1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
|
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2013 Christoph Hertzberg <chtz@informatik.uni-bremen.de>
//
// 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 <unsupported/Eigen/AutoDiff>
/*
* In this file scalar derivations are tested for correctness.
* TODO add more tests!
*/
template<typename Scalar> void check_atan2()
{
typedef Matrix<Scalar, 1, 1> Deriv1;
typedef AutoDiffScalar<Deriv1> AD;
AD x(internal::random<Scalar>(-3.0, 3.0), Deriv1::UnitX());
using std::exp;
Scalar r = exp(internal::random<Scalar>(-10, 10));
AD s = sin(x), c = cos(x);
AD res = atan2(r*s, r*c);
VERIFY_IS_APPROX(res.value(), x.value());
VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
res = atan2(r*s+0, r*c+0);
VERIFY_IS_APPROX(res.value(), x.value());
VERIFY_IS_APPROX(res.derivatives(), x.derivatives());
}
template<typename Scalar> void check_hyperbolic_functions()
{
using std::sinh;
using std::cosh;
using std::tanh;
typedef Matrix<Scalar, 1, 1> Deriv1;
typedef AutoDiffScalar<Deriv1> AD;
Deriv1 p = Deriv1::Random();
AD val(p.x(),Deriv1::UnitX());
Scalar cosh_px = std::cosh(p.x());
AD res1 = tanh(val);
VERIFY_IS_APPROX(res1.value(), std::tanh(p.x()));
VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(1.0) / (cosh_px * cosh_px));
AD res2 = sinh(val);
VERIFY_IS_APPROX(res2.value(), std::sinh(p.x()));
VERIFY_IS_APPROX(res2.derivatives().x(), cosh_px);
AD res3 = cosh(val);
VERIFY_IS_APPROX(res3.value(), cosh_px);
VERIFY_IS_APPROX(res3.derivatives().x(), std::sinh(p.x()));
// Check constant values.
const Scalar sample_point = Scalar(1) / Scalar(3);
val = AD(sample_point,Deriv1::UnitX());
res1 = tanh(val);
VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(0.896629559604914));
res2 = sinh(val);
VERIFY_IS_APPROX(res2.derivatives().x(), Scalar(1.056071867829939));
res3 = cosh(val);
VERIFY_IS_APPROX(res3.derivatives().x(), Scalar(0.339540557256150));
}
template <typename Scalar>
void check_limits_specialization()
{
typedef Eigen::Matrix<Scalar, 1, 1> Deriv;
typedef Eigen::AutoDiffScalar<Deriv> AD;
typedef std::numeric_limits<AD> A;
typedef std::numeric_limits<Scalar> B;
#if EIGEN_HAS_CXX11
VERIFY(bool(std::is_base_of<B, A>::value));
#endif
}
void test_autodiff_scalar()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( check_atan2<float>() );
CALL_SUBTEST_2( check_atan2<double>() );
CALL_SUBTEST_3( check_hyperbolic_functions<float>() );
CALL_SUBTEST_4( check_hyperbolic_functions<double>() );
CALL_SUBTEST_5( check_limits_specialization<double>());
}
}
|
