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Several improvements in sparse module:
* add a LDL^T factorization with solver using code from T. Davis's LDL library (LPGL2.1+) * various bug fixes in trianfular solver, matrix product, etc. * improve cmake files for the supported libraries * split the sparse unit test * etc.
This commit is contained in:
Gael Guennebaud
208
test/sparse_basic.cpp
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208
test/sparse_basic.cpp
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra. Eigen itself is part of the KDE project.
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//
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// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
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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
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// 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
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// 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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#include "sparse.h"
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template<typename SetterType,typename DenseType, typename SparseType>
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bool test_random_setter(SparseType& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
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{
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{
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sm.setZero();
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SetterType w(sm);
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std::vector<Vector2i> remaining = nonzeroCoords;
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while(!remaining.empty())
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{
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int i = ei_random<int>(0,remaining.size()-1);
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w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
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remaining[i] = remaining.back();
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remaining.pop_back();
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}
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}
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return sm.isApprox(ref);
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}
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template<typename Scalar> void sparse_basic(int rows, int cols)
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{
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double density = std::max(8./(rows*cols), 0.01);
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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Scalar eps = 1e-6;
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SparseMatrix<Scalar> m(rows, cols);
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DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
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DenseVector vec1 = DenseVector::Random(rows);
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std::vector<Vector2i> zeroCoords;
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std::vector<Vector2i> nonzeroCoords;
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initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
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if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
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return;
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// test coeff and coeffRef
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for (int i=0; i<(int)zeroCoords.size(); ++i)
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{
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VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
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VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
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}
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VERIFY_IS_APPROX(m, refMat);
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m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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VERIFY_IS_APPROX(m, refMat);
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/*
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// test InnerIterators and Block expressions
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for (int t=0; t<10; ++t)
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{
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int j = ei_random<int>(0,cols-1);
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int i = ei_random<int>(0,rows-1);
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int w = ei_random<int>(1,cols-j-1);
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int h = ei_random<int>(1,rows-i-1);
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VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
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for(int c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
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for(int r=0; r<h; r++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
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}
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}
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for(int r=0; r<h; r++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
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for(int c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
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}
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}
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}
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for(int c=0; c<cols; c++)
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{
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VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
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VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
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}
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for(int r=0; r<rows; r++)
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{
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VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
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VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
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}
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*/
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// test SparseSetters
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// coherent setter
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// TODO extend the MatrixSetter
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// {
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// m.setZero();
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// VERIFY_IS_NOT_APPROX(m, refMat);
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// SparseSetter<SparseMatrix<Scalar>, FullyCoherentAccessPattern> w(m);
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// for (int i=0; i<nonzeroCoords.size(); ++i)
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// {
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// w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y());
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// }
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// }
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// VERIFY_IS_APPROX(m, refMat);
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// random setter
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// {
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// m.setZero();
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// VERIFY_IS_NOT_APPROX(m, refMat);
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// SparseSetter<SparseMatrix<Scalar>, RandomAccessPattern> w(m);
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// std::vector<Vector2i> remaining = nonzeroCoords;
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// while(!remaining.empty())
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// {
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// int i = ei_random<int>(0,remaining.size()-1);
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// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
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// remaining[i] = remaining.back();
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// remaining.pop_back();
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// }
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// }
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// VERIFY_IS_APPROX(m, refMat);
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VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, StdMapTraits> >(m,refMat,nonzeroCoords) ));
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#ifdef _HASH_MAP
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VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GnuHashMapTraits> >(m,refMat,nonzeroCoords) ));
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#endif
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#ifdef _DENSE_HASH_MAP_H_
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VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) ));
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#endif
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#ifdef _SPARSE_HASH_MAP_H_
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VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) ));
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#endif
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// {
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// m.setZero();
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// VERIFY_IS_NOT_APPROX(m, refMat);
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// // RandomSetter<SparseMatrix<Scalar> > w(m);
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// RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits > w(m);
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// // RandomSetter<SparseMatrix<Scalar>, GnuHashMapTraits > w(m);
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// std::vector<Vector2i> remaining = nonzeroCoords;
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// while(!remaining.empty())
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// {
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// int i = ei_random<int>(0,remaining.size()-1);
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// w(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
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// remaining[i] = remaining.back();
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// remaining.pop_back();
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// }
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// }
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// std::cerr << m.transpose() << "\n\n" << refMat.transpose() << "\n\n";
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// VERIFY_IS_APPROX(m, refMat);
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// test transpose
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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SparseMatrix<Scalar> m2(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
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VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
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}
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// test matrix product
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows);
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DenseMatrix refMat4 = DenseMatrix::Zero(rows, rows);
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SparseMatrix<Scalar> m2(rows, rows);
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SparseMatrix<Scalar> m3(rows, rows);
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SparseMatrix<Scalar> m4(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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initSparse<Scalar>(density, refMat3, m3);
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initSparse<Scalar>(density, refMat4, m4);
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VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3);
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VERIFY_IS_APPROX(m4=m2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
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VERIFY_IS_APPROX(m4=m2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
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VERIFY_IS_APPROX(m4=m2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
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}
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}
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void test_sparse_basic()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST( sparse_basic<double>(8, 8) );
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CALL_SUBTEST( sparse_basic<std::complex<double> >(16, 16) );
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CALL_SUBTEST( sparse_basic<double>(33, 33) );
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}
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}
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