| // 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 |
| |
| // IWYU pragma: private |
| #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> { |
| #if EIGEN_MAX_STATIC_ALIGN_BYTES != 0 |
| internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize), 0, AlignedMax> |
| 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) + EIGEN_MAX_ALIGN_BYTES, 0> |
| m_data; |
| EIGEN_STRONG_INLINE Scalar* data() { |
| return reinterpret_cast<Scalar*>((std::uintptr_t(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES - 1))) + EIGEN_MAX_ALIGN_BYTES); |
| } |
| #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 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 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 |
