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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@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 "main.h"
#include <Eigen/SVD>
template<typename MatrixType, typename JacobiScalar>
void jacobi(const MatrixType& m = MatrixType())
{
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::Index Index;
Index rows = m.rows();
Index cols = m.cols();
enum {
RowsAtCompileTime = MatrixType::RowsAtCompileTime,
ColsAtCompileTime = MatrixType::ColsAtCompileTime
};
typedef Matrix<JacobiScalar, 2, 1> JacobiVector;
const MatrixType a(MatrixType::Random(rows, cols));
JacobiVector v = JacobiVector::Random().normalized();
JacobiScalar c = v.x(), s = v.y();
JacobiRotation<JacobiScalar> rot(c, s);
{
Index p = internal::random<Index>(0, rows-1);
Index q;
do {
q = internal::random<Index>(0, rows-1);
} while (q == p);
MatrixType b = a;
b.applyOnTheLeft(p, q, rot);
VERIFY_IS_APPROX(b.row(p), c * a.row(p) + internal::conj(s) * a.row(q));
VERIFY_IS_APPROX(b.row(q), -s * a.row(p) + internal::conj(c) * a.row(q));
}
{
Index p = internal::random<Index>(0, cols-1);
Index q;
do {
q = internal::random<Index>(0, cols-1);
} while (q == p);
MatrixType b = a;
b.applyOnTheRight(p, q, rot);
VERIFY_IS_APPROX(b.col(p), c * a.col(p) - s * a.col(q));
VERIFY_IS_APPROX(b.col(q), internal::conj(s) * a.col(p) + internal::conj(c) * a.col(q));
}
}
void test_jacobi()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(( jacobi<Matrix3f, float>() ));
CALL_SUBTEST_2(( jacobi<Matrix4d, double>() ));
CALL_SUBTEST_3(( jacobi<Matrix4cf, float>() ));
CALL_SUBTEST_3(( jacobi<Matrix4cf, std::complex<float> >() ));
int r = internal::random<int>(2, 20),
c = internal::random<int>(2, 20);
CALL_SUBTEST_4(( jacobi<MatrixXf, float>(MatrixXf(r,c)) ));
CALL_SUBTEST_5(( jacobi<MatrixXcd, double>(MatrixXcd(r,c)) ));
CALL_SUBTEST_5(( jacobi<MatrixXcd, std::complex<double> >(MatrixXcd(r,c)) ));
// complex<float> is really important to test as it is the only way to cover conjugation issues in certain unaligned paths
CALL_SUBTEST_6(( jacobi<MatrixXcf, float>(MatrixXcf(r,c)) ));
CALL_SUBTEST_6(( jacobi<MatrixXcf, std::complex<float> >(MatrixXcf(r,c)) ));
(void) r;
(void) c;
}
}
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