| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> |
| // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #ifndef EIGEN_PARTIAL_REDUX_H |
| #define EIGEN_PARTIAL_REDUX_H |
| |
| namespace Eigen { |
| |
| /** \class PartialReduxExpr |
| * \ingroup Core_Module |
| * |
| * \brief Generic expression of a partially reduxed matrix |
| * |
| * \tparam MatrixType the type of the matrix we are applying the redux operation |
| * \tparam MemberOp type of the member functor |
| * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal) |
| * |
| * This class represents an expression of a partial redux operator of a matrix. |
| * It is the return type of some VectorwiseOp functions, |
| * and most of the time this is the only way it is used. |
| * |
| * \sa class VectorwiseOp |
| */ |
| |
| template< typename MatrixType, typename MemberOp, int Direction> |
| class PartialReduxExpr; |
| |
| namespace internal { |
| template<typename MatrixType, typename MemberOp, int Direction> |
| struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> > |
| : traits<MatrixType> |
| { |
| typedef typename MemberOp::result_type Scalar; |
| typedef typename traits<MatrixType>::StorageKind StorageKind; |
| typedef typename traits<MatrixType>::XprKind XprKind; |
| typedef typename MatrixType::Scalar InputScalar; |
| enum { |
| RowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::RowsAtCompileTime, |
| ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime, |
| MaxRowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::MaxRowsAtCompileTime, |
| MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime, |
| Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0, |
| TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime : MatrixType::ColsAtCompileTime |
| }; |
| }; |
| } |
| |
| template< typename MatrixType, typename MemberOp, int Direction> |
| class PartialReduxExpr : public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type, |
| internal::no_assignment_operator |
| { |
| public: |
| |
| typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base; |
| EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr) |
| |
| EIGEN_DEVICE_FUNC |
| explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp()) |
| : m_matrix(mat), m_functor(func) {} |
| |
| EIGEN_DEVICE_FUNC |
| Index rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); } |
| EIGEN_DEVICE_FUNC |
| Index cols() const { return (Direction==Horizontal ? 1 : m_matrix.cols()); } |
| |
| EIGEN_DEVICE_FUNC |
| typename MatrixType::Nested nestedExpression() const { return m_matrix; } |
| |
| EIGEN_DEVICE_FUNC |
| const MemberOp& functor() const { return m_functor; } |
| |
| protected: |
| typename MatrixType::Nested m_matrix; |
| const MemberOp m_functor; |
| }; |
| |
| template<typename A,typename B> struct partial_redux_dummy_func; |
| |
| #define EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,VECTORIZABLE,BINARYOP) \ |
| template <typename ResultType,typename Scalar> \ |
| struct member_##MEMBER { \ |
| EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER) \ |
| typedef ResultType result_type; \ |
| typedef BINARYOP<Scalar,Scalar> BinaryOp; \ |
| template<int Size> struct Cost { enum { value = COST }; }; \ |
| enum { Vectorizable = VECTORIZABLE }; \ |
| template<typename XprType> \ |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE \ |
| ResultType operator()(const XprType& mat) const \ |
| { return mat.MEMBER(); } \ |
| BinaryOp binaryFunc() const { return BinaryOp(); } \ |
| } |
| |
| #define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \ |
| EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,0,partial_redux_dummy_func) |
| |
| namespace internal { |
| |
| EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); |
| EIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); |
| EIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); |
| EIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits<scalar_hypot_op<Scalar> >::Cost ); |
| EIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost); |
| EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost); |
| EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost); |
| |
| EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_sum_op); |
| EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_min_op); |
| EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_max_op); |
| EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost, 1, internal::scalar_product_op); |
| |
| template <int p, typename ResultType,typename Scalar> |
| struct member_lpnorm { |
| typedef ResultType result_type; |
| enum { Vectorizable = 0 }; |
| template<int Size> struct Cost |
| { enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; }; |
| EIGEN_DEVICE_FUNC member_lpnorm() {} |
| template<typename XprType> |
| EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const |
| { return mat.template lpNorm<p>(); } |
| }; |
| |
| template <typename BinaryOpT, typename Scalar> |
| struct member_redux { |
| typedef BinaryOpT BinaryOp; |
| typedef typename result_of< |
| BinaryOp(const Scalar&,const Scalar&) |
| >::type result_type; |
| |
| enum { Vectorizable = functor_traits<BinaryOp>::PacketAccess }; |
| template<int Size> struct Cost { enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; }; |
| EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {} |
| template<typename Derived> |
| EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const |
| { return mat.redux(m_functor); } |
| const BinaryOp& binaryFunc() const { return m_functor; } |
| const BinaryOp m_functor; |
| }; |
| } |
| |
| /** \class VectorwiseOp |
| * \ingroup Core_Module |
| * |
| * \brief Pseudo expression providing broadcasting and partial reduction operations |
| * |
| * \tparam ExpressionType the type of the object on which to do partial reductions |
| * \tparam Direction indicates whether to operate on columns (#Vertical) or rows (#Horizontal) |
| * |
| * This class represents a pseudo expression with broadcasting and partial reduction features. |
| * It is the return type of DenseBase::colwise() and DenseBase::rowwise() |
| * and most of the time this is the only way it is explicitly used. |
| * |
| * To understand the logic of rowwise/colwise expression, let's consider a generic case `A.colwise().foo()` |
| * where `foo` is any method of `VectorwiseOp`. This expression is equivalent to applying `foo()` to each |
| * column of `A` and then re-assemble the outputs in a matrix expression: |
| * \code [A.col(0).foo(), A.col(1).foo(), ..., A.col(A.cols()-1).foo()] \endcode |
| * |
| * Example: \include MatrixBase_colwise.cpp |
| * Output: \verbinclude MatrixBase_colwise.out |
| * |
| * The begin() and end() methods are obviously exceptions to the previous rule as they |
| * return STL-compatible begin/end iterators to the rows or columns of the nested expression. |
| * Typical use cases include for-range-loop and calls to STL algorithms: |
| * |
| * Example: \include MatrixBase_colwise_iterator_cxx11.cpp |
| * Output: \verbinclude MatrixBase_colwise_iterator_cxx11.out |
| * |
| * For a partial reduction on an empty input, some rules apply. |
| * For the sake of clarity, let's consider a vertical reduction: |
| * - If the number of columns is zero, then a 1x0 row-major vector expression is returned. |
| * - Otherwise, if the number of rows is zero, then |
| * - a row vector of zeros is returned for sum-like reductions (sum, squaredNorm, norm, etc.) |
| * - a row vector of ones is returned for a product reduction (e.g., <code>MatrixXd(n,0).colwise().prod()</code>) |
| * - an assert is triggered for all other reductions (minCoeff,maxCoeff,redux(bin_op)) |
| * |
| * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr |
| */ |
| template<typename ExpressionType, int Direction> class VectorwiseOp |
| { |
| public: |
| |
| typedef typename ExpressionType::Scalar Scalar; |
| typedef typename ExpressionType::RealScalar RealScalar; |
| typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3 |
| typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested; |
| typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned; |
| |
| template<template<typename OutScalar,typename InputScalar> class Functor, |
| typename ReturnScalar=Scalar> struct ReturnType |
| { |
| typedef PartialReduxExpr<ExpressionType, |
| Functor<ReturnScalar,Scalar>, |
| Direction |
| > Type; |
| }; |
| |
| template<typename BinaryOp> struct ReduxReturnType |
| { |
| typedef PartialReduxExpr<ExpressionType, |
| internal::member_redux<BinaryOp,Scalar>, |
| Direction |
| > Type; |
| }; |
| |
| enum { |
| isVertical = (Direction==Vertical) ? 1 : 0, |
| isHorizontal = (Direction==Horizontal) ? 1 : 0 |
| }; |
| |
| protected: |
| |
| template<typename OtherDerived> struct ExtendedType { |
| typedef Replicate<OtherDerived, |
| isVertical ? 1 : ExpressionType::RowsAtCompileTime, |
| isHorizontal ? 1 : ExpressionType::ColsAtCompileTime> Type; |
| }; |
| |
| /** \internal |
| * Replicates a vector to match the size of \c *this */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| typename ExtendedType<OtherDerived>::Type |
| extendedTo(const DenseBase<OtherDerived>& other) const |
| { |
| EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxColsAtCompileTime==1), |
| YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED) |
| EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1), |
| YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED) |
| return typename ExtendedType<OtherDerived>::Type |
| (other.derived(), |
| isVertical ? 1 : m_matrix.rows(), |
| isHorizontal ? 1 : m_matrix.cols()); |
| } |
| |
| template<typename OtherDerived> struct OppositeExtendedType { |
| typedef Replicate<OtherDerived, |
| isHorizontal ? 1 : ExpressionType::RowsAtCompileTime, |
| isVertical ? 1 : ExpressionType::ColsAtCompileTime> Type; |
| }; |
| |
| /** \internal |
| * Replicates a vector in the opposite direction to match the size of \c *this */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| typename OppositeExtendedType<OtherDerived>::Type |
| extendedToOpposite(const DenseBase<OtherDerived>& other) const |
| { |
| EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxColsAtCompileTime==1), |
| YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED) |
| EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxRowsAtCompileTime==1), |
| YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED) |
| return typename OppositeExtendedType<OtherDerived>::Type |
| (other.derived(), |
| isHorizontal ? 1 : m_matrix.rows(), |
| isVertical ? 1 : m_matrix.cols()); |
| } |
| |
| public: |
| EIGEN_DEVICE_FUNC |
| explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {} |
| |
| /** \internal */ |
| EIGEN_DEVICE_FUNC |
| inline const ExpressionType& _expression() const { return m_matrix; } |
| |
| #ifdef EIGEN_PARSED_BY_DOXYGEN |
| /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a> |
| * iterator type over the columns or rows as returned by the begin() and end() methods. |
| */ |
| random_access_iterator_type iterator; |
| /** This is the const version of iterator (aka read-only) */ |
| random_access_iterator_type const_iterator; |
| #else |
| typedef internal::subvector_stl_iterator<ExpressionType, DirectionType(Direction)> iterator; |
| typedef internal::subvector_stl_iterator<const ExpressionType, DirectionType(Direction)> const_iterator; |
| #endif |
| |
| /** returns an iterator to the first row (rowwise) or column (colwise) of the nested expression. |
| * \sa end(), cbegin() |
| */ |
| iterator begin() const { return iterator (m_matrix, 0); } |
| /** const version of begin() */ |
| const_iterator cbegin() const { return const_iterator(m_matrix, 0); } |
| |
| /** returns an iterator to the row (resp. column) following the last row (resp. column) of the nested expression |
| * \sa begin(), cend() |
| */ |
| iterator end() const { return iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); } |
| /** const version of end() */ |
| const_iterator cend() const { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); } |
| |
| /** \returns a row or column vector expression of \c *this reduxed by \a func |
| * |
| * The template parameter \a BinaryOp is the type of the functor |
| * of the custom redux operator. Note that func must be an associative operator. |
| * |
| * \warning the size along the reduction direction must be strictly positive, |
| * otherwise an assertion is triggered. |
| * |
| * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise() |
| */ |
| template<typename BinaryOp> |
| EIGEN_DEVICE_FUNC |
| const typename ReduxReturnType<BinaryOp>::Type |
| redux(const BinaryOp& func = BinaryOp()) const |
| { |
| eigen_assert(redux_length()>0 && "you are using an empty matrix"); |
| return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp,Scalar>(func)); |
| } |
| |
| typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType; |
| typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType; |
| typedef PartialReduxExpr<const CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const ExpressionTypeNestedCleaned>,internal::member_sum<RealScalar,RealScalar>,Direction> SquaredNormReturnType; |
| typedef CwiseUnaryOp<internal::scalar_sqrt_op<RealScalar>, const SquaredNormReturnType> NormReturnType; |
| typedef typename ReturnType<internal::member_blueNorm,RealScalar>::Type BlueNormReturnType; |
| typedef typename ReturnType<internal::member_stableNorm,RealScalar>::Type StableNormReturnType; |
| typedef typename ReturnType<internal::member_hypotNorm,RealScalar>::Type HypotNormReturnType; |
| typedef typename ReturnType<internal::member_sum>::Type SumReturnType; |
| typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SumReturnType,Scalar,quotient) MeanReturnType; |
| typedef typename ReturnType<internal::member_all>::Type AllReturnType; |
| typedef typename ReturnType<internal::member_any>::Type AnyReturnType; |
| typedef PartialReduxExpr<ExpressionType, internal::member_count<Index,Scalar>, Direction> CountReturnType; |
| typedef typename ReturnType<internal::member_prod>::Type ProdReturnType; |
| typedef Reverse<const ExpressionType, Direction> ConstReverseReturnType; |
| typedef Reverse<ExpressionType, Direction> ReverseReturnType; |
| |
| template<int p> struct LpNormReturnType { |
| typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar,Scalar>,Direction> Type; |
| }; |
| |
| /** \returns a row (or column) vector expression of the smallest coefficient |
| * of each column (or row) of the referenced expression. |
| * |
| * \warning the size along the reduction direction must be strictly positive, |
| * otherwise an assertion is triggered. |
| * |
| * \warning the result is undefined if \c *this contains NaN. |
| * |
| * Example: \include PartialRedux_minCoeff.cpp |
| * Output: \verbinclude PartialRedux_minCoeff.out |
| * |
| * \sa DenseBase::minCoeff() */ |
| EIGEN_DEVICE_FUNC |
| const MinCoeffReturnType minCoeff() const |
| { |
| eigen_assert(redux_length()>0 && "you are using an empty matrix"); |
| return MinCoeffReturnType(_expression()); |
| } |
| |
| /** \returns a row (or column) vector expression of the largest coefficient |
| * of each column (or row) of the referenced expression. |
| * |
| * \warning the size along the reduction direction must be strictly positive, |
| * otherwise an assertion is triggered. |
| * |
| * \warning the result is undefined if \c *this contains NaN. |
| * |
| * Example: \include PartialRedux_maxCoeff.cpp |
| * Output: \verbinclude PartialRedux_maxCoeff.out |
| * |
| * \sa DenseBase::maxCoeff() */ |
| EIGEN_DEVICE_FUNC |
| const MaxCoeffReturnType maxCoeff() const |
| { |
| eigen_assert(redux_length()>0 && "you are using an empty matrix"); |
| return MaxCoeffReturnType(_expression()); |
| } |
| |
| /** \returns a row (or column) vector expression of the squared norm |
| * of each column (or row) of the referenced expression. |
| * This is a vector with real entries, even if the original matrix has complex entries. |
| * |
| * Example: \include PartialRedux_squaredNorm.cpp |
| * Output: \verbinclude PartialRedux_squaredNorm.out |
| * |
| * \sa DenseBase::squaredNorm() */ |
| EIGEN_DEVICE_FUNC |
| const SquaredNormReturnType squaredNorm() const |
| { return SquaredNormReturnType(m_matrix.cwiseAbs2()); } |
| |
| /** \returns a row (or column) vector expression of the norm |
| * of each column (or row) of the referenced expression. |
| * This is a vector with real entries, even if the original matrix has complex entries. |
| * |
| * Example: \include PartialRedux_norm.cpp |
| * Output: \verbinclude PartialRedux_norm.out |
| * |
| * \sa DenseBase::norm() */ |
| EIGEN_DEVICE_FUNC |
| const NormReturnType norm() const |
| { return NormReturnType(squaredNorm()); } |
| |
| /** \returns a row (or column) vector expression of the norm |
| * of each column (or row) of the referenced expression. |
| * This is a vector with real entries, even if the original matrix has complex entries. |
| * |
| * Example: \include PartialRedux_norm.cpp |
| * Output: \verbinclude PartialRedux_norm.out |
| * |
| * \sa DenseBase::norm() */ |
| template<int p> |
| EIGEN_DEVICE_FUNC |
| const typename LpNormReturnType<p>::Type lpNorm() const |
| { return typename LpNormReturnType<p>::Type(_expression()); } |
| |
| |
| /** \returns a row (or column) vector expression of the norm |
| * of each column (or row) of the referenced expression, using |
| * Blue's algorithm. |
| * This is a vector with real entries, even if the original matrix has complex entries. |
| * |
| * \sa DenseBase::blueNorm() */ |
| EIGEN_DEVICE_FUNC |
| const BlueNormReturnType blueNorm() const |
| { return BlueNormReturnType(_expression()); } |
| |
| |
| /** \returns a row (or column) vector expression of the norm |
| * of each column (or row) of the referenced expression, avoiding |
| * underflow and overflow. |
| * This is a vector with real entries, even if the original matrix has complex entries. |
| * |
| * \sa DenseBase::stableNorm() */ |
| EIGEN_DEVICE_FUNC |
| const StableNormReturnType stableNorm() const |
| { return StableNormReturnType(_expression()); } |
| |
| |
| /** \returns a row (or column) vector expression of the norm |
| * of each column (or row) of the referenced expression, avoiding |
| * underflow and overflow using a concatenation of hypot() calls. |
| * This is a vector with real entries, even if the original matrix has complex entries. |
| * |
| * \sa DenseBase::hypotNorm() */ |
| EIGEN_DEVICE_FUNC |
| const HypotNormReturnType hypotNorm() const |
| { return HypotNormReturnType(_expression()); } |
| |
| /** \returns a row (or column) vector expression of the sum |
| * of each column (or row) of the referenced expression. |
| * |
| * Example: \include PartialRedux_sum.cpp |
| * Output: \verbinclude PartialRedux_sum.out |
| * |
| * \sa DenseBase::sum() */ |
| EIGEN_DEVICE_FUNC |
| const SumReturnType sum() const |
| { return SumReturnType(_expression()); } |
| |
| /** \returns a row (or column) vector expression of the mean |
| * of each column (or row) of the referenced expression. |
| * |
| * \sa DenseBase::mean() */ |
| EIGEN_DEVICE_FUNC |
| const MeanReturnType mean() const |
| { return sum() / Scalar(Direction==Vertical?m_matrix.rows():m_matrix.cols()); } |
| |
| /** \returns a row (or column) vector expression representing |
| * whether \b all coefficients of each respective column (or row) are \c true. |
| * This expression can be assigned to a vector with entries of type \c bool. |
| * |
| * \sa DenseBase::all() */ |
| EIGEN_DEVICE_FUNC |
| const AllReturnType all() const |
| { return AllReturnType(_expression()); } |
| |
| /** \returns a row (or column) vector expression representing |
| * whether \b at \b least one coefficient of each respective column (or row) is \c true. |
| * This expression can be assigned to a vector with entries of type \c bool. |
| * |
| * \sa DenseBase::any() */ |
| EIGEN_DEVICE_FUNC |
| const AnyReturnType any() const |
| { return AnyReturnType(_expression()); } |
| |
| /** \returns a row (or column) vector expression representing |
| * the number of \c true coefficients of each respective column (or row). |
| * This expression can be assigned to a vector whose entries have the same type as is used to |
| * index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t . |
| * |
| * Example: \include PartialRedux_count.cpp |
| * Output: \verbinclude PartialRedux_count.out |
| * |
| * \sa DenseBase::count() */ |
| EIGEN_DEVICE_FUNC |
| const CountReturnType count() const |
| { return CountReturnType(_expression()); } |
| |
| /** \returns a row (or column) vector expression of the product |
| * of each column (or row) of the referenced expression. |
| * |
| * Example: \include PartialRedux_prod.cpp |
| * Output: \verbinclude PartialRedux_prod.out |
| * |
| * \sa DenseBase::prod() */ |
| EIGEN_DEVICE_FUNC |
| const ProdReturnType prod() const |
| { return ProdReturnType(_expression()); } |
| |
| |
| /** \returns a matrix expression |
| * where each column (or row) are reversed. |
| * |
| * Example: \include Vectorwise_reverse.cpp |
| * Output: \verbinclude Vectorwise_reverse.out |
| * |
| * \sa DenseBase::reverse() */ |
| EIGEN_DEVICE_FUNC |
| const ConstReverseReturnType reverse() const |
| { return ConstReverseReturnType( _expression() ); } |
| |
| /** \returns a writable matrix expression |
| * where each column (or row) are reversed. |
| * |
| * \sa reverse() const */ |
| EIGEN_DEVICE_FUNC |
| ReverseReturnType reverse() |
| { return ReverseReturnType( _expression() ); } |
| |
| typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType; |
| EIGEN_DEVICE_FUNC |
| const ReplicateReturnType replicate(Index factor) const; |
| |
| /** |
| * \return an expression of the replication of each column (or row) of \c *this |
| * |
| * Example: \include DirectionWise_replicate.cpp |
| * Output: \verbinclude DirectionWise_replicate.out |
| * |
| * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate |
| */ |
| // NOTE implemented here because of sunstudio's compilation errors |
| // isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator |
| template<int Factor> const Replicate<ExpressionType,isVertical*Factor+isHorizontal,isHorizontal*Factor+isVertical> |
| EIGEN_DEVICE_FUNC |
| replicate(Index factor = Factor) const |
| { |
| return Replicate<ExpressionType,(isVertical?Factor:1),(isHorizontal?Factor:1)> |
| (_expression(),isVertical?factor:1,isHorizontal?factor:1); |
| } |
| |
| /////////// Artithmetic operators /////////// |
| |
| /** Copies the vector \a other to each subvector of \c *this */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| ExpressionType& operator=(const DenseBase<OtherDerived>& other) |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| //eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME |
| return m_matrix = extendedTo(other.derived()); |
| } |
| |
| /** Adds the vector \a other to each subvector of \c *this */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| ExpressionType& operator+=(const DenseBase<OtherDerived>& other) |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| return m_matrix += extendedTo(other.derived()); |
| } |
| |
| /** Substracts the vector \a other to each subvector of \c *this */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| ExpressionType& operator-=(const DenseBase<OtherDerived>& other) |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| return m_matrix -= extendedTo(other.derived()); |
| } |
| |
| /** Multiples each subvector of \c *this by the vector \a other */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| ExpressionType& operator*=(const DenseBase<OtherDerived>& other) |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| m_matrix *= extendedTo(other.derived()); |
| return m_matrix; |
| } |
| |
| /** Divides each subvector of \c *this by the vector \a other */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| ExpressionType& operator/=(const DenseBase<OtherDerived>& other) |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| m_matrix /= extendedTo(other.derived()); |
| return m_matrix; |
| } |
| |
| /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */ |
| template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC |
| CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>, |
| const ExpressionTypeNestedCleaned, |
| const typename ExtendedType<OtherDerived>::Type> |
| operator+(const DenseBase<OtherDerived>& other) const |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| return m_matrix + extendedTo(other.derived()); |
| } |
| |
| /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>, |
| const ExpressionTypeNestedCleaned, |
| const typename ExtendedType<OtherDerived>::Type> |
| operator-(const DenseBase<OtherDerived>& other) const |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| return m_matrix - extendedTo(other.derived()); |
| } |
| |
| /** Returns the expression where each subvector is the product of the vector \a other |
| * by the corresponding subvector of \c *this */ |
| template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC |
| CwiseBinaryOp<internal::scalar_product_op<Scalar>, |
| const ExpressionTypeNestedCleaned, |
| const typename ExtendedType<OtherDerived>::Type> |
| EIGEN_DEVICE_FUNC |
| operator*(const DenseBase<OtherDerived>& other) const |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| return m_matrix * extendedTo(other.derived()); |
| } |
| |
| /** Returns the expression where each subvector is the quotient of the corresponding |
| * subvector of \c *this by the vector \a other */ |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, |
| const ExpressionTypeNestedCleaned, |
| const typename ExtendedType<OtherDerived>::Type> |
| operator/(const DenseBase<OtherDerived>& other) const |
| { |
| EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) |
| EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) |
| EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) |
| return m_matrix / extendedTo(other.derived()); |
| } |
| |
| /** \returns an expression where each column (or row) of the referenced matrix are normalized. |
| * The referenced matrix is \b not modified. |
| * \sa MatrixBase::normalized(), normalize() |
| */ |
| EIGEN_DEVICE_FUNC |
| CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, |
| const ExpressionTypeNestedCleaned, |
| const typename OppositeExtendedType<NormReturnType>::Type> |
| normalized() const { return m_matrix.cwiseQuotient(extendedToOpposite(this->norm())); } |
| |
| |
| /** Normalize in-place each row or columns of the referenced matrix. |
| * \sa MatrixBase::normalize(), normalized() |
| */ |
| EIGEN_DEVICE_FUNC void normalize() { |
| m_matrix = this->normalized(); |
| } |
| |
| EIGEN_DEVICE_FUNC inline void reverseInPlace(); |
| |
| /////////// Geometry module /////////// |
| |
| typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType; |
| EIGEN_DEVICE_FUNC |
| HomogeneousReturnType homogeneous() const; |
| |
| typedef typename ExpressionType::PlainObject CrossReturnType; |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC |
| const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const; |
| |
| enum { |
| HNormalized_Size = Direction==Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime |
| : internal::traits<ExpressionType>::ColsAtCompileTime, |
| HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1 |
| }; |
| typedef Block<const ExpressionType, |
| Direction==Vertical ? int(HNormalized_SizeMinusOne) |
| : int(internal::traits<ExpressionType>::RowsAtCompileTime), |
| Direction==Horizontal ? int(HNormalized_SizeMinusOne) |
| : int(internal::traits<ExpressionType>::ColsAtCompileTime)> |
| HNormalized_Block; |
| typedef Block<const ExpressionType, |
| Direction==Vertical ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime), |
| Direction==Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)> |
| HNormalized_Factors; |
| typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>, |
| const HNormalized_Block, |
| const Replicate<HNormalized_Factors, |
| Direction==Vertical ? HNormalized_SizeMinusOne : 1, |
| Direction==Horizontal ? HNormalized_SizeMinusOne : 1> > |
| HNormalizedReturnType; |
| |
| EIGEN_DEVICE_FUNC |
| const HNormalizedReturnType hnormalized() const; |
| |
| protected: |
| Index redux_length() const |
| { |
| return Direction==Vertical ? m_matrix.rows() : m_matrix.cols(); |
| } |
| ExpressionTypeNested m_matrix; |
| }; |
| |
| //const colwise moved to DenseBase.h due to CUDA compiler bug |
| |
| |
| /** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations |
| * |
| * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting |
| */ |
| template<typename Derived> |
| EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ColwiseReturnType |
| DenseBase<Derived>::colwise() |
| { |
| return ColwiseReturnType(derived()); |
| } |
| |
| //const rowwise moved to DenseBase.h due to CUDA compiler bug |
| |
| |
| /** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations |
| * |
| * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting |
| */ |
| template<typename Derived> |
| EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::RowwiseReturnType |
| DenseBase<Derived>::rowwise() |
| { |
| return RowwiseReturnType(derived()); |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_PARTIAL_REDUX_H |
