mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-27 07:29:52 +08:00
100 lines
3.4 KiB
C++
100 lines
3.4 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra. Eigen itself is part of the KDE project.
|
|
//
|
|
// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
|
|
//
|
|
// 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/>.
|
|
|
|
#include "sparse.h"
|
|
|
|
template<typename Scalar> void sparse_vector(int rows, int cols)
|
|
{
|
|
double densityMat = std::max(8./(rows*cols), 0.01);
|
|
double densityVec = std::max(8./float(rows), 0.1);
|
|
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
|
|
typedef Matrix<Scalar,Dynamic,1> DenseVector;
|
|
typedef SparseVector<Scalar> SparseVectorType;
|
|
typedef SparseMatrix<Scalar> SparseMatrixType;
|
|
Scalar eps = 1e-6;
|
|
|
|
SparseMatrixType m1(rows,cols);
|
|
SparseVectorType v1(rows), v2(rows), v3(rows);
|
|
DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
|
|
DenseVector refV1 = DenseVector::Random(rows),
|
|
refV2 = DenseVector::Random(rows),
|
|
refV3 = DenseVector::Random(rows);
|
|
|
|
std::vector<int> zerocoords, nonzerocoords;
|
|
initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
|
|
initSparse<Scalar>(densityMat, refM1, m1);
|
|
|
|
initSparse<Scalar>(densityVec, refV2, v2);
|
|
initSparse<Scalar>(densityVec, refV3, v3);
|
|
|
|
Scalar s1 = ei_random<Scalar>();
|
|
|
|
// test coeff and coeffRef
|
|
for (unsigned int i=0; i<zerocoords.size(); ++i)
|
|
{
|
|
VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps );
|
|
//VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
|
|
}
|
|
{
|
|
VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
|
|
int j=0;
|
|
for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j)
|
|
{
|
|
VERIFY(nonzerocoords[j]==it.index());
|
|
VERIFY(it.value()==v1.coeff(it.index()));
|
|
VERIFY(it.value()==refV1.coeff(it.index()));
|
|
}
|
|
}
|
|
VERIFY_IS_APPROX(v1, refV1);
|
|
|
|
v1.coeffRef(nonzerocoords[0]) = Scalar(5);
|
|
refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
|
|
VERIFY_IS_APPROX(v1, refV1);
|
|
|
|
VERIFY_IS_APPROX(v1+v2, refV1+refV2);
|
|
VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3);
|
|
|
|
VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2);
|
|
|
|
VERIFY_IS_APPROX(v1*=s1, refV1*=s1);
|
|
VERIFY_IS_APPROX(v1/=s1, refV1/=s1);
|
|
|
|
VERIFY_IS_APPROX(v1+=v2, refV1+=refV2);
|
|
VERIFY_IS_APPROX(v1-=v2, refV1-=refV2);
|
|
|
|
VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
|
|
VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2));
|
|
|
|
}
|
|
|
|
void test_sparse_vector()
|
|
{
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
CALL_SUBTEST( sparse_vector<double>(8, 8) );
|
|
CALL_SUBTEST( sparse_vector<std::complex<double> >(16, 16) );
|
|
CALL_SUBTEST( sparse_vector<double>(299, 535) );
|
|
}
|
|
}
|
|
|