VectorwiseOp.h revision 2b8756b6f1de65d3f8bffab45be6c44ceb7411fc
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 142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangnamespace 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 enum { 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::RowsAtCompileTime, 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime, 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MaxRowsAtCompileTime = Direction==Vertical ? 1 : MatrixType::MaxRowsAtCompileTime, 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime, 492b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0, 507faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime : MatrixType::ColsAtCompileTime 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate< typename MatrixType, typename MemberOp, int Direction> 562b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangclass PartialReduxExpr : public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type, 572b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang internal::no_assignment_operator 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base; 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr) 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 642b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 652b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp()) 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : m_matrix(mat), m_functor(func) {} 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 682b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows() const { return (Direction==Vertical ? 1 : m_matrix.rows()); } 702b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols() const { return (Direction==Horizontal ? 1 : m_matrix.cols()); } 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 732b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 742b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typename MatrixType::Nested nestedExpression() const { return m_matrix; } 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 762b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 772b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const MemberOp& functor() const { return m_functor; } 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath protected: 802b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typename MatrixType::Nested m_matrix; 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const MemberOp m_functor; 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \ 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template <typename ResultType> \ 867faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez struct member_##MEMBER { \ 877faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez EIGEN_EMPTY_STRUCT_CTOR(member_##MEMBER) \ 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef ResultType result_type; \ 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename Scalar, int Size> struct Cost \ 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { enum { value = COST }; }; \ 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename XprType> \ 922b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE \ 932b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang ResultType operator()(const XprType& mat) const \ 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return mat.MEMBER(); } \ 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal { 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(squaredNorm, Size * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost); 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits<scalar_hypot_op<Scalar> >::Cost ); 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost); 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(mean, (Size-1)*NumTraits<Scalar>::AddCost + NumTraits<Scalar>::MulCost); 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost); 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost); 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost); 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost); 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost); 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathEIGEN_MEMBER_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost); 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 1132b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangtemplate <int p, typename ResultType> 1142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wangstruct member_lpnorm { 1152b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef ResultType result_type; 1162b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang template<typename Scalar, int Size> struct Cost 1172b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; }; 1182b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC member_lpnorm() {} 1192b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang template<typename XprType> 1202b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const 1212b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return mat.template lpNorm<p>(); } 1222b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang}; 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate <typename BinaryOp, typename Scalar> 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct member_redux { 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename result_of< 1272b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang BinaryOp(const Scalar&,const Scalar&) 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath >::type result_type; 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename _Scalar, int Size> struct Cost 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; }; 1312b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {} 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename Derived> 1332b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { return mat.redux(m_functor); } 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const BinaryOp m_functor; 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \class VectorwiseOp 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \ingroup Core_Module 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \brief Pseudo expression providing partial reduction operations 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 1442b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * \tparam ExpressionType the type of the object on which to do partial reductions 1452b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal) 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * This class represents a pseudo expression with partial reduction features. 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * It is the return type of DenseBase::colwise() and DenseBase::rowwise() 149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * and most of the time this is the only way it is used. 150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include MatrixBase_colwise.cpp 152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude MatrixBase_colwise.out 153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr 155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename ExpressionType, int Direction> class VectorwiseOp 157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ExpressionType::Scalar Scalar; 161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ExpressionType::RealScalar RealScalar; 1622b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3 1632b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested; 164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::remove_all<ExpressionTypeNested>::type ExpressionTypeNestedCleaned; 165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<template<typename _Scalar> class Functor, 1672b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typename Scalar_=Scalar> struct ReturnType 168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef PartialReduxExpr<ExpressionType, 1702b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang Functor<Scalar_>, 171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction 172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath > Type; 173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 175c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename BinaryOp> struct ReduxReturnType 176c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef PartialReduxExpr<ExpressionType, 1782b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang internal::member_redux<BinaryOp,Scalar>, 179c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction 180c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath > Type; 181c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 182c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 183c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 1842b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isVertical = (Direction==Vertical) ? 1 : 0, 1852b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isHorizontal = (Direction==Horizontal) ? 1 : 0 186c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 187c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 188c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath protected: 189c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 1902b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename internal::conditional<isVertical, 191c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename ExpressionType::ColXpr, 192c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename ExpressionType::RowXpr>::type SubVector; 1932b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang /** \internal 1942b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * \returns the i-th subvector according to the \c Direction */ 1952b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 196c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SubVector subVector(Index i) 197c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 198c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return SubVector(m_matrix.derived(),i); 199c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \internal 202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \returns the number of subvectors in the direction \c Direction */ 2032b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index subVectors() const 2052b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return isVertical?m_matrix.cols():m_matrix.rows(); } 206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> struct ExtendedType { 208c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Replicate<OtherDerived, 2092b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isVertical ? 1 : ExpressionType::RowsAtCompileTime, 2102b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isHorizontal ? 1 : ExpressionType::ColsAtCompileTime> Type; 211c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 212c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 213c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \internal 214c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Replicates a vector to match the size of \c *this */ 215c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 2162b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 217c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename ExtendedType<OtherDerived>::Type 218c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath extendedTo(const DenseBase<OtherDerived>& other) const 219c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 2202b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxColsAtCompileTime==1), 221c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED) 2222b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1), 223c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED) 224c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return typename ExtendedType<OtherDerived>::Type 225c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (other.derived(), 2262b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isVertical ? 1 : m_matrix.rows(), 2272b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isHorizontal ? 1 : m_matrix.cols()); 228c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 2292b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 2307faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename OtherDerived> struct OppositeExtendedType { 2317faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez typedef Replicate<OtherDerived, 2322b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isHorizontal ? 1 : ExpressionType::RowsAtCompileTime, 2332b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isVertical ? 1 : ExpressionType::ColsAtCompileTime> Type; 2347faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez }; 2357faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 2367faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez /** \internal 2377faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez * Replicates a vector in the opposite direction to match the size of \c *this */ 2387faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename OtherDerived> 2392b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 2407faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez typename OppositeExtendedType<OtherDerived>::Type 2417faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez extendedToOpposite(const DenseBase<OtherDerived>& other) const 2427faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez { 2432b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isHorizontal, OtherDerived::MaxColsAtCompileTime==1), 2447faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED) 2452b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_STATIC_ASSERT(EIGEN_IMPLIES(isVertical, OtherDerived::MaxRowsAtCompileTime==1), 2467faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED) 2477faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez return typename OppositeExtendedType<OtherDerived>::Type 2487faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez (other.derived(), 2492b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isHorizontal ? 1 : m_matrix.rows(), 2502b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang isVertical ? 1 : m_matrix.cols()); 2517faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 252c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 253c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 2542b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 2552b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {} 256c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 257c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \internal */ 2582b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 259c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath inline const ExpressionType& _expression() const { return m_matrix; } 260c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 261c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row or column vector expression of \c *this reduxed by \a func 262c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 263c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * The template parameter \a BinaryOp is the type of the functor 264c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of the custom redux operator. Note that func must be an associative operator. 265c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 266c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise() 267c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 268c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename BinaryOp> 2692b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 270c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ReduxReturnType<BinaryOp>::Type 271c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath redux(const BinaryOp& func = BinaryOp()) const 2722b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp,Scalar>(func)); } 2732b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 2742b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType; 2752b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType; 2762b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_squaredNorm,RealScalar>::Type SquaredNormReturnType; 2772b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_norm,RealScalar>::Type NormReturnType; 2782b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_blueNorm,RealScalar>::Type BlueNormReturnType; 2792b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_stableNorm,RealScalar>::Type StableNormReturnType; 2802b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_hypotNorm,RealScalar>::Type HypotNormReturnType; 2812b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_sum>::Type SumReturnType; 2822b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_mean>::Type MeanReturnType; 2832b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_all>::Type AllReturnType; 2842b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_any>::Type AnyReturnType; 2852b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef PartialReduxExpr<ExpressionType, internal::member_count<Index>, Direction> CountReturnType; 2862b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef typename ReturnType<internal::member_prod>::Type ProdReturnType; 2872b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Reverse<const ExpressionType, Direction> ConstReverseReturnType; 2882b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Reverse<ExpressionType, Direction> ReverseReturnType; 2892b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 2902b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang template<int p> struct LpNormReturnType { 2912b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar>,Direction> Type; 2922b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang }; 293c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 294c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the smallest coefficient 295c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 2962b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * 2977faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez * \warning the result is undefined if \c *this contains NaN. 298c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 299c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_minCoeff.cpp 300c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_minCoeff.out 301c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 302c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::minCoeff() */ 3032b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3042b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const MinCoeffReturnType minCoeff() const 3052b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return MinCoeffReturnType(_expression()); } 306c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 307c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the largest coefficient 308c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 3092b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * 3107faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez * \warning the result is undefined if \c *this contains NaN. 311c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 312c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_maxCoeff.cpp 313c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_maxCoeff.out 314c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 315c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::maxCoeff() */ 3162b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3172b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const MaxCoeffReturnType maxCoeff() const 3182b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return MaxCoeffReturnType(_expression()); } 319c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 320c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the squared norm 321c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 3222b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This is a vector with real entries, even if the original matrix has complex entries. 323c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 324c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_squaredNorm.cpp 325c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_squaredNorm.out 326c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 327c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::squaredNorm() */ 3282b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3292b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const SquaredNormReturnType squaredNorm() const 3302b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return SquaredNormReturnType(_expression()); } 3312b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 3322b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang /** \returns a row (or column) vector expression of the norm 3332b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * of each column (or row) of the referenced expression. 3342b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This is a vector with real entries, even if the original matrix has complex entries. 3352b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * 3362b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * Example: \include PartialRedux_norm.cpp 3372b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * Output: \verbinclude PartialRedux_norm.out 3382b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * 3392b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * \sa DenseBase::norm() */ 3402b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3412b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const NormReturnType norm() const 3422b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return NormReturnType(_expression()); } 343c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 344c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the norm 345c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 3462b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This is a vector with real entries, even if the original matrix has complex entries. 347c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 348c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_norm.cpp 349c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_norm.out 350c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 351c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::norm() */ 3522b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang template<int p> 3532b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3542b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const typename LpNormReturnType<p>::Type lpNorm() const 3552b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return typename LpNormReturnType<p>::Type(_expression()); } 356c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 357c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 358c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the norm 359c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression, using 3602b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * Blue's algorithm. 3612b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This is a vector with real entries, even if the original matrix has complex entries. 362c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 363c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::blueNorm() */ 3642b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3652b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const BlueNormReturnType blueNorm() const 3662b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return BlueNormReturnType(_expression()); } 367c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 368c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 369c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the norm 370c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression, avoiding 371c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * underflow and overflow. 3722b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This is a vector with real entries, even if the original matrix has complex entries. 373c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 374c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::stableNorm() */ 3752b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3762b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const StableNormReturnType stableNorm() const 3772b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return StableNormReturnType(_expression()); } 378c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 379c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 380c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the norm 381c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression, avoiding 382c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * underflow and overflow using a concatenation of hypot() calls. 3832b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This is a vector with real entries, even if the original matrix has complex entries. 384c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 385c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::hypotNorm() */ 3862b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3872b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const HypotNormReturnType hypotNorm() const 3882b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return HypotNormReturnType(_expression()); } 389c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 390c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the sum 391c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 392c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 393c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_sum.cpp 394c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_sum.out 395c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 396c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::sum() */ 3972b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 3982b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const SumReturnType sum() const 3992b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return SumReturnType(_expression()); } 400c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 401c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the mean 402c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 403c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 404c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::mean() */ 4052b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 4062b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const MeanReturnType mean() const 4072b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return MeanReturnType(_expression()); } 408c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 409c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression representing 410c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * whether \b all coefficients of each respective column (or row) are \c true. 4112b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This expression can be assigned to a vector with entries of type \c bool. 412c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 413c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::all() */ 4142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 4152b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const AllReturnType all() const 4162b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return AllReturnType(_expression()); } 417c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 418c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression representing 419c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * whether \b at \b least one coefficient of each respective column (or row) is \c true. 4202b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This expression can be assigned to a vector with entries of type \c bool. 421c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 422c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::any() */ 4232b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 4242b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const AnyReturnType any() const 4252b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return AnyReturnType(_expression()); } 426c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 427c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression representing 428c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * the number of \c true coefficients of each respective column (or row). 4292b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * This expression can be assigned to a vector whose entries have the same type as is used to 4302b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t . 431c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 432c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_count.cpp 433c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_count.out 434c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 435c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::count() */ 4362b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 4372b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const CountReturnType count() const 4382b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return CountReturnType(_expression()); } 439c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 440c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a row (or column) vector expression of the product 441c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * of each column (or row) of the referenced expression. 442c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 443c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include PartialRedux_prod.cpp 444c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude PartialRedux_prod.out 445c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 446c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::prod() */ 4472b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 4482b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const ProdReturnType prod() const 4492b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return ProdReturnType(_expression()); } 450c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 451c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 452c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** \returns a matrix expression 453c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * where each column (or row) are reversed. 454c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 455c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include Vectorwise_reverse.cpp 456c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude Vectorwise_reverse.out 457c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 458c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa DenseBase::reverse() */ 4592b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 4602b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang const ConstReverseReturnType reverse() const 4612b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return ConstReverseReturnType( _expression() ); } 462c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 4632b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang /** \returns a writable matrix expression 4642b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * where each column (or row) are reversed. 4652b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * 4662b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang * \sa reverse() const */ 4672b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 4682b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang ReverseReturnType reverse() 4692b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang { return ReverseReturnType( _expression() ); } 4702b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 4712b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType; 4722b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 473c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ReplicateReturnType replicate(Index factor) const; 474c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 475c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** 476c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \return an expression of the replication of each column (or row) of \c *this 477c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 478c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Example: \include DirectionWise_replicate.cpp 479c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Output: \verbinclude DirectionWise_replicate.out 480c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 481c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate 482c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 483c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // NOTE implemented here because of sunstudio's compilation errors 4842b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang // isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator 4852b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang template<int Factor> const Replicate<ExpressionType,isVertical*Factor+isHorizontal,isHorizontal*Factor+isVertical> 4862b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 487c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath replicate(Index factor = Factor) const 488c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 4892b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang return Replicate<ExpressionType,(isVertical?Factor:1),(isHorizontal?Factor:1)> 4902b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang (_expression(),isVertical?factor:1,isHorizontal?factor:1); 491c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 492c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 493c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/////////// Artithmetic operators /////////// 494c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 495c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Copies the vector \a other to each subvector of \c *this */ 496c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 4972b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 498c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator=(const DenseBase<OtherDerived>& other) 499c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 500c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 501c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 502c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME 503c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix = extendedTo(other.derived())); 504c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 505c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 506c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Adds the vector \a other to each subvector of \c *this */ 507c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 5082b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 509c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator+=(const DenseBase<OtherDerived>& other) 510c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 511c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 512c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 513c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix += extendedTo(other.derived())); 514c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 515c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 516c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Substracts the vector \a other to each subvector of \c *this */ 517c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 5182b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 519c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator-=(const DenseBase<OtherDerived>& other) 520c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 521c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 522c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 523c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix -= extendedTo(other.derived())); 524c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 525c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 526c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Multiples each subvector of \c *this by the vector \a other */ 527c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 5282b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 529c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator*=(const DenseBase<OtherDerived>& other) 530c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 531c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 532c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) 533c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 534c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m_matrix *= extendedTo(other.derived()); 535c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix); 536c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 537c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 538c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Divides each subvector of \c *this by the vector \a other */ 539c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 5402b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 541c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionType& operator/=(const DenseBase<OtherDerived>& other) 542c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 543c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 544c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) 545c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 546c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m_matrix /= extendedTo(other.derived()); 547c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return const_cast<ExpressionType&>(m_matrix); 548c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 549c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 550c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */ 5512b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC 5522b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>, 553c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ExpressionTypeNestedCleaned, 554c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ExtendedType<OtherDerived>::Type> 555c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator+(const DenseBase<OtherDerived>& other) const 556c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 557c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 558c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 559c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_matrix + extendedTo(other.derived()); 560c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 561c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 562c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */ 563c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 5642b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 5652b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>, 566c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ExpressionTypeNestedCleaned, 567c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ExtendedType<OtherDerived>::Type> 568c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator-(const DenseBase<OtherDerived>& other) const 569c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 570c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 571c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 572c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_matrix - extendedTo(other.derived()); 573c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 574c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 575c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Returns the expression where each subvector is the product of the vector \a other 576c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * by the corresponding subvector of \c *this */ 5772b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC 578c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CwiseBinaryOp<internal::scalar_product_op<Scalar>, 579c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ExpressionTypeNestedCleaned, 580c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ExtendedType<OtherDerived>::Type> 5812b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 582c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator*(const DenseBase<OtherDerived>& other) const 583c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 584c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 585c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) 586c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 587c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_matrix * extendedTo(other.derived()); 588c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 589c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 590c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Returns the expression where each subvector is the quotient of the corresponding 591c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * subvector of \c *this by the vector \a other */ 592c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 5932b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 594c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, 595c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const ExpressionTypeNestedCleaned, 596c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const typename ExtendedType<OtherDerived>::Type> 597c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator/(const DenseBase<OtherDerived>& other) const 598c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 599c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived) 600c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType) 601c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived) 602c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return m_matrix / extendedTo(other.derived()); 603c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 6042b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 6052b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang /** \returns an expression where each column (or row) of the referenced matrix are normalized. 6067faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez * The referenced matrix is \b not modified. 6077faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez * \sa MatrixBase::normalized(), normalize() 6087faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez */ 6092b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 6107faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, 6117faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez const ExpressionTypeNestedCleaned, 6127faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez const typename OppositeExtendedType<typename ReturnType<internal::member_norm,RealScalar>::Type>::Type> 6137faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez normalized() const { return m_matrix.cwiseQuotient(extendedToOpposite(this->norm())); } 6142b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 6152b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 6167faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez /** Normalize in-place each row or columns of the referenced matrix. 6177faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez * \sa MatrixBase::normalize(), normalized() 6187faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez */ 6192b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC void normalize() { 6207faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez m_matrix = this->normalized(); 6217faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 622c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 6232b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC inline void reverseInPlace(); 6242b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 625c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/////////// Geometry module /////////// 626c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 6272b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType; 6282b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 6292b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang HomogeneousReturnType homogeneous() const; 630c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 631c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ExpressionType::PlainObject CrossReturnType; 632c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename OtherDerived> 6332b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 634c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const; 635c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 636c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 637c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalized_Size = Direction==Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime 638c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : internal::traits<ExpressionType>::ColsAtCompileTime, 639c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1 640c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 641c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Block<const ExpressionType, 642c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Vertical ? int(HNormalized_SizeMinusOne) 643c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : int(internal::traits<ExpressionType>::RowsAtCompileTime), 644c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Horizontal ? int(HNormalized_SizeMinusOne) 645c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : int(internal::traits<ExpressionType>::ColsAtCompileTime)> 646c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalized_Block; 647c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Block<const ExpressionType, 648c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Vertical ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime), 649c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)> 650c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalized_Factors; 651c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>, 652c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const HNormalized_Block, 653c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Replicate<HNormalized_Factors, 654c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Vertical ? HNormalized_SizeMinusOne : 1, 655c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Direction==Horizontal ? HNormalized_SizeMinusOne : 1> > 656c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath HNormalizedReturnType; 657c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 6582b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang EIGEN_DEVICE_FUNC 659c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const HNormalizedReturnType hnormalized() const; 660c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 661c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath protected: 662c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ExpressionTypeNested m_matrix; 663c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 664c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 6652b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang//const colwise moved to DenseBase.h due to CUDA compiler bug 6662b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 667c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 668c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations 669c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 670c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting 671c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 672c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived> 673c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline typename DenseBase<Derived>::ColwiseReturnType 674c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathDenseBase<Derived>::colwise() 675c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 6762b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang return ColwiseReturnType(derived()); 677c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 678c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 6792b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang//const rowwise moved to DenseBase.h due to CUDA compiler bug 6802b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang 681c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 682c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations 683c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 684c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting 685c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 686c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived> 687c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline typename DenseBase<Derived>::RowwiseReturnType 688c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathDenseBase<Derived>::rowwise() 689c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 6902b8756b6f1de65d3f8bffab45be6c44ceb7411fcMiao Wang return RowwiseReturnType(derived()); 691c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 692c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 693c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace Eigen 694c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 695c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif // EIGEN_PARTIAL_REDUX_H 696