// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2008, 2010 Benoit Jacob // // Eigen is free software; you can redistribute it and/or // modify it under the terms of the GNU Lesser General Public // License as published by the Free Software Foundation; either // version 3 of the License, or (at your option) any later version. // // Alternatively, you can redistribute it and/or // modify it under the terms of the GNU General Public License as // published by the Free Software Foundation; either version 2 of // the License, or (at your option) any later version. // // Eigen is distributed in the hope that it will be useful, but WITHOUT ANY // WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS // FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the // GNU General Public License for more details. // // You should have received a copy of the GNU Lesser General Public // License and a copy of the GNU General Public License along with // Eigen. If not, see . #ifndef EIGEN_DOT_H #define EIGEN_DOT_H // 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::ret, // 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 ei_dot_nocheck { static inline typename ei_traits::Scalar run(const MatrixBase& a, const MatrixBase& b) { return a.conjugate().cwiseProduct(b).sum(); } }; template struct ei_dot_nocheck { static inline typename ei_traits::Scalar run(const MatrixBase& a, const MatrixBase& b) { return a.adjoint().cwiseProduct(b).sum(); } }; template struct ei_dot_nocheck { static inline typename ei_traits::Scalar run(const MatrixBase&, const MatrixBase&) { return typename ei_traits::Scalar(0); } }; /** \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 template typename ei_traits::Scalar MatrixBase::dot(const MatrixBase& 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((ei_is_same_type::ret), YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) ei_assert(size() == other.size()); return ei_dot_nocheck::run(*this, other); } /** \returns the squared \em l2 norm of *this, i.e., for vectors, the dot product of *this with itself. * * \sa dot(), norm() */ template inline typename NumTraits::Scalar>::Real MatrixBase::squaredNorm() const { return ei_real((*this).cwiseAbs2().sum()); } /** \returns the \em l2 norm of *this, i.e., for vectors, the square root of the dot product of *this with itself. * * \sa dot(), squaredNorm() */ template inline typename NumTraits::Scalar>::Real MatrixBase::norm() const { return ei_sqrt(squaredNorm()); } /** \returns an expression of the quotient of *this by its own norm. * * \only_for_vectors * * \sa norm(), normalize() */ template inline const typename MatrixBase::PlainObject MatrixBase::normalized() const { typedef typename ei_nested::type Nested; typedef typename ei_unref::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 inline void MatrixBase::normalize() { *this /= norm(); } /** \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 template bool MatrixBase::isOrthogonal (const MatrixBase& other, RealScalar prec) const { typename ei_nested::type nested(derived()); typename ei_nested::type otherNested(other.derived()); return ei_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 bool MatrixBase::isUnitary(RealScalar prec) const { typename Derived::Nested nested(derived()); for(int i = 0; i < cols(); ++i) { if(!ei_isApprox(nested.col(i).squaredNorm(), static_cast(1), prec)) return false; for(int j = 0; j < i; ++j) if(!ei_isMuchSmallerThan(nested.col(i).dot(nested.col(j)), static_cast(1), prec)) return false; } return true; } #endif // EIGEN_DOT_H