1// This file is part of Eigen, a lightweight C++ template library
2// for linear algebra.
3//
4// Copyright (C) 2013 Christoph Hertzberg <chtz@informatik.uni-bremen.de>
5//
6// This Source Code Form is subject to the terms of the Mozilla
7// Public License v. 2.0. If a copy of the MPL was not distributed
8// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9
10#include "main.h"
11#include <unsupported/Eigen/AutoDiff>
12
13/*
14 * In this file scalar derivations are tested for correctness.
15 * TODO add more tests!
16 */
17
18template<typename Scalar> void check_atan2()
19{
20  typedef Matrix<Scalar, 1, 1> Deriv1;
21  typedef AutoDiffScalar<Deriv1> AD;
22
23  AD x(internal::random<Scalar>(-3.0, 3.0), Deriv1::UnitX());
24
25  using std::exp;
26  Scalar r = exp(internal::random<Scalar>(-10, 10));
27
28  AD s = sin(x), c = cos(x);
29  AD res = atan2(r*s, r*c);
30
31  VERIFY_IS_APPROX(res.value(), x.value());
32  VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
33
34  res = atan2(r*s+0, r*c+0);
35  VERIFY_IS_APPROX(res.value(), x.value());
36  VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
37}
38
39template<typename Scalar> void check_hyperbolic_functions()
40{
41  using std::sinh;
42  using std::cosh;
43  using std::tanh;
44  typedef Matrix<Scalar, 1, 1> Deriv1;
45  typedef AutoDiffScalar<Deriv1> AD;
46  Deriv1 p = Deriv1::Random();
47  AD val(p.x(),Deriv1::UnitX());
48
49  Scalar cosh_px = std::cosh(p.x());
50  AD res1 = tanh(val);
51  VERIFY_IS_APPROX(res1.value(), std::tanh(p.x()));
52  VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(1.0) / (cosh_px * cosh_px));
53
54  AD res2 = sinh(val);
55  VERIFY_IS_APPROX(res2.value(), std::sinh(p.x()));
56  VERIFY_IS_APPROX(res2.derivatives().x(), cosh_px);
57
58  AD res3 = cosh(val);
59  VERIFY_IS_APPROX(res3.value(), cosh_px);
60  VERIFY_IS_APPROX(res3.derivatives().x(), std::sinh(p.x()));
61
62  // Check constant values.
63  const Scalar sample_point = Scalar(1) / Scalar(3);
64  val = AD(sample_point,Deriv1::UnitX());
65  res1 = tanh(val);
66  VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(0.896629559604914));
67
68  res2 = sinh(val);
69  VERIFY_IS_APPROX(res2.derivatives().x(), Scalar(1.056071867829939));
70
71  res3 = cosh(val);
72  VERIFY_IS_APPROX(res3.derivatives().x(), Scalar(0.339540557256150));
73}
74
75template <typename Scalar>
76void check_limits_specialization()
77{
78  typedef Eigen::Matrix<Scalar, 1, 1> Deriv;
79  typedef Eigen::AutoDiffScalar<Deriv> AD;
80
81  typedef std::numeric_limits<AD> A;
82  typedef std::numeric_limits<Scalar> B;
83
84#if EIGEN_HAS_CXX11
85  VERIFY(bool(std::is_base_of<B, A>::value));
86#endif
87}
88
89void test_autodiff_scalar()
90{
91  for(int i = 0; i < g_repeat; i++) {
92    CALL_SUBTEST_1( check_atan2<float>() );
93    CALL_SUBTEST_2( check_atan2<double>() );
94    CALL_SUBTEST_3( check_hyperbolic_functions<float>() );
95    CALL_SUBTEST_4( check_hyperbolic_functions<double>() );
96    CALL_SUBTEST_5( check_limits_specialization<double>());
97  }
98}
99