1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com> 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h" 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/SVD> 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType, typename JacobiScalar> 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid jacobi(const MatrixType& m = MatrixType()) 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows = m.rows(); 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols = m.cols(); 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RowsAtCompileTime = MatrixType::RowsAtCompileTime, 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ColsAtCompileTime = MatrixType::ColsAtCompileTime 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<JacobiScalar, 2, 1> JacobiVector; 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const MatrixType a(MatrixType::Random(rows, cols)); 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath JacobiVector v = JacobiVector::Random().normalized(); 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath JacobiScalar c = v.x(), s = v.y(); 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath JacobiRotation<JacobiScalar> rot(c, s); 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index p = internal::random<Index>(0, rows-1); 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index q; 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath do { 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath q = internal::random<Index>(0, rows-1); 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } while (q == p); 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType b = a; 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath b.applyOnTheLeft(p, q, rot); 437faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez VERIFY_IS_APPROX(b.row(p), c * a.row(p) + numext::conj(s) * a.row(q)); 447faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez VERIFY_IS_APPROX(b.row(q), -s * a.row(p) + numext::conj(c) * a.row(q)); 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index p = internal::random<Index>(0, cols-1); 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index q; 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath do { 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath q = internal::random<Index>(0, cols-1); 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } while (q == p); 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType b = a; 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath b.applyOnTheRight(p, q, rot); 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(b.col(p), c * a.col(p) - s * a.col(q)); 577faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez VERIFY_IS_APPROX(b.col(q), numext::conj(s) * a.col(p) + numext::conj(c) * a.col(q)); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_jacobi() 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1(( jacobi<Matrix3f, float>() )); 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2(( jacobi<Matrix4d, double>() )); 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3(( jacobi<Matrix4cf, float>() )); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3(( jacobi<Matrix4cf, std::complex<float> >() )); 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int r = internal::random<int>(2, internal::random<int>(1,EIGEN_TEST_MAX_SIZE)/2), 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath c = internal::random<int>(2, internal::random<int>(1,EIGEN_TEST_MAX_SIZE)/2); 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4(( jacobi<MatrixXf, float>(MatrixXf(r,c)) )); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5(( jacobi<MatrixXcd, double>(MatrixXcd(r,c)) )); 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5(( jacobi<MatrixXcd, std::complex<double> >(MatrixXcd(r,c)) )); 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // complex<float> is really important to test as it is the only way to cover conjugation issues in certain unaligned paths 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6(( jacobi<MatrixXcf, float>(MatrixXcf(r,c)) )); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6(( jacobi<MatrixXcf, std::complex<float> >(MatrixXcf(r,c)) )); 777faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 787faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez TEST_SET_BUT_UNUSED_VARIABLE(r); 797faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez TEST_SET_BUT_UNUSED_VARIABLE(c); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 82