mirror of
https://gitlab.com/libeigen/eigen.git
synced 2025-06-04 02:33:59 +08:00
merge with main repository
This commit is contained in:
Thomas Capricelli
commit
6a56262bf4
@ -781,6 +781,11 @@ template<typename Derived> class MatrixBase
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template<typename Derived1, typename Derived2>
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Derived& lazyAssign(const SparseProduct<Derived1,Derived2,DenseTimeSparseProduct>& product);
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// dense = skyline * dense
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template<typename Derived1, typename Derived2>
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Derived& lazyAssign(const SkylineProduct<Derived1,Derived2,SkylineTimeDenseProduct>& product);
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////////// Householder module ///////////
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void makeHouseholderInPlace(Scalar *tau, RealScalar *beta);
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@ -75,7 +75,6 @@ template<typename DecompositionType> struct ei_image_retval;
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template<typename _Scalar, int Rows=Dynamic, int Cols=Dynamic, int Supers=Dynamic, int Subs=Dynamic, int Options=0> class BandMatrix;
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template<typename Lhs, typename Rhs> struct ei_product_type;
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template<typename Lhs, typename Rhs,
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int ProductType = ei_product_type<Lhs,Rhs>::value>
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@ -151,4 +150,5 @@ template<typename MatrixType,int Direction> class Homogeneous;
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// Sparse module:
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template<typename Lhs, typename Rhs, int ProductMode> class SparseProduct;
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#endif // EIGEN_FORWARDDECLARATIONS_H
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34
unsupported/Eigen/Skyline
Normal file
34
unsupported/Eigen/Skyline
Normal file
@ -0,0 +1,34 @@
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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/DisableMSVCWarnings.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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namespace Eigen {
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/** \defgroup Skyline_Module Skyline module
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*
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* \nonstableyet
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*
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*
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*/
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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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} // namespace Eigen
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#include "Eigen/src/Core/util/EnableMSVCWarnings.h"
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#endif // EIGEN_SKYLINE_MODULE_H
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@ -2,3 +2,6 @@ ADD_SUBDIRECTORY(IterativeSolvers)
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ADD_SUBDIRECTORY(BVH)
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ADD_SUBDIRECTORY(AutoDiff)
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ADD_SUBDIRECTORY(MoreVectorization)
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ADD_SUBDIRECTORY(FFT)
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ADD_SUBDIRECTORY(Skyline)
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ADD_SUBDIRECTORY(MatrixFunctions)
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361
unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
Normal file
361
unsupported/Eigen/src/Skyline/SkylineInplaceLU.h
Normal file
@ -0,0 +1,361 @@
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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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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
|
||||
// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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||||
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
||||
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
||||
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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||||
// GNU General Public License for more details.
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//
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||||
// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#ifndef EIGEN_SKYLINEINPLACELU_H
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#define EIGEN_SKYLINEINPLACELU_H
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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 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::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 imcomplete
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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) {
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m_precision = v;
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}
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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 {
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return m_precision;
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}
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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) {
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m_flags = f;
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}
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/** \returns the current flags */
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int flags() const {
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return m_flags;
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}
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void setOrderingMethod(int m) {
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m_flags = m;
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}
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int orderingMethod() const {
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return m_flags;
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}
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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,
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const int transposed = 0) const;
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/** \returns true if the factorization succeeded */
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inline bool succeeded(void) const {
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return m_succeeded;
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}
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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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ei_assert(rows == cols && "We do not (yet) support rectangular LU.");
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ei_assert(!m_lu.IsRowMajor && "LU decomposition does not work with rowMajor Storage");
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for (unsigned int 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 unsigned int& 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 (unsigned int 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 (unsigned int 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 (unsigned int 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 (unsigned int 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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ei_assert(rows == cols && "We do not (yet) support rectangular LU.");
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ei_assert(m_lu.IsRowMajor && "You're trying to apply rowMajor decomposition on a ColMajor matrix !");
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for (unsigned int row = 0; row < rows; row++) {
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typename MatrixType::InnerLowerIterator llIt(m_lu, row);
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for (unsigned int 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 int offset = lIt.col() - uIt.row();
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||||
|
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int 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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||||
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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 (int k = 0; k < stop; ++k) {
|
||||
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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||||
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||||
m_lu.coeffRefLower(row, col) = newCoeff / diag;
|
||||
}
|
||||
}
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||||
|
||||
//Upper matrix update
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||||
const int col = row;
|
||||
typename MatrixType::InnerUpperIterator uuIt(m_lu, col);
|
||||
for (unsigned int rrow = uuIt.row(); rrow < col; rrow++) {
|
||||
|
||||
typename MatrixType::InnerLowerIterator lIt(m_lu, rrow);
|
||||
typename MatrixType::InnerUpperIterator uIt(m_lu, col);
|
||||
const int offset = lIt.col() - uIt.row();
|
||||
|
||||
int stop = offset > 0 ? rrow - lIt.col() : rrow - uIt.row();
|
||||
|
||||
#ifdef VECTORIZE
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||||
Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
|
||||
Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
|
||||
|
||||
Scalar newCoeff = m_lu.coeffUpper(rrow, col) - rowVal.dot(colVal);
|
||||
#else
|
||||
if (offset > 0) //Skip zero value of lIt
|
||||
uIt += offset;
|
||||
else //Skip zero values of uIt
|
||||
lIt += -offset;
|
||||
Scalar newCoeff = m_lu.coeffUpper(rrow, col);
|
||||
for (int k = 0; k < stop; ++k) {
|
||||
const Scalar tmp = newCoeff;
|
||||
newCoeff = tmp - lIt.value() * uIt.value();
|
||||
|
||||
++lIt;
|
||||
++uIt;
|
||||
}
|
||||
#endif
|
||||
m_lu.coeffRefUpper(rrow, col) = newCoeff;
|
||||
}
|
||||
|
||||
|
||||
//Diag matrix update
|
||||
typename MatrixType::InnerLowerIterator lIt(m_lu, row);
|
||||
typename MatrixType::InnerUpperIterator uIt(m_lu, row);
|
||||
|
||||
const int offset = lIt.col() - uIt.row();
|
||||
|
||||
|
||||
int stop = offset > 0 ? lIt.size() : uIt.size();
|
||||
#ifdef VECTORIZE
|
||||
Map<VectorXd > rowVal(lIt.valuePtr() + (offset > 0 ? 0 : -offset), stop);
|
||||
Map<VectorXd > colVal(uIt.valuePtr() + (offset > 0 ? offset : 0), stop);
|
||||
Scalar newCoeff = m_lu.coeffDiag(row) - rowVal.dot(colVal);
|
||||
#else
|
||||
if (offset > 0) //Skip zero value of lIt
|
||||
uIt += offset;
|
||||
else //Skip zero values of uIt
|
||||
lIt += -offset;
|
||||
Scalar newCoeff = m_lu.coeffDiag(row);
|
||||
for (unsigned int k = 0; k < stop; ++k) {
|
||||
const Scalar tmp = newCoeff;
|
||||
newCoeff = tmp - lIt.value() * uIt.value();
|
||||
++lIt;
|
||||
++uIt;
|
||||
}
|
||||
#endif
|
||||
m_lu.coeffRefDiag(row) = newCoeff;
|
||||
}
|
||||
}
|
||||
|
||||
/** Computes *x = U^-1 L^-1 b
|
||||
*
|
||||
* If \a transpose is set to SvTranspose or SvAdjoint, the solution
|
||||
* of the transposed/adjoint system is computed instead.
|
||||
*
|
||||
* Not all backends implement the solution of the transposed or
|
||||
* adjoint system.
|
||||
*/
|
||||
template<typename MatrixType>
|
||||
template<typename BDerived, typename XDerived>
|
||||
bool SkylineInplaceLU<MatrixType>::solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x, const int transposed) const {
|
||||
const size_t rows = m_lu.rows();
|
||||
const size_t cols = m_lu.cols();
|
||||
|
||||
|
||||
for (int row = 0; row < rows; row++) {
|
||||
x->coeffRef(row) = b.coeff(row);
|
||||
Scalar newVal = x->coeff(row);
|
||||
typename MatrixType::InnerLowerIterator lIt(m_lu, row);
|
||||
|
||||
unsigned int col = lIt.col();
|
||||
while (lIt.col() < row) {
|
||||
|
||||
newVal -= x->coeff(col++) * lIt.value();
|
||||
++lIt;
|
||||
}
|
||||
|
||||
x->coeffRef(row) = newVal;
|
||||
}
|
||||
|
||||
|
||||
for (int col = rows - 1; col > 0; col--) {
|
||||
x->coeffRef(col) = x->coeff(col) / m_lu.coeffDiag(col);
|
||||
|
||||
const Scalar x_col = x->coeff(col);
|
||||
|
||||
typename MatrixType::InnerUpperIterator uIt(m_lu, col);
|
||||
uIt += uIt.size()-1;
|
||||
|
||||
|
||||
while (uIt) {
|
||||
x->coeffRef(uIt.row()) -= x_col * uIt.value();
|
||||
//TODO : introduce --operator
|
||||
uIt += -1;
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
x->coeffRef(0) = x->coeff(0) / m_lu.coeffDiag(0);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
#endif // EIGEN_SKYLINELU_H
|
||||
870
unsupported/Eigen/src/Skyline/SkylineMatrix.h
Normal file
870
unsupported/Eigen/src/Skyline/SkylineMatrix.h
Normal file
@ -0,0 +1,870 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
|
||||
//
|
||||
// Eigen is free software; you can redistribute it and/or
|
||||
// modify it under the terms of the GNU Lesser General Public
|
||||
// License as published by the Free Software Foundation; either
|
||||
// version 3 of the License, or (at your option) any later version.
|
||||
//
|
||||
// Alternatively, you can redistribute it and/or
|
||||
// modify it under the terms of the GNU General Public License as
|
||||
// published by the Free Software Foundation; either version 2 of
|
||||
// the License, or (at your option) any later version.
|
||||
//
|
||||
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
||||
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
||||
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
|
||||
// GNU General Public License for more details.
|
||||
//
|
||||
// You should have received a copy of the GNU Lesser General Public
|
||||
// License and a copy of the GNU General Public License along with
|
||||
// Eigen. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
#ifndef EIGEN_SKYLINEMATRIX_H
|
||||
#define EIGEN_SKYLINEMATRIX_H
|
||||
|
||||
#include "SkylineStorage.h"
|
||||
#include "SkylineMatrixBase.h"
|
||||
|
||||
/** \ingroup Skyline_Module
|
||||
*
|
||||
* \class SkylineMatrix
|
||||
*
|
||||
* \brief The main skyline matrix class
|
||||
*
|
||||
* This class implements a skyline matrix using the very uncommon storage
|
||||
* scheme.
|
||||
*
|
||||
* \param _Scalar the scalar type, i.e. the type of the coefficients
|
||||
* \param _Options Union of bit flags controlling the storage scheme. Currently the only possibility
|
||||
* is RowMajor. The default is 0 which means column-major.
|
||||
*
|
||||
*
|
||||
*/
|
||||
template<typename _Scalar, int _Options>
|
||||
struct ei_traits<SkylineMatrix<_Scalar, _Options> > {
|
||||
typedef _Scalar Scalar;
|
||||
|
||||
enum {
|
||||
RowsAtCompileTime = Dynamic,
|
||||
ColsAtCompileTime = Dynamic,
|
||||
MaxRowsAtCompileTime = Dynamic,
|
||||
MaxColsAtCompileTime = Dynamic,
|
||||
Flags = SkylineBit | _Options,
|
||||
CoeffReadCost = NumTraits<Scalar>::ReadCost,
|
||||
};
|
||||
};
|
||||
|
||||
template<typename _Scalar, int _Options>
|
||||
class SkylineMatrix
|
||||
: public SkylineMatrixBase<SkylineMatrix<_Scalar, _Options> > {
|
||||
public:
|
||||
EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(SkylineMatrix)
|
||||
EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, +=)
|
||||
EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, -=)
|
||||
|
||||
using Base::IsRowMajor;
|
||||
|
||||
protected:
|
||||
|
||||
typedef SkylineMatrix<Scalar, (Flags&~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0) > TransposedSkylineMatrix;
|
||||
|
||||
int m_outerSize;
|
||||
int m_innerSize;
|
||||
|
||||
public:
|
||||
int* m_colStartIndex;
|
||||
int* m_rowStartIndex;
|
||||
SkylineStorage<Scalar> m_data;
|
||||
|
||||
public:
|
||||
|
||||
inline int rows() const {
|
||||
return IsRowMajor ? m_outerSize : m_innerSize;
|
||||
}
|
||||
|
||||
inline int cols() const {
|
||||
return IsRowMajor ? m_innerSize : m_outerSize;
|
||||
}
|
||||
|
||||
inline int innerSize() const {
|
||||
return m_innerSize;
|
||||
}
|
||||
|
||||
inline int outerSize() const {
|
||||
return m_outerSize;
|
||||
}
|
||||
|
||||
inline int upperNonZeros() const {
|
||||
return m_data.upperSize();
|
||||
}
|
||||
|
||||
inline int lowerNonZeros() const {
|
||||
return m_data.lowerSize();
|
||||
}
|
||||
|
||||
inline int upperNonZeros(int j) const {
|
||||
return m_colStartIndex[j + 1] - m_colStartIndex[j];
|
||||
}
|
||||
|
||||
inline int lowerNonZeros(int 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 int* _upperProfilePtr() const {
|
||||
return &m_data.upperProfile(0);
|
||||
}
|
||||
|
||||
inline int* _upperProfilePtr() {
|
||||
return &m_data.upperProfile(0);
|
||||
}
|
||||
|
||||
inline const int* _lowerProfilePtr() const {
|
||||
return &m_data.lowerProfile(0);
|
||||
}
|
||||
|
||||
inline int* _lowerProfilePtr() {
|
||||
return &m_data.lowerProfile(0);
|
||||
}
|
||||
|
||||
inline Scalar coeff(int row, int col) const {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
|
||||
if (outer == inner)
|
||||
return this->m_data.diag(outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
if (inner > outer) //upper matrix
|
||||
{
|
||||
const int 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 int 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 int 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 int 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(int row, int col) {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
|
||||
if (outer == inner)
|
||||
return this->m_data.diag(outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
if (col > row) //upper matrix
|
||||
{
|
||||
const int minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
ei_assert(outer >= minOuterIndex && "you try to acces a coeff that do 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 int minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
ei_assert(inner >= minInnerIndex && "you try to acces a coeff that do 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 int maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
ei_assert(outer <= maxOuterIndex && "you try to acces a coeff that do not exist in the storage");
|
||||
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
|
||||
}
|
||||
if (outer < inner) //lower matrix
|
||||
{
|
||||
const int maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
ei_assert(inner <= maxInnerIndex && "you try to acces a coeff that do not exist in the storage");
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
inline Scalar coeffDiag(int idx) const {
|
||||
ei_assert(idx < outerSize());
|
||||
ei_assert(idx < innerSize());
|
||||
return this->m_data.diag(idx);
|
||||
}
|
||||
|
||||
inline Scalar coeffLower(int row, int col) const {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
ei_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const int 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 int 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(int row, int col) const {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
ei_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const int 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 int 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(int idx) {
|
||||
ei_assert(idx < outerSize());
|
||||
ei_assert(idx < innerSize());
|
||||
return this->m_data.diag(idx);
|
||||
}
|
||||
|
||||
inline Scalar& coeffRefLower(int row, int col) {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
ei_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const int minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
ei_assert(inner >= minInnerIndex && "you try to acces a coeff that do not exist in the storage");
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
|
||||
} else {
|
||||
const int maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
ei_assert(inner <= maxInnerIndex && "you try to acces a coeff that do not exist in the storage");
|
||||
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
|
||||
}
|
||||
}
|
||||
|
||||
inline bool coeffExistLower(int row, int col) {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
ei_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const int minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
return inner >= minInnerIndex;
|
||||
} else {
|
||||
const int maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
return inner <= maxInnerIndex;
|
||||
}
|
||||
}
|
||||
|
||||
inline Scalar& coeffRefUpper(int row, int col) {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
ei_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const int minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
ei_assert(outer >= minOuterIndex && "you try to acces a coeff that do not exist in the storage");
|
||||
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
|
||||
} else {
|
||||
const int maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
ei_assert(outer <= maxOuterIndex && "you try to acces a coeff that do not exist in the storage");
|
||||
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
|
||||
}
|
||||
}
|
||||
|
||||
inline bool coeffExistUpper(int row, int col) {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
ei_assert(inner != outer);
|
||||
|
||||
if (IsRowMajor) {
|
||||
const int minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
return outer >= minOuterIndex;
|
||||
} else {
|
||||
const int 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();
|
||||
memset(m_colStartIndex, 0, (m_outerSize + 1) * sizeof (int));
|
||||
memset(m_rowStartIndex, 0, (m_outerSize + 1) * sizeof (int));
|
||||
}
|
||||
|
||||
/** \returns the number of non zero coefficients */
|
||||
inline int nonZeros() const {
|
||||
return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize();
|
||||
}
|
||||
|
||||
/** Preallocates \a reserveSize non zeros */
|
||||
inline void reserve(int reserveSize, int reserveUpperSize, int 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(int row, int col) {
|
||||
const int outer = IsRowMajor ? row : col;
|
||||
const int inner = IsRowMajor ? col : row;
|
||||
|
||||
ei_assert(outer < outerSize());
|
||||
ei_assert(inner < innerSize());
|
||||
|
||||
if (outer == inner)
|
||||
return m_data.diag(col);
|
||||
|
||||
if (IsRowMajor) {
|
||||
if (outer < inner) //upper matrix
|
||||
{
|
||||
int minOuterIndex = 0;
|
||||
minOuterIndex = inner - m_data.upperProfile(inner);
|
||||
|
||||
if (outer < minOuterIndex) //The value does not yet exist
|
||||
{
|
||||
const int previousProfile = m_data.upperProfile(inner);
|
||||
|
||||
m_data.upperProfile(inner) = inner - outer;
|
||||
|
||||
|
||||
const int bandIncrement = m_data.upperProfile(inner) - previousProfile;
|
||||
//shift data stored after this new one
|
||||
const int stop = m_colStartIndex[cols()];
|
||||
const int start = m_colStartIndex[inner];
|
||||
|
||||
|
||||
for (int innerIdx = stop; innerIdx >= start; innerIdx--) {
|
||||
m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
|
||||
}
|
||||
|
||||
for (int innerIdx = cols(); innerIdx > inner; innerIdx--) {
|
||||
m_colStartIndex[innerIdx] += bandIncrement;
|
||||
}
|
||||
|
||||
//zeros new data
|
||||
memset(this->_upperPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
|
||||
|
||||
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 int minInnerIndex = outer - m_data.lowerProfile(outer);
|
||||
if (inner < minInnerIndex) //The value does not yet exist
|
||||
{
|
||||
const int previousProfile = m_data.lowerProfile(outer);
|
||||
m_data.lowerProfile(outer) = outer - inner;
|
||||
|
||||
const int bandIncrement = m_data.lowerProfile(outer) - previousProfile;
|
||||
//shift data stored after this new one
|
||||
const int stop = m_rowStartIndex[rows()];
|
||||
const int start = m_rowStartIndex[outer];
|
||||
|
||||
|
||||
for (int innerIdx = stop; innerIdx >= start; innerIdx--) {
|
||||
m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
|
||||
}
|
||||
|
||||
for (int innerIdx = rows(); innerIdx > outer; innerIdx--) {
|
||||
m_rowStartIndex[innerIdx] += bandIncrement;
|
||||
}
|
||||
|
||||
//zeros new data
|
||||
memset(this->_lowerPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
|
||||
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 int maxOuterIndex = inner + m_data.upperProfile(inner);
|
||||
if (outer > maxOuterIndex) //The value does not yet exist
|
||||
{
|
||||
const int previousProfile = m_data.upperProfile(inner);
|
||||
m_data.upperProfile(inner) = outer - inner;
|
||||
|
||||
const int bandIncrement = m_data.upperProfile(inner) - previousProfile;
|
||||
//shift data stored after this new one
|
||||
const int stop = m_rowStartIndex[rows()];
|
||||
const int start = m_rowStartIndex[inner + 1];
|
||||
|
||||
for (int innerIdx = stop; innerIdx >= start; innerIdx--) {
|
||||
m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
|
||||
}
|
||||
|
||||
for (int innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) {
|
||||
m_rowStartIndex[innerIdx] += bandIncrement;
|
||||
}
|
||||
memset(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
|
||||
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 int maxInnerIndex = outer + m_data.lowerProfile(outer);
|
||||
if (inner > maxInnerIndex) //The value does not yet exist
|
||||
{
|
||||
const int previousProfile = m_data.lowerProfile(outer);
|
||||
m_data.lowerProfile(outer) = inner - outer;
|
||||
|
||||
const int bandIncrement = m_data.lowerProfile(outer) - previousProfile;
|
||||
//shift data stored after this new one
|
||||
const int stop = m_colStartIndex[cols()];
|
||||
const int start = m_colStartIndex[outer + 1];
|
||||
|
||||
for (int innerIdx = stop; innerIdx >= start; innerIdx--) {
|
||||
m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
|
||||
}
|
||||
|
||||
for (int innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) {
|
||||
m_colStartIndex[innerIdx] += bandIncrement;
|
||||
}
|
||||
memset(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
|
||||
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);
|
||||
|
||||
// ei_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix");
|
||||
//
|
||||
// Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1];
|
||||
// unsigned int dataIdx = 0;
|
||||
// for (unsigned int row = 0; row < rows(); row++) {
|
||||
//
|
||||
// const unsigned int 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 unsigned int 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 = precision<RealScalar > ()) {
|
||||
//TODO
|
||||
}
|
||||
|
||||
/** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
|
||||
* \sa resizeNonZeros(int), reserve(), setZero()
|
||||
*/
|
||||
void resize(size_t rows, size_t cols) {
|
||||
const int diagSize = rows > cols ? cols : rows;
|
||||
m_innerSize = IsRowMajor ? cols : rows;
|
||||
|
||||
ei_assert(rows == cols && "Skyline matrix must be square matrix");
|
||||
|
||||
if (diagSize % 2) { // diagSize is odd
|
||||
const int 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 int 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 int [cols + 1];
|
||||
m_rowStartIndex = new int [rows + 1];
|
||||
m_outerSize = diagSize;
|
||||
|
||||
m_data.reset();
|
||||
m_data.clear();
|
||||
|
||||
m_outerSize = diagSize;
|
||||
memset(m_colStartIndex, 0, (cols + 1) * sizeof (int));
|
||||
memset(m_rowStartIndex, 0, (rows + 1) * sizeof (int));
|
||||
}
|
||||
|
||||
void resizeNonZeros(int 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 (int));
|
||||
memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (int));
|
||||
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 (unsigned int 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 (unsigned int 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 (unsigned int 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 (unsigned int 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 (unsigned int 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 (unsigned int 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 (unsigned int rowIdx = 0; rowIdx < m.rows(); rowIdx++) {
|
||||
for (unsigned int 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, int 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+=(unsigned int 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 int 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 int row() const {
|
||||
return IsRowMajor ? index() : m_outer;
|
||||
}
|
||||
|
||||
inline int 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 int m_outer;
|
||||
int m_id;
|
||||
const int m_start;
|
||||
const int m_end;
|
||||
};
|
||||
|
||||
template<typename Scalar, int _Options>
|
||||
class SkylineMatrix<Scalar, _Options>::InnerLowerIterator {
|
||||
public:
|
||||
|
||||
InnerLowerIterator(const SkylineMatrix& mat, int 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+=(unsigned int 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 int 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 int row() const {
|
||||
return IsRowMajor ? m_outer : index();
|
||||
}
|
||||
|
||||
inline int 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 int m_outer;
|
||||
int m_id;
|
||||
const int m_start;
|
||||
const int m_end;
|
||||
};
|
||||
|
||||
#endif // EIGEN_SkylineMatrix_H
|
||||
221
unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
Normal file
221
unsupported/Eigen/src/Skyline/SkylineMatrixBase.h
Normal file
@ -0,0 +1,221 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
|
||||
//
|
||||
// Eigen is free software; you can redistribute it and/or
|
||||
// modify it under the terms of the GNU Lesser General Public
|
||||
// License as published by the Free Software Foundation; either
|
||||
// version 3 of the License, or (at your option) any later version.
|
||||
//
|
||||
// Alternatively, you can redistribute it and/or
|
||||
// modify it under the terms of the GNU General Public License as
|
||||
// published by the Free Software Foundation; either version 2 of
|
||||
// the License, or (at your option) any later version.
|
||||
//
|
||||
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
||||
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
||||
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
|
||||
// GNU General Public License for more details.
|
||||
//
|
||||
// You should have received a copy of the GNU Lesser General Public
|
||||
// License and a copy of the GNU General Public License along with
|
||||
// Eigen. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
#ifndef EIGEN_SKYLINEMATRIXBASE_H
|
||||
#define EIGEN_SKYLINEMATRIXBASE_H
|
||||
|
||||
#include "SkylineUtil.h"
|
||||
|
||||
/** \ingroup Skyline_Module
|
||||
*
|
||||
* \class SkylineMatrixBase
|
||||
*
|
||||
* \brief Base class of any skyline matrices or skyline expressions
|
||||
*
|
||||
* \param Derived
|
||||
*
|
||||
*/
|
||||
template<typename Derived> class SkylineMatrixBase : public AnyMatrixBase<Derived> {
|
||||
public:
|
||||
|
||||
typedef typename ei_traits<Derived>::Scalar Scalar;
|
||||
|
||||
enum {
|
||||
RowsAtCompileTime = ei_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 = ei_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 = (ei_size_at_compile_time<ei_traits<Derived>::RowsAtCompileTime,
|
||||
ei_traits<Derived>::ColsAtCompileTime>::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 = (ei_size_at_compile_time<MaxRowsAtCompileTime,
|
||||
MaxColsAtCompileTime>::ret),
|
||||
|
||||
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 = ei_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 = ei_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, EIGEN_ENUM_MAX(RowsAtCompileTime, ColsAtCompileTime),
|
||||
EIGEN_ENUM_MAX(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 int rows() const {
|
||||
return derived().rows();
|
||||
}
|
||||
|
||||
/** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
|
||||
inline int cols() const {
|
||||
return derived().cols();
|
||||
}
|
||||
|
||||
/** \returns the number of coefficients, which is \a rows()*cols().
|
||||
* \sa rows(), cols(), SizeAtCompileTime. */
|
||||
inline int size() const {
|
||||
return rows() * cols();
|
||||
}
|
||||
|
||||
/** \returns the number of nonzero coefficients which is in practice the number
|
||||
* of stored coefficients. */
|
||||
inline int 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 */
|
||||
int 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 */
|
||||
int 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 (unsigned int row = 0; row < rows(); row++)
|
||||
for (unsigned int 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 (unsigned int i = 0; i < rows(); i++)
|
||||
for (unsigned int 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 ei_eval<Derived, IsSkyline>::type eval() const {
|
||||
return typename ei_eval<Derived>::type(derived());
|
||||
}
|
||||
|
||||
protected:
|
||||
bool m_isRValue;
|
||||
};
|
||||
|
||||
#endif // EIGEN_SkylineMatrixBase_H
|
||||
314
unsupported/Eigen/src/Skyline/SkylineProduct.h
Normal file
314
unsupported/Eigen/src/Skyline/SkylineProduct.h
Normal file
@ -0,0 +1,314 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
|
||||
//
|
||||
// Eigen is free software; you can redistribute it and/or
|
||||
// modify it under the terms of the GNU Lesser General Public
|
||||
// License as published by the Free Software Foundation; either
|
||||
// version 3 of the License, or (at your option) any later version.
|
||||
//
|
||||
// Alternatively, you can redistribute it and/or
|
||||
// modify it under the terms of the GNU General Public License as
|
||||
// published by the Free Software Foundation; either version 2 of
|
||||
// the License, or (at your option) any later version.
|
||||
//
|
||||
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
||||
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
||||
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
|
||||
// GNU General Public License for more details.
|
||||
//
|
||||
// You should have received a copy of the GNU Lesser General Public
|
||||
// License and a copy of the GNU General Public License along with
|
||||
// Eigen. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
#ifndef EIGEN_SKYLINEPRODUCT_H
|
||||
#define EIGEN_SKYLINEPRODUCT_H
|
||||
|
||||
template<typename Lhs, typename Rhs> struct ei_skyline_product_mode {
|
||||
|
||||
enum {
|
||||
value = (Rhs::Flags & Lhs::Flags & SkylineBit) == SkylineBit
|
||||
? SkylineTimeSkylineProduct
|
||||
: (Lhs::Flags & SkylineBit) == SkylineBit
|
||||
? SkylineTimeDenseProduct
|
||||
: DenseTimeSkylineProduct
|
||||
};
|
||||
};
|
||||
|
||||
template<typename Lhs, typename Rhs, int ProductMode>
|
||||
struct SkylineProductReturnType {
|
||||
typedef const typename ei_nested<Lhs, Rhs::RowsAtCompileTime>::type LhsNested;
|
||||
typedef const typename ei_nested<Rhs, Lhs::RowsAtCompileTime>::type RhsNested;
|
||||
|
||||
typedef SkylineProduct<LhsNested, RhsNested, ProductMode> Type;
|
||||
};
|
||||
|
||||
template<typename LhsNested, typename RhsNested, int ProductMode>
|
||||
struct ei_traits<SkylineProduct<LhsNested, RhsNested, ProductMode> > {
|
||||
// clean the nested types:
|
||||
typedef typename ei_cleantype<LhsNested>::type _LhsNested;
|
||||
typedef typename ei_cleantype<RhsNested>::type _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 = EIGEN_ENUM_MIN(_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 = Dynamic
|
||||
};
|
||||
|
||||
typedef typename ei_meta_if<ResultIsSkyline,
|
||||
SkylineMatrixBase<SkylineProduct<LhsNested, RhsNested, ProductMode> >,
|
||||
MatrixBase<SkylineProduct<LhsNested, RhsNested, ProductMode> > >::ret Base;
|
||||
};
|
||||
|
||||
template<typename LhsNested, typename RhsNested, int ProductMode>
|
||||
class SkylineProduct : ei_no_assignment_operator,
|
||||
public ei_traits<SkylineProduct<LhsNested, RhsNested, ProductMode> >::Base {
|
||||
public:
|
||||
|
||||
EIGEN_GENERIC_PUBLIC_INTERFACE(SkylineProduct)
|
||||
|
||||
private:
|
||||
|
||||
typedef typename ei_traits<SkylineProduct>::_LhsNested _LhsNested;
|
||||
typedef typename ei_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) {
|
||||
ei_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 int rows() const {
|
||||
return m_lhs.rows();
|
||||
}
|
||||
|
||||
EIGEN_STRONG_INLINE int 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 ei_skyline_row_major_time_dense_product(const Lhs& lhs, const Rhs& rhs, Dest& dst) {
|
||||
typedef typename ei_cleantype<Lhs>::type _Lhs;
|
||||
typedef typename ei_cleantype<Rhs>::type _Rhs;
|
||||
typedef typename ei_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 (unsigned int col = 0; col < rhs.cols(); col++) {
|
||||
for (unsigned int row = 0; row < lhs.rows(); row++) {
|
||||
dst(row, col) = lhs.coeffDiag(row) * rhs(row, col);
|
||||
}
|
||||
}
|
||||
//Use matrix lower triangular part
|
||||
for (unsigned int row = 0; row < lhs.rows(); row++) {
|
||||
typename _Lhs::InnerLowerIterator lIt(lhs, row);
|
||||
const int stop = lIt.col() + lIt.size();
|
||||
for (unsigned int col = 0; col < rhs.cols(); col++) {
|
||||
|
||||
unsigned int 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 (unsigned int lhscol = 0; lhscol < lhs.cols(); lhscol++) {
|
||||
typename _Lhs::InnerUpperIterator uIt(lhs, lhscol);
|
||||
const int stop = uIt.size() + uIt.row();
|
||||
for (unsigned int rhscol = 0; rhscol < rhs.cols(); rhscol++) {
|
||||
|
||||
|
||||
const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol);
|
||||
unsigned int 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 ei_skyline_col_major_time_dense_product(const Lhs& lhs, const Rhs& rhs, Dest& dst) {
|
||||
typedef typename ei_cleantype<Lhs>::type _Lhs;
|
||||
typedef typename ei_cleantype<Rhs>::type _Rhs;
|
||||
typedef typename ei_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 (unsigned int col = 0; col < rhs.cols(); col++) {
|
||||
for (unsigned int row = 0; row < lhs.rows(); row++) {
|
||||
dst(row, col) = lhs.coeffDiag(row) * rhs(row, col);
|
||||
}
|
||||
}
|
||||
|
||||
//Use matrix upper triangular part
|
||||
for (unsigned int row = 0; row < lhs.rows(); row++) {
|
||||
typename _Lhs::InnerUpperIterator uIt(lhs, row);
|
||||
const int stop = uIt.col() + uIt.size();
|
||||
for (unsigned int col = 0; col < rhs.cols(); col++) {
|
||||
|
||||
unsigned int 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 (unsigned int lhscol = 0; lhscol < lhs.cols(); lhscol++) {
|
||||
typename _Lhs::InnerLowerIterator lIt(lhs, lhscol);
|
||||
const int stop = lIt.size() + lIt.row();
|
||||
for (unsigned int rhscol = 0; rhscol < rhs.cols(); rhscol++) {
|
||||
|
||||
const Scalar rhsCoeff = rhs.coeff(lhscol, rhscol);
|
||||
unsigned int 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 = ei_traits<Lhs>::Flags&RowMajorBit>
|
||||
struct ei_skyline_product_selector;
|
||||
|
||||
template<typename Lhs, typename Rhs, typename ResultType>
|
||||
struct ei_skyline_product_selector<Lhs, Rhs, ResultType, RowMajor> {
|
||||
typedef typename ei_traits<typename ei_cleantype<Lhs>::type>::Scalar Scalar;
|
||||
|
||||
static void run(const Lhs& lhs, const Rhs& rhs, ResultType & res) {
|
||||
ei_skyline_row_major_time_dense_product<Lhs, Rhs, ResultType > (lhs, rhs, res);
|
||||
}
|
||||
};
|
||||
|
||||
template<typename Lhs, typename Rhs, typename ResultType>
|
||||
struct ei_skyline_product_selector<Lhs, Rhs, ResultType, ColMajor> {
|
||||
typedef typename ei_traits<typename ei_cleantype<Lhs>::type>::Scalar Scalar;
|
||||
|
||||
static void run(const Lhs& lhs, const Rhs& rhs, ResultType & res) {
|
||||
ei_skyline_col_major_time_dense_product<Lhs, Rhs, ResultType > (lhs, rhs, res);
|
||||
}
|
||||
};
|
||||
|
||||
template<typename Derived>
|
||||
template<typename Lhs, typename Rhs >
|
||||
Derived & MatrixBase<Derived>::lazyAssign(const SkylineProduct<Lhs, Rhs, SkylineTimeDenseProduct>& product) {
|
||||
typedef typename ei_cleantype<Lhs>::type _Lhs;
|
||||
ei_skyline_product_selector<typename ei_cleantype<Lhs>::type,
|
||||
typename ei_cleantype<Rhs>::type,
|
||||
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());
|
||||
}
|
||||
|
||||
#endif // EIGEN_SKYLINEPRODUCT_H
|
||||
269
unsupported/Eigen/src/Skyline/SkylineStorage.h
Normal file
269
unsupported/Eigen/src/Skyline/SkylineStorage.h
Normal file
@ -0,0 +1,269 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
|
||||
//
|
||||
// Eigen is free software; you can redistribute it and/or
|
||||
// modify it under the terms of the GNU Lesser General Public
|
||||
// License as published by the Free Software Foundation; either
|
||||
// version 3 of the License, or (at your option) any later version.
|
||||
//
|
||||
// Alternatively, you can redistribute it and/or
|
||||
// modify it under the terms of the GNU General Public License as
|
||||
// published by the Free Software Foundation; either version 2 of
|
||||
// the License, or (at your option) any later version.
|
||||
//
|
||||
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
||||
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
||||
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
|
||||
// GNU General Public License for more details.
|
||||
//
|
||||
// You should have received a copy of the GNU Lesser General Public
|
||||
// License and a copy of the GNU General Public License along with
|
||||
// Eigen. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
#ifndef EIGEN_SKYLINE_STORAGE_H
|
||||
#define EIGEN_SKYLINE_STORAGE_H
|
||||
|
||||
/** 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;
|
||||
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 (int));
|
||||
memcpy(m_lowerProfile, other.m_lowerProfile, m_lowerProfileSize * sizeof (int));
|
||||
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(size_t size, size_t upperProfileSize, size_t lowerProfileSize, size_t upperSize, size_t lowerSize) {
|
||||
int 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(size_t diagSize, size_t upperProfileSize, size_t lowerProfileSize, size_t upperSize, size_t lowerSize, float reserveSizeFactor = 0) {
|
||||
if (m_allocatedSize < diagSize + upperSize + lowerSize)
|
||||
reallocate(diagSize, upperProfileSize, lowerProfileSize, upperSize + size_t(reserveSizeFactor * upperSize), lowerSize + size_t(reserveSizeFactor * lowerSize));
|
||||
m_diagSize = diagSize;
|
||||
m_upperSize = upperSize;
|
||||
m_lowerSize = lowerSize;
|
||||
m_upperProfileSize = upperProfileSize;
|
||||
m_lowerProfileSize = lowerProfileSize;
|
||||
}
|
||||
|
||||
inline size_t diagSize() const {
|
||||
return m_diagSize;
|
||||
}
|
||||
|
||||
inline size_t upperSize() const {
|
||||
return m_upperSize;
|
||||
}
|
||||
|
||||
inline size_t lowerSize() const {
|
||||
return m_lowerSize;
|
||||
}
|
||||
|
||||
inline size_t upperProfileSize() const {
|
||||
return m_upperProfileSize;
|
||||
}
|
||||
|
||||
inline size_t lowerProfileSize() const {
|
||||
return m_lowerProfileSize;
|
||||
}
|
||||
|
||||
inline size_t allocatedSize() const {
|
||||
return m_allocatedSize;
|
||||
}
|
||||
|
||||
inline void clear() {
|
||||
m_diagSize = 0;
|
||||
}
|
||||
|
||||
inline Scalar& diag(size_t i) {
|
||||
return m_diag[i];
|
||||
}
|
||||
|
||||
inline const Scalar& diag(size_t i) const {
|
||||
return m_diag[i];
|
||||
}
|
||||
|
||||
inline Scalar& upper(size_t i) {
|
||||
return m_upper[i];
|
||||
}
|
||||
|
||||
inline const Scalar& upper(size_t i) const {
|
||||
return m_upper[i];
|
||||
}
|
||||
|
||||
inline Scalar& lower(size_t i) {
|
||||
return m_lower[i];
|
||||
}
|
||||
|
||||
inline const Scalar& lower(size_t i) const {
|
||||
return m_lower[i];
|
||||
}
|
||||
|
||||
inline int& upperProfile(size_t i) {
|
||||
return m_upperProfile[i];
|
||||
}
|
||||
|
||||
inline const int& upperProfile(size_t i) const {
|
||||
return m_upperProfile[i];
|
||||
}
|
||||
|
||||
inline int& lowerProfile(size_t i) {
|
||||
return m_lowerProfile[i];
|
||||
}
|
||||
|
||||
inline const int& lowerProfile(size_t i) const {
|
||||
return m_lowerProfile[i];
|
||||
}
|
||||
|
||||
static SkylineStorage Map(int* upperProfile, int* lowerProfile, Scalar* diag, Scalar* upper, Scalar* lower, size_t size, size_t upperSize, size_t 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() {
|
||||
memset(m_diag, 0, m_diagSize * sizeof (Scalar));
|
||||
memset(m_upper, 0, m_upperSize * sizeof (Scalar));
|
||||
memset(m_lower, 0, m_lowerSize * sizeof (Scalar));
|
||||
memset(m_upperProfile, 0, m_diagSize * sizeof (int));
|
||||
memset(m_lowerProfile, 0, m_diagSize * sizeof (int));
|
||||
}
|
||||
|
||||
void prune(Scalar reference, RealScalar epsilon = precision<RealScalar>()) {
|
||||
//TODO
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
inline void reallocate(size_t diagSize, size_t upperProfileSize, size_t lowerProfileSize, size_t upperSize, size_t lowerSize) {
|
||||
|
||||
Scalar* diag = new Scalar[diagSize];
|
||||
Scalar* upper = new Scalar[upperSize];
|
||||
Scalar* lower = new Scalar[lowerSize];
|
||||
int* upperProfile = new int[upperProfileSize];
|
||||
int* lowerProfile = new int[lowerProfileSize];
|
||||
|
||||
size_t copyDiagSize = std::min(diagSize, m_diagSize);
|
||||
size_t copyUpperSize = std::min(upperSize, m_upperSize);
|
||||
size_t copyLowerSize = std::min(lowerSize, m_lowerSize);
|
||||
size_t copyUpperProfileSize = std::min(upperProfileSize, m_upperProfileSize);
|
||||
size_t 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 (int));
|
||||
memcpy(lowerProfile, m_lowerProfile, copyLowerProfileSize * sizeof (int));
|
||||
|
||||
|
||||
|
||||
// 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;
|
||||
int* m_upperProfile;
|
||||
int* m_lowerProfile;
|
||||
size_t m_diagSize;
|
||||
size_t m_upperSize;
|
||||
size_t m_lowerSize;
|
||||
size_t m_upperProfileSize;
|
||||
size_t m_lowerProfileSize;
|
||||
size_t m_allocatedSize;
|
||||
|
||||
};
|
||||
|
||||
#endif // EIGEN_COMPRESSED_STORAGE_H
|
||||
96
unsupported/Eigen/src/Skyline/SkylineUtil.h
Normal file
96
unsupported/Eigen/src/Skyline/SkylineUtil.h
Normal file
@ -0,0 +1,96 @@
|
||||
// This file is part of Eigen, a lightweight C++ template library
|
||||
// for linear algebra.
|
||||
//
|
||||
// Copyright (C) 2009 Guillaume Saupin <guillaume.saupin@cea.fr>
|
||||
//
|
||||
// Eigen is free software; you can redistribute it and/or
|
||||
// modify it under the terms of the GNU Lesser General Public
|
||||
// License as published by the Free Software Foundation; either
|
||||
// version 3 of the License, or (at your option) any later version.
|
||||
//
|
||||
// Alternatively, you can redistribute it and/or
|
||||
// modify it under the terms of the GNU General Public License as
|
||||
// published by the Free Software Foundation; either version 2 of
|
||||
// the License, or (at your option) any later version.
|
||||
//
|
||||
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
|
||||
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
|
||||
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
|
||||
// GNU General Public License for more details.
|
||||
//
|
||||
// You should have received a copy of the GNU Lesser General Public
|
||||
// License and a copy of the GNU General Public License along with
|
||||
// Eigen. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
#ifndef EIGEN_SKYLINEUTIL_H
|
||||
#define EIGEN_SKYLINEUTIL_H
|
||||
|
||||
#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::ei_traits<Derived>::Scalar Scalar; \
|
||||
typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; \
|
||||
enum { Flags = Eigen::ei_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;
|
||||
|
||||
template<typename Lhs, typename Rhs> struct ei_skyline_product_mode;
|
||||
template<typename Lhs, typename Rhs, int ProductMode = ei_skyline_product_mode<Lhs,Rhs>::value> struct SkylineProductReturnType;
|
||||
|
||||
|
||||
template<typename T> class ei_eval<T,IsSkyline>
|
||||
{
|
||||
typedef typename ei_traits<T>::Scalar _Scalar;
|
||||
enum {
|
||||
_Flags = ei_traits<T>::Flags
|
||||
};
|
||||
|
||||
public:
|
||||
typedef SkylineMatrix<_Scalar, _Flags> type;
|
||||
};
|
||||
|
||||
|
||||
#endif // EIGEN_SKYLINEUTIL_H
|
||||
@ -39,10 +39,11 @@ using namespace Eigen;
|
||||
template <typename T>
|
||||
void take_std( std::complex<T> * dst, int n )
|
||||
{
|
||||
for (int i=0;i<n;++i)
|
||||
dst[i] = std::complex<T>(i,i);
|
||||
cout << dst[n-1] << endl;
|
||||
}
|
||||
|
||||
|
||||
template <typename T>
|
||||
void syntax()
|
||||
{
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user