| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2008-2014 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_SPARSEMATRIX_H |
| #define EIGEN_SPARSEMATRIX_H |
| |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| /** \ingroup SparseCore_Module |
| * |
| * \class SparseMatrix |
| * |
| * \brief A versatible sparse matrix representation |
| * |
| * This class implements a more versatile variants of the common \em compressed row/column storage format. |
| * Each colmun's (resp. row) non zeros are stored as a pair of value with associated row (resp. colmiun) index. |
| * All the non zeros are stored in a single large buffer. Unlike the \em compressed format, there might be extra |
| * space in between the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero |
| * can be done with limited memory reallocation and copies. |
| * |
| * A call to the function makeCompressed() turns the matrix into the standard \em compressed format |
| * compatible with many library. |
| * |
| * More details on this storage sceheme are given in the \ref TutorialSparse "manual pages". |
| * |
| * \tparam Scalar_ the scalar type, i.e. the type of the coefficients |
| * \tparam Options_ Union of bit flags controlling the storage scheme. Currently the only possibility |
| * is ColMajor or RowMajor. The default is 0 which means column-major. |
| * \tparam StorageIndex_ the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int. |
| * |
| * \warning In %Eigen 3.2, the undocumented type \c SparseMatrix::Index was improperly defined as the storage index type (e.g., int), |
| * whereas it is now (starting from %Eigen 3.3) deprecated and always defined as Eigen::Index. |
| * Codes making use of \c SparseMatrix::Index, might thus likely have to be changed to use \c SparseMatrix::StorageIndex instead. |
| * |
| * 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_SPARSEMATRIX_PLUGIN. |
| */ |
| |
| namespace internal { |
| template<typename Scalar_, int Options_, typename StorageIndex_> |
| struct traits<SparseMatrix<Scalar_, Options_, StorageIndex_> > |
| { |
| typedef Scalar_ Scalar; |
| typedef StorageIndex_ StorageIndex; |
| typedef Sparse StorageKind; |
| typedef MatrixXpr XprKind; |
| enum { |
| RowsAtCompileTime = Dynamic, |
| ColsAtCompileTime = Dynamic, |
| MaxRowsAtCompileTime = Dynamic, |
| MaxColsAtCompileTime = Dynamic, |
| Flags = Options_ | NestByRefBit | LvalueBit | CompressedAccessBit, |
| SupportedAccessPatterns = InnerRandomAccessPattern |
| }; |
| }; |
| |
| template<typename Scalar_, int Options_, typename StorageIndex_, int DiagIndex> |
| struct traits<Diagonal<SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex> > |
| { |
| typedef SparseMatrix<Scalar_, Options_, StorageIndex_> MatrixType; |
| typedef typename ref_selector<MatrixType>::type MatrixTypeNested; |
| typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_; |
| |
| typedef Scalar_ Scalar; |
| typedef Dense StorageKind; |
| typedef StorageIndex_ StorageIndex; |
| typedef MatrixXpr XprKind; |
| |
| enum { |
| RowsAtCompileTime = Dynamic, |
| ColsAtCompileTime = 1, |
| MaxRowsAtCompileTime = Dynamic, |
| MaxColsAtCompileTime = 1, |
| Flags = LvalueBit |
| }; |
| }; |
| |
| template<typename Scalar_, int Options_, typename StorageIndex_, int DiagIndex> |
| struct traits<Diagonal<const SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex> > |
| : public traits<Diagonal<SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex> > |
| { |
| enum { |
| Flags = 0 |
| }; |
| }; |
| |
| template <typename StorageIndex> |
| struct sparse_reserve_op { |
| EIGEN_DEVICE_FUNC sparse_reserve_op(Index begin, Index end, Index size) { |
| Index range = numext::mini(end - begin, size); |
| m_begin = begin; |
| m_end = begin + range; |
| m_val = StorageIndex(size / range); |
| m_remainder = StorageIndex(size % range); |
| } |
| template <typename IndexType> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex operator()(IndexType i) const { |
| if ((i >= m_begin) && (i < m_end)) |
| return m_val + ((i - m_begin) < m_remainder ? 1 : 0); |
| else |
| return 0; |
| } |
| StorageIndex m_val, m_remainder; |
| Index m_begin, m_end; |
| }; |
| |
| template <typename Scalar> |
| struct functor_traits<sparse_reserve_op<Scalar>> { |
| enum { Cost = 1, PacketAccess = false, IsRepeatable = true }; |
| }; |
| |
| } // end namespace internal |
| |
| template<typename Scalar_, int Options_, typename StorageIndex_> |
| class SparseMatrix |
| : public SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_> > |
| { |
| typedef SparseCompressedBase<SparseMatrix> Base; |
| using Base::convert_index; |
| friend class SparseVector<Scalar_,0,StorageIndex_>; |
| template<typename, typename, typename, typename, typename> |
| friend struct internal::Assignment; |
| public: |
| using Base::isCompressed; |
| using Base::nonZeros; |
| EIGEN_SPARSE_PUBLIC_INTERFACE(SparseMatrix) |
| using Base::operator+=; |
| using Base::operator-=; |
| |
| typedef Eigen::Map<SparseMatrix<Scalar,Flags,StorageIndex>> Map; |
| typedef Diagonal<SparseMatrix> DiagonalReturnType; |
| typedef Diagonal<const SparseMatrix> ConstDiagonalReturnType; |
| typedef typename Base::InnerIterator InnerIterator; |
| typedef typename Base::ReverseInnerIterator ReverseInnerIterator; |
| |
| |
| using Base::IsRowMajor; |
| typedef internal::CompressedStorage<Scalar,StorageIndex> Storage; |
| enum { |
| Options = Options_ |
| }; |
| |
| typedef typename Base::IndexVector IndexVector; |
| typedef typename Base::ScalarVector ScalarVector; |
| protected: |
| typedef SparseMatrix<Scalar,(Flags&~RowMajorBit)|(IsRowMajor?RowMajorBit:0),StorageIndex> TransposedSparseMatrix; |
| |
| Index m_outerSize; |
| Index m_innerSize; |
| StorageIndex* m_outerIndex; |
| StorageIndex* m_innerNonZeros; // optional, if null then the data is compressed |
| Storage m_data; |
| |
| public: |
| |
| /** \returns the number of rows of the matrix */ |
| inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; } |
| /** \returns the number of columns of the matrix */ |
| inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; } |
| |
| /** \returns the number of rows (resp. columns) of the matrix if the storage order column major (resp. row major) */ |
| inline Index innerSize() const { return m_innerSize; } |
| /** \returns the number of columns (resp. rows) of the matrix if the storage order column major (resp. row major) */ |
| inline Index outerSize() const { return m_outerSize; } |
| |
| /** \returns a const pointer to the array of values. |
| * This function is aimed at interoperability with other libraries. |
| * \sa innerIndexPtr(), outerIndexPtr() */ |
| inline const Scalar* valuePtr() const { return m_data.valuePtr(); } |
| /** \returns a non-const pointer to the array of values. |
| * This function is aimed at interoperability with other libraries. |
| * \sa innerIndexPtr(), outerIndexPtr() */ |
| inline Scalar* valuePtr() { return m_data.valuePtr(); } |
| |
| /** \returns a const pointer to the array of inner indices. |
| * This function is aimed at interoperability with other libraries. |
| * \sa valuePtr(), outerIndexPtr() */ |
| inline const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); } |
| /** \returns a non-const pointer to the array of inner indices. |
| * This function is aimed at interoperability with other libraries. |
| * \sa valuePtr(), outerIndexPtr() */ |
| inline StorageIndex* innerIndexPtr() { return m_data.indexPtr(); } |
| |
| /** \returns a const pointer to the array of the starting positions of the inner vectors. |
| * This function is aimed at interoperability with other libraries. |
| * \sa valuePtr(), innerIndexPtr() */ |
| inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; } |
| /** \returns a non-const pointer to the array of the starting positions of the inner vectors. |
| * This function is aimed at interoperability with other libraries. |
| * \sa valuePtr(), innerIndexPtr() */ |
| inline StorageIndex* outerIndexPtr() { return m_outerIndex; } |
| |
| /** \returns a const pointer to the array of the number of non zeros of the inner vectors. |
| * This function is aimed at interoperability with other libraries. |
| * \warning it returns the null pointer 0 in compressed mode */ |
| inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; } |
| /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors. |
| * This function is aimed at interoperability with other libraries. |
| * \warning it returns the null pointer 0 in compressed mode */ |
| inline StorageIndex* innerNonZeroPtr() { return m_innerNonZeros; } |
| |
| /** \internal */ |
| inline Storage& data() { return m_data; } |
| /** \internal */ |
| inline const Storage& data() const { return m_data; } |
| |
| /** \returns the value of the matrix at position \a i, \a j |
| * This function returns Scalar(0) if the element is an explicit \em zero */ |
| inline Scalar coeff(Index row, Index col) const |
| { |
| eigen_assert(row>=0 && row<rows() && col>=0 && col<cols()); |
| |
| const Index outer = IsRowMajor ? row : col; |
| const Index inner = IsRowMajor ? col : row; |
| Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1]; |
| return m_data.atInRange(m_outerIndex[outer], end, inner); |
| } |
| |
| /** \returns a non-const reference to the value of the matrix at position \a i, \a j |
| * |
| * If the element does not exist then it is inserted via the insert(Index,Index) function |
| * which itself turns the matrix into a non compressed form if that was not the case. |
| * |
| * This is a O(log(nnz_j)) operation (binary search) plus the cost of insert(Index,Index) |
| * function if the element does not already exist. |
| */ |
| inline Scalar& coeffRef(Index row, Index col) |
| { |
| eigen_assert(row>=0 && row<rows() && col>=0 && col<cols()); |
| const Index outer = IsRowMajor ? row : col; |
| const Index inner = IsRowMajor ? col : row; |
| Index start = m_outerIndex[outer]; |
| Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer + 1]; |
| eigen_assert(end >= start && "you probably called coeffRef on a non finalized matrix"); |
| if (end <= start) return insertAtByOuterInner(outer, inner, start); |
| Index dst = m_data.searchLowerIndex(start, end, inner); |
| if ((dst < end) && (m_data.index(dst) == inner)) |
| return m_data.value(dst); |
| else |
| return insertAtByOuterInner(outer, inner, dst); |
| } |
| |
| /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col. |
| * The non zero coefficient must \b not already exist. |
| * |
| * If the matrix \c *this is in compressed mode, then \c *this is turned into uncompressed |
| * mode while reserving room for 2 x this->innerSize() non zeros if reserve(Index) has not been called earlier. |
| * In this case, the insertion procedure is optimized for a \e sequential insertion mode where elements are assumed to be |
| * inserted by increasing outer-indices. |
| * |
| * If that's not the case, then it is strongly recommended to either use a triplet-list to assemble the matrix, or to first |
| * call reserve(const SizesType &) to reserve the appropriate number of non-zero elements per inner vector. |
| * |
| * Assuming memory has been appropriately reserved, this function performs a sorted insertion in O(1) |
| * if the elements of each inner vector are inserted in increasing inner index order, and in O(nnz_j) for a random insertion. |
| * |
| */ |
| inline Scalar& insert(Index row, Index col); |
| |
| public: |
| |
| /** Removes all non zeros but keep allocated memory |
| * |
| * This function does not free the currently allocated memory. To release as much as memory as possible, |
| * call \code mat.data().squeeze(); \endcode after resizing it. |
| * |
| * \sa resize(Index,Index), data() |
| */ |
| inline void setZero() |
| { |
| m_data.clear(); |
| std::fill_n(m_outerIndex, m_outerSize + 1, StorageIndex(0)); |
| if(m_innerNonZeros) { |
| std::fill_n(m_innerNonZeros, m_outerSize, StorageIndex(0)); |
| } |
| } |
| |
| /** Preallocates \a reserveSize non zeros. |
| * |
| * Precondition: the matrix must be in compressed mode. */ |
| inline void reserve(Index reserveSize) |
| { |
| eigen_assert(isCompressed() && "This function does not make sense in non compressed mode."); |
| m_data.reserve(reserveSize); |
| } |
| |
| #ifdef EIGEN_PARSED_BY_DOXYGEN |
| /** Preallocates \a reserveSize[\c j] non zeros for each column (resp. row) \c j. |
| * |
| * This function turns the matrix in non-compressed mode. |
| * |
| * The type \c SizesType must expose the following interface: |
| \code |
| typedef value_type; |
| const value_type& operator[](i) const; |
| \endcode |
| * for \c i in the [0,this->outerSize()[ range. |
| * Typical choices include std::vector<int>, Eigen::VectorXi, Eigen::VectorXi::Constant, etc. |
| */ |
| template<class SizesType> |
| inline void reserve(const SizesType& reserveSizes); |
| #else |
| template<class SizesType> |
| inline void reserve(const SizesType& reserveSizes, const typename SizesType::value_type& enableif = |
| typename SizesType::value_type()) |
| { |
| EIGEN_UNUSED_VARIABLE(enableif); |
| reserveInnerVectors(reserveSizes); |
| } |
| #endif // EIGEN_PARSED_BY_DOXYGEN |
| protected: |
| template<class SizesType> |
| inline void reserveInnerVectors(const SizesType& reserveSizes) |
| { |
| if(isCompressed()) |
| { |
| Index totalReserveSize = 0; |
| for (Index j = 0; j < m_outerSize; ++j) totalReserveSize += reserveSizes[j]; |
| |
| // if reserveSizes is empty, don't do anything! |
| if (totalReserveSize == 0) return; |
| |
| // turn the matrix into non-compressed mode |
| m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize); |
| |
| // temporarily use m_innerSizes to hold the new starting points. |
| StorageIndex* newOuterIndex = m_innerNonZeros; |
| |
| StorageIndex count = 0; |
| for(Index j=0; j<m_outerSize; ++j) |
| { |
| newOuterIndex[j] = count; |
| count += reserveSizes[j] + (m_outerIndex[j+1]-m_outerIndex[j]); |
| } |
| |
| m_data.reserve(totalReserveSize); |
| StorageIndex previousOuterIndex = m_outerIndex[m_outerSize]; |
| for(Index j=m_outerSize-1; j>=0; --j) |
| { |
| StorageIndex innerNNZ = previousOuterIndex - m_outerIndex[j]; |
| StorageIndex begin = m_outerIndex[j]; |
| StorageIndex end = begin + innerNNZ; |
| StorageIndex target = newOuterIndex[j]; |
| internal::smart_memmove(innerIndexPtr() + begin, innerIndexPtr() + end, innerIndexPtr() + target); |
| internal::smart_memmove(valuePtr() + begin, valuePtr() + end, valuePtr() + target); |
| previousOuterIndex = m_outerIndex[j]; |
| m_outerIndex[j] = newOuterIndex[j]; |
| m_innerNonZeros[j] = innerNNZ; |
| } |
| if(m_outerSize>0) |
| m_outerIndex[m_outerSize] = m_outerIndex[m_outerSize-1] + m_innerNonZeros[m_outerSize-1] + reserveSizes[m_outerSize-1]; |
| |
| m_data.resize(m_outerIndex[m_outerSize]); |
| } |
| else |
| { |
| StorageIndex* newOuterIndex = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize + 1); |
| |
| StorageIndex count = 0; |
| for(Index j=0; j<m_outerSize; ++j) |
| { |
| newOuterIndex[j] = count; |
| StorageIndex alreadyReserved = (m_outerIndex[j+1]-m_outerIndex[j]) - m_innerNonZeros[j]; |
| StorageIndex toReserve = std::max<StorageIndex>(reserveSizes[j], alreadyReserved); |
| count += toReserve + m_innerNonZeros[j]; |
| } |
| newOuterIndex[m_outerSize] = count; |
| |
| m_data.resize(count); |
| for(Index j=m_outerSize-1; j>=0; --j) |
| { |
| StorageIndex offset = newOuterIndex[j] - m_outerIndex[j]; |
| if(offset>0) |
| { |
| StorageIndex innerNNZ = m_innerNonZeros[j]; |
| StorageIndex begin = m_outerIndex[j]; |
| StorageIndex end = begin + innerNNZ; |
| StorageIndex target = newOuterIndex[j]; |
| internal::smart_memmove(innerIndexPtr() + begin, innerIndexPtr() + end, innerIndexPtr() + target); |
| internal::smart_memmove(valuePtr() + begin, valuePtr() + end, valuePtr() + target); |
| } |
| } |
| |
| std::swap(m_outerIndex, newOuterIndex); |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(newOuterIndex, m_outerSize + 1); |
| } |
| |
| } |
| public: |
| |
| //--- low level purely coherent filling --- |
| |
| /** \internal |
| * \returns a reference to the non zero coefficient at position \a row, \a col assuming that: |
| * - the nonzero does not already exist |
| * - the new coefficient is the last one according to the storage order |
| * |
| * Before filling a given inner vector you must call the statVec(Index) function. |
| * |
| * After an insertion session, you should call the finalize() function. |
| * |
| * \sa insert, insertBackByOuterInner, startVec */ |
| inline Scalar& insertBack(Index row, Index col) |
| { |
| return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row); |
| } |
| |
| /** \internal |
| * \sa insertBack, startVec */ |
| inline Scalar& insertBackByOuterInner(Index outer, Index inner) |
| { |
| eigen_assert(Index(m_outerIndex[outer+1]) == m_data.size() && "Invalid ordered insertion (invalid outer index)"); |
| eigen_assert( (m_outerIndex[outer+1]-m_outerIndex[outer]==0 || m_data.index(m_data.size()-1)<inner) && "Invalid ordered insertion (invalid inner index)"); |
| StorageIndex p = m_outerIndex[outer+1]; |
| ++m_outerIndex[outer+1]; |
| m_data.append(Scalar(0), inner); |
| return m_data.value(p); |
| } |
| |
| /** \internal |
| * \warning use it only if you know what you are doing */ |
| inline Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner) |
| { |
| StorageIndex p = m_outerIndex[outer+1]; |
| ++m_outerIndex[outer+1]; |
| m_data.append(Scalar(0), inner); |
| return m_data.value(p); |
| } |
| |
| /** \internal |
| * \sa insertBack, insertBackByOuterInner */ |
| inline void startVec(Index outer) |
| { |
| eigen_assert(m_outerIndex[outer]==Index(m_data.size()) && "You must call startVec for each inner vector sequentially"); |
| eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially"); |
| m_outerIndex[outer+1] = m_outerIndex[outer]; |
| } |
| |
| /** \internal |
| * Must be called after inserting a set of non zero entries using the low level compressed API. |
| */ |
| inline void finalize() |
| { |
| if(isCompressed()) |
| { |
| StorageIndex size = internal::convert_index<StorageIndex>(m_data.size()); |
| Index i = m_outerSize; |
| // find the last filled column |
| while (i>=0 && m_outerIndex[i]==0) |
| --i; |
| ++i; |
| while (i<=m_outerSize) |
| { |
| m_outerIndex[i] = size; |
| ++i; |
| } |
| } |
| } |
| |
| //--- |
| |
| template<typename InputIterators> |
| void setFromTriplets(const InputIterators& begin, const InputIterators& end); |
| |
| template<typename InputIterators,typename DupFunctor> |
| void setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func); |
| |
| template<typename Derived, typename DupFunctor> |
| void collapseDuplicates(DenseBase<Derived>& wi, DupFunctor dup_func = DupFunctor()); |
| |
| template<typename InputIterators> |
| void setFromSortedTriplets(const InputIterators& begin, const InputIterators& end); |
| |
| template<typename InputIterators, typename DupFunctor> |
| void setFromSortedTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func); |
| |
| //--- |
| |
| /** \internal |
| * same as insert(Index,Index) except that the indices are given relative to the storage order */ |
| Scalar& insertByOuterInner(Index j, Index i) |
| { |
| return insert(IsRowMajor ? j : i, IsRowMajor ? i : j); |
| } |
| |
| /** Turns the matrix into the \em compressed format. |
| */ |
| void makeCompressed() |
| { |
| if (isCompressed()) return; |
| |
| eigen_internal_assert(m_outerIndex!=0 && m_outerSize>0); |
| |
| StorageIndex start = m_outerIndex[1]; |
| m_outerIndex[1] = m_innerNonZeros[0]; |
| for(Index j=1; j<m_outerSize; ++j) |
| { |
| StorageIndex end = start + m_innerNonZeros[j]; |
| StorageIndex target = m_outerIndex[j]; |
| if (start != target) |
| { |
| internal::smart_memmove(innerIndexPtr() + start, innerIndexPtr() + end, innerIndexPtr() + target); |
| internal::smart_memmove(valuePtr() + start, valuePtr() + end, valuePtr() + target); |
| } |
| start = m_outerIndex[j + 1]; |
| m_outerIndex[j + 1] = m_outerIndex[j] + m_innerNonZeros[j]; |
| } |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| m_innerNonZeros = 0; |
| m_data.resize(m_outerIndex[m_outerSize]); |
| m_data.squeeze(); |
| } |
| |
| /** Turns the matrix into the uncompressed mode */ |
| void uncompress() |
| { |
| if(m_innerNonZeros != 0) return; |
| m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize); |
| typename IndexVector::AlignedMapType innerNonZeroMap(m_innerNonZeros, m_outerSize); |
| typename IndexVector::ConstAlignedMapType outerIndexMap(m_outerIndex, m_outerSize); |
| typename IndexVector::ConstMapType nextOuterIndexMap(m_outerIndex + 1, m_outerSize); |
| innerNonZeroMap = nextOuterIndexMap - outerIndexMap; |
| } |
| |
| /** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerance \a epsilon */ |
| void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) |
| { |
| prune(default_prunning_func(reference,epsilon)); |
| } |
| |
| /** Turns the matrix into compressed format, and suppresses all nonzeros which do not satisfy the predicate \a keep. |
| * The functor type \a KeepFunc must implement the following function: |
| * \code |
| * bool operator() (const Index& row, const Index& col, const Scalar& value) const; |
| * \endcode |
| * \sa prune(Scalar,RealScalar) |
| */ |
| template<typename KeepFunc> |
| void prune(const KeepFunc& keep = KeepFunc()) |
| { |
| StorageIndex k = 0; |
| for(Index j=0; j<m_outerSize; ++j) |
| { |
| StorageIndex previousStart = m_outerIndex[j]; |
| if (isCompressed()) |
| m_outerIndex[j] = k; |
| else |
| k = m_outerIndex[j]; |
| StorageIndex end = isCompressed() ? m_outerIndex[j+1] : previousStart + m_innerNonZeros[j]; |
| for(StorageIndex i=previousStart; i<end; ++i) |
| { |
| StorageIndex row = IsRowMajor ? StorageIndex(j) : m_data.index(i); |
| StorageIndex col = IsRowMajor ? m_data.index(i) : StorageIndex(j); |
| bool keepEntry = keep(row, col, m_data.value(i)); |
| if (keepEntry) { |
| m_data.value(k) = m_data.value(i); |
| m_data.index(k) = m_data.index(i); |
| ++k; |
| } else if (!isCompressed()) |
| m_innerNonZeros[j]--; |
| } |
| } |
| if (isCompressed()) { |
| m_outerIndex[m_outerSize] = k; |
| m_data.resize(k, 0); |
| } |
| } |
| |
| /** Resizes the matrix to a \a rows x \a cols matrix leaving old values untouched. |
| * |
| * If the sizes of the matrix are decreased, then the matrix is turned to \b uncompressed-mode |
| * and the storage of the out of bounds coefficients is kept and reserved. |
| * Call makeCompressed() to pack the entries and squeeze extra memory. |
| * |
| * \sa reserve(), setZero(), makeCompressed() |
| */ |
| void conservativeResize(Index rows, Index cols) { |
| |
| // If one dimension is null, then there is nothing to be preserved |
| if (rows == 0 || cols == 0) return resize(rows, cols); |
| |
| Index newOuterSize = IsRowMajor ? rows : cols; |
| Index newInnerSize = IsRowMajor ? cols : rows; |
| |
| Index innerChange = newInnerSize - innerSize(); |
| Index outerChange = newOuterSize - outerSize(); |
| |
| if (outerChange != 0) { |
| m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>( |
| outerIndexPtr(), newOuterSize + 1, outerSize() + 1); |
| |
| if (!isCompressed()) |
| m_innerNonZeros = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>( |
| innerNonZeroPtr(), newOuterSize, outerSize()); |
| |
| if (outerChange > 0) { |
| StorageIndex lastIdx = outerSize() == 0 ? StorageIndex(0) : outerIndexPtr()[outerSize()]; |
| std::fill_n(outerIndexPtr() + outerSize(), outerChange + 1, lastIdx); |
| |
| if (!isCompressed()) std::fill_n(innerNonZeroPtr() + outerSize(), outerChange, StorageIndex(0)); |
| } |
| } |
| m_outerSize = newOuterSize; |
| |
| if (innerChange < 0) { |
| for (Index j = 0; j < outerSize(); j++) { |
| Index start = outerIndexPtr()[j]; |
| Index end = isCompressed() ? outerIndexPtr()[j + 1] : start + innerNonZeroPtr()[j]; |
| Index lb = data().searchLowerIndex(start, end, newInnerSize); |
| if (lb != end) { |
| uncompress(); |
| innerNonZeroPtr()[j] = StorageIndex(lb - start); |
| } |
| } |
| } |
| m_innerSize = newInnerSize; |
| |
| Index newSize = outerIndexPtr()[outerSize()]; |
| eigen_assert(newSize <= m_data.size()); |
| m_data.resize(newSize); |
| } |
| |
| /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero. |
| * |
| * This function does not free the currently allocated memory. To release as much as memory as possible, |
| * call \code mat.data().squeeze(); \endcode after resizing it. |
| * |
| * \sa reserve(), setZero() |
| */ |
| void resize(Index rows, Index cols) |
| { |
| const Index outerSize = IsRowMajor ? rows : cols; |
| m_innerSize = IsRowMajor ? cols : rows; |
| m_data.clear(); |
| if (m_outerSize != outerSize || m_outerSize==0) |
| { |
| m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(m_outerIndex, outerSize + 1, |
| m_outerSize + 1); |
| m_outerSize = outerSize; |
| } |
| if(m_innerNonZeros) |
| { |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| m_innerNonZeros = 0; |
| } |
| std::fill_n(m_outerIndex, m_outerSize + 1, StorageIndex(0)); |
| } |
| |
| /** \internal |
| * Resize the nonzero vector to \a size */ |
| void resizeNonZeros(Index size) |
| { |
| m_data.resize(size); |
| } |
| |
| /** \returns a const expression of the diagonal coefficients. */ |
| const ConstDiagonalReturnType diagonal() const { return ConstDiagonalReturnType(*this); } |
| |
| /** \returns a read-write expression of the diagonal coefficients. |
| * \warning If the diagonal entries are written, then all diagonal |
| * entries \b must already exist, otherwise an assertion will be raised. |
| */ |
| DiagonalReturnType diagonal() { return DiagonalReturnType(*this); } |
| |
| /** Default constructor yielding an empty \c 0 \c x \c 0 matrix */ |
| inline SparseMatrix() |
| : m_outerSize(-1), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) |
| { |
| resize(0, 0); |
| } |
| |
| /** Constructs a \a rows \c x \a cols empty matrix */ |
| inline SparseMatrix(Index rows, Index cols) |
| : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) |
| { |
| resize(rows, cols); |
| } |
| |
| /** Constructs a sparse matrix from the sparse expression \a other */ |
| template<typename OtherDerived> |
| inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other) |
| : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) |
| { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), |
| YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) |
| const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit); |
| if (needToTranspose) |
| *this = other.derived(); |
| else |
| { |
| #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN |
| EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN |
| #endif |
| internal::call_assignment_no_alias(*this, other.derived()); |
| } |
| } |
| |
| /** Constructs a sparse matrix from the sparse selfadjoint view \a other */ |
| template<typename OtherDerived, unsigned int UpLo> |
| inline SparseMatrix(const SparseSelfAdjointView<OtherDerived, UpLo>& other) |
| : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) |
| { |
| Base::operator=(other); |
| } |
| |
| inline SparseMatrix(SparseMatrix&& other) : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) |
| { |
| *this = other.derived().markAsRValue(); |
| } |
| |
| /** Copy constructor (it performs a deep copy) */ |
| inline SparseMatrix(const SparseMatrix& other) |
| : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) |
| { |
| *this = other.derived(); |
| } |
| |
| /** \brief Copy constructor with in-place evaluation */ |
| template<typename OtherDerived> |
| SparseMatrix(const ReturnByValue<OtherDerived>& other) |
| : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) |
| { |
| initAssignment(other); |
| other.evalTo(*this); |
| } |
| |
| /** \brief Copy constructor with in-place evaluation */ |
| template<typename OtherDerived> |
| explicit SparseMatrix(const DiagonalBase<OtherDerived>& other) |
| : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0) |
| { |
| *this = other.derived(); |
| } |
| |
| /** Swaps the content of two sparse matrices of the same type. |
| * This is a fast operation that simply swaps the underlying pointers and parameters. */ |
| inline void swap(SparseMatrix& other) |
| { |
| //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n"); |
| std::swap(m_outerIndex, other.m_outerIndex); |
| std::swap(m_innerSize, other.m_innerSize); |
| std::swap(m_outerSize, other.m_outerSize); |
| std::swap(m_innerNonZeros, other.m_innerNonZeros); |
| m_data.swap(other.m_data); |
| } |
| |
| /** Sets *this to the identity matrix. |
| * This function also turns the matrix into compressed mode, and drop any reserved memory. */ |
| inline void setIdentity() |
| { |
| eigen_assert(rows() == cols() && "ONLY FOR SQUARED MATRICES"); |
| if (m_innerNonZeros) { |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| m_innerNonZeros = 0; |
| } |
| m_data.resize(rows()); |
| // is it necessary to squeeze? |
| m_data.squeeze(); |
| typename IndexVector::AlignedMapType outerIndexMap(outerIndexPtr(), rows() + 1); |
| typename IndexVector::AlignedMapType innerIndexMap(innerIndexPtr(), rows()); |
| typename ScalarVector::AlignedMapType valueMap(valuePtr(), rows()); |
| outerIndexMap.setEqualSpaced(StorageIndex(0), StorageIndex(1)); |
| innerIndexMap.setEqualSpaced(StorageIndex(0), StorageIndex(1)); |
| valueMap.setOnes(); |
| } |
| |
| inline SparseMatrix& operator=(const SparseMatrix& other) |
| { |
| if (other.isRValue()) |
| { |
| swap(other.const_cast_derived()); |
| } |
| else if(this!=&other) |
| { |
| #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN |
| EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN |
| #endif |
| initAssignment(other); |
| if(other.isCompressed()) |
| { |
| internal::smart_copy(other.m_outerIndex, other.m_outerIndex + m_outerSize + 1, m_outerIndex); |
| m_data = other.m_data; |
| } |
| else |
| { |
| Base::operator=(other); |
| } |
| } |
| return *this; |
| } |
| |
| inline SparseMatrix& operator=(SparseMatrix&& other) { |
| return *this = other.derived().markAsRValue(); |
| } |
| |
| #ifndef EIGEN_PARSED_BY_DOXYGEN |
| template<typename OtherDerived> |
| inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other) |
| { return Base::operator=(other.derived()); } |
| |
| template<typename Lhs, typename Rhs> |
| inline SparseMatrix& operator=(const Product<Lhs,Rhs,AliasFreeProduct>& other); |
| #endif // EIGEN_PARSED_BY_DOXYGEN |
| |
| template<typename OtherDerived> |
| EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other); |
| |
| #ifndef EIGEN_NO_IO |
| friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m) |
| { |
| EIGEN_DBG_SPARSE( |
| s << "Nonzero entries:\n"; |
| if(m.isCompressed()) |
| { |
| for (Index i=0; i<m.nonZeros(); ++i) |
| s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") "; |
| } |
| else |
| { |
| for (Index i=0; i<m.outerSize(); ++i) |
| { |
| Index p = m.m_outerIndex[i]; |
| Index pe = m.m_outerIndex[i]+m.m_innerNonZeros[i]; |
| Index k=p; |
| for (; k<pe; ++k) { |
| s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") "; |
| } |
| for (; k<m.m_outerIndex[i+1]; ++k) { |
| s << "(_,_) "; |
| } |
| } |
| } |
| s << std::endl; |
| s << std::endl; |
| s << "Outer pointers:\n"; |
| for (Index i=0; i<m.outerSize(); ++i) { |
| s << m.m_outerIndex[i] << " "; |
| } |
| s << " $" << std::endl; |
| if(!m.isCompressed()) |
| { |
| s << "Inner non zeros:\n"; |
| for (Index i=0; i<m.outerSize(); ++i) { |
| s << m.m_innerNonZeros[i] << " "; |
| } |
| s << " $" << std::endl; |
| } |
| s << std::endl; |
| ); |
| s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m); |
| return s; |
| } |
| #endif |
| |
| /** Destructor */ |
| inline ~SparseMatrix() |
| { |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_outerIndex, m_outerSize + 1); |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| } |
| |
| /** Overloaded for performance */ |
| Scalar sum() const; |
| |
| # ifdef EIGEN_SPARSEMATRIX_PLUGIN |
| # include EIGEN_SPARSEMATRIX_PLUGIN |
| # endif |
| |
| protected: |
| |
| template<typename Other> |
| void initAssignment(const Other& other) |
| { |
| resize(other.rows(), other.cols()); |
| if(m_innerNonZeros) |
| { |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| m_innerNonZeros = 0; |
| } |
| } |
| |
| /** \internal |
| * \sa insert(Index,Index) */ |
| EIGEN_DEPRECATED EIGEN_DONT_INLINE Scalar& insertCompressed(Index row, Index col); |
| |
| /** \internal |
| * A vector object that is equal to 0 everywhere but v at the position i */ |
| class SingletonVector |
| { |
| StorageIndex m_index; |
| StorageIndex m_value; |
| public: |
| typedef StorageIndex value_type; |
| SingletonVector(Index i, Index v) |
| : m_index(convert_index(i)), m_value(convert_index(v)) |
| {} |
| |
| StorageIndex operator[](Index i) const { return i==m_index ? m_value : 0; } |
| }; |
| |
| /** \internal |
| * \sa insert(Index,Index) */ |
| EIGEN_DEPRECATED EIGEN_DONT_INLINE Scalar& insertUncompressed(Index row, Index col); |
| |
| public: |
| /** \internal |
| * \sa insert(Index,Index) */ |
| EIGEN_STRONG_INLINE Scalar& insertBackUncompressed(Index row, Index col) |
| { |
| const Index outer = IsRowMajor ? row : col; |
| const Index inner = IsRowMajor ? col : row; |
| |
| eigen_assert(!isCompressed()); |
| eigen_assert(m_innerNonZeros[outer]<=(m_outerIndex[outer+1] - m_outerIndex[outer])); |
| |
| Index p = m_outerIndex[outer] + m_innerNonZeros[outer]++; |
| m_data.index(p) = convert_index(inner); |
| return (m_data.value(p) = Scalar(0)); |
| } |
| protected: |
| struct IndexPosPair { |
| IndexPosPair(Index a_i, Index a_p) : i(a_i), p(a_p) {} |
| Index i; |
| Index p; |
| }; |
| |
| /** \internal assign \a diagXpr to the diagonal of \c *this |
| * There are different strategies: |
| * 1 - if *this is overwritten (Func==assign_op) or *this is empty, then we can work treat *this as a dense vector expression. |
| * 2 - otherwise, for each diagonal coeff, |
| * 2.a - if it already exists, then we update it, |
| * 2.b - if the correct position is at the end of the vector, and there is capacity, push to back |
| * 2.b - otherwise, the insertion requires a data move, record insertion locations and handle in a second pass |
| * 3 - at the end, if some entries failed to be updated in-place, then we alloc a new buffer, copy each chunk at the right position, and insert the new elements. |
| */ |
| template<typename DiagXpr, typename Func> |
| void assignDiagonal(const DiagXpr diagXpr, const Func& assignFunc) |
| { |
| |
| constexpr StorageIndex kEmptyIndexVal(-1); |
| typedef typename IndexVector::AlignedMapType IndexMap; |
| typedef typename ScalarVector::AlignedMapType ValueMap; |
| |
| Index n = diagXpr.size(); |
| |
| const bool overwrite = internal::is_same<Func, internal::assign_op<Scalar,Scalar> >::value; |
| if(overwrite) |
| { |
| if((this->rows()!=n) || (this->cols()!=n)) |
| this->resize(n, n); |
| } |
| |
| if(data().size()==0 || overwrite) |
| { |
| if (!isCompressed()) { |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| m_innerNonZeros = 0; |
| } |
| resizeNonZeros(n); |
| IndexMap outerIndexMap(outerIndexPtr(), n + 1); |
| IndexMap innerIndexMap(innerIndexPtr(), n); |
| ValueMap valueMap(valuePtr(), n); |
| outerIndexMap.setEqualSpaced(StorageIndex(0), StorageIndex(1)); |
| innerIndexMap.setEqualSpaced(StorageIndex(0), StorageIndex(1)); |
| valueMap.setZero(); |
| internal::call_assignment_no_alias(valueMap, diagXpr, assignFunc); |
| } |
| else |
| { |
| internal::evaluator<DiagXpr> diaEval(diagXpr); |
| |
| ei_declare_aligned_stack_constructed_variable(StorageIndex, tmp, n, 0); |
| typename IndexVector::AlignedMapType insertionLocations(tmp, n); |
| insertionLocations.setConstant(kEmptyIndexVal); |
| |
| Index deferredInsertions = 0; |
| Index shift = 0; |
| |
| for (Index j = 0; j < n; j++) { |
| Index begin = outerIndexPtr()[j]; |
| Index end = isCompressed() ? outerIndexPtr()[j + 1] : begin + innerNonZeroPtr()[j]; |
| Index capacity = outerIndexPtr()[j + 1] - end; |
| Index dst = data().searchLowerIndex(begin, end, j); |
| // the entry exists: update it now |
| if (dst != end && data().index(dst) == j) assignFunc.assignCoeff(data().value(dst), diaEval.coeff(j)); |
| // the entry belongs at the back of the vector: push to back |
| else if (dst == end && capacity > 0) |
| assignFunc.assignCoeff(insertBackUncompressed(j, j), diaEval.coeff(j)); |
| // the insertion requires a data move, record insertion location and handle in second pass |
| else { |
| insertionLocations.coeffRef(j) = StorageIndex(dst); |
| deferredInsertions++; |
| // if there is no capacity, all vectors to the right of this are shifted |
| if (capacity == 0) shift++; |
| } |
| } |
| |
| if (deferredInsertions > 0) { |
| |
| data().resize(data().size() + shift); |
| Index copyEnd = isCompressed() ? outerIndexPtr()[outerSize()] |
| : outerIndexPtr()[outerSize() - 1] + innerNonZeroPtr()[outerSize() - 1]; |
| for (Index j = outerSize() - 1; deferredInsertions > 0; j--) { |
| Index begin = outerIndexPtr()[j]; |
| Index end = isCompressed() ? outerIndexPtr()[j + 1] : begin + innerNonZeroPtr()[j]; |
| Index capacity = outerIndexPtr()[j + 1] - end; |
| |
| bool doInsertion = insertionLocations(j) >= 0; |
| bool breakUpCopy = doInsertion && (capacity > 0); |
| // break up copy for sorted insertion into inactive nonzeros |
| // optionally, add another criterium, i.e. 'breakUpCopy || (capacity > threhsold)' |
| // where `threshold >= 0` to skip inactive nonzeros in each vector |
| // this reduces the total number of copied elements, but requires more moveChunk calls |
| if (breakUpCopy) { |
| Index copyBegin = outerIndexPtr()[j + 1]; |
| Index to = copyBegin + shift; |
| Index chunkSize = copyEnd - copyBegin; |
| if (chunkSize > 0) data().moveChunk(copyBegin, to, chunkSize); |
| copyEnd = end; |
| } |
| |
| outerIndexPtr()[j + 1] += shift; |
| |
| if (doInsertion) { |
| // if there is capacity, shift into the inactive nonzeros |
| if (capacity > 0) shift++; |
| Index copyBegin = insertionLocations(j); |
| Index to = copyBegin + shift; |
| Index chunkSize = copyEnd - copyBegin; |
| if (chunkSize > 0) data().moveChunk(copyBegin, to, chunkSize); |
| Index dst = to - 1; |
| data().index(dst) = StorageIndex(j); |
| assignFunc.assignCoeff(data().value(dst) = Scalar(0), diaEval.coeff(j)); |
| if (!isCompressed()) innerNonZeroPtr()[j]++; |
| shift--; |
| deferredInsertions--; |
| copyEnd = copyBegin; |
| } |
| } |
| } |
| eigen_assert((shift == 0) && (deferredInsertions == 0)); |
| } |
| } |
| |
| /* Provides a consistent reserve and reallocation strategy for insertCompressed and insertUncompressed |
| If there is insufficient space for one insertion, the size of the memory buffer is doubled */ |
| inline void checkAllocatedSpaceAndMaybeExpand(); |
| /* These functions are used to avoid a redundant binary search operation in functions such as coeffRef() and assume `dst` is the appropriate sorted insertion point */ |
| EIGEN_STRONG_INLINE Scalar& insertAtByOuterInner(Index outer, Index inner, Index dst); |
| Scalar& insertCompressedAtByOuterInner(Index outer, Index inner, Index dst); |
| Scalar& insertUncompressedAtByOuterInner(Index outer, Index inner, Index dst); |
| |
| private: |
| EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE) |
| EIGEN_STATIC_ASSERT((Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS) |
| |
| struct default_prunning_func { |
| default_prunning_func(const Scalar& ref, const RealScalar& eps) : reference(ref), epsilon(eps) {} |
| inline bool operator() (const Index&, const Index&, const Scalar& value) const |
| { |
| return !internal::isMuchSmallerThan(value, reference, epsilon); |
| } |
| Scalar reference; |
| RealScalar epsilon; |
| }; |
| }; |
| |
| namespace internal { |
| |
| // Creates a compressed sparse matrix from a range of unsorted triplets |
| // Requires temporary storage to handle duplicate entries |
| template <typename InputIterator, typename SparseMatrixType, typename DupFunctor> |
| void set_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat, |
| DupFunctor dup_func) { |
| |
| constexpr bool IsRowMajor = SparseMatrixType::IsRowMajor; |
| typedef typename SparseMatrixType::StorageIndex StorageIndex; |
| typedef typename VectorX<StorageIndex>::AlignedMapType IndexMap; |
| if (begin == end) return; |
| |
| // free innerNonZeroPtr (if present) and zero outerIndexPtr |
| mat.resize(mat.rows(), mat.cols()); |
| // allocate temporary storage for nonzero insertion (outer size) and duplicate removal (inner size) |
| ei_declare_aligned_stack_constructed_variable(StorageIndex, tmp, numext::maxi(mat.innerSize(), mat.outerSize()), 0); |
| // scan triplets to determine allocation size before constructing matrix |
| IndexMap outerIndexMap(mat.outerIndexPtr(), mat.outerSize() + 1); |
| for (InputIterator it(begin); it != end; ++it) { |
| eigen_assert(it->row() >= 0 && it->row() < mat.rows() && it->col() >= 0 && it->col() < mat.cols()); |
| StorageIndex j = IsRowMajor ? it->row() : it->col(); |
| outerIndexMap.coeffRef(j + 1)++; |
| } |
| |
| // finalize outer indices and allocate memory |
| std::partial_sum(outerIndexMap.begin(), outerIndexMap.end(), outerIndexMap.begin()); |
| Index nonZeros = mat.outerIndexPtr()[mat.outerSize()]; |
| mat.resizeNonZeros(nonZeros); |
| |
| // use tmp to track nonzero insertions |
| IndexMap back(tmp, mat.outerSize()); |
| back = outerIndexMap.head(mat.outerSize()); |
| |
| // push triplets to back of each inner vector |
| for (InputIterator it(begin); it != end; ++it) { |
| StorageIndex j = IsRowMajor ? it->row() : it->col(); |
| StorageIndex i = IsRowMajor ? it->col() : it->row(); |
| mat.data().index(back.coeff(j)) = i; |
| mat.data().value(back.coeff(j)) = it->value(); |
| back.coeffRef(j)++; |
| } |
| |
| // use tmp to collapse duplicates |
| IndexMap wi(tmp, mat.innerSize()); |
| mat.collapseDuplicates(wi, dup_func); |
| mat.sortInnerIndices(); |
| } |
| |
| // Creates a compressed sparse matrix from a sorted range of triplets |
| template <typename InputIterator, typename SparseMatrixType, typename DupFunctor> |
| void set_from_triplets_sorted(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat, |
| DupFunctor dup_func) { |
| constexpr bool IsRowMajor = SparseMatrixType::IsRowMajor; |
| typedef typename SparseMatrixType::StorageIndex StorageIndex; |
| typedef typename VectorX<StorageIndex>::AlignedMapType IndexMap; |
| if (begin == end) return; |
| |
| constexpr StorageIndex kEmptyIndexValue(-1); |
| // deallocate inner nonzeros if present and zero outerIndexPtr |
| mat.resize(mat.rows(), mat.cols()); |
| // use outer indices to count non zero entries (excluding duplicate entries) |
| StorageIndex previous_j = kEmptyIndexValue; |
| StorageIndex previous_i = kEmptyIndexValue; |
| // scan triplets to determine allocation size before constructing matrix |
| IndexMap outerIndexMap(mat.outerIndexPtr(), mat.outerSize() + 1); |
| for (InputIterator it(begin); it != end; ++it) { |
| eigen_assert(it->row() >= 0 && it->row() < mat.rows() && it->col() >= 0 && it->col() < mat.cols()); |
| StorageIndex j = IsRowMajor ? it->row() : it->col(); |
| StorageIndex i = IsRowMajor ? it->col() : it->row(); |
| eigen_assert(j > previous_j || (j == previous_j && i >= previous_i)); |
| // identify duplicates by examining previous location |
| bool duplicate = (previous_j == j) && (previous_i == i); |
| if (!duplicate) outerIndexMap.coeffRef(j + 1)++; |
| previous_j = j; |
| previous_i = i; |
| } |
| |
| // finalize outer indices and allocate memory |
| std::partial_sum(outerIndexMap.begin(), outerIndexMap.end(), outerIndexMap.begin()); |
| Index nonZeros = mat.outerIndexPtr()[mat.outerSize()]; |
| mat.resizeNonZeros(nonZeros); |
| |
| previous_i = kEmptyIndexValue; |
| previous_j = kEmptyIndexValue; |
| Index back = 0; |
| for (InputIterator it(begin); it != end; ++it) { |
| StorageIndex j = IsRowMajor ? it->row() : it->col(); |
| StorageIndex i = IsRowMajor ? it->col() : it->row(); |
| bool duplicate = (previous_j == j) && (previous_i == i); |
| if (duplicate) { |
| mat.data().value(back - 1) = dup_func(mat.data().value(back - 1), it->value()); |
| } else { |
| // push triplets to back |
| mat.data().index(back) = i; |
| mat.data().value(back) = it->value(); |
| previous_j = j; |
| previous_i = i; |
| back++; |
| } |
| } |
| // matrix is finalized |
| } |
| |
| } |
| |
| |
| /** Fill the matrix \c *this with the list of \em triplets defined by the iterator range \a begin - \a end. |
| * |
| * A \em triplet is a tuple (i,j,value) defining a non-zero element. |
| * The input list of triplets does not have to be sorted, and can contains duplicated elements. |
| * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up. |
| * This is a \em O(n) operation, with \em n the number of triplet elements. |
| * The initial contents of \c *this is destroyed. |
| * The matrix \c *this must be properly resized beforehand using the SparseMatrix(Index,Index) constructor, |
| * or the resize(Index,Index) method. The sizes are not extracted from the triplet list. |
| * |
| * The \a InputIterators value_type must provide the following interface: |
| * \code |
| * Scalar value() const; // the value |
| * Scalar row() const; // the row index i |
| * Scalar col() const; // the column index j |
| * \endcode |
| * See for instance the Eigen::Triplet template class. |
| * |
| * Here is a typical usage example: |
| * \code |
| typedef Triplet<double> T; |
| std::vector<T> tripletList; |
| tripletList.reserve(estimation_of_entries); |
| for(...) |
| { |
| // ... |
| tripletList.push_back(T(i,j,v_ij)); |
| } |
| SparseMatrixType m(rows,cols); |
| m.setFromTriplets(tripletList.begin(), tripletList.end()); |
| // m is ready to go! |
| * \endcode |
| * |
| * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define |
| * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather |
| * be explicitly stored into a std::vector for instance. |
| */ |
| template<typename Scalar, int Options_, typename StorageIndex_> |
| template<typename InputIterators> |
| void SparseMatrix<Scalar,Options_,StorageIndex_>::setFromTriplets(const InputIterators& begin, const InputIterators& end) |
| { |
| internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,Options_,StorageIndex_> >(begin, end, *this, internal::scalar_sum_op<Scalar,Scalar>()); |
| } |
| |
| /** The same as setFromTriplets but triplets are assumed to be pre-sorted. This is faster and requires less temporary storage. |
| * Two triplets `a` and `b` are appropriately ordered if: |
| * \code |
| * ColMajor: ((a.col() != b.col()) ? (a.col() < b.col()) : (a.row() < b.row()) |
| * RowMajor: ((a.row() != b.row()) ? (a.row() < b.row()) : (a.col() < b.col()) |
| * \endcode |
| */ |
| template<typename Scalar, int Options_, typename StorageIndex_> |
| template<typename InputIterators> |
| void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromSortedTriplets(const InputIterators& begin, const InputIterators& end) |
| { |
| internal::set_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_> >(begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>()); |
| } |
| |
| /** The same as setFromTriplets but when duplicates are met the functor \a dup_func is applied: |
| * \code |
| * value = dup_func(OldValue, NewValue) |
| * \endcode |
| * Here is a C++11 example keeping the latest entry only: |
| * \code |
| * mat.setFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; }); |
| * \endcode |
| */ |
| template<typename Scalar, int Options_, typename StorageIndex_> |
| template<typename InputIterators, typename DupFunctor> |
| void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func) |
| { |
| internal::set_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(begin, end, *this, dup_func); |
| } |
| |
| /** The same as setFromSortedTriplets but when duplicates are met the functor \a dup_func is applied: |
| * \code |
| * value = dup_func(OldValue, NewValue) |
| * \endcode |
| * Here is a C++11 example keeping the latest entry only: |
| * \code |
| * mat.setFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; }); |
| * \endcode |
| */ |
| template<typename Scalar, int Options_, typename StorageIndex_> |
| template<typename InputIterators, typename DupFunctor> |
| void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromSortedTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func) |
| { |
| internal::set_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(begin, end, *this, dup_func); |
| } |
| |
| /** \internal */ |
| template<typename Scalar, int Options_, typename StorageIndex_> |
| template<typename Derived, typename DupFunctor> |
| void SparseMatrix<Scalar, Options_, StorageIndex_>::collapseDuplicates(DenseBase<Derived>& wi, DupFunctor dup_func) |
| { |
| eigen_assert(wi.size() >= m_innerSize); |
| constexpr StorageIndex kEmptyIndexValue(-1); |
| wi.setConstant(kEmptyIndexValue); |
| StorageIndex count = 0; |
| // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers |
| for (Index j = 0; j < m_outerSize; ++j) { |
| StorageIndex start = count; |
| StorageIndex oldEnd = isCompressed() ? m_outerIndex[j + 1] : m_outerIndex[j] + m_innerNonZeros[j]; |
| for (StorageIndex k = m_outerIndex[j]; k < oldEnd; ++k) { |
| StorageIndex i = m_data.index(k); |
| if (wi(i) >= start) { |
| // we already meet this entry => accumulate it |
| m_data.value(wi(i)) = dup_func(m_data.value(wi(i)), m_data.value(k)); |
| } else { |
| m_data.value(count) = m_data.value(k); |
| m_data.index(count) = m_data.index(k); |
| wi(i) = count; |
| ++count; |
| } |
| } |
| m_outerIndex[j] = start; |
| } |
| m_outerIndex[m_outerSize] = count; |
| // turn the matrix into compressed form |
| if (m_innerNonZeros) { |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| m_innerNonZeros = 0; |
| } |
| m_data.resize(m_outerIndex[m_outerSize]); |
| } |
| |
| |
| |
| /** \internal */ |
| template<typename Scalar, int Options_, typename StorageIndex_> |
| template<typename OtherDerived> |
| EIGEN_DONT_INLINE SparseMatrix<Scalar,Options_,StorageIndex_>& SparseMatrix<Scalar,Options_,StorageIndex_>::operator=(const SparseMatrixBase<OtherDerived>& other) |
| { |
| EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value), |
| YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY) |
| |
| #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN |
| EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN |
| #endif |
| |
| const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit); |
| if (needToTranspose) |
| { |
| #ifdef EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN |
| EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN |
| #endif |
| // two passes algorithm: |
| // 1 - compute the number of coeffs per dest inner vector |
| // 2 - do the actual copy/eval |
| // Since each coeff of the rhs has to be evaluated twice, let's evaluate it if needed |
| typedef typename internal::nested_eval<OtherDerived,2,typename internal::plain_matrix_type<OtherDerived>::type >::type OtherCopy; |
| typedef internal::remove_all_t<OtherCopy> OtherCopy_; |
| typedef internal::evaluator<OtherCopy_> OtherCopyEval; |
| OtherCopy otherCopy(other.derived()); |
| OtherCopyEval otherCopyEval(otherCopy); |
| |
| SparseMatrix dest(other.rows(),other.cols()); |
| Eigen::Map<IndexVector> (dest.m_outerIndex,dest.outerSize()).setZero(); |
| |
| // pass 1 |
| // FIXME the above copy could be merged with that pass |
| for (Index j=0; j<otherCopy.outerSize(); ++j) |
| for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it) |
| ++dest.m_outerIndex[it.index()]; |
| |
| // prefix sum |
| StorageIndex count = 0; |
| IndexVector positions(dest.outerSize()); |
| for (Index j=0; j<dest.outerSize(); ++j) |
| { |
| StorageIndex tmp = dest.m_outerIndex[j]; |
| dest.m_outerIndex[j] = count; |
| positions[j] = count; |
| count += tmp; |
| } |
| dest.m_outerIndex[dest.outerSize()] = count; |
| // alloc |
| dest.m_data.resize(count); |
| // pass 2 |
| for (StorageIndex j=0; j<otherCopy.outerSize(); ++j) |
| { |
| for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it) |
| { |
| Index pos = positions[it.index()]++; |
| dest.m_data.index(pos) = j; |
| dest.m_data.value(pos) = it.value(); |
| } |
| } |
| this->swap(dest); |
| return *this; |
| } |
| else |
| { |
| if(other.isRValue()) |
| { |
| initAssignment(other.derived()); |
| } |
| // there is no special optimization |
| return Base::operator=(other.derived()); |
| } |
| } |
| |
| template <typename Scalar_, int Options_, typename StorageIndex_> |
| inline typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar& SparseMatrix<Scalar_, Options_, StorageIndex_>::insert( |
| Index row, Index col) { |
| Index outer = IsRowMajor ? row : col; |
| Index inner = IsRowMajor ? col : row; |
| Index start = outerIndexPtr()[outer]; |
| Index end = isCompressed() ? outerIndexPtr()[outer + 1] : start + innerNonZeroPtr()[outer]; |
| Index dst = data().searchLowerIndex(start, end, inner); |
| eigen_assert((dst == end || data().index(dst) != inner) && |
| "you cannot insert an element that already exists, you must call coeffRef to this end"); |
| return insertAtByOuterInner(outer, inner, dst); |
| } |
| |
| template <typename Scalar_, int Options_, typename StorageIndex_> |
| EIGEN_STRONG_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar& |
| SparseMatrix<Scalar_, Options_, StorageIndex_>::insertAtByOuterInner(Index outer, Index inner, Index dst) { |
| uncompress(); |
| return insertUncompressedAtByOuterInner(outer, inner, dst); |
| } |
| |
| template <typename Scalar_, int Options_, typename StorageIndex_> |
| EIGEN_DEPRECATED EIGEN_DONT_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar& |
| SparseMatrix<Scalar_, Options_, StorageIndex_>::insertUncompressed(Index row, Index col) { |
| eigen_assert(!isCompressed()); |
| Index outer = IsRowMajor ? row : col; |
| Index inner = IsRowMajor ? col : row; |
| Index start = outerIndexPtr()[outer]; |
| Index end = start + innerNonZeroPtr()[outer]; |
| Index dst = data().searchLowerIndex(start, end, inner); |
| eigen_assert((dst == end || data().index(dst) != inner) && |
| "you cannot insert an element that already exists, you must call coeffRef to this end"); |
| return insertUncompressedAtByOuterInner(outer, inner, dst); |
| } |
| |
| template <typename Scalar_, int Options_, typename StorageIndex_> |
| EIGEN_DEPRECATED EIGEN_DONT_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar& |
| SparseMatrix<Scalar_, Options_, StorageIndex_>::insertCompressed(Index row, Index col) { |
| eigen_assert(isCompressed()); |
| Index outer = IsRowMajor ? row : col; |
| Index inner = IsRowMajor ? col : row; |
| Index start = outerIndexPtr()[outer]; |
| Index end = outerIndexPtr()[outer + 1]; |
| Index dst = data().searchLowerIndex(start, end, inner); |
| eigen_assert((dst == end || data().index(dst) != inner) && |
| "you cannot insert an element that already exists, you must call coeffRef to this end"); |
| return insertCompressedAtByOuterInner(outer, inner, dst); |
| } |
| |
| template <typename Scalar_, int Options_, typename StorageIndex_> |
| EIGEN_STRONG_INLINE void SparseMatrix<Scalar_, Options_, StorageIndex_>::checkAllocatedSpaceAndMaybeExpand() { |
| // if there is no capacity for a single insertion, double the capacity |
| if (data().allocatedSize() <= data().size()) { |
| // increase capacity by a mininum of 32 |
| Index minReserve = 32; |
| Index reserveSize = numext::maxi(minReserve, data().allocatedSize()); |
| data().reserve(reserveSize); |
| } |
| } |
| |
| template <typename Scalar_, int Options_, typename StorageIndex_> |
| typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar& |
| SparseMatrix<Scalar_, Options_, StorageIndex_>::insertCompressedAtByOuterInner(Index outer, Index inner, Index dst) { |
| eigen_assert(isCompressed()); |
| // compressed insertion always requires expanding the buffer |
| checkAllocatedSpaceAndMaybeExpand(); |
| data().resize(data().size() + 1); |
| Index chunkSize = outerIndexPtr()[outerSize()] - dst; |
| // shift the existing data to the right if necessary |
| if (chunkSize > 0) data().moveChunk(dst, dst + 1, chunkSize); |
| // update nonzero counts |
| typename IndexVector::AlignedMapType outerIndexMap(outerIndexPtr(), outerSize() + 1); |
| outerIndexMap.segment(outer + 1, outerSize() - outer).array() += 1; |
| // initialize the coefficient |
| data().index(dst) = StorageIndex(inner); |
| // return a reference to the coefficient |
| return data().value(dst) = Scalar(0); |
| } |
| |
| template <typename Scalar_, int Options_, typename StorageIndex_> |
| typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar& |
| SparseMatrix<Scalar_, Options_, StorageIndex_>::insertUncompressedAtByOuterInner(Index outer, Index inner, Index dst) { |
| eigen_assert(!isCompressed()); |
| // find nearest outer vector to the right with capacity (if any) to minimize copy size |
| Index target = outer; |
| for (; target < outerSize(); target++) { |
| Index start = outerIndexPtr()[target]; |
| Index end = start + innerNonZeroPtr()[target]; |
| Index capacity = outerIndexPtr()[target + 1] - end; |
| if (capacity > 0) { |
| // `target` has room for interior insertion |
| Index chunkSize = end - dst; |
| // shift the existing data to the right if necessary |
| if (chunkSize > 0) data().moveChunk(dst, dst + 1, chunkSize); |
| break; |
| } |
| } |
| if (target == outerSize()) { |
| // no room for interior insertion (to the right of `outer`) |
| target = outer; |
| Index dst_offset = dst - outerIndexPtr()[target]; |
| Index totalCapacity = data().allocatedSize() - data().size(); |
| eigen_assert(totalCapacity >= 0); |
| if (totalCapacity == 0) { |
| // there is no room left. we must reallocate. reserve space in each vector |
| constexpr StorageIndex kReserveSizePerVector(2); |
| reserveInnerVectors(IndexVector::Constant(outerSize(), kReserveSizePerVector)); |
| } else { |
| // dont reallocate, but re-distribute the remaining capacity to the right of `outer` |
| // each vector in the range [outer,outerSize) will receive totalCapacity / (outerSize - outer) nonzero |
| // reservations each vector in the range [outer,remainder) will receive an additional nonzero reservation where |
| // remainder = totalCapacity % (outerSize - outer) |
| typedef internal::sparse_reserve_op<StorageIndex> ReserveSizesOp; |
| typedef CwiseNullaryOp<ReserveSizesOp, IndexVector> ReserveSizesXpr; |
| ReserveSizesXpr reserveSizesXpr(outerSize(), 1, ReserveSizesOp(target, outerSize(), totalCapacity)); |
| eigen_assert(reserveSizesXpr.sum() == totalCapacity); |
| reserveInnerVectors(reserveSizesXpr); |
| } |
| Index start = outerIndexPtr()[target]; |
| Index end = start + innerNonZeroPtr()[target]; |
| dst = start + dst_offset; |
| Index chunkSize = end - dst; |
| if (chunkSize > 0) data().moveChunk(dst, dst + 1, chunkSize); |
| } |
| // update nonzero counts |
| innerNonZeroPtr()[outer]++; |
| typename IndexVector::AlignedMapType outerIndexMap(outerIndexPtr(), outerSize() + 1); |
| outerIndexMap.segment(outer + 1, target - outer).array() += 1; |
| // initialize the coefficient |
| data().index(dst) = StorageIndex(inner); |
| // return a reference to the coefficient |
| return data().value(dst) = Scalar(0); |
| } |
| |
| namespace internal { |
| |
| template <typename Scalar_, int Options_, typename StorageIndex_> |
| struct evaluator<SparseMatrix<Scalar_, Options_, StorageIndex_>> |
| : evaluator<SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_>>> { |
| typedef evaluator<SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_>>> Base; |
| typedef SparseMatrix<Scalar_, Options_, StorageIndex_> SparseMatrixType; |
| evaluator() : Base() {} |
| explicit evaluator(const SparseMatrixType& mat) : Base(mat) {} |
| }; |
| |
| } |
| |
| // Specialization for SparseMatrix. |
| // Serializes [rows, cols, isCompressed, outerSize, innerBufferSize, |
| // innerNonZeros, outerIndices, innerIndices, values]. |
| template <typename Scalar, int Options, typename StorageIndex> |
| class Serializer<SparseMatrix<Scalar, Options, StorageIndex>, void> { |
| public: |
| typedef SparseMatrix<Scalar, Options, StorageIndex> SparseMat; |
| |
| struct Header { |
| typename SparseMat::Index rows; |
| typename SparseMat::Index cols; |
| bool compressed; |
| Index outer_size; |
| Index inner_buffer_size; |
| }; |
| |
| EIGEN_DEVICE_FUNC size_t size(const SparseMat& value) const { |
| // innerNonZeros. |
| std::size_t num_storage_indices = value.isCompressed() ? 0 : value.outerSize(); |
| // Outer indices. |
| num_storage_indices += value.outerSize() + 1; |
| // Inner indices. |
| const StorageIndex inner_buffer_size = value.outerIndexPtr()[value.outerSize()]; |
| num_storage_indices += inner_buffer_size; |
| // Values. |
| std::size_t num_values = inner_buffer_size; |
| return sizeof(Header) + sizeof(Scalar) * num_values + |
| sizeof(StorageIndex) * num_storage_indices; |
| } |
| |
| 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.rows(), value.cols(), value.isCompressed(), |
| value.outerSize(), value.outerIndexPtr()[value.outerSize()]}; |
| EIGEN_USING_STD(memcpy) |
| memcpy(dest, &header, header_bytes); |
| dest += header_bytes; |
| |
| // innerNonZeros. |
| if (!header.compressed) { |
| std::size_t data_bytes = sizeof(StorageIndex) * header.outer_size; |
| memcpy(dest, value.innerNonZeroPtr(), data_bytes); |
| dest += data_bytes; |
| } |
| |
| // Outer indices. |
| std::size_t data_bytes = sizeof(StorageIndex) * (header.outer_size + 1); |
| memcpy(dest, value.outerIndexPtr(), data_bytes); |
| dest += data_bytes; |
| |
| // Inner indices. |
| data_bytes = sizeof(StorageIndex) * header.inner_buffer_size; |
| memcpy(dest, value.innerIndexPtr(), data_bytes); |
| dest += data_bytes; |
| |
| // Values. |
| data_bytes = sizeof(Scalar) * header.inner_buffer_size; |
| 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.rows, header.cols); |
| if (header.compressed) { |
| value.makeCompressed(); |
| } else { |
| value.uncompress(); |
| } |
| |
| // Adjust value ptr size. |
| value.data().resize(header.inner_buffer_size); |
| |
| // Initialize compressed state and inner non-zeros. |
| if (!header.compressed) { |
| // Inner non-zero counts. |
| std::size_t data_bytes = sizeof(StorageIndex) * header.outer_size; |
| if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr; |
| memcpy(value.innerNonZeroPtr(), src, data_bytes); |
| src += data_bytes; |
| } |
| |
| // Outer indices. |
| std::size_t data_bytes = sizeof(StorageIndex) * (header.outer_size + 1); |
| if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr; |
| memcpy(value.outerIndexPtr(), src, data_bytes); |
| src += data_bytes; |
| |
| // Inner indices. |
| data_bytes = sizeof(StorageIndex) * header.inner_buffer_size; |
| 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.inner_buffer_size; |
| 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_SPARSEMATRIX_H |
