| // 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> >() )); | 
 |  | 
 |     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); | 
 |   } | 
 | } |