eigen/test/sparse_basic.cpp
Gael Guennebaud 5015e48361 Sparse module: add a more flexible SparseMatrix::fillrand() function
which allows to fill a matrix with random inner coordinates (makes sense
only when a very few coeffs are inserted per col/row)
2008-12-11 18:26:24 +00:00

229 lines
7.9 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 SetterType,typename DenseType, typename SparseType>
bool test_random_setter(SparseType& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
{
{
sm.setZero();
SetterType w(sm);
std::vector<Vector2i> remaining = nonzeroCoords;
while(!remaining.empty())
{
int i = ei_random<int>(0,remaining.size()-1);
w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
remaining[i] = remaining.back();
remaining.pop_back();
}
}
return sm.isApprox(ref);
}
template<typename Scalar> void sparse_basic(int rows, int cols)
{
double density = std::max(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
Scalar eps = 1e-6;
SparseMatrix<Scalar> m(rows, cols);
DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
DenseVector vec1 = DenseVector::Random(rows);
std::vector<Vector2i> zeroCoords;
std::vector<Vector2i> nonzeroCoords;
initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
return;
// test coeff and coeffRef
for (int i=0; i<(int)zeroCoords.size(); ++i)
{
VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
}
VERIFY_IS_APPROX(m, refMat);
m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
VERIFY_IS_APPROX(m, refMat);
/*
// test InnerIterators and Block expressions
for (int t=0; t<10; ++t)
{
int j = ei_random<int>(0,cols-1);
int i = ei_random<int>(0,rows-1);
int w = ei_random<int>(1,cols-j-1);
int h = ei_random<int>(1,rows-i-1);
VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
for(int c=0; c<w; c++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
for(int r=0; r<h; r++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
}
}
for(int r=0; r<h; r++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
for(int c=0; c<w; c++)
{
VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
}
}
}
for(int c=0; c<cols; c++)
{
VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
}
for(int r=0; r<rows; r++)
{
VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
}
*/
// test SparseSetters
// coherent setter
// TODO extend the MatrixSetter
// {
// m.setZero();
// VERIFY_IS_NOT_APPROX(m, refMat);
// SparseSetter<SparseMatrix<Scalar>, FullyCoherentAccessPattern> w(m);
// for (int i=0; i<nonzeroCoords.size(); ++i)
// {
// w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y());
// }
// }
// VERIFY_IS_APPROX(m, refMat);
// random setter
// {
// m.setZero();
// VERIFY_IS_NOT_APPROX(m, refMat);
// SparseSetter<SparseMatrix<Scalar>, RandomAccessPattern> w(m);
// std::vector<Vector2i> remaining = nonzeroCoords;
// while(!remaining.empty())
// {
// int i = ei_random<int>(0,remaining.size()-1);
// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
// remaining[i] = remaining.back();
// remaining.pop_back();
// }
// }
// VERIFY_IS_APPROX(m, refMat);
VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, StdMapTraits> >(m,refMat,nonzeroCoords) ));
#ifdef _HASH_MAP
VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GnuHashMapTraits> >(m,refMat,nonzeroCoords) ));
#endif
#ifdef _DENSE_HASH_MAP_H_
VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) ));
#endif
#ifdef _SPARSE_HASH_MAP_H_
VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) ));
#endif
// test fillrand
{
DenseMatrix m1(rows,cols);
m1.setZero();
SparseMatrix<Scalar> m2(rows,cols);
m2.startFill();
for (int j=0; j<cols; ++j)
{
for (int k=0; k<rows/2; ++k)
{
int i = ei_random<int>(0,rows-1);
if (m1.coeff(i,j)==Scalar(0))
m2.fillrand(i,j) = m1(i,j) = ei_random<Scalar>();
}
}
m2.endFill();
std::cerr << m1 << "\n\n" << m2 << "\n";
VERIFY_IS_APPROX(m1,m2);
}
// {
// m.setZero();
// VERIFY_IS_NOT_APPROX(m, refMat);
// // RandomSetter<SparseMatrix<Scalar> > w(m);
// RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits > w(m);
// // RandomSetter<SparseMatrix<Scalar>, GnuHashMapTraits > w(m);
// std::vector<Vector2i> remaining = nonzeroCoords;
// while(!remaining.empty())
// {
// int i = ei_random<int>(0,remaining.size()-1);
// w(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
// remaining[i] = remaining.back();
// remaining.pop_back();
// }
// }
// std::cerr << m.transpose() << "\n\n" << refMat.transpose() << "\n\n";
// VERIFY_IS_APPROX(m, refMat);
// test transpose
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
SparseMatrix<Scalar> m2(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
}
// test matrix product
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows);
DenseMatrix refMat4 = DenseMatrix::Zero(rows, rows);
SparseMatrix<Scalar> m2(rows, rows);
SparseMatrix<Scalar> m3(rows, rows);
SparseMatrix<Scalar> m4(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
initSparse<Scalar>(density, refMat3, m3);
initSparse<Scalar>(density, refMat4, m4);
VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3);
VERIFY_IS_APPROX(m4=m2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
VERIFY_IS_APPROX(m4=m2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
VERIFY_IS_APPROX(m4=m2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
}
}
void test_sparse_basic()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( sparse_basic<double>(8, 8) );
CALL_SUBTEST( sparse_basic<std::complex<double> >(16, 16) );
CALL_SUBTEST( sparse_basic<double>(33, 33) );
}
}