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Yousef Khadadeh
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include/Eigen/src/SparseCore/SparseMatrixBase.h
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include/Eigen/src/SparseCore/SparseMatrixBase.h
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_SPARSEMATRIXBASE_H
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#define EIGEN_SPARSEMATRIXBASE_H
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// IWYU pragma: private
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#include "./InternalHeaderCheck.h"
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namespace Eigen {
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/** \ingroup SparseCore_Module
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*
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* \class SparseMatrixBase
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*
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* \brief Base class of any sparse matrices or sparse expressions
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*
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* \tparam Derived is the derived type, e.g. a sparse matrix type, or an expression, etc.
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*
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* This class can be extended with the help of the plugin mechanism described on the page
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* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIXBASE_PLUGIN.
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*/
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template <typename Derived>
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class SparseMatrixBase : public EigenBase<Derived> {
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public:
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typedef typename internal::traits<Derived>::Scalar Scalar;
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/** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
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*
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* It is an alias for the Scalar type */
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typedef Scalar value_type;
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typedef typename internal::packet_traits<Scalar>::type PacketScalar;
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typedef typename internal::traits<Derived>::StorageKind StorageKind;
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/** The integer type used to \b store indices within a SparseMatrix.
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* For a \c SparseMatrix<Scalar,Options,IndexType> it an alias of the third template parameter \c IndexType. */
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typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
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typedef typename internal::add_const_on_value_type_if_arithmetic<typename internal::packet_traits<Scalar>::type>::type
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PacketReturnType;
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typedef SparseMatrixBase StorageBaseType;
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typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
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typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
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template <typename OtherDerived>
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Derived& operator=(const EigenBase<OtherDerived>& other);
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enum {
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RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
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/**< The number of rows at compile-time. This is just a copy of the value provided
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* by the \a Derived type. If a value is not known at compile-time,
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* it is set to the \a Dynamic constant.
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* \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
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ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
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/**< The number of columns at compile-time. This is just a copy of the value provided
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* by the \a Derived type. If a value is not known at compile-time,
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* it is set to the \a Dynamic constant.
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* \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
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SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
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/**< This is equal to the number of coefficients, i.e. the number of
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* rows times the number of columns, or to \a Dynamic if this is not
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* known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
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MaxRowsAtCompileTime = RowsAtCompileTime,
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MaxColsAtCompileTime = ColsAtCompileTime,
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MaxSizeAtCompileTime = internal::size_at_compile_time(MaxRowsAtCompileTime, MaxColsAtCompileTime),
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IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
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/**< This is set to true if either the number of rows or the number of
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* columns is known at compile-time to be equal to 1. Indeed, in that case,
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* we are dealing with a column-vector (if there is only one column) or with
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* a row-vector (if there is only one row). */
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NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0
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: bool(IsVectorAtCompileTime) ? 1
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: 2,
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/**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
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* and 2 for matrices.
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*/
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Flags = internal::traits<Derived>::Flags,
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/**< This stores expression \ref flags flags which may or may not be inherited by new expressions
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* constructed from this one. See the \ref flags "list of flags".
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*/
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IsRowMajor = Flags & RowMajorBit ? 1 : 0,
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InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
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: int(IsRowMajor) ? int(ColsAtCompileTime)
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: int(RowsAtCompileTime),
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#ifndef EIGEN_PARSED_BY_DOXYGEN
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HasDirectAccess_ = (int(Flags) & DirectAccessBit) ? 1 : 0 // workaround sunCC
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#endif
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};
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/** \internal the return type of MatrixBase::adjoint() */
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typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
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CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Eigen::Transpose<const Derived> >,
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Transpose<const Derived> >
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AdjointReturnType;
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typedef Transpose<Derived> TransposeReturnType;
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typedef Transpose<const Derived> ConstTransposeReturnType;
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// FIXME storage order do not match evaluator storage order
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typedef SparseMatrix<Scalar, Flags & RowMajorBit ? RowMajor : ColMajor, StorageIndex> PlainObject;
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#ifndef EIGEN_PARSED_BY_DOXYGEN
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/** This is the "real scalar" type; if the \a Scalar type is already real numbers
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* (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
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* \a Scalar is \a std::complex<T> then RealScalar is \a T.
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*
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* \sa class NumTraits
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*/
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typedef typename NumTraits<Scalar>::Real RealScalar;
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/** \internal the return type of coeff()
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*/
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typedef std::conditional_t<HasDirectAccess_, const Scalar&, Scalar> CoeffReturnType;
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/** \internal Represents a matrix with all coefficients equal to one another*/
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typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, Matrix<Scalar, Dynamic, Dynamic> > ConstantReturnType;
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/** type of the equivalent dense matrix */
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typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime> DenseMatrixType;
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/** type of the equivalent square matrix */
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typedef Matrix<Scalar, internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
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internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)>
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SquareMatrixType;
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inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
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inline Derived& derived() { return *static_cast<Derived*>(this); }
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inline Derived& const_cast_derived() const { return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); }
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typedef EigenBase<Derived> Base;
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#endif // not EIGEN_PARSED_BY_DOXYGEN
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#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::SparseMatrixBase
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#ifdef EIGEN_PARSED_BY_DOXYGEN
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#define EIGEN_DOC_UNARY_ADDONS(METHOD, \
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OP) /** <p>This method does not change the sparsity of \c *this: the OP is applied to \
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explicitly stored coefficients only. \sa SparseCompressedBase::coeffs() </p> */
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#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL /** <p> \warning This method returns a read-only expression for any \
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sparse matrices. \sa \ref TutorialSparse_SubMatrices "Sparse block \
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operations" </p> */
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#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF( \
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COND) /** <p> \warning This method returns a read-write expression for COND sparse matrices only. Otherwise, the \
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returned expression is read-only. \sa \ref TutorialSparse_SubMatrices "Sparse block operations" </p> */
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#else
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#define EIGEN_DOC_UNARY_ADDONS(X, Y)
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#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
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#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
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#endif
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#include "../plugins/CommonCwiseUnaryOps.inc"
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#include "../plugins/CommonCwiseBinaryOps.inc"
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#include "../plugins/MatrixCwiseUnaryOps.inc"
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#include "../plugins/MatrixCwiseBinaryOps.inc"
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#include "../plugins/BlockMethods.inc"
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#ifdef EIGEN_SPARSEMATRIXBASE_PLUGIN
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#include EIGEN_SPARSEMATRIXBASE_PLUGIN
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#endif
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#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
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#undef EIGEN_DOC_UNARY_ADDONS
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#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
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#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
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/** \returns the number of rows. \sa cols() */
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inline Index rows() const { return derived().rows(); }
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/** \returns the number of columns. \sa rows() */
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inline Index cols() const { return derived().cols(); }
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/** \returns the number of coefficients, which is \a rows()*cols().
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* \sa rows(), cols(). */
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inline Index size() const { return rows() * cols(); }
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/** \returns true if either the number of rows or the number of columns is equal to 1.
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* In other words, this function returns
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* \code rows()==1 || cols()==1 \endcode
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* \sa rows(), cols(), IsVectorAtCompileTime. */
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inline bool isVector() const { return rows() == 1 || cols() == 1; }
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/** \returns the size of the storage major dimension,
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* i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
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Index outerSize() const { return (int(Flags) & RowMajorBit) ? this->rows() : this->cols(); }
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/** \returns the size of the inner dimension according to the storage order,
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* i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
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Index innerSize() const { return (int(Flags) & RowMajorBit) ? this->cols() : this->rows(); }
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bool isRValue() const { return m_isRValue; }
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Derived& markAsRValue() {
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m_isRValue = true;
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return derived();
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}
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SparseMatrixBase() : m_isRValue(false) { /* TODO check flags */
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}
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template <typename OtherDerived>
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Derived& operator=(const ReturnByValue<OtherDerived>& other);
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template <typename OtherDerived>
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inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other);
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inline Derived& operator=(const Derived& other);
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protected:
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template <typename OtherDerived>
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inline Derived& assign(const OtherDerived& other);
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template <typename OtherDerived>
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inline void assignGeneric(const OtherDerived& other);
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public:
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#ifndef EIGEN_NO_IO
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friend std::ostream& operator<<(std::ostream& s, const SparseMatrixBase& m) {
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typedef typename Derived::Nested Nested;
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typedef internal::remove_all_t<Nested> NestedCleaned;
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if (Flags & RowMajorBit) {
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Nested nm(m.derived());
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internal::evaluator<NestedCleaned> thisEval(nm);
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for (Index row = 0; row < nm.outerSize(); ++row) {
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Index col = 0;
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for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, row); it; ++it) {
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for (; col < it.index(); ++col) s << "0 ";
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s << it.value() << " ";
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++col;
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}
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for (; col < m.cols(); ++col) s << "0 ";
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s << std::endl;
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}
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} else {
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Nested nm(m.derived());
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internal::evaluator<NestedCleaned> thisEval(nm);
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if (m.cols() == 1) {
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Index row = 0;
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for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, 0); it; ++it) {
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for (; row < it.index(); ++row) s << "0" << std::endl;
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s << it.value() << std::endl;
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++row;
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}
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for (; row < m.rows(); ++row) s << "0" << std::endl;
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} else {
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SparseMatrix<Scalar, RowMajorBit, StorageIndex> trans = m;
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s << static_cast<const SparseMatrixBase<SparseMatrix<Scalar, RowMajorBit, StorageIndex> >&>(trans);
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}
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}
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return s;
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}
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#endif
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template <typename OtherDerived>
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Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
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template <typename OtherDerived>
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Derived& operator-=(const SparseMatrixBase<OtherDerived>& other);
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template <typename OtherDerived>
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Derived& operator+=(const DiagonalBase<OtherDerived>& other);
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template <typename OtherDerived>
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Derived& operator-=(const DiagonalBase<OtherDerived>& other);
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template <typename OtherDerived>
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Derived& operator+=(const EigenBase<OtherDerived>& other);
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template <typename OtherDerived>
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Derived& operator-=(const EigenBase<OtherDerived>& other);
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Derived& operator*=(const Scalar& other);
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Derived& operator/=(const Scalar& other);
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template <typename OtherDerived>
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struct CwiseProductDenseReturnType {
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typedef CwiseBinaryOp<
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internal::scalar_product_op<typename ScalarBinaryOpTraits<
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typename internal::traits<Derived>::Scalar, typename internal::traits<OtherDerived>::Scalar>::ReturnType>,
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const Derived, const OtherDerived>
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Type;
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};
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template <typename OtherDerived>
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EIGEN_STRONG_INLINE const typename CwiseProductDenseReturnType<OtherDerived>::Type cwiseProduct(
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const MatrixBase<OtherDerived>& other) const;
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// sparse * diagonal
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template <typename OtherDerived>
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const Product<Derived, OtherDerived> operator*(const DiagonalBase<OtherDerived>& other) const {
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return Product<Derived, OtherDerived>(derived(), other.derived());
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}
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// diagonal * sparse
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template <typename OtherDerived>
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friend const Product<OtherDerived, Derived> operator*(const DiagonalBase<OtherDerived>& lhs,
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const SparseMatrixBase& rhs) {
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return Product<OtherDerived, Derived>(lhs.derived(), rhs.derived());
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}
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// sparse * sparse
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template <typename OtherDerived>
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const Product<Derived, OtherDerived, AliasFreeProduct> operator*(const SparseMatrixBase<OtherDerived>& other) const;
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// sparse * dense
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template <typename OtherDerived>
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const Product<Derived, OtherDerived> operator*(const MatrixBase<OtherDerived>& other) const {
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return Product<Derived, OtherDerived>(derived(), other.derived());
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}
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// dense * sparse
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template <typename OtherDerived>
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friend const Product<OtherDerived, Derived> operator*(const MatrixBase<OtherDerived>& lhs,
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const SparseMatrixBase& rhs) {
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return Product<OtherDerived, Derived>(lhs.derived(), rhs.derived());
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}
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/** \returns an expression of P H P^-1 where H is the matrix represented by \c *this */
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SparseSymmetricPermutationProduct<Derived, Upper | Lower> twistedBy(
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const PermutationMatrix<Dynamic, Dynamic, StorageIndex>& perm) const {
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return SparseSymmetricPermutationProduct<Derived, Upper | Lower>(derived(), perm);
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}
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template <typename OtherDerived>
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Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
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template <int Mode>
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inline const TriangularView<const Derived, Mode> triangularView() const;
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template <unsigned int UpLo>
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struct SelfAdjointViewReturnType {
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typedef SparseSelfAdjointView<Derived, UpLo> Type;
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};
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template <unsigned int UpLo>
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struct ConstSelfAdjointViewReturnType {
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typedef const SparseSelfAdjointView<const Derived, UpLo> Type;
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};
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template <unsigned int UpLo>
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inline typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
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template <unsigned int UpLo>
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inline typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
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template <typename OtherDerived>
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Scalar dot(const MatrixBase<OtherDerived>& other) const;
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template <typename OtherDerived>
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Scalar dot(const SparseMatrixBase<OtherDerived>& other) const;
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RealScalar squaredNorm() const;
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RealScalar norm() const;
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RealScalar blueNorm() const;
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TransposeReturnType transpose() { return TransposeReturnType(derived()); }
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const ConstTransposeReturnType transpose() const { return ConstTransposeReturnType(derived()); }
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const AdjointReturnType adjoint() const { return AdjointReturnType(transpose()); }
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DenseMatrixType toDense() const { return DenseMatrixType(derived()); }
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template <typename OtherDerived>
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bool isApprox(const SparseMatrixBase<OtherDerived>& other,
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const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
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template <typename OtherDerived>
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bool isApprox(const MatrixBase<OtherDerived>& other,
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const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const {
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return toDense().isApprox(other, prec);
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}
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/** \returns the matrix or vector obtained by evaluating this expression.
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*
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* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
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* a const reference, in order to avoid a useless copy.
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*/
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inline const typename internal::eval<Derived>::type eval() const {
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return typename internal::eval<Derived>::type(derived());
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}
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Scalar sum() const;
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inline const SparseView<Derived> pruned(const Scalar& reference = Scalar(0),
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const RealScalar& epsilon = NumTraits<Scalar>::dummy_precision()) const;
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protected:
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bool m_isRValue;
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static inline StorageIndex convert_index(const Index idx) { return internal::convert_index<StorageIndex>(idx); }
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private:
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template <typename Dest>
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void evalTo(Dest&) const;
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};
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} // end namespace Eigen
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#endif // EIGEN_SPARSEMATRIXBASE_H
|
||||
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