Google Git
Sign in
eigen / mirror / 7c636dd5db213e50ff88d6c94fba5b9427977448 / . / Eigen / src / SparseCore / SparseMatrix.h
blob: 7ddb6fab901621f15ff026d4ec405509f13abf68 [file] [log] [blame]
// 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
// IWYU pragma: private
#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,
Options = Options_,
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, Options_, 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, IsRowMajor ? ColMajor : RowMajor, 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 */
constexpr Storage& data() { return m_data; }
/** \internal */
constexpr 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.
* The output parameter `inserted` is set to true.
*
* Otherwise, if the element does exist, `inserted` will be set to false.
*
* 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& findOrInsertCoeff(Index row, Index col, bool* inserted) {
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 = isCompressed() ? m_outerIndex[outer + 1] : m_outerIndex[outer] + m_innerNonZeros[outer];
eigen_assert(end >= start && "you probably called coeffRef on a non finalized matrix");
Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
if (dst == end) {
Index capacity = m_outerIndex[outer + 1] - end;
if (capacity > 0) {
// implies uncompressed: push to back of vector
m_innerNonZeros[outer]++;
m_data.index(end) = StorageIndex(inner);
m_data.value(end) = Scalar(0);
if (inserted != nullptr) {
*inserted = true;
}
return m_data.value(end);
}
}
if ((dst < end) && (m_data.index(dst) == inner)) {
// this coefficient exists, return a reference to it
if (inserted != nullptr) {
*inserted = false;
}
return m_data.value(dst);
} else {
if (inserted != nullptr) {
*inserted = true;
}
// insertion will require reconfiguring the buffer
return insertAtByOuterInner(outer, inner, dst);
}
}
/** \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) { return findOrInsertCoeff(row, col, nullptr); }
/** \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();
using std::fill_n;
fill_n(m_outerIndex, m_outerSize + 1, StorageIndex(0));
if (m_innerNonZeros) {
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 += internal::convert_index<Index>(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;
Index count = 0;
for (Index j = 0; j < m_outerSize; ++j) {
newOuterIndex[j] = internal::convert_index<StorageIndex>(count);
Index reserveSize = internal::convert_index<Index>(reserveSizes[j]);
count += reserveSize + internal::convert_index<Index>(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] +
internal::convert_index<StorageIndex>(reserveSizes[m_outerSize - 1]);
m_data.resize(m_outerIndex[m_outerSize]);
} else {
StorageIndex* newOuterIndex = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize + 1);
Index count = 0;
for (Index j = 0; j < m_outerSize; ++j) {
newOuterIndex[j] = internal::convert_index<StorageIndex>(count);
Index alreadyReserved =
internal::convert_index<Index>(m_outerIndex[j + 1] - m_outerIndex[j] - m_innerNonZeros[j]);
Index reserveSize = internal::convert_index<Index>(reserveSizes[j]);
Index toReserve = numext::maxi(reserveSize, alreadyReserved);
count += toReserve + internal::convert_index<Index>(m_innerNonZeros[j]);
}
newOuterIndex[m_outerSize] = internal::convert_index<StorageIndex>(count);
m_data.resize(count);
for (Index j = m_outerSize - 1; j >= 0; --j) {
StorageIndex innerNNZ = m_innerNonZeros[j];
StorageIndex begin = m_outerIndex[j];
StorageIndex target = newOuterIndex[j];
m_data.moveChunk(begin, target, innerNNZ);
}
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;
}
}
}
// remove outer vectors j, j+1 ... j+num-1 and resize the matrix
void removeOuterVectors(Index j, Index num = 1) {
eigen_assert(num >= 0 && j >= 0 && j + num <= m_outerSize && "Invalid parameters");
const Index newRows = IsRowMajor ? m_outerSize - num : rows();
const Index newCols = IsRowMajor ? cols() : m_outerSize - num;
const Index begin = j + num;
const Index end = m_outerSize;
const Index target = j;
// if the removed vectors are not empty, uncompress the matrix
if (m_outerIndex[j + num] > m_outerIndex[j]) uncompress();
// shift m_outerIndex and m_innerNonZeros [num] to the left
internal::smart_memmove(m_outerIndex + begin, m_outerIndex + end + 1, m_outerIndex + target);
if (!isCompressed())
internal::smart_memmove(m_innerNonZeros + begin, m_innerNonZeros + end, m_innerNonZeros + target);
// if m_outerIndex[0] > 0, shift the data within the first vector while it is easy to do so
if (m_outerIndex[0] > StorageIndex(0)) {
uncompress();
const Index from = internal::convert_index<Index>(m_outerIndex[0]);
const Index to = Index(0);
const Index chunkSize = internal::convert_index<Index>(m_innerNonZeros[0]);
m_data.moveChunk(from, to, chunkSize);
m_outerIndex[0] = StorageIndex(0);
}
// truncate the matrix to the smaller size
conservativeResize(newRows, newCols);
}
// insert empty outer vectors at indices j, j+1 ... j+num-1 and resize the matrix
void insertEmptyOuterVectors(Index j, Index num = 1) {
using std::fill_n;
eigen_assert(num >= 0 && j >= 0 && j < m_outerSize && "Invalid parameters");
const Index newRows = IsRowMajor ? m_outerSize + num : rows();
const Index newCols = IsRowMajor ? cols() : m_outerSize + num;
const Index begin = j;
const Index end = m_outerSize;
const Index target = j + num;
// expand the matrix to the larger size
conservativeResize(newRows, newCols);
// shift m_outerIndex and m_innerNonZeros [num] to the right
internal::smart_memmove(m_outerIndex + begin, m_outerIndex + end + 1, m_outerIndex + target);
// m_outerIndex[begin] == m_outerIndex[target], set all indices in this range to same value
fill_n(m_outerIndex + begin, num, m_outerIndex[begin]);
if (!isCompressed()) {
internal::smart_memmove(m_innerNonZeros + begin, m_innerNonZeros + end, m_innerNonZeros + target);
// set the nonzeros of the newly inserted vectors to 0
fill_n(m_innerNonZeros + begin, num, StorageIndex(0));
}
}
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);
template <typename InputIterators>
void insertFromTriplets(const InputIterators& begin, const InputIterators& end);
template <typename InputIterators, typename DupFunctor>
void insertFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
template <typename InputIterators>
void insertFromSortedTriplets(const InputIterators& begin, const InputIterators& end);
template <typename InputIterators, typename DupFunctor>
void insertFromSortedTriplets(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) {
eigen_assert(j >= 0 && j < m_outerSize && "invalid outer index");
eigen_assert(i >= 0 && i < m_innerSize && "invalid inner index");
Index start = m_outerIndex[j];
Index end = isCompressed() ? m_outerIndex[j + 1] : start + m_innerNonZeros[j];
Index dst = start == end ? end : m_data.searchLowerIndex(start, end, i);
if (dst == end) {
Index capacity = m_outerIndex[j + 1] - end;
if (capacity > 0) {
// implies uncompressed: push to back of vector
m_innerNonZeros[j]++;
m_data.index(end) = StorageIndex(i);
m_data.value(end) = Scalar(0);
return m_data.value(end);
}
}
eigen_assert((dst == end || m_data.index(dst) != i) &&
"you cannot insert an element that already exists, you must call coeffRef to this end");
return insertAtByOuterInner(j, i, dst);
}
/** 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];
// try to move fewer, larger contiguous chunks
Index copyStart = start;
Index copyTarget = m_innerNonZeros[0];
for (Index j = 1; j < m_outerSize; j++) {
StorageIndex end = start + m_innerNonZeros[j];
StorageIndex nextStart = m_outerIndex[j + 1];
// dont forget to move the last chunk!
bool breakUpCopy = (end != nextStart) || (j == m_outerSize - 1);
if (breakUpCopy) {
Index chunkSize = end - copyStart;
if (chunkSize > 0) m_data.moveChunk(copyStart, copyTarget, chunkSize);
copyStart = nextStart;
copyTarget += chunkSize;
}
start = nextStart;
m_outerIndex[j + 1] = m_outerIndex[j] + m_innerNonZeros[j];
}
m_data.resize(m_outerIndex[m_outerSize]);
// release as much memory as possible
internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
m_innerNonZeros = 0;
m_data.squeeze();
}
/** Turns the matrix into the uncompressed mode */
void uncompress() {
if (!isCompressed()) return;
m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize);
if (m_outerIndex[m_outerSize] == 0) {
using std::fill_n;
fill_n(m_innerNonZeros, m_outerSize, StorageIndex(0));
} else {
for (Index j = 0; j < m_outerSize; j++) m_innerNonZeros[j] = m_outerIndex[j + 1] - m_outerIndex[j];
}
}
/** 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 - m_innerSize;
Index outerChange = newOuterSize - m_outerSize;
if (outerChange != 0) {
m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(m_outerIndex, newOuterSize + 1,
m_outerSize + 1);
if (!isCompressed())
m_innerNonZeros = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(m_innerNonZeros,
newOuterSize, m_outerSize);
if (outerChange > 0) {
StorageIndex lastIdx = m_outerSize == 0 ? StorageIndex(0) : m_outerIndex[m_outerSize];
using std::fill_n;
fill_n(m_outerIndex + m_outerSize, outerChange + 1, lastIdx);
if (!isCompressed()) fill_n(m_innerNonZeros + m_outerSize, outerChange, StorageIndex(0));
}
}
m_outerSize = newOuterSize;
if (innerChange < 0) {
for (Index j = 0; j < m_outerSize; j++) {
Index start = m_outerIndex[j];
Index end = isCompressed() ? m_outerIndex[j + 1] : start + m_innerNonZeros[j];
Index lb = m_data.searchLowerIndex(start, end, newInnerSize);
if (lb != end) {
uncompress();
m_innerNonZeros[j] = StorageIndex(lb - start);
}
}
}
m_innerSize = newInnerSize;
Index newSize = m_outerIndex[m_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_outerIndex == 0) || (m_outerSize != outerSize)) {
m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(m_outerIndex, outerSize + 1,
m_outerSize + 1);
m_outerSize = outerSize;
}
internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
m_innerNonZeros = 0;
using std::fill_n;
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(0), 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);
}
/** Move constructor */
inline SparseMatrix(SparseMatrix&& other) : SparseMatrix() { this->swap(other); }
template <typename OtherDerived>
inline SparseMatrix(SparseCompressedBase<OtherDerived>&& other) : SparseMatrix() {
*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);
}
/** Free-function swap. */
friend EIGEN_DEVICE_FUNC void swap(SparseMatrix& a, SparseMatrix& b) { a.swap(b); }
/** 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(m_outerSize == m_innerSize && "ONLY FOR SQUARED MATRICES");
internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
m_innerNonZeros = 0;
m_data.resize(m_outerSize);
// is it necessary to squeeze?
m_data.squeeze();
std::iota(m_outerIndex, m_outerIndex + m_outerSize + 1, StorageIndex(0));
std::iota(innerIndexPtr(), innerIndexPtr() + m_outerSize, StorageIndex(0));
using std::fill_n;
fill_n(valuePtr(), m_outerSize, Scalar(1));
}
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) {
this->swap(other);
return *this;
}
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);
template <typename OtherDerived>
EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other);
template <typename OtherDerived>
inline SparseMatrix& operator=(SparseCompressedBase<OtherDerived>&& other) {
*this = other.derived().markAsRValue();
return *this;
}
#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());
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) = StorageIndex(inner);
m_data.value(p) = Scalar(0);
return m_data.value(p);
}
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 ScalarVector::AlignedMapType ValueMap;
Index n = diagXpr.size();
const bool overwrite = internal::is_same<Func, internal::assign_op<Scalar, Scalar>>::value;
if (overwrite) {
if ((m_outerSize != n) || (m_innerSize != n)) resize(n, n);
}
if (m_data.size() == 0 || overwrite) {
internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
m_innerNonZeros = 0;
resizeNonZeros(n);
ValueMap valueMap(valuePtr(), n);
std::iota(m_outerIndex, m_outerIndex + n + 1, StorageIndex(0));
std::iota(innerIndexPtr(), innerIndexPtr() + n, StorageIndex(0));
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 = m_outerIndex[j];
Index end = isCompressed() ? m_outerIndex[j + 1] : begin + m_innerNonZeros[j];
Index capacity = m_outerIndex[j + 1] - end;
Index dst = m_data.searchLowerIndex(begin, end, j);
// the entry exists: update it now
if (dst != end && m_data.index(dst) == StorageIndex(j))
assignFunc.assignCoeff(m_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) {
m_data.resize(m_data.size() + shift);
Index copyEnd = isCompressed() ? m_outerIndex[m_outerSize]
: m_outerIndex[m_outerSize - 1] + m_innerNonZeros[m_outerSize - 1];
for (Index j = m_outerSize - 1; deferredInsertions > 0; j--) {
Index begin = m_outerIndex[j];
Index end = isCompressed() ? m_outerIndex[j + 1] : begin + m_innerNonZeros[j];
Index capacity = m_outerIndex[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 = m_outerIndex[j + 1];
Index to = copyBegin + shift;
Index chunkSize = copyEnd - copyBegin;
m_data.moveChunk(copyBegin, to, chunkSize);
copyEnd = end;
}
m_outerIndex[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;
m_data.moveChunk(copyBegin, to, chunkSize);
Index dst = to - 1;
m_data.index(dst) = StorageIndex(j);
m_data.value(dst) = Scalar(0);
assignFunc.assignCoeff(m_data.value(dst), diaEval.coeff(j));
if (!isCompressed()) m_innerNonZeros[j]++;
shift--;
deferredInsertions--;
copyEnd = copyBegin;
}
}
}
eigen_assert((shift == 0) && (deferredInsertions == 0));
}
}
/* 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;
using StorageIndex = typename SparseMatrixType::StorageIndex;
using IndexMap = typename VectorX<StorageIndex>::AlignedMapType;
using TransposedSparseMatrix =
SparseMatrix<typename SparseMatrixType::Scalar, IsRowMajor ? ColMajor : RowMajor, StorageIndex>;
if (begin == end) return;
// There are two strategies to consider for constructing a matrix from unordered triplets:
// A) construct the 'mat' in its native storage order and sort in-place (less memory); or,
// B) construct the transposed matrix and use an implicit sort upon assignment to `mat` (less time).
// This routine uses B) for faster execution time.
TransposedSparseMatrix trmat(mat.rows(), mat.cols());
// scan triplets to determine allocation size before constructing matrix
Index nonZeros = 0;
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 = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
if (nonZeros == NumTraits<StorageIndex>::highest()) internal::throw_std_bad_alloc();
trmat.outerIndexPtr()[j + 1]++;
nonZeros++;
}
std::partial_sum(trmat.outerIndexPtr(), trmat.outerIndexPtr() + trmat.outerSize() + 1, trmat.outerIndexPtr());
eigen_assert(nonZeros == trmat.outerIndexPtr()[trmat.outerSize()]);
trmat.resizeNonZeros(nonZeros);
// construct temporary array to track insertions (outersize) and collapse duplicates (innersize)
ei_declare_aligned_stack_constructed_variable(StorageIndex, tmp, numext::maxi(mat.innerSize(), mat.outerSize()), 0);
smart_copy(trmat.outerIndexPtr(), trmat.outerIndexPtr() + trmat.outerSize(), tmp);
// push triplets to back of each vector
for (InputIterator it(begin); it != end; ++it) {
StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
StorageIndex i = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
StorageIndex k = tmp[j];
trmat.data().index(k) = i;
trmat.data().value(k) = it->value();
tmp[j]++;
}
IndexMap wi(tmp, trmat.innerSize());
trmat.collapseDuplicates(wi, dup_func);
// implicit sorting
mat = trmat;
}
// 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;
using StorageIndex = typename SparseMatrixType::StorageIndex;
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
Index nonZeros = 0;
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 = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
StorageIndex i = convert_index<StorageIndex>(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) {
if (nonZeros == NumTraits<StorageIndex>::highest()) internal::throw_std_bad_alloc();
nonZeros++;
mat.outerIndexPtr()[j + 1]++;
previous_j = j;
previous_i = i;
}
}
// finalize outer indices and allocate memory
std::partial_sum(mat.outerIndexPtr(), mat.outerIndexPtr() + mat.outerSize() + 1, mat.outerIndexPtr());
eigen_assert(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 = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
StorageIndex i = convert_index<StorageIndex>(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++;
}
}
eigen_assert(back == nonZeros);
// matrix is finalized
}
// thin wrapper around a generic binary functor to use the sparse disjunction evaluator instead of the default
// "arithmetic" evaluator
template <typename DupFunctor, typename LhsScalar, typename RhsScalar = LhsScalar>
struct scalar_disjunction_op {
using result_type = typename result_of<DupFunctor(LhsScalar, RhsScalar)>::type;
scalar_disjunction_op(const DupFunctor& op) : m_functor(op) {}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
return m_functor(a, b);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DupFunctor& functor() const { return m_functor; }
const DupFunctor& m_functor;
};
template <typename DupFunctor, typename LhsScalar, typename RhsScalar>
struct functor_traits<scalar_disjunction_op<DupFunctor, LhsScalar, RhsScalar>> : public functor_traits<DupFunctor> {};
// Creates a compressed sparse matrix from its existing entries and those from an unsorted range of triplets
template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
void insert_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
DupFunctor dup_func) {
using Scalar = typename SparseMatrixType::Scalar;
using SrcXprType =
CwiseBinaryOp<scalar_disjunction_op<DupFunctor, Scalar>, const SparseMatrixType, const SparseMatrixType>;
// set_from_triplets is necessary to sort the inner indices and remove the duplicate entries
SparseMatrixType trips(mat.rows(), mat.cols());
set_from_triplets(begin, end, trips, dup_func);
SrcXprType src = mat.binaryExpr(trips, scalar_disjunction_op<DupFunctor, Scalar>(dup_func));
// the sparse assignment procedure creates a temporary matrix and swaps the final result
assign_sparse_to_sparse<SparseMatrixType, SrcXprType>(mat, src);
}
// Creates a compressed sparse matrix from its existing entries and those from an sorted range of triplets
template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
void insert_from_triplets_sorted(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
DupFunctor dup_func) {
using Scalar = typename SparseMatrixType::Scalar;
using SrcXprType =
CwiseBinaryOp<scalar_disjunction_op<DupFunctor, Scalar>, const SparseMatrixType, const SparseMatrixType>;
// TODO: process triplets without making a copy
SparseMatrixType trips(mat.rows(), mat.cols());
set_from_triplets_sorted(begin, end, trips, dup_func);
SrcXprType src = mat.binaryExpr(trips, scalar_disjunction_op<DupFunctor, Scalar>(dup_func));
// the sparse assignment procedure creates a temporary matrix and swaps the final result
assign_sparse_to_sparse<SparseMatrixType, SrcXprType>(mat, src);
}
} // namespace internal
/** Fill the matrix \c *this with the list of \em triplets defined in the half-open range from \a begin to \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 may contain 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 are 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
* IndexType row() const; // the row index i
* IndexType 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);
}
/** 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 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.setFromSortedTriplets(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);
}
/** Insert a batch of elements into the matrix \c *this with the list of \em triplets defined in the half-open range
from \a begin to \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 may contain 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 are preserved (except for the summation of duplicate elements).
* The matrix \c *this must be properly sized beforehand. 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
* IndexType row() const; // the row index i
* IndexType col() const; // the column index j
* \endcode
* See for instance the Eigen::Triplet template class.
*
* Here is a typical usage example:
* \code
SparseMatrixType m(rows,cols); // m contains nonzero entries
typedef Triplet<double> T;
std::vector<T> tripletList;
tripletList.reserve(estimation_of_entries);
for(...)
{
// ...
tripletList.push_back(T(i,j,v_ij));
}
m.insertFromTriplets(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_>::insertFromTriplets(const InputIterators& begin,
const InputIterators& end) {
internal::insert_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>>(
begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
}
/** The same as insertFromTriplets 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.insertFromTriplets(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_>::insertFromTriplets(const InputIterators& begin,
const InputIterators& end, DupFunctor dup_func) {
internal::insert_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(
begin, end, *this, dup_func);
}
/** The same as insertFromTriplets 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_>::insertFromSortedTriplets(const InputIterators& begin,
const InputIterators& end) {
internal::insert_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>>(
begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
}
/** The same as insertFromSortedTriplets 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.insertFromSortedTriplets(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_>::insertFromSortedTriplets(const InputIterators& begin,
const InputIterators& end,
DupFunctor dup_func) {
internal::insert_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) {
// removes duplicate entries and compresses the matrix
// the excess allocated memory is not released
// the inner indices do not need to be sorted, nor is the matrix returned in a sorted state
eigen_assert(wi.size() == m_innerSize);
constexpr StorageIndex kEmptyIndexValue(-1);
wi.setConstant(kEmptyIndexValue);
StorageIndex count = 0;
const bool is_compressed = isCompressed();
// 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) {
const StorageIndex newBegin = count;
const StorageIndex end = is_compressed ? m_outerIndex[j + 1] : m_outerIndex[j] + m_innerNonZeros[j];
for (StorageIndex k = m_outerIndex[j]; k < end; ++k) {
StorageIndex i = m_data.index(k);
if (wi(i) >= newBegin) {
// entry at k is a duplicate
// accumulate it into the primary entry located at wi(i)
m_data.value(wi(i)) = dup_func(m_data.value(wi(i)), m_data.value(k));
} else {
// k is the primary entry in j with inner index i
// shift it to the left and record its location at wi(i)
m_data.index(count) = i;
m_data.value(count) = m_data.value(k);
wi(i) = count;
++count;
}
}
m_outerIndex[j] = newBegin;
}
m_outerIndex[m_outerSize] = count;
m_data.resize(count);
// turn the matrix into compressed form (if it is not already)
internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
m_innerNonZeros = 0;
}
/** \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) {
return insertByOuterInner(IsRowMajor ? row : col, IsRowMajor ? col : row);
}
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) {
// random insertion into compressed matrix is very slow
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 = m_outerIndex[outer];
Index end = start + m_innerNonZeros[outer];
Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
if (dst == end) {
Index capacity = m_outerIndex[outer + 1] - end;
if (capacity > 0) {
// implies uncompressed: push to back of vector
m_innerNonZeros[outer]++;
m_data.index(end) = StorageIndex(inner);
m_data.value(end) = Scalar(0);
return m_data.value(end);
}
}
eigen_assert((dst == end || m_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 = m_outerIndex[outer];
Index end = m_outerIndex[outer + 1];
Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
eigen_assert((dst == end || m_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_>
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
// first, check if there is adequate allocated memory
if (m_data.allocatedSize() <= m_data.size()) {
// if there is no capacity for a single insertion, double the capacity
// increase capacity by a minimum of 32
Index minReserve = 32;
Index reserveSize = numext::maxi(minReserve, m_data.allocatedSize());
m_data.reserve(reserveSize);
}
m_data.resize(m_data.size() + 1);
Index chunkSize = m_outerIndex[m_outerSize] - dst;
// shift the existing data to the right if necessary
m_data.moveChunk(dst, dst + 1, chunkSize);
// update nonzero counts
// potentially O(outerSize) bottleneck!
for (Index j = outer; j < m_outerSize; j++) m_outerIndex[j + 1]++;
// initialize the coefficient
m_data.index(dst) = StorageIndex(inner);
m_data.value(dst) = Scalar(0);
// return a reference to the coefficient
return m_data.value(dst);
}
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 a vector with capacity, starting at `outer` and searching to the left and right
for (Index leftTarget = outer - 1, rightTarget = outer; (leftTarget >= 0) || (rightTarget < m_outerSize);) {
if (rightTarget < m_outerSize) {
Index start = m_outerIndex[rightTarget];
Index end = start + m_innerNonZeros[rightTarget];
Index nextStart = m_outerIndex[rightTarget + 1];
Index capacity = nextStart - end;
if (capacity > 0) {
// move [dst, end) to dst+1 and insert at dst
Index chunkSize = end - dst;
if (chunkSize > 0) m_data.moveChunk(dst, dst + 1, chunkSize);
m_innerNonZeros[outer]++;
for (Index j = outer; j < rightTarget; j++) m_outerIndex[j + 1]++;
m_data.index(dst) = StorageIndex(inner);
m_data.value(dst) = Scalar(0);
return m_data.value(dst);
}
rightTarget++;
}
if (leftTarget >= 0) {
Index start = m_outerIndex[leftTarget];
Index end = start + m_innerNonZeros[leftTarget];
Index nextStart = m_outerIndex[leftTarget + 1];
Index capacity = nextStart - end;
if (capacity > 0) {
// tricky: dst is a lower bound, so we must insert at dst-1 when shifting left
// move [nextStart, dst) to nextStart-1 and insert at dst-1
Index chunkSize = dst - nextStart;
if (chunkSize > 0) m_data.moveChunk(nextStart, nextStart - 1, chunkSize);
m_innerNonZeros[outer]++;
for (Index j = leftTarget; j < outer; j++) m_outerIndex[j + 1]--;
m_data.index(dst - 1) = StorageIndex(inner);
m_data.value(dst - 1) = Scalar(0);
return m_data.value(dst - 1);
}
leftTarget--;
}
}
// no room for interior insertion
// nonZeros() == m_data.size()
// record offset as outerIndxPtr will change
Index dst_offset = dst - m_outerIndex[outer];
// allocate space for random insertion
if (m_data.allocatedSize() == 0) {
// fast method to allocate space for one element per vector in empty matrix
m_data.resize(m_outerSize);
std::iota(m_outerIndex, m_outerIndex + m_outerSize + 1, StorageIndex(0));
} else {
// check for integer overflow: if maxReserveSize == 0, insertion is not possible
Index maxReserveSize = static_cast<Index>(NumTraits<StorageIndex>::highest()) - m_data.allocatedSize();
eigen_assert(maxReserveSize > 0);
if (m_outerSize <= maxReserveSize) {
// allocate space for one additional element per vector
reserveInnerVectors(IndexVector::Constant(m_outerSize, 1));
} else {
// handle the edge case where StorageIndex is insufficient to reserve outerSize additional elements
// allocate space for one additional element in the interval [outer,maxReserveSize)
typedef internal::sparse_reserve_op<StorageIndex> ReserveSizesOp;
typedef CwiseNullaryOp<ReserveSizesOp, IndexVector> ReserveSizesXpr;
ReserveSizesXpr reserveSizesXpr(m_outerSize, 1, ReserveSizesOp(outer, m_outerSize, maxReserveSize));
reserveInnerVectors(reserveSizesXpr);
}
}
// insert element at `dst` with new outer indices
Index start = m_outerIndex[outer];
Index end = start + m_innerNonZeros[outer];
Index new_dst = start + dst_offset;
Index chunkSize = end - new_dst;
if (chunkSize > 0) m_data.moveChunk(new_dst, new_dst + 1, chunkSize);
m_innerNonZeros[outer]++;
m_data.index(new_dst) = StorageIndex(inner);
m_data.value(new_dst) = Scalar(0);
return m_data.value(new_dst);
}
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) {}
};
} // namespace internal
// 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