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// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2008-2015 Gael Guennebaud // // 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_SPARSEVECTOR_H #define EIGEN_SPARSEVECTOR_H // IWYU pragma: private #include "./InternalHeaderCheck.h" namespace Eigen { /** \ingroup SparseCore_Module * \class SparseVector * * \brief a sparse vector class * * \tparam Scalar_ the scalar type, i.e. the type of the coefficients * * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme. * * This class can be extended with the help of the plugin mechanism described on the page * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN. */ namespace internal { template struct traits > { typedef Scalar_ Scalar; typedef StorageIndex_ StorageIndex; typedef Sparse StorageKind; typedef MatrixXpr XprKind; enum { IsColVector = (Options_ & RowMajorBit) ? 0 : 1, RowsAtCompileTime = IsColVector ? Dynamic : 1, ColsAtCompileTime = IsColVector ? 1 : Dynamic, MaxRowsAtCompileTime = RowsAtCompileTime, MaxColsAtCompileTime = ColsAtCompileTime, Flags = Options_ | NestByRefBit | LvalueBit | (IsColVector ? 0 : RowMajorBit) | CompressedAccessBit, SupportedAccessPatterns = InnerRandomAccessPattern }; }; // Sparse-Vector-Assignment kinds: enum { SVA_RuntimeSwitch, SVA_Inner, SVA_Outer }; template struct sparse_vector_assign_selector; } // namespace internal template class SparseVector : public SparseCompressedBase > { typedef SparseCompressedBase Base; using Base::convert_index; public: EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector) EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=) EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=) typedef internal::CompressedStorage Storage; enum { IsColVector = internal::traits::IsColVector }; enum { Options = Options_ }; EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; } EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; } EIGEN_STRONG_INLINE Index innerSize() const { return m_size; } EIGEN_STRONG_INLINE Index outerSize() const { return 1; } EIGEN_STRONG_INLINE const Scalar* valuePtr() const { return m_data.valuePtr(); } EIGEN_STRONG_INLINE Scalar* valuePtr() { return m_data.valuePtr(); } EIGEN_STRONG_INLINE const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); } EIGEN_STRONG_INLINE StorageIndex* innerIndexPtr() { return m_data.indexPtr(); } inline const StorageIndex* outerIndexPtr() const { return 0; } inline StorageIndex* outerIndexPtr() { return 0; } inline const StorageIndex* innerNonZeroPtr() const { return 0; } inline StorageIndex* innerNonZeroPtr() { return 0; } /** \internal */ constexpr Storage& data() { return m_data; } /** \internal */ constexpr const Storage& data() const { return m_data; } inline Scalar coeff(Index row, Index col) const { eigen_assert(IsColVector ? (col == 0 && row >= 0 && row < m_size) : (row == 0 && col >= 0 && col < m_size)); return coeff(IsColVector ? row : col); } inline Scalar coeff(Index i) const { eigen_assert(i >= 0 && i < m_size); return m_data.at(StorageIndex(i)); } inline Scalar& coeffRef(Index row, Index col) { eigen_assert(IsColVector ? (col == 0 && row >= 0 && row < m_size) : (row == 0 && col >= 0 && col < m_size)); return coeffRef(IsColVector ? row : col); } /** \returns a reference to the coefficient value at given index \a i * This operation involves a log(rho*size) binary search. If the coefficient does not * exist yet, then a sorted insertion into a sequential buffer is performed. * * This insertion might be very costly if the number of nonzeros above \a i is large. */ inline Scalar& coeffRef(Index i) { eigen_assert(i >= 0 && i < m_size); return m_data.atWithInsertion(StorageIndex(i)); } public: typedef typename Base::InnerIterator InnerIterator; typedef typename Base::ReverseInnerIterator ReverseInnerIterator; inline void setZero() { m_data.clear(); } /** \returns the number of non zero coefficients */ inline Index nonZeros() const { return m_data.size(); } inline void startVec(Index outer) { EIGEN_UNUSED_VARIABLE(outer); eigen_assert(outer == 0); } inline Scalar& insertBackByOuterInner(Index outer, Index inner) { EIGEN_UNUSED_VARIABLE(outer); eigen_assert(outer == 0); return insertBack(inner); } inline Scalar& insertBack(Index i) { m_data.append(Scalar(0), i); return m_data.value(m_data.size() - 1); } Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner) { EIGEN_UNUSED_VARIABLE(outer); eigen_assert(outer == 0); return insertBackUnordered(inner); } inline Scalar& insertBackUnordered(Index i) { m_data.append(Scalar(0), i); return m_data.value(m_data.size() - 1); } inline Scalar& insert(Index row, Index col) { eigen_assert(IsColVector ? (col == 0 && row >= 0 && row < m_size) : (row == 0 && col >= 0 && col < m_size)); Index inner = IsColVector ? row : col; Index outer = IsColVector ? col : row; EIGEN_ONLY_USED_FOR_DEBUG(outer); eigen_assert(outer == 0); return insert(inner); } Scalar& insert(Index i) { eigen_assert(i >= 0 && i < m_size); Index startId = 0; Index p = Index(m_data.size()) - 1; // TODO smart realloc m_data.resize(p + 2, 1); while ((p >= startId) && (m_data.index(p) > i)) { m_data.index(p + 1) = m_data.index(p); m_data.value(p + 1) = m_data.value(p); --p; } m_data.index(p + 1) = convert_index(i); m_data.value(p + 1) = Scalar(0); return m_data.value(p + 1); } /** */ inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); } inline void finalize() {} /** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */ Index prune(const Scalar& reference, const RealScalar& epsilon = NumTraits::dummy_precision()) { return prune([&](const Scalar& val) { return !internal::isMuchSmallerThan(val, reference, epsilon); }); } /** * \brief Prunes the entries of the vector based on a `predicate` * \tparam F Type of the predicate. * \param keep_predicate The predicate that is used to test whether a value should be kept. A callable that * gets passed om a `Scalar` value and returns a boolean. If the predicate returns true, the value is kept. * \return The new number of structural non-zeros. */ template Index prune(F&& keep_predicate) { Index k = 0; Index n = m_data.size(); for (Index i = 0; i < n; ++i) { if (keep_predicate(m_data.value(i))) { m_data.value(k) = std::move(m_data.value(i)); m_data.index(k) = m_data.index(i); ++k; } } m_data.resize(k); return k; } /** Resizes the sparse vector to \a rows x \a cols * * This method is provided for compatibility with matrices. * For a column vector, \a cols must be equal to 1. * For a row vector, \a rows must be equal to 1. * * \sa resize(Index) */ void resize(Index rows, Index cols) { eigen_assert((IsColVector ? cols : rows) == 1 && "Outer dimension must equal 1"); resize(IsColVector ? rows : cols); } /** Resizes the sparse vector to \a newSize * This method deletes all entries, thus leaving an empty sparse vector * * \sa conservativeResize(), setZero() */ void resize(Index newSize) { m_size = newSize; m_data.clear(); } /** Resizes the sparse vector to \a newSize, while leaving old values untouched. * * If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved. * Call .data().squeeze() to free extra memory. * * \sa reserve(), setZero() */ void conservativeResize(Index newSize) { if (newSize < m_size) { Index i = 0; while (i < m_data.size() && m_data.index(i) < newSize) ++i; m_data.resize(i); } m_size = newSize; } void resizeNonZeros(Index size) { m_data.resize(size); } inline SparseVector() : m_size(0) { resize(0); } explicit inline SparseVector(Index size) : m_size(0) { resize(size); } inline SparseVector(Index rows, Index cols) : m_size(0) { resize(rows, cols); } template inline SparseVector(const SparseMatrixBase& other) : m_size(0) { #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN #endif *this = other.derived(); } inline SparseVector(const SparseVector& other) : Base(other), m_size(0) { *this = other.derived(); } /** Swaps the values of \c *this and \a other. * Overloaded for performance: this version performs a \em shallow swap by swapping pointers and attributes only. * \sa SparseMatrixBase::swap() */ inline void swap(SparseVector& other) { std::swap(m_size, other.m_size); m_data.swap(other.m_data); } friend EIGEN_DEVICE_FUNC void swap(SparseVector& a, SparseVector& b) { a.swap(b); } template inline void swap(SparseMatrix& other) { eigen_assert(other.outerSize() == 1); std::swap(m_size, other.m_innerSize); m_data.swap(other.m_data); } template friend EIGEN_DEVICE_FUNC void swap(SparseVector& a, SparseMatrix& b) { a.swap(b); } template friend EIGEN_DEVICE_FUNC void swap(SparseMatrix& a, SparseVector& b) { b.swap(a); } inline SparseVector& operator=(const SparseVector& other) { if (other.isRValue()) { swap(other.const_cast_derived()); } else { resize(other.size()); m_data = other.m_data; } return *this; } template inline SparseVector& operator=(const SparseMatrixBase& other) { SparseVector tmp(other.size()); internal::sparse_vector_assign_selector::run(tmp, other.derived()); this->swap(tmp); return *this; } inline SparseVector(SparseVector&& other) : SparseVector() { this->swap(other); } template inline SparseVector(SparseCompressedBase&& other) : SparseVector() { *this = other.derived().markAsRValue(); } inline SparseVector& operator=(SparseVector&& other) { this->swap(other); return *this; } template inline SparseVector& operator=(SparseCompressedBase&& other) { *this = other.derived().markAsRValue(); return *this; } #ifndef EIGEN_PARSED_BY_DOXYGEN template inline SparseVector& operator=(const SparseSparseProduct& product) { return Base::operator=(product); } #endif #ifndef EIGEN_NO_IO friend std::ostream& operator<<(std::ostream& s, const SparseVector& m) { for (Index i = 0; i < m.nonZeros(); ++i) s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") "; s << std::endl; return s; } #endif /** Destructor */ inline ~SparseVector() {} /** Overloaded for performance */ Scalar sum() const; public: /** \internal \deprecated use setZero() and reserve() */ EIGEN_DEPRECATED_WITH_REASON("Use .setZero() and .reserve() instead.") void startFill(Index reserve) { setZero(); m_data.reserve(reserve); } /** \internal \deprecated use insertBack(Index,Index) */ EIGEN_DEPRECATED_WITH_REASON("Use .insertBack() instead.") Scalar& fill(Index r, Index c) { eigen_assert(r == 0 || c == 0); return fill(IsColVector ? r : c); } /** \internal \deprecated use insertBack(Index) */ EIGEN_DEPRECATED_WITH_REASON("Use .insertBack() instead.") Scalar& fill(Index i) { m_data.append(Scalar(0), i); return m_data.value(m_data.size() - 1); } /** \internal \deprecated use insert(Index,Index) */ EIGEN_DEPRECATED_WITH_REASON("Use .insert() instead.") Scalar& fillrand(Index r, Index c) { eigen_assert(r == 0 || c == 0); return fillrand(IsColVector ? r : c); } /** \internal \deprecated use insert(Index) */ EIGEN_DEPRECATED_WITH_REASON("Use .insert() instead.") Scalar& fillrand(Index i) { return insert(i); } /** \internal \deprecated use finalize() */ EIGEN_DEPRECATED_WITH_REASON("Use .finalize() instead.") void endFill() {} // These two functions were here in the 3.1 release, so let's keep them in case some code rely on them. /** \internal \deprecated use data() */ EIGEN_DEPRECATED_WITH_REASON("Use .data() instead.") Storage& _data() { return m_data; } /** \internal \deprecated use data() */ EIGEN_DEPRECATED_WITH_REASON("Use .data() instead.") const Storage& _data() const { return m_data; } #ifdef EIGEN_SPARSEVECTOR_PLUGIN #include EIGEN_SPARSEVECTOR_PLUGIN #endif protected: EIGEN_STATIC_ASSERT(NumTraits::IsSigned, THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE) EIGEN_STATIC_ASSERT((Options_ & (ColMajor | RowMajor)) == Options, INVALID_MATRIX_TEMPLATE_PARAMETERS) Storage m_data; Index m_size; }; namespace internal { template struct evaluator > : evaluator_base > { typedef SparseVector SparseVectorType; typedef evaluator_base Base; typedef typename SparseVectorType::InnerIterator InnerIterator; typedef typename SparseVectorType::ReverseInnerIterator ReverseInnerIterator; enum { CoeffReadCost = NumTraits::ReadCost, Flags = SparseVectorType::Flags }; evaluator() : Base() {} explicit evaluator(const SparseVectorType& mat) : m_matrix(&mat) { EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost); } inline Index nonZerosEstimate() const { return m_matrix->nonZeros(); } operator SparseVectorType&() { return m_matrix->const_cast_derived(); } operator const SparseVectorType&() const { return *m_matrix; } const SparseVectorType* m_matrix; }; template struct sparse_vector_assign_selector { static void run(Dest& dst, const Src& src) { eigen_internal_assert(src.innerSize() == src.size()); typedef internal::evaluator SrcEvaluatorType; SrcEvaluatorType srcEval(src); for (typename SrcEvaluatorType::InnerIterator it(srcEval, 0); it; ++it) dst.insert(it.index()) = it.value(); } }; template struct sparse_vector_assign_selector { static void run(Dest& dst, const Src& src) { eigen_internal_assert(src.outerSize() == src.size()); typedef internal::evaluator SrcEvaluatorType; SrcEvaluatorType srcEval(src); for (Index i = 0; i < src.size(); ++i) { typename SrcEvaluatorType::InnerIterator it(srcEval, i); if (it) dst.insert(i) = it.value(); } } }; template struct sparse_vector_assign_selector { static void run(Dest& dst, const Src& src) { if (src.outerSize() == 1) sparse_vector_assign_selector::run(dst, src); else sparse_vector_assign_selector::run(dst, src); } }; } // namespace internal // Specialization for SparseVector. // Serializes [size, numNonZeros, innerIndices, values]. template class Serializer, void> { public: typedef SparseVector SparseMat; struct Header { typename SparseMat::Index size; Index num_non_zeros; }; EIGEN_DEVICE_FUNC size_t size(const SparseMat& value) const { return sizeof(Header) + (sizeof(Scalar) + sizeof(StorageIndex)) * value.nonZeros(); } EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end, const SparseMat& value) { if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr; if (EIGEN_PREDICT_FALSE(dest + size(value) > end)) return nullptr; const size_t header_bytes = sizeof(Header); Header header = {value.innerSize(), value.nonZeros()}; EIGEN_USING_STD(memcpy) memcpy(dest, &header, header_bytes); dest += header_bytes; // Inner indices. std::size_t data_bytes = sizeof(StorageIndex) * header.num_non_zeros; memcpy(dest, value.innerIndexPtr(), data_bytes); dest += data_bytes; // Values. data_bytes = sizeof(Scalar) * header.num_non_zeros; memcpy(dest, value.valuePtr(), data_bytes); dest += data_bytes; return dest; } EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, SparseMat& value) const { if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr; if (EIGEN_PREDICT_FALSE(src + sizeof(Header) > end)) return nullptr; const size_t header_bytes = sizeof(Header); Header header; EIGEN_USING_STD(memcpy) memcpy(&header, src, header_bytes); src += header_bytes; value.setZero(); value.resize(header.size); value.resizeNonZeros(header.num_non_zeros); // Inner indices. std::size_t data_bytes = sizeof(StorageIndex) * header.num_non_zeros; if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr; memcpy(value.innerIndexPtr(), src, data_bytes); src += data_bytes; // Values. data_bytes = sizeof(Scalar) * header.num_non_zeros; if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr; memcpy(value.valuePtr(), src, data_bytes); src += data_bytes; return src; } }; } // end namespace Eigen #endif // EIGEN_SPARSEVECTOR_H