| // 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 |
| }; |
| }; |
| |
| } // 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, StorageIndex(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 insert(row,col); |
| const Index p = m_data.searchLowerIndex(start,end-1,StorageIndex(inner)); |
| if((p<end) && (m_data.index(p)==inner)) |
| return m_data.value(p); |
| else |
| return insert(row,col); |
| } |
| |
| /** \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. |
| * |
| */ |
| 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; |
| // 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]); |
| totalReserveSize += reserveSizes[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]; |
| for(Index i=innerNNZ-1; i>=0; --i) |
| { |
| m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i); |
| m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i); |
| } |
| 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) |
| { |
| Index offset = newOuterIndex[j] - m_outerIndex[j]; |
| if(offset>0) |
| { |
| StorageIndex innerNNZ = m_innerNonZeros[j]; |
| for(Index i=innerNNZ-1; i>=0; --i) |
| { |
| m_data.index(newOuterIndex[j]+i) = m_data.index(m_outerIndex[j]+i); |
| m_data.value(newOuterIndex[j]+i) = m_data.value(m_outerIndex[j]+i); |
| } |
| } |
| } |
| |
| 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)"); |
| Index 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) |
| { |
| Index 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); |
| |
| void sumupDuplicates() { collapseDuplicates(internal::scalar_sum_op<Scalar,Scalar>()); } |
| |
| template<typename DupFunctor> |
| void collapseDuplicates(DupFunctor dup_func = DupFunctor()); |
| |
| //--- |
| |
| /** \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); |
| |
| Index oldStart = m_outerIndex[1]; |
| m_outerIndex[1] = m_innerNonZeros[0]; |
| for(Index j=1; j<m_outerSize; ++j) |
| { |
| Index nextOldStart = m_outerIndex[j+1]; |
| Index offset = oldStart - m_outerIndex[j]; |
| if(offset>0) |
| { |
| for(Index k=0; k<m_innerNonZeros[j]; ++k) |
| { |
| m_data.index(m_outerIndex[j]+k) = m_data.index(oldStart+k); |
| m_data.value(m_outerIndex[j]+k) = m_data.value(oldStart+k); |
| } |
| } |
| m_outerIndex[j+1] = m_outerIndex[j] + m_innerNonZeros[j]; |
| oldStart = nextOldStart; |
| } |
| 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); |
| for (Index i = 0; i < m_outerSize; i++) |
| { |
| m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i]; |
| } |
| } |
| |
| /** 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()) |
| { |
| // TODO optimize the uncompressed mode to avoid moving and allocating the data twice |
| makeCompressed(); |
| |
| StorageIndex k = 0; |
| for(Index j=0; j<m_outerSize; ++j) |
| { |
| Index previousStart = m_outerIndex[j]; |
| m_outerIndex[j] = k; |
| Index end = m_outerIndex[j+1]; |
| for(Index i=previousStart; i<end; ++i) |
| { |
| if(keep(IsRowMajor?j:m_data.index(i), IsRowMajor?m_data.index(i):j, m_data.value(i))) |
| { |
| m_data.value(k) = m_data.value(i); |
| m_data.index(k) = m_data.index(i); |
| ++k; |
| } |
| } |
| } |
| 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) |
| { |
| // No change |
| if (this->rows() == rows && this->cols() == cols) return; |
| |
| // If one dimension is null, then there is nothing to be preserved |
| if(rows==0 || cols==0) return resize(rows,cols); |
| |
| Index innerChange = IsRowMajor ? cols - this->cols() : rows - this->rows(); |
| Index outerChange = IsRowMajor ? rows - this->rows() : cols - this->cols(); |
| StorageIndex newInnerSize = convert_index(IsRowMajor ? cols : rows); |
| |
| // Deals with inner non zeros |
| if (m_innerNonZeros) |
| { |
| // Resize m_innerNonZeros |
| m_innerNonZeros = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>( |
| m_innerNonZeros, m_outerSize + outerChange, m_outerSize); |
| |
| for(Index i=m_outerSize; i<m_outerSize+outerChange; i++) |
| m_innerNonZeros[i] = 0; |
| } |
| else if (innerChange < 0) |
| { |
| // Inner size decreased: allocate a new m_innerNonZeros |
| m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize + outerChange); |
| for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++) |
| m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i]; |
| for(Index i = m_outerSize; i < m_outerSize + outerChange; i++) |
| m_innerNonZeros[i] = 0; |
| } |
| |
| // Change the m_innerNonZeros in case of a decrease of inner size |
| if (m_innerNonZeros && innerChange < 0) |
| { |
| for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++) |
| { |
| StorageIndex &n = m_innerNonZeros[i]; |
| StorageIndex start = m_outerIndex[i]; |
| while (n > 0 && m_data.index(start+n-1) >= newInnerSize) --n; |
| } |
| } |
| |
| m_innerSize = newInnerSize; |
| |
| // Re-allocate outer index structure if necessary |
| if (outerChange == 0) |
| return; |
| |
| m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>( |
| m_outerIndex, m_outerSize + outerChange + 1, m_outerSize + 1); |
| if (outerChange > 0) |
| { |
| StorageIndex lastIdx = m_outerSize == 0 ? 0 : m_outerIndex[m_outerSize]; |
| for(Index i=m_outerSize; i<m_outerSize+outerChange+1; i++) |
| m_outerIndex[i] = lastIdx; |
| } |
| m_outerSize += outerChange; |
| } |
| |
| /** 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); |
| } |
| |
| /** 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"); |
| this->m_data.resize(rows()); |
| Eigen::Map<IndexVector>(this->m_data.indexPtr(), rows()).setLinSpaced(0, StorageIndex(rows()-1)); |
| Eigen::Map<ScalarVector>(this->m_data.valuePtr(), rows()).setOnes(); |
| Eigen::Map<IndexVector>(this->m_outerIndex, rows()+1).setLinSpaced(0, StorageIndex(rows())); |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| m_innerNonZeros = 0; |
| } |
| 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; |
| } |
| |
| #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_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_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 - otherwise, if *this is uncompressed and that the current inner-vector has empty room for at least 1 element, then we perform an in-place insertion. |
| * 2.c - otherwise, we'll have to reallocate and copy everything, so instead of doing so for each new element, it is recorded in a std::vector. |
| * 3 - at the end, if some entries failed to be inserted in-place, then we alloc a new buffer, copy each chunk at the right position, and insert the new elements. |
| * |
| * TODO: some piece of code could be isolated and reused for a general in-place update strategy. |
| * TODO: if we start to defer the insertion of some elements (i.e., case 2.c executed once), |
| * then it *might* be better to disable case 2.b since they will have to be copied anyway. |
| */ |
| template<typename DiagXpr, typename Func> |
| void assignDiagonal(const DiagXpr diagXpr, const Func& assignFunc) |
| { |
| 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(m_data.size()==0 || overwrite) |
| { |
| typedef Array<StorageIndex,Dynamic,1> ArrayXI; |
| this->makeCompressed(); |
| this->resizeNonZeros(n); |
| Eigen::Map<ArrayXI>(this->innerIndexPtr(), n).setLinSpaced(0,StorageIndex(n)-1); |
| Eigen::Map<ArrayXI>(this->outerIndexPtr(), n+1).setLinSpaced(0,StorageIndex(n)); |
| Eigen::Map<Array<Scalar,Dynamic,1> > values = this->coeffs(); |
| values.setZero(); |
| internal::call_assignment_no_alias(values, diagXpr, assignFunc); |
| } |
| else |
| { |
| bool isComp = isCompressed(); |
| internal::evaluator<DiagXpr> diaEval(diagXpr); |
| std::vector<IndexPosPair> newEntries; |
| |
| // 1 - try in-place update and record insertion failures |
| for(Index i = 0; i<n; ++i) |
| { |
| internal::LowerBoundIndex lb = this->lower_bound(i,i); |
| Index p = lb.value; |
| if(lb.found) |
| { |
| // the coeff already exists |
| assignFunc.assignCoeff(m_data.value(p), diaEval.coeff(i)); |
| } |
| else if((!isComp) && m_innerNonZeros[i] < (m_outerIndex[i+1]-m_outerIndex[i])) |
| { |
| // non compressed mode with local room for inserting one element |
| m_data.moveChunk(p, p+1, m_outerIndex[i]+m_innerNonZeros[i]-p); |
| m_innerNonZeros[i]++; |
| m_data.value(p) = Scalar(0); |
| m_data.index(p) = StorageIndex(i); |
| assignFunc.assignCoeff(m_data.value(p), diaEval.coeff(i)); |
| } |
| else |
| { |
| // defer insertion |
| newEntries.push_back(IndexPosPair(i,p)); |
| } |
| } |
| // 2 - insert deferred entries |
| Index n_entries = Index(newEntries.size()); |
| if(n_entries>0) |
| { |
| Storage newData(m_data.size()+n_entries); |
| Index prev_p = 0; |
| Index prev_i = 0; |
| for(Index k=0; k<n_entries;++k) |
| { |
| Index i = newEntries[k].i; |
| Index p = newEntries[k].p; |
| internal::smart_copy(m_data.valuePtr()+prev_p, m_data.valuePtr()+p, newData.valuePtr()+prev_p+k); |
| internal::smart_copy(m_data.indexPtr()+prev_p, m_data.indexPtr()+p, newData.indexPtr()+prev_p+k); |
| for(Index j=prev_i;j<i;++j) |
| m_outerIndex[j+1] += k; |
| if(!isComp) |
| m_innerNonZeros[i]++; |
| prev_p = p; |
| prev_i = i; |
| newData.value(p+k) = Scalar(0); |
| newData.index(p+k) = StorageIndex(i); |
| assignFunc.assignCoeff(newData.value(p+k), diaEval.coeff(i)); |
| } |
| { |
| internal::smart_copy(m_data.valuePtr()+prev_p, m_data.valuePtr()+m_data.size(), newData.valuePtr()+prev_p+n_entries); |
| internal::smart_copy(m_data.indexPtr()+prev_p, m_data.indexPtr()+m_data.size(), newData.indexPtr()+prev_p+n_entries); |
| for(Index j=prev_i+1;j<=m_outerSize;++j) |
| m_outerIndex[j] += n_entries; |
| } |
| m_data.swap(newData); |
| } |
| } |
| } |
| |
| 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 { |
| |
| template<typename InputIterator, typename SparseMatrixType, typename DupFunctor> |
| void set_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat, DupFunctor dup_func) |
| { |
| enum { IsRowMajor = SparseMatrixType::IsRowMajor }; |
| typedef typename SparseMatrixType::Scalar Scalar; |
| typedef typename SparseMatrixType::StorageIndex StorageIndex; |
| SparseMatrix<Scalar,IsRowMajor?ColMajor:RowMajor,StorageIndex> trMat(mat.rows(),mat.cols()); |
| |
| if(begin!=end) |
| { |
| // pass 1: count the nnz per inner-vector |
| typename SparseMatrixType::IndexVector wi(trMat.outerSize()); |
| wi.setZero(); |
| for(InputIterator it(begin); it!=end; ++it) |
| { |
| eigen_assert(it->row()>=0 && it->row()<mat.rows() && it->col()>=0 && it->col()<mat.cols()); |
| wi(IsRowMajor ? it->col() : it->row())++; |
| } |
| |
| // pass 2: insert all the elements into trMat |
| trMat.reserve(wi); |
| for(InputIterator it(begin); it!=end; ++it) |
| trMat.insertBackUncompressed(it->row(),it->col()) = it->value(); |
| |
| // pass 3: |
| trMat.collapseDuplicates(dup_func); |
| } |
| |
| // pass 4: transposed copy -> implicit sorting |
| mat = trMat; |
| } |
| |
| } |
| |
| |
| /** 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 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); |
| } |
| |
| /** \internal */ |
| template<typename Scalar, int Options_, typename StorageIndex_> |
| template<typename DupFunctor> |
| void SparseMatrix<Scalar,Options_,StorageIndex_>::collapseDuplicates(DupFunctor dup_func) |
| { |
| eigen_assert(!isCompressed()); |
| // TODO, in practice we should be able to use m_innerNonZeros for that task |
| IndexVector wi(innerSize()); |
| wi.fill(-1); |
| 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<outerSize(); ++j) |
| { |
| StorageIndex start = count; |
| Index oldEnd = m_outerIndex[j]+m_innerNonZeros[j]; |
| for(Index k=m_outerIndex[j]; k<oldEnd; ++k) |
| { |
| Index 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 |
| internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize); |
| m_innerNonZeros = 0; |
| m_data.resize(m_outerIndex[m_outerSize]); |
| } |
| |
| 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_> |
| typename SparseMatrix<Scalar_,Options_,StorageIndex_>::Scalar& SparseMatrix<Scalar_,Options_,StorageIndex_>::insert(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; |
| |
| if(isCompressed()) |
| { |
| if(nonZeros()==0) |
| { |
| // reserve space if not already done |
| if(m_data.allocatedSize()==0) |
| m_data.reserve(2*m_innerSize); |
| |
| // turn the matrix into non-compressed mode |
| m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize); |
| |
| std::fill(m_innerNonZeros, m_innerNonZeros + m_outerSize, StorageIndex(0)); |
| |
| // pack all inner-vectors to the end of the pre-allocated space |
| // and allocate the entire free-space to the first inner-vector |
| StorageIndex end = convert_index(m_data.allocatedSize()); |
| for(Index j=1; j<=m_outerSize; ++j) |
| m_outerIndex[j] = end; |
| } |
| else |
| { |
| // turn the matrix into non-compressed mode |
| m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize); |
| for(Index j=0; j<m_outerSize; ++j) |
| m_innerNonZeros[j] = m_outerIndex[j+1]-m_outerIndex[j]; |
| } |
| } |
| |
| // check whether we can do a fast "push back" insertion |
| Index data_end = m_data.allocatedSize(); |
| |
| // First case: we are filling a new inner vector which is packed at the end. |
| // We assume that all remaining inner-vectors are also empty and packed to the end. |
| if(m_outerIndex[outer]==data_end) |
| { |
| eigen_internal_assert(m_innerNonZeros[outer]==0); |
| |
| // pack previous empty inner-vectors to end of the used-space |
| // and allocate the entire free-space to the current inner-vector. |
| StorageIndex p = convert_index(m_data.size()); |
| Index j = outer; |
| while(j>=0 && m_innerNonZeros[j]==0) |
| m_outerIndex[j--] = p; |
| |
| // push back the new element |
| ++m_innerNonZeros[outer]; |
| m_data.append(Scalar(0), inner); |
| |
| // check for reallocation |
| if(data_end != m_data.allocatedSize()) |
| { |
| // m_data has been reallocated |
| // -> move remaining inner-vectors back to the end of the free-space |
| // so that the entire free-space is allocated to the current inner-vector. |
| eigen_internal_assert(data_end < m_data.allocatedSize()); |
| StorageIndex new_end = convert_index(m_data.allocatedSize()); |
| for(Index k=outer+1; k<=m_outerSize; ++k) |
| if(m_outerIndex[k]==data_end) |
| m_outerIndex[k] = new_end; |
| } |
| return m_data.value(p); |
| } |
| |
| // Second case: the next inner-vector is packed to the end |
| // and the current inner-vector end match the used-space. |
| if(m_outerIndex[outer+1]==data_end && m_outerIndex[outer]+m_innerNonZeros[outer]==m_data.size()) |
| { |
| eigen_internal_assert(outer+1==m_outerSize || m_innerNonZeros[outer+1]==0); |
| |
| // add space for the new element |
| ++m_innerNonZeros[outer]; |
| m_data.resize(m_data.size()+1); |
| |
| // check for reallocation |
| if(data_end != m_data.allocatedSize()) |
| { |
| // m_data has been reallocated |
| // -> move remaining inner-vectors back to the end of the free-space |
| // so that the entire free-space is allocated to the current inner-vector. |
| eigen_internal_assert(data_end < m_data.allocatedSize()); |
| StorageIndex new_end = convert_index(m_data.allocatedSize()); |
| for(Index k=outer+1; k<=m_outerSize; ++k) |
| if(m_outerIndex[k]==data_end) |
| m_outerIndex[k] = new_end; |
| } |
| |
| // and insert it at the right position (sorted insertion) |
| Index startId = m_outerIndex[outer]; |
| Index p = m_outerIndex[outer]+m_innerNonZeros[outer]-1; |
| while ( (p > startId) && (m_data.index(p-1) > inner) ) |
| { |
| m_data.index(p) = m_data.index(p-1); |
| m_data.value(p) = m_data.value(p-1); |
| --p; |
| } |
| |
| m_data.index(p) = convert_index(inner); |
| return (m_data.value(p) = Scalar(0)); |
| } |
| |
| if(m_data.size() != m_data.allocatedSize()) |
| { |
| // make sure the matrix is compatible to random un-compressed insertion: |
| m_data.resize(m_data.allocatedSize()); |
| this->reserveInnerVectors(Array<StorageIndex,Dynamic,1>::Constant(m_outerSize, 2)); |
| } |
| |
| return insertUncompressed(row,col); |
| } |
| |
| template<typename Scalar_, int Options_, typename StorageIndex_> |
| EIGEN_DONT_INLINE typename SparseMatrix<Scalar_,Options_,StorageIndex_>::Scalar& SparseMatrix<Scalar_,Options_,StorageIndex_>::insertUncompressed(Index row, Index col) |
| { |
| eigen_assert(!isCompressed()); |
| |
| const Index outer = IsRowMajor ? row : col; |
| const StorageIndex inner = convert_index(IsRowMajor ? col : row); |
| |
| Index room = m_outerIndex[outer+1] - m_outerIndex[outer]; |
| StorageIndex innerNNZ = m_innerNonZeros[outer]; |
| if(innerNNZ>=room) |
| { |
| // this inner vector is full, we need to reallocate the whole buffer :( |
| reserve(SingletonVector(outer,std::max<StorageIndex>(2,innerNNZ))); |
| } |
| |
| Index startId = m_outerIndex[outer]; |
| Index p = startId + m_innerNonZeros[outer]; |
| while ( (p > startId) && (m_data.index(p-1) > inner) ) |
| { |
| m_data.index(p) = m_data.index(p-1); |
| m_data.value(p) = m_data.value(p-1); |
| --p; |
| } |
| eigen_assert((p<=startId || m_data.index(p-1)!=inner) && "you cannot insert an element that already exists, you must call coeffRef to this end"); |
| |
| m_innerNonZeros[outer]++; |
| |
| m_data.index(p) = inner; |
| return (m_data.value(p) = Scalar(0)); |
| } |
| |
| template<typename Scalar_, int Options_, typename StorageIndex_> |
| EIGEN_DONT_INLINE typename SparseMatrix<Scalar_,Options_,StorageIndex_>::Scalar& SparseMatrix<Scalar_,Options_,StorageIndex_>::insertCompressed(Index row, Index col) |
| { |
| eigen_assert(isCompressed()); |
| |
| const Index outer = IsRowMajor ? row : col; |
| const Index inner = IsRowMajor ? col : row; |
| |
| Index previousOuter = outer; |
| if (m_outerIndex[outer+1]==0) |
| { |
| // we start a new inner vector |
| while (previousOuter>=0 && m_outerIndex[previousOuter]==0) |
| { |
| m_outerIndex[previousOuter] = convert_index(m_data.size()); |
| --previousOuter; |
| } |
| m_outerIndex[outer+1] = m_outerIndex[outer]; |
| } |
| |
| // here we have to handle the tricky case where the outerIndex array |
| // starts with: [ 0 0 0 0 0 1 ...] and we are inserted in, e.g., |
| // the 2nd inner vector... |
| bool isLastVec = (!(previousOuter==-1 && m_data.size()!=0)) |
| && (std::size_t(m_outerIndex[outer+1]) == m_data.size()); |
| |
| std::size_t startId = m_outerIndex[outer]; |
| // FIXME let's make sure sizeof(long int) == sizeof(std::size_t) |
| std::size_t p = m_outerIndex[outer+1]; |
| ++m_outerIndex[outer+1]; |
| |
| double reallocRatio = 1; |
| if (m_data.allocatedSize()<=m_data.size()) |
| { |
| // if there is no preallocated memory, let's reserve a minimum of 32 elements |
| if (m_data.size()==0) |
| { |
| m_data.reserve(32); |
| } |
| else |
| { |
| // we need to reallocate the data, to reduce multiple reallocations |
| // we use a smart resize algorithm based on the current filling ratio |
| // in addition, we use double to avoid integers overflows |
| double nnzEstimate = double(m_outerIndex[outer])*double(m_outerSize)/double(outer+1); |
| reallocRatio = (nnzEstimate-double(m_data.size()))/double(m_data.size()); |
| // furthermore we bound the realloc ratio to: |
| // 1) reduce multiple minor realloc when the matrix is almost filled |
| // 2) avoid to allocate too much memory when the matrix is almost empty |
| reallocRatio = (std::min)((std::max)(reallocRatio,1.5),8.); |
| } |
| } |
| m_data.resize(m_data.size()+1,reallocRatio); |
| |
| if (!isLastVec) |
| { |
| if (previousOuter==-1) |
| { |
| // oops wrong guess. |
| // let's correct the outer offsets |
| for (Index k=0; k<=(outer+1); ++k) |
| m_outerIndex[k] = 0; |
| Index k=outer+1; |
| while(m_outerIndex[k]==0) |
| m_outerIndex[k++] = 1; |
| while (k<=m_outerSize && m_outerIndex[k]!=0) |
| m_outerIndex[k++]++; |
| p = 0; |
| --k; |
| k = m_outerIndex[k]-1; |
| while (k>0) |
| { |
| m_data.index(k) = m_data.index(k-1); |
| m_data.value(k) = m_data.value(k-1); |
| k--; |
| } |
| } |
| else |
| { |
| // we are not inserting into the last inner vec |
| // update outer indices: |
| Index j = outer+2; |
| while (j<=m_outerSize && m_outerIndex[j]!=0) |
| m_outerIndex[j++]++; |
| --j; |
| // shift data of last vecs: |
| Index k = m_outerIndex[j]-1; |
| while (k>=Index(p)) |
| { |
| m_data.index(k) = m_data.index(k-1); |
| m_data.value(k) = m_data.value(k-1); |
| k--; |
| } |
| } |
| } |
| |
| while ( (p > startId) && (m_data.index(p-1) > inner) ) |
| { |
| m_data.index(p) = m_data.index(p-1); |
| m_data.value(p) = m_data.value(p-1); |
| --p; |
| } |
| |
| m_data.index(p) = inner; |
| return (m_data.value(p) = 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) {} |
| }; |
| |
| } |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_SPARSEMATRIX_H |
