| // 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> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #include "main.h" |
| #include <Eigen/SVD> |
| |
| template<typename MatrixType, typename JacobiScalar> |
| void jacobi(const MatrixType& m = MatrixType()) |
| { |
| 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) + numext::conj(s) * a.row(q)); |
| VERIFY_IS_APPROX(b.row(q), -s * a.row(p) + numext::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), numext::conj(s) * a.col(p) + numext::conj(c) * a.col(q)); |
| } |
| } |
| |
| EIGEN_DECLARE_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> >() )); |
| |
| CALL_SUBTEST_1(( jacobi<Matrix<float, 3, 3, RowMajor>, float>() )); |
| CALL_SUBTEST_2(( jacobi<Matrix<double, 4, 4, RowMajor>, double>() )); |
| CALL_SUBTEST_3(( jacobi<Matrix<std::complex<float>, 4, 4, RowMajor>, float>() )); |
| CALL_SUBTEST_3(( jacobi<Matrix<std::complex<float>, 4, 4, RowMajor>, std::complex<float> >() )); |
| |
| int r = internal::random<int>(2, internal::random<int>(1,EIGEN_TEST_MAX_SIZE)/2), |
| c = internal::random<int>(2, internal::random<int>(1,EIGEN_TEST_MAX_SIZE)/2); |
| 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)) )); |
| |
| TEST_SET_BUT_UNUSED_VARIABLE(r); |
| TEST_SET_BUT_UNUSED_VARIABLE(c); |
| } |
| } |