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709e903335
* extend unit tests * add support for generic sum reduction and dot product * optimize the cwise()* : this is a special case of CwiseBinaryOp where we only have to process the coeffs which are not null for *both* matrices. Perhaps there exist some other binary operations like that ?
93 lines
3.2 KiB
C++
93 lines
3.2 KiB
C++
// 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 Scalar> void sparse_vector(int rows, int cols)
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{
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double densityMat = std::max(8./(rows*cols), 0.01);
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double densityVec = std::max(8./float(rows), 0.1);
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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typedef SparseVector<Scalar> SparseVectorType;
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typedef SparseMatrix<Scalar> SparseMatrixType;
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Scalar eps = 1e-6;
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SparseMatrixType m1(rows,cols);
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SparseVectorType v1(rows), v2(rows), v3(rows);
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DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
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DenseVector refV1 = DenseVector::Random(rows),
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refV2 = DenseVector::Random(rows),
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refV3 = DenseVector::Random(rows);
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std::vector<int> zerocoords, nonzerocoords;
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initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
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initSparse<Scalar>(densityMat, refM1, m1);
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initSparse<Scalar>(densityVec, refV2, v2);
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initSparse<Scalar>(densityVec, refV3, v3);
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Scalar s1 = ei_random<Scalar>();
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// test coeff and coeffRef
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for (unsigned int i=0; i<zerocoords.size(); ++i)
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{
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VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps );
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VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
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}
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{
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VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
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int j=0;
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for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j)
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{
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VERIFY(nonzerocoords[j]==it.index());
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VERIFY(it.value()==v1[it.index()]);
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}
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}
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VERIFY_IS_APPROX(v1, refV1);
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v1.coeffRef(nonzerocoords[0]) = Scalar(5);
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refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
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VERIFY_IS_APPROX(v1, refV1);
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VERIFY_IS_APPROX(v1+v2, refV1+refV2);
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VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3);
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VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2);
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std::cerr << v1.dot(v2) << " == " << refV1.dot(refV2) << "\n";
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VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
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}
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void test_sparse_vector()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST( sparse_vector<double>(8, 8) );
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// CALL_SUBTEST( sparse_vector<std::complex<double> >(16, 16) );
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CALL_SUBTEST( sparse_vector<double>(299, 535) );
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}
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}
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