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Remove Skyline.
This commit is contained in:
Antonio Sánchez
parent
0f0c76dc29
commit
69ee52ed13
@ -16,7 +16,6 @@ set(Eigen_HEADERS
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NumericalDiff
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OpenGLSupport
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Polynomials
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Skyline
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SparseExtra
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SpecialFunctions
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Splines
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@ -1,40 +0,0 @@
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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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//
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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_SKYLINE_MODULE_H
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#define EIGEN_SKYLINE_MODULE_H
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#include "../../Eigen/Core"
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#include "../../Eigen/src/Core/util/DisableStupidWarnings.h"
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#include <map>
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#include <cstdlib>
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#include <cstring>
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#include <algorithm>
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/**
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* \defgroup Skyline_Module Skyline module
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*
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*
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*
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*
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*/
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// IWYU pragma: begin_exports
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#include "src/Skyline/SkylineUtil.h"
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#include "src/Skyline/SkylineMatrixBase.h"
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#include "src/Skyline/SkylineStorage.h"
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#include "src/Skyline/SkylineMatrix.h"
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#include "src/Skyline/SkylineInplaceLU.h"
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#include "src/Skyline/SkylineProduct.h"
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// IWYU pragma: end_exports
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#include "../../Eigen/src/Core/util/ReenableStupidWarnings.h"
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#endif // EIGEN_SKYLINE_MODULE_H
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@ -1,3 +0,0 @@
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#ifndef EIGEN_SKYLINE_MODULE_H
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#error "Please include unsupported/Eigen/Skyline instead of including headers inside the src directory directly."
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#endif
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@ -1,326 +0,0 @@
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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 Guillaume Saupin <guillaume.saupin@cea.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_SKYLINEINPLACELU_H
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#define EIGEN_SKYLINEINPLACELU_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 Skyline_Module
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*
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* \class SkylineInplaceLU
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*
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* \brief Inplace LU decomposition of a skyline matrix and associated features
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*
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* \param MatrixType the type of the matrix of which we are computing the LU factorization
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*
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*/
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template <typename MatrixType>
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class SkylineInplaceLU {
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protected:
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::Index Index;
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typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
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public:
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/** Creates a LU object and compute the respective factorization of \a matrix using
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* flags \a flags. */
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SkylineInplaceLU(MatrixType& matrix, int flags = 0)
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: /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0), m_lu(matrix) {
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m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar>();
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m_lu.IsRowMajor ? computeRowMajor() : compute();
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}
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/** Sets the relative threshold value used to prune zero coefficients during the decomposition.
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*
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* Setting a value greater than zero speeds up computation, and yields to an incomplete
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* factorization with fewer non zero coefficients. Such approximate factors are especially
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* useful to initialize an iterative solver.
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*
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* Note that the exact meaning of this parameter might depends on the actual
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* backend. Moreover, not all backends support this feature.
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*
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* \sa precision() */
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void setPrecision(RealScalar v) { m_precision = v; }
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/** \returns the current precision.
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*
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* \sa setPrecision() */
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RealScalar precision() const { return m_precision; }
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/** Sets the flags. Possible values are:
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* - CompleteFactorization
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* - IncompleteFactorization
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* - MemoryEfficient
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* - one of the ordering methods
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* - etc...
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*
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* \sa flags() */
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void setFlags(int f) { m_flags = f; }
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/** \returns the current flags */
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int flags() const { return m_flags; }
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void setOrderingMethod(int m) { m_flags = m; }
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int orderingMethod() const { return m_flags; }
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/** Computes/re-computes the LU factorization */
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void compute();
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void computeRowMajor();
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/** \returns the lower triangular matrix L */
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// inline const MatrixType& matrixL() const { return m_matrixL; }
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/** \returns the upper triangular matrix U */
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// inline const MatrixType& matrixU() const { return m_matrixU; }
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template <typename BDerived, typename XDerived>
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bool solve(const MatrixBase<BDerived>& b, MatrixBase<XDerived>* x, const int transposed = 0) const;
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/** \returns true if the factorization succeeded */
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inline bool succeeded(void) const { return m_succeeded; }
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protected:
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RealScalar m_precision;
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int m_flags;
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mutable int m_status;
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bool m_succeeded;
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MatrixType& m_lu;
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};
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/** Computes / recomputes the in place LU decomposition of the SkylineInplaceLU.
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* using the default algorithm.
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*/
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template <typename MatrixType>
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// template<typename Scalar_>
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void SkylineInplaceLU<MatrixType>::compute() {
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const size_t rows = m_lu.rows();
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const size_t cols = m_lu.cols();
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eigen_assert(rows == cols && "We do not (yet) support rectangular LU.");
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eigen_assert(!m_lu.IsRowMajor && "LU decomposition does not work with rowMajor Storage");
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for (Index row = 0; row < rows; row++) {
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const double pivot = m_lu.coeffDiag(row);
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// Lower matrix Columns update
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const Index& col = row;
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for (typename MatrixType::InnerLowerIterator lIt(m_lu, col); lIt; ++lIt) {
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lIt.valueRef() /= pivot;
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}
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// Upper matrix update -> contiguous memory access
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typename MatrixType::InnerLowerIterator lIt(m_lu, col);
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for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
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typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
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typename MatrixType::InnerUpperIterator uIt(m_lu, rrow);
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const double coef = lIt.value();
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uItPivot += (rrow - row - 1);
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// update upper part -> contiguous memory access
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for (++uItPivot; uIt && uItPivot;) {
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uIt.valueRef() -= uItPivot.value() * coef;
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++uIt;
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++uItPivot;
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}
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++lIt;
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}
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// Upper matrix update -> non contiguous memory access
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typename MatrixType::InnerLowerIterator lIt3(m_lu, col);
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for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
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typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
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const double coef = lIt3.value();
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// update lower part -> non contiguous memory access
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for (Index i = 0; i < rrow - row - 1; i++) {
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m_lu.coeffRefLower(rrow, row + i + 1) -= uItPivot.value() * coef;
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++uItPivot;
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}
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++lIt3;
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}
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// update diag -> contiguous
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typename MatrixType::InnerLowerIterator lIt2(m_lu, col);
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for (Index rrow = row + 1; rrow < m_lu.rows(); rrow++) {
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typename MatrixType::InnerUpperIterator uItPivot(m_lu, row);
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typename MatrixType::InnerUpperIterator uIt(m_lu, rrow);
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const double coef = lIt2.value();
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uItPivot += (rrow - row - 1);
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m_lu.coeffRefDiag(rrow) -= uItPivot.value() * coef;
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++lIt2;
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}
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}
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}
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template <typename MatrixType>
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void SkylineInplaceLU<MatrixType>::computeRowMajor() {
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const size_t rows = m_lu.rows();
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const size_t cols = m_lu.cols();
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eigen_assert(rows == cols && "We do not (yet) support rectangular LU.");
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eigen_assert(m_lu.IsRowMajor && "You're trying to apply rowMajor decomposition on a ColMajor matrix !");
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for (Index row = 0; row < rows; row++) {
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typename MatrixType::InnerLowerIterator llIt(m_lu, row);
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for (Index col = llIt.col(); col < row; col++) {
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if (m_lu.coeffExistLower(row, col)) {
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const double diag = m_lu.coeffDiag(col);
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typename MatrixType::InnerLowerIterator lIt(m_lu, row);
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typename MatrixType::InnerUpperIterator uIt(m_lu, col);
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const Index offset = lIt.col() - uIt.row();
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Index stop = offset > 0 ? col - lIt.col() : col - uIt.row();
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// #define VECTORIZE
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#ifdef VECTORIZE
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Map<VectorXd> rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
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Map<VectorXd> colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
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Scalar newCoeff = m_lu.coeffLower(row, col) - rowVal.dot(colVal);
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#else
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if (offset > 0) // Skip zero value of lIt
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uIt += offset;
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else // Skip zero values of uIt
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lIt += -offset;
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Scalar newCoeff = m_lu.coeffLower(row, col);
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for (Index k = 0; k < stop; ++k) {
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const Scalar tmp = newCoeff;
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newCoeff = tmp - lIt.value() * uIt.value();
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++lIt;
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++uIt;
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}
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#endif
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m_lu.coeffRefLower(row, col) = newCoeff / diag;
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}
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}
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// Upper matrix update
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const Index col = row;
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typename MatrixType::InnerUpperIterator uuIt(m_lu, col);
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for (Index rrow = uuIt.row(); rrow < col; rrow++) {
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typename MatrixType::InnerLowerIterator lIt(m_lu, rrow);
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typename MatrixType::InnerUpperIterator uIt(m_lu, col);
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const Index offset = lIt.col() - uIt.row();
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Index stop = offset > 0 ? rrow - lIt.col() : rrow - uIt.row();
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#ifdef VECTORIZE
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Map<VectorXd> rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
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Map<VectorXd> colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
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Scalar newCoeff = m_lu.coeffUpper(rrow, col) - rowVal.dot(colVal);
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#else
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if (offset > 0) // Skip zero value of lIt
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uIt += offset;
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else // Skip zero values of uIt
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lIt += -offset;
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Scalar newCoeff = m_lu.coeffUpper(rrow, col);
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for (Index k = 0; k < stop; ++k) {
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const Scalar tmp = newCoeff;
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newCoeff = tmp - lIt.value() * uIt.value();
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++lIt;
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++uIt;
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}
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#endif
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m_lu.coeffRefUpper(rrow, col) = newCoeff;
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}
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// Diag matrix update
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typename MatrixType::InnerLowerIterator lIt(m_lu, row);
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typename MatrixType::InnerUpperIterator uIt(m_lu, row);
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const Index offset = lIt.col() - uIt.row();
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Index stop = offset > 0 ? lIt.size() : uIt.size();
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#ifdef VECTORIZE
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Map<VectorXd> rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
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Map<VectorXd> colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
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Scalar newCoeff = m_lu.coeffDiag(row) - rowVal.dot(colVal);
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#else
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if (offset > 0) // Skip zero value of lIt
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uIt += offset;
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else // Skip zero values of uIt
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lIt += -offset;
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Scalar newCoeff = m_lu.coeffDiag(row);
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for (Index k = 0; k < stop; ++k) {
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const Scalar tmp = newCoeff;
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newCoeff = tmp - lIt.value() * uIt.value();
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++lIt;
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++uIt;
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}
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#endif
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m_lu.coeffRefDiag(row) = newCoeff;
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}
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}
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/** Computes *x = U^-1 L^-1 b
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*
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* If \a transpose is set to SvTranspose or SvAdjoint, the solution
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* of the transposed/adjoint system is computed instead.
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*
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* Not all backends implement the solution of the transposed or
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* adjoint system.
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*/
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template <typename MatrixType>
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template <typename BDerived, typename XDerived>
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bool SkylineInplaceLU<MatrixType>::solve(const MatrixBase<BDerived>& b, MatrixBase<XDerived>* x,
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const int transposed) const {
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const size_t rows = m_lu.rows();
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const size_t cols = m_lu.cols();
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for (Index row = 0; row < rows; row++) {
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x->coeffRef(row) = b.coeff(row);
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Scalar newVal = x->coeff(row);
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typename MatrixType::InnerLowerIterator lIt(m_lu, row);
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Index col = lIt.col();
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while (lIt.col() < row) {
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newVal -= x->coeff(col++) * lIt.value();
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++lIt;
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}
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x->coeffRef(row) = newVal;
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}
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for (Index col = rows - 1; col > 0; col--) {
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x->coeffRef(col) = x->coeff(col) / m_lu.coeffDiag(col);
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const Scalar x_col = x->coeff(col);
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typename MatrixType::InnerUpperIterator uIt(m_lu, col);
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uIt += uIt.size() - 1;
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while (uIt) {
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x->coeffRef(uIt.row()) -= x_col * uIt.value();
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// TODO : introduce --operator
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uIt += -1;
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}
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}
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x->coeffRef(0) = x->coeff(0) / m_lu.coeffDiag(0);
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return true;
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}
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} // end namespace Eigen
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#endif // EIGEN_SKYLINEINPLACELU_H
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@ -1,763 +0,0 @@
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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) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.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_SKYLINEMATRIX_H
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#define EIGEN_SKYLINEMATRIX_H
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#include "SkylineStorage.h"
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#include "SkylineMatrixBase.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 Skyline_Module
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*
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* \class SkylineMatrix
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*
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* \brief The main skyline matrix class
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*
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* This class implements a skyline matrix using the very uncommon storage
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* scheme.
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*
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* \param Scalar_ the scalar type, i.e. the type of the coefficients
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* \param Options_ Union of bit flags controlling the storage scheme. Currently the only possibility
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* is RowMajor. The default is 0 which means column-major.
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*
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*
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*/
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namespace internal {
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template <typename Scalar_, int Options_>
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struct traits<SkylineMatrix<Scalar_, Options_> > {
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typedef Scalar_ Scalar;
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typedef Sparse StorageKind;
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enum {
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RowsAtCompileTime = Dynamic,
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ColsAtCompileTime = Dynamic,
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MaxRowsAtCompileTime = Dynamic,
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MaxColsAtCompileTime = Dynamic,
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Flags = SkylineBit | Options_,
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CoeffReadCost = NumTraits<Scalar>::ReadCost,
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};
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};
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} // namespace internal
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template <typename Scalar_, int Options_>
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class SkylineMatrix : public SkylineMatrixBase<SkylineMatrix<Scalar_, Options_> > {
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public:
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EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(SkylineMatrix)
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EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, +=)
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EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, -=)
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using Base::IsRowMajor;
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protected:
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typedef SkylineMatrix<Scalar, (Flags & ~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0)> TransposedSkylineMatrix;
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Index m_outerSize;
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Index m_innerSize;
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public:
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Index* m_colStartIndex;
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Index* m_rowStartIndex;
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SkylineStorage<Scalar> m_data;
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public:
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inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
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inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
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inline Index innerSize() const { return m_innerSize; }
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inline Index outerSize() const { return m_outerSize; }
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inline Index upperNonZeros() const { return m_data.upperSize(); }
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inline Index lowerNonZeros() const { return m_data.lowerSize(); }
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inline Index upperNonZeros(Index j) const { return m_colStartIndex[j + 1] - m_colStartIndex[j]; }
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||||
|
||||
inline Index lowerNonZeros(Index j) const { return m_rowStartIndex[j + 1] - m_rowStartIndex[j]; }
|
||||
|
||||
inline const Scalar* _diagPtr() const { return &m_data.diag(0); }
|
||||
|
||||
inline Scalar* _diagPtr() { return &m_data.diag(0); }
|
||||
|
||||
inline const Scalar* _upperPtr() const { return &m_data.upper(0); }
|
||||
|
||||
inline Scalar* _upperPtr() { return &m_data.upper(0); }
|
||||
|
||||
inline const Scalar* _lowerPtr() const { return &m_data.lower(0); }
|
||||
|
||||
inline Scalar* _lowerPtr() { return &m_data.lower(0); }
|
||||
|
||||
inline const Index* _upperProfilePtr() const { return &m_data.upperProfile(0); }
|
||||
|
||||
inline Index* _upperProfilePtr() { return &m_data.upperProfile(0); }
|
||||
|
||||
inline const Index* _lowerProfilePtr() const { return &m_data.lowerProfile(0); }
|
||||
|
||||
inline Index* _lowerProfilePtr() { return &m_data.lowerProfile(0); }
|
||||
|
||||
inline Scalar coeff(Index row, Index col) const {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
|
||||
if (outer == inner) return this->m_data.diag(outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
if (inner > outer) // upper matrix
|
||||
{
|
||||
const Index minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
if (outer >= minOuterIndex)
|
||||
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
|
||||
else
|
||||
return Scalar(0);
|
||||
}
|
||||
if (inner < outer) // lower matrix
|
||||
{
|
||||
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
if (inner >= minInnerIndex)
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
|
||||
else
|
||||
return Scalar(0);
|
||||
}
|
||||
return m_data.upper(m_colStartIndex[inner] + outer - inner);
|
||||
} else {
|
||||
if (outer > inner) // upper matrix
|
||||
{
|
||||
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
if (outer <= maxOuterIndex)
|
||||
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
|
||||
else
|
||||
return Scalar(0);
|
||||
}
|
||||
if (outer < inner) // lower matrix
|
||||
{
|
||||
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
|
||||
if (inner <= maxInnerIndex)
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
|
||||
else
|
||||
return Scalar(0);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
inline Scalar& coeffRef(Index row, Index col) {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
|
||||
if (outer == inner) return this->m_data.diag(outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
if (col > row) // upper matrix
|
||||
{
|
||||
const Index minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
|
||||
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
|
||||
}
|
||||
if (col < row) // lower matrix
|
||||
{
|
||||
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
|
||||
}
|
||||
} else {
|
||||
if (outer > inner) // upper matrix
|
||||
{
|
||||
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
|
||||
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
|
||||
}
|
||||
if (outer < inner) // lower matrix
|
||||
{
|
||||
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
inline Scalar coeffDiag(Index idx) const {
|
||||
eigen_assert(idx < outerSize());
|
||||
eigen_assert(idx < innerSize());
|
||||
return this->m_data.diag(idx);
|
||||
}
|
||||
|
||||
inline Scalar coeffLower(Index row, Index col) const {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
eigen_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
if (inner >= minInnerIndex)
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
|
||||
else
|
||||
return Scalar(0);
|
||||
|
||||
} else {
|
||||
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
if (inner <= maxInnerIndex)
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
|
||||
else
|
||||
return Scalar(0);
|
||||
}
|
||||
}
|
||||
|
||||
inline Scalar coeffUpper(Index row, Index col) const {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
eigen_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const Index minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
if (outer >= minOuterIndex)
|
||||
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
|
||||
else
|
||||
return Scalar(0);
|
||||
} else {
|
||||
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
if (outer <= maxOuterIndex)
|
||||
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
|
||||
else
|
||||
return Scalar(0);
|
||||
}
|
||||
}
|
||||
|
||||
inline Scalar& coeffRefDiag(Index idx) {
|
||||
eigen_assert(idx < outerSize());
|
||||
eigen_assert(idx < innerSize());
|
||||
return this->m_data.diag(idx);
|
||||
}
|
||||
|
||||
inline Scalar& coeffRefLower(Index row, Index col) {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
eigen_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
|
||||
} else {
|
||||
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
|
||||
}
|
||||
}
|
||||
|
||||
inline bool coeffExistLower(Index row, Index col) {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
eigen_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
return inner >= minInnerIndex;
|
||||
} else {
|
||||
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
return inner <= maxInnerIndex;
|
||||
}
|
||||
}
|
||||
|
||||
inline Scalar& coeffRefUpper(Index row, Index col) {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
eigen_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const Index minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
|
||||
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
|
||||
} else {
|
||||
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
|
||||
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
|
||||
}
|
||||
}
|
||||
|
||||
inline bool coeffExistUpper(Index row, Index col) {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
eigen_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const Index minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
return outer >= minOuterIndex;
|
||||
} else {
|
||||
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
return outer <= maxOuterIndex;
|
||||
}
|
||||
}
|
||||
|
||||
protected:
|
||||
public:
|
||||
class InnerUpperIterator;
|
||||
class InnerLowerIterator;
|
||||
|
||||
class OuterUpperIterator;
|
||||
class OuterLowerIterator;
|
||||
|
||||
/** Removes all non zeros */
|
||||
inline void setZero() {
|
||||
m_data.clear();
|
||||
std::fill_n(m_colStartIndex, m_outerSize + 1, Index(0));
|
||||
std::fill_n(m_rowStartIndex, m_outerSize + 1, Index(0));
|
||||
}
|
||||
|
||||
/** \returns the number of non zero coefficients */
|
||||
inline Index nonZeros() const { return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize(); }
|
||||
|
||||
/** Preallocates \a reserveSize non zeros */
|
||||
inline void reserve(Index reserveSize, Index reserveUpperSize, Index reserveLowerSize) {
|
||||
m_data.reserve(reserveSize, reserveUpperSize, reserveLowerSize);
|
||||
}
|
||||
|
||||
/** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
|
||||
|
||||
*
|
||||
* \warning This function can be extremely slow if the non zero coefficients
|
||||
* are not inserted in a coherent order.
|
||||
*
|
||||
* After an insertion session, you should call the finalize() function.
|
||||
*/
|
||||
EIGEN_DONT_INLINE Scalar& insert(Index row, Index col) {
|
||||
const Index outer = IsRowMajor ? row : col;
|
||||
const Index inner = IsRowMajor ? col : row;
|
||||
|
||||
eigen_assert(outer < outerSize());
|
||||
eigen_assert(inner < innerSize());
|
||||
|
||||
if (outer == inner) return m_data.diag(col);
|
||||
|
||||
if (IsRowMajor) {
|
||||
if (outer < inner) // upper matrix
|
||||
{
|
||||
Index minOuterIndex = 0;
|
||||
minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
|
||||
if (outer < minOuterIndex) // The value does not yet exist
|
||||
{
|
||||
const Index previousProfile = m_data.upperProfile(inner);
|
||||
|
||||
m_data.upperProfile(inner) = inner - outer;
|
||||
|
||||
const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
|
||||
// shift data stored after this new one
|
||||
const Index stop = m_colStartIndex[cols()];
|
||||
const Index start = m_colStartIndex[inner];
|
||||
|
||||
for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
||||
m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
|
||||
}
|
||||
|
||||
for (Index innerIdx = cols(); innerIdx > inner; innerIdx--) {
|
||||
m_colStartIndex[innerIdx] += bandIncrement;
|
||||
}
|
||||
|
||||
// zeros new data
|
||||
std::fill_n(this->_upperPtr() + start, bandIncrement - 1, Scalar(0));
|
||||
|
||||
return m_data.upper(m_colStartIndex[inner]);
|
||||
} else {
|
||||
return m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
|
||||
}
|
||||
}
|
||||
|
||||
if (outer > inner) // lower matrix
|
||||
{
|
||||
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
if (inner < minInnerIndex) // The value does not yet exist
|
||||
{
|
||||
const Index previousProfile = m_data.lowerProfile(outer);
|
||||
m_data.lowerProfile(outer) = outer - inner;
|
||||
|
||||
const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
|
||||
// shift data stored after this new one
|
||||
const Index stop = m_rowStartIndex[rows()];
|
||||
const Index start = m_rowStartIndex[outer];
|
||||
|
||||
for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
||||
m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
|
||||
}
|
||||
|
||||
for (Index innerIdx = rows(); innerIdx > outer; innerIdx--) {
|
||||
m_rowStartIndex[innerIdx] += bandIncrement;
|
||||
}
|
||||
|
||||
// zeros new data
|
||||
std::fill_n(this->_lowerPtr() + start, bandIncrement - 1, Scalar(0));
|
||||
return m_data.lower(m_rowStartIndex[outer]);
|
||||
} else {
|
||||
return m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
|
||||
}
|
||||
}
|
||||
} else {
|
||||
if (outer > inner) // upper matrix
|
||||
{
|
||||
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
if (outer > maxOuterIndex) // The value does not yet exist
|
||||
{
|
||||
const Index previousProfile = m_data.upperProfile(inner);
|
||||
m_data.upperProfile(inner) = outer - inner;
|
||||
|
||||
const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
|
||||
// shift data stored after this new one
|
||||
const Index stop = m_rowStartIndex[rows()];
|
||||
const Index start = m_rowStartIndex[inner + 1];
|
||||
|
||||
for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
||||
m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
|
||||
}
|
||||
|
||||
for (Index innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) {
|
||||
m_rowStartIndex[innerIdx] += bandIncrement;
|
||||
}
|
||||
std::fill_n(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, bandIncrement - 1, Scalar(0));
|
||||
return m_data.upper(m_rowStartIndex[inner] + m_data.upperProfile(inner));
|
||||
} else {
|
||||
return m_data.upper(m_rowStartIndex[inner] + (outer - inner));
|
||||
}
|
||||
}
|
||||
|
||||
if (outer < inner) // lower matrix
|
||||
{
|
||||
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
if (inner > maxInnerIndex) // The value does not yet exist
|
||||
{
|
||||
const Index previousProfile = m_data.lowerProfile(outer);
|
||||
m_data.lowerProfile(outer) = inner - outer;
|
||||
|
||||
const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
|
||||
// shift data stored after this new one
|
||||
const Index stop = m_colStartIndex[cols()];
|
||||
const Index start = m_colStartIndex[outer + 1];
|
||||
|
||||
for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
||||
m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
|
||||
}
|
||||
|
||||
for (Index innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) {
|
||||
m_colStartIndex[innerIdx] += bandIncrement;
|
||||
}
|
||||
std::fill_n(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, bandIncrement - 1, Scalar(0));
|
||||
return m_data.lower(m_colStartIndex[outer] + m_data.lowerProfile(outer));
|
||||
} else {
|
||||
return m_data.lower(m_colStartIndex[outer] + (inner - outer));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/** Must be called after inserting a set of non zero entries.
|
||||
*/
|
||||
inline void finalize() {
|
||||
if (IsRowMajor) {
|
||||
if (rows() > cols())
|
||||
m_data.resize(cols(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
|
||||
else
|
||||
m_data.resize(rows(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
|
||||
|
||||
// eigen_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix");
|
||||
//
|
||||
// Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1];
|
||||
// Index dataIdx = 0;
|
||||
// for (Index row = 0; row < rows(); row++) {
|
||||
//
|
||||
// const Index nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row];
|
||||
// // std::cout << "nbLowerElts" << nbLowerElts << std::endl;
|
||||
// memcpy(newArray + dataIdx, m_data.m_lower + m_rowStartIndex[row], nbLowerElts * sizeof
|
||||
// (Scalar)); m_rowStartIndex[row] = dataIdx; dataIdx += nbLowerElts;
|
||||
//
|
||||
// const Index nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row];
|
||||
// memcpy(newArray + dataIdx, m_data.m_upper + m_colStartIndex[row], nbUpperElts * sizeof
|
||||
// (Scalar)); m_colStartIndex[row] = dataIdx; dataIdx += nbUpperElts;
|
||||
//
|
||||
//
|
||||
// }
|
||||
// //todo : don't access m_data profile directly : add an accessor from SkylineMatrix
|
||||
// m_rowStartIndex[rows()] = m_rowStartIndex[rows()-1] + m_data.lowerProfile(rows()-1);
|
||||
// m_colStartIndex[cols()] = m_colStartIndex[cols()-1] + m_data.upperProfile(cols()-1);
|
||||
//
|
||||
// delete[] m_data.m_lower;
|
||||
// delete[] m_data.m_upper;
|
||||
//
|
||||
// m_data.m_lower = newArray;
|
||||
// m_data.m_upper = newArray;
|
||||
} else {
|
||||
if (rows() > cols())
|
||||
m_data.resize(cols(), rows(), cols(), m_rowStartIndex[cols()] + 1, m_colStartIndex[cols()] + 1);
|
||||
else
|
||||
m_data.resize(rows(), rows(), cols(), m_rowStartIndex[rows()] + 1, m_colStartIndex[rows()] + 1);
|
||||
}
|
||||
}
|
||||
|
||||
inline void squeeze() {
|
||||
finalize();
|
||||
m_data.squeeze();
|
||||
}
|
||||
|
||||
void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>()) {
|
||||
// TODO
|
||||
}
|
||||
|
||||
/** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
|
||||
* \sa resizeNonZeros(Index), reserve(), setZero()
|
||||
*/
|
||||
void resize(size_t rows, size_t cols) {
|
||||
const Index diagSize = rows > cols ? cols : rows;
|
||||
m_innerSize = IsRowMajor ? cols : rows;
|
||||
|
||||
eigen_assert(rows == cols && "Skyline matrix must be square matrix");
|
||||
|
||||
if (diagSize % 2) { // diagSize is odd
|
||||
const Index k = (diagSize - 1) / 2;
|
||||
|
||||
m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols, 2 * k * k + k + 1, 2 * k * k + k + 1);
|
||||
|
||||
} else // diagSize is even
|
||||
{
|
||||
const Index k = diagSize / 2;
|
||||
m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols, 2 * k * k - k + 1, 2 * k * k - k + 1);
|
||||
}
|
||||
|
||||
if (m_colStartIndex && m_rowStartIndex) {
|
||||
delete[] m_colStartIndex;
|
||||
delete[] m_rowStartIndex;
|
||||
}
|
||||
m_colStartIndex = new Index[cols + 1];
|
||||
m_rowStartIndex = new Index[rows + 1];
|
||||
m_outerSize = diagSize;
|
||||
|
||||
m_data.reset();
|
||||
m_data.clear();
|
||||
|
||||
m_outerSize = diagSize;
|
||||
std::fill_n(m_colStartIndex, cols + 1, Index(0));
|
||||
std::fill_n(m_rowStartIndex, rows + 1, Index(0));
|
||||
}
|
||||
|
||||
void resizeNonZeros(Index size) { m_data.resize(size); }
|
||||
|
||||
inline SkylineMatrix() : m_outerSize(-1), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) { resize(0, 0); }
|
||||
|
||||
inline SkylineMatrix(size_t rows, size_t cols)
|
||||
: m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
||||
resize(rows, cols);
|
||||
}
|
||||
|
||||
template <typename OtherDerived>
|
||||
inline SkylineMatrix(const SkylineMatrixBase<OtherDerived>& other)
|
||||
: m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
||||
*this = other.derived();
|
||||
}
|
||||
|
||||
inline SkylineMatrix(const SkylineMatrix& other)
|
||||
: Base(), m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
||||
*this = other.derived();
|
||||
}
|
||||
|
||||
inline void swap(SkylineMatrix& other) {
|
||||
// EIGEN_DBG_SKYLINE(std::cout << "SkylineMatrix:: swap\n");
|
||||
std::swap(m_colStartIndex, other.m_colStartIndex);
|
||||
std::swap(m_rowStartIndex, other.m_rowStartIndex);
|
||||
std::swap(m_innerSize, other.m_innerSize);
|
||||
std::swap(m_outerSize, other.m_outerSize);
|
||||
m_data.swap(other.m_data);
|
||||
}
|
||||
|
||||
inline SkylineMatrix& operator=(const SkylineMatrix& other) {
|
||||
std::cout << "SkylineMatrix& operator=(const SkylineMatrix& other)\n";
|
||||
if (other.isRValue()) {
|
||||
swap(other.const_cast_derived());
|
||||
} else {
|
||||
resize(other.rows(), other.cols());
|
||||
memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof(Index));
|
||||
memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof(Index));
|
||||
m_data = other.m_data;
|
||||
}
|
||||
return *this;
|
||||
}
|
||||
|
||||
template <typename OtherDerived>
|
||||
inline SkylineMatrix& operator=(const SkylineMatrixBase<OtherDerived>& other) {
|
||||
const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
|
||||
if (needToTranspose) {
|
||||
// TODO
|
||||
// return *this;
|
||||
} else {
|
||||
// there is no special optimization
|
||||
return SkylineMatrixBase<SkylineMatrix>::operator=(other.derived());
|
||||
}
|
||||
}
|
||||
|
||||
friend std::ostream& operator<<(std::ostream& s, const SkylineMatrix& m) {
|
||||
EIGEN_DBG_SKYLINE(
|
||||
std::cout << "upper elements : " << std::endl;
|
||||
for (Index i = 0; i < m.m_data.upperSize(); i++) std::cout << m.m_data.upper(i) << "\t"; std::cout << std::endl;
|
||||
std::cout << "upper profile : " << std::endl;
|
||||
for (Index i = 0; i < m.m_data.upperProfileSize(); i++) std::cout << m.m_data.upperProfile(i) << "\t";
|
||||
std::cout << std::endl; std::cout << "lower startIdx : " << std::endl;
|
||||
for (Index i = 0; i < m.m_data.upperProfileSize(); i++) std::cout
|
||||
<< (IsRowMajor ? m.m_colStartIndex[i] : m.m_rowStartIndex[i]) << "\t";
|
||||
std::cout << std::endl;
|
||||
|
||||
std::cout << "lower elements : " << std::endl;
|
||||
for (Index i = 0; i < m.m_data.lowerSize(); i++) std::cout << m.m_data.lower(i) << "\t"; std::cout << std::endl;
|
||||
std::cout << "lower profile : " << std::endl;
|
||||
for (Index i = 0; i < m.m_data.lowerProfileSize(); i++) std::cout << m.m_data.lowerProfile(i) << "\t";
|
||||
std::cout << std::endl; std::cout << "lower startIdx : " << std::endl;
|
||||
for (Index i = 0; i < m.m_data.lowerProfileSize(); i++) std::cout
|
||||
<< (IsRowMajor ? m.m_rowStartIndex[i] : m.m_colStartIndex[i]) << "\t";
|
||||
std::cout << std::endl;);
|
||||
for (Index rowIdx = 0; rowIdx < m.rows(); rowIdx++) {
|
||||
for (Index colIdx = 0; colIdx < m.cols(); colIdx++) {
|
||||
s << m.coeff(rowIdx, colIdx) << "\t";
|
||||
}
|
||||
s << std::endl;
|
||||
}
|
||||
return s;
|
||||
}
|
||||
|
||||
/** Destructor */
|
||||
inline ~SkylineMatrix() {
|
||||
delete[] m_colStartIndex;
|
||||
delete[] m_rowStartIndex;
|
||||
}
|
||||
|
||||
/** Overloaded for performance */
|
||||
Scalar sum() const;
|
||||
};
|
||||
|
||||
template <typename Scalar, int Options_>
|
||||
class SkylineMatrix<Scalar, Options_>::InnerUpperIterator {
|
||||
public:
|
||||
InnerUpperIterator(const SkylineMatrix& mat, Index outer)
|
||||
: m_matrix(mat),
|
||||
m_outer(outer),
|
||||
m_id(Options_ == RowMajor ? mat.m_colStartIndex[outer] : mat.m_rowStartIndex[outer] + 1),
|
||||
m_start(m_id),
|
||||
m_end(Options_ == RowMajor ? mat.m_colStartIndex[outer + 1] : mat.m_rowStartIndex[outer + 1] + 1) {}
|
||||
|
||||
inline InnerUpperIterator& operator++() {
|
||||
m_id++;
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline InnerUpperIterator& operator+=(Index shift) {
|
||||
m_id += shift;
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline Scalar value() const { return m_matrix.m_data.upper(m_id); }
|
||||
|
||||
inline Scalar* valuePtr() { return const_cast<Scalar*>(&(m_matrix.m_data.upper(m_id))); }
|
||||
|
||||
inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.m_data.upper(m_id)); }
|
||||
|
||||
inline Index index() const {
|
||||
return IsRowMajor ? m_outer - m_matrix.m_data.upperProfile(m_outer) + (m_id - m_start)
|
||||
: m_outer + (m_id - m_start) + 1;
|
||||
}
|
||||
|
||||
inline Index row() const { return IsRowMajor ? index() : m_outer; }
|
||||
|
||||
inline Index col() const { return IsRowMajor ? m_outer : index(); }
|
||||
|
||||
inline size_t size() const { return m_matrix.m_data.upperProfile(m_outer); }
|
||||
|
||||
inline operator bool() const { return (m_id < m_end) && (m_id >= m_start); }
|
||||
|
||||
protected:
|
||||
const SkylineMatrix& m_matrix;
|
||||
const Index m_outer;
|
||||
Index m_id;
|
||||
const Index m_start;
|
||||
const Index m_end;
|
||||
};
|
||||
|
||||
template <typename Scalar, int Options_>
|
||||
class SkylineMatrix<Scalar, Options_>::InnerLowerIterator {
|
||||
public:
|
||||
InnerLowerIterator(const SkylineMatrix& mat, Index outer)
|
||||
: m_matrix(mat),
|
||||
m_outer(outer),
|
||||
m_id(Options_ == RowMajor ? mat.m_rowStartIndex[outer] : mat.m_colStartIndex[outer] + 1),
|
||||
m_start(m_id),
|
||||
m_end(Options_ == RowMajor ? mat.m_rowStartIndex[outer + 1] : mat.m_colStartIndex[outer + 1] + 1) {}
|
||||
|
||||
inline InnerLowerIterator& operator++() {
|
||||
m_id++;
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline InnerLowerIterator& operator+=(Index shift) {
|
||||
m_id += shift;
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline Scalar value() const { return m_matrix.m_data.lower(m_id); }
|
||||
|
||||
inline Scalar* valuePtr() { return const_cast<Scalar*>(&(m_matrix.m_data.lower(m_id))); }
|
||||
|
||||
inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.m_data.lower(m_id)); }
|
||||
|
||||
inline Index index() const {
|
||||
return IsRowMajor ? m_outer - m_matrix.m_data.lowerProfile(m_outer) + (m_id - m_start)
|
||||
: m_outer + (m_id - m_start) + 1;
|
||||
;
|
||||
}
|
||||
|
||||
inline Index row() const { return IsRowMajor ? m_outer : index(); }
|
||||
|
||||
inline Index col() const { return IsRowMajor ? index() : m_outer; }
|
||||
|
||||
inline size_t size() const { return m_matrix.m_data.lowerProfile(m_outer); }
|
||||
|
||||
inline operator bool() const { return (m_id < m_end) && (m_id >= m_start); }
|
||||
|
||||
protected:
|
||||
const SkylineMatrix& m_matrix;
|
||||
const Index m_outer;
|
||||
Index m_id;
|
||||
const Index m_start;
|
||||
const Index m_end;
|
||||
};
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif // EIGEN_SKYLINEMATRIX_H
|
||||
@ -1,188 +0,0 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.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_SKYLINEMATRIXBASE_H
|
||||
#define EIGEN_SKYLINEMATRIXBASE_H
|
||||
|
||||
#include "SkylineUtil.h"
|
||||
|
||||
// IWYU pragma: private
|
||||
#include "./InternalHeaderCheck.h"
|
||||
|
||||
namespace Eigen {
|
||||
|
||||
/** \ingroup Skyline_Module
|
||||
*
|
||||
* \class SkylineMatrixBase
|
||||
*
|
||||
* \brief Base class of any skyline matrices or skyline expressions
|
||||
*
|
||||
* \param Derived
|
||||
*
|
||||
*/
|
||||
template <typename Derived>
|
||||
class SkylineMatrixBase : public EigenBase<Derived> {
|
||||
public:
|
||||
typedef typename internal::traits<Derived>::Scalar Scalar;
|
||||
typedef typename internal::traits<Derived>::StorageKind StorageKind;
|
||||
typedef typename internal::index<StorageKind>::type Index;
|
||||
|
||||
enum {
|
||||
RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
|
||||
/**< The number of rows at compile-time. This is just a copy of the value provided
|
||||
* by the \a Derived type. If a value is not known at compile-time,
|
||||
* it is set to the \a Dynamic constant.
|
||||
* \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
|
||||
|
||||
ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
|
||||
/**< The number of columns at compile-time. This is just a copy of the value provided
|
||||
* by the \a Derived type. If a value is not known at compile-time,
|
||||
* it is set to the \a Dynamic constant.
|
||||
* \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
|
||||
|
||||
SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
|
||||
/**< This is equal to the number of coefficients, i.e. the number of
|
||||
* rows times the number of columns, or to \a Dynamic if this is not
|
||||
* known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
|
||||
|
||||
MaxRowsAtCompileTime = RowsAtCompileTime,
|
||||
MaxColsAtCompileTime = ColsAtCompileTime,
|
||||
|
||||
MaxSizeAtCompileTime = (internal::size_at_compile_time(MaxRowsAtCompileTime, MaxColsAtCompileTime)),
|
||||
|
||||
IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
|
||||
/**< This is set to true if either the number of rows or the number of
|
||||
* columns is known at compile-time to be equal to 1. Indeed, in that case,
|
||||
* we are dealing with a column-vector (if there is only one column) or with
|
||||
* a row-vector (if there is only one row). */
|
||||
|
||||
Flags = internal::traits<Derived>::Flags,
|
||||
/**< This stores expression \ref flags flags which may or may not be inherited by new expressions
|
||||
* constructed from this one. See the \ref flags "list of flags".
|
||||
*/
|
||||
|
||||
CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
|
||||
/**< This is a rough measure of how expensive it is to read one coefficient from
|
||||
* this expression.
|
||||
*/
|
||||
|
||||
IsRowMajor = Flags & RowMajorBit ? 1 : 0
|
||||
};
|
||||
|
||||
#ifndef EIGEN_PARSED_BY_DOXYGEN
|
||||
/** This is the "real scalar" type; if the \a Scalar type is already real numbers
|
||||
* (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
|
||||
* \a Scalar is \a std::complex<T> then RealScalar is \a T.
|
||||
*
|
||||
* \sa class NumTraits
|
||||
*/
|
||||
typedef typename NumTraits<Scalar>::Real RealScalar;
|
||||
|
||||
/** type of the equivalent square matrix */
|
||||
typedef Matrix<Scalar, internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
|
||||
internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)>
|
||||
SquareMatrixType;
|
||||
|
||||
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
|
||||
|
||||
inline Derived& derived() { return *static_cast<Derived*>(this); }
|
||||
|
||||
inline Derived& const_cast_derived() const { return *static_cast<Derived*>(const_cast<SkylineMatrixBase*>(this)); }
|
||||
#endif // not EIGEN_PARSED_BY_DOXYGEN
|
||||
|
||||
/** \returns the number of rows. \sa cols(), RowsAtCompileTime */
|
||||
inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return derived().rows(); }
|
||||
|
||||
/** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
|
||||
inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return derived().cols(); }
|
||||
|
||||
/** \returns the number of coefficients, which is \a rows()*cols().
|
||||
* \sa rows(), cols(), SizeAtCompileTime. */
|
||||
inline EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return rows() * cols(); }
|
||||
|
||||
/** \returns the number of nonzero coefficients which is in practice the number
|
||||
* of stored coefficients. */
|
||||
inline Index nonZeros() const { return derived().nonZeros(); }
|
||||
|
||||
/** \returns the size of the storage major dimension,
|
||||
* i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
|
||||
Index outerSize() const { return (int(Flags) & RowMajorBit) ? this->rows() : this->cols(); }
|
||||
|
||||
/** \returns the size of the inner dimension according to the storage order,
|
||||
* i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
|
||||
Index innerSize() const { return (int(Flags) & RowMajorBit) ? this->cols() : this->rows(); }
|
||||
|
||||
bool isRValue() const { return m_isRValue; }
|
||||
|
||||
Derived& markAsRValue() {
|
||||
m_isRValue = true;
|
||||
return derived();
|
||||
}
|
||||
|
||||
SkylineMatrixBase() : m_isRValue(false) { /* TODO check flags */
|
||||
}
|
||||
|
||||
inline Derived& operator=(const Derived& other) {
|
||||
this->operator= <Derived>(other);
|
||||
return derived();
|
||||
}
|
||||
|
||||
template <typename OtherDerived>
|
||||
inline void assignGeneric(const OtherDerived& other) {
|
||||
derived().resize(other.rows(), other.cols());
|
||||
for (Index row = 0; row < rows(); row++)
|
||||
for (Index col = 0; col < cols(); col++) {
|
||||
if (other.coeff(row, col) != Scalar(0)) derived().insert(row, col) = other.coeff(row, col);
|
||||
}
|
||||
derived().finalize();
|
||||
}
|
||||
|
||||
template <typename OtherDerived>
|
||||
inline Derived& operator=(const SkylineMatrixBase<OtherDerived>& other) {
|
||||
// TODO
|
||||
}
|
||||
|
||||
template <typename Lhs, typename Rhs>
|
||||
inline Derived& operator=(const SkylineProduct<Lhs, Rhs, SkylineTimeSkylineProduct>& product);
|
||||
|
||||
friend std::ostream& operator<<(std::ostream& s, const SkylineMatrixBase& m) {
|
||||
s << m.derived();
|
||||
return s;
|
||||
}
|
||||
|
||||
template <typename OtherDerived>
|
||||
const typename SkylineProductReturnType<Derived, OtherDerived>::Type operator*(
|
||||
const MatrixBase<OtherDerived>& other) const;
|
||||
|
||||
/** \internal use operator= */
|
||||
template <typename DenseDerived>
|
||||
void evalTo(MatrixBase<DenseDerived>& dst) const {
|
||||
dst.setZero();
|
||||
for (Index i = 0; i < rows(); i++)
|
||||
for (Index j = 0; j < rows(); j++) dst(i, j) = derived().coeff(i, j);
|
||||
}
|
||||
|
||||
Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime> toDense() const { return derived(); }
|
||||
|
||||
/** \returns the matrix or vector obtained by evaluating this expression.
|
||||
*
|
||||
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
|
||||
* a const reference, in order to avoid a useless copy.
|
||||
*/
|
||||
EIGEN_STRONG_INLINE const typename internal::eval<Derived, IsSkyline>::type eval() const {
|
||||
return typename internal::eval<Derived>::type(derived());
|
||||
}
|
||||
|
||||
protected:
|
||||
bool m_isRValue;
|
||||
};
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif // EIGEN_SKYLINEMATRIXBASE_H
|
||||
@ -1,262 +0,0 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.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_SKYLINEPRODUCT_H
|
||||
#define EIGEN_SKYLINEPRODUCT_H
|
||||
|
||||
// IWYU pragma: private
|
||||
#include "./InternalHeaderCheck.h"
|
||||
|
||||
namespace Eigen {
|
||||
|
||||
template <typename Lhs, typename Rhs, int ProductMode>
|
||||
struct SkylineProductReturnType {
|
||||
typedef const typename internal::nested_eval<Lhs, Rhs::RowsAtCompileTime>::type LhsNested;
|
||||
typedef const typename internal::nested_eval<Rhs, Lhs::RowsAtCompileTime>::type RhsNested;
|
||||
|
||||
typedef SkylineProduct<LhsNested, RhsNested, ProductMode> Type;
|
||||
};
|
||||
|
||||
template <typename LhsNested, typename RhsNested, int ProductMode>
|
||||
struct internal::traits<SkylineProduct<LhsNested, RhsNested, ProductMode>> {
|
||||
// clean the nested types:
|
||||
typedef internal::remove_all_t<LhsNested> LhsNested_;
|
||||
typedef internal::remove_all_t<RhsNested> RhsNested_;
|
||||
typedef typename LhsNested_::Scalar Scalar;
|
||||
|
||||
enum {
|
||||
LhsCoeffReadCost = LhsNested_::CoeffReadCost,
|
||||
RhsCoeffReadCost = RhsNested_::CoeffReadCost,
|
||||
LhsFlags = LhsNested_::Flags,
|
||||
RhsFlags = RhsNested_::Flags,
|
||||
|
||||
RowsAtCompileTime = LhsNested_::RowsAtCompileTime,
|
||||
ColsAtCompileTime = RhsNested_::ColsAtCompileTime,
|
||||
InnerSize = internal::min_size_prefer_fixed(LhsNested_::ColsAtCompileTime, RhsNested_::RowsAtCompileTime),
|
||||
|
||||
MaxRowsAtCompileTime = LhsNested_::MaxRowsAtCompileTime,
|
||||
MaxColsAtCompileTime = RhsNested_::MaxColsAtCompileTime,
|
||||
|
||||
EvalToRowMajor = (RhsFlags & LhsFlags & RowMajorBit),
|
||||
ResultIsSkyline = ProductMode == SkylineTimeSkylineProduct,
|
||||
|
||||
RemovedBits = ~((EvalToRowMajor ? 0 : RowMajorBit) | (ResultIsSkyline ? 0 : SkylineBit)),
|
||||
|
||||
Flags = (int(LhsFlags | RhsFlags) & HereditaryBits & RemovedBits) | EvalBeforeAssigningBit | EvalBeforeNestingBit,
|
||||
|
||||
CoeffReadCost = HugeCost
|
||||
};
|
||||
|
||||
typedef std::conditional_t<ResultIsSkyline, SkylineMatrixBase<SkylineProduct<LhsNested, RhsNested, ProductMode>>,
|
||||
MatrixBase<SkylineProduct<LhsNested, RhsNested, ProductMode>>>
|
||||
Base;
|
||||
};
|
||||
|
||||
namespace internal {
|
||||
template <typename LhsNested, typename RhsNested, int ProductMode>
|
||||
class SkylineProduct : no_assignment_operator, public traits<SkylineProduct<LhsNested, RhsNested, ProductMode>>::Base {
|
||||
public:
|
||||
EIGEN_GENERIC_PUBLIC_INTERFACE(SkylineProduct)
|
||||
|
||||
private:
|
||||
typedef typename traits<SkylineProduct>::LhsNested_ LhsNested_;
|
||||
typedef typename traits<SkylineProduct>::RhsNested_ RhsNested_;
|
||||
|
||||
public:
|
||||
template <typename Lhs, typename Rhs>
|
||||
EIGEN_STRONG_INLINE SkylineProduct(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs) {
|
||||
eigen_assert(lhs.cols() == rhs.rows());
|
||||
|
||||
enum {
|
||||
ProductIsValid = LhsNested_::ColsAtCompileTime == Dynamic || RhsNested_::RowsAtCompileTime == Dynamic ||
|
||||
int(LhsNested_::ColsAtCompileTime) == int(RhsNested_::RowsAtCompileTime),
|
||||
AreVectors = LhsNested_::IsVectorAtCompileTime && RhsNested_::IsVectorAtCompileTime,
|
||||
SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(LhsNested_, RhsNested_)
|
||||
};
|
||||
// note to the lost user:
|
||||
// * for a dot product use: v1.dot(v2)
|
||||
// * for a coeff-wise product use: v1.cwise()*v2
|
||||
EIGEN_STATIC_ASSERT(
|
||||
ProductIsValid || !(AreVectors && SameSizes),
|
||||
INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
|
||||
EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
|
||||
INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
|
||||
EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
|
||||
}
|
||||
|
||||
EIGEN_STRONG_INLINE Index rows() const { return m_lhs.rows(); }
|
||||
|
||||
EIGEN_STRONG_INLINE Index cols() const { return m_rhs.cols(); }
|
||||
|
||||
EIGEN_STRONG_INLINE const LhsNested_& lhs() const { return m_lhs; }
|
||||
|
||||
EIGEN_STRONG_INLINE const RhsNested_& rhs() const { return m_rhs; }
|
||||
|
||||
protected:
|
||||
LhsNested m_lhs;
|
||||
RhsNested m_rhs;
|
||||
};
|
||||
|
||||
// dense = skyline * dense
|
||||
// Note that here we force no inlining and separate the setZero() because GCC messes up otherwise
|
||||
|
||||
template <typename Lhs, typename Rhs, typename Dest>
|
||||
EIGEN_DONT_INLINE void skyline_row_major_time_dense_product(const Lhs& lhs, const Rhs& rhs, Dest& dst) {
|
||||
typedef remove_all_t<Lhs> Lhs_;
|
||||
typedef remove_all_t<Rhs> Rhs_;
|
||||
typedef typename traits<Lhs>::Scalar Scalar;
|
||||
|
||||
enum {
|
||||
LhsIsRowMajor = (Lhs_::Flags & RowMajorBit) == RowMajorBit,
|
||||
LhsIsSelfAdjoint = (Lhs_::Flags & SelfAdjointBit) == SelfAdjointBit,
|
||||
ProcessFirstHalf = LhsIsSelfAdjoint && (((Lhs_::Flags & (UpperTriangularBit | LowerTriangularBit)) == 0) ||
|
||||
((Lhs_::Flags & UpperTriangularBit) && !LhsIsRowMajor) ||
|
||||
((Lhs_::Flags & LowerTriangularBit) && LhsIsRowMajor)),
|
||||
ProcessSecondHalf = LhsIsSelfAdjoint && (!ProcessFirstHalf)
|
||||
};
|
||||
|
||||
// Use matrix diagonal part <- Improvement : use inner iterator on dense matrix.
|
||||
for (Index col = 0; col < rhs.cols(); col++) {
|
||||
for (Index row = 0; row < lhs.rows(); row++) {
|
||||
dst(row, col) = lhs.coeffDiag(row) * rhs(row, col);
|
||||
}
|
||||
}
|
||||
// Use matrix lower triangular part
|
||||
for (Index row = 0; row < lhs.rows(); row++) {
|
||||
typename Lhs_::InnerLowerIterator lIt(lhs, row);
|
||||
const Index stop = lIt.col() + lIt.size();
|
||||
for (Index col = 0; col < rhs.cols(); col++) {
|
||||
Index k = lIt.col();
|
||||
Scalar tmp = 0;
|
||||
while (k < stop) {
|
||||
tmp += lIt.value() * rhs(k++, col);
|
||||
++lIt;
|
||||
}
|
||||
dst(row, col) += tmp;
|
||||
lIt += -lIt.size();
|
||||
}
|
||||
}
|
||||
|
||||
// Use matrix upper triangular part
|
||||
for (Index lhscol = 0; lhscol < lhs.cols(); lhscol++) {
|
||||
typename Lhs_::InnerUpperIterator uIt(lhs, lhscol);
|
||||
const Index stop = uIt.size() + uIt.row();
|
||||
for (Index rhscol = 0; rhscol < rhs.cols(); rhscol++) {
|
||||
const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol);
|
||||
Index k = uIt.row();
|
||||
while (k < stop) {
|
||||
dst(k++, rhscol) += uIt.value() * rhsCoeff;
|
||||
++uIt;
|
||||
}
|
||||
uIt += -uIt.size();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Lhs, typename Rhs, typename Dest>
|
||||
EIGEN_DONT_INLINE void skyline_col_major_time_dense_product(const Lhs& lhs, const Rhs& rhs, Dest& dst) {
|
||||
typedef remove_all_t<Lhs> Lhs_;
|
||||
typedef remove_all_t<Rhs> Rhs_;
|
||||
typedef typename traits<Lhs>::Scalar Scalar;
|
||||
|
||||
enum {
|
||||
LhsIsRowMajor = (Lhs_::Flags & RowMajorBit) == RowMajorBit,
|
||||
LhsIsSelfAdjoint = (Lhs_::Flags & SelfAdjointBit) == SelfAdjointBit,
|
||||
ProcessFirstHalf = LhsIsSelfAdjoint && (((Lhs_::Flags & (UpperTriangularBit | LowerTriangularBit)) == 0) ||
|
||||
((Lhs_::Flags & UpperTriangularBit) && !LhsIsRowMajor) ||
|
||||
((Lhs_::Flags & LowerTriangularBit) && LhsIsRowMajor)),
|
||||
ProcessSecondHalf = LhsIsSelfAdjoint && (!ProcessFirstHalf)
|
||||
};
|
||||
|
||||
// Use matrix diagonal part <- Improvement : use inner iterator on dense matrix.
|
||||
for (Index col = 0; col < rhs.cols(); col++) {
|
||||
for (Index row = 0; row < lhs.rows(); row++) {
|
||||
dst(row, col) = lhs.coeffDiag(row) * rhs(row, col);
|
||||
}
|
||||
}
|
||||
|
||||
// Use matrix upper triangular part
|
||||
for (Index row = 0; row < lhs.rows(); row++) {
|
||||
typename Lhs_::InnerUpperIterator uIt(lhs, row);
|
||||
const Index stop = uIt.col() + uIt.size();
|
||||
for (Index col = 0; col < rhs.cols(); col++) {
|
||||
Index k = uIt.col();
|
||||
Scalar tmp = 0;
|
||||
while (k < stop) {
|
||||
tmp += uIt.value() * rhs(k++, col);
|
||||
++uIt;
|
||||
}
|
||||
|
||||
dst(row, col) += tmp;
|
||||
uIt += -uIt.size();
|
||||
}
|
||||
}
|
||||
|
||||
// Use matrix lower triangular part
|
||||
for (Index lhscol = 0; lhscol < lhs.cols(); lhscol++) {
|
||||
typename Lhs_::InnerLowerIterator lIt(lhs, lhscol);
|
||||
const Index stop = lIt.size() + lIt.row();
|
||||
for (Index rhscol = 0; rhscol < rhs.cols(); rhscol++) {
|
||||
const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol);
|
||||
Index k = lIt.row();
|
||||
while (k < stop) {
|
||||
dst(k++, rhscol) += lIt.value() * rhsCoeff;
|
||||
++lIt;
|
||||
}
|
||||
lIt += -lIt.size();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Lhs, typename Rhs, typename ResultType, int LhsStorageOrder = traits<Lhs>::Flags & RowMajorBit>
|
||||
struct skyline_product_selector;
|
||||
|
||||
template <typename Lhs, typename Rhs, typename ResultType>
|
||||
struct skyline_product_selector<Lhs, Rhs, ResultType, RowMajor> {
|
||||
typedef typename traits<remove_all_t<Lhs>>::Scalar Scalar;
|
||||
|
||||
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
|
||||
skyline_row_major_time_dense_product<Lhs, Rhs, ResultType>(lhs, rhs, res);
|
||||
}
|
||||
};
|
||||
|
||||
template <typename Lhs, typename Rhs, typename ResultType>
|
||||
struct skyline_product_selector<Lhs, Rhs, ResultType, ColMajor> {
|
||||
typedef typename traits<remove_all_t<Lhs>>::Scalar Scalar;
|
||||
|
||||
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res) {
|
||||
skyline_col_major_time_dense_product<Lhs, Rhs, ResultType>(lhs, rhs, res);
|
||||
}
|
||||
};
|
||||
|
||||
} // end namespace internal
|
||||
|
||||
// template<typename Derived>
|
||||
// template<typename Lhs, typename Rhs >
|
||||
// Derived & MatrixBase<Derived>::lazyAssign(const SkylineProduct<Lhs, Rhs, SkylineTimeDenseProduct>& product) {
|
||||
// typedef internal::remove_all_t<Lhs> Lhs_;
|
||||
// internal::skyline_product_selector<internal::remove_all_t<Lhs>,
|
||||
// internal::remove_all_t<Rhs>,
|
||||
// Derived>::run(product.lhs(), product.rhs(), derived());
|
||||
//
|
||||
// return derived();
|
||||
// }
|
||||
|
||||
// skyline * dense
|
||||
|
||||
template <typename Derived>
|
||||
template <typename OtherDerived>
|
||||
EIGEN_STRONG_INLINE const typename SkylineProductReturnType<Derived, OtherDerived>::Type
|
||||
SkylineMatrixBase<Derived>::operator*(const MatrixBase<OtherDerived>& other) const {
|
||||
return typename SkylineProductReturnType<Derived, OtherDerived>::Type(derived(), other.derived());
|
||||
}
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif // EIGEN_SKYLINEPRODUCT_H
|
||||
@ -1,224 +0,0 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.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_SKYLINE_STORAGE_H
|
||||
#define EIGEN_SKYLINE_STORAGE_H
|
||||
|
||||
// IWYU pragma: private
|
||||
#include "./InternalHeaderCheck.h"
|
||||
|
||||
namespace Eigen {
|
||||
|
||||
/** Stores a skyline set of values in three structures :
|
||||
* The diagonal elements
|
||||
* The upper elements
|
||||
* The lower elements
|
||||
*
|
||||
*/
|
||||
template <typename Scalar>
|
||||
class SkylineStorage {
|
||||
typedef typename NumTraits<Scalar>::Real RealScalar;
|
||||
typedef SparseIndex Index;
|
||||
|
||||
public:
|
||||
SkylineStorage()
|
||||
: m_diag(0),
|
||||
m_lower(0),
|
||||
m_upper(0),
|
||||
m_lowerProfile(0),
|
||||
m_upperProfile(0),
|
||||
m_diagSize(0),
|
||||
m_upperSize(0),
|
||||
m_lowerSize(0),
|
||||
m_upperProfileSize(0),
|
||||
m_lowerProfileSize(0),
|
||||
m_allocatedSize(0) {}
|
||||
|
||||
SkylineStorage(const SkylineStorage& other)
|
||||
: m_diag(0),
|
||||
m_lower(0),
|
||||
m_upper(0),
|
||||
m_lowerProfile(0),
|
||||
m_upperProfile(0),
|
||||
m_diagSize(0),
|
||||
m_upperSize(0),
|
||||
m_lowerSize(0),
|
||||
m_upperProfileSize(0),
|
||||
m_lowerProfileSize(0),
|
||||
m_allocatedSize(0) {
|
||||
*this = other;
|
||||
}
|
||||
|
||||
SkylineStorage& operator=(const SkylineStorage& other) {
|
||||
resize(other.diagSize(), other.m_upperProfileSize, other.m_lowerProfileSize, other.upperSize(), other.lowerSize());
|
||||
memcpy(m_diag, other.m_diag, m_diagSize * sizeof(Scalar));
|
||||
memcpy(m_upper, other.m_upper, other.upperSize() * sizeof(Scalar));
|
||||
memcpy(m_lower, other.m_lower, other.lowerSize() * sizeof(Scalar));
|
||||
memcpy(m_upperProfile, other.m_upperProfile, m_upperProfileSize * sizeof(Index));
|
||||
memcpy(m_lowerProfile, other.m_lowerProfile, m_lowerProfileSize * sizeof(Index));
|
||||
return *this;
|
||||
}
|
||||
|
||||
void swap(SkylineStorage& other) {
|
||||
std::swap(m_diag, other.m_diag);
|
||||
std::swap(m_upper, other.m_upper);
|
||||
std::swap(m_lower, other.m_lower);
|
||||
std::swap(m_upperProfile, other.m_upperProfile);
|
||||
std::swap(m_lowerProfile, other.m_lowerProfile);
|
||||
std::swap(m_diagSize, other.m_diagSize);
|
||||
std::swap(m_upperSize, other.m_upperSize);
|
||||
std::swap(m_lowerSize, other.m_lowerSize);
|
||||
std::swap(m_allocatedSize, other.m_allocatedSize);
|
||||
}
|
||||
|
||||
~SkylineStorage() {
|
||||
delete[] m_diag;
|
||||
delete[] m_upper;
|
||||
if (m_upper != m_lower) delete[] m_lower;
|
||||
delete[] m_upperProfile;
|
||||
delete[] m_lowerProfile;
|
||||
}
|
||||
|
||||
void reserve(Index size, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize) {
|
||||
Index newAllocatedSize = size + upperSize + lowerSize;
|
||||
if (newAllocatedSize > m_allocatedSize) reallocate(size, upperProfileSize, lowerProfileSize, upperSize, lowerSize);
|
||||
}
|
||||
|
||||
void squeeze() {
|
||||
if (m_allocatedSize > m_diagSize + m_upperSize + m_lowerSize)
|
||||
reallocate(m_diagSize, m_upperProfileSize, m_lowerProfileSize, m_upperSize, m_lowerSize);
|
||||
}
|
||||
|
||||
void resize(Index diagSize, Index upperProfileSize, Index lowerProfileSize, Index upperSize, Index lowerSize,
|
||||
float reserveSizeFactor = 0) {
|
||||
if (m_allocatedSize < diagSize + upperSize + lowerSize)
|
||||
reallocate(diagSize, upperProfileSize, lowerProfileSize, upperSize + Index(reserveSizeFactor * upperSize),
|
||||
lowerSize + Index(reserveSizeFactor * lowerSize));
|
||||
m_diagSize = diagSize;
|
||||
m_upperSize = upperSize;
|
||||
m_lowerSize = lowerSize;
|
||||
m_upperProfileSize = upperProfileSize;
|
||||
m_lowerProfileSize = lowerProfileSize;
|
||||
}
|
||||
|
||||
inline Index diagSize() const { return m_diagSize; }
|
||||
|
||||
inline Index upperSize() const { return m_upperSize; }
|
||||
|
||||
inline Index lowerSize() const { return m_lowerSize; }
|
||||
|
||||
inline Index upperProfileSize() const { return m_upperProfileSize; }
|
||||
|
||||
inline Index lowerProfileSize() const { return m_lowerProfileSize; }
|
||||
|
||||
inline Index allocatedSize() const { return m_allocatedSize; }
|
||||
|
||||
inline void clear() { m_diagSize = 0; }
|
||||
|
||||
inline Scalar& diag(Index i) { return m_diag[i]; }
|
||||
|
||||
inline const Scalar& diag(Index i) const { return m_diag[i]; }
|
||||
|
||||
inline Scalar& upper(Index i) { return m_upper[i]; }
|
||||
|
||||
inline const Scalar& upper(Index i) const { return m_upper[i]; }
|
||||
|
||||
inline Scalar& lower(Index i) { return m_lower[i]; }
|
||||
|
||||
inline const Scalar& lower(Index i) const { return m_lower[i]; }
|
||||
|
||||
inline Index& upperProfile(Index i) { return m_upperProfile[i]; }
|
||||
|
||||
inline const Index& upperProfile(Index i) const { return m_upperProfile[i]; }
|
||||
|
||||
inline Index& lowerProfile(Index i) { return m_lowerProfile[i]; }
|
||||
|
||||
inline const Index& lowerProfile(Index i) const { return m_lowerProfile[i]; }
|
||||
|
||||
static SkylineStorage Map(Index* upperProfile, Index* lowerProfile, Scalar* diag, Scalar* upper, Scalar* lower,
|
||||
Index size, Index upperSize, Index lowerSize) {
|
||||
SkylineStorage res;
|
||||
res.m_upperProfile = upperProfile;
|
||||
res.m_lowerProfile = lowerProfile;
|
||||
res.m_diag = diag;
|
||||
res.m_upper = upper;
|
||||
res.m_lower = lower;
|
||||
res.m_allocatedSize = res.m_diagSize = size;
|
||||
res.m_upperSize = upperSize;
|
||||
res.m_lowerSize = lowerSize;
|
||||
return res;
|
||||
}
|
||||
|
||||
inline void reset() {
|
||||
std::fill_n(m_diag, m_diagSize, Scalar(0));
|
||||
std::fill_n(m_upper, m_upperSize, Scalar(0));
|
||||
std::fill_n(m_lower, m_lowerSize, Scalar(0));
|
||||
std::fill_n(m_upperProfile, m_diagSize, Index(0));
|
||||
std::fill_n(m_lowerProfile, m_diagSize, Index(0));
|
||||
}
|
||||
|
||||
void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>()) {
|
||||
// TODO
|
||||
}
|
||||
|
||||
protected:
|
||||
inline void reallocate(Index diagSize, Index upperProfileSize, Index lowerProfileSize, Index upperSize,
|
||||
Index lowerSize) {
|
||||
Scalar* diag = new Scalar[diagSize];
|
||||
Scalar* upper = new Scalar[upperSize];
|
||||
Scalar* lower = new Scalar[lowerSize];
|
||||
Index* upperProfile = new Index[upperProfileSize];
|
||||
Index* lowerProfile = new Index[lowerProfileSize];
|
||||
|
||||
Index copyDiagSize = (std::min)(diagSize, m_diagSize);
|
||||
Index copyUpperSize = (std::min)(upperSize, m_upperSize);
|
||||
Index copyLowerSize = (std::min)(lowerSize, m_lowerSize);
|
||||
Index copyUpperProfileSize = (std::min)(upperProfileSize, m_upperProfileSize);
|
||||
Index copyLowerProfileSize = (std::min)(lowerProfileSize, m_lowerProfileSize);
|
||||
|
||||
// copy
|
||||
memcpy(diag, m_diag, copyDiagSize * sizeof(Scalar));
|
||||
memcpy(upper, m_upper, copyUpperSize * sizeof(Scalar));
|
||||
memcpy(lower, m_lower, copyLowerSize * sizeof(Scalar));
|
||||
memcpy(upperProfile, m_upperProfile, copyUpperProfileSize * sizeof(Index));
|
||||
memcpy(lowerProfile, m_lowerProfile, copyLowerProfileSize * sizeof(Index));
|
||||
|
||||
// delete old stuff
|
||||
delete[] m_diag;
|
||||
delete[] m_upper;
|
||||
delete[] m_lower;
|
||||
delete[] m_upperProfile;
|
||||
delete[] m_lowerProfile;
|
||||
m_diag = diag;
|
||||
m_upper = upper;
|
||||
m_lower = lower;
|
||||
m_upperProfile = upperProfile;
|
||||
m_lowerProfile = lowerProfile;
|
||||
m_allocatedSize = diagSize + upperSize + lowerSize;
|
||||
m_upperSize = upperSize;
|
||||
m_lowerSize = lowerSize;
|
||||
}
|
||||
|
||||
public:
|
||||
Scalar* m_diag;
|
||||
Scalar* m_upper;
|
||||
Scalar* m_lower;
|
||||
Index* m_upperProfile;
|
||||
Index* m_lowerProfile;
|
||||
Index m_diagSize;
|
||||
Index m_upperSize;
|
||||
Index m_lowerSize;
|
||||
Index m_upperProfileSize;
|
||||
Index m_lowerProfileSize;
|
||||
Index m_allocatedSize;
|
||||
};
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif // EIGEN_SKYLINE_STORAGE_H
|
||||
@ -1,94 +0,0 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2009 Guillaume Saupin <guillaume.saupin@cea.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_SKYLINEUTIL_H
|
||||
#define EIGEN_SKYLINEUTIL_H
|
||||
|
||||
// IWYU pragma: private
|
||||
#include "./InternalHeaderCheck.h"
|
||||
|
||||
namespace Eigen {
|
||||
|
||||
#ifdef NDEBUG
|
||||
#define EIGEN_DBG_SKYLINE(X)
|
||||
#else
|
||||
#define EIGEN_DBG_SKYLINE(X) X
|
||||
#endif
|
||||
|
||||
const unsigned int SkylineBit = 0x1200;
|
||||
template <typename Lhs, typename Rhs, int ProductMode>
|
||||
class SkylineProduct;
|
||||
enum AdditionalProductEvaluationMode { SkylineTimeDenseProduct, SkylineTimeSkylineProduct, DenseTimeSkylineProduct };
|
||||
enum { IsSkyline = SkylineBit };
|
||||
|
||||
#define EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
|
||||
template <typename OtherDerived> \
|
||||
EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SkylineMatrixBase<OtherDerived>& other) { \
|
||||
return Base::operator Op(other.derived()); \
|
||||
} \
|
||||
EIGEN_STRONG_INLINE Derived& operator Op(const Derived & other) { return Base::operator Op(other); }
|
||||
|
||||
#define EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
|
||||
template <typename Other> \
|
||||
EIGEN_STRONG_INLINE Derived& operator Op(const Other & scalar) { \
|
||||
return Base::operator Op(scalar); \
|
||||
}
|
||||
|
||||
#define EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
|
||||
EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =) \
|
||||
EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, +=) \
|
||||
EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(Derived, -=) \
|
||||
EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, *=) \
|
||||
EIGEN_SKYLINE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, /=)
|
||||
|
||||
#define EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE_(Derived, BaseClass) \
|
||||
typedef BaseClass Base; \
|
||||
typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; \
|
||||
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \
|
||||
typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \
|
||||
typedef typename Eigen::internal::index<StorageKind>::type Index; \
|
||||
enum { \
|
||||
Flags = Eigen::internal::traits<Derived>::Flags, \
|
||||
};
|
||||
|
||||
#define EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(Derived) \
|
||||
EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE_(Derived, Eigen::SkylineMatrixBase<Derived>)
|
||||
|
||||
template <typename Derived>
|
||||
class SkylineMatrixBase;
|
||||
template <typename Scalar_, int Flags_ = 0>
|
||||
class SkylineMatrix;
|
||||
template <typename Scalar_, int Flags_ = 0>
|
||||
class DynamicSkylineMatrix;
|
||||
template <typename Scalar_, int Flags_ = 0>
|
||||
class SkylineVector;
|
||||
template <typename Scalar_, int Flags_ = 0>
|
||||
class MappedSkylineMatrix;
|
||||
|
||||
namespace internal {
|
||||
|
||||
template <typename Lhs, typename Rhs>
|
||||
struct skyline_product_mode;
|
||||
template <typename Lhs, typename Rhs, int ProductMode = skyline_product_mode<Lhs, Rhs>::value>
|
||||
struct SkylineProductReturnType;
|
||||
|
||||
template <typename T>
|
||||
class eval<T, IsSkyline> {
|
||||
typedef typename traits<T>::Scalar Scalar_;
|
||||
enum { Flags_ = traits<T>::Flags };
|
||||
|
||||
public:
|
||||
typedef SkylineMatrix<Scalar_, Flags_> type;
|
||||
};
|
||||
|
||||
} // end namespace internal
|
||||
|
||||
} // end namespace Eigen
|
||||
|
||||
#endif // EIGEN_SKYLINEUTIL_H
|
||||
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Reference in New Issue
Block a user