| // 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 |
| |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| enum { |
| Large = 2, |
| Small = 3 |
| }; |
| |
| // Define the threshold value to fallback from the generic matrix-matrix product |
| // implementation (heavy) to the lightweight coeff-based product one. |
| // See generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct> |
| // in products/GeneralMatrixMatrix.h for more details. |
| // TODO This threshold should also be used in the compile-time selector below. |
| #ifndef EIGEN_GEMM_TO_COEFFBASED_THRESHOLD |
| // This default value has been obtained on a Haswell architecture. |
| #define EIGEN_GEMM_TO_COEFFBASED_THRESHOLD 20 |
| #endif |
| |
| namespace internal { |
| |
| template<int Rows, int Cols, int Depth> struct product_type_selector; |
| |
| template<int Size, int MaxSize> struct product_size_category |
| { |
| enum { |
| #ifndef EIGEN_GPU_COMPILE_PHASE |
| is_large = MaxSize == Dynamic || |
| Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD || |
| (Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD), |
| #else |
| is_large = 0, |
| #endif |
| value = is_large ? Large |
| : Size == 1 ? 1 |
| : Small |
| }; |
| }; |
| |
| template<typename Lhs, typename Rhs> struct product_type |
| { |
| typedef remove_all_t<Lhs> Lhs_; |
| typedef remove_all_t<Rhs> Rhs_; |
| enum { |
| MaxRows = traits<Lhs_>::MaxRowsAtCompileTime, |
| Rows = traits<Lhs_>::RowsAtCompileTime, |
| MaxCols = traits<Rhs_>::MaxColsAtCompileTime, |
| Cols = traits<Rhs_>::ColsAtCompileTime, |
| MaxDepth = min_size_prefer_fixed(traits<Lhs_>::MaxColsAtCompileTime, |
| traits<Rhs_>::MaxRowsAtCompileTime), |
| Depth = min_size_prefer_fixed(traits<Lhs_>::ColsAtCompileTime, |
| traits<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, |
| ret = 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 M> struct product_type_selector<M, 1, 1> { enum { ret = LazyCoeffBasedProductMode }; }; |
| template<int N> struct product_type_selector<1, N, 1> { enum { ret = LazyCoeffBasedProductMode }; }; |
| 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 = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Small,Large,Small> { enum { ret = CoeffBasedProductMode }; }; |
| template<> struct product_type_selector<Large,Large,Small> { enum { ret = GemmProduct }; }; |
| |
| } // end namespace internal |
| |
| /*********************************************************************** |
| * 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); |
| |
| /*********************************************************************** |
| * Implementation of Outer Vector Vector Product |
| ***********************************************************************/ |
| |
| /*********************************************************************** |
| * 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<int Side, int StorageOrder, bool BlasCompatible> |
| struct gemv_dense_selector; |
| |
| } // end namespace internal |
| |
| namespace internal { |
| |
| 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 EIGEN_DEVICE_FUNC 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 EIGEN_DEVICE_FUNC Scalar* data() { return 0; } |
| }; |
| |
| template<typename Scalar,int Size,int MaxSize> |
| struct gemv_static_vector_if<Scalar,Size,MaxSize,true> |
| { |
| enum { |
| ForceAlignment = internal::packet_traits<Scalar>::Vectorizable, |
| PacketSize = internal::packet_traits<Scalar>::size |
| }; |
| #if EIGEN_MAX_STATIC_ALIGN_BYTES!=0 |
| internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize), 0, |
| internal::plain_enum_min(AlignedMax, PacketSize)> 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. |
| internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data; |
| EIGEN_STRONG_INLINE Scalar* data() { |
| return ForceAlignment |
| ? reinterpret_cast<Scalar*>((internal::UIntPtr(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES) |
| : m_data.array; |
| } |
| #endif |
| }; |
| |
| // The vector is on the left => transposition |
| template<int StorageOrder, bool BlasCompatible> |
| struct gemv_dense_selector<OnTheLeft,StorageOrder,BlasCompatible> |
| { |
| template<typename Lhs, typename Rhs, typename Dest> |
| static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) |
| { |
| Transpose<Dest> destT(dest); |
| enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor }; |
| gemv_dense_selector<OnTheRight,OtherStorageOrder,BlasCompatible> |
| ::run(rhs.transpose(), lhs.transpose(), destT, alpha); |
| } |
| }; |
| |
| template<> struct gemv_dense_selector<OnTheRight,ColMajor,true> |
| { |
| template<typename Lhs, typename Rhs, typename Dest> |
| static inline void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) |
| { |
| typedef typename Lhs::Scalar LhsScalar; |
| typedef typename Rhs::Scalar RhsScalar; |
| typedef typename Dest::Scalar ResScalar; |
| |
| typedef internal::blas_traits<Lhs> LhsBlasTraits; |
| typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType; |
| typedef internal::blas_traits<Rhs> RhsBlasTraits; |
| typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType; |
| |
| typedef Map<Matrix<ResScalar,Dynamic,1>, plain_enum_min(AlignedMax, internal::packet_traits<ResScalar>::size)> MappedDest; |
| |
| ActualLhsType actualLhs = LhsBlasTraits::extract(lhs); |
| ActualRhsType actualRhs = RhsBlasTraits::extract(rhs); |
| |
| ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs); |
| |
| // make sure Dest is a compile-time vector type (bug 1166) |
| typedef std::conditional_t<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr> 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 = ((!EvalToDestAtCompileTime) || ComplexByReal) && (ActualDest::MaxSizeAtCompileTime!=0) |
| }; |
| |
| typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper; |
| typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper; |
| RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha); |
| |
| if(!MightCannotUseDest) |
| { |
| // shortcut if we are sure to be able to use dest directly, |
| // this ease the compiler to generate cleaner and more optimzized code for most common cases |
| 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()), |
| dest.data(), 1, |
| compatibleAlpha); |
| } |
| else |
| { |
| gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest; |
| |
| const bool alphaIsCompatible = (!ComplexByReal) || (numext::is_exactly_zero(numext::imag(actualAlpha))); |
| const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible; |
| |
| ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(), |
| evalToDest ? dest.data() : static_dest.data()); |
| |
| if(!evalToDest) |
| { |
| #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN |
| Index 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,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.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size()); |
| else |
| dest = MappedDest(actualDestPtr, dest.size()); |
| } |
| } |
| } |
| }; |
| |
| template<> struct gemv_dense_selector<OnTheRight,RowMajor,true> |
| { |
| template<typename Lhs, typename Rhs, typename Dest> |
| static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) |
| { |
| typedef typename Lhs::Scalar LhsScalar; |
| typedef typename Rhs::Scalar RhsScalar; |
| typedef typename Dest::Scalar ResScalar; |
| |
| typedef internal::blas_traits<Lhs> LhsBlasTraits; |
| typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType; |
| typedef internal::blas_traits<Rhs> RhsBlasTraits; |
| typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType; |
| typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned; |
| |
| std::add_const_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs); |
| std::add_const_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs); |
| |
| ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, 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 = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1 || ActualRhsTypeCleaned::MaxSizeAtCompileTime==0 |
| }; |
| |
| gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::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 |
| Index size = actualRhs.size(); |
| EIGEN_DENSE_STORAGE_CTOR_PLUGIN |
| #endif |
| Map<typename ActualRhsTypeCleaned::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.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_dense_selector<OnTheRight,ColMajor,false> |
| { |
| template<typename Lhs, typename Rhs, typename Dest> |
| static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) |
| { |
| EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE); |
| // TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory, otherwise use a temp |
| typename nested_eval<Rhs,1>::type actual_rhs(rhs); |
| const Index size = rhs.rows(); |
| for(Index k=0; k<size; ++k) |
| dest += (alpha*actual_rhs.coeff(k)) * lhs.col(k); |
| } |
| }; |
| |
| template<> struct gemv_dense_selector<OnTheRight,RowMajor,false> |
| { |
| template<typename Lhs, typename Rhs, typename Dest> |
| static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha) |
| { |
| EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE); |
| typename nested_eval<Rhs,Lhs::RowsAtCompileTime>::type actual_rhs(rhs); |
| const Index rows = dest.rows(); |
| for(Index i=0; i<rows; ++i) |
| dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(actual_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> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR |
| 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()); |
| } |
| |
| /** \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 EIGEN_STRONG_INLINE EIGEN_CONSTEXPR |
| const Product<Derived,OtherDerived,LazyProduct> |
| 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 Product<Derived,OtherDerived,LazyProduct>(derived(), other.derived()); |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_PRODUCT_H |
