Eigne Matrix Class Library
Dependents: Eigen_test Odometry_test AttitudeEstimation_usingTicker MPU9250_Quaternion_Binary_Serial ... more
Eigen Matrix Class Library for mbed.
Finally, you can use Eigen on your mbed!!!
src/Core/GeneralProduct.h
- Committer:
- ykuroda
- Date:
- 2016-10-13
- Revision:
- 0:13a5d365ba16
File content as of revision 0:13a5d365ba16:
// 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), LargeThreshold = EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD }; // 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(0,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,0) * 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 is_row_major : 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(), is_row_major<Dest>()); } template<typename Dest> inline void addTo(Dest& dest) const { internal::outer_product_selector_run(*this, dest, add(), is_row_major<Dest>()); } template<typename Dest> inline void subTo(Dest& dest) const { internal::outer_product_selector_run(*this, dest, sub(), is_row_major<Dest>()); } template<typename Dest> void scaleAndAddTo(Dest& dest, const Scalar& alpha) const { internal::outer_product_selector_run(*this, dest, adds(alpha), is_row_major<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(15))) + 16) : 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()); // make sure Dest is a compile-time vector type (bug 1166) typedef typename conditional<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr>::type ActualDest; 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 = (ActualDest::InnerStrideAtCompileTime==1), ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex), MightCannotUseDest = (ActualDest::InnerStrideAtCompileTime!=1) || ComplexByReal }; gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::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; } general_matrix_vector_product <Index,LhsScalar,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run( actualLhs.rows(), actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), 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; } general_matrix_vector_product <Index,LhsScalar,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsBlasTraits::NeedToConjugate>::run( actualLhs.rows(), actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhsPtr, 1, dest.data(), dest.col(0).innerStride(), //NOTE if dest is not a vector at compile-time, then dest.innerStride() might be wrong. (bug 1166) 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*() */ 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()); } /** \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> 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