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}
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