| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> |
| // Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #ifndef EIGEN_GENERAL_PRODUCT_H |
| #define EIGEN_GENERAL_PRODUCT_H |
| |
| namespace Eigen { |
| |
| /** \class GeneralProduct |
| * \ingroup Core_Module |
| * |
| * \brief Expression of the product of two general matrices or vectors |
| * |
| * \param LhsNested the type used to store the left-hand side |
| * \param RhsNested the type used to store the right-hand side |
| * \param ProductMode the type of the product |
| * |
| * This class represents an expression of the product of two general matrices. |
| * We call a general matrix, a dense matrix with full storage. For instance, |
| * This excludes triangular, selfadjoint, and sparse matrices. |
| * It is the return type of the operator* between general matrices. Its template |
| * arguments are determined automatically by ProductReturnType. Therefore, |
| * GeneralProduct should never be used direclty. To determine the result type of a |
| * function which involves a matrix product, use ProductReturnType::Type. |
| * |
| * \sa ProductReturnType, MatrixBase::operator*(const MatrixBase<OtherDerived>&) |
| */ |
| template<typename Lhs, typename Rhs, int ProductType = internal::product_type<Lhs,Rhs>::value> |
| class GeneralProduct; |
| |
| enum { |
| Large = 2, |
| Small = 3 |
| }; |
| |
| namespace internal { |
| |
| template<int Rows, int Cols, int Depth> struct product_type_selector; |
| |
| template<int Size, int MaxSize> struct product_size_category |
| { |
| enum { is_large = MaxSize == Dynamic || |
| Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD, |
| value = is_large ? Large |
| : Size == 1 ? 1 |
| : Small |
| }; |
| }; |
| |
| template<typename Lhs, typename Rhs> struct product_type |
| { |
| typedef typename remove_all<Lhs>::type _Lhs; |
| typedef typename remove_all<Rhs>::type _Rhs; |
| enum { |
| MaxRows = _Lhs::MaxRowsAtCompileTime, |
| Rows = _Lhs::RowsAtCompileTime, |
| MaxCols = _Rhs::MaxColsAtCompileTime, |
| Cols = _Rhs::ColsAtCompileTime, |
| MaxDepth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::MaxColsAtCompileTime, |
| _Rhs::MaxRowsAtCompileTime), |
| Depth = EIGEN_SIZE_MIN_PREFER_FIXED(_Lhs::ColsAtCompileTime, |
| _Rhs::RowsAtCompileTime) |
| }; |
| |
| // the splitting into different lines of code here, introducing the _select enums and the typedef below, |
| // is to work around an internal compiler error with gcc 4.1 and 4.2. |
| private: |
| enum { |
| rows_select = product_size_category<Rows,MaxRows>::value, |
| cols_select = product_size_category<Cols,MaxCols>::value, |
| depth_select = product_size_category<Depth,MaxDepth>::value |
| }; |
| typedef product_type_selector<rows_select, cols_select, depth_select> selector; |
| |
| public: |
| enum { |
| value = selector::ret |
| }; |
| #ifdef EIGEN_DEBUG_PRODUCT |
| static void debug() |
| { |
| EIGEN_DEBUG_VAR(Rows); |
| EIGEN_DEBUG_VAR(Cols); |
| EIGEN_DEBUG_VAR(Depth); |
| EIGEN_DEBUG_VAR(rows_select); |
| EIGEN_DEBUG_VAR(cols_select); |
| EIGEN_DEBUG_VAR(depth_select); |
| EIGEN_DEBUG_VAR(value); |
| } |
| #endif |
| }; |
| |
| |
| /* The following allows to select the kind of product at compile time |
| * based on the three dimensions of the product. |
| * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */ |
| // FIXME I'm not sure the current mapping is the ideal one. |
| template<int M, int N> struct product_type_selector<M,N,1> { enum { ret = OuterProduct }; }; |
| template<int Depth> struct product_type_selector<1, 1, Depth> { enum { ret = InnerProduct }; }; |
| template<> struct product_type_selector<1, 1, 1> { enum { ret = InnerProduct }; }; |
| template<> struct product_type_selector<Small,1, Small> { enum { ret = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<1, Small,Small> { enum { ret = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Small,Small,Small> { enum { ret = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Small, Small, 1> { enum { ret = LazyCoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Small, Large, 1> { enum { ret = LazyCoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Large, Small, 1> { enum { ret = LazyCoeffBasedProductMode }; }; |
| template<> struct product_type_selector<1, Large,Small> { enum { ret = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<1, Large,Large> { enum { ret = GemvProduct }; }; |
| template<> struct product_type_selector<1, Small,Large> { enum { ret = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Large,1, Small> { enum { ret = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Large,1, Large> { enum { ret = GemvProduct }; }; |
| template<> struct product_type_selector<Small,1, Large> { enum { ret = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Small,Small,Large> { enum { ret = GemmProduct }; }; |
| template<> struct product_type_selector<Large,Small,Large> { enum { ret = GemmProduct }; }; |
| template<> struct product_type_selector<Small,Large,Large> { enum { ret = GemmProduct }; }; |
| template<> struct product_type_selector<Large,Large,Large> { enum { ret = GemmProduct }; }; |
| template<> struct product_type_selector<Large,Small,Small> { enum { ret = GemmProduct }; }; |
| template<> struct product_type_selector<Small,Large,Small> { enum { ret = GemmProduct }; }; |
| template<> struct product_type_selector<Large,Large,Small> { enum { ret = GemmProduct }; }; |
| |
| } // end namespace internal |
| |
| /** \class ProductReturnType |
| * \ingroup Core_Module |
| * |
| * \brief Helper class to get the correct and optimized returned type of operator* |
| * |
| * \param Lhs the type of the left-hand side |
| * \param Rhs the type of the right-hand side |
| * \param ProductMode the type of the product (determined automatically by internal::product_mode) |
| * |
| * This class defines the typename Type representing the optimized product expression |
| * between two matrix expressions. In practice, using ProductReturnType<Lhs,Rhs>::Type |
| * is the recommended way to define the result type of a function returning an expression |
| * which involve a matrix product. The class Product should never be |
| * used directly. |
| * |
| * \sa class Product, MatrixBase::operator*(const MatrixBase<OtherDerived>&) |
| */ |
| template<typename Lhs, typename Rhs, int ProductType> |
| struct ProductReturnType |
| { |
| // TODO use the nested type to reduce instanciations ???? |
| // typedef typename internal::nested<Lhs,Rhs::ColsAtCompileTime>::type LhsNested; |
| // typedef typename internal::nested<Rhs,Lhs::RowsAtCompileTime>::type RhsNested; |
| |
| typedef GeneralProduct<Lhs/*Nested*/, Rhs/*Nested*/, ProductType> Type; |
| }; |
| |
| template<typename Lhs, typename Rhs> |
| struct ProductReturnType<Lhs,Rhs,CoeffBasedProductMode> |
| { |
| typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested; |
| typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested; |
| typedef CoeffBasedProduct<LhsNested, RhsNested, EvalBeforeAssigningBit | EvalBeforeNestingBit> Type; |
| }; |
| |
| template<typename Lhs, typename Rhs> |
| struct ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode> |
| { |
| typedef typename internal::nested<Lhs, Rhs::ColsAtCompileTime, typename internal::plain_matrix_type<Lhs>::type >::type LhsNested; |
| typedef typename internal::nested<Rhs, Lhs::RowsAtCompileTime, typename internal::plain_matrix_type<Rhs>::type >::type RhsNested; |
| typedef CoeffBasedProduct<LhsNested, RhsNested, NestByRefBit> Type; |
| }; |
| |
| // this is a workaround for sun CC |
| template<typename Lhs, typename Rhs> |
| struct LazyProductReturnType : public ProductReturnType<Lhs,Rhs,LazyCoeffBasedProductMode> |
| {}; |
| |
| /*********************************************************************** |
| * Implementation of Inner Vector Vector Product |
| ***********************************************************************/ |
| |
| // FIXME : maybe the "inner product" could return a Scalar |
| // instead of a 1x1 matrix ?? |
| // Pro: more natural for the user |
| // Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix |
| // product ends up to a row-vector times col-vector product... To tackle this use |
| // case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x); |
| |
| namespace internal { |
| |
| template<typename Lhs, typename Rhs> |
| struct traits<GeneralProduct<Lhs,Rhs,InnerProduct> > |
| : traits<Matrix<typename scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> > |
| {}; |
| |
| } |
| |
| template<typename Lhs, typename Rhs> |
| class GeneralProduct<Lhs, Rhs, InnerProduct> |
| : internal::no_assignment_operator, |
| public Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> |
| { |
| typedef Matrix<typename internal::scalar_product_traits<typename Lhs::Scalar, typename Rhs::Scalar>::ReturnType,1,1> Base; |
| public: |
| GeneralProduct(const Lhs& lhs, const Rhs& rhs) |
| { |
| EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value), |
| YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) |
| |
| Base::coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum(); |
| } |
| |
| /** Convertion to scalar */ |
| operator const typename Base::Scalar() const { |
| return Base::coeff(0,0); |
| } |
| }; |
| |
| /*********************************************************************** |
| * Implementation of Outer Vector Vector Product |
| ***********************************************************************/ |
| |
| namespace internal { |
| |
| // Column major |
| template<typename ProductType, typename Dest, typename Func> |
| EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const false_type&) |
| { |
| typedef typename Dest::Index Index; |
| // FIXME make sure lhs is sequentially stored |
| // FIXME not very good if rhs is real and lhs complex while alpha is real too |
| const Index cols = dest.cols(); |
| for (Index j=0; j<cols; ++j) |
| func(dest.col(j), prod.rhs().coeff(j) * prod.lhs()); |
| } |
| |
| // Row major |
| template<typename ProductType, typename Dest, typename Func> |
| EIGEN_DONT_INLINE void outer_product_selector_run(const ProductType& prod, Dest& dest, const Func& func, const true_type&) { |
| typedef typename Dest::Index Index; |
| // FIXME make sure rhs is sequentially stored |
| // FIXME not very good if lhs is real and rhs complex while alpha is real too |
| const Index rows = dest.rows(); |
| for (Index i=0; i<rows; ++i) |
| func(dest.row(i), prod.lhs().coeff(i) * prod.rhs()); |
| } |
| |
| template<typename Lhs, typename Rhs> |
| struct traits<GeneralProduct<Lhs,Rhs,OuterProduct> > |
| : traits<ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> > |
| {}; |
| |
| } |
| |
| template<typename Lhs, typename Rhs> |
| class GeneralProduct<Lhs, Rhs, OuterProduct> |
| : public ProductBase<GeneralProduct<Lhs,Rhs,OuterProduct>, Lhs, Rhs> |
| { |
| template<typename T> struct IsRowMajor : internal::conditional<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type>::type {}; |
| |
| public: |
| EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct) |
| |
| GeneralProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs) |
| { |
| EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::RealScalar, typename Rhs::RealScalar>::value), |
| YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) |
| } |
| |
| struct set { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() = src; } }; |
| struct add { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } }; |
| struct sub { template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } }; |
| struct adds { |
| Scalar m_scale; |
| adds(const Scalar& s) : m_scale(s) {} |
| template<typename Dst, typename Src> void operator()(const Dst& dst, const Src& src) const { |
| dst.const_cast_derived() += m_scale * src; |
| } |
| }; |
| |
| template<typename Dest> |
| inline void evalTo(Dest& dest) const { |
| internal::outer_product_selector_run(*this, dest, set(), IsRowMajor<Dest>()); |
| } |
| |
| template<typename Dest> |
| inline void addTo(Dest& dest) const { |
| internal::outer_product_selector_run(*this, dest, add(), IsRowMajor<Dest>()); |
| } |
| |
| template<typename Dest> |
| inline void subTo(Dest& dest) const { |
| internal::outer_product_selector_run(*this, dest, sub(), IsRowMajor<Dest>()); |
| } |
| |
| template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const |
| { |
| internal::outer_product_selector_run(*this, dest, adds(alpha), IsRowMajor<Dest>()); |
| } |
| }; |
| |
| /*********************************************************************** |
| * Implementation of General Matrix Vector Product |
| ***********************************************************************/ |
| |
| /* According to the shape/flags of the matrix we have to distinghish 3 different cases: |
| * 1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine |
| * 2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine |
| * 3 - all other cases are handled using a simple loop along the outer-storage direction. |
| * Therefore we need a lower level meta selector. |
| * Furthermore, if the matrix is the rhs, then the product has to be transposed. |
| */ |
| namespace internal { |
| |
| template<typename Lhs, typename Rhs> |
| struct traits<GeneralProduct<Lhs,Rhs,GemvProduct> > |
| : traits<ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> > |
| {}; |
| |
| template<int Side, int StorageOrder, bool BlasCompatible> |
| struct gemv_selector; |
| |
| } // end namespace internal |
| |
| template<typename Lhs, typename Rhs> |
| class GeneralProduct<Lhs, Rhs, GemvProduct> |
| : public ProductBase<GeneralProduct<Lhs,Rhs,GemvProduct>, Lhs, Rhs> |
| { |
| public: |
| EIGEN_PRODUCT_PUBLIC_INTERFACE(GeneralProduct) |
| |
| typedef typename Lhs::Scalar LhsScalar; |
| typedef typename Rhs::Scalar RhsScalar; |
| |
| GeneralProduct(const Lhs& a_lhs, const Rhs& a_rhs) : Base(a_lhs,a_rhs) |
| { |
| // EIGEN_STATIC_ASSERT((internal::is_same<typename Lhs::Scalar, typename Rhs::Scalar>::value), |
| // YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) |
| } |
| |
| enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight }; |
| typedef typename internal::conditional<int(Side)==OnTheRight,_LhsNested,_RhsNested>::type MatrixType; |
| |
| template<typename Dest> void scaleAndAddTo(Dest& dst, const Scalar& alpha) const |
| { |
| eigen_assert(m_lhs.rows() == dst.rows() && m_rhs.cols() == dst.cols()); |
| internal::gemv_selector<Side,(int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor, |
| bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)>::run(*this, dst, alpha); |
| } |
| }; |
| |
| namespace internal { |
| |
| // The vector is on the left => transposition |
| template<int StorageOrder, bool BlasCompatible> |
| struct gemv_selector<OnTheLeft,StorageOrder,BlasCompatible> |
| { |
| template<typename ProductType, typename Dest> |
| static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) |
| { |
| Transpose<Dest> destT(dest); |
| enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor }; |
| gemv_selector<OnTheRight,OtherStorageOrder,BlasCompatible> |
| ::run(GeneralProduct<Transpose<const typename ProductType::_RhsNested>,Transpose<const typename ProductType::_LhsNested>, GemvProduct> |
| (prod.rhs().transpose(), prod.lhs().transpose()), destT, alpha); |
| } |
| }; |
| |
| template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if; |
| |
| template<typename Scalar,int Size,int MaxSize> |
| struct gemv_static_vector_if<Scalar,Size,MaxSize,false> |
| { |
| EIGEN_STRONG_INLINE Scalar* data() { eigen_internal_assert(false && "should never be called"); return 0; } |
| }; |
| |
| template<typename Scalar,int Size> |
| struct gemv_static_vector_if<Scalar,Size,Dynamic,true> |
| { |
| EIGEN_STRONG_INLINE Scalar* data() { return 0; } |
| }; |
| |
| template<typename Scalar,int Size,int MaxSize> |
| struct gemv_static_vector_if<Scalar,Size,MaxSize,true> |
| { |
| #if EIGEN_ALIGN_STATICALLY |
| internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize),0> m_data; |
| EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; } |
| #else |
| // Some architectures cannot align on the stack, |
| // => let's manually enforce alignment by allocating more data and return the address of the first aligned element. |
| enum { |
| ForceAlignment = internal::packet_traits<Scalar>::Vectorizable, |
| PacketSize = internal::packet_traits<Scalar>::size |
| }; |
| internal::plain_array<Scalar,EIGEN_SIZE_MIN_PREFER_FIXED(Size,MaxSize)+(ForceAlignment?PacketSize:0),0> m_data; |
| EIGEN_STRONG_INLINE Scalar* data() { |
| return ForceAlignment |
| ? reinterpret_cast<Scalar*>((reinterpret_cast<size_t>(m_data.array) & ~(size_t(EIGEN_ALIGN_BYTES-1))) + EIGEN_ALIGN_BYTES) |
| : m_data.array; |
| } |
| #endif |
| }; |
| |
| template<> struct gemv_selector<OnTheRight,ColMajor,true> |
| { |
| template<typename ProductType, typename Dest> |
| static inline void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) |
| { |
| typedef typename ProductType::Index Index; |
| typedef typename ProductType::LhsScalar LhsScalar; |
| typedef typename ProductType::RhsScalar RhsScalar; |
| typedef typename ProductType::Scalar ResScalar; |
| typedef typename ProductType::RealScalar RealScalar; |
| typedef typename ProductType::ActualLhsType ActualLhsType; |
| typedef typename ProductType::ActualRhsType ActualRhsType; |
| typedef typename ProductType::LhsBlasTraits LhsBlasTraits; |
| typedef typename ProductType::RhsBlasTraits RhsBlasTraits; |
| typedef Map<Matrix<ResScalar,Dynamic,1>, Aligned> MappedDest; |
| |
| ActualLhsType actualLhs = LhsBlasTraits::extract(prod.lhs()); |
| ActualRhsType actualRhs = RhsBlasTraits::extract(prod.rhs()); |
| |
| ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) |
| * RhsBlasTraits::extractScalarFactor(prod.rhs()); |
| |
| enum { |
| // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1 |
| // on, the other hand it is good for the cache to pack the vector anyways... |
| EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1, |
| ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex), |
| MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal |
| }; |
| |
| gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest; |
| |
| bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0)); |
| bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible; |
| |
| RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha); |
| |
| ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), |
| evalToDest ? dest.data() : static_dest.data()); |
| |
| if(!evalToDest) |
| { |
| #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN |
| int size = dest.size(); |
| EIGEN_DENSE_STORAGE_CTOR_PLUGIN |
| #endif |
| if(!alphaIsCompatible) |
| { |
| MappedDest(actualDestPtr, dest.size()).setZero(); |
| compatibleAlpha = RhsScalar(1); |
| } |
| else |
| MappedDest(actualDestPtr, dest.size()) = dest; |
| } |
| |
| typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper; |
| typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper; |
| general_matrix_vector_product |
| <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run( |
| actualLhs.rows(), actualLhs.cols(), |
| LhsMapper(actualLhs.data(), actualLhs.outerStride()), |
| RhsMapper(actualRhs.data(), actualRhs.innerStride()), |
| actualDestPtr, 1, |
| compatibleAlpha); |
| |
| if (!evalToDest) |
| { |
| if(!alphaIsCompatible) |
| dest += actualAlpha * MappedDest(actualDestPtr, dest.size()); |
| else |
| dest = MappedDest(actualDestPtr, dest.size()); |
| } |
| } |
| }; |
| |
| template<> struct gemv_selector<OnTheRight,RowMajor,true> |
| { |
| template<typename ProductType, typename Dest> |
| static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) |
| { |
| typedef typename ProductType::LhsScalar LhsScalar; |
| typedef typename ProductType::RhsScalar RhsScalar; |
| typedef typename ProductType::Scalar ResScalar; |
| typedef typename ProductType::Index Index; |
| typedef typename ProductType::ActualLhsType ActualLhsType; |
| typedef typename ProductType::ActualRhsType ActualRhsType; |
| typedef typename ProductType::_ActualRhsType _ActualRhsType; |
| typedef typename ProductType::LhsBlasTraits LhsBlasTraits; |
| typedef typename ProductType::RhsBlasTraits RhsBlasTraits; |
| |
| typename add_const<ActualLhsType>::type actualLhs = LhsBlasTraits::extract(prod.lhs()); |
| typename add_const<ActualRhsType>::type actualRhs = RhsBlasTraits::extract(prod.rhs()); |
| |
| ResScalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs()) |
| * RhsBlasTraits::extractScalarFactor(prod.rhs()); |
| |
| enum { |
| // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1 |
| // on, the other hand it is good for the cache to pack the vector anyways... |
| DirectlyUseRhs = _ActualRhsType::InnerStrideAtCompileTime==1 |
| }; |
| |
| gemv_static_vector_if<RhsScalar,_ActualRhsType::SizeAtCompileTime,_ActualRhsType::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs; |
| |
| ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(), |
| DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data()); |
| |
| if(!DirectlyUseRhs) |
| { |
| #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN |
| int size = actualRhs.size(); |
| EIGEN_DENSE_STORAGE_CTOR_PLUGIN |
| #endif |
| Map<typename _ActualRhsType::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs; |
| } |
| |
| typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper; |
| typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper; |
| general_matrix_vector_product |
| <Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run( |
| actualLhs.rows(), actualLhs.cols(), |
| LhsMapper(actualLhs.data(), actualLhs.outerStride()), |
| RhsMapper(actualRhsPtr, 1), |
| dest.data(), dest.innerStride(), |
| actualAlpha); |
| } |
| }; |
| |
| template<> struct gemv_selector<OnTheRight,ColMajor,false> |
| { |
| template<typename ProductType, typename Dest> |
| static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) |
| { |
| typedef typename Dest::Index Index; |
| // TODO makes sure dest is sequentially stored in memory, otherwise use a temp |
| const Index size = prod.rhs().rows(); |
| for(Index k=0; k<size; ++k) |
| dest += (alpha*prod.rhs().coeff(k)) * prod.lhs().col(k); |
| } |
| }; |
| |
| template<> struct gemv_selector<OnTheRight,RowMajor,false> |
| { |
| template<typename ProductType, typename Dest> |
| static void run(const ProductType& prod, Dest& dest, const typename ProductType::Scalar& alpha) |
| { |
| typedef typename Dest::Index Index; |
| // TODO makes sure rhs is sequentially stored in memory, otherwise use a temp |
| const Index rows = prod.rows(); |
| for(Index i=0; i<rows; ++i) |
| dest.coeffRef(i) += alpha * (prod.lhs().row(i).cwiseProduct(prod.rhs().transpose())).sum(); |
| } |
| }; |
| |
| } // end namespace internal |
| |
| /*************************************************************************** |
| * Implementation of matrix base methods |
| ***************************************************************************/ |
| |
| /** \returns the matrix product of \c *this and \a other. |
| * |
| * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*(). |
| * |
| * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*() |
| */ |
| #ifndef __CUDACC__ |
| |
| #ifdef EIGEN_TEST_EVALUATORS |
| template<typename Derived> |
| template<typename OtherDerived> |
| inline const Product<Derived, OtherDerived> |
| MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const |
| { |
| // A note regarding the function declaration: In MSVC, this function will sometimes |
| // not be inlined since DenseStorage is an unwindable object for dynamic |
| // matrices and product types are holding a member to store the result. |
| // Thus it does not help tagging this function with EIGEN_STRONG_INLINE. |
| enum { |
| ProductIsValid = Derived::ColsAtCompileTime==Dynamic |
| || OtherDerived::RowsAtCompileTime==Dynamic |
| || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), |
| AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, |
| SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) |
| }; |
| // note to the lost user: |
| // * for a dot product use: v1.dot(v2) |
| // * for a coeff-wise product use: v1.cwiseProduct(v2) |
| EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), |
| INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) |
| EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), |
| INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) |
| EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) |
| #ifdef EIGEN_DEBUG_PRODUCT |
| internal::product_type<Derived,OtherDerived>::debug(); |
| #endif |
| |
| return Product<Derived, OtherDerived>(derived(), other.derived()); |
| } |
| #else |
| template<typename Derived> |
| template<typename OtherDerived> |
| inline const typename ProductReturnType<Derived, OtherDerived>::Type |
| MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const |
| { |
| // A note regarding the function declaration: In MSVC, this function will sometimes |
| // not be inlined since DenseStorage is an unwindable object for dynamic |
| // matrices and product types are holding a member to store the result. |
| // Thus it does not help tagging this function with EIGEN_STRONG_INLINE. |
| enum { |
| ProductIsValid = Derived::ColsAtCompileTime==Dynamic |
| || OtherDerived::RowsAtCompileTime==Dynamic |
| || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), |
| AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, |
| SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) |
| }; |
| // note to the lost user: |
| // * for a dot product use: v1.dot(v2) |
| // * for a coeff-wise product use: v1.cwiseProduct(v2) |
| EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), |
| INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) |
| EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), |
| INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) |
| EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) |
| #ifdef EIGEN_DEBUG_PRODUCT |
| internal::product_type<Derived,OtherDerived>::debug(); |
| #endif |
| return typename ProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); |
| } |
| #endif |
| |
| #endif |
| /** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation. |
| * |
| * The returned product will behave like any other expressions: the coefficients of the product will be |
| * computed once at a time as requested. This might be useful in some extremely rare cases when only |
| * a small and no coherent fraction of the result's coefficients have to be computed. |
| * |
| * \warning This version of the matrix product can be much much slower. So use it only if you know |
| * what you are doing and that you measured a true speed improvement. |
| * |
| * \sa operator*(const MatrixBase&) |
| */ |
| template<typename Derived> |
| template<typename OtherDerived> |
| EIGEN_DEVICE_FUNC const typename LazyProductReturnType<Derived,OtherDerived>::Type |
| MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const |
| { |
| enum { |
| ProductIsValid = Derived::ColsAtCompileTime==Dynamic |
| || OtherDerived::RowsAtCompileTime==Dynamic |
| || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime), |
| AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime, |
| SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived) |
| }; |
| // note to the lost user: |
| // * for a dot product use: v1.dot(v2) |
| // * for a coeff-wise product use: v1.cwiseProduct(v2) |
| EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes), |
| INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS) |
| EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors), |
| INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION) |
| EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT) |
| |
| return typename LazyProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived()); |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_PRODUCT_H |
