1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifndef EIGEN_PARTIAL_REDUX_H 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_PARTIAL_REDUX_H 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace Eigen { 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \class PartialReduxExpr 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \ingroup Core_Module 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \brief Generic expression of a partially reduxed matrix 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \tparam MatrixType the type of the matrix we are applying the redux operation 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \tparam MemberOp type of the member functor 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal) 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * This class represents an expression of a partial redux operator of a matrix. 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * It is the return type of some VectorwiseOp functions, 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * and most of the time this is the only way it is used. 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa class VectorwiseOp 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate< typename MatrixType, typename MemberOp, int Direction> 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathclass PartialReduxExpr; 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal { 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename MemberOp, int Direction> 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> > 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : traits<MatrixType> 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MemberOp::result_type Scalar; 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename traits<MatrixType>::StorageKind StorageKind; 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename traits<MatrixType>::XprKind XprKind; 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar InputScalar; 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename nested<MatrixType>::type MatrixTypeNested; 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename remove_all<MatrixTypeNested>::type _MatrixTypeNested; 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::RowsAtCompileTime, 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime, 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MaxRowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::MaxRowsAtCompileTime, 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime, 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Flags0 = (unsigned int)_MatrixTypeNested::Flags & HereditaryBits, 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Flags = (Flags0 & ~RowMajorBit) | (RowsAtCompileTime == 1 ? RowMajorBit : 0), 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath TraversalSize = Direction==Vertical ? RowsAtCompileTime : ColsAtCompileTime 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #if EIGEN_GNUC_AT_LEAST(3,4) 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MemberOp::template Cost<InputScalar,int(TraversalSize)> CostOpType; 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #else 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MemberOp::template Cost<InputScalar,TraversalSize> CostOpType; 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CoeffReadCost = TraversalSize * traits<_MatrixTypeNested>::CoeffReadCost + int(CostOpType::value) 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate< typename MatrixType, typename MemberOp, int Direction> 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathclass PartialReduxExpr : internal::no_assignment_operator, 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base; 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr) 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::traits<PartialReduxExpr>::MatrixTypeNested MatrixTypeNested; 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::traits<PartialReduxExpr>::_MatrixTypeNested _MatrixTypeNested; 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp()) 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : m_matrix(mat), m_functor(func) {} 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); } 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols() const { return (Direction==Horizontal ? 1 : m_matrix.cols()); } 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STRONG_INLINE const Scalar coeff(Index i, Index j) const 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (Direction==Vertical) 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_functor(m_matrix.col(j)); 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_functor(m_matrix.row(i)); 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Scalar coeff(Index index) const 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (Direction==Vertical) 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_functor(m_matrix.col(index)); 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_functor(m_matrix.row(index)); 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath protected: 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixTypeNested m_matrix; 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const MemberOp m_functor; 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \ 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template <typename ResultType> \ 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath struct member_##MEMBER { \ 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER) \ 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef ResultType result_type; \ 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename Scalar, int Size> struct Cost \ 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { enum { value = COST }; }; \ 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename XprType> \ 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STRONG_INLINE ResultType operator()(const XprType& mat) const \ 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return mat.MEMBER(); } \ 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal { 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(squaredNorm, Size * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits<scalar_hypot_op<Scalar> >::Cost ); 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost); 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(mean, (Size-1)*NumTraits<Scalar>::AddCost + NumTraits<Scalar>::MulCost); 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost); 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost); 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost); 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost); 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost); 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost); 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate <typename BinaryOp, typename Scalar> 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct member_redux { 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename result_of< 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath BinaryOp(Scalar) 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath >::type result_type; 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename _Scalar, int Size> struct Cost 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; }; 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath member_redux(const BinaryOp func) : m_functor(func) {} 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename Derived> 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath inline result_type operator()(const DenseBase<Derived>& mat) const 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return mat.redux(m_functor); } 144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const BinaryOp m_functor; 145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \class VectorwiseOp 149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \ingroup Core_Module 150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \brief Pseudo expression providing partial reduction operations 152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \param ExpressionType the type of the object on which to do partial reductions 154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \param Direction indicates the direction of the redux (#Vertical or #Horizontal) 155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * This class represents a pseudo expression with partial reduction features. 157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * It is the return type of DenseBase::colwise() and DenseBase::rowwise() 158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * and most of the time this is the only way it is used. 159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include MatrixBase_colwise.cpp 161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude MatrixBase_colwise.out 162c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 163c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr 164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename ExpressionType, int Direction> class VectorwiseOp 166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ExpressionType::Scalar Scalar; 170c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ExpressionType::RealScalar RealScalar; 171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ExpressionType::Index Index; 172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::conditional<internal::must_nest_by_value<ExpressionType>::ret, 173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType, ExpressionType&>::type ExpressionTypeNested; 174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned; 175c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 176c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<template<typename _Scalar> class Functor, 177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename Scalar=typename internal::traits<ExpressionType>::Scalar> struct ReturnType 178c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 179c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef PartialReduxExpr<ExpressionType, 180c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Functor<Scalar>, 181c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction 182c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath > Type; 183c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 184c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 185c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename BinaryOp> struct ReduxReturnType 186c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 187c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef PartialReduxExpr<ExpressionType, 188c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath internal::member_redux<BinaryOp,typename internal::traits<ExpressionType>::Scalar>, 189c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction 190c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath > Type; 191c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 192c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 193c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 194c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath IsVertical = (Direction==Vertical) ? 1 : 0, 195c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath IsHorizontal = (Direction==Horizontal) ? 1 : 0 196c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 197c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 198c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath protected: 199c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \internal 201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \returns the i-th subvector according to the \c Direction */ 202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::conditional<Direction==Vertical, 203c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename ExpressionType::ColXpr, 204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename ExpressionType::RowXpr>::type SubVector; 205c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SubVector subVector(Index i) 206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return SubVector(m_matrix.derived(),i); 208c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 209c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 210c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \internal 211c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \returns the number of subvectors in the direction \c Direction */ 212c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index subVectors() const 213c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return Direction==Vertical?m_matrix.cols():m_matrix.rows(); } 214c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 215c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> struct ExtendedType { 216c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Replicate<OtherDerived, 217c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Vertical ? 1 : ExpressionType::RowsAtCompileTime, 218c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Horizontal ? 1 : ExpressionType::ColsAtCompileTime> Type; 219c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 220c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 221c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \internal 222c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Replicates a vector to match the size of \c *this */ 223c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 224c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename ExtendedType<OtherDerived>::Type 225c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath extendedTo(const DenseBase<OtherDerived>& other) const 226c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 227c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Vertical, OtherDerived::MaxColsAtCompileTime==1), 228c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED) 229c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(Direction==Horizontal, OtherDerived::MaxRowsAtCompileTime==1), 230c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED) 231c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return typename ExtendedType<OtherDerived>::Type 232c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (other.derived(), 233c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Vertical ? 1 : m_matrix.rows(), 234c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Horizontal ? 1 : m_matrix.cols()); 235c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 236c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 237c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 238c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 239c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {} 240c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 241c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \internal */ 242c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath inline const ExpressionType& _expression() const { return m_matrix; } 243c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 244c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row or column vector expression of \c *this reduxed by \a func 245c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 246c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * The template parameter \a BinaryOp is the type of the functor 247c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of the custom redux operator. Note that func must be an associative operator. 248c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 249c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise() 250c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 251c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename BinaryOp> 252c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReduxReturnType<BinaryOp>::Type 253c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath redux(const BinaryOp& func = BinaryOp()) const 254c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return typename ReduxReturnType<BinaryOp>::Type(_expression(), func); } 255c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 256c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the smallest coefficient 257c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 258c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 259c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_minCoeff.cpp 260c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_minCoeff.out 261c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 262c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::minCoeff() */ 263c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_minCoeff>::Type minCoeff() const 264c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 265c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 266c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the largest coefficient 267c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 268c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 269c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_maxCoeff.cpp 270c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_maxCoeff.out 271c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 272c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::maxCoeff() */ 273c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_maxCoeff>::Type maxCoeff() const 274c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 275c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 276c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the squared norm 277c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 278c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 279c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_squaredNorm.cpp 280c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_squaredNorm.out 281c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 282c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::squaredNorm() */ 283c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_squaredNorm,RealScalar>::Type squaredNorm() const 284c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 285c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 286c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the norm 287c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 288c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 289c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_norm.cpp 290c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_norm.out 291c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 292c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::norm() */ 293c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_norm,RealScalar>::Type norm() const 294c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 295c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 296c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 297c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the norm 298c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression, using 299c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * blue's algorithm. 300c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 301c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::blueNorm() */ 302c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_blueNorm,RealScalar>::Type blueNorm() const 303c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 304c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 305c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 306c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the norm 307c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression, avoiding 308c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * underflow and overflow. 309c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 310c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::stableNorm() */ 311c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_stableNorm,RealScalar>::Type stableNorm() const 312c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 313c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 314c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 315c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the norm 316c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression, avoiding 317c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * underflow and overflow using a concatenation of hypot() calls. 318c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 319c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::hypotNorm() */ 320c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_hypotNorm,RealScalar>::Type hypotNorm() const 321c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 322c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 323c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the sum 324c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 325c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 326c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_sum.cpp 327c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_sum.out 328c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 329c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::sum() */ 330c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_sum>::Type sum() const 331c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 332c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 333c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the mean 334c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 335c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 336c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::mean() */ 337c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_mean>::Type mean() const 338c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 339c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 340c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression representing 341c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * whether \b all coefficients of each respective column (or row) are \c true. 342c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 343c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::all() */ 344c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_all>::Type all() const 345c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 346c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 347c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression representing 348c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * whether \b at \b least one coefficient of each respective column (or row) is \c true. 349c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 350c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::any() */ 351c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_any>::Type any() const 352c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 353c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 354c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression representing 355c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * the number of \c true coefficients of each respective column (or row). 356c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 357c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_count.cpp 358c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_count.out 359c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 360c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::count() */ 361c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> count() const 362c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 363c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 364c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the product 365c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 366c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 367c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_prod.cpp 368c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_prod.out 369c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 370c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::prod() */ 371c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReturnType<internal::member_prod>::Type prod() const 372c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return _expression(); } 373c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 374c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 375c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a matrix expression 376c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * where each column (or row) are reversed. 377c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 378c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include Vectorwise_reverse.cpp 379c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude Vectorwise_reverse.out 380c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 381c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::reverse() */ 382c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Reverse<ExpressionType, Direction> reverse() const 383c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return Reverse<ExpressionType, Direction>( _expression() ); } 384c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 385c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Replicate<ExpressionType,Direction==Vertical?Dynamic:1,Direction==Horizontal?Dynamic:1> ReplicateReturnType; 386c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ReplicateReturnType replicate(Index factor) const; 387c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 388c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** 389c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \return an expression of the replication of each column (or row) of \c *this 390c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 391c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include DirectionWise_replicate.cpp 392c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude DirectionWise_replicate.out 393c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 394c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate 395c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 396c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // NOTE implemented here because of sunstudio's compilation errors 397c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<int Factor> const Replicate<ExpressionType,(IsVertical?Factor:1),(IsHorizontal?Factor:1)> 398c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath replicate(Index factor = Factor) const 399c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 400c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return Replicate<ExpressionType,Direction==Vertical?Factor:1,Direction==Horizontal?Factor:1> 401c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1); 402c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 403c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 404c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/////////// Artithmetic operators /////////// 405c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 406c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Copies the vector \a other to each subvector of \c *this */ 407c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 408c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator=(const DenseBase<OtherDerived>& other) 409c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 410c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 411c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 412c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME 413c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix = extendedTo(other.derived())); 414c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 415c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 416c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Adds the vector \a other to each subvector of \c *this */ 417c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 418c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator+=(const DenseBase<OtherDerived>& other) 419c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 420c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 421c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 422c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix += extendedTo(other.derived())); 423c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 424c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 425c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Substracts the vector \a other to each subvector of \c *this */ 426c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 427c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator-=(const DenseBase<OtherDerived>& other) 428c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 429c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 430c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 431c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix -= extendedTo(other.derived())); 432c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 433c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 434c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Multiples each subvector of \c *this by the vector \a other */ 435c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 436c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator*=(const DenseBase<OtherDerived>& other) 437c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 438c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 439c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) 440c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 441c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m_matrix *= extendedTo(other.derived()); 442c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix); 443c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 444c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 445c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Divides each subvector of \c *this by the vector \a other */ 446c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 447c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator/=(const DenseBase<OtherDerived>& other) 448c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 449c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 450c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) 451c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 452c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m_matrix /= extendedTo(other.derived()); 453c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix); 454c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 455c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 456c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */ 457c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> EIGEN_STRONG_INLINE 458c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CwiseBinaryOp<internal::scalar_sum_op<Scalar>, 459c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ExpressionTypeNestedCleaned, 460c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ExtendedType<OtherDerived>::Type> 461c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator+(const DenseBase<OtherDerived>& other) const 462c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 463c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 464c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 465c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_matrix + extendedTo(other.derived()); 466c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 467c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 468c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */ 469c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 470c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CwiseBinaryOp<internal::scalar_difference_op<Scalar>, 471c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ExpressionTypeNestedCleaned, 472c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ExtendedType<OtherDerived>::Type> 473c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator-(const DenseBase<OtherDerived>& other) const 474c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 475c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 476c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 477c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_matrix - extendedTo(other.derived()); 478c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 479c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 480c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Returns the expression where each subvector is the product of the vector \a other 481c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * by the corresponding subvector of \c *this */ 482c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> EIGEN_STRONG_INLINE 483c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CwiseBinaryOp<internal::scalar_product_op<Scalar>, 484c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ExpressionTypeNestedCleaned, 485c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ExtendedType<OtherDerived>::Type> 486c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator*(const DenseBase<OtherDerived>& other) const 487c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 488c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 489c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) 490c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 491c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_matrix * extendedTo(other.derived()); 492c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 493c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 494c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Returns the expression where each subvector is the quotient of the corresponding 495c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * subvector of \c *this by the vector \a other */ 496c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 497c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, 498c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ExpressionTypeNestedCleaned, 499c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ExtendedType<OtherDerived>::Type> 500c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator/(const DenseBase<OtherDerived>& other) const 501c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 502c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 503c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) 504c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 505c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_matrix / extendedTo(other.derived()); 506c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 507c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 508c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/////////// Geometry module /////////// 509c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 510c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #if EIGEN2_SUPPORT_STAGE > STAGE20_RESOLVE_API_CONFLICTS 511c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Homogeneous<ExpressionType,Direction> homogeneous() const; 512c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 513c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 514c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ExpressionType::PlainObject CrossReturnType; 515c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 516c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const; 517c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 518c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 519c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalized_Size = Direction==Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime 520c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : internal::traits<ExpressionType>::ColsAtCompileTime, 521c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1 522c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 523c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Block<const ExpressionType, 524c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Vertical ? int(HNormalized_SizeMinusOne) 525c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : int(internal::traits<ExpressionType>::RowsAtCompileTime), 526c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Horizontal ? int(HNormalized_SizeMinusOne) 527c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : int(internal::traits<ExpressionType>::ColsAtCompileTime)> 528c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalized_Block; 529c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Block<const ExpressionType, 530c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Vertical ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime), 531c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)> 532c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalized_Factors; 533c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>, 534c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const HNormalized_Block, 535c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Replicate<HNormalized_Factors, 536c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Vertical ? HNormalized_SizeMinusOne : 1, 537c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Horizontal ? HNormalized_SizeMinusOne : 1> > 538c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalizedReturnType; 539c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 540c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const HNormalizedReturnType hnormalized() const; 541c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 542c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath protected: 543c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionTypeNested m_matrix; 544c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 545c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 546c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations 547c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 548c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include MatrixBase_colwise.cpp 549c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude MatrixBase_colwise.out 550c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 551c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting 552c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 553c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived> 554c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline const typename DenseBase<Derived>::ConstColwiseReturnType 555c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathDenseBase<Derived>::colwise() const 556c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 557c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return derived(); 558c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 559c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 560c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations 561c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 562c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting 563c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 564c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived> 565c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline typename DenseBase<Derived>::ColwiseReturnType 566c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathDenseBase<Derived>::colwise() 567c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 568c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return derived(); 569c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 570c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 571c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns a VectorwiseOp wrapper of *this providing additional partial reduction operations 572c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 573c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include MatrixBase_rowwise.cpp 574c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude MatrixBase_rowwise.out 575c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 576c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting 577c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 578c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived> 579c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline const typename DenseBase<Derived>::ConstRowwiseReturnType 580c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathDenseBase<Derived>::rowwise() const 581c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 582c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return derived(); 583c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 584c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 585c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations 586c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 587c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting 588c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 589c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived> 590c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline typename DenseBase<Derived>::RowwiseReturnType 591c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathDenseBase<Derived>::rowwise() 592c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 593c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return derived(); 594c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 595c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 596c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace Eigen 597c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 598c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif // EIGEN_PARTIAL_REDUX_H 599