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