autodiff_scalar.cpp
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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 
18 template<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 
39 template<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 
75 template <typename Scalar>
77 {
78  typedef Eigen::Matrix<Scalar, 1, 1> Deriv;
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 
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 }
A scalar type replacement with automatic differentation capability.
EIGEN_DEVICE_FUNC const ExpReturnType exp() const
void check_hyperbolic_functions()
XmlRpcServer s
EIGEN_DEVICE_FUNC const TanhReturnType tanh() const
EIGEN_DEVICE_FUNC const CoshReturnType cosh() const
EIGEN_DEVICE_FUNC const CosReturnType cos() const
EIGEN_DEVICE_FUNC const SinhReturnType sinh() const
void check_limits_specialization()
void check_atan2()
void test_autodiff_scalar()
const AutoDiffScalar< Matrix< typename internal::traits< typename internal::remove_all< DerTypeA >::type >::Scalar, Dynamic, 1 > > atan2(const AutoDiffScalar< DerTypeA > &a, const AutoDiffScalar< DerTypeB > &b)
EIGEN_DEVICE_FUNC const SinReturnType sin() const
The matrix class, also used for vectors and row-vectors.
Definition: Matrix.h:178


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Author(s): Xavier Artache , Matthew Tesch
autogenerated on Thu Sep 3 2020 04:08:00