1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h"
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void diagonal(const MatrixType& m)
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::Index Index;
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::Scalar Scalar;
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::RealScalar RealScalar;
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Index rows = m.rows();
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Index cols = m.cols();
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixType m1 = MatrixType::Random(rows, cols),
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             m2 = MatrixType::Random(rows, cols);
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  //check diagonal()
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1.diagonal(), m1.transpose().diagonal());
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m2.diagonal() = 2 * m1.diagonal();
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m2.diagonal()[0] *= 3;
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  if (rows>2)
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    enum {
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      N1 = MatrixType::RowsAtCompileTime>1 ?  1 : 0,
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      N2 = MatrixType::RowsAtCompileTime>2 ? -2 : 0
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    };
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    // check sub/super diagonal
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    if(m1.template diagonal<N1>().RowsAtCompileTime!=Dynamic)
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      VERIFY(m1.template diagonal<N1>().RowsAtCompileTime == m1.diagonal(N1).size());
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    if(m1.template diagonal<N2>().RowsAtCompileTime!=Dynamic)
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      VERIFY(m1.template diagonal<N2>().RowsAtCompileTime == m1.diagonal(N2).size());
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.template diagonal<N1>() = 2 * m1.template diagonal<N1>();
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m2.template diagonal<N1>(), static_cast<Scalar>(2) * m1.diagonal(N1));
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.template diagonal<N1>()[0] *= 3;
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m2.template diagonal<N1>()[0], static_cast<Scalar>(6) * m1.template diagonal<N1>()[0]);
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.template diagonal<N2>() = 2 * m1.template diagonal<N2>();
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.template diagonal<N2>()[0] *= 3;
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m2.template diagonal<N2>()[0], static_cast<Scalar>(6) * m1.template diagonal<N2>()[0]);
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.diagonal(N1) = 2 * m1.diagonal(N1);
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m2.diagonal<N1>(), static_cast<Scalar>(2) * m1.diagonal(N1));
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.diagonal(N1)[0] *= 3;
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m2.diagonal(N1)[0], static_cast<Scalar>(6) * m1.diagonal(N1)[0]);
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.diagonal(N2) = 2 * m1.diagonal(N2);
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m2.diagonal<N2>(), static_cast<Scalar>(2) * m1.diagonal(N2));
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    m2.diagonal(N2)[0] *= 3;
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m2.diagonal(N2)[0], static_cast<Scalar>(6) * m1.diagonal(N2)[0]);
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_diagonal()
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for(int i = 0; i < g_repeat; i++) {
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( diagonal(Matrix<float, 1, 1>()) );
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( diagonal(Matrix<float, 4, 9>()) );
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( diagonal(Matrix<float, 7, 3>()) );
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( diagonal(Matrix4d()) );
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( diagonal(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( diagonal(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( diagonal(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( diagonal(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( diagonal(Matrix<float,Dynamic,4>(3, 4)) );
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
84