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Added a SVD module:

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- the decompostion code has been adfapted from JAMA
 - handles non square matrices of size MxN with M>=N
 - does not work for complex matrices
 - includes a solver where the parts corresponding to zero singular values are set to zero
This commit is contained in:
Gael Guennebaud
2008-08-19 17:52:04 +00:00
parent 54137f1ca7
commit 104f8fd36e
7 changed files with 587 additions and 0 deletions
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test/svd.cpp Normal file
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// 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 Gael Guennebaud <g.gael@free.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/>.
#include "main.h"
#include <Eigen/SVD>
template<typename MatrixType> void svd(const MatrixType& m)
{
/* this test covers the following files:
SVD.h
*/
int rows = m.rows();
int cols = m.cols();
typedef typename MatrixType::Scalar Scalar;
MatrixType a = MatrixType::Random(rows,cols);
Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> b =
Matrix<Scalar, MatrixType::RowsAtCompileTime, 1>::Random(rows,1);
Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> x(cols,1), x2(cols,1);
SVD<MatrixType> svd(a);
MatrixType sigma = MatrixType::Zero(rows,cols);
MatrixType matU = MatrixType::Zero(rows,rows);
sigma.block(0,0,cols,cols) = svd.singularValues().asDiagonal();
matU.block(0,0,rows,cols) = svd.matrixU();
VERIFY_IS_APPROX(a, matU * sigma * svd.matrixV().transpose());
if (rows==cols)
{
svd.solve(b, &x);
VERIFY_IS_APPROX(a * x, b);
}
}
void test_svd()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( svd(Matrix3f()) );
CALL_SUBTEST( svd(Matrix4d()) );
CALL_SUBTEST( svd(MatrixXf(7,7)) );
CALL_SUBTEST( svd(MatrixXf(14,7)) );
// complex are not implemented yet
// CALL_SUBTEST( svd(MatrixXcd(6,6)) );
// CALL_SUBTEST( svd(MatrixXcf(3,3)) );
}
}