diff options
Diffstat (limited to 'unsupported/Eigen')
26 files changed, 440 insertions, 397 deletions
diff --git a/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h b/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h index b3983f8a6..6a6516c82 100644 --- a/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h +++ b/unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h @@ -51,6 +51,7 @@ public: typedef typename Functor::ValueType ValueType; typedef typename Functor::JacobianType JacobianType; typedef typename JacobianType::Scalar Scalar; + typedef typename JacobianType::Index Index; typedef Matrix<Scalar,InputsAtCompileTime,1> DerivativeType; typedef AutoDiffScalar<DerivativeType> ActiveScalar; @@ -74,15 +75,15 @@ public: ActiveValue av(jac.rows()); if(InputsAtCompileTime==Dynamic) - for (int j=0; j<jac.rows(); j++) + for (Index j=0; j<jac.rows(); j++) av[j].derivatives().resize(this->inputs()); - for (int i=0; i<jac.cols(); i++) + for (Index i=0; i<jac.cols(); i++) ax[i].derivatives() = DerivativeType::Unit(this->inputs(),i); Functor::operator()(ax, &av); - for (int i=0; i<jac.rows(); i++) + for (Index i=0; i<jac.rows(); i++) { (*v)[i] = av[i].value(); jac.row(i) = av[i].derivatives(); diff --git a/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h b/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h index c0765d494..9b9dbcc88 100644 --- a/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h +++ b/unsupported/Eigen/src/AutoDiff/AutoDiffVector.h @@ -53,6 +53,7 @@ class AutoDiffVector typedef AutoDiffScalar<Matrix<BaseScalar,JacobianType::RowsAtCompileTime,1> > ActiveScalar; typedef ActiveScalar Scalar; typedef AutoDiffScalar<typename JacobianType::ColXpr> CoeffType; + typedef typename JacobianType::Index Index; inline AutoDiffVector() {} @@ -63,16 +64,16 @@ class AutoDiffVector } - CoeffType operator[] (int i) { return CoeffType(m_values[i], m_jacobian.col(i)); } - const CoeffType operator[] (int i) const { return CoeffType(m_values[i], m_jacobian.col(i)); } + CoeffType operator[] (Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); } + const CoeffType operator[] (Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); } - CoeffType operator() (int i) { return CoeffType(m_values[i], m_jacobian.col(i)); } - const CoeffType operator() (int i) const { return CoeffType(m_values[i], m_jacobian.col(i)); } + CoeffType operator() (Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); } + const CoeffType operator() (Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); } - CoeffType coeffRef(int i) { return CoeffType(m_values[i], m_jacobian.col(i)); } - const CoeffType coeffRef(int i) const { return CoeffType(m_values[i], m_jacobian.col(i)); } + CoeffType coeffRef(Index i) { return CoeffType(m_values[i], m_jacobian.col(i)); } + const CoeffType coeffRef(Index i) const { return CoeffType(m_values[i], m_jacobian.col(i)); } - int size() const { return m_values.size(); } + Index size() const { return m_values.size(); } // FIXME here we could return an expression of the sum Scalar sum() const { /*std::cerr << "sum \n\n";*/ /*std::cerr << m_jacobian.rowwise().sum() << "\n\n";*/ return Scalar(m_values.sum(), m_jacobian.rowwise().sum()); } diff --git a/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h b/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h index c2b77636c..8f2a35319 100644 --- a/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h +++ b/unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h @@ -61,17 +61,18 @@ void ei_pseudo_inverse(const CMatrix &C, CINVMatrix &CINV) { // optimisable : copie de la ligne, precalcul de C * trans(C). typedef typename CMatrix::Scalar Scalar; + typedef typename CMatrix::Index Index; // FIXME use sparse vectors ? typedef Matrix<Scalar,Dynamic,1> TmpVec; - int rows = C.rows(), cols = C.cols(); + Index rows = C.rows(), cols = C.cols(); TmpVec d(rows), e(rows), l(cols), p(rows), q(rows), r(rows); Scalar rho, rho_1, alpha; d.setZero(); CINV.startFill(); // FIXME estimate the number of non-zeros - for (int i = 0; i < rows; ++i) + for (Index i = 0; i < rows; ++i) { d[i] = 1.0; rho = 1.0; @@ -94,7 +95,7 @@ void ei_pseudo_inverse(const CMatrix &C, CINVMatrix &CINV) l = C.transpose() * e; // l is the i-th row of CINV // FIXME add a generic "prune/filter" expression for both dense and sparse object to sparse - for (int j=0; j<l.size(); ++j) + for (Index j=0; j<l.size(); ++j) if (l[j]<1e-15) CINV.fill(i,j) = l[j]; @@ -116,10 +117,11 @@ void ei_constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x, const VectorB& b, const VectorF& f, IterationController &iter) { typedef typename TMatrix::Scalar Scalar; + typedef typename TMatrix::Index Index; typedef Matrix<Scalar,Dynamic,1> TmpVec; Scalar rho = 1.0, rho_1, lambda, gamma; - int xSize = x.size(); + Index xSize = x.size(); TmpVec p(xSize), q(xSize), q2(xSize), r(xSize), old_z(xSize), z(xSize), memox(xSize); @@ -140,7 +142,7 @@ void ei_constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x, r += A * -x; z = r; bool transition = false; - for (int i = 0; i < C.rows(); ++i) + for (Index i = 0; i < C.rows(); ++i) { Scalar al = C.row(i).dot(x) - f.coeff(i); if (al >= -1.0E-15) @@ -175,7 +177,7 @@ void ei_constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x, // one dimensionnal optimization q = A * p; lambda = rho / q.dot(p); - for (int i = 0; i < C.rows(); ++i) + for (Index i = 0; i < C.rows(); ++i) { if (!satured[i]) { diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h index d7eb1b2fe..5d47f2cec 100644 --- a/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h +++ b/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h @@ -296,6 +296,7 @@ void MatrixExponential<MatrixType>::computeUV(double) template<typename Derived> struct MatrixExponentialReturnValue : public ReturnByValue<MatrixExponentialReturnValue<Derived> > { + typedef typename Derived::Index Index; public: /** \brief Constructor. * @@ -317,8 +318,8 @@ template<typename Derived> struct MatrixExponentialReturnValue me.compute(result); } - int rows() const { return m_src.rows(); } - int cols() const { return m_src.cols(); } + Index rows() const { return m_src.rows(); } + Index cols() const { return m_src.cols(); } protected: const Derived& m_src; diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h b/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h index 8ed856770..be27c5037 100644 --- a/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h +++ b/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h @@ -131,14 +131,15 @@ class MatrixFunction<MatrixType, 1> private: typedef ei_traits<MatrixType> Traits; - typedef typename Traits::Scalar Scalar; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::Index Index; static const int RowsAtCompileTime = Traits::RowsAtCompileTime; static const int ColsAtCompileTime = Traits::ColsAtCompileTime; static const int Options = MatrixType::Options; typedef typename NumTraits<Scalar>::Real RealScalar; typedef typename ei_stem_function<Scalar>::type StemFunction; typedef Matrix<Scalar, Traits::RowsAtCompileTime, 1> VectorType; - typedef Matrix<int, Traits::RowsAtCompileTime, 1> IntVectorType; + typedef Matrix<Index, Traits::RowsAtCompileTime, 1> IntVectorType; typedef std::list<Scalar> Cluster; typedef std::list<Cluster> ListOfClusters; typedef Matrix<Scalar, Dynamic, Dynamic, Options, RowsAtCompileTime, ColsAtCompileTime> DynMatrixType; @@ -157,9 +158,9 @@ class MatrixFunction<MatrixType, 1> void computeBlockStart(); void constructPermutation(); void permuteSchur(); - void swapEntriesInSchur(int index); + void swapEntriesInSchur(Index index); void computeBlockAtomic(); - Block<MatrixType> block(const MatrixType& A, int i, int j); + Block<MatrixType> block(const MatrixType& A, Index i, Index j); void computeOffDiagonal(); DynMatrixType solveTriangularSylvester(const DynMatrixType& A, const DynMatrixType& B, const DynMatrixType& C); @@ -238,10 +239,10 @@ void MatrixFunction<MatrixType,1>::computeSchurDecomposition() template <typename MatrixType> void MatrixFunction<MatrixType,1>::partitionEigenvalues() { - const int rows = m_T.rows(); + const Index rows = m_T.rows(); VectorType diag = m_T.diagonal(); // contains eigenvalues of A - for (int i=0; i<rows; ++i) { + for (Index i=0; i<rows; ++i) { // Find set containing diag(i), adding a new set if necessary typename ListOfClusters::iterator qi = findCluster(diag(i)); if (qi == m_clusters.end()) { @@ -253,7 +254,7 @@ void MatrixFunction<MatrixType,1>::partitionEigenvalues() } // Look for other element to add to the set - for (int j=i+1; j<rows; ++j) { + for (Index j=i+1; j<rows; ++j) { if (ei_abs(diag(j) - diag(i)) <= separation() && std::find(qi->begin(), qi->end(), diag(j)) == qi->end()) { typename ListOfClusters::iterator qj = findCluster(diag(j)); if (qj == m_clusters.end()) { @@ -288,15 +289,15 @@ typename MatrixFunction<MatrixType,1>::ListOfClusters::iterator MatrixFunction<M template <typename MatrixType> void MatrixFunction<MatrixType,1>::computeClusterSize() { - const int rows = m_T.rows(); + const Index rows = m_T.rows(); VectorType diag = m_T.diagonal(); - const int numClusters = static_cast<int>(m_clusters.size()); + const Index numClusters = static_cast<Index>(m_clusters.size()); m_clusterSize.setZero(numClusters); m_eivalToCluster.resize(rows); - int clusterIndex = 0; + Index clusterIndex = 0; for (typename ListOfClusters::const_iterator cluster = m_clusters.begin(); cluster != m_clusters.end(); ++cluster) { - for (int i = 0; i < diag.rows(); ++i) { + for (Index i = 0; i < diag.rows(); ++i) { if (std::find(cluster->begin(), cluster->end(), diag(i)) != cluster->end()) { ++m_clusterSize[clusterIndex]; m_eivalToCluster[i] = clusterIndex; @@ -312,7 +313,7 @@ void MatrixFunction<MatrixType,1>::computeBlockStart() { m_blockStart.resize(m_clusterSize.rows()); m_blockStart(0) = 0; - for (int i = 1; i < m_clusterSize.rows(); i++) { + for (Index i = 1; i < m_clusterSize.rows(); i++) { m_blockStart(i) = m_blockStart(i-1) + m_clusterSize(i-1); } } @@ -323,8 +324,8 @@ void MatrixFunction<MatrixType,1>::constructPermutation() { VectorXi indexNextEntry = m_blockStart; m_permutation.resize(m_T.rows()); - for (int i = 0; i < m_T.rows(); i++) { - int cluster = m_eivalToCluster[i]; + for (Index i = 0; i < m_T.rows(); i++) { + Index cluster = m_eivalToCluster[i]; m_permutation[i] = indexNextEntry[cluster]; ++indexNextEntry[cluster]; } @@ -335,13 +336,13 @@ template <typename MatrixType> void MatrixFunction<MatrixType,1>::permuteSchur() { IntVectorType p = m_permutation; - for (int i = 0; i < p.rows() - 1; i++) { - int j; + for (Index i = 0; i < p.rows() - 1; i++) { + Index j; for (j = i; j < p.rows(); j++) { if (p(j) == i) break; } ei_assert(p(j) == i); - for (int k = j-1; k >= i; k--) { + for (Index k = j-1; k >= i; k--) { swapEntriesInSchur(k); std::swap(p.coeffRef(k), p.coeffRef(k+1)); } @@ -350,7 +351,7 @@ void MatrixFunction<MatrixType,1>::permuteSchur() /** \brief Swap rows \a index and \a index+1 in Schur decomposition in #m_U and #m_T */ template <typename MatrixType> -void MatrixFunction<MatrixType,1>::swapEntriesInSchur(int index) +void MatrixFunction<MatrixType,1>::swapEntriesInSchur(Index index) { PlanarRotation<Scalar> rotation; rotation.makeGivens(m_T(index, index+1), m_T(index+1, index+1) - m_T(index, index)); @@ -372,14 +373,14 @@ void MatrixFunction<MatrixType,1>::computeBlockAtomic() m_fT.resize(m_T.rows(), m_T.cols()); m_fT.setZero(); MatrixFunctionAtomic<DynMatrixType> mfa(m_f); - for (int i = 0; i < m_clusterSize.rows(); ++i) { + for (Index i = 0; i < m_clusterSize.rows(); ++i) { block(m_fT, i, i) = mfa.compute(block(m_T, i, i)); } } /** \brief Return block of matrix according to blocking given by #m_blockStart */ template <typename MatrixType> -Block<MatrixType> MatrixFunction<MatrixType,1>::block(const MatrixType& A, int i, int j) +Block<MatrixType> MatrixFunction<MatrixType,1>::block(const MatrixType& A, Index i, Index j) { return A.block(m_blockStart(i), m_blockStart(j), m_clusterSize(i), m_clusterSize(j)); } @@ -394,14 +395,14 @@ Block<MatrixType> MatrixFunction<MatrixType,1>::block(const MatrixType& A, int i template <typename MatrixType> void MatrixFunction<MatrixType,1>::computeOffDiagonal() { - for (int diagIndex = 1; diagIndex < m_clusterSize.rows(); diagIndex++) { - for (int blockIndex = 0; blockIndex < m_clusterSize.rows() - diagIndex; blockIndex++) { + for (Index diagIndex = 1; diagIndex < m_clusterSize.rows(); diagIndex++) { + for (Index blockIndex = 0; blockIndex < m_clusterSize.rows() - diagIndex; blockIndex++) { // compute (blockIndex, blockIndex+diagIndex) block DynMatrixType A = block(m_T, blockIndex, blockIndex); DynMatrixType B = -block(m_T, blockIndex+diagIndex, blockIndex+diagIndex); DynMatrixType C = block(m_fT, blockIndex, blockIndex) * block(m_T, blockIndex, blockIndex+diagIndex); C -= block(m_T, blockIndex, blockIndex+diagIndex) * block(m_fT, blockIndex+diagIndex, blockIndex+diagIndex); - for (int k = blockIndex + 1; k < blockIndex + diagIndex; k++) { + for (Index k = blockIndex + 1; k < blockIndex + diagIndex; k++) { C += block(m_fT, blockIndex, k) * block(m_T, k, blockIndex+diagIndex); C -= block(m_T, blockIndex, k) * block(m_fT, k, blockIndex+diagIndex); } @@ -446,12 +447,12 @@ typename MatrixFunction<MatrixType,1>::DynMatrixType MatrixFunction<MatrixType,1 ei_assert(C.rows() == A.rows()); ei_assert(C.cols() == B.rows()); - int m = A.rows(); - int n = B.rows(); + Index m = A.rows(); + Index n = B.rows(); DynMatrixType X(m, n); - for (int i = m - 1; i >= 0; --i) { - for (int j = 0; j < n; ++j) { + for (Index i = m - 1; i >= 0; --i) { + for (Index j = 0; j < n; ++j) { // Compute AX = \sum_{k=i+1}^m A_{ik} X_{kj} Scalar AX; @@ -494,7 +495,8 @@ template<typename Derived> class MatrixFunctionReturnValue { public: - typedef typename ei_traits<Derived>::Scalar Scalar; + typedef typename Derived::Scalar Scalar; + typedef typename Derived::Index Index; typedef typename ei_stem_function<Scalar>::type StemFunction; /** \brief Constructor. @@ -518,8 +520,8 @@ template<typename Derived> class MatrixFunctionReturnValue mf.compute(result); } - int rows() const { return m_A.rows(); } - int cols() const { return m_A.cols(); } + Index rows() const { return m_A.rows(); } + Index cols() const { return m_A.cols(); } private: const Derived& m_A; diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h index 4bcae47c0..b578c1301 100644 --- a/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h +++ b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h @@ -38,11 +38,11 @@ class MatrixFunctionAtomic { public: - typedef ei_traits<MatrixType> Traits; - typedef typename Traits::Scalar Scalar; + typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::Index Index; typedef typename NumTraits<Scalar>::Real RealScalar; typedef typename ei_stem_function<Scalar>::type StemFunction; - typedef Matrix<Scalar, Traits::RowsAtCompileTime, 1> VectorType; + typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; /** \brief Constructor * \param[in] f matrix function to compute. @@ -62,13 +62,13 @@ class MatrixFunctionAtomic MatrixFunctionAtomic& operator=(const MatrixFunctionAtomic&); void computeMu(); - bool taylorConverged(int s, const MatrixType& F, const MatrixType& Fincr, const MatrixType& P); + bool taylorConverged(Index s, const MatrixType& F, const MatrixType& Fincr, const MatrixType& P); /** \brief Pointer to scalar function */ StemFunction* m_f; /** \brief Size of matrix function */ - int m_Arows; + Index m_Arows; /** \brief Mean of eigenvalues */ Scalar m_avgEival; @@ -91,7 +91,7 @@ MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A) MatrixType F = m_f(m_avgEival, 0) * MatrixType::Identity(m_Arows, m_Arows); MatrixType P = m_Ashifted; MatrixType Fincr; - for (int s = 1; s < 1.1 * m_Arows + 10; s++) { // upper limit is fairly arbitrary + for (Index s = 1; s < 1.1 * m_Arows + 10; s++) { // upper limit is fairly arbitrary Fincr = m_f(m_avgEival, s) * P; F += Fincr; P = Scalar(RealScalar(1.0/(s + 1))) * P * m_Ashifted; @@ -115,18 +115,18 @@ void MatrixFunctionAtomic<MatrixType>::computeMu() /** \brief Determine whether Taylor series has converged */ template <typename MatrixType> -bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType& F, +bool MatrixFunctionAtomic<MatrixType>::taylorConverged(Index s, const MatrixType& F, const MatrixType& Fincr, const MatrixType& P) { - const int n = F.rows(); + const Index n = F.rows(); const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff(); const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff(); if (Fincr_norm < NumTraits<Scalar>::epsilon() * F_norm) { RealScalar delta = 0; RealScalar rfactorial = 1; - for (int r = 0; r < n; r++) { + for (Index r = 0; r < n; r++) { RealScalar mx = 0; - for (int i = 0; i < n; i++) + for (Index i = 0; i < n; i++) mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, s+r))); if (r != 0) rfactorial *= RealScalar(r); diff --git a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h index d75b1407c..aba31b238 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h +++ b/unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h @@ -56,6 +56,8 @@ template<typename FunctorType, typename Scalar=double> class HybridNonLinearSolver { public: + typedef DenseIndex Index; + HybridNonLinearSolver(FunctorType &_functor) : functor(_functor) { nfev=njev=iter = 0; fnorm= 0.; useExternalScaling=false;} @@ -68,10 +70,10 @@ public: , nb_of_superdiagonals(-1) , epsfcn(Scalar(0.)) {} Scalar factor; - int maxfev; // maximum number of function evaluation + Index maxfev; // maximum number of function evaluation Scalar xtol; - int nb_of_subdiagonals; - int nb_of_superdiagonals; + Index nb_of_subdiagonals; + Index nb_of_superdiagonals; Scalar epsfcn; }; typedef Matrix< Scalar, Dynamic, 1 > FVectorType; @@ -102,24 +104,24 @@ public: FVectorType fvec, qtf, diag; JacobianType fjac; UpperTriangularType R; - int nfev; - int njev; - int iter; + Index nfev; + Index njev; + Index iter; Scalar fnorm; bool useExternalScaling; private: FunctorType &functor; - int n; + Index n; Scalar sum; bool sing; Scalar temp; Scalar delta; bool jeval; - int ncsuc; + Index ncsuc; Scalar ratio; Scalar pnorm, xnorm, fnorm1; - int nslow1, nslow2; - int ncfail; + Index nslow1, nslow2; + Index ncfail; Scalar actred, prered; FVectorType wa1, wa2, wa3, wa4; }; @@ -169,7 +171,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveInit(FVectorType &x) if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.factor <= 0. ) return HybridNonLinearSolverSpace::ImproperInputParameters; if (useExternalScaling) - for (int j = 0; j < n; ++j) + for (Index j = 0; j < n; ++j) if (diag[j] <= 0.) return HybridNonLinearSolverSpace::ImproperInputParameters; @@ -196,7 +198,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x) { assert(x.size()==n); // check the caller is not cheating us - int j; + Index j; std::vector<PlanarRotation<Scalar> > v_givens(n), w_givens(n); jeval = true; @@ -408,7 +410,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffInit(FVectorType & if (n <= 0 || parameters.xtol < 0. || parameters.maxfev <= 0 || parameters.nb_of_subdiagonals< 0 || parameters.nb_of_superdiagonals< 0 || parameters.factor <= 0. ) return HybridNonLinearSolverSpace::ImproperInputParameters; if (useExternalScaling) - for (int j = 0; j < n; ++j) + for (Index j = 0; j < n; ++j) if (diag[j] <= 0.) return HybridNonLinearSolverSpace::ImproperInputParameters; @@ -435,7 +437,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType { assert(x.size()==n); // check the caller is not cheating us - int j; + Index j; std::vector<PlanarRotation<Scalar> > v_givens(n), w_givens(n); jeval = true; diff --git a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h index 3d5b6ea70..63eb66738 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h +++ b/unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h @@ -63,6 +63,8 @@ public: LevenbergMarquardt(FunctorType &_functor) : functor(_functor) { nfev = njev = iter = 0; fnorm = gnorm = 0.; useExternalScaling=false; } + typedef DenseIndex Index; + struct Parameters { Parameters() : factor(Scalar(100.)) @@ -72,7 +74,7 @@ public: , gtol(Scalar(0.)) , epsfcn(Scalar(0.)) {} Scalar factor; - int maxfev; // maximum number of function evaluation + Index maxfev; // maximum number of function evaluation Scalar ftol; Scalar xtol; Scalar gtol; @@ -94,7 +96,7 @@ public: static LevenbergMarquardtSpace::Status lmdif1( FunctorType &functor, FVectorType &x, - int *nfev, + Index *nfev, const Scalar tol = ei_sqrt(NumTraits<Scalar>::epsilon()) ); @@ -113,17 +115,17 @@ public: FVectorType fvec, qtf, diag; JacobianType fjac; PermutationMatrix<Dynamic,Dynamic> permutation; - int nfev; - int njev; - int iter; + Index nfev; + Index njev; + Index iter; Scalar fnorm, gnorm; bool useExternalScaling; Scalar lm_param(void) { return par; } private: FunctorType &functor; - int n; - int m; + Index n; + Index m; FVectorType wa1, wa2, wa3, wa4; Scalar par, sum; @@ -194,7 +196,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeInit(FVectorType &x) return LevenbergMarquardtSpace::ImproperInputParameters; if (useExternalScaling) - for (int j = 0; j < n; ++j) + for (Index j = 0; j < n; ++j) if (diag[j] <= 0.) return LevenbergMarquardtSpace::ImproperInputParameters; @@ -219,7 +221,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x) assert(x.size()==n); // check the caller is not cheating us /* calculate the jacobian matrix. */ - int df_ret = functor.df(x, fjac); + Index df_ret = functor.df(x, fjac); if (df_ret<0) return LevenbergMarquardtSpace::UserAsked; if (df_ret>0) @@ -237,7 +239,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x) /* to the norms of the columns of the initial jacobian. */ if (iter == 1) { if (!useExternalScaling) - for (int j = 0; j < n; ++j) + for (Index j = 0; j < n; ++j) diag[j] = (wa2[j]==0.)? 1. : wa2[j]; /* on the first iteration, calculate the norm of the scaled x */ @@ -257,7 +259,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x) /* compute the norm of the scaled gradient. */ gnorm = 0.; if (fnorm != 0.) - for (int j = 0; j < n; ++j) + for (Index j = 0; j < n; ++j) if (wa2[permutation.indices()[j]] != 0.) gnorm = std::max(gnorm, ei_abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]])); @@ -410,7 +412,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageInit(FVectorType return LevenbergMarquardtSpace::ImproperInputParameters; if (useExternalScaling) - for (int j = 0; j < n; ++j) + for (Index j = 0; j < n; ++j) if (diag[j] <= 0.) return LevenbergMarquardtSpace::ImproperInputParameters; @@ -435,7 +437,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp { assert(x.size()==n); // check the caller is not cheating us - int i, j; + Index i, j; bool sing; /* compute the qr factorization of the jacobian matrix */ @@ -444,7 +446,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp /* n components in qtf. */ qtf.fill(0.); fjac.fill(0.); - int rownb = 2; + Index rownb = 2; for (i = 0; i < m; ++i) { if (functor.df(x, wa3, rownb) < 0) return LevenbergMarquardtSpace::UserAsked; ei_rwupdt<Scalar>(fjac, wa3, qtf, fvec[i]); @@ -471,7 +473,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp fjac.diagonal() = qrfac.hCoeffs(); permutation = qrfac.colsPermutation(); // TODO : avoid this: - for(int ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors + for(Index ii=0; ii< fjac.cols(); ii++) fjac.col(ii).segment(ii+1, fjac.rows()-ii-1) *= fjac(ii,ii); // rescale vectors for (j = 0; j < n; ++j) { if (fjac(j,j) != 0.) { @@ -623,12 +625,12 @@ LevenbergMarquardtSpace::Status LevenbergMarquardt<FunctorType,Scalar>::lmdif1( FunctorType &functor, FVectorType &x, - int *nfev, + Index *nfev, const Scalar tol ) { - int n = x.size(); - int m = functor.values(); + Index n = x.size(); + Index m = functor.values(); /* check the input parameters for errors. */ if (n <= 0 || m < n || tol < 0.) diff --git a/unsupported/Eigen/src/NonLinearOptimization/chkder.h b/unsupported/Eigen/src/NonLinearOptimization/chkder.h index 591e8bef7..4cb4fbdef 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/chkder.h +++ b/unsupported/Eigen/src/NonLinearOptimization/chkder.h @@ -13,17 +13,19 @@ void ei_chkder( Matrix< Scalar, Dynamic, 1 > &err ) { + typedef DenseIndex Index; + const Scalar eps = ei_sqrt(NumTraits<Scalar>::epsilon()); const Scalar epsf = chkder_factor * NumTraits<Scalar>::epsilon(); const Scalar epslog = chkder_log10e * ei_log(eps); Scalar temp; - const int m = fvec.size(), n = x.size(); + const Index m = fvec.size(), n = x.size(); if (mode != 2) { /* mode = 1. */ xp.resize(n); - for (int j = 0; j < n; ++j) { + for (Index j = 0; j < n; ++j) { temp = eps * ei_abs(x[j]); if (temp == 0.) temp = eps; @@ -33,13 +35,13 @@ void ei_chkder( else { /* mode = 2. */ err.setZero(m); - for (int j = 0; j < n; ++j) { + for (Index j = 0; j < n; ++j) { temp = ei_abs(x[j]); if (temp == 0.) temp = 1.; err += temp * fjac.col(j); } - for (int i = 0; i < m; ++i) { + for (Index i = 0; i < m; ++i) { temp = 1.; if (fvec[i] != 0. && fvecp[i] != 0. && ei_abs(fvecp[i] - fvec[i]) >= epsf * ei_abs(fvec[i])) temp = eps * ei_abs((fvecp[i] - fvec[i]) / eps - err[i]) / (ei_abs(fvec[i]) + ei_abs(fvecp[i])); diff --git a/unsupported/Eigen/src/NonLinearOptimization/covar.h b/unsupported/Eigen/src/NonLinearOptimization/covar.h index 7cfaa22d4..104898a30 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/covar.h +++ b/unsupported/Eigen/src/NonLinearOptimization/covar.h @@ -5,13 +5,15 @@ void ei_covar( const VectorXi &ipvt, Scalar tol = ei_sqrt(NumTraits<Scalar>::epsilon()) ) { + typedef DenseIndex Index; + /* Local variables */ - int i, j, k, l, ii, jj; - int sing; + Index i, j, k, l, ii, jj; + bool sing; Scalar temp; /* Function Body */ - const int n = r.cols(); + const Index n = r.cols(); const Scalar tolr = tol * ei_abs(r(0,0)); Matrix< Scalar, Dynamic, 1 > wa(n); assert(ipvt.size()==n); diff --git a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h index 9c1d38dea..ab01d5c47 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/dogleg.h +++ b/unsupported/Eigen/src/NonLinearOptimization/dogleg.h @@ -7,15 +7,17 @@ void ei_dogleg( Scalar delta, Matrix< Scalar, Dynamic, 1 > &x) { + typedef DenseIndex Index; + /* Local variables */ - int i, j; + Index i, j; Scalar sum, temp, alpha, bnorm; Scalar gnorm, qnorm; Scalar sgnorm; /* Function Body */ const Scalar epsmch = NumTraits<Scalar>::epsilon(); - const int n = qrfac.cols(); + const Index n = qrfac.cols(); assert(n==qtb.size()); assert(n==x.size()); assert(n==diag.size()); diff --git a/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h b/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h index 3dc1e8070..74cf53b90 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h +++ b/unsupported/Eigen/src/NonLinearOptimization/fdjac1.h @@ -1,24 +1,26 @@ template<typename FunctorType, typename Scalar> -int ei_fdjac1( +DenseIndex ei_fdjac1( const FunctorType &Functor, Matrix< Scalar, Dynamic, 1 > &x, Matrix< Scalar, Dynamic, 1 > &fvec, Matrix< Scalar, Dynamic, Dynamic > &fjac, - int ml, int mu, + DenseIndex ml, DenseIndex mu, Scalar epsfcn) { + typedef DenseIndex Index; + /* Local variables */ Scalar h; - int j, k; + Index j, k; Scalar eps, temp; - int msum; + Index msum; int iflag; - int start, length; + Index start, length; /* Function Body */ const Scalar epsmch = NumTraits<Scalar>::epsilon(); - const int n = x.size(); + const Index n = x.size(); assert(fvec.size()==n); Matrix< Scalar, Dynamic, 1 > wa1(n); Matrix< Scalar, Dynamic, 1 > wa2(n); @@ -57,7 +59,7 @@ int ei_fdjac1( h = eps * ei_abs(wa2[j]); if (h == 0.) h = eps; fjac.col(j).setZero(); - start = std::max(0,j-mu); + start = std::max<Index>(0,j-mu); length = std::min(n-1, j+ml) - start + 1; fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h; } diff --git a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h index 850011912..27138de96 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/lmpar.h +++ b/unsupported/Eigen/src/NonLinearOptimization/lmpar.h @@ -9,11 +9,13 @@ void ei_lmpar( Scalar &par, Matrix< Scalar, Dynamic, 1 > &x) { + typedef DenseIndex Index; + /* Local variables */ - int i, j, l; + Index i, j, l; Scalar fp; Scalar parc, parl; - int iter; + Index iter; Scalar temp, paru; Scalar gnorm; Scalar dxnorm; @@ -21,7 +23,7 @@ void ei_lmpar( /* Function Body */ const Scalar dwarf = std::numeric_limits<Scalar>::min(); - const int n = r.cols(); + const Index n = r.cols(); assert(n==diag.size()); assert(n==qtb.size()); assert(n==x.size()); @@ -30,7 +32,7 @@ void ei_lmpar( /* compute and store in x the gauss-newton direction. if the */ /* jacobian is rank-deficient, obtain a least squares solution. */ - int nsing = n-1; + Index nsing = n-1; wa1 = qtb; for (j = 0; j < n; ++j) { if (r(j,j) == 0. && nsing == n-1) @@ -163,11 +165,13 @@ void ei_lmpar2( Matrix< Scalar, Dynamic, 1 > &x) { + typedef DenseIndex Index; + /* Local variables */ - int j; + Index j; Scalar fp; Scalar parc, parl; - int iter; + Index iter; Scalar temp, paru; Scalar gnorm; Scalar dxnorm; @@ -175,7 +179,7 @@ void ei_lmpar2( /* Function Body */ const Scalar dwarf = std::numeric_limits<Scalar>::min(); - const int n = qr.matrixQR().cols(); + const Index n = qr.matrixQR().cols(); assert(n==diag.size()); assert(n==qtb.size()); @@ -184,8 +188,8 @@ void ei_lmpar2( /* compute and store in x the gauss-newton direction. if the */ /* jacobian is rank-deficient, obtain a least squares solution. */ -// const int rank = qr.nonzeroPivots(); // exactly double(0.) - const int rank = qr.rank(); // use a threshold +// const Index rank = qr.nonzeroPivots(); // exactly double(0.) + const Index rank = qr.rank(); // use a threshold wa1 = qtb; wa1.tail(n-rank).setZero(); qr.matrixQR().topLeftCorner(rank, rank).template triangularView<Upper>().solveInPlace(wa1.head(rank)); @@ -262,7 +266,7 @@ void ei_lmpar2( for (j = 0; j < n; ++j) { wa1[j] /= sdiag[j]; temp = wa1[j]; - for (int i = j+1; i < n; ++i) + for (Index i = j+1; i < n; ++i) wa1[i] -= s(i,j) * temp; } temp = wa1.blueNorm(); diff --git a/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h b/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h index 205d934bd..bce8a4441 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h +++ b/unsupported/Eigen/src/NonLinearOptimization/qrsolv.h @@ -11,10 +11,12 @@ void ei_qrsolv( Matrix< Scalar, Dynamic, 1 > &sdiag) { + typedef DenseIndex Index; + /* Local variables */ - int i, j, k, l; + Index i, j, k, l; Scalar temp; - int n = s.cols(); + Index n = s.cols(); Matrix< Scalar, Dynamic, 1 > wa(n); PlanarRotation<Scalar> givens; @@ -67,7 +69,7 @@ void ei_qrsolv( /* solve the triangular system for z. if the system is */ /* singular, then obtain a least squares solution. */ - int nsing; + Index nsing; for (nsing=0; nsing<n && sdiag[nsing]!=0; nsing++); wa.tail(n-nsing).setZero(); diff --git a/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h b/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h index 855cb7a1b..ad319d9eb 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h +++ b/unsupported/Eigen/src/NonLinearOptimization/r1mpyq.h @@ -2,18 +2,20 @@ // TODO : move this to GivensQR once there's such a thing in Eigen template <typename Scalar> -void ei_r1mpyq(int m, int n, Scalar *a, const std::vector<PlanarRotation<Scalar> > &v_givens, const std::vector<PlanarRotation<Scalar> > &w_givens) +void ei_r1mpyq(DenseIndex m, DenseIndex n, Scalar *a, const std::vector<PlanarRotation<Scalar> > &v_givens, const std::vector<PlanarRotation<Scalar> > &w_givens) { + typedef DenseIndex Index; + /* apply the first set of givens rotations to a. */ - for (int j = n-2; j>=0; --j) - for (int i = 0; i<m; ++i) { + for (Index j = n-2; j>=0; --j) + for (Index i = 0; i<m; ++i) { Scalar temp = v_givens[j].c() * a[i+m*j] - v_givens[j].s() * a[i+m*(n-1)]; a[i+m*(n-1)] = v_givens[j].s() * a[i+m*j] + v_givens[j].c() * a[i+m*(n-1)]; a[i+m*j] = temp; } /* apply the second set of givens rotations to a. */ - for (int j = 0; j<n-1; ++j) - for (int i = 0; i<m; ++i) { + for (Index j = 0; j<n-1; ++j) + for (Index i = 0; i<m; ++i) { Scalar temp = w_givens[j].c() * a[i+m*j] + w_givens[j].s() * a[i+m*(n-1)]; a[i+m*(n-1)] = -w_givens[j].s() * a[i+m*j] + w_givens[j].c() * a[i+m*(n-1)]; a[i+m*j] = temp; diff --git a/unsupported/Eigen/src/NonLinearOptimization/r1updt.h b/unsupported/Eigen/src/NonLinearOptimization/r1updt.h index 3d8978837..e01d02910 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/r1updt.h +++ b/unsupported/Eigen/src/NonLinearOptimization/r1updt.h @@ -9,10 +9,12 @@ void ei_r1updt( Matrix< Scalar, Dynamic, 1> &w, bool *sing) { + typedef DenseIndex Index; + /* Local variables */ - const int m = s.rows(); - const int n = s.cols(); - int i, j=1; + const Index m = s.rows(); + const Index n = s.cols(); + Index i, j=1; Scalar temp; PlanarRotation<Scalar> givens; diff --git a/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h b/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h index b0bf72923..aa0bf7d0f 100644 --- a/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h +++ b/unsupported/Eigen/src/NonLinearOptimization/rwupdt.h @@ -6,7 +6,9 @@ void ei_rwupdt( Matrix< Scalar, Dynamic, 1> &b, Scalar alpha) { - const int n = r.cols(); + typedef DenseIndex Index; + + const Index n = r.cols(); assert(r.rows()>=n); std::vector<PlanarRotation<Scalar> > givens(n); @@ -14,12 +16,12 @@ void ei_rwupdt( Scalar temp, rowj; /* Function Body */ - for (int j = 0; j < n; ++j) { + for (Index j = 0; j < n; ++j) { rowj = w[j]; /* apply the previous transformations to */ /* r(i,j), i=0,1,...,j-1, and to w(j). */ - for (int i = 0; i < j; ++i) { + for (Index i = 0; i < j; ++i) { temp = givens[i].c() * r(i,j) + givens[i].s() * rowj; rowj = -givens[i].s() * r(i,j) + givens[i].c() * rowj; r(i,j) = temp; diff --git a/unsupported/Eigen/src/Polynomials/Companion.h b/unsupported/Eigen/src/Polynomials/Companion.h index 7c9e9c518..5a3bacc20 100644 --- a/unsupported/Eigen/src/Polynomials/Companion.h +++ b/unsupported/Eigen/src/Polynomials/Companion.h @@ -68,8 +68,10 @@ class ei_companion typedef Matrix< Scalar, Deg_1, Deg_1 > BottomLeftBlock; typedef Matrix< Scalar, 1, Deg_1 > LeftBlockFirstRow; + typedef DenseIndex Index; + public: - EIGEN_STRONG_INLINE const _Scalar operator()( int row, int col ) const + EIGEN_STRONG_INLINE const _Scalar operator()(Index row, Index col ) const { if( m_bl_diag.rows() > col ) { @@ -83,7 +85,7 @@ class ei_companion template<typename VectorType> void setPolynomial( const VectorType& poly ) { - const int deg = poly.size()-1; + const Index deg = poly.size()-1; m_monic = -1/poly[deg] * poly.head(deg); //m_bl_diag.setIdentity( deg-1 ); m_bl_diag.setOnes(deg-1); @@ -96,8 +98,8 @@ class ei_companion public: DenseCompanionMatrixType denseMatrix() const { - const int deg = m_monic.size(); - const int deg_1 = deg-1; + const Index deg = m_monic.size(); + const Index deg_1 = deg-1; DenseCompanionMatrixType companion(deg,deg); companion << ( LeftBlock(deg,deg_1) @@ -220,8 +222,8 @@ template< typename _Scalar, int _Deg > void ei_companion<_Scalar,_Deg>::balance() { EIGEN_STATIC_ASSERT( 1 < Deg, YOU_MADE_A_PROGRAMMING_MISTAKE ); - const int deg = m_monic.size(); - const int deg_1 = deg-1; + const Index deg = m_monic.size(); + const Index deg_1 = deg-1; bool hasConverged=false; while( !hasConverged ) @@ -244,7 +246,7 @@ void ei_companion<_Scalar,_Deg>::balance() //Middle rows and columns excluding the diagonal //============================================== - for( int i=1; i<deg_1; ++i ) + for( Index i=1; i<deg_1; ++i ) { // column norm, excluding the diagonal colNorm = ei_abs(m_bl_diag[i]); @@ -263,7 +265,7 @@ void ei_companion<_Scalar,_Deg>::balance() //Last row, last column excluding the diagonal //============================================ - const int ebl = m_bl_diag.size()-1; + const Index ebl = m_bl_diag.size()-1; VectorBlock<RightColumn,Deg_1> headMonic( m_monic, 0, deg_1 ); colNorm = headMonic.array().abs().sum(); rowNorm = ei_abs( m_bl_diag[ebl] ); diff --git a/unsupported/Eigen/src/Polynomials/PolynomialSolver.h b/unsupported/Eigen/src/Polynomials/PolynomialSolver.h index 207836508..ba14b5911 100644 --- a/unsupported/Eigen/src/Polynomials/PolynomialSolver.h +++ b/unsupported/Eigen/src/Polynomials/PolynomialSolver.h @@ -49,6 +49,8 @@ class PolynomialSolverBase typedef std::complex<RealScalar> RootType; typedef Matrix<RootType,_Deg,1> RootsType; + typedef DenseIndex Index; + protected: template< typename OtherPolynomial > inline void setPolynomial( const OtherPolynomial& poly ){ @@ -81,7 +83,7 @@ class PolynomialSolverBase const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const { bi_seq.clear(); - for( int i=0; i<m_roots.size(); ++i ) + for(Index i=0; i<m_roots.size(); ++i ) { if( ei_abs( m_roots[i].imag() ) < absImaginaryThreshold ){ bi_seq.push_back( m_roots[i].real() ); } @@ -92,9 +94,9 @@ class PolynomialSolverBase template<typename squaredNormBinaryPredicate> inline const RootType& selectComplexRoot_withRespectToNorm( squaredNormBinaryPredicate& pred ) const { - int res=0; + Index res=0; RealScalar norm2 = ei_abs2( m_roots[0] ); - for( int i=1; i<m_roots.size(); ++i ) + for( Index i=1; i<m_roots.size(); ++i ) { const RealScalar currNorm2 = ei_abs2( m_roots[i] ); if( pred( currNorm2, norm2 ) ){ @@ -130,10 +132,10 @@ class PolynomialSolverBase const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const { hasArealRoot = false; - int res=0; + Index res=0; RealScalar abs2(0); - for( int i=0; i<m_roots.size(); ++i ) + for( Index i=0; i<m_roots.size(); ++i ) { if( ei_abs( m_roots[i].imag() ) < absImaginaryThreshold ) { @@ -170,10 +172,10 @@ class PolynomialSolverBase const RealScalar& absImaginaryThreshold = NumTraits<Scalar>::dummy_precision() ) const { hasArealRoot = false; - int res=0; + Index res=0; RealScalar val(0); - for( int i=0; i<m_roots.size(); ++i ) + for( Index i=0; i<m_roots.size(); ++i ) { if( ei_abs( m_roots[i].imag() ) < absImaginaryThreshold ) { diff --git a/unsupported/Eigen/src/Polynomials/PolynomialUtils.h b/unsupported/Eigen/src/Polynomials/PolynomialUtils.h index d78821f90..d10b8f4dc 100644 --- a/unsupported/Eigen/src/Polynomials/PolynomialUtils.h +++ b/unsupported/Eigen/src/Polynomials/PolynomialUtils.h @@ -41,7 +41,7 @@ inline T poly_eval_horner( const Polynomials& poly, const T& x ) { T val=poly[poly.size()-1]; - for( int i=poly.size()-2; i>=0; --i ){ + for(DenseIndex i=poly.size()-2; i>=0; --i ){ val = val*x + poly[i]; } return val; } @@ -66,7 +66,7 @@ T poly_eval( const Polynomials& poly, const T& x ) { T val=poly[0]; T inv_x = T(1)/x; - for( int i=1; i<poly.size(); ++i ){ + for( DenseIndex i=1; i<poly.size(); ++i ){ val = val*inv_x + poly[i]; } return std::pow(x,(T)(poly.size()-1)) * val; @@ -94,7 +94,7 @@ typename NumTraits<typename Polynomial::Scalar>::Real cauchy_max_bound( const Po const Scalar inv_leading_coeff = Scalar(1)/poly[poly.size()-1]; Real cb(0); - for( int i=0; i<poly.size()-1; ++i ){ + for( DenseIndex i=0; i<poly.size()-1; ++i ){ cb += ei_abs(poly[i]*inv_leading_coeff); } return cb + Real(1); } @@ -112,14 +112,14 @@ typename NumTraits<typename Polynomial::Scalar>::Real cauchy_min_bound( const Po typedef typename Polynomial::Scalar Scalar; typedef typename NumTraits<Scalar>::Real Real; - int i=0; + DenseIndex i=0; while( i<poly.size()-1 && Scalar(0) == poly(i) ){ ++i; } if( poly.size()-1 == i ){ return Real(1); } const Scalar inv_min_coeff = Scalar(1)/poly[i]; Real cb(1); - for( int j=i+1; j<poly.size(); ++j ){ + for( DenseIndex j=i+1; j<poly.size(); ++j ){ cb += ei_abs(poly[j]*inv_min_coeff); } return Real(1)/cb; } @@ -142,9 +142,9 @@ void roots_to_monicPolynomial( const RootVector& rv, Polynomial& poly ) poly.setZero( rv.size()+1 ); poly[0] = -rv[0]; poly[1] = Scalar(1); - for( int i=1; i<(int)rv.size(); ++i ) + for( DenseIndex i=1; i< rv.size(); ++i ) { - for( int j=i+1; j>0; --j ){ poly[j] = poly[j-1] - rv[i]*poly[j]; } + for( DenseIndex j=i+1; j>0; --j ){ poly[j] = poly[j-1] - rv[i]*poly[j]; } poly[0] = -rv[i]*poly[0]; } } diff --git a/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h b/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h index c8c5f7575..fa8f81908 100644 --- a/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h +++ b/unsupported/Eigen/src/Skyline/SkylineInplaceLU.h @@ -38,6 +38,8 @@ template<typename MatrixType> class SkylineInplaceLU { protected: typedef typename MatrixType::Scalar Scalar; + typedef typename MatrixType::Index Index; + typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar; public: @@ -135,18 +137,18 @@ void SkylineInplaceLU<MatrixType>::compute() { ei_assert(rows == cols && "We do not (yet) support rectangular LU."); ei_assert(!m_lu.IsRowMajor && "LU decomposition does not work with rowMajor Storage"); - for (unsigned int row = 0; row < rows; row++) { + for (Index row = 0; row < rows; row++) { const double pivot = m_lu.coeffDiag(row); //Lower matrix Columns update - const unsigned int& col = row; + const Index& col = row; for (typename MatrixType::InnerLowerIterator lIt(m_lu, col); lIt; ++lIt) { lIt.valueRef() /= pivot; } //Upper matrix update -> contiguous memory access typename MatrixType::InnerLowerIterator lIt(m_lu, col); - for (unsigned int rrow = row + 1; rrow < m_lu.rows(); rrow++) { + for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) { typename MatrixType::InnerUpperIterator uItPivot(m_lu, row); typename MatrixType::InnerUpperIterator uIt(m_lu, rrow); const double coef = lIt.value(); @@ -165,12 +167,12 @@ void SkylineInplaceLU<MatrixType>::compute() { //Upper matrix update -> non contiguous memory access typename MatrixType::InnerLowerIterator lIt3(m_lu, col); - for (unsigned int rrow = row + 1; rrow < m_lu.rows(); rrow++) { + for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) { typename MatrixType::InnerUpperIterator uItPivot(m_lu, row); const double coef = lIt3.value(); //update lower part -> non contiguous memory access - for (unsigned int i = 0; i < rrow - row - 1; i++) { + for (Index i = 0; i < rrow - row - 1; i++) { m_lu.coeffRefLower(rrow, row + i + 1) -= uItPivot.value() * coef; ++uItPivot; } @@ -178,7 +180,7 @@ void SkylineInplaceLU<MatrixType>::compute() { } //update diag -> contiguous typename MatrixType::InnerLowerIterator lIt2(m_lu, col); - for (unsigned int rrow = row + 1; rrow < m_lu.rows(); rrow++) { + for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) { typename MatrixType::InnerUpperIterator uItPivot(m_lu, row); typename MatrixType::InnerUpperIterator uIt(m_lu, rrow); @@ -199,11 +201,11 @@ void SkylineInplaceLU<MatrixType>::computeRowMajor() { ei_assert(rows == cols && "We do not (yet) support rectangular LU."); ei_assert(m_lu.IsRowMajor && "You're trying to apply rowMajor decomposition on a ColMajor matrix !"); - for (unsigned int row = 0; row < rows; row++) { + for (Index row = 0; row < rows; row++) { typename MatrixType::InnerLowerIterator llIt(m_lu, row); - for (unsigned int col = llIt.col(); col < row; col++) { + for (Index col = llIt.col(); col < row; col++) { if (m_lu.coeffExistLower(row, col)) { const double diag = m_lu.coeffDiag(col); @@ -211,10 +213,10 @@ void SkylineInplaceLU<MatrixType>::computeRowMajor() { typename MatrixType::InnerUpperIterator uIt(m_lu, col); - const int offset = lIt.col() - uIt.row(); + const Index offset = lIt.col() - uIt.row(); - int stop = offset > 0 ? col - lIt.col() : col - uIt.row(); + Index stop = offset > 0 ? col - lIt.col() : col - uIt.row(); //#define VECTORIZE #ifdef VECTORIZE @@ -230,7 +232,7 @@ void SkylineInplaceLU<MatrixType>::computeRowMajor() { lIt += -offset; Scalar newCoeff = m_lu.coeffLower(row, col); - for (int k = 0; k < stop; ++k) { + for (Index k = 0; k < stop; ++k) { const Scalar tmp = newCoeff; newCoeff = tmp - lIt.value() * uIt.value(); ++lIt; @@ -243,15 +245,15 @@ void SkylineInplaceLU<MatrixType>::computeRowMajor() { } //Upper matrix update - const int col = row; + const Index col = row; typename MatrixType::InnerUpperIterator uuIt(m_lu, col); - for (unsigned int rrow = uuIt.row(); rrow < col; rrow++) { + for (Index rrow = uuIt.row(); rrow < col; rrow++) { typename MatrixType::InnerLowerIterator lIt(m_lu, rrow); typename MatrixType::InnerUpperIterator uIt(m_lu, col); - const int offset = lIt.col() - uIt.row(); + const Index offset = lIt.col() - uIt.row(); - int stop = offset > 0 ? rrow - lIt.col() : rrow - uIt.row(); + Index stop = offset > 0 ? rrow - lIt.col() : rrow - uIt.row(); #ifdef VECTORIZE Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop); @@ -264,7 +266,7 @@ void SkylineInplaceLU<MatrixType>::computeRowMajor() { else //Skip zero values of uIt lIt += -offset; Scalar newCoeff = m_lu.coeffUpper(rrow, col); - for (int k = 0; k < stop; ++k) { + for (Index k = 0; k < stop; ++k) { const Scalar tmp = newCoeff; newCoeff = tmp - lIt.value() * uIt.value(); @@ -280,10 +282,10 @@ void SkylineInplaceLU<MatrixType>::computeRowMajor() { typename MatrixType::InnerLowerIterator lIt(m_lu, row); typename MatrixType::InnerUpperIterator uIt(m_lu, row); - const int offset = lIt.col() - uIt.row(); + const Index offset = lIt.col() - uIt.row(); - int stop = offset > 0 ? lIt.size() : uIt.size(); + Index stop = offset > 0 ? lIt.size() : uIt.size(); #ifdef VECTORIZE Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop); Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop); @@ -294,7 +296,7 @@ void SkylineInplaceLU<MatrixType>::computeRowMajor() { else //Skip zero values of uIt lIt += -offset; Scalar newCoeff = m_lu.coeffDiag(row); - for (unsigned int k = 0; k < stop; ++k) { + for (Index k = 0; k < stop; ++k) { const Scalar tmp = newCoeff; newCoeff = tmp - lIt.value() * uIt.value(); ++lIt; @@ -320,12 +322,12 @@ bool SkylineInplaceLU<MatrixType>::solve(const MatrixBase<BDerived> &b, MatrixBa const size_t cols = m_lu.cols(); - for (int row = 0; row < rows; row++) { + for (Index row = 0; row < rows; row++) { x->coeffRef(row) = b.coeff(row); Scalar newVal = x->coeff(row); typename MatrixType::InnerLowerIterator lIt(m_lu, row); - unsigned int col = lIt.col(); + Index col = lIt.col(); while (lIt.col() < row) { newVal -= x->coeff(col++) * lIt.value(); @@ -336,7 +338,7 @@ bool SkylineInplaceLU<MatrixType>::solve(const MatrixBase<BDerived> &b, MatrixBa } - for (int col = rows - 1; col > 0; col--) { + for (Index col = rows - 1; col > 0; col--) { x->coeffRef(col) = x->coeff(col) / m_lu.coeffDiag(col); const Scalar x_col = x->coeff(col); diff --git a/unsupported/Eigen/src/Skyline/SkylineMatrix.h b/unsupported/Eigen/src/Skyline/SkylineMatrix.h index 6dd4f1736..20fafafa8 100644 --- a/unsupported/Eigen/src/Skyline/SkylineMatrix.h +++ b/unsupported/Eigen/src/Skyline/SkylineMatrix.h @@ -46,6 +46,7 @@ template<typename _Scalar, int _Options> struct ei_traits<SkylineMatrix<_Scalar, _Options> > { typedef _Scalar Scalar; + typedef Sparse StorageKind; enum { RowsAtCompileTime = Dynamic, @@ -71,45 +72,45 @@ protected: typedef SkylineMatrix<Scalar, (Flags&~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0) > TransposedSkylineMatrix; - int m_outerSize; - int m_innerSize; + Index m_outerSize; + Index m_innerSize; public: - int* m_colStartIndex; - int* m_rowStartIndex; + Index* m_colStartIndex; + Index* m_rowStartIndex; SkylineStorage<Scalar> m_data; public: - inline int rows() const { + inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; } - inline int cols() const { + inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; } - inline int innerSize() const { + inline Index innerSize() const { return m_innerSize; } - inline int outerSize() const { + inline Index outerSize() const { return m_outerSize; } - inline int upperNonZeros() const { + inline Index upperNonZeros() const { return m_data.upperSize(); } - inline int lowerNonZeros() const { + inline Index lowerNonZeros() const { return m_data.lowerSize(); } - inline int upperNonZeros(int j) const { + inline Index upperNonZeros(Index j) const { return m_colStartIndex[j + 1] - m_colStartIndex[j]; } - inline int lowerNonZeros(int j) const { + inline Index lowerNonZeros(Index j) const { return m_rowStartIndex[j + 1] - m_rowStartIndex[j]; } @@ -137,25 +138,25 @@ public: return &m_data.lower(0); } - inline const int* _upperProfilePtr() const { + inline const Index* _upperProfilePtr() const { return &m_data.upperProfile(0); } - inline int* _upperProfilePtr() { + inline Index* _upperProfilePtr() { return &m_data.upperProfile(0); } - inline const int* _lowerProfilePtr() const { + inline const Index* _lowerProfilePtr() const { return &m_data.lowerProfile(0); } - inline int* _lowerProfilePtr() { + inline Index* _lowerProfilePtr() { return &m_data.lowerProfile(0); } - inline Scalar coeff(int row, int col) const { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + inline Scalar coeff(Index row, Index col) const { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); @@ -166,7 +167,7 @@ public: if (IsRowMajor) { if (inner > outer) //upper matrix { - const int minOuterIndex = inner - m_data.upperProfile(inner); + const Index minOuterIndex = inner - m_data.upperProfile(inner); if (outer >= minOuterIndex) return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner))); else @@ -174,7 +175,7 @@ public: } if (inner < outer) //lower matrix { - const int minInnerIndex = outer - m_data.lowerProfile(outer); + const Index minInnerIndex = outer - m_data.lowerProfile(outer); if (inner >= minInnerIndex) return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer))); else @@ -184,7 +185,7 @@ public: } else { if (outer > inner) //upper matrix { - const int maxOuterIndex = inner + m_data.upperProfile(inner); + const Index maxOuterIndex = inner + m_data.upperProfile(inner); if (outer <= maxOuterIndex) return this->m_data.upper(m_colStartIndex[inner] + (outer - inner)); else @@ -192,7 +193,7 @@ public: } if (outer < inner) //lower matrix { - const int maxInnerIndex = outer + m_data.lowerProfile(outer); + const Index maxInnerIndex = outer + m_data.lowerProfile(outer); if (inner <= maxInnerIndex) return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer)); @@ -202,9 +203,9 @@ public: } } - inline Scalar& coeffRef(int row, int col) { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + inline Scalar& coeffRef(Index row, Index col) { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); @@ -215,55 +216,55 @@ public: if (IsRowMajor) { if (col > row) //upper matrix { - const int minOuterIndex = inner - m_data.upperProfile(inner); + const Index minOuterIndex = inner - m_data.upperProfile(inner); ei_assert(outer >= minOuterIndex && "you try to acces a coeff that do not exist in the storage"); return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner))); } if (col < row) //lower matrix { - const int minInnerIndex = outer - m_data.lowerProfile(outer); + const Index minInnerIndex = outer - m_data.lowerProfile(outer); ei_assert(inner >= minInnerIndex && "you try to acces a coeff that do not exist in the storage"); return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer))); } } else { if (outer > inner) //upper matrix { - const int maxOuterIndex = inner + m_data.upperProfile(inner); + const Index maxOuterIndex = inner + m_data.upperProfile(inner); ei_assert(outer <= maxOuterIndex && "you try to acces a coeff that do not exist in the storage"); return this->m_data.upper(m_colStartIndex[inner] + (outer - inner)); } if (outer < inner) //lower matrix { - const int maxInnerIndex = outer + m_data.lowerProfile(outer); + const Index maxInnerIndex = outer + m_data.lowerProfile(outer); ei_assert(inner <= maxInnerIndex && "you try to acces a coeff that do not exist in the storage"); return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer)); } } } - inline Scalar coeffDiag(int idx) const { + inline Scalar coeffDiag(Index idx) const { ei_assert(idx < outerSize()); ei_assert(idx < innerSize()); return this->m_data.diag(idx); } - inline Scalar coeffLower(int row, int col) const { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + inline Scalar coeffLower(Index row, Index col) const { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); ei_assert(inner != outer); if (IsRowMajor) { - const int minInnerIndex = outer - m_data.lowerProfile(outer); + const Index minInnerIndex = outer - m_data.lowerProfile(outer); if (inner >= minInnerIndex) return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer))); else return Scalar(0); } else { - const int maxInnerIndex = outer + m_data.lowerProfile(outer); + const Index maxInnerIndex = outer + m_data.lowerProfile(outer); if (inner <= maxInnerIndex) return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer)); else @@ -271,22 +272,22 @@ public: } } - inline Scalar coeffUpper(int row, int col) const { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + inline Scalar coeffUpper(Index row, Index col) const { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); ei_assert(inner != outer); if (IsRowMajor) { - const int minOuterIndex = inner - m_data.upperProfile(inner); + const Index minOuterIndex = inner - m_data.upperProfile(inner); if (outer >= minOuterIndex) return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner))); else return Scalar(0); } else { - const int maxOuterIndex = inner + m_data.upperProfile(inner); + const Index maxOuterIndex = inner + m_data.upperProfile(inner); if (outer <= maxOuterIndex) return this->m_data.upper(m_colStartIndex[inner] + (outer - inner)); else @@ -294,80 +295,80 @@ public: } } - inline Scalar& coeffRefDiag(int idx) { + inline Scalar& coeffRefDiag(Index idx) { ei_assert(idx < outerSize()); ei_assert(idx < innerSize()); return this->m_data.diag(idx); } - inline Scalar& coeffRefLower(int row, int col) { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + inline Scalar& coeffRefLower(Index row, Index col) { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); ei_assert(inner != outer); if (IsRowMajor) { - const int minInnerIndex = outer - m_data.lowerProfile(outer); + const Index minInnerIndex = outer - m_data.lowerProfile(outer); ei_assert(inner >= minInnerIndex && "you try to acces a coeff that do not exist in the storage"); return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer))); } else { - const int maxInnerIndex = outer + m_data.lowerProfile(outer); + const Index maxInnerIndex = outer + m_data.lowerProfile(outer); ei_assert(inner <= maxInnerIndex && "you try to acces a coeff that do not exist in the storage"); return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer)); } } - inline bool coeffExistLower(int row, int col) { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + inline bool coeffExistLower(Index row, Index col) { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); ei_assert(inner != outer); if (IsRowMajor) { - const int minInnerIndex = outer - m_data.lowerProfile(outer); + const Index minInnerIndex = outer - m_data.lowerProfile(outer); return inner >= minInnerIndex; } else { - const int maxInnerIndex = outer + m_data.lowerProfile(outer); + const Index maxInnerIndex = outer + m_data.lowerProfile(outer); return inner <= maxInnerIndex; } } - inline Scalar& coeffRefUpper(int row, int col) { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + inline Scalar& coeffRefUpper(Index row, Index col) { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); ei_assert(inner != outer); if (IsRowMajor) { - const int minOuterIndex = inner - m_data.upperProfile(inner); + const Index minOuterIndex = inner - m_data.upperProfile(inner); ei_assert(outer >= minOuterIndex && "you try to acces a coeff that do not exist in the storage"); return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner))); } else { - const int maxOuterIndex = inner + m_data.upperProfile(inner); + const Index maxOuterIndex = inner + m_data.upperProfile(inner); ei_assert(outer <= maxOuterIndex && "you try to acces a coeff that do not exist in the storage"); return this->m_data.upper(m_colStartIndex[inner] + (outer - inner)); } } - inline bool coeffExistUpper(int row, int col) { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + inline bool coeffExistUpper(Index row, Index col) { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); ei_assert(inner != outer); if (IsRowMajor) { - const int minOuterIndex = inner - m_data.upperProfile(inner); + const Index minOuterIndex = inner - m_data.upperProfile(inner); return outer >= minOuterIndex; } else { - const int maxOuterIndex = inner + m_data.upperProfile(inner); + const Index maxOuterIndex = inner + m_data.upperProfile(inner); return outer <= maxOuterIndex; } } @@ -385,17 +386,17 @@ public: /** Removes all non zeros */ inline void setZero() { m_data.clear(); - memset(m_colStartIndex, 0, (m_outerSize + 1) * sizeof (int)); - memset(m_rowStartIndex, 0, (m_outerSize + 1) * sizeof (int)); + memset(m_colStartIndex, 0, (m_outerSize + 1) * sizeof (Index)); + memset(m_rowStartIndex, 0, (m_outerSize + 1) * sizeof (Index)); } /** \returns the number of non zero coefficients */ - inline int nonZeros() const { + inline Index nonZeros() const { return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize(); } /** Preallocates \a reserveSize non zeros */ - inline void reserve(int reserveSize, int reserveUpperSize, int reserveLowerSize) { + inline void reserve(Index reserveSize, Index reserveUpperSize, Index reserveLowerSize) { m_data.reserve(reserveSize, reserveUpperSize, reserveLowerSize); } @@ -407,9 +408,9 @@ public: * * After an insertion session, you should call the finalize() function. */ - EIGEN_DONT_INLINE Scalar & insert(int row, int col) { - const int outer = IsRowMajor ? row : col; - const int inner = IsRowMajor ? col : row; + EIGEN_DONT_INLINE Scalar & insert(Index row, Index col) { + const Index outer = IsRowMajor ? row : col; + const Index inner = IsRowMajor ? col : row; ei_assert(outer < outerSize()); ei_assert(inner < innerSize()); @@ -420,27 +421,27 @@ public: if (IsRowMajor) { if (outer < inner) //upper matrix { - int minOuterIndex = 0; + Index minOuterIndex = 0; minOuterIndex = inner - m_data.upperProfile(inner); if (outer < minOuterIndex) //The value does not yet exist { - const int previousProfile = m_data.upperProfile(inner); + const Index previousProfile = m_data.upperProfile(inner); m_data.upperProfile(inner) = inner - outer; - const int bandIncrement = m_data.upperProfile(inner) - previousProfile; + const Index bandIncrement = m_data.upperProfile(inner) - previousProfile; //shift data stored after this new one - const int stop = m_colStartIndex[cols()]; - const int start = m_colStartIndex[inner]; + const Index stop = m_colStartIndex[cols()]; + const Index start = m_colStartIndex[inner]; - for (int innerIdx = stop; innerIdx >= start; innerIdx--) { + for (Index innerIdx = stop; innerIdx >= start; innerIdx--) { m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx); } - for (int innerIdx = cols(); innerIdx > inner; innerIdx--) { + for (Index innerIdx = cols(); innerIdx > inner; innerIdx--) { m_colStartIndex[innerIdx] += bandIncrement; } @@ -455,23 +456,23 @@ public: if (outer > inner) //lower matrix { - const int minInnerIndex = outer - m_data.lowerProfile(outer); + const Index minInnerIndex = outer - m_data.lowerProfile(outer); if (inner < minInnerIndex) //The value does not yet exist { - const int previousProfile = m_data.lowerProfile(outer); + const Index previousProfile = m_data.lowerProfile(outer); m_data.lowerProfile(outer) = outer - inner; - const int bandIncrement = m_data.lowerProfile(outer) - previousProfile; + const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile; //shift data stored after this new one - const int stop = m_rowStartIndex[rows()]; - const int start = m_rowStartIndex[outer]; + const Index stop = m_rowStartIndex[rows()]; + const Index start = m_rowStartIndex[outer]; - for (int innerIdx = stop; innerIdx >= start; innerIdx--) { + for (Index innerIdx = stop; innerIdx >= start; innerIdx--) { m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx); } - for (int innerIdx = rows(); innerIdx > outer; innerIdx--) { + for (Index innerIdx = rows(); innerIdx > outer; innerIdx--) { m_rowStartIndex[innerIdx] += bandIncrement; } @@ -485,22 +486,22 @@ public: } else { if (outer > inner) //upper matrix { - const int maxOuterIndex = inner + m_data.upperProfile(inner); + const Index maxOuterIndex = inner + m_data.upperProfile(inner); if (outer > maxOuterIndex) //The value does not yet exist { - const int previousProfile = m_data.upperProfile(inner); + const Index previousProfile = m_data.upperProfile(inner); m_data.upperProfile(inner) = outer - inner; - const int bandIncrement = m_data.upperProfile(inner) - previousProfile; + const Index bandIncrement = m_data.upperProfile(inner) - previousProfile; //shift data stored after this new one - const int stop = m_rowStartIndex[rows()]; - const int start = m_rowStartIndex[inner + 1]; + const Index stop = m_rowStartIndex[rows()]; + const Index start = m_rowStartIndex[inner + 1]; - for (int innerIdx = stop; innerIdx >= start; innerIdx--) { + for (Index innerIdx = stop; innerIdx >= start; innerIdx--) { m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx); } - for (int innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) { + for (Index innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) { m_rowStartIndex[innerIdx] += bandIncrement; } memset(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar)); @@ -512,22 +513,22 @@ public: if (outer < inner) //lower matrix { - const int maxInnerIndex = outer + m_data.lowerProfile(outer); + const Index maxInnerIndex = outer + m_data.lowerProfile(outer); if (inner > maxInnerIndex) //The value does not yet exist { - const int previousProfile = m_data.lowerProfile(outer); + const Index previousProfile = m_data.lowerProfile(outer); m_data.lowerProfile(outer) = inner - outer; - const int bandIncrement = m_data.lowerProfile(outer) - previousProfile; + const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile; //shift data stored after this new one - const int stop = m_colStartIndex[cols()]; - const int start = m_colStartIndex[outer + 1]; + const Index stop = m_colStartIndex[cols()]; + const Index start = m_colStartIndex[outer + 1]; - for (int innerIdx = stop; innerIdx >= start; innerIdx--) { + for (Index innerIdx = stop; innerIdx >= start; innerIdx--) { m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx); } - for (int innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) { + for (Index innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) { m_colStartIndex[innerIdx] += bandIncrement; } memset(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar)); @@ -551,16 +552,16 @@ public: // ei_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix"); // // Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1]; - // unsigned int dataIdx = 0; - // for (unsigned int row = 0; row < rows(); row++) { + // Index dataIdx = 0; + // for (Index row = 0; row < rows(); row++) { // - // const unsigned int nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row]; + // const Index nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row]; // // std::cout << "nbLowerElts" << nbLowerElts << std::endl; // memcpy(newArray + dataIdx, m_data.m_lower + m_rowStartIndex[row], nbLowerElts * sizeof (Scalar)); // m_rowStartIndex[row] = dataIdx; // dataIdx += nbLowerElts; // - // const unsigned int nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row]; + // const Index nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row]; // memcpy(newArray + dataIdx, m_data.m_upper + m_colStartIndex[row], nbUpperElts * sizeof (Scalar)); // m_colStartIndex[row] = dataIdx; // dataIdx += nbUpperElts; @@ -594,16 +595,16 @@ public: } /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero - * \sa resizeNonZeros(int), reserve(), setZero() + * \sa resizeNonZeros(Index), reserve(), setZero() */ void resize(size_t rows, size_t cols) { - const int diagSize = rows > cols ? cols : rows; + const Index diagSize = rows > cols ? cols : rows; m_innerSize = IsRowMajor ? cols : rows; ei_assert(rows == cols && "Skyline matrix must be square matrix"); if (diagSize % 2) { // diagSize is odd - const int k = (diagSize - 1) / 2; + const Index k = (diagSize - 1) / 2; m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols, 2 * k * k + k + 1, @@ -611,7 +612,7 @@ public: } else // diagSize is even { - const int k = diagSize / 2; + const Index k = diagSize / 2; m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols, 2 * k * k - k + 1, 2 * k * k - k + 1); @@ -621,19 +622,19 @@ public: delete[] m_colStartIndex; delete[] m_rowStartIndex; } - m_colStartIndex = new int [cols + 1]; - m_rowStartIndex = new int [rows + 1]; + m_colStartIndex = new Index [cols + 1]; + m_rowStartIndex = new Index [rows + 1]; m_outerSize = diagSize; m_data.reset(); m_data.clear(); m_outerSize = diagSize; - memset(m_colStartIndex, 0, (cols + 1) * sizeof (int)); - memset(m_rowStartIndex, 0, (rows + 1) * sizeof (int)); + memset(m_colStartIndex, 0, (cols + 1) * sizeof (Index)); + memset(m_rowStartIndex, 0, (rows + 1) * sizeof (Index)); } - void resizeNonZeros(int size) { + void resizeNonZeros(Index size) { m_data.resize(size); } @@ -673,8 +674,8 @@ public: swap(other.const_cast_derived()); } else { resize(other.rows(), other.cols()); - memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof (int)); - memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (int)); + memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof (Index)); + memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (Index)); m_data = other.m_data; } return *this; @@ -696,34 +697,34 @@ public: EIGEN_DBG_SKYLINE( std::cout << "upper elements : " << std::endl; - for (unsigned int i = 0; i < m.m_data.upperSize(); i++) + for (Index i = 0; i < m.m_data.upperSize(); i++) std::cout << m.m_data.upper(i) << "\t"; std::cout << std::endl; std::cout << "upper profile : " << std::endl; - for (unsigned int i = 0; i < m.m_data.upperProfileSize(); i++) + for (Index i = 0; i < m.m_data.upperProfileSize(); i++) std::cout << m.m_data.upperProfile(i) << "\t"; std::cout << std::endl; std::cout << "lower startIdx : " << std::endl; - for (unsigned int i = 0; i < m.m_data.upperProfileSize(); i++) + for (Index i = 0; i < m.m_data.upperProfileSize(); i++) std::cout << (IsRowMajor ? m.m_colStartIndex[i] : m.m_rowStartIndex[i]) << "\t"; std::cout << std::endl; std::cout << "lower elements : " << std::endl; - for (unsigned int i = 0; i < m.m_data.lowerSize(); i++) + for (Index i = 0; i < m.m_data.lowerSize(); i++) std::cout << m.m_data.lower(i) << "\t"; std::cout << std::endl; std::cout << "lower profile : " << std::endl; - for (unsigned int i = 0; i < m.m_data.lowerProfileSize(); i++) + for (Index i = 0; i < m.m_data.lowerProfileSize(); i++) std::cout << m.m_data.lowerProfile(i) << "\t"; std::cout << std::endl; std::cout << "lower startIdx : " << std::endl; - for (unsigned int i = 0; i < m.m_data.lowerProfileSize(); i++) + for (Index i = 0; i < m.m_data.lowerProfileSize(); i++) std::cout << (IsRowMajor ? m.m_rowStartIndex[i] : m.m_colStartIndex[i]) << "\t"; std::cout << std::endl; ); - for (unsigned int rowIdx = 0; rowIdx < m.rows(); rowIdx++) { - for (unsigned int colIdx = 0; colIdx < m.cols(); colIdx++) { + for (Index rowIdx = 0; rowIdx < m.rows(); rowIdx++) { + for (Index colIdx = 0; colIdx < m.cols(); colIdx++) { s << m.coeff(rowIdx, colIdx) << "\t"; } s << std::endl; @@ -745,7 +746,7 @@ template<typename Scalar, int _Options> class SkylineMatrix<Scalar, _Options>::InnerUpperIterator { public: - InnerUpperIterator(const SkylineMatrix& mat, int outer) + InnerUpperIterator(const SkylineMatrix& mat, Index outer) : m_matrix(mat), m_outer(outer), m_id(_Options == RowMajor ? mat.m_colStartIndex[outer] : mat.m_rowStartIndex[outer] + 1), m_start(m_id), @@ -757,7 +758,7 @@ public: return *this; } - inline InnerUpperIterator & operator+=(unsigned int shift) { + inline InnerUpperIterator & operator+=(Index shift) { m_id += shift; return *this; } @@ -774,16 +775,16 @@ public: return const_cast<Scalar&> (m_matrix.m_data.upper(m_id)); } - inline int index() const { + inline Index index() const { return IsRowMajor ? m_outer - m_matrix.m_data.upperProfile(m_outer) + (m_id - m_start) : m_outer + (m_id - m_start) + 1; } - inline int row() const { + inline Index row() const { return IsRowMajor ? index() : m_outer; } - inline int col() const { + inline Index col() const { return IsRowMajor ? m_outer : index(); } @@ -797,17 +798,17 @@ public: protected: const SkylineMatrix& m_matrix; - const int m_outer; - int m_id; - const int m_start; - const int m_end; + const Index m_outer; + Index m_id; + const Index m_start; + const Index m_end; }; template<typename Scalar, int _Options> class SkylineMatrix<Scalar, _Options>::InnerLowerIterator { public: - InnerLowerIterator(const SkylineMatrix& mat, int outer) + InnerLowerIterator(const SkylineMatrix& mat, Index outer) : m_matrix(mat), m_outer(outer), m_id(_Options == RowMajor ? mat.m_rowStartIndex[outer] : mat.m_colStartIndex[outer] + 1), @@ -820,7 +821,7 @@ public: return *this; } - inline InnerLowerIterator & operator+=(unsigned int shift) { + inline InnerLowerIterator & operator+=(Index shift) { m_id += shift; return *this; } @@ -837,17 +838,17 @@ public: return const_cast<Scalar&> (m_matrix.m_data.lower(m_id)); } - inline int index() const { + inline Index index() const { return IsRowMajor ? m_outer - m_matrix.m_data.lowerProfile(m_outer) + (m_id - m_start) : m_outer + (m_id - m_start) + 1; ; } - inline int row() const { + inline Index row() const { return IsRowMajor ? m_outer : index(); } - inline int col() const { + inline Index col() const { return IsRowMajor ? index() : m_outer; } @@ -861,10 +862,10 @@ public: protected: const SkylineMatrix& m_matrix; - const int m_outer; - int m_id; - const int m_start; - const int m_end; + const Index m_outer; + Index m_id; + const Index m_start; + const Index m_end; }; #endif // EIGEN_SkylineMatrix_H diff --git a/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h b/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h index ff20b830c..17349c187 100644 --- a/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h +++ b/unsupported/Eigen/src/Skyline/SkylineMatrixBase.h @@ -40,6 +40,8 @@ template<typename Derived> class SkylineMatrixBase : public EigenBase<Derived> { public: typedef typename ei_traits<Derived>::Scalar Scalar; + typedef typename ei_traits<Derived>::StorageKind StorageKind; + typedef typename ei_index<StorageKind>::type Index; enum { RowsAtCompileTime = ei_traits<Derived>::RowsAtCompileTime, @@ -113,36 +115,36 @@ public: #endif // not EIGEN_PARSED_BY_DOXYGEN /** \returns the number of rows. \sa cols(), RowsAtCompileTime */ - inline int rows() const { + inline Index rows() const { return derived().rows(); } /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/ - inline int cols() const { + inline Index cols() const { return derived().cols(); } /** \returns the number of coefficients, which is \a rows()*cols(). * \sa rows(), cols(), SizeAtCompileTime. */ - inline int size() const { + inline Index size() const { return rows() * cols(); } /** \returns the number of nonzero coefficients which is in practice the number * of stored coefficients. */ - inline int nonZeros() const { + inline Index nonZeros() const { return derived().nonZeros(); } /** \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 */ - int outerSize() const { + 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 */ - int innerSize() const { + Index innerSize() const { return (int(Flags) & RowMajorBit) ? this->cols() : this->rows(); } @@ -167,8 +169,8 @@ public: template<typename OtherDerived> inline void assignGeneric(const OtherDerived& other) { derived().resize(other.rows(), other.cols()); - for (unsigned int row = 0; row < rows(); row++) - for (unsigned int col = 0; col < cols(); col++) { + for (Index row = 0; row < rows(); row++) + for (Index col = 0; col < cols(); col++) { if (other.coeff(row, col) != Scalar(0)) derived().insert(row, col) = other.coeff(row, col); } @@ -196,8 +198,8 @@ public: template<typename DenseDerived> void evalTo(MatrixBase<DenseDerived>& dst) const { dst.setZero(); - for (unsigned int i = 0; i < rows(); i++) - for (unsigned int j = 0; j < rows(); j++) + for (Index i = 0; i < rows(); i++) + for (Index j = 0; j < rows(); j++) dst(i, j) = derived().coeff(i, j); } diff --git a/unsupported/Eigen/src/Skyline/SkylineProduct.h b/unsupported/Eigen/src/Skyline/SkylineProduct.h index 9586576b5..e6484ad13 100644 --- a/unsupported/Eigen/src/Skyline/SkylineProduct.h +++ b/unsupported/Eigen/src/Skyline/SkylineProduct.h @@ -106,11 +106,11 @@ public: EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) } - EIGEN_STRONG_INLINE int rows() const { + EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); } - EIGEN_STRONG_INLINE int cols() const { + EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); } @@ -147,18 +147,18 @@ EIGEN_DONT_INLINE void ei_skyline_row_major_time_dense_product(const Lhs& lhs, c }; //Use matrix diagonal part <- Improvement : use inner iterator on dense matrix. - for (unsigned int col = 0; col < rhs.cols(); col++) { - for (unsigned int row = 0; row < lhs.rows(); row++) { + for (Index col = 0; col < rhs.cols(); col++) { + for (Index row = 0; row < lhs.rows(); row++) { dst(row, col) = lhs.coeffDiag(row) * rhs(row, col); } } //Use matrix lower triangular part - for (unsigned int row = 0; row < lhs.rows(); row++) { + for (Index row = 0; row < lhs.rows(); row++) { typename _Lhs::InnerLowerIterator lIt(lhs, row); - const int stop = lIt.col() + lIt.size(); - for (unsigned int col = 0; col < rhs.cols(); col++) { + const Index stop = lIt.col() + lIt.size(); + for (Index col = 0; col < rhs.cols(); col++) { - unsigned int k = lIt.col(); + Index k = lIt.col(); Scalar tmp = 0; while (k < stop) { tmp += @@ -173,14 +173,14 @@ EIGEN_DONT_INLINE void ei_skyline_row_major_time_dense_product(const Lhs& lhs, c } //Use matrix upper triangular part - for (unsigned int lhscol = 0; lhscol < lhs.cols(); lhscol++) { + for (Index lhscol = 0; lhscol < lhs.cols(); lhscol++) { typename _Lhs::InnerUpperIterator uIt(lhs, lhscol); - const int stop = uIt.size() + uIt.row(); - for (unsigned int rhscol = 0; rhscol < rhs.cols(); rhscol++) { + const Index stop = uIt.size() + uIt.row(); + for (Index rhscol = 0; rhscol < rhs.cols(); rhscol++) { const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol); - unsigned int k = uIt.row(); + Index k = uIt.row(); while (k < stop) { dst(k++, rhscol) += uIt.value() * @@ -210,19 +210,19 @@ EIGEN_DONT_INLINE void ei_skyline_col_major_time_dense_product(const Lhs& lhs, c }; //Use matrix diagonal part <- Improvement : use inner iterator on dense matrix. - for (unsigned int col = 0; col < rhs.cols(); col++) { - for (unsigned int row = 0; row < lhs.rows(); row++) { + for (Index col = 0; col < rhs.cols(); col++) { + for (Index row = 0; row < lhs.rows(); row++) { dst(row, col) = lhs.coeffDiag(row) * rhs(row, col); } } //Use matrix upper triangular part - for (unsigned int row = 0; row < lhs.rows(); row++) { + for (Index row = 0; row < lhs.rows(); row++) { typename _Lhs::InnerUpperIterator uIt(lhs, row); - const int stop = uIt.col() + uIt.size(); - for (unsigned int col = 0; col < rhs.cols(); col++) { + const Index stop = uIt.col() + uIt.size(); + for (Index col = 0; col < rhs.cols(); col++) { - unsigned int k = uIt.col(); + Index k = uIt.col(); Scalar tmp = 0; while (k < stop) { tmp += @@ -238,13 +238,13 @@ EIGEN_DONT_INLINE void ei_skyline_col_major_time_dense_product(const Lhs& lhs, c } //Use matrix lower triangular part - for (unsigned int lhscol = 0; lhscol < lhs.cols(); lhscol++) { + for (Index lhscol = 0; lhscol < lhs.cols(); lhscol++) { typename _Lhs::InnerLowerIterator lIt(lhs, lhscol); - const int stop = lIt.size() + lIt.row(); - for (unsigned int rhscol = 0; rhscol < rhs.cols(); rhscol++) { + const Index stop = lIt.size() + lIt.row(); + for (Index rhscol = 0; rhscol < rhs.cols(); rhscol++) { const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol); - unsigned int k = lIt.row(); + Index k = lIt.row(); while (k < stop) { dst(k++, rhscol) += lIt.value() * diff --git a/unsupported/Eigen/src/Skyline/SkylineStorage.h b/unsupported/Eigen/src/Skyline/SkylineStorage.h index 641508f75..13f3e0ced 100644 --- a/unsupported/Eigen/src/Skyline/SkylineStorage.h +++ b/unsupported/Eigen/src/Skyline/SkylineStorage.h @@ -34,6 +34,7 @@ template<typename Scalar> class SkylineStorage { typedef typename NumTraits<Scalar>::Real RealScalar; + typedef SparseIndex Index; public: SkylineStorage() @@ -70,8 +71,8 @@ public: memcpy(m_diag, other.m_diag, m_diagSize * sizeof (Scalar)); memcpy(m_upper, other.m_upper, other.upperSize() * sizeof (Scalar)); memcpy(m_lower, other.m_lower, other.lowerSize() * sizeof (Scalar)); - memcpy(m_upperProfile, other.m_upperProfile, m_upperProfileSize * sizeof (int)); - memcpy(m_lowerProfile, other.m_lowerProfile, m_lowerProfileSize * sizeof (int)); + memcpy(m_upperProfile, other.m_upperProfile, m_upperProfileSize * sizeof (Index)); + memcpy(m_lowerProfile, other.m_lowerProfile, m_lowerProfileSize * sizeof (Index)); return *this; } @@ -96,8 +97,8 @@ public: delete[] m_lowerProfile; } - void reserve(size_t size, size_t upperProfileSize, size_t lowerProfileSize, size_t upperSize, size_t lowerSize) { - int newAllocatedSize = size + upperSize + lowerSize; + void reserve(Index size, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize) { + Index newAllocatedSize = size + upperSize + lowerSize; if (newAllocatedSize > m_allocatedSize) reallocate(size, upperProfileSize, lowerProfileSize, upperSize, lowerSize); } @@ -107,9 +108,9 @@ public: reallocate(m_diagSize, m_upperProfileSize, m_lowerProfileSize, m_upperSize, m_lowerSize); } - void resize(size_t diagSize, size_t upperProfileSize, size_t lowerProfileSize, size_t upperSize, size_t lowerSize, float reserveSizeFactor = 0) { + void resize(Index diagSize, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize, float reserveSizeFactor = 0) { if (m_allocatedSize < diagSize + upperSize + lowerSize) - reallocate(diagSize, upperProfileSize, lowerProfileSize, upperSize + size_t(reserveSizeFactor * upperSize), lowerSize + size_t(reserveSizeFactor * lowerSize)); + reallocate(diagSize, upperProfileSize, lowerProfileSize, upperSize + Index(reserveSizeFactor * upperSize), lowerSize + Index(reserveSizeFactor * lowerSize)); m_diagSize = diagSize; m_upperSize = upperSize; m_lowerSize = lowerSize; @@ -117,27 +118,27 @@ public: m_lowerProfileSize = lowerProfileSize; } - inline size_t diagSize() const { + inline Index diagSize() const { return m_diagSize; } - inline size_t upperSize() const { + inline Index upperSize() const { return m_upperSize; } - inline size_t lowerSize() const { + inline Index lowerSize() const { return m_lowerSize; } - inline size_t upperProfileSize() const { + inline Index upperProfileSize() const { return m_upperProfileSize; } - inline size_t lowerProfileSize() const { + inline Index lowerProfileSize() const { return m_lowerProfileSize; } - inline size_t allocatedSize() const { + inline Index allocatedSize() const { return m_allocatedSize; } @@ -145,47 +146,47 @@ public: m_diagSize = 0; } - inline Scalar& diag(size_t i) { + inline Scalar& diag(Index i) { return m_diag[i]; } - inline const Scalar& diag(size_t i) const { + inline const Scalar& diag(Index i) const { return m_diag[i]; } - inline Scalar& upper(size_t i) { + inline Scalar& upper(Index i) { return m_upper[i]; } - inline const Scalar& upper(size_t i) const { + inline const Scalar& upper(Index i) const { return m_upper[i]; } - inline Scalar& lower(size_t i) { + inline Scalar& lower(Index i) { return m_lower[i]; } - inline const Scalar& lower(size_t i) const { + inline const Scalar& lower(Index i) const { return m_lower[i]; } - inline int& upperProfile(size_t i) { + inline Index& upperProfile(Index i) { return m_upperProfile[i]; } - inline const int& upperProfile(size_t i) const { + inline const Index& upperProfile(Index i) const { return m_upperProfile[i]; } - inline int& lowerProfile(size_t i) { + inline Index& lowerProfile(Index i) { return m_lowerProfile[i]; } - inline const int& lowerProfile(size_t i) const { + inline const Index& lowerProfile(Index i) const { return m_lowerProfile[i]; } - static SkylineStorage Map(int* upperProfile, int* lowerProfile, Scalar* diag, Scalar* upper, Scalar* lower, size_t size, size_t upperSize, size_t lowerSize) { + static SkylineStorage Map(Index* upperProfile, Index* lowerProfile, Scalar* diag, Scalar* upper, Scalar* lower, Index size, Index upperSize, Index lowerSize) { SkylineStorage res; res.m_upperProfile = upperProfile; res.m_lowerProfile = lowerProfile; @@ -202,8 +203,8 @@ public: memset(m_diag, 0, m_diagSize * sizeof (Scalar)); memset(m_upper, 0, m_upperSize * sizeof (Scalar)); memset(m_lower, 0, m_lowerSize * sizeof (Scalar)); - memset(m_upperProfile, 0, m_diagSize * sizeof (int)); - memset(m_lowerProfile, 0, m_diagSize * sizeof (int)); + memset(m_upperProfile, 0, m_diagSize * sizeof (Index)); + memset(m_lowerProfile, 0, m_diagSize * sizeof (Index)); } void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>()) { @@ -212,26 +213,26 @@ public: protected: - inline void reallocate(size_t diagSize, size_t upperProfileSize, size_t lowerProfileSize, size_t upperSize, size_t lowerSize) { + inline void reallocate(Index diagSize, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize) { Scalar* diag = new Scalar[diagSize]; Scalar* upper = new Scalar[upperSize]; Scalar* lower = new Scalar[lowerSize]; - int* upperProfile = new int[upperProfileSize]; - int* lowerProfile = new int[lowerProfileSize]; + Index* upperProfile = new Index[upperProfileSize]; + Index* lowerProfile = new Index[lowerProfileSize]; - size_t copyDiagSize = std::min(diagSize, m_diagSize); - size_t copyUpperSize = std::min(upperSize, m_upperSize); - size_t copyLowerSize = std::min(lowerSize, m_lowerSize); - size_t copyUpperProfileSize = std::min(upperProfileSize, m_upperProfileSize); - size_t copyLowerProfileSize = std::min(lowerProfileSize, m_lowerProfileSize); + Index copyDiagSize = std::min(diagSize, m_diagSize); + Index copyUpperSize = std::min(upperSize, m_upperSize); + Index copyLowerSize = std::min(lowerSize, m_lowerSize); + Index copyUpperProfileSize = std::min(upperProfileSize, m_upperProfileSize); + Index copyLowerProfileSize = std::min(lowerProfileSize, m_lowerProfileSize); // copy memcpy(diag, m_diag, copyDiagSize * sizeof (Scalar)); memcpy(upper, m_upper, copyUpperSize * sizeof (Scalar)); memcpy(lower, m_lower, copyLowerSize * sizeof (Scalar)); - memcpy(upperProfile, m_upperProfile, copyUpperProfileSize * sizeof (int)); - memcpy(lowerProfile, m_lowerProfile, copyLowerProfileSize * sizeof (int)); + memcpy(upperProfile, m_upperProfile, copyUpperProfileSize * sizeof (Index)); + memcpy(lowerProfile, m_lowerProfile, copyLowerProfileSize * sizeof (Index)); @@ -255,14 +256,14 @@ public: Scalar* m_diag; Scalar* m_upper; Scalar* m_lower; - int* m_upperProfile; - int* m_lowerProfile; - size_t m_diagSize; - size_t m_upperSize; - size_t m_lowerSize; - size_t m_upperProfileSize; - size_t m_lowerProfileSize; - size_t m_allocatedSize; + Index* m_upperProfile; + Index* m_lowerProfile; + Index m_diagSize; + Index m_upperSize; + Index m_lowerSize; + Index m_upperProfileSize; + Index m_lowerProfileSize; + Index m_allocatedSize; }; diff --git a/unsupported/Eigen/src/Skyline/SkylineUtil.h b/unsupported/Eigen/src/Skyline/SkylineUtil.h index 71563adfb..7781c33e7 100644 --- a/unsupported/Eigen/src/Skyline/SkylineUtil.h +++ b/unsupported/Eigen/src/Skyline/SkylineUtil.h @@ -56,20 +56,22 @@ EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \ } #define EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \ -EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =) \ -EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, +=) \ -EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, -=) \ -EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, *=) \ -EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, /=) + EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =) \ + EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, +=) \ + EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, -=) \ + EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, *=) \ + EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, /=) #define _EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived, BaseClass) \ -typedef BaseClass Base; \ -typedef typename Eigen::ei_traits<Derived>::Scalar Scalar; \ -typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \ -enum { Flags = Eigen::ei_traits<Derived>::Flags, }; + typedef BaseClass Base; \ + typedef typename Eigen::ei_traits<Derived>::Scalar Scalar; \ + typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \ + typedef typename Eigen::ei_traits<Derived>::StorageKind StorageKind; \ + typedef typename Eigen::ei_index<StorageKind>::type Index; \ + enum { Flags = Eigen::ei_traits<Derived>::Flags, }; #define EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived) \ -_EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived, Eigen::SkylineMatrixBase<Derived>) + _EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived, Eigen::SkylineMatrixBase<Derived>) template<typename Derived> class SkylineMatrixBase; template<typename _Scalar, int _Flags = 0> class SkylineMatrix; |