != 0.0) - Lrow[lastl++] = L_SUB(istart+i); - } - Lcol[j+1] = lastl; - - ++upper; - } /* for j ... */ - - } /* for k ... */ - - // squeeze the matrices : - m_l.resizeNonZeros(lastl); - m_u.resizeNonZeros(lastu); - - m_extractedDataAreDirty = false; - } -} - -template<typename MatrixType> -typename SuperLU<MatrixType>::Scalar SuperLU<MatrixType>::determinant() const -{ - eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for computing the determinant, you must first call either compute() or analyzePattern()/factorize()"); - - if (m_extractedDataAreDirty) - this->extractData(); - - Scalar det = Scalar(1); - for (int j=0; j<m_u.cols(); ++j) - { - if (m_u.outerIndexPtr()[j+1]-m_u.outerIndexPtr()[j] > 0) - { - int lastId = m_u.outerIndexPtr()[j+1]-1; - eigen_assert(m_u.innerIndexPtr()[lastId]<=j); - if (m_u.innerIndexPtr()[lastId]==j) - det *= m_u.valuePtr()[lastId]; - } - } - if(m_sluEqued!='N') - return det/m_sluRscale.prod()/m_sluCscale.prod(); - else - return det; -} - -#ifdef EIGEN_PARSED_BY_DOXYGEN -#define EIGEN_SUPERLU_HAS_ILU -#endif - -#ifdef EIGEN_SUPERLU_HAS_ILU - -/** \ingroup SuperLUSupport_Module - * \class SuperILU - * \brief A sparse direct \b incomplete LU factorization and solver based on the SuperLU library - * - * This class allows to solve for an approximate solution of A.X = B sparse linear problems via an incomplete LU factorization - * using the SuperLU library. This class is aimed to be used as a preconditioner of the iterative linear solvers. - * - * \warning This class requires SuperLU 4 or later. - * - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * - * \sa \ref TutorialSparseDirectSolvers, class ConjugateGradient, class BiCGSTAB - */ - -template<typename _MatrixType> -class SuperILU : public SuperLUBase<_MatrixType,SuperILU<_MatrixType> > -{ - public: - typedef SuperLUBase<_MatrixType,SuperILU> Base; - typedef _MatrixType MatrixType; - typedef typename Base::Scalar Scalar; - typedef typename Base::RealScalar RealScalar; - typedef typename Base::Index Index; - - public: - - SuperILU() : Base() { init(); } - - SuperILU(const MatrixType& matrix) : Base() - { - init(); - Base::compute(matrix); - } - - ~SuperILU() - { - } - - /** Performs a symbolic decomposition on the sparcity of \a matrix. - * - * This function is particularly useful when solving for several problems having the same structure. - * - * \sa factorize() - */ - void analyzePattern(const MatrixType& matrix) - { - Base::analyzePattern(matrix); - } - - /** Performs a numeric decomposition of \a matrix - * - * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed. - * - * \sa analyzePattern() - */ - void factorize(const MatrixType& matrix); - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** \internal */ - template<typename Rhs,typename Dest> - void _solve(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const; - #endif // EIGEN_PARSED_BY_DOXYGEN - - protected: - - using Base::m_matrix; - using Base::m_sluOptions; - using Base::m_sluA; - using Base::m_sluB; - using Base::m_sluX; - using Base::m_p; - using Base::m_q; - using Base::m_sluEtree; - using Base::m_sluEqued; - using Base::m_sluRscale; - using Base::m_sluCscale; - using Base::m_sluL; - using Base::m_sluU; - using Base::m_sluStat; - using Base::m_sluFerr; - using Base::m_sluBerr; - using Base::m_l; - using Base::m_u; - - using Base::m_analysisIsOk; - using Base::m_factorizationIsOk; - using Base::m_extractedDataAreDirty; - using Base::m_isInitialized; - using Base::m_info; - - void init() - { - Base::init(); - - ilu_set_default_options(&m_sluOptions); - m_sluOptions.PrintStat = NO; - m_sluOptions.ConditionNumber = NO; - m_sluOptions.Trans = NOTRANS; - m_sluOptions.ColPerm = MMD_AT_PLUS_A; - - // no attempt to preserve column sum - m_sluOptions.ILU_MILU = SILU; - // only basic ILU(k) support -- no direct control over memory consumption - // better to use ILU_DropRule = DROP_BASIC | DROP_AREA - // and set ILU_FillFactor to max memory growth - m_sluOptions.ILU_DropRule = DROP_BASIC; - m_sluOptions.ILU_DropTol = NumTraits<Scalar>::dummy_precision()*10; - } - - private: - SuperILU(SuperILU& ) { } -}; - -template<typename MatrixType> -void SuperILU<MatrixType>::factorize(const MatrixType& a) -{ - eigen_assert(m_analysisIsOk && "You must first call analyzePattern()"); - if(!m_analysisIsOk) - { - m_info = InvalidInput; - return; - } - - this->initFactorization(a); - - int info = 0; - RealScalar recip_pivot_growth, rcond; - - StatInit(&m_sluStat); - SuperLU_gsisx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0], - &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0], - &m_sluL, &m_sluU, - NULL, 0, - &m_sluB, &m_sluX, - &recip_pivot_growth, &rcond, - &m_sluStat, &info, Scalar()); - StatFree(&m_sluStat); - - // FIXME how to better check for errors ??? - m_info = info == 0 ? Success : NumericalIssue; - m_factorizationIsOk = true; -} - -template<typename MatrixType> -template<typename Rhs,typename Dest> -void SuperILU<MatrixType>::_solve(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const -{ - eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()"); - - const int size = m_matrix.rows(); - const int rhsCols = b.cols(); - eigen_assert(size==b.rows()); - - m_sluOptions.Trans = NOTRANS; - m_sluOptions.Fact = FACTORED; - m_sluOptions.IterRefine = NOREFINE; - - m_sluFerr.resize(rhsCols); - m_sluBerr.resize(rhsCols); - m_sluB = SluMatrix::Map(b.const_cast_derived()); - m_sluX = SluMatrix::Map(x.derived()); - - typename Rhs::PlainObject b_cpy; - if(m_sluEqued!='N') - { - b_cpy = b; - m_sluB = SluMatrix::Map(b_cpy.const_cast_derived()); - } - - int info = 0; - RealScalar recip_pivot_growth, rcond; - - StatInit(&m_sluStat); - SuperLU_gsisx(&m_sluOptions, &m_sluA, - m_q.data(), m_p.data(), - &m_sluEtree[0], &m_sluEqued, - &m_sluRscale[0], &m_sluCscale[0], - &m_sluL, &m_sluU, - NULL, 0, - &m_sluB, &m_sluX, - &recip_pivot_growth, &rcond, - &m_sluStat, &info, Scalar()); - StatFree(&m_sluStat); - - m_info = info==0 ? Success : NumericalIssue; -} -#endif - -namespace internal { - -template<typename _MatrixType, typename Derived, typename Rhs> -struct solve_retval<SuperLUBase<_MatrixType,Derived>, Rhs> - : solve_retval_base<SuperLUBase<_MatrixType,Derived>, Rhs> -{ - typedef SuperLUBase<_MatrixType,Derived> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec().derived()._solve(rhs(),dst); - } -}; - -template<typename _MatrixType, typename Derived, typename Rhs> -struct sparse_solve_retval<SuperLUBase<_MatrixType,Derived>, Rhs> - : sparse_solve_retval_base<SuperLUBase<_MatrixType,Derived>, Rhs> -{ - typedef SuperLUBase<_MatrixType,Derived> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - this->defaultEvalTo(dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_SUPERLUSUPPORT_H diff --git a/third_party/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h b/third_party/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h deleted file mode 100644 index 3a48cecf76..0000000000 --- a/third_party/eigen3/Eigen/src/UmfPackSupport/UmfPackSupport.h +++ /dev/null @@ -1,432 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_UMFPACKSUPPORT_H -#define EIGEN_UMFPACKSUPPORT_H - -namespace Eigen { - -/* TODO extract L, extract U, compute det, etc... */ - -// generic double/complex<double> wrapper functions: - -inline void umfpack_free_numeric(void **Numeric, double) -{ umfpack_di_free_numeric(Numeric); *Numeric = 0; } - -inline void umfpack_free_numeric(void **Numeric, std::complex<double>) -{ umfpack_zi_free_numeric(Numeric); *Numeric = 0; } - -inline void umfpack_free_symbolic(void **Symbolic, double) -{ umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; } - -inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>) -{ umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; } - -inline int umfpack_symbolic(int n_row,int n_col, - const int Ap[], const int Ai[], const double Ax[], void **Symbolic, - const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO]) -{ - return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info); -} - -inline int umfpack_symbolic(int n_row,int n_col, - const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic, - const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO]) -{ - return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info); -} - -inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[], - void *Symbolic, void **Numeric, - const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO]) -{ - return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info); -} - -inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[], - void *Symbolic, void **Numeric, - const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO]) -{ - return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info); -} - -inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[], - double X[], const double B[], void *Numeric, - const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) -{ - return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info); -} - -inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[], - std::complex<double> X[], const std::complex<double> B[], void *Numeric, - const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO]) -{ - return umfpack_zi_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info); -} - -inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double) -{ - return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric); -} - -inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>) -{ - return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric); -} - -inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[], - int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric) -{ - return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric); -} - -inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[], - int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric) -{ - double& lx0_real = numext::real_ref(Lx[0]); - double& ux0_real = numext::real_ref(Ux[0]); - double& dx0_real = numext::real_ref(Dx[0]); - return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q, - Dx?&dx0_real:0,0,do_recip,Rs,Numeric); -} - -inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO]) -{ - return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info); -} - -inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO]) -{ - double& mx_real = numext::real_ref(*Mx); - return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info); -} - -/** \ingroup UmfPackSupport_Module - * \brief A sparse LU factorization and solver based on UmfPack - * - * This class allows to solve for A.X = B sparse linear problems via a LU factorization - * using the UmfPack library. The sparse matrix A must be squared and full rank. - * The vectors or matrices X and B can be either dense or sparse. - * - * \warning The input matrix A should be in a \b compressed and \b column-major form. - * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix. - * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> - * - * \sa \ref TutorialSparseDirectSolvers - */ -template<typename _MatrixType> -class UmfPackLU : internal::noncopyable -{ - public: - typedef _MatrixType MatrixType; - typedef typename MatrixType::Scalar Scalar; - typedef typename MatrixType::RealScalar RealScalar; - typedef typename MatrixType::Index Index; - typedef Matrix<Scalar,Dynamic,1> Vector; - typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType; - typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType; - typedef SparseMatrix<Scalar> LUMatrixType; - typedef SparseMatrix<Scalar,ColMajor,int> UmfpackMatrixType; - - public: - - UmfPackLU() { init(); } - - UmfPackLU(const MatrixType& matrix) - { - init(); - compute(matrix); - } - - ~UmfPackLU() - { - if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar()); - if(m_numeric) umfpack_free_numeric(&m_numeric,Scalar()); - } - - inline Index rows() const { return m_copyMatrix.rows(); } - inline Index cols() const { return m_copyMatrix.cols(); } - - /** \brief Reports whether previous computation was successful. - * - * \returns \c Success if computation was succesful, - * \c NumericalIssue if the matrix.appears to be negative. - */ - ComputationInfo info() const - { - eigen_assert(m_isInitialized && "Decomposition is not initialized."); - return m_info; - } - - inline const LUMatrixType& matrixL() const - { - if (m_extractedDataAreDirty) extractData(); - return m_l; - } - - inline const LUMatrixType& matrixU() const - { - if (m_extractedDataAreDirty) extractData(); - return m_u; - } - - inline const IntColVectorType& permutationP() const - { - if (m_extractedDataAreDirty) extractData(); - return m_p; - } - - inline const IntRowVectorType& permutationQ() const - { - if (m_extractedDataAreDirty) extractData(); - return m_q; - } - - /** Computes the sparse Cholesky decomposition of \a matrix - * Note that the matrix should be column-major, and in compressed format for best performance. - * \sa SparseMatrix::makeCompressed(). - */ - void compute(const MatrixType& matrix) - { - analyzePattern(matrix); - factorize(matrix); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::solve_retval<UmfPackLU, Rhs> solve(const MatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "UmfPackLU is not initialized."); - eigen_assert(rows()==b.rows() - && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b"); - return internal::solve_retval<UmfPackLU, Rhs>(*this, b.derived()); - } - - /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A. - * - * \sa compute() - */ - template<typename Rhs> - inline const internal::sparse_solve_retval<UmfPackLU, Rhs> solve(const SparseMatrixBase<Rhs>& b) const - { - eigen_assert(m_isInitialized && "UmfPackLU is not initialized."); - eigen_assert(rows()==b.rows() - && "UmfPackLU::solve(): invalid number of rows of the right hand side matrix b"); - return internal::sparse_solve_retval<UmfPackLU, Rhs>(*this, b.derived()); - } - - /** Performs a symbolic decomposition on the sparcity of \a matrix. - * - * This function is particularly useful when solving for several problems having the same structure. - * - * \sa factorize(), compute() - */ - void analyzePattern(const MatrixType& matrix) - { - if(m_symbolic) - umfpack_free_symbolic(&m_symbolic,Scalar()); - if(m_numeric) - umfpack_free_numeric(&m_numeric,Scalar()); - - grapInput(matrix); - - int errorCode = 0; - errorCode = umfpack_symbolic(matrix.rows(), matrix.cols(), m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, - &m_symbolic, 0, 0); - - m_isInitialized = true; - m_info = errorCode ? InvalidInput : Success; - m_analysisIsOk = true; - m_factorizationIsOk = false; - } - - /** Performs a numeric decomposition of \a matrix - * - * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed. - * - * \sa analyzePattern(), compute() - */ - void factorize(const MatrixType& matrix) - { - eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()"); - if(m_numeric) - umfpack_free_numeric(&m_numeric,Scalar()); - - grapInput(matrix); - - int errorCode; - errorCode = umfpack_numeric(m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, - m_symbolic, &m_numeric, 0, 0); - - m_info = errorCode ? NumericalIssue : Success; - m_factorizationIsOk = true; - } - - #ifndef EIGEN_PARSED_BY_DOXYGEN - /** \internal */ - template<typename BDerived,typename XDerived> - bool _solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const; - #endif - - Scalar determinant() const; - - void extractData() const; - - protected: - - - void init() - { - m_info = InvalidInput; - m_isInitialized = false; - m_numeric = 0; - m_symbolic = 0; - m_outerIndexPtr = 0; - m_innerIndexPtr = 0; - m_valuePtr = 0; - } - - void grapInput(const MatrixType& mat) - { - m_copyMatrix.resize(mat.rows(), mat.cols()); - if( ((MatrixType::Flags&RowMajorBit)==RowMajorBit) || sizeof(typename MatrixType::Index)!=sizeof(int) || !mat.isCompressed() ) - { - // non supported input -> copy - m_copyMatrix = mat; - m_outerIndexPtr = m_copyMatrix.outerIndexPtr(); - m_innerIndexPtr = m_copyMatrix.innerIndexPtr(); - m_valuePtr = m_copyMatrix.valuePtr(); - } - else - { - m_outerIndexPtr = mat.outerIndexPtr(); - m_innerIndexPtr = mat.innerIndexPtr(); - m_valuePtr = mat.valuePtr(); - } - } - - // cached data to reduce reallocation, etc. - mutable LUMatrixType m_l; - mutable LUMatrixType m_u; - mutable IntColVectorType m_p; - mutable IntRowVectorType m_q; - - UmfpackMatrixType m_copyMatrix; - const Scalar* m_valuePtr; - const int* m_outerIndexPtr; - const int* m_innerIndexPtr; - void* m_numeric; - void* m_symbolic; - - mutable ComputationInfo m_info; - bool m_isInitialized; - int m_factorizationIsOk; - int m_analysisIsOk; - mutable bool m_extractedDataAreDirty; - - private: - UmfPackLU(UmfPackLU& ) { } -}; - - -template<typename MatrixType> -void UmfPackLU<MatrixType>::extractData() const -{ - if (m_extractedDataAreDirty) - { - // get size of the data - int lnz, unz, rows, cols, nz_udiag; - umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar()); - - // allocate data - m_l.resize(rows,(std::min)(rows,cols)); - m_l.resizeNonZeros(lnz); - - m_u.resize((std::min)(rows,cols),cols); - m_u.resizeNonZeros(unz); - - m_p.resize(rows); - m_q.resize(cols); - - // extract - umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(), - m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(), - m_p.data(), m_q.data(), 0, 0, 0, m_numeric); - - m_extractedDataAreDirty = false; - } -} - -template<typename MatrixType> -typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const -{ - Scalar det; - umfpack_get_determinant(&det, 0, m_numeric, 0); - return det; -} - -template<typename MatrixType> -template<typename BDerived,typename XDerived> -bool UmfPackLU<MatrixType>::_solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const -{ - const int rhsCols = b.cols(); - eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet"); - eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet"); - eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve"); - - int errorCode; - for (int j=0; j<rhsCols; ++j) - { - errorCode = umfpack_solve(UMFPACK_A, - m_outerIndexPtr, m_innerIndexPtr, m_valuePtr, - &x.col(j).coeffRef(0), &b.const_cast_derived().col(j).coeffRef(0), m_numeric, 0, 0); - if (errorCode!=0) - return false; - } - - return true; -} - - -namespace internal { - -template<typename _MatrixType, typename Rhs> -struct solve_retval<UmfPackLU<_MatrixType>, Rhs> - : solve_retval_base<UmfPackLU<_MatrixType>, Rhs> -{ - typedef UmfPackLU<_MatrixType> Dec; - EIGEN_MAKE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - dec()._solve(rhs(),dst); - } -}; - -template<typename _MatrixType, typename Rhs> -struct sparse_solve_retval<UmfPackLU<_MatrixType>, Rhs> - : sparse_solve_retval_base<UmfPackLU<_MatrixType>, Rhs> -{ - typedef UmfPackLU<_MatrixType> Dec; - EIGEN_MAKE_SPARSE_SOLVE_HELPERS(Dec,Rhs) - - template<typename Dest> void evalTo(Dest& dst) const - { - this->defaultEvalTo(dst); - } -}; - -} // end namespace internal - -} // end namespace Eigen - -#endif // EIGEN_UMFPACKSUPPORT_H diff --git a/third_party/eigen3/Eigen/src/misc/Image.h b/third_party/eigen3/Eigen/src/misc/Image.h deleted file mode 100644 index 75c5f433a8..0000000000 --- a/third_party/eigen3/Eigen/src/misc/Image.h +++ /dev/null @@ -1,84 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MISC_IMAGE_H -#define EIGEN_MISC_IMAGE_H - -namespace Eigen { - -namespace internal { - -/** \class image_retval_base - * - */ -template<typename DecompositionType> -struct traits<image_retval_base<DecompositionType> > -{ - typedef typename DecompositionType::MatrixType MatrixType; - typedef Matrix< - typename MatrixType::Scalar, - MatrixType::RowsAtCompileTime, // the image is a subspace of the destination space, whose - // dimension is the number of rows of the original matrix - Dynamic, // we don't know at compile time the dimension of the image (the rank) - MatrixType::Options, - MatrixType::MaxRowsAtCompileTime, // the image matrix will consist of columns from the original matrix, - MatrixType::MaxColsAtCompileTime // so it has the same number of rows and at most as many columns. - > ReturnType; -}; - -template<typename _DecompositionType> struct image_retval_base - : public ReturnByValue<image_retval_base<_DecompositionType> > -{ - typedef _DecompositionType DecompositionType; - typedef typename DecompositionType::MatrixType MatrixType; - typedef ReturnByValue<image_retval_base> Base; - typedef typename Base::Index Index; - - image_retval_base(const DecompositionType& dec, const MatrixType& originalMatrix) - : m_dec(dec), m_rank(dec.rank()), - m_cols(m_rank == 0 ? 1 : m_rank), - m_originalMatrix(originalMatrix) - {} - - inline Index rows() const { return m_dec.rows(); } - inline Index cols() const { return m_cols; } - inline Index rank() const { return m_rank; } - inline const DecompositionType& dec() const { return m_dec; } - inline const MatrixType& originalMatrix() const { return m_originalMatrix; } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - static_cast<const image_retval<DecompositionType>*>(this)->evalTo(dst); - } - - protected: - const DecompositionType& m_dec; - Index m_rank, m_cols; - const MatrixType& m_originalMatrix; -}; - -} // end namespace internal - -#define EIGEN_MAKE_IMAGE_HELPERS(DecompositionType) \ - typedef typename DecompositionType::MatrixType MatrixType; \ - typedef typename MatrixType::Scalar Scalar; \ - typedef typename MatrixType::RealScalar RealScalar; \ - typedef typename MatrixType::Index Index; \ - typedef Eigen::internal::image_retval_base<DecompositionType> Base; \ - using Base::dec; \ - using Base::originalMatrix; \ - using Base::rank; \ - using Base::rows; \ - using Base::cols; \ - image_retval(const DecompositionType& dec, const MatrixType& originalMatrix) \ - : Base(dec, originalMatrix) {} - -} // end namespace Eigen - -#endif // EIGEN_MISC_IMAGE_H diff --git a/third_party/eigen3/Eigen/src/misc/Kernel.h b/third_party/eigen3/Eigen/src/misc/Kernel.h deleted file mode 100644 index b9e1518fd4..0000000000 --- a/third_party/eigen3/Eigen/src/misc/Kernel.h +++ /dev/null @@ -1,81 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MISC_KERNEL_H -#define EIGEN_MISC_KERNEL_H - -namespace Eigen { - -namespace internal { - -/** \class kernel_retval_base - * - */ -template<typename DecompositionType> -struct traits<kernel_retval_base<DecompositionType> > -{ - typedef typename DecompositionType::MatrixType MatrixType; - typedef Matrix< - typename MatrixType::Scalar, - MatrixType::ColsAtCompileTime, // the number of rows in the "kernel matrix" - // is the number of cols of the original matrix - // so that the product "matrix * kernel = zero" makes sense - Dynamic, // we don't know at compile-time the dimension of the kernel - MatrixType::Options, - MatrixType::MaxColsAtCompileTime, // see explanation for 2nd template parameter - MatrixType::MaxColsAtCompileTime // the kernel is a subspace of the domain space, - // whose dimension is the number of columns of the original matrix - > ReturnType; -}; - -template<typename _DecompositionType> struct kernel_retval_base - : public ReturnByValue<kernel_retval_base<_DecompositionType> > -{ - typedef _DecompositionType DecompositionType; - typedef ReturnByValue<kernel_retval_base> Base; - typedef typename Base::Index Index; - - kernel_retval_base(const DecompositionType& dec) - : m_dec(dec), - m_rank(dec.rank()), - m_cols(m_rank==dec.cols() ? 1 : dec.cols() - m_rank) - {} - - inline Index rows() const { return m_dec.cols(); } - inline Index cols() const { return m_cols; } - inline Index rank() const { return m_rank; } - inline const DecompositionType& dec() const { return m_dec; } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - static_cast<const kernel_retval<DecompositionType>*>(this)->evalTo(dst); - } - - protected: - const DecompositionType& m_dec; - Index m_rank, m_cols; -}; - -} // end namespace internal - -#define EIGEN_MAKE_KERNEL_HELPERS(DecompositionType) \ - typedef typename DecompositionType::MatrixType MatrixType; \ - typedef typename MatrixType::Scalar Scalar; \ - typedef typename MatrixType::RealScalar RealScalar; \ - typedef typename MatrixType::Index Index; \ - typedef Eigen::internal::kernel_retval_base<DecompositionType> Base; \ - using Base::dec; \ - using Base::rank; \ - using Base::rows; \ - using Base::cols; \ - kernel_retval(const DecompositionType& dec) : Base(dec) {} - -} // end namespace Eigen - -#endif // EIGEN_MISC_KERNEL_H diff --git a/third_party/eigen3/Eigen/src/misc/Solve.h b/third_party/eigen3/Eigen/src/misc/Solve.h deleted file mode 100644 index 7f70d60afb..0000000000 --- a/third_party/eigen3/Eigen/src/misc/Solve.h +++ /dev/null @@ -1,76 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_MISC_SOLVE_H -#define EIGEN_MISC_SOLVE_H - -namespace Eigen { - -namespace internal { - -/** \class solve_retval_base - * - */ -template<typename DecompositionType, typename Rhs> -struct traits<solve_retval_base<DecompositionType, Rhs> > -{ - typedef typename DecompositionType::MatrixType MatrixType; - typedef Matrix<typename Rhs::Scalar, - MatrixType::ColsAtCompileTime, - Rhs::ColsAtCompileTime, - Rhs::PlainObject::Options, - MatrixType::MaxColsAtCompileTime, - Rhs::MaxColsAtCompileTime> ReturnType; -}; - -template<typename _DecompositionType, typename Rhs> struct solve_retval_base - : public ReturnByValue<solve_retval_base<_DecompositionType, Rhs> > -{ - typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned; - typedef _DecompositionType DecompositionType; - typedef ReturnByValue<solve_retval_base> Base; - typedef typename Base::Index Index; - - solve_retval_base(const DecompositionType& dec, const Rhs& rhs) - : m_dec(dec), m_rhs(rhs) - {} - - inline Index rows() const { return m_dec.cols(); } - inline Index cols() const { return m_rhs.cols(); } - inline const DecompositionType& dec() const { return m_dec; } - inline const RhsNestedCleaned& rhs() const { return m_rhs; } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - static_cast<const solve_retval<DecompositionType,Rhs>*>(this)->evalTo(dst); - } - - protected: - const DecompositionType& m_dec; - typename Rhs::Nested m_rhs; -}; - -} // end namespace internal - -#define EIGEN_MAKE_SOLVE_HELPERS(DecompositionType,Rhs) \ - typedef typename DecompositionType::MatrixType MatrixType; \ - typedef typename MatrixType::Scalar Scalar; \ - typedef typename MatrixType::RealScalar RealScalar; \ - typedef typename MatrixType::Index Index; \ - typedef Eigen::internal::solve_retval_base<DecompositionType,Rhs> Base; \ - using Base::dec; \ - using Base::rhs; \ - using Base::rows; \ - using Base::cols; \ - solve_retval(const DecompositionType& dec, const Rhs& rhs) \ - : Base(dec, rhs) {} - -} // end namespace Eigen - -#endif // EIGEN_MISC_SOLVE_H diff --git a/third_party/eigen3/Eigen/src/misc/SparseSolve.h b/third_party/eigen3/Eigen/src/misc/SparseSolve.h deleted file mode 100644 index 05caa9266b..0000000000 --- a/third_party/eigen3/Eigen/src/misc/SparseSolve.h +++ /dev/null @@ -1,130 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_SPARSE_SOLVE_H -#define EIGEN_SPARSE_SOLVE_H - -namespace Eigen { - -namespace internal { - -template<typename _DecompositionType, typename Rhs> struct sparse_solve_retval_base; -template<typename _DecompositionType, typename Rhs> struct sparse_solve_retval; - -template<typename DecompositionType, typename Rhs> -struct traits<sparse_solve_retval_base<DecompositionType, Rhs> > -{ - typedef typename DecompositionType::MatrixType MatrixType; - typedef SparseMatrix<typename Rhs::Scalar, Rhs::Options, typename Rhs::Index> ReturnType; -}; - -template<typename _DecompositionType, typename Rhs> struct sparse_solve_retval_base - : public ReturnByValue<sparse_solve_retval_base<_DecompositionType, Rhs> > -{ - typedef typename remove_all<typename Rhs::Nested>::type RhsNestedCleaned; - typedef _DecompositionType DecompositionType; - typedef ReturnByValue<sparse_solve_retval_base> Base; - typedef typename Base::Index Index; - - sparse_solve_retval_base(const DecompositionType& dec, const Rhs& rhs) - : m_dec(dec), m_rhs(rhs) - {} - - inline Index rows() const { return m_dec.cols(); } - inline Index cols() const { return m_rhs.cols(); } - inline const DecompositionType& dec() const { return m_dec; } - inline const RhsNestedCleaned& rhs() const { return m_rhs; } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - static_cast<const sparse_solve_retval<DecompositionType,Rhs>*>(this)->evalTo(dst); - } - - protected: - template<typename DestScalar, int DestOptions, typename DestIndex> - inline void defaultEvalTo(SparseMatrix<DestScalar,DestOptions,DestIndex>& dst) const - { - // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix. - static const int NbColsAtOnce = 4; - int rhsCols = m_rhs.cols(); - int size = m_rhs.rows(); - // the temporary matrices do not need more columns than NbColsAtOnce: - int tmpCols = (std::min)(rhsCols, NbColsAtOnce); - Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmp(size,tmpCols); - Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmpX(size,tmpCols); - for(int k=0; k<rhsCols; k+=NbColsAtOnce) - { - int actualCols = std::min<int>(rhsCols-k, NbColsAtOnce); - tmp.leftCols(actualCols) = m_rhs.middleCols(k,actualCols); - tmpX.leftCols(actualCols) = m_dec.solve(tmp.leftCols(actualCols)); - dst.middleCols(k,actualCols) = tmpX.leftCols(actualCols).sparseView(); - } - } - const DecompositionType& m_dec; - typename Rhs::Nested m_rhs; -}; - -#define EIGEN_MAKE_SPARSE_SOLVE_HELPERS(DecompositionType,Rhs) \ - typedef typename DecompositionType::MatrixType MatrixType; \ - typedef typename MatrixType::Scalar Scalar; \ - typedef typename MatrixType::RealScalar RealScalar; \ - typedef typename MatrixType::Index Index; \ - typedef Eigen::internal::sparse_solve_retval_base<DecompositionType,Rhs> Base; \ - using Base::dec; \ - using Base::rhs; \ - using Base::rows; \ - using Base::cols; \ - sparse_solve_retval(const DecompositionType& dec, const Rhs& rhs) \ - : Base(dec, rhs) {} - - - -template<typename DecompositionType, typename Rhs, typename Guess> struct solve_retval_with_guess; - -template<typename DecompositionType, typename Rhs, typename Guess> -struct traits<solve_retval_with_guess<DecompositionType, Rhs, Guess> > -{ - typedef typename DecompositionType::MatrixType MatrixType; - typedef Matrix<typename Rhs::Scalar, - MatrixType::ColsAtCompileTime, - Rhs::ColsAtCompileTime, - Rhs::PlainObject::Options, - MatrixType::MaxColsAtCompileTime, - Rhs::MaxColsAtCompileTime> ReturnType; -}; - -template<typename DecompositionType, typename Rhs, typename Guess> struct solve_retval_with_guess - : public ReturnByValue<solve_retval_with_guess<DecompositionType, Rhs, Guess> > -{ - typedef typename DecompositionType::Index Index; - - solve_retval_with_guess(const DecompositionType& dec, const Rhs& rhs, const Guess& guess) - : m_dec(dec), m_rhs(rhs), m_guess(guess) - {} - - inline Index rows() const { return m_dec.cols(); } - inline Index cols() const { return m_rhs.cols(); } - - template<typename Dest> inline void evalTo(Dest& dst) const - { - dst = m_guess; - m_dec._solveWithGuess(m_rhs,dst); - } - - protected: - const DecompositionType& m_dec; - const typename Rhs::Nested m_rhs; - const typename Guess::Nested m_guess; -}; - -} // namepsace internal - -} // end namespace Eigen - -#endif // EIGEN_SPARSE_SOLVE_H diff --git a/third_party/eigen3/Eigen/src/misc/blas.h b/third_party/eigen3/Eigen/src/misc/blas.h deleted file mode 100644 index 6fce99ed5c..0000000000 --- a/third_party/eigen3/Eigen/src/misc/blas.h +++ /dev/null @@ -1,658 +0,0 @@ -#ifndef BLAS_H -#define BLAS_H - -#ifdef __cplusplus -extern "C" -{ -#endif - -#define BLASFUNC(FUNC) FUNC##_ - -#ifdef __WIN64__ -typedef long long BLASLONG; -typedef unsigned long long BLASULONG; -#else -typedef long BLASLONG; -typedef unsigned long BLASULONG; -#endif - -int BLASFUNC(xerbla)(const char *, int *info, int); - -float BLASFUNC(sdot) (int *, float *, int *, float *, int *); -float BLASFUNC(sdsdot)(int *, float *, float *, int *, float *, int *); - -double BLASFUNC(dsdot) (int *, float *, int *, float *, int *); -double BLASFUNC(ddot) (int *, double *, int *, double *, int *); -double BLASFUNC(qdot) (int *, double *, int *, double *, int *); - -int BLASFUNC(cdotuw) (int *, float *, int *, float *, int *, float*); -int BLASFUNC(cdotcw) (int *, float *, int *, float *, int *, float*); -int BLASFUNC(zdotuw) (int *, double *, int *, double *, int *, double*); -int BLASFUNC(zdotcw) (int *, double *, int *, double *, int *, double*); - -int BLASFUNC(saxpy) (int *, float *, float *, int *, float *, int *); -int BLASFUNC(daxpy) (int *, double *, double *, int *, double *, int *); -int BLASFUNC(qaxpy) (int *, double *, double *, int *, double *, int *); -int BLASFUNC(caxpy) (int *, float *, float *, int *, float *, int *); -int BLASFUNC(zaxpy) (int *, double *, double *, int *, double *, int *); -int BLASFUNC(xaxpy) (int *, double *, double *, int *, double *, int *); -int BLASFUNC(caxpyc)(int *, float *, float *, int *, float *, int *); -int BLASFUNC(zaxpyc)(int *, double *, double *, int *, double *, int *); -int BLASFUNC(xaxpyc)(int *, double *, double *, int *, double *, int *); - -int BLASFUNC(scopy) (int *, float *, int *, float *, int *); -int BLASFUNC(dcopy) (int *, double *, int *, double *, int *); -int BLASFUNC(qcopy) (int *, double *, int *, double *, int *); -int BLASFUNC(ccopy) (int *, float *, int *, float *, int *); -int BLASFUNC(zcopy) (int *, double *, int *, double *, int *); -int BLASFUNC(xcopy) (int *, double *, int *, double *, int *); - -int BLASFUNC(sswap) (int *, float *, int *, float *, int *); -int BLASFUNC(dswap) (int *, double *, int *, double *, int *); -int BLASFUNC(qswap) (int *, double *, int *, double *, int *); -int BLASFUNC(cswap) (int *, float *, int *, float *, int *); -int BLASFUNC(zswap) (int *, double *, int *, double *, int *); -int BLASFUNC(xswap) (int *, double *, int *, double *, int *); - -float BLASFUNC(sasum) (int *, float *, int *); -float BLASFUNC(scasum)(int *, float *, int *); -double BLASFUNC(dasum) (int *, double *, int *); -double BLASFUNC(qasum) (int *, double *, int *); -double BLASFUNC(dzasum)(int *, double *, int *); -double BLASFUNC(qxasum)(int *, double *, int *); - -int BLASFUNC(isamax)(int *, float *, int *); -int BLASFUNC(idamax)(int *, double *, int *); -int BLASFUNC(iqamax)(int *, double *, int *); -int BLASFUNC(icamax)(int *, float *, int *); -int BLASFUNC(izamax)(int *, double *, int *); -int BLASFUNC(ixamax)(int *, double *, int *); - -int BLASFUNC(ismax) (int *, float *, int *); -int BLASFUNC(idmax) (int *, double *, int *); -int BLASFUNC(iqmax) (int *, double *, int *); -int BLASFUNC(icmax) (int *, float *, int *); -int BLASFUNC(izmax) (int *, double *, int *); -int BLASFUNC(ixmax) (int *, double *, int *); - -int BLASFUNC(isamin)(int *, float *, int *); -int BLASFUNC(idamin)(int *, double *, int *); -int BLASFUNC(iqamin)(int *, double *, int *); -int BLASFUNC(icamin)(int *, float *, int *); -int BLASFUNC(izamin)(int *, double *, int *); -int BLASFUNC(ixamin)(int *, double *, int *); - -int BLASFUNC(ismin)(int *, float *, int *); -int BLASFUNC(idmin)(int *, double *, int *); -int BLASFUNC(iqmin)(int *, double *, int *); -int BLASFUNC(icmin)(int *, float *, int *); -int BLASFUNC(izmin)(int *, double *, int *); -int BLASFUNC(ixmin)(int *, double *, int *); - -float BLASFUNC(samax) (int *, float *, int *); -double BLASFUNC(damax) (int *, double *, int *); -double BLASFUNC(qamax) (int *, double *, int *); -float BLASFUNC(scamax)(int *, float *, int *); -double BLASFUNC(dzamax)(int *, double *, int *); -double BLASFUNC(qxamax)(int *, double *, int *); - -float BLASFUNC(samin) (int *, float *, int *); -double BLASFUNC(damin) (int *, double *, int *); -double BLASFUNC(qamin) (int *, double *, int *); -float BLASFUNC(scamin)(int *, float *, int *); -double BLASFUNC(dzamin)(int *, double *, int *); -double BLASFUNC(qxamin)(int *, double *, int *); - -float BLASFUNC(smax) (int *, float *, int *); -double BLASFUNC(dmax) (int *, double *, int *); -double BLASFUNC(qmax) (int *, double *, int *); -float BLASFUNC(scmax) (int *, float *, int *); -double BLASFUNC(dzmax) (int *, double *, int *); -double BLASFUNC(qxmax) (int *, double *, int *); - -float BLASFUNC(smin) (int *, float *, int *); -double BLASFUNC(dmin) (int *, double *, int *); -double BLASFUNC(qmin) (int *, double *, int *); -float BLASFUNC(scmin) (int *, float *, int *); -double BLASFUNC(dzmin) (int *, double *, int *); -double BLASFUNC(qxmin) (int *, double *, int *); - -int BLASFUNC(sscal) (int *, float *, float *, int *); -int BLASFUNC(dscal) (int *, double *, double *, int *); -int BLASFUNC(qscal) (int *, double *, double *, int *); -int BLASFUNC(cscal) (int *, float *, float *, int *); -int BLASFUNC(zscal) (int *, double *, double *, int *); -int BLASFUNC(xscal) (int *, double *, double *, int *); -int BLASFUNC(csscal)(int *, float *, float *, int *); -int BLASFUNC(zdscal)(int *, double *, double *, int *); -int BLASFUNC(xqscal)(int *, double *, double *, int *); - -float BLASFUNC(snrm2) (int *, float *, int *); -float BLASFUNC(scnrm2)(int *, float *, int *); - -double BLASFUNC(dnrm2) (int *, double *, int *); -double BLASFUNC(qnrm2) (int *, double *, int *); -double BLASFUNC(dznrm2)(int *, double *, int *); -double BLASFUNC(qxnrm2)(int *, double *, int *); - -int BLASFUNC(srot) (int *, float *, int *, float *, int *, float *, float *); -int BLASFUNC(drot) (int *, double *, int *, double *, int *, double *, double *); -int BLASFUNC(qrot) (int *, double *, int *, double *, int *, double *, double *); -int BLASFUNC(csrot) (int *, float *, int *, float *, int *, float *, float *); -int BLASFUNC(zdrot) (int *, double *, int *, double *, int *, double *, double *); -int BLASFUNC(xqrot) (int *, double *, int *, double *, int *, double *, double *); - -int BLASFUNC(srotg) (float *, float *, float *, float *); -int BLASFUNC(drotg) (double *, double *, double *, double *); -int BLASFUNC(qrotg) (double *, double *, double *, double *); -int BLASFUNC(crotg) (float *, float *, float *, float *); -int BLASFUNC(zrotg) (double *, double *, double *, double *); -int BLASFUNC(xrotg) (double *, double *, double *, double *); - -int BLASFUNC(srotmg)(float *, float *, float *, float *, float *); -int BLASFUNC(drotmg)(double *, double *, double *, double *, double *); - -int BLASFUNC(srotm) (int *, float *, int *, float *, int *, float *); -int BLASFUNC(drotm) (int *, double *, int *, double *, int *, double *); -int BLASFUNC(qrotm) (int *, double *, int *, double *, int *, double *); - -/* Level 2 routines */ - -int BLASFUNC(sger)(int *, int *, float *, float *, int *, - float *, int *, float *, int *); -int BLASFUNC(dger)(int *, int *, double *, double *, int *, - double *, int *, double *, int *); -int BLASFUNC(qger)(int *, int *, double *, double *, int *, - double *, int *, double *, int *); -int BLASFUNC(cgeru)(int *, int *, float *, float *, int *, - float *, int *, float *, int *); -int BLASFUNC(cgerc)(int *, int *, float *, float *, int *, - float *, int *, float *, int *); -int BLASFUNC(zgeru)(int *, int *, double *, double *, int *, - double *, int *, double *, int *); -int BLASFUNC(zgerc)(int *, int *, double *, double *, int *, - double *, int *, double *, int *); -int BLASFUNC(xgeru)(int *, int *, double *, double *, int *, - double *, int *, double *, int *); -int BLASFUNC(xgerc)(int *, int *, double *, double *, int *, - double *, int *, double *, int *); - -int BLASFUNC(sgemv)(char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(dgemv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(qgemv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(cgemv)(char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zgemv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xgemv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(strsv) (char *, char *, char *, int *, float *, int *, - float *, int *); -int BLASFUNC(dtrsv) (char *, char *, char *, int *, double *, int *, - double *, int *); -int BLASFUNC(qtrsv) (char *, char *, char *, int *, double *, int *, - double *, int *); -int BLASFUNC(ctrsv) (char *, char *, char *, int *, float *, int *, - float *, int *); -int BLASFUNC(ztrsv) (char *, char *, char *, int *, double *, int *, - double *, int *); -int BLASFUNC(xtrsv) (char *, char *, char *, int *, double *, int *, - double *, int *); - -int BLASFUNC(stpsv) (char *, char *, char *, int *, float *, float *, int *); -int BLASFUNC(dtpsv) (char *, char *, char *, int *, double *, double *, int *); -int BLASFUNC(qtpsv) (char *, char *, char *, int *, double *, double *, int *); -int BLASFUNC(ctpsv) (char *, char *, char *, int *, float *, float *, int *); -int BLASFUNC(ztpsv) (char *, char *, char *, int *, double *, double *, int *); -int BLASFUNC(xtpsv) (char *, char *, char *, int *, double *, double *, int *); - -int BLASFUNC(strmv) (char *, char *, char *, int *, float *, int *, - float *, int *); -int BLASFUNC(dtrmv) (char *, char *, char *, int *, double *, int *, - double *, int *); -int BLASFUNC(qtrmv) (char *, char *, char *, int *, double *, int *, - double *, int *); -int BLASFUNC(ctrmv) (char *, char *, char *, int *, float *, int *, - float *, int *); -int BLASFUNC(ztrmv) (char *, char *, char *, int *, double *, int *, - double *, int *); -int BLASFUNC(xtrmv) (char *, char *, char *, int *, double *, int *, - double *, int *); - -int BLASFUNC(stpmv) (char *, char *, char *, int *, float *, float *, int *); -int BLASFUNC(dtpmv) (char *, char *, char *, int *, double *, double *, int *); -int BLASFUNC(qtpmv) (char *, char *, char *, int *, double *, double *, int *); -int BLASFUNC(ctpmv) (char *, char *, char *, int *, float *, float *, int *); -int BLASFUNC(ztpmv) (char *, char *, char *, int *, double *, double *, int *); -int BLASFUNC(xtpmv) (char *, char *, char *, int *, double *, double *, int *); - -int BLASFUNC(stbmv) (char *, char *, char *, int *, int *, float *, int *, float *, int *); -int BLASFUNC(dtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *); -int BLASFUNC(qtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *); -int BLASFUNC(ctbmv) (char *, char *, char *, int *, int *, float *, int *, float *, int *); -int BLASFUNC(ztbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *); -int BLASFUNC(xtbmv) (char *, char *, char *, int *, int *, double *, int *, double *, int *); - -int BLASFUNC(stbsv) (char *, char *, char *, int *, int *, float *, int *, float *, int *); -int BLASFUNC(dtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *); -int BLASFUNC(qtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *); -int BLASFUNC(ctbsv) (char *, char *, char *, int *, int *, float *, int *, float *, int *); -int BLASFUNC(ztbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *); -int BLASFUNC(xtbsv) (char *, char *, char *, int *, int *, double *, int *, double *, int *); - -int BLASFUNC(ssymv) (char *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(dsymv) (char *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(qsymv) (char *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(csymv) (char *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zsymv) (char *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xsymv) (char *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(sspmv) (char *, int *, float *, float *, - float *, int *, float *, float *, int *); -int BLASFUNC(dspmv) (char *, int *, double *, double *, - double *, int *, double *, double *, int *); -int BLASFUNC(qspmv) (char *, int *, double *, double *, - double *, int *, double *, double *, int *); -int BLASFUNC(cspmv) (char *, int *, float *, float *, - float *, int *, float *, float *, int *); -int BLASFUNC(zspmv) (char *, int *, double *, double *, - double *, int *, double *, double *, int *); -int BLASFUNC(xspmv) (char *, int *, double *, double *, - double *, int *, double *, double *, int *); - -int BLASFUNC(ssyr) (char *, int *, float *, float *, int *, - float *, int *); -int BLASFUNC(dsyr) (char *, int *, double *, double *, int *, - double *, int *); -int BLASFUNC(qsyr) (char *, int *, double *, double *, int *, - double *, int *); -int BLASFUNC(csyr) (char *, int *, float *, float *, int *, - float *, int *); -int BLASFUNC(zsyr) (char *, int *, double *, double *, int *, - double *, int *); -int BLASFUNC(xsyr) (char *, int *, double *, double *, int *, - double *, int *); - -int BLASFUNC(ssyr2) (char *, int *, float *, - float *, int *, float *, int *, float *, int *); -int BLASFUNC(dsyr2) (char *, int *, double *, - double *, int *, double *, int *, double *, int *); -int BLASFUNC(qsyr2) (char *, int *, double *, - double *, int *, double *, int *, double *, int *); -int BLASFUNC(csyr2) (char *, int *, float *, - float *, int *, float *, int *, float *, int *); -int BLASFUNC(zsyr2) (char *, int *, double *, - double *, int *, double *, int *, double *, int *); -int BLASFUNC(xsyr2) (char *, int *, double *, - double *, int *, double *, int *, double *, int *); - -int BLASFUNC(sspr) (char *, int *, float *, float *, int *, - float *); -int BLASFUNC(dspr) (char *, int *, double *, double *, int *, - double *); -int BLASFUNC(qspr) (char *, int *, double *, double *, int *, - double *); -int BLASFUNC(cspr) (char *, int *, float *, float *, int *, - float *); -int BLASFUNC(zspr) (char *, int *, double *, double *, int *, - double *); -int BLASFUNC(xspr) (char *, int *, double *, double *, int *, - double *); - -int BLASFUNC(sspr2) (char *, int *, float *, - float *, int *, float *, int *, float *); -int BLASFUNC(dspr2) (char *, int *, double *, - double *, int *, double *, int *, double *); -int BLASFUNC(qspr2) (char *, int *, double *, - double *, int *, double *, int *, double *); -int BLASFUNC(cspr2) (char *, int *, float *, - float *, int *, float *, int *, float *); -int BLASFUNC(zspr2) (char *, int *, double *, - double *, int *, double *, int *, double *); -int BLASFUNC(xspr2) (char *, int *, double *, - double *, int *, double *, int *, double *); - -int BLASFUNC(cher) (char *, int *, float *, float *, int *, - float *, int *); -int BLASFUNC(zher) (char *, int *, double *, double *, int *, - double *, int *); -int BLASFUNC(xher) (char *, int *, double *, double *, int *, - double *, int *); - -int BLASFUNC(chpr) (char *, int *, float *, float *, int *, float *); -int BLASFUNC(zhpr) (char *, int *, double *, double *, int *, double *); -int BLASFUNC(xhpr) (char *, int *, double *, double *, int *, double *); - -int BLASFUNC(cher2) (char *, int *, float *, - float *, int *, float *, int *, float *, int *); -int BLASFUNC(zher2) (char *, int *, double *, - double *, int *, double *, int *, double *, int *); -int BLASFUNC(xher2) (char *, int *, double *, - double *, int *, double *, int *, double *, int *); - -int BLASFUNC(chpr2) (char *, int *, float *, - float *, int *, float *, int *, float *); -int BLASFUNC(zhpr2) (char *, int *, double *, - double *, int *, double *, int *, double *); -int BLASFUNC(xhpr2) (char *, int *, double *, - double *, int *, double *, int *, double *); - -int BLASFUNC(chemv) (char *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zhemv) (char *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xhemv) (char *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(chpmv) (char *, int *, float *, float *, - float *, int *, float *, float *, int *); -int BLASFUNC(zhpmv) (char *, int *, double *, double *, - double *, int *, double *, double *, int *); -int BLASFUNC(xhpmv) (char *, int *, double *, double *, - double *, int *, double *, double *, int *); - -int BLASFUNC(snorm)(char *, int *, int *, float *, int *); -int BLASFUNC(dnorm)(char *, int *, int *, double *, int *); -int BLASFUNC(cnorm)(char *, int *, int *, float *, int *); -int BLASFUNC(znorm)(char *, int *, int *, double *, int *); - -int BLASFUNC(sgbmv)(char *, int *, int *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(dgbmv)(char *, int *, int *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(qgbmv)(char *, int *, int *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(cgbmv)(char *, int *, int *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zgbmv)(char *, int *, int *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xgbmv)(char *, int *, int *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(ssbmv)(char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(dsbmv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(qsbmv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(csbmv)(char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zsbmv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xsbmv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(chbmv)(char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zhbmv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xhbmv)(char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -/* Level 3 routines */ - -int BLASFUNC(sgemm)(char *, char *, int *, int *, int *, float *, - float *, int *, float *, int *, float *, float *, int *); -int BLASFUNC(dgemm)(char *, char *, int *, int *, int *, double *, - double *, int *, double *, int *, double *, double *, int *); -int BLASFUNC(qgemm)(char *, char *, int *, int *, int *, double *, - double *, int *, double *, int *, double *, double *, int *); -int BLASFUNC(cgemm)(char *, char *, int *, int *, int *, float *, - float *, int *, float *, int *, float *, float *, int *); -int BLASFUNC(zgemm)(char *, char *, int *, int *, int *, double *, - double *, int *, double *, int *, double *, double *, int *); -int BLASFUNC(xgemm)(char *, char *, int *, int *, int *, double *, - double *, int *, double *, int *, double *, double *, int *); - -int BLASFUNC(cgemm3m)(char *, char *, int *, int *, int *, float *, - float *, int *, float *, int *, float *, float *, int *); -int BLASFUNC(zgemm3m)(char *, char *, int *, int *, int *, double *, - double *, int *, double *, int *, double *, double *, int *); -int BLASFUNC(xgemm3m)(char *, char *, int *, int *, int *, double *, - double *, int *, double *, int *, double *, double *, int *); - -int BLASFUNC(sge2mm)(char *, char *, char *, int *, int *, - float *, float *, int *, float *, int *, - float *, float *, int *); -int BLASFUNC(dge2mm)(char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *, - double *, double *, int *); -int BLASFUNC(cge2mm)(char *, char *, char *, int *, int *, - float *, float *, int *, float *, int *, - float *, float *, int *); -int BLASFUNC(zge2mm)(char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *, - double *, double *, int *); - -int BLASFUNC(strsm)(char *, char *, char *, char *, int *, int *, - float *, float *, int *, float *, int *); -int BLASFUNC(dtrsm)(char *, char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *); -int BLASFUNC(qtrsm)(char *, char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *); -int BLASFUNC(ctrsm)(char *, char *, char *, char *, int *, int *, - float *, float *, int *, float *, int *); -int BLASFUNC(ztrsm)(char *, char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *); -int BLASFUNC(xtrsm)(char *, char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *); - -int BLASFUNC(strmm)(char *, char *, char *, char *, int *, int *, - float *, float *, int *, float *, int *); -int BLASFUNC(dtrmm)(char *, char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *); -int BLASFUNC(qtrmm)(char *, char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *); -int BLASFUNC(ctrmm)(char *, char *, char *, char *, int *, int *, - float *, float *, int *, float *, int *); -int BLASFUNC(ztrmm)(char *, char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *); -int BLASFUNC(xtrmm)(char *, char *, char *, char *, int *, int *, - double *, double *, int *, double *, int *); - -int BLASFUNC(ssymm)(char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(dsymm)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(qsymm)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(csymm)(char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zsymm)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xsymm)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(csymm3m)(char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zsymm3m)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xsymm3m)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(ssyrk)(char *, char *, int *, int *, float *, float *, int *, - float *, float *, int *); -int BLASFUNC(dsyrk)(char *, char *, int *, int *, double *, double *, int *, - double *, double *, int *); -int BLASFUNC(qsyrk)(char *, char *, int *, int *, double *, double *, int *, - double *, double *, int *); -int BLASFUNC(csyrk)(char *, char *, int *, int *, float *, float *, int *, - float *, float *, int *); -int BLASFUNC(zsyrk)(char *, char *, int *, int *, double *, double *, int *, - double *, double *, int *); -int BLASFUNC(xsyrk)(char *, char *, int *, int *, double *, double *, int *, - double *, double *, int *); - -int BLASFUNC(ssyr2k)(char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(dsyr2k)(char *, char *, int *, int *, double *, double *, int *, - double*, int *, double *, double *, int *); -int BLASFUNC(qsyr2k)(char *, char *, int *, int *, double *, double *, int *, - double*, int *, double *, double *, int *); -int BLASFUNC(csyr2k)(char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zsyr2k)(char *, char *, int *, int *, double *, double *, int *, - double*, int *, double *, double *, int *); -int BLASFUNC(xsyr2k)(char *, char *, int *, int *, double *, double *, int *, - double*, int *, double *, double *, int *); - -int BLASFUNC(chemm)(char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zhemm)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xhemm)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(chemm3m)(char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zhemm3m)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); -int BLASFUNC(xhemm3m)(char *, char *, int *, int *, double *, double *, int *, - double *, int *, double *, double *, int *); - -int BLASFUNC(cherk)(char *, char *, int *, int *, float *, float *, int *, - float *, float *, int *); -int BLASFUNC(zherk)(char *, char *, int *, int *, double *, double *, int *, - double *, double *, int *); -int BLASFUNC(xherk)(char *, char *, int *, int *, double *, double *, int *, - double *, double *, int *); - -int BLASFUNC(cher2k)(char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zher2k)(char *, char *, int *, int *, double *, double *, int *, - double*, int *, double *, double *, int *); -int BLASFUNC(xher2k)(char *, char *, int *, int *, double *, double *, int *, - double*, int *, double *, double *, int *); -int BLASFUNC(cher2m)(char *, char *, char *, int *, int *, float *, float *, int *, - float *, int *, float *, float *, int *); -int BLASFUNC(zher2m)(char *, char *, char *, int *, int *, double *, double *, int *, - double*, int *, double *, double *, int *); -int BLASFUNC(xher2m)(char *, char *, char *, int *, int *, double *, double *, int *, - double*, int *, double *, double *, int *); - -int BLASFUNC(sgemt)(char *, int *, int *, float *, float *, int *, - float *, int *); -int BLASFUNC(dgemt)(char *, int *, int *, double *, double *, int *, - double *, int *); -int BLASFUNC(cgemt)(char *, int *, int *, float *, float *, int *, - float *, int *); -int BLASFUNC(zgemt)(char *, int *, int *, double *, double *, int *, - double *, int *); - -int BLASFUNC(sgema)(char *, char *, int *, int *, float *, - float *, int *, float *, float *, int *, float *, int *); -int BLASFUNC(dgema)(char *, char *, int *, int *, double *, - double *, int *, double*, double *, int *, double*, int *); -int BLASFUNC(cgema)(char *, char *, int *, int *, float *, - float *, int *, float *, float *, int *, float *, int *); -int BLASFUNC(zgema)(char *, char *, int *, int *, double *, - double *, int *, double*, double *, int *, double*, int *); - -int BLASFUNC(sgems)(char *, char *, int *, int *, float *, - float *, int *, float *, float *, int *, float *, int *); -int BLASFUNC(dgems)(char *, char *, int *, int *, double *, - double *, int *, double*, double *, int *, double*, int *); -int BLASFUNC(cgems)(char *, char *, int *, int *, float *, - float *, int *, float *, float *, int *, float *, int *); -int BLASFUNC(zgems)(char *, char *, int *, int *, double *, - double *, int *, double*, double *, int *, double*, int *); - -int BLASFUNC(sgetf2)(int *, int *, float *, int *, int *, int *); -int BLASFUNC(dgetf2)(int *, int *, double *, int *, int *, int *); -int BLASFUNC(qgetf2)(int *, int *, double *, int *, int *, int *); -int BLASFUNC(cgetf2)(int *, int *, float *, int *, int *, int *); -int BLASFUNC(zgetf2)(int *, int *, double *, int *, int *, int *); -int BLASFUNC(xgetf2)(int *, int *, double *, int *, int *, int *); - -int BLASFUNC(sgetrf)(int *, int *, float *, int *, int *, int *); -int BLASFUNC(dgetrf)(int *, int *, double *, int *, int *, int *); -int BLASFUNC(qgetrf)(int *, int *, double *, int *, int *, int *); -int BLASFUNC(cgetrf)(int *, int *, float *, int *, int *, int *); -int BLASFUNC(zgetrf)(int *, int *, double *, int *, int *, int *); -int BLASFUNC(xgetrf)(int *, int *, double *, int *, int *, int *); - -int BLASFUNC(slaswp)(int *, float *, int *, int *, int *, int *, int *); -int BLASFUNC(dlaswp)(int *, double *, int *, int *, int *, int *, int *); -int BLASFUNC(qlaswp)(int *, double *, int *, int *, int *, int *, int *); -int BLASFUNC(claswp)(int *, float *, int *, int *, int *, int *, int *); -int BLASFUNC(zlaswp)(int *, double *, int *, int *, int *, int *, int *); -int BLASFUNC(xlaswp)(int *, double *, int *, int *, int *, int *, int *); - -int BLASFUNC(sgetrs)(char *, int *, int *, float *, int *, int *, float *, int *, int *); -int BLASFUNC(dgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *); -int BLASFUNC(qgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *); -int BLASFUNC(cgetrs)(char *, int *, int *, float *, int *, int *, float *, int *, int *); -int BLASFUNC(zgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *); -int BLASFUNC(xgetrs)(char *, int *, int *, double *, int *, int *, double *, int *, int *); - -int BLASFUNC(sgesv)(int *, int *, float *, int *, int *, float *, int *, int *); -int BLASFUNC(dgesv)(int *, int *, double *, int *, int *, double*, int *, int *); -int BLASFUNC(qgesv)(int *, int *, double *, int *, int *, double*, int *, int *); -int BLASFUNC(cgesv)(int *, int *, float *, int *, int *, float *, int *, int *); -int BLASFUNC(zgesv)(int *, int *, double *, int *, int *, double*, int *, int *); -int BLASFUNC(xgesv)(int *, int *, double *, int *, int *, double*, int *, int *); - -int BLASFUNC(spotf2)(char *, int *, float *, int *, int *); -int BLASFUNC(dpotf2)(char *, int *, double *, int *, int *); -int BLASFUNC(qpotf2)(char *, int *, double *, int *, int *); -int BLASFUNC(cpotf2)(char *, int *, float *, int *, int *); -int BLASFUNC(zpotf2)(char *, int *, double *, int *, int *); -int BLASFUNC(xpotf2)(char *, int *, double *, int *, int *); - -int BLASFUNC(spotrf)(char *, int *, float *, int *, int *); -int BLASFUNC(dpotrf)(char *, int *, double *, int *, int *); -int BLASFUNC(qpotrf)(char *, int *, double *, int *, int *); -int BLASFUNC(cpotrf)(char *, int *, float *, int *, int *); -int BLASFUNC(zpotrf)(char *, int *, double *, int *, int *); -int BLASFUNC(xpotrf)(char *, int *, double *, int *, int *); - -int BLASFUNC(slauu2)(char *, int *, float *, int *, int *); -int BLASFUNC(dlauu2)(char *, int *, double *, int *, int *); -int BLASFUNC(qlauu2)(char *, int *, double *, int *, int *); -int BLASFUNC(clauu2)(char *, int *, float *, int *, int *); -int BLASFUNC(zlauu2)(char *, int *, double *, int *, int *); -int BLASFUNC(xlauu2)(char *, int *, double *, int *, int *); - -int BLASFUNC(slauum)(char *, int *, float *, int *, int *); -int BLASFUNC(dlauum)(char *, int *, double *, int *, int *); -int BLASFUNC(qlauum)(char *, int *, double *, int *, int *); -int BLASFUNC(clauum)(char *, int *, float *, int *, int *); -int BLASFUNC(zlauum)(char *, int *, double *, int *, int *); -int BLASFUNC(xlauum)(char *, int *, double *, int *, int *); - -int BLASFUNC(strti2)(char *, char *, int *, float *, int *, int *); -int BLASFUNC(dtrti2)(char *, char *, int *, double *, int *, int *); -int BLASFUNC(qtrti2)(char *, char *, int *, double *, int *, int *); -int BLASFUNC(ctrti2)(char *, char *, int *, float *, int *, int *); -int BLASFUNC(ztrti2)(char *, char *, int *, double *, int *, int *); -int BLASFUNC(xtrti2)(char *, char *, int *, double *, int *, int *); - -int BLASFUNC(strtri)(char *, char *, int *, float *, int *, int *); -int BLASFUNC(dtrtri)(char *, char *, int *, double *, int *, int *); -int BLASFUNC(qtrtri)(char *, char *, int *, double *, int *, int *); -int BLASFUNC(ctrtri)(char *, char *, int *, float *, int *, int *); -int BLASFUNC(ztrtri)(char *, char *, int *, double *, int *, int *); -int BLASFUNC(xtrtri)(char *, char *, int *, double *, int *, int *); - -int BLASFUNC(spotri)(char *, int *, float *, int *, int *); -int BLASFUNC(dpotri)(char *, int *, double *, int *, int *); -int BLASFUNC(qpotri)(char *, int *, double *, int *, int *); -int BLASFUNC(cpotri)(char *, int *, float *, int *, int *); -int BLASFUNC(zpotri)(char *, int *, double *, int *, int *); -int BLASFUNC(xpotri)(char *, int *, double *, int *, int *); - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/third_party/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h b/third_party/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h deleted file mode 100644 index 6e3f674573..0000000000 --- a/third_party/eigen3/Eigen/src/plugins/ArrayCwiseBinaryOps.h +++ /dev/null @@ -1,241 +0,0 @@ -/** \returns an expression of the coefficient wise product of \c *this and \a other - * - * \sa MatrixBase::cwiseProduct - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const EIGEN_CWISE_PRODUCT_RETURN_TYPE(Derived,OtherDerived) -operator*(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - return EIGEN_CWISE_PRODUCT_RETURN_TYPE(Derived,OtherDerived)(derived(), other.derived()); -} - -/** \returns an expression of the coefficient wise quotient of \c *this and \a other - * - * \sa MatrixBase::cwiseQuotient - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived> -operator/(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - return CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); -} - -/** \returns an expression of the coefficient-wise min of \c *this and \a other - * - * Example: \include Cwise_min.cpp - * Output: \verbinclude Cwise_min.out - * - * \sa max() - */ -EIGEN_MAKE_CWISE_BINARY_OP(min,internal::scalar_min_op) - -/** \returns an expression of the coefficient-wise min of \c *this and scalar \a other - * - * \sa max() - */ -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, - const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> > -#ifdef EIGEN_PARSED_BY_DOXYGEN -min -#else -(min) -#endif -(const Scalar &other) const -{ - return (min)(Derived::PlainObject::Constant(rows(), cols(), other)); -} - -/** \returns an expression of the coefficient-wise max of \c *this and \a other - * - * Example: \include Cwise_max.cpp - * Output: \verbinclude Cwise_max.out - * - * \sa min() - */ -EIGEN_MAKE_CWISE_BINARY_OP(max,internal::scalar_max_op) - -/** \returns an expression of the coefficient-wise max of \c *this and scalar \a other - * - * \sa min() - */ -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, - const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> > -#ifdef EIGEN_PARSED_BY_DOXYGEN -max -#else -(max) -#endif -(const Scalar &other) const -{ - return (max)(Derived::PlainObject::Constant(rows(), cols(), other)); -} - -/** \returns an expression of the coefficient-wise \< operator of *this and \a other - * - * Example: \include Cwise_less.cpp - * Output: \verbinclude Cwise_less.out - * - * \sa all(), any(), operator>(), operator<=() - */ -EIGEN_MAKE_CWISE_BINARY_OP(operator<,std::less) - -/** \returns an expression of the coefficient-wise \<= operator of *this and \a other - * - * Example: \include Cwise_less_equal.cpp - * Output: \verbinclude Cwise_less_equal.out - * - * \sa all(), any(), operator>=(), operator<() - */ -EIGEN_MAKE_CWISE_BINARY_OP(operator<=,std::less_equal) - -/** \returns an expression of the coefficient-wise \> operator of *this and \a other - * - * Example: \include Cwise_greater.cpp - * Output: \verbinclude Cwise_greater.out - * - * \sa all(), any(), operator>=(), operator<() - */ -EIGEN_MAKE_CWISE_BINARY_OP(operator>,std::greater) - -/** \returns an expression of the coefficient-wise \>= operator of *this and \a other - * - * Example: \include Cwise_greater_equal.cpp - * Output: \verbinclude Cwise_greater_equal.out - * - * \sa all(), any(), operator>(), operator<=() - */ -EIGEN_MAKE_CWISE_BINARY_OP(operator>=,std::greater_equal) - -/** \returns an expression of the coefficient-wise == operator of *this and \a other - * - * \warning this performs an exact comparison, which is generally a bad idea with floating-point types. - * In order to check for equality between two vectors or matrices with floating-point coefficients, it is - * generally a far better idea to use a fuzzy comparison as provided by isApprox() and - * isMuchSmallerThan(). - * - * Example: \include Cwise_equal_equal.cpp - * Output: \verbinclude Cwise_equal_equal.out - * - * \sa all(), any(), isApprox(), isMuchSmallerThan() - */ -EIGEN_MAKE_CWISE_BINARY_OP(operator==,std::equal_to) - -/** \returns an expression of the coefficient-wise != operator of *this and \a other - * - * \warning this performs an exact comparison, which is generally a bad idea with floating-point types. - * In order to check for equality between two vectors or matrices with floating-point coefficients, it is - * generally a far better idea to use a fuzzy comparison as provided by isApprox() and - * isMuchSmallerThan(). - * - * Example: \include Cwise_not_equal.cpp - * Output: \verbinclude Cwise_not_equal.out - * - * \sa all(), any(), isApprox(), isMuchSmallerThan() - */ -EIGEN_MAKE_CWISE_BINARY_OP(operator!=,std::not_equal_to) - -// scalar addition - -/** \returns an expression of \c *this with each coeff incremented by the constant \a scalar - * - * Example: \include Cwise_plus.cpp - * Output: \verbinclude Cwise_plus.out - * - * \sa operator+=(), operator-() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived> -operator+(const Scalar& scalar) const -{ - return CwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived>(derived(), internal::scalar_add_op<Scalar>(scalar)); -} - -EIGEN_DEVICE_FUNC -friend inline const CwiseUnaryOp<internal::scalar_add_op<Scalar>, const Derived> -operator+(const Scalar& scalar,const EIGEN_CURRENT_STORAGE_BASE_CLASS<Derived>& other) -{ - return other + scalar; -} - -/** \returns an expression of \c *this with each coeff decremented by the constant \a scalar - * - * Example: \include Cwise_minus.cpp - * Output: \verbinclude Cwise_minus.out - * - * \sa operator+(), operator-=() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_sub_op<Scalar>, const Derived> -operator-(const Scalar& scalar) const -{ - return CwiseUnaryOp<internal::scalar_sub_op<Scalar>, const Derived>(derived(), internal::scalar_sub_op<Scalar>(scalar));; -} - -EIGEN_DEVICE_FUNC -friend inline const CwiseUnaryOp<internal::scalar_rsub_op<Scalar>, const Derived> -operator-(const Scalar& scalar,const EIGEN_CURRENT_STORAGE_BASE_CLASS<Derived>& other) -{ - return CwiseUnaryOp<internal::scalar_rsub_op<Scalar>, const Derived>(other.derived(), internal::scalar_rsub_op<Scalar>(scalar));; -} - -/** \returns an expression of the coefficient-wise && operator of *this and \a other - * - * \warning this operator is for expression of bool only. - * - * Example: \include Cwise_boolean_and.cpp - * Output: \verbinclude Cwise_boolean_and.out - * - * \sa operator||(), select() - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -inline const CwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived> -operator&&(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value), - THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL); - return CwiseBinaryOp<internal::scalar_boolean_and_op, const Derived, const OtherDerived>(derived(),other.derived()); -} - -/** \returns an expression of the coefficient-wise || operator of *this and \a other - * - * \warning this operator is for expression of bool only. - * - * Example: \include Cwise_boolean_or.cpp - * Output: \verbinclude Cwise_boolean_or.out - * - * \sa operator&&(), select() - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -inline const CwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived> -operator||(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value), - THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL); - return CwiseBinaryOp<internal::scalar_boolean_or_op, const Derived, const OtherDerived>(derived(),other.derived()); -} - -/** \returns an expression of the coefficient-wise ^ operator of *this and \a other - * - * \warning this operator is for expression of bool only. - * - * Example: \include Cwise_boolean_xor.cpp - * Output: \verbinclude Cwise_boolean_xor.out - * - * \sa operator^(), select() - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -inline const CwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived> -operator^(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - EIGEN_STATIC_ASSERT((internal::is_same<bool,Scalar>::value && internal::is_same<bool,typename OtherDerived::Scalar>::value), - THIS_METHOD_IS_ONLY_FOR_EXPRESSIONS_OF_BOOL); - return CwiseBinaryOp<internal::scalar_boolean_xor_op, const Derived, const OtherDerived>(derived(),other.derived()); -} - diff --git a/third_party/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h b/third_party/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h deleted file mode 100644 index fbf0d2031b..0000000000 --- a/third_party/eigen3/Eigen/src/plugins/ArrayCwiseUnaryOps.h +++ /dev/null @@ -1,283 +0,0 @@ - - -/** \returns an expression of the coefficient-wise absolute value of \c *this - * - * Example: \include Cwise_abs.cpp - * Output: \verbinclude Cwise_abs.out - * - * \sa abs2() - */ -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> -abs() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise squared absolute value of \c *this - * - * Example: \include Cwise_abs2.cpp - * Output: \verbinclude Cwise_abs2.out - * - * \sa abs(), square() - */ -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> -abs2() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise exponential of *this. - * - * Example: \include Cwise_exp.cpp - * Output: \verbinclude Cwise_exp.out - * - * \sa pow(), log(), sin(), cos() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived> -exp() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise logarithm of *this. - * - * Example: \include Cwise_log.cpp - * Output: \verbinclude Cwise_log.out - * - * \sa exp() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived> -log() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise square root of *this. - * - * Example: \include Cwise_sqrt.cpp - * Output: \verbinclude Cwise_sqrt.out - * - * \sa rsqrt(), pow(), square() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> -sqrt() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise reciprocal square root of *this. - * - * \sa sqrt(), pow(), square() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived> -rsqrt() const -{ - return derived(); -} - - -/** \returns an expression of the coefficient-wise cosine of *this. - * - * Example: \include Cwise_cos.cpp - * Output: \verbinclude Cwise_cos.out - * - * \sa sin(), acos() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_cos_op<Scalar>, const Derived> -cos() const -{ - return derived(); -} - - -/** \returns an expression of the coefficient-wise sine of *this. - * - * Example: \include Cwise_sin.cpp - * Output: \verbinclude Cwise_sin.out - * - * \sa cos(), asin() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_sin_op<Scalar>, const Derived> -sin() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise arc cosine of *this. - * - * Example: \include Cwise_acos.cpp - * Output: \verbinclude Cwise_acos.out - * - * \sa cos(), asin() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_acos_op<Scalar>, const Derived> -acos() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise arc sine of *this. - * - * Example: \include Cwise_asin.cpp - * Output: \verbinclude Cwise_asin.out - * - * \sa sin(), acos() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_asin_op<Scalar>, const Derived> -asin() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise tan of *this. - * - * Example: \include Cwise_tan.cpp - * Output: \verbinclude Cwise_tan.out - * - * \sa cos(), sin() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_tan_op<Scalar>, Derived> -tan() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise arc tan of *this. - * - * Example: \include Cwise_atan.cpp - * Output: \verbinclude Cwise_atan.out - * - * \sa cos(), sin(), tan() - */ -inline const CwiseUnaryOp<internal::scalar_atan_op<Scalar>, Derived> -atan() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise ln(|gamma(*this)|). - * - * Example: \include Cwise_lgamma.cpp - * Output: \verbinclude Cwise_lgamma.out - * - * \sa cos(), sin(), tan() - */ -inline const CwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, Derived> -lgamma() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise Gauss error function of *this. - * - * Example: \include Cwise_erf.cpp - * Output: \verbinclude Cwise_erf.out - * - * \sa cos(), sin(), tan() - */ -inline const CwiseUnaryOp<internal::scalar_erf_op<Scalar>, Derived> -erf() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise Complementary error function of *this. - * - * Example: \include Cwise_erfc.cpp - * Output: \verbinclude Cwise_erfc.out - * - * \sa cos(), sin(), tan() - */ -inline const CwiseUnaryOp<internal::scalar_erfc_op<Scalar>, Derived> -erfc() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise power of *this to the given exponent. - * - * Example: \include Cwise_pow.cpp - * Output: \verbinclude Cwise_pow.out - * - * \sa exp(), log() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_pow_op<Scalar>, const Derived> -pow(const Scalar& exponent) const -{ - return CwiseUnaryOp<internal::scalar_pow_op<Scalar>, const Derived> - (derived(), internal::scalar_pow_op<Scalar>(exponent)); -} - -/** \returns an expression of the coefficient-wise inverse of *this. - * - * Example: \include Cwise_inverse.cpp - * Output: \verbinclude Cwise_inverse.out - * - * \sa operator/(), operator*() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> -inverse() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise square of *this. - * - * Example: \include Cwise_square.cpp - * Output: \verbinclude Cwise_square.out - * - * \sa operator/(), operator*(), abs2() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> -square() const -{ - return derived(); -} - -/** \returns an expression of the coefficient-wise cube of *this. - * - * Example: \include Cwise_cube.cpp - * Output: \verbinclude Cwise_cube.out - * - * \sa square(), pow() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived> -cube() const -{ - return derived(); -} - -#define EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(METHOD_NAME,FUNCTOR) \ - EIGEN_DEVICE_FUNC \ - inline const CwiseUnaryOp<std::binder2nd<FUNCTOR<Scalar> >, const Derived> \ - METHOD_NAME(const Scalar& s) const { \ - return CwiseUnaryOp<std::binder2nd<FUNCTOR<Scalar> >, const Derived> \ - (derived(), std::bind2nd(FUNCTOR<Scalar>(), s)); \ - } \ - friend inline const CwiseUnaryOp<std::binder1st<FUNCTOR<Scalar> >, const Derived> \ - METHOD_NAME(const Scalar& s, const Derived& d) { \ - return CwiseUnaryOp<std::binder1st<FUNCTOR<Scalar> >, const Derived> \ - (d, std::bind1st(FUNCTOR<Scalar>(), s)); \ - } - -EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator==, std::equal_to) -EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator!=, std::not_equal_to) -EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator<, std::less) -EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator<=, std::less_equal) -EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator>, std::greater) -EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator>=, std::greater_equal) diff --git a/third_party/eigen3/Eigen/src/plugins/BlockMethods.h b/third_party/eigen3/Eigen/src/plugins/BlockMethods.h deleted file mode 100644 index 9b7fdc4aa7..0000000000 --- a/third_party/eigen3/Eigen/src/plugins/BlockMethods.h +++ /dev/null @@ -1,995 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - - -#ifndef EIGEN_PARSED_BY_DOXYGEN - -/** \internal expression type of a column */ -typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ColXpr; -typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ConstColXpr; -/** \internal expression type of a row */ -typedef Block<Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowXpr; -typedef const Block<const Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowXpr; -/** \internal expression type of a block of whole columns */ -typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ColsBlockXpr; -typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ConstColsBlockXpr; -/** \internal expression type of a block of whole rows */ -typedef Block<Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowsBlockXpr; -typedef const Block<const Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowsBlockXpr; -/** \internal expression type of a block of whole columns */ -template<int N> struct NColsBlockXpr { typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type; }; -template<int N> struct ConstNColsBlockXpr { typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type; }; -/** \internal expression type of a block of whole rows */ -template<int N> struct NRowsBlockXpr { typedef Block<Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type; }; -template<int N> struct ConstNRowsBlockXpr { typedef const Block<const Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type; }; - -typedef VectorBlock<Derived> SegmentReturnType; -typedef const VectorBlock<const Derived> ConstSegmentReturnType; -template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; }; -template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; }; - -#endif // not EIGEN_PARSED_BY_DOXYGEN - -/** \returns a dynamic-size expression of a block in *this. - * - * \param startRow the first row in the block - * \param startCol the first column in the block - * \param blockRows the number of rows in the block - * \param blockCols the number of columns in the block - * - * Example: \include MatrixBase_block_int_int_int_int.cpp - * Output: \verbinclude MatrixBase_block_int_int_int_int.out - * - * \note Even though the returned expression has dynamic size, in the case - * when it is applied to a fixed-size matrix, it inherits a fixed maximal size, - * which means that evaluating it does not cause a dynamic memory allocation. - * - * \sa class Block, block(Index,Index) - */ -EIGEN_DEVICE_FUNC -inline Block<Derived> block(Index startRow, Index startCol, Index blockRows, Index blockCols) -{ - return Block<Derived>(derived(), startRow, startCol, blockRows, blockCols); -} - -/** This is the const version of block(Index,Index,Index,Index). */ -EIGEN_DEVICE_FUNC -inline const Block<const Derived> block(Index startRow, Index startCol, Index blockRows, Index blockCols) const -{ - return Block<const Derived>(derived(), startRow, startCol, blockRows, blockCols); -} - - - - -/** \returns a dynamic-size expression of a top-right corner of *this. - * - * \param cRows the number of rows in the corner - * \param cCols the number of columns in the corner - * - * Example: \include MatrixBase_topRightCorner_int_int.cpp - * Output: \verbinclude MatrixBase_topRightCorner_int_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline Block<Derived> topRightCorner(Index cRows, Index cCols) -{ - return Block<Derived>(derived(), 0, cols() - cCols, cRows, cCols); -} - -/** This is the const version of topRightCorner(Index, Index).*/ -EIGEN_DEVICE_FUNC -inline const Block<const Derived> topRightCorner(Index cRows, Index cCols) const -{ - return Block<const Derived>(derived(), 0, cols() - cCols, cRows, cCols); -} - -/** \returns an expression of a fixed-size top-right corner of *this. - * - * \tparam CRows the number of rows in the corner - * \tparam CCols the number of columns in the corner - * - * Example: \include MatrixBase_template_int_int_topRightCorner.cpp - * Output: \verbinclude MatrixBase_template_int_int_topRightCorner.out - * - * \sa class Block, block<int,int>(Index,Index) - */ -template<int CRows, int CCols> -EIGEN_DEVICE_FUNC -inline Block<Derived, CRows, CCols> topRightCorner() -{ - return Block<Derived, CRows, CCols>(derived(), 0, cols() - CCols); -} - -/** This is the const version of topRightCorner<int, int>().*/ -template<int CRows, int CCols> -EIGEN_DEVICE_FUNC -inline const Block<const Derived, CRows, CCols> topRightCorner() const -{ - return Block<const Derived, CRows, CCols>(derived(), 0, cols() - CCols); -} - -/** \returns an expression of a top-right corner of *this. - * - * \tparam CRows number of rows in corner as specified at compile-time - * \tparam CCols number of columns in corner as specified at compile-time - * \param cRows number of rows in corner as specified at run-time - * \param cCols number of columns in corner as specified at run-time - * - * This function is mainly useful for corners where the number of rows is specified at compile-time - * and the number of columns is specified at run-time, or vice versa. The compile-time and run-time - * information should not contradict. In other words, \a cRows should equal \a CRows unless - * \a CRows is \a Dynamic, and the same for the number of columns. - * - * Example: \include MatrixBase_template_int_int_topRightCorner_int_int.cpp - * Output: \verbinclude MatrixBase_template_int_int_topRightCorner_int_int.out - * - * \sa class Block - */ -template<int CRows, int CCols> -inline Block<Derived, CRows, CCols> topRightCorner(Index cRows, Index cCols) -{ - return Block<Derived, CRows, CCols>(derived(), 0, cols() - cCols, cRows, cCols); -} - -/** This is the const version of topRightCorner<int, int>(Index, Index).*/ -template<int CRows, int CCols> -inline const Block<const Derived, CRows, CCols> topRightCorner(Index cRows, Index cCols) const -{ - return Block<const Derived, CRows, CCols>(derived(), 0, cols() - cCols, cRows, cCols); -} - - - -/** \returns a dynamic-size expression of a top-left corner of *this. - * - * \param cRows the number of rows in the corner - * \param cCols the number of columns in the corner - * - * Example: \include MatrixBase_topLeftCorner_int_int.cpp - * Output: \verbinclude MatrixBase_topLeftCorner_int_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline Block<Derived> topLeftCorner(Index cRows, Index cCols) -{ - return Block<Derived>(derived(), 0, 0, cRows, cCols); -} - -/** This is the const version of topLeftCorner(Index, Index).*/ -EIGEN_DEVICE_FUNC -inline const Block<const Derived> topLeftCorner(Index cRows, Index cCols) const -{ - return Block<const Derived>(derived(), 0, 0, cRows, cCols); -} - -/** \returns an expression of a fixed-size top-left corner of *this. - * - * The template parameters CRows and CCols are the number of rows and columns in the corner. - * - * Example: \include MatrixBase_template_int_int_topLeftCorner.cpp - * Output: \verbinclude MatrixBase_template_int_int_topLeftCorner.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int CRows, int CCols> -EIGEN_DEVICE_FUNC -inline Block<Derived, CRows, CCols> topLeftCorner() -{ - return Block<Derived, CRows, CCols>(derived(), 0, 0); -} - -/** This is the const version of topLeftCorner<int, int>().*/ -template<int CRows, int CCols> -EIGEN_DEVICE_FUNC -inline const Block<const Derived, CRows, CCols> topLeftCorner() const -{ - return Block<const Derived, CRows, CCols>(derived(), 0, 0); -} - -/** \returns an expression of a top-left corner of *this. - * - * \tparam CRows number of rows in corner as specified at compile-time - * \tparam CCols number of columns in corner as specified at compile-time - * \param cRows number of rows in corner as specified at run-time - * \param cCols number of columns in corner as specified at run-time - * - * This function is mainly useful for corners where the number of rows is specified at compile-time - * and the number of columns is specified at run-time, or vice versa. The compile-time and run-time - * information should not contradict. In other words, \a cRows should equal \a CRows unless - * \a CRows is \a Dynamic, and the same for the number of columns. - * - * Example: \include MatrixBase_template_int_int_topLeftCorner_int_int.cpp - * Output: \verbinclude MatrixBase_template_int_int_topLeftCorner_int_int.out - * - * \sa class Block - */ -template<int CRows, int CCols> -inline Block<Derived, CRows, CCols> topLeftCorner(Index cRows, Index cCols) -{ - return Block<Derived, CRows, CCols>(derived(), 0, 0, cRows, cCols); -} - -/** This is the const version of topLeftCorner<int, int>(Index, Index).*/ -template<int CRows, int CCols> -inline const Block<const Derived, CRows, CCols> topLeftCorner(Index cRows, Index cCols) const -{ - return Block<const Derived, CRows, CCols>(derived(), 0, 0, cRows, cCols); -} - - - -/** \returns a dynamic-size expression of a bottom-right corner of *this. - * - * \param cRows the number of rows in the corner - * \param cCols the number of columns in the corner - * - * Example: \include MatrixBase_bottomRightCorner_int_int.cpp - * Output: \verbinclude MatrixBase_bottomRightCorner_int_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline Block<Derived> bottomRightCorner(Index cRows, Index cCols) -{ - return Block<Derived>(derived(), rows() - cRows, cols() - cCols, cRows, cCols); -} - -/** This is the const version of bottomRightCorner(Index, Index).*/ -EIGEN_DEVICE_FUNC -inline const Block<const Derived> bottomRightCorner(Index cRows, Index cCols) const -{ - return Block<const Derived>(derived(), rows() - cRows, cols() - cCols, cRows, cCols); -} - -/** \returns an expression of a fixed-size bottom-right corner of *this. - * - * The template parameters CRows and CCols are the number of rows and columns in the corner. - * - * Example: \include MatrixBase_template_int_int_bottomRightCorner.cpp - * Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int CRows, int CCols> -EIGEN_DEVICE_FUNC -inline Block<Derived, CRows, CCols> bottomRightCorner() -{ - return Block<Derived, CRows, CCols>(derived(), rows() - CRows, cols() - CCols); -} - -/** This is the const version of bottomRightCorner<int, int>().*/ -template<int CRows, int CCols> -EIGEN_DEVICE_FUNC -inline const Block<const Derived, CRows, CCols> bottomRightCorner() const -{ - return Block<const Derived, CRows, CCols>(derived(), rows() - CRows, cols() - CCols); -} - -/** \returns an expression of a bottom-right corner of *this. - * - * \tparam CRows number of rows in corner as specified at compile-time - * \tparam CCols number of columns in corner as specified at compile-time - * \param cRows number of rows in corner as specified at run-time - * \param cCols number of columns in corner as specified at run-time - * - * This function is mainly useful for corners where the number of rows is specified at compile-time - * and the number of columns is specified at run-time, or vice versa. The compile-time and run-time - * information should not contradict. In other words, \a cRows should equal \a CRows unless - * \a CRows is \a Dynamic, and the same for the number of columns. - * - * Example: \include MatrixBase_template_int_int_bottomRightCorner_int_int.cpp - * Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner_int_int.out - * - * \sa class Block - */ -template<int CRows, int CCols> -inline Block<Derived, CRows, CCols> bottomRightCorner(Index cRows, Index cCols) -{ - return Block<Derived, CRows, CCols>(derived(), rows() - cRows, cols() - cCols, cRows, cCols); -} - -/** This is the const version of bottomRightCorner<int, int>(Index, Index).*/ -template<int CRows, int CCols> -inline const Block<const Derived, CRows, CCols> bottomRightCorner(Index cRows, Index cCols) const -{ - return Block<const Derived, CRows, CCols>(derived(), rows() - cRows, cols() - cCols, cRows, cCols); -} - - - -/** \returns a dynamic-size expression of a bottom-left corner of *this. - * - * \param cRows the number of rows in the corner - * \param cCols the number of columns in the corner - * - * Example: \include MatrixBase_bottomLeftCorner_int_int.cpp - * Output: \verbinclude MatrixBase_bottomLeftCorner_int_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline Block<Derived> bottomLeftCorner(Index cRows, Index cCols) -{ - return Block<Derived>(derived(), rows() - cRows, 0, cRows, cCols); -} - -/** This is the const version of bottomLeftCorner(Index, Index).*/ -EIGEN_DEVICE_FUNC -inline const Block<const Derived> bottomLeftCorner(Index cRows, Index cCols) const -{ - return Block<const Derived>(derived(), rows() - cRows, 0, cRows, cCols); -} - -/** \returns an expression of a fixed-size bottom-left corner of *this. - * - * The template parameters CRows and CCols are the number of rows and columns in the corner. - * - * Example: \include MatrixBase_template_int_int_bottomLeftCorner.cpp - * Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int CRows, int CCols> -EIGEN_DEVICE_FUNC -inline Block<Derived, CRows, CCols> bottomLeftCorner() -{ - return Block<Derived, CRows, CCols>(derived(), rows() - CRows, 0); -} - -/** This is the const version of bottomLeftCorner<int, int>().*/ -template<int CRows, int CCols> -EIGEN_DEVICE_FUNC -inline const Block<const Derived, CRows, CCols> bottomLeftCorner() const -{ - return Block<const Derived, CRows, CCols>(derived(), rows() - CRows, 0); -} - -/** \returns an expression of a bottom-left corner of *this. - * - * \tparam CRows number of rows in corner as specified at compile-time - * \tparam CCols number of columns in corner as specified at compile-time - * \param cRows number of rows in corner as specified at run-time - * \param cCols number of columns in corner as specified at run-time - * - * This function is mainly useful for corners where the number of rows is specified at compile-time - * and the number of columns is specified at run-time, or vice versa. The compile-time and run-time - * information should not contradict. In other words, \a cRows should equal \a CRows unless - * \a CRows is \a Dynamic, and the same for the number of columns. - * - * Example: \include MatrixBase_template_int_int_bottomLeftCorner_int_int.cpp - * Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner_int_int.out - * - * \sa class Block - */ -template<int CRows, int CCols> -inline Block<Derived, CRows, CCols> bottomLeftCorner(Index cRows, Index cCols) -{ - return Block<Derived, CRows, CCols>(derived(), rows() - cRows, 0, cRows, cCols); -} - -/** This is the const version of bottomLeftCorner<int, int>(Index, Index).*/ -template<int CRows, int CCols> -inline const Block<const Derived, CRows, CCols> bottomLeftCorner(Index cRows, Index cCols) const -{ - return Block<const Derived, CRows, CCols>(derived(), rows() - cRows, 0, cRows, cCols); -} - - - -/** \returns a block consisting of the top rows of *this. - * - * \param n the number of rows in the block - * - * Example: \include MatrixBase_topRows_int.cpp - * Output: \verbinclude MatrixBase_topRows_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline RowsBlockXpr topRows(Index n) -{ - return RowsBlockXpr(derived(), 0, 0, n, cols()); -} - -/** This is the const version of topRows(Index).*/ -EIGEN_DEVICE_FUNC -inline ConstRowsBlockXpr topRows(Index n) const -{ - return ConstRowsBlockXpr(derived(), 0, 0, n, cols()); -} - -/** \returns a block consisting of the top rows of *this. - * - * \tparam N the number of rows in the block as specified at compile-time - * \param n the number of rows in the block as specified at run-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include MatrixBase_template_int_topRows.cpp - * Output: \verbinclude MatrixBase_template_int_topRows.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename NRowsBlockXpr<N>::Type topRows(Index n = N) -{ - return typename NRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols()); -} - -/** This is the const version of topRows<int>().*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstNRowsBlockXpr<N>::Type topRows(Index n = N) const -{ - return typename ConstNRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols()); -} - - - -/** \returns a block consisting of the bottom rows of *this. - * - * \param n the number of rows in the block - * - * Example: \include MatrixBase_bottomRows_int.cpp - * Output: \verbinclude MatrixBase_bottomRows_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline RowsBlockXpr bottomRows(Index n) -{ - return RowsBlockXpr(derived(), rows() - n, 0, n, cols()); -} - -/** This is the const version of bottomRows(Index).*/ -EIGEN_DEVICE_FUNC -inline ConstRowsBlockXpr bottomRows(Index n) const -{ - return ConstRowsBlockXpr(derived(), rows() - n, 0, n, cols()); -} - -/** \returns a block consisting of the bottom rows of *this. - * - * \tparam N the number of rows in the block as specified at compile-time - * \param n the number of rows in the block as specified at run-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include MatrixBase_template_int_bottomRows.cpp - * Output: \verbinclude MatrixBase_template_int_bottomRows.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename NRowsBlockXpr<N>::Type bottomRows(Index n = N) -{ - return typename NRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols()); -} - -/** This is the const version of bottomRows<int>().*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstNRowsBlockXpr<N>::Type bottomRows(Index n = N) const -{ - return typename ConstNRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols()); -} - - - -/** \returns a block consisting of a range of rows of *this. - * - * \param startRow the index of the first row in the block - * \param n the number of rows in the block - * - * Example: \include DenseBase_middleRows_int.cpp - * Output: \verbinclude DenseBase_middleRows_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline RowsBlockXpr middleRows(Index startRow, Index n) -{ - return RowsBlockXpr(derived(), startRow, 0, n, cols()); -} - -/** This is the const version of middleRows(Index,Index).*/ -EIGEN_DEVICE_FUNC -inline ConstRowsBlockXpr middleRows(Index startRow, Index n) const -{ - return ConstRowsBlockXpr(derived(), startRow, 0, n, cols()); -} - -/** \returns a block consisting of a range of rows of *this. - * - * \tparam N the number of rows in the block as specified at compile-time - * \param startRow the index of the first row in the block - * \param n the number of rows in the block as specified at run-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include DenseBase_template_int_middleRows.cpp - * Output: \verbinclude DenseBase_template_int_middleRows.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename NRowsBlockXpr<N>::Type middleRows(Index startRow, Index n = N) -{ - return typename NRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols()); -} - -/** This is the const version of middleRows<int>().*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstNRowsBlockXpr<N>::Type middleRows(Index startRow, Index n = N) const -{ - return typename ConstNRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols()); -} - - - -/** \returns a block consisting of the left columns of *this. - * - * \param n the number of columns in the block - * - * Example: \include MatrixBase_leftCols_int.cpp - * Output: \verbinclude MatrixBase_leftCols_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline ColsBlockXpr leftCols(Index n) -{ - return ColsBlockXpr(derived(), 0, 0, rows(), n); -} - -/** This is the const version of leftCols(Index).*/ -EIGEN_DEVICE_FUNC -inline ConstColsBlockXpr leftCols(Index n) const -{ - return ConstColsBlockXpr(derived(), 0, 0, rows(), n); -} - -/** \returns a block consisting of the left columns of *this. - * - * \tparam N the number of columns in the block as specified at compile-time - * \param n the number of columns in the block as specified at run-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include MatrixBase_template_int_leftCols.cpp - * Output: \verbinclude MatrixBase_template_int_leftCols.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename NColsBlockXpr<N>::Type leftCols(Index n = N) -{ - return typename NColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n); -} - -/** This is the const version of leftCols<int>().*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstNColsBlockXpr<N>::Type leftCols(Index n = N) const -{ - return typename ConstNColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n); -} - - - -/** \returns a block consisting of the right columns of *this. - * - * \param n the number of columns in the block - * - * Example: \include MatrixBase_rightCols_int.cpp - * Output: \verbinclude MatrixBase_rightCols_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline ColsBlockXpr rightCols(Index n) -{ - return ColsBlockXpr(derived(), 0, cols() - n, rows(), n); -} - -/** This is the const version of rightCols(Index).*/ -EIGEN_DEVICE_FUNC -inline ConstColsBlockXpr rightCols(Index n) const -{ - return ConstColsBlockXpr(derived(), 0, cols() - n, rows(), n); -} - -/** \returns a block consisting of the right columns of *this. - * - * \tparam N the number of columns in the block as specified at compile-time - * \param n the number of columns in the block as specified at run-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include MatrixBase_template_int_rightCols.cpp - * Output: \verbinclude MatrixBase_template_int_rightCols.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename NColsBlockXpr<N>::Type rightCols(Index n = N) -{ - return typename NColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n); -} - -/** This is the const version of rightCols<int>().*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstNColsBlockXpr<N>::Type rightCols(Index n = N) const -{ - return typename ConstNColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n); -} - - - -/** \returns a block consisting of a range of columns of *this. - * - * \param startCol the index of the first column in the block - * \param numCols the number of columns in the block - * - * Example: \include DenseBase_middleCols_int.cpp - * Output: \verbinclude DenseBase_middleCols_int.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -EIGEN_DEVICE_FUNC -inline ColsBlockXpr middleCols(Index startCol, Index numCols) -{ - return ColsBlockXpr(derived(), 0, startCol, rows(), numCols); -} - -/** This is the const version of middleCols(Index,Index).*/ -EIGEN_DEVICE_FUNC -inline ConstColsBlockXpr middleCols(Index startCol, Index numCols) const -{ - return ConstColsBlockXpr(derived(), 0, startCol, rows(), numCols); -} - -/** \returns a block consisting of a range of columns of *this. - * - * \tparam N the number of columns in the block as specified at compile-time - * \param startCol the index of the first column in the block - * \param n the number of columns in the block as specified at run-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include DenseBase_template_int_middleCols.cpp - * Output: \verbinclude DenseBase_template_int_middleCols.out - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename NColsBlockXpr<N>::Type middleCols(Index startCol, Index n = N) -{ - return typename NColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n); -} - -/** This is the const version of middleCols<int>().*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstNColsBlockXpr<N>::Type middleCols(Index startCol, Index n = N) const -{ - return typename ConstNColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n); -} - - - -/** \returns a fixed-size expression of a block in *this. - * - * The template parameters \a BlockRows and \a BlockCols are the number of - * rows and columns in the block. - * - * \param startRow the first row in the block - * \param startCol the first column in the block - * - * Example: \include MatrixBase_block_int_int.cpp - * Output: \verbinclude MatrixBase_block_int_int.out - * - * \note since block is a templated member, the keyword template has to be used - * if the matrix type is also a template parameter: \code m.template block<3,3>(1,1); \endcode - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int BlockRows, int BlockCols> -EIGEN_DEVICE_FUNC -inline Block<Derived, BlockRows, BlockCols> block(Index startRow, Index startCol) -{ - return Block<Derived, BlockRows, BlockCols>(derived(), startRow, startCol); -} - -/** This is the const version of block<>(Index, Index). */ -template<int BlockRows, int BlockCols> -EIGEN_DEVICE_FUNC -inline const Block<const Derived, BlockRows, BlockCols> block(Index startRow, Index startCol) const -{ - return Block<const Derived, BlockRows, BlockCols>(derived(), startRow, startCol); -} - -/** \returns an expression of a block in *this. - * - * \tparam BlockRows number of rows in block as specified at compile-time - * \tparam BlockCols number of columns in block as specified at compile-time - * \param startRow the first row in the block - * \param startCol the first column in the block - * \param blockRows number of rows in block as specified at run-time - * \param blockCols number of columns in block as specified at run-time - * - * This function is mainly useful for blocks where the number of rows is specified at compile-time - * and the number of columns is specified at run-time, or vice versa. The compile-time and run-time - * information should not contradict. In other words, \a blockRows should equal \a BlockRows unless - * \a BlockRows is \a Dynamic, and the same for the number of columns. - * - * Example: \include MatrixBase_template_int_int_block_int_int_int_int.cpp - * Output: \verbinclude MatrixBase_template_int_int_block_int_int_int_int.cpp - * - * \sa class Block, block(Index,Index,Index,Index) - */ -template<int BlockRows, int BlockCols> -inline Block<Derived, BlockRows, BlockCols> block(Index startRow, Index startCol, - Index blockRows, Index blockCols) -{ - return Block<Derived, BlockRows, BlockCols>(derived(), startRow, startCol, blockRows, blockCols); -} - -/** This is the const version of block<>(Index, Index, Index, Index). */ -template<int BlockRows, int BlockCols> -inline const Block<const Derived, BlockRows, BlockCols> block(Index startRow, Index startCol, - Index blockRows, Index blockCols) const -{ - return Block<const Derived, BlockRows, BlockCols>(derived(), startRow, startCol, blockRows, blockCols); -} - -/** \returns an expression of the \a i-th column of *this. Note that the numbering starts at 0. - * - * Example: \include MatrixBase_col.cpp - * Output: \verbinclude MatrixBase_col.out - * - * \sa row(), class Block */ -EIGEN_DEVICE_FUNC -inline ColXpr col(Index i) -{ - return ColXpr(derived(), i); -} - -/** This is the const version of col(). */ -EIGEN_DEVICE_FUNC -inline ConstColXpr col(Index i) const -{ - return ConstColXpr(derived(), i); -} - -/** \returns an expression of the \a i-th row of *this. Note that the numbering starts at 0. - * - * Example: \include MatrixBase_row.cpp - * Output: \verbinclude MatrixBase_row.out - * - * \sa col(), class Block */ -EIGEN_DEVICE_FUNC -inline RowXpr row(Index i) -{ - return RowXpr(derived(), i); -} - -/** This is the const version of row(). */ -EIGEN_DEVICE_FUNC -inline ConstRowXpr row(Index i) const -{ - return ConstRowXpr(derived(), i); -} - -/** \returns a dynamic-size expression of a segment (i.e. a vector block) in *this. - * - * \only_for_vectors - * - * \param start the first coefficient in the segment - * \param n the number of coefficients in the segment - * - * Example: \include MatrixBase_segment_int_int.cpp - * Output: \verbinclude MatrixBase_segment_int_int.out - * - * \note Even though the returned expression has dynamic size, in the case - * when it is applied to a fixed-size vector, it inherits a fixed maximal size, - * which means that evaluating it does not cause a dynamic memory allocation. - * - * \sa class Block, segment(Index) - */ -EIGEN_DEVICE_FUNC -inline SegmentReturnType segment(Index start, Index n) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return SegmentReturnType(derived(), start, n); -} - - -/** This is the const version of segment(Index,Index).*/ -EIGEN_DEVICE_FUNC -inline ConstSegmentReturnType segment(Index start, Index n) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return ConstSegmentReturnType(derived(), start, n); -} - -/** \returns a dynamic-size expression of the first coefficients of *this. - * - * \only_for_vectors - * - * \param n the number of coefficients in the segment - * - * Example: \include MatrixBase_start_int.cpp - * Output: \verbinclude MatrixBase_start_int.out - * - * \note Even though the returned expression has dynamic size, in the case - * when it is applied to a fixed-size vector, it inherits a fixed maximal size, - * which means that evaluating it does not cause a dynamic memory allocation. - * - * \sa class Block, block(Index,Index) - */ -EIGEN_DEVICE_FUNC -inline SegmentReturnType head(Index n) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return SegmentReturnType(derived(), 0, n); -} - -/** This is the const version of head(Index).*/ -EIGEN_DEVICE_FUNC -inline ConstSegmentReturnType head(Index n) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return ConstSegmentReturnType(derived(), 0, n); -} - -/** \returns a dynamic-size expression of the last coefficients of *this. - * - * \only_for_vectors - * - * \param n the number of coefficients in the segment - * - * Example: \include MatrixBase_end_int.cpp - * Output: \verbinclude MatrixBase_end_int.out - * - * \note Even though the returned expression has dynamic size, in the case - * when it is applied to a fixed-size vector, it inherits a fixed maximal size, - * which means that evaluating it does not cause a dynamic memory allocation. - * - * \sa class Block, block(Index,Index) - */ -EIGEN_DEVICE_FUNC -inline SegmentReturnType tail(Index n) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return SegmentReturnType(derived(), this->size() - n, n); -} - -/** This is the const version of tail(Index).*/ -EIGEN_DEVICE_FUNC -inline ConstSegmentReturnType tail(Index n) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return ConstSegmentReturnType(derived(), this->size() - n, n); -} - -/** \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this - * - * \only_for_vectors - * - * \tparam N the number of coefficients in the segment as specified at compile-time - * \param start the index of the first element in the segment - * \param n the number of coefficients in the segment as specified at compile-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include MatrixBase_template_int_segment.cpp - * Output: \verbinclude MatrixBase_template_int_segment.out - * - * \sa class Block - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename FixedSegmentReturnType<N>::Type segment(Index start, Index n = N) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return typename FixedSegmentReturnType<N>::Type(derived(), start, n); -} - -/** This is the const version of segment<int>(Index).*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstFixedSegmentReturnType<N>::Type segment(Index start, Index n = N) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return typename ConstFixedSegmentReturnType<N>::Type(derived(), start, n); -} - -/** \returns a fixed-size expression of the first coefficients of *this. - * - * \only_for_vectors - * - * \tparam N the number of coefficients in the segment as specified at compile-time - * \param n the number of coefficients in the segment as specified at run-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include MatrixBase_template_int_start.cpp - * Output: \verbinclude MatrixBase_template_int_start.out - * - * \sa class Block - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename FixedSegmentReturnType<N>::Type head(Index n = N) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return typename FixedSegmentReturnType<N>::Type(derived(), 0, n); -} - -/** This is the const version of head<int>().*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstFixedSegmentReturnType<N>::Type head(Index n = N) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return typename ConstFixedSegmentReturnType<N>::Type(derived(), 0, n); -} - -/** \returns a fixed-size expression of the last coefficients of *this. - * - * \only_for_vectors - * - * \tparam N the number of coefficients in the segment as specified at compile-time - * \param n the number of coefficients in the segment as specified at run-time - * - * The compile-time and run-time information should not contradict. In other words, - * \a n should equal \a N unless \a N is \a Dynamic. - * - * Example: \include MatrixBase_template_int_end.cpp - * Output: \verbinclude MatrixBase_template_int_end.out - * - * \sa class Block - */ -template<int N> -EIGEN_DEVICE_FUNC -inline typename FixedSegmentReturnType<N>::Type tail(Index n = N) -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return typename FixedSegmentReturnType<N>::Type(derived(), size() - n); -} - -/** This is the const version of tail<int>.*/ -template<int N> -EIGEN_DEVICE_FUNC -inline typename ConstFixedSegmentReturnType<N>::Type tail(Index n = N) const -{ - EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived) - return typename ConstFixedSegmentReturnType<N>::Type(derived(), size() - n); -} diff --git a/third_party/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h b/third_party/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h deleted file mode 100644 index a8fa287c90..0000000000 --- a/third_party/eigen3/Eigen/src/plugins/CommonCwiseBinaryOps.h +++ /dev/null @@ -1,47 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// This file is a base class plugin containing common coefficient wise functions. - -/** \returns an expression of the difference of \c *this and \a other - * - * \note If you want to substract a given scalar from all coefficients, see Cwise::operator-(). - * - * \sa class CwiseBinaryOp, operator-=() - */ -EIGEN_MAKE_CWISE_BINARY_OP(operator-,internal::scalar_difference_op) - -/** \returns an expression of the sum of \c *this and \a other - * - * \note If you want to add a given scalar to all coefficients, see Cwise::operator+(). - * - * \sa class CwiseBinaryOp, operator+=() - */ -EIGEN_MAKE_CWISE_BINARY_OP(operator+,internal::scalar_sum_op) - -/** \returns an expression of a custom coefficient-wise operator \a func of *this and \a other - * - * The template parameter \a CustomBinaryOp is the type of the functor - * of the custom operator (see class CwiseBinaryOp for an example) - * - * Here is an example illustrating the use of custom functors: - * \include class_CwiseBinaryOp.cpp - * Output: \verbinclude class_CwiseBinaryOp.out - * - * \sa class CwiseBinaryOp, operator+(), operator-(), cwiseProduct() - */ -template<typename CustomBinaryOp, typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived> -binaryExpr(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other, const CustomBinaryOp& func = CustomBinaryOp()) const -{ - return CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other.derived(), func); -} - diff --git a/third_party/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h b/third_party/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h deleted file mode 100644 index aa20215745..0000000000 --- a/third_party/eigen3/Eigen/src/plugins/CommonCwiseUnaryOps.h +++ /dev/null @@ -1,201 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// This file is a base class plugin containing common coefficient wise functions. - -#ifndef EIGEN_PARSED_BY_DOXYGEN - -/** \internal Represents a scalar multiple of an expression */ -typedef CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Derived> ScalarMultipleReturnType; -/** \internal Represents a quotient of an expression by a scalar*/ -typedef CwiseUnaryOp<internal::scalar_quotient1_op<Scalar>, const Derived> ScalarQuotient1ReturnType; -/** \internal the return type of conjugate() */ -typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, - const CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>, - const Derived& - >::type ConjugateReturnType; -/** \internal the return type of real() const */ -typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, - const CwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived>, - const Derived& - >::type RealReturnType; -/** \internal the return type of real() */ -typedef typename internal::conditional<NumTraits<Scalar>::IsComplex, - CwiseUnaryView<internal::scalar_real_ref_op<Scalar>, Derived>, - Derived& - >::type NonConstRealReturnType; -/** \internal the return type of imag() const */ -typedef CwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived> ImagReturnType; -/** \internal the return type of imag() */ -typedef CwiseUnaryView<internal::scalar_imag_ref_op<Scalar>, Derived> NonConstImagReturnType; - -#endif // not EIGEN_PARSED_BY_DOXYGEN - -/** \returns an expression of the opposite of \c *this - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_opposite_op<typename internal::traits<Derived>::Scalar>, const Derived> -operator-() const { return derived(); } - - -/** \returns an expression of \c *this scaled by the scalar factor \a scalar */ -EIGEN_DEVICE_FUNC -inline const ScalarMultipleReturnType -operator*(const Scalar& scalar) const -{ - return CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const Derived> - (derived(), internal::scalar_multiple_op<Scalar>(scalar)); -} - -#ifdef EIGEN_PARSED_BY_DOXYGEN -const ScalarMultipleReturnType operator*(const RealScalar& scalar) const; -#endif - -/** \returns an expression of \c *this divided by the scalar value \a scalar */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_quotient1_op<typename internal::traits<Derived>::Scalar>, const Derived> -operator/(const Scalar& scalar) const -{ - return CwiseUnaryOp<internal::scalar_quotient1_op<Scalar>, const Derived> - (derived(), internal::scalar_quotient1_op<Scalar>(scalar)); -} - -/** Overloaded for efficient real matrix times complex scalar value */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_multiple2_op<Scalar,std::complex<Scalar> >, const Derived> -operator*(const std::complex<Scalar>& scalar) const -{ - return CwiseUnaryOp<internal::scalar_multiple2_op<Scalar,std::complex<Scalar> >, const Derived> - (*static_cast<const Derived*>(this), internal::scalar_multiple2_op<Scalar,std::complex<Scalar> >(scalar)); -} - -EIGEN_DEVICE_FUNC -inline friend const ScalarMultipleReturnType -operator*(const Scalar& scalar, const StorageBaseType& matrix) -{ return matrix*scalar; } - -EIGEN_DEVICE_FUNC -inline friend const CwiseUnaryOp<internal::scalar_multiple2_op<Scalar,std::complex<Scalar> >, const Derived> -operator*(const std::complex<Scalar>& scalar, const StorageBaseType& matrix) -{ return matrix*scalar; } - -/** \returns an expression of *this with the \a Scalar type casted to - * \a NewScalar. - * - * The template parameter \a NewScalar is the type we are casting the scalars to. - * - * \sa class CwiseUnaryOp - */ -template<typename NewType> -EIGEN_DEVICE_FUNC -typename internal::cast_return_type<Derived,const CwiseUnaryOp<internal::scalar_cast_op<typename internal::traits<Derived>::Scalar, NewType>, const Derived> >::type -cast() const -{ - return derived(); -} - -/** \returns an expression of *this with the \a Scalar type converted to - * \a NewScalar using the custom conversion functor \a ConvertOp. - * - * The template parameter \a NewType is the type we are casting the scalars to. - * The template parameter \a ConvertOp is the conversion functor. - * - * \sa class CwiseUnaryOp - */ -template<typename NewType, typename ConvertOp> -typename internal::cast_return_type<Derived,const CwiseUnaryOp<internal::scalar_convert_op<typename internal::traits<Derived>::Scalar, NewType, ConvertOp>, const Derived> >::type -convert() const -{ - return derived(); -} - -/** \returns an expression of the complex conjugate of \c *this. - * - * \sa adjoint() */ -EIGEN_DEVICE_FUNC -inline ConjugateReturnType -conjugate() const -{ - return ConjugateReturnType(derived()); -} - -/** \returns a read-only expression of the real part of \c *this. - * - * \sa imag() */ -EIGEN_DEVICE_FUNC -inline RealReturnType -real() const { return derived(); } - -/** \returns an read-only expression of the imaginary part of \c *this. - * - * \sa real() */ -EIGEN_DEVICE_FUNC -inline const ImagReturnType -imag() const { return derived(); } - -/** \brief Apply a unary operator coefficient-wise - * \param[in] func Functor implementing the unary operator - * \tparam CustomUnaryOp Type of \a func - * \returns An expression of a custom coefficient-wise unary operator \a func of *this - * - * The function \c ptr_fun() from the C++ standard library can be used to make functors out of normal functions. - * - * Example: - * \include class_CwiseUnaryOp_ptrfun.cpp - * Output: \verbinclude class_CwiseUnaryOp_ptrfun.out - * - * Genuine functors allow for more possibilities, for instance it may contain a state. - * - * Example: - * \include class_CwiseUnaryOp.cpp - * Output: \verbinclude class_CwiseUnaryOp.out - * - * \sa class CwiseUnaryOp, class CwiseBinaryOp - */ -template<typename CustomUnaryOp> -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<CustomUnaryOp, const Derived> -unaryExpr(const CustomUnaryOp& func = CustomUnaryOp()) const -{ - return CwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func); -} - -/** \returns an expression of a custom coefficient-wise unary operator \a func of *this - * - * The template parameter \a CustomUnaryOp is the type of the functor - * of the custom unary operator. - * - * Example: - * \include class_CwiseUnaryOp.cpp - * Output: \verbinclude class_CwiseUnaryOp.out - * - * \sa class CwiseUnaryOp, class CwiseBinaryOp - */ -template<typename CustomViewOp> -EIGEN_DEVICE_FUNC -inline const CwiseUnaryView<CustomViewOp, const Derived> -unaryViewExpr(const CustomViewOp& func = CustomViewOp()) const -{ - return CwiseUnaryView<CustomViewOp, const Derived>(derived(), func); -} - -/** \returns a non const expression of the real part of \c *this. - * - * \sa imag() */ -EIGEN_DEVICE_FUNC -inline NonConstRealReturnType -real() { return derived(); } - -/** \returns a non const expression of the imaginary part of \c *this. - * - * \sa real() */ -EIGEN_DEVICE_FUNC -inline NonConstImagReturnType -imag() { return derived(); } diff --git a/third_party/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h b/third_party/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h deleted file mode 100644 index b9582a5a06..0000000000 --- a/third_party/eigen3/Eigen/src/plugins/MatrixCwiseBinaryOps.h +++ /dev/null @@ -1,134 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// This file is a base class plugin containing matrix specifics coefficient wise functions. - -/** \returns an expression of the Schur product (coefficient wise product) of *this and \a other - * - * Example: \include MatrixBase_cwiseProduct.cpp - * Output: \verbinclude MatrixBase_cwiseProduct.out - * - * \sa class CwiseBinaryOp, cwiseAbs2 - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const EIGEN_CWISE_PRODUCT_RETURN_TYPE(Derived,OtherDerived) -cwiseProduct(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - return EIGEN_CWISE_PRODUCT_RETURN_TYPE(Derived,OtherDerived)(derived(), other.derived()); -} - -/** \returns an expression of the coefficient-wise == operator of *this and \a other - * - * \warning this performs an exact comparison, which is generally a bad idea with floating-point types. - * In order to check for equality between two vectors or matrices with floating-point coefficients, it is - * generally a far better idea to use a fuzzy comparison as provided by isApprox() and - * isMuchSmallerThan(). - * - * Example: \include MatrixBase_cwiseEqual.cpp - * Output: \verbinclude MatrixBase_cwiseEqual.out - * - * \sa cwiseNotEqual(), isApprox(), isMuchSmallerThan() - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -inline const CwiseBinaryOp<std::equal_to<Scalar>, const Derived, const OtherDerived> -cwiseEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - return CwiseBinaryOp<std::equal_to<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); -} - -/** \returns an expression of the coefficient-wise != operator of *this and \a other - * - * \warning this performs an exact comparison, which is generally a bad idea with floating-point types. - * In order to check for equality between two vectors or matrices with floating-point coefficients, it is - * generally a far better idea to use a fuzzy comparison as provided by isApprox() and - * isMuchSmallerThan(). - * - * Example: \include MatrixBase_cwiseNotEqual.cpp - * Output: \verbinclude MatrixBase_cwiseNotEqual.out - * - * \sa cwiseEqual(), isApprox(), isMuchSmallerThan() - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -inline const CwiseBinaryOp<std::not_equal_to<Scalar>, const Derived, const OtherDerived> -cwiseNotEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - return CwiseBinaryOp<std::not_equal_to<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); -} - -/** \returns an expression of the coefficient-wise min of *this and \a other - * - * Example: \include MatrixBase_cwiseMin.cpp - * Output: \verbinclude MatrixBase_cwiseMin.out - * - * \sa class CwiseBinaryOp, max() - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const OtherDerived> -cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - return CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); -} - -/** \returns an expression of the coefficient-wise min of *this and scalar \a other - * - * \sa class CwiseBinaryOp, min() - */ -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const ConstantReturnType> -cwiseMin(const Scalar &other) const -{ - return cwiseMin(Derived::Constant(rows(), cols(), other)); -} - -/** \returns an expression of the coefficient-wise max of *this and \a other - * - * Example: \include MatrixBase_cwiseMax.cpp - * Output: \verbinclude MatrixBase_cwiseMax.out - * - * \sa class CwiseBinaryOp, min() - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const OtherDerived> -cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - return CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); -} - -/** \returns an expression of the coefficient-wise max of *this and scalar \a other - * - * \sa class CwiseBinaryOp, min() - */ -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const ConstantReturnType> -cwiseMax(const Scalar &other) const -{ - return cwiseMax(Derived::Constant(rows(), cols(), other)); -} - - -/** \returns an expression of the coefficient-wise quotient of *this and \a other - * - * Example: \include MatrixBase_cwiseQuotient.cpp - * Output: \verbinclude MatrixBase_cwiseQuotient.out - * - * \sa class CwiseBinaryOp, cwiseProduct(), cwiseInverse() - */ -template<typename OtherDerived> -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived> -cwiseQuotient(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const -{ - return CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); -} diff --git a/third_party/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h b/third_party/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h deleted file mode 100644 index 1bb15f862d..0000000000 --- a/third_party/eigen3/Eigen/src/plugins/MatrixCwiseUnaryOps.h +++ /dev/null @@ -1,72 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> -// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -// This file is a base class plugin containing matrix specifics coefficient wise functions. - -/** \returns an expression of the coefficient-wise absolute value of \c *this - * - * Example: \include MatrixBase_cwiseAbs.cpp - * Output: \verbinclude MatrixBase_cwiseAbs.out - * - * \sa cwiseAbs2() - */ -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> -cwiseAbs() const { return derived(); } - -/** \returns an expression of the coefficient-wise squared absolute value of \c *this - * - * Example: \include MatrixBase_cwiseAbs2.cpp - * Output: \verbinclude MatrixBase_cwiseAbs2.out - * - * \sa cwiseAbs() - */ -EIGEN_DEVICE_FUNC -EIGEN_STRONG_INLINE const CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> -cwiseAbs2() const { return derived(); } - -/** \returns an expression of the coefficient-wise square root of *this. - * - * Example: \include MatrixBase_cwiseSqrt.cpp - * Output: \verbinclude MatrixBase_cwiseSqrt.out - * - * \sa cwisePow(), cwiseSquare() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> -cwiseSqrt() const { return derived(); } - -/** \returns an expression of the coefficient-wise inverse of *this. - * - * Example: \include MatrixBase_cwiseInverse.cpp - * Output: \verbinclude MatrixBase_cwiseInverse.out - * - * \sa cwiseProduct() - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> -cwiseInverse() const { return derived(); } - -/** \returns an expression of the coefficient-wise == operator of \c *this and a scalar \a s - * - * \warning this performs an exact comparison, which is generally a bad idea with floating-point types. - * In order to check for equality between two vectors or matrices with floating-point coefficients, it is - * generally a far better idea to use a fuzzy comparison as provided by isApprox() and - * isMuchSmallerThan(). - * - * \sa cwiseEqual(const MatrixBase<OtherDerived> &) const - */ -EIGEN_DEVICE_FUNC -inline const CwiseUnaryOp<std::binder1st<std::equal_to<Scalar> >, const Derived> -cwiseEqual(const Scalar& s) const -{ - return CwiseUnaryOp<std::binder1st<std::equal_to<Scalar> >,const Derived> - (derived(), std::bind1st(std::equal_to<Scalar>(), s)); -} |