|  | // 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::cosh; | 
|  | using std::sinh; | 
|  | 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; | 
|  |  | 
|  | // workaround "unused typedef" warning: | 
|  | VERIFY(!bool(internal::is_same<B, A>::value)); | 
|  |  | 
|  | VERIFY(bool(std::is_base_of<B, A>::value)); | 
|  | } | 
|  |  | 
|  | EIGEN_DECLARE_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>()); | 
|  | } | 
|  | } |