1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> 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_GENERAL_PRODUCT_H 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_GENERAL_PRODUCT_H 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace Eigen { 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \class GeneralProduct 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \ingroup Core_Module 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \brief Expression of the product of two general matrices or vectors 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \param LhsNested the type used to store the left-hand side 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \param RhsNested the type used to store the right-hand side 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \param ProductMode the type of the product 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * This class represents an expression of the product of two general matrices. 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * We call a general matrix, a dense matrix with full storage. For instance, 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * This excludes triangular, selfadjoint, and sparse matrices. 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * It is the return type of the operator* between general matrices. Its template 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * arguments are determined automatically by ProductReturnType. Therefore, 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * GeneralProduct should never be used direclty. To determine the result type of a 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * function which involves a matrix product, use ProductReturnType::Type. 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa ProductReturnType, MatrixBase::operator*(const MatrixBase<OtherDerived>&) 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs, int ProductType = internal::product_type<Lhs,Rhs>::value> 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathclass GeneralProduct; 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathenum { 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Large = 2, 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Small = 3 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal { 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<int Rows, int Cols, int Depth> struct product_type_selector; 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<int Size, int MaxSize> struct product_size_category 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { is_large = MaxSize == Dynamic || 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD, 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath value = is_large ? Large 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : Size == 1 ? 1 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : Small 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> struct product_type 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename remove_all<Lhs>::type _Lhs; 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename remove_all<Rhs>::type _Rhs; 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MaxRows = _Lhs::MaxRowsAtCompileTime, 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Rows = _Lhs::RowsAtCompileTime, 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MaxCols = _Rhs::MaxColsAtCompileTime, 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Cols = _Rhs::ColsAtCompileTime, 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::MaxColsAtCompileTime, 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath _Rhs::MaxRowsAtCompileTime), 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Depth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::ColsAtCompileTime, 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath _Rhs::RowsAtCompileTime), 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath LargeThreshold = EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // the splitting into different lines of code here, introducing the _select enums and the typedef below, 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // is to work around an internal compiler error with gcc 4.1 and 4.2. 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathprivate: 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath rows_select = product_size_category<Rows,MaxRows>::value, 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath cols_select = product_size_category<Cols,MaxCols>::value, 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath depth_select = product_size_category<Depth,MaxDepth>::value 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef product_type_selector<rows_select, cols_select, depth_select> selector; 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathpublic: 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath value = selector::ret 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifdef EIGEN_DEBUG_PRODUCT 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath static void debug() 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DEBUG_VAR(Rows); 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DEBUG_VAR(Cols); 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DEBUG_VAR(Depth); 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DEBUG_VAR(rows_select); 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DEBUG_VAR(cols_select); 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DEBUG_VAR(depth_select); 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DEBUG_VAR(value); 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/* The following allows to select the kind of product at compile time 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * based on the three dimensions of the product. 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */ 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// FIXME I'm not sure the current mapping is the ideal one. 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<int M, int N> struct product_type_selector<M,N,1> { enum { ret = OuterProduct }; }; 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<int Depth> struct product_type_selector<1, 1, Depth> { enum { ret = InnerProduct }; }; 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<1, 1, 1> { enum { ret = InnerProduct }; }; 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Small,1, Small> { enum { ret = CoeffBasedProductMode }; }; 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<1, Small,Small> { enum { ret = CoeffBasedProductMode }; }; 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Small,Small,Small> { enum { ret = CoeffBasedProductMode }; }; 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Small, Small, 1> { enum { ret = LazyCoeffBasedProductMode }; }; 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Small, Large, 1> { enum { ret = LazyCoeffBasedProductMode }; }; 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Large, Small, 1> { enum { ret = LazyCoeffBasedProductMode }; }; 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<1, Large,Small> { enum { ret = CoeffBasedProductMode }; }; 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<1, Large,Large> { enum { ret = GemvProduct }; }; 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<1, Small,Large> { enum { ret = CoeffBasedProductMode }; }; 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Large,1, Small> { enum { ret = CoeffBasedProductMode }; }; 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Large,1, Large> { enum { ret = GemvProduct }; }; 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Small,1, Large> { enum { ret = CoeffBasedProductMode }; }; 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Small,Small,Large> { enum { ret = GemmProduct }; }; 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Large,Small,Large> { enum { ret = GemmProduct }; }; 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Small,Large,Large> { enum { ret = GemmProduct }; }; 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Large,Large,Large> { enum { ret = GemmProduct }; }; 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Large,Small,Small> { enum { ret = GemmProduct }; }; 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Small,Large,Small> { enum { ret = GemmProduct }; }; 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct product_type_selector<Large,Large,Small> { enum { ret = GemmProduct }; }; 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace internal 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \class ProductReturnType 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \ingroup Core_Module 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \brief Helper class to get the correct and optimized returned type of operator* 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \param Lhs the type of the left-hand side 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \param Rhs the type of the right-hand side 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \param ProductMode the type of the product (determined automatically by internal::product_mode) 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * This class defines the typename Type representing the optimized product expression 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * between two matrix expressions. In practice, using ProductReturnType<Lhs,Rhs>::Type 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * is the recommended way to define the result type of a function returning an expression 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * which involve a matrix product. The class Product should never be 144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * used directly. 145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa class Product, MatrixBase::operator*(const MatrixBase<OtherDerived>&) 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs, int ProductType> 149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct ProductReturnType 150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // TODO use the nested type to reduce instanciations ???? 152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// typedef typename internal::nested<Lhs,Rhs::ColsAtCompileTime>::type LhsNested; 153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// typedef typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type RhsNested; 154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef GeneralProduct<Lhs/*Nested*/, Rhs/*Nested*/, ProductType> Type; 156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct ProductReturnType<Lhs,Rhs,CoeffBasedProductMode> 160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested; 162c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested; 163c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef CoeffBasedProduct<LhsNested, RhsNested, EvalBeforeAssigningBit | EvalBeforeNestingBit> Type; 164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode> 168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested; 170c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested; 171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef CoeffBasedProduct<LhsNested, RhsNested, NestByRefBit> Type; 172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// this is a workaround for sun CC 175c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 176c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode> 177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{}; 178c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 179c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/*********************************************************************** 180c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath* Implementation of Inner Vector Vector Product 181c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath***********************************************************************/ 182c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 183c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// FIXME : maybe the "inner product" could return a Scalar 184c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// instead of a 1x1 matrix ?? 185c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Pro: more natural for the user 186c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix 187c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// product ends up to a row-vector times col-vector product... To tackle this use 188c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x); 189c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 190c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal { 191c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 192c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 193c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct traits<GeneralProduct<Lhs,Rhs,InnerProduct> > 194c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : traits<Matrix<typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> > 195c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{}; 196c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 197c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 198c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 199c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathclass GeneralProduct<Lhs, Rhs, InnerProduct> 201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : internal::no_assignment_operator, 202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> 203c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> Base; 205c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath GeneralProduct(const Lhs& lhs, const Rhs& rhs) 207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 208c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value), 209c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) 210c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 211c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Base::coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum(); 212c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 213c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 214c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /** Convertion to scalar */ 215c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath operator const typename Base::Scalar() const { 216c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return Base::coeff(0,0); 217c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 218c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 219c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 220c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/*********************************************************************** 221c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath* Implementation of Outer Vector Vector Product 222c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath***********************************************************************/ 223c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 224c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal { 2257faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 2267faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez// Column major 2277faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandeztemplate<typename ProductType, typename Dest, typename Func> 2287faaa9f3f0df9d23790277834d426c3d992ac3baCarlos HernandezEIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const false_type&) 2297faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez{ 2307faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez typedef typename Dest::Index Index; 2317faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // FIXME make sure lhs is sequentially stored 2327faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // FIXME not very good if rhs is real and lhs complex while alpha is real too 2337faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez const Index cols = dest.cols(); 2347faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez for (Index j=0; j<cols; ++j) 2357faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez func(dest.col(j), prod.rhs().coeff(j) * prod.lhs()); 2367faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez} 2377faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 2387faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez// Row major 2397faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandeztemplate<typename ProductType, typename Dest, typename Func> 2407faaa9f3f0df9d23790277834d426c3d992ac3baCarlos HernandezEIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const true_type&) { 2417faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez typedef typename Dest::Index Index; 2427faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // FIXME make sure rhs is sequentially stored 2437faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // FIXME not very good if lhs is real and rhs complex while alpha is real too 2447faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez const Index rows = dest.rows(); 2457faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez for (Index i=0; i<rows; ++i) 2467faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez func(dest.row(i), prod.lhs().coeff(i) * prod.rhs()); 2477faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez} 248c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 249c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 250c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct traits<GeneralProduct<Lhs,Rhs,OuterProduct> > 251c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : traits<ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> > 252c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{}; 253c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 254c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 255c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 256c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 257c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathclass GeneralProduct<Lhs, Rhs, OuterProduct> 258c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> 259c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 2607faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename T> struct IsRowMajor : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {}; 2617faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 262c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 263c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct) 264c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 265c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) 266c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 267c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value), 268c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) 269c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 2707faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 2717faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez struct set { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } }; 2727faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez struct add { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } }; 2737faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez struct sub { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } }; 2747faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez struct adds { 2757faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez Scalar m_scale; 2767faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez adds(const Scalar& s) : m_scale(s) {} 2777faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { 2787faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez dst.const_cast_derived() += m_scale * src; 2797faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 2807faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez }; 2817faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 2827faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename Dest> 2837faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez inline void evalTo(Dest& dest) const { 2847faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez internal::outer_product_selector_run(*this, dest, set(), IsRowMajor<Dest>()); 2857faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 2867faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 2877faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename Dest> 2887faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez inline void addTo(Dest& dest) const { 2897faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez internal::outer_product_selector_run(*this, dest, add(), IsRowMajor<Dest>()); 290c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 291c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 2927faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename Dest> 2937faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez inline void subTo(Dest& dest) const { 2947faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez internal::outer_product_selector_run(*this, dest, sub(), IsRowMajor<Dest>()); 2957faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 296c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 2977faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const 2987faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez { 2997faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez internal::outer_product_selector_run(*this, dest, adds(alpha), IsRowMajor<Dest>()); 3007faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 301c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 302c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 303c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/*********************************************************************** 304c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath* Implementation of General Matrix Vector Product 305c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath***********************************************************************/ 306c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 307c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/* According to the shape/flags of the matrix we have to distinghish 3 different cases: 308c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine 309c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine 310c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 3 - all other cases are handled using a simple loop along the outer-storage direction. 311c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Therefore we need a lower level meta selector. 312c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * Furthermore, if the matrix is the rhs, then the product has to be transposed. 313c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 314c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal { 315c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 316c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 317c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct traits<GeneralProduct<Lhs,Rhs,GemvProduct> > 318c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : traits<ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> > 319c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{}; 320c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 321c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<int Side, int StorageOrder, bool BlasCompatible> 322c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct gemv_selector; 323c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 324c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace internal 325c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 326c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Lhs, typename Rhs> 327c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathclass GeneralProduct<Lhs, Rhs, GemvProduct> 328c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : public ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> 329c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 330c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath public: 331c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct) 332c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 333c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename Lhs::Scalar LhsScalar; 334c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename Rhs::Scalar RhsScalar; 335c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 3367faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez GeneralProduct(const Lhs& a_lhs, const Rhs& a_rhs) : Base(a_lhs,a_rhs) 337c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 338c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value), 339c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) 340c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 341c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 342c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight }; 343c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType; 344c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 3457faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const 346c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 347c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath eigen_assert(m_lhs.rows() == dst.rows() && m_rhs.cols() == dst.cols()); 348c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath internal::gemv_selector<Side,(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor, 349c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(*this, dst, alpha); 350c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 351c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 352c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 353c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathnamespace internal { 354c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 355c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// The vector is on the left => transposition 356c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<int StorageOrder, bool BlasCompatible> 357c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible> 358c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 359c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename ProductType, typename Dest> 3607faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) 361c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 362c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Transpose<Dest> destT(dest); 363c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor }; 364c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath gemv_selector<OnTheRight,OtherStorageOrder,BlasCompatible> 365c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ::run(GeneralProduct<Transpose<const typename ProductType::_RhsNested>,Transpose<const typename ProductType::_LhsNested>, GemvProduct> 366c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath (prod.rhs().transpose(), prod.lhs().transpose()), destT, alpha); 367c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 368c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 369c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 370c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if; 371c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 372c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar,int Size,int MaxSize> 373c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct gemv_static_vector_if<Scalar,Size,MaxSize,false> 374c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 375c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STRONG_INLINE Scalar* data() { eigen_internal_assert(false && "should never be called"); return 0; } 376c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 377c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 378c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar,int Size> 379c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct gemv_static_vector_if<Scalar,Size,Dynamic,true> 380c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 381c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STRONG_INLINE Scalar* data() { return 0; } 382c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 383c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 384c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar,int Size,int MaxSize> 385c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct gemv_static_vector_if<Scalar,Size,MaxSize,true> 386c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 387c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #if EIGEN_ALIGN_STATICALLY 388c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0> m_data; 389c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; } 390c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #else 391c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Some architectures cannot align on the stack, 392c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // => let's manually enforce alignment by allocating more data and return the address of the first aligned element. 393c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 394c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ForceAlignment = internal::packet_traits<Scalar>::Vectorizable, 395c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath PacketSize = internal::packet_traits<Scalar>::size 396c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 397c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?PacketSize:0),0> m_data; 398c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STRONG_INLINE Scalar* data() { 399c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return ForceAlignment 400c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ? reinterpret_cast<Scalar*>((reinterpret_cast<size_t>(m_data.array) & ~(size_t(15))) + 16) 401c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath : m_data.array; 402c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 403c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 404c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 405c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 406c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct gemv_selector<OnTheRight,ColMajor,true> 407c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 408c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename ProductType, typename Dest> 4097faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez static inline void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) 410c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 411c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::Index Index; 412c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::LhsScalar LhsScalar; 413c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::RhsScalar RhsScalar; 414c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::Scalar ResScalar; 415c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::RealScalar RealScalar; 416c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::ActualLhsType ActualLhsType; 417c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::ActualRhsType ActualRhsType; 418c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::LhsBlasTraits LhsBlasTraits; 419c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::RhsBlasTraits RhsBlasTraits; 420c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest; 421c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 422c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ActualLhsType actualLhs = LhsBlasTraits::extract(prod.lhs()); 423c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ActualRhsType actualRhs = RhsBlasTraits::extract(prod.rhs()); 424c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 425c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) 426c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * RhsBlasTraits::extractScalarFactor(prod.rhs()); 427c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 428c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 429c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1 430c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // on, the other hand it is good for the cache to pack the vector anyways... 431c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1, 432c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex), 433c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal 434c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 435c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 436c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest; 437c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 4387faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0)); 439c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible; 440c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 441c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha); 442c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 443c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), 444c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath evalToDest ? dest.data() : static_dest.data()); 445c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 446c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(!evalToDest) 447c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 448c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN 449c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int size = dest.size(); 450c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DENSE_STORAGE_CTOR_PLUGIN 451c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 452c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(!alphaIsCompatible) 453c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 454c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MappedDest(actualDestPtr, dest.size()).setZero(); 455c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath compatibleAlpha = RhsScalar(1); 456c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 457c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else 458c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MappedDest(actualDestPtr, dest.size()) = dest; 459c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 460c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 461c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath general_matrix_vector_product 462c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath <Index,LhsScalar,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run( 463c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath actualLhs.rows(), actualLhs.cols(), 464c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath actualLhs.data(), actualLhs.outerStride(), 465c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath actualRhs.data(), actualRhs.innerStride(), 466c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath actualDestPtr, 1, 467c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath compatibleAlpha); 468c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 469c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (!evalToDest) 470c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 471c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(!alphaIsCompatible) 472c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dest += actualAlpha * MappedDest(actualDestPtr, dest.size()); 473c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else 474c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dest = MappedDest(actualDestPtr, dest.size()); 475c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 476c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 477c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 478c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 479c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct gemv_selector<OnTheRight,RowMajor,true> 480c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 481c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename ProductType, typename Dest> 4827faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) 483c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 484c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::LhsScalar LhsScalar; 485c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::RhsScalar RhsScalar; 486c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::Scalar ResScalar; 487c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::Index Index; 488c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::ActualLhsType ActualLhsType; 489c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::ActualRhsType ActualRhsType; 490c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::_ActualRhsType _ActualRhsType; 491c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::LhsBlasTraits LhsBlasTraits; 492c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename ProductType::RhsBlasTraits RhsBlasTraits; 493c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 494c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs()); 495c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs()); 496c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 497c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) 498c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * RhsBlasTraits::extractScalarFactor(prod.rhs()); 499c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 500c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 501c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1 502c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // on, the other hand it is good for the cache to pack the vector anyways... 503c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1 504c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 505c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 506c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs; 507c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 508c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(), 509c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data()); 510c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 511c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(!DirectlyUseRhs) 512c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 513c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN 514c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int size = actualRhs.size(); 515c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_DENSE_STORAGE_CTOR_PLUGIN 516c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 517c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs; 518c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 519c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 520c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath general_matrix_vector_product 521c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath <Index,LhsScalar,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run( 522c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath actualLhs.rows(), actualLhs.cols(), 523c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath actualLhs.data(), actualLhs.outerStride(), 524c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath actualRhsPtr, 1, 525c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dest.data(), dest.innerStride(), 526c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath actualAlpha); 527c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 528c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 529c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 530c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct gemv_selector<OnTheRight,ColMajor,false> 531c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 532c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename ProductType, typename Dest> 5337faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) 534c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 535c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename Dest::Index Index; 536c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // TODO makes sure dest is sequentially stored in memory, otherwise use a temp 537c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Index size = prod.rhs().rows(); 538c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(Index k=0; k<size; ++k) 539c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dest += (alpha*prod.rhs().coeff(k)) * prod.lhs().col(k); 540c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 541c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 542c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 543c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<> struct gemv_selector<OnTheRight,RowMajor,false> 544c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 545c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath template<typename ProductType, typename Dest> 5467faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) 547c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 548c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename Dest::Index Index; 549c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // TODO makes sure rhs is sequentially stored in memory, otherwise use a temp 550c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Index rows = prod.rows(); 551c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(Index i=0; i<rows; ++i) 552c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dest.coeffRef(i) += alpha * (prod.lhs().row(i).cwiseProduct(prod.rhs().transpose())).sum(); 553c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 554c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 555c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 556c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace internal 557c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 558c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/*************************************************************************** 559c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath* Implementation of matrix base methods 560c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath***************************************************************************/ 561c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 562c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns the matrix product of \c *this and \a other. 563c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 564c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*(). 565c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 566c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*() 567c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 568c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived> 569c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename OtherDerived> 570c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathinline const typename ProductReturnType<Derived, OtherDerived>::Type 571c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathMatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const 572c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 573c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // A note regarding the function declaration: In MSVC, this function will sometimes 574c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // not be inlined since DenseStorage is an unwindable object for dynamic 575c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // matrices and product types are holding a member to store the result. 576c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Thus it does not help tagging this function with EIGEN_STRONG_INLINE. 577c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 578c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ProductIsValid = Derived::ColsAtCompileTime==Dynamic 579c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath || OtherDerived::RowsAtCompileTime==Dynamic 580c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), 581c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, 582c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) 583c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 584c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // note to the lost user: 585c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // * for a dot product use: v1.dot(v2) 586c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // * for a coeff-wise product use: v1.cwiseProduct(v2) 587c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), 588c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) 589c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), 590c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) 591c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) 592c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifdef EIGEN_DEBUG_PRODUCT 593c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath internal::product_type<Derived,OtherDerived>::debug(); 594c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif 595c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); 596c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 597c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 598c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation. 599c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 600c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * The returned product will behave like any other expressions: the coefficients of the product will be 601c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * computed once at a time as requested. This might be useful in some extremely rare cases when only 602c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * a small and no coherent fraction of the result's coefficients have to be computed. 603c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 604c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \warning This version of the matrix product can be much much slower. So use it only if you know 605c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * what you are doing and that you measured a true speed improvement. 606c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * 607c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath * \sa operator*(const MatrixBase&) 608c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 609c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Derived> 610c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename OtherDerived> 611c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathconst typename LazyProductReturnType<Derived,OtherDerived>::Type 612c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan KamathMatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const 613c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 614c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 615c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ProductIsValid = Derived::ColsAtCompileTime==Dynamic 616c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath || OtherDerived::RowsAtCompileTime==Dynamic 617c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), 618c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, 619c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) 620c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 621c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // note to the lost user: 622c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // * for a dot product use: v1.dot(v2) 623c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // * for a coeff-wise product use: v1.cwiseProduct(v2) 624c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), 625c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) 626c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), 627c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) 628c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) 629c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 630c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return typename LazyProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); 631c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 632c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 633c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} // end namespace Eigen 634c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 635c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif // EIGEN_PRODUCT_H 636