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101 lines
2.9 KiB
101 lines
2.9 KiB
// This file is part of Eigen, a lightweight C++ template library |
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// for linear algebra. |
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// |
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// Copyright (C) 2013 Christoph Hertzberg <chtz@informatik.uni-bremen.de> |
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// |
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// This Source Code Form is subject to the terms of the Mozilla |
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// Public License v. 2.0. If a copy of the MPL was not distributed |
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// with this file, You can obtain one at the mozilla.org home page |
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#include "main.h" |
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#include <unsupported/Eigen/AutoDiff> |
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/* |
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* In this file scalar derivations are tested for correctness. |
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* TODO add more tests! |
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*/ |
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template<typename Scalar> void check_atan2() |
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{ |
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typedef Matrix<Scalar, 1, 1> Deriv1; |
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typedef AutoDiffScalar<Deriv1> AD; |
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AD x(internal::random<Scalar>(-3.0, 3.0), Deriv1::UnitX()); |
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using std::exp; |
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Scalar r = exp(internal::random<Scalar>(-10, 10)); |
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AD s = sin(x), c = cos(x); |
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AD res = atan2(r*s, r*c); |
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VERIFY_IS_APPROX(res.value(), x.value()); |
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VERIFY_IS_APPROX(res.derivatives(), x.derivatives()); |
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res = atan2(r*s+0, r*c+0); |
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VERIFY_IS_APPROX(res.value(), x.value()); |
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VERIFY_IS_APPROX(res.derivatives(), x.derivatives()); |
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} |
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template<typename Scalar> void check_hyperbolic_functions() |
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{ |
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using std::sinh; |
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using std::cosh; |
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using std::tanh; |
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typedef Matrix<Scalar, 1, 1> Deriv1; |
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typedef AutoDiffScalar<Deriv1> AD; |
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Deriv1 p = Deriv1::Random(); |
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AD val(p.x(),Deriv1::UnitX()); |
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Scalar cosh_px = std::cosh(p.x()); |
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AD res1 = tanh(val); |
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VERIFY_IS_APPROX(res1.value(), std::tanh(p.x())); |
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VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(1.0) / (cosh_px * cosh_px)); |
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AD res2 = sinh(val); |
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VERIFY_IS_APPROX(res2.value(), std::sinh(p.x())); |
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VERIFY_IS_APPROX(res2.derivatives().x(), cosh_px); |
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AD res3 = cosh(val); |
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VERIFY_IS_APPROX(res3.value(), cosh_px); |
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VERIFY_IS_APPROX(res3.derivatives().x(), std::sinh(p.x())); |
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// Check constant values. |
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const Scalar sample_point = Scalar(1) / Scalar(3); |
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val = AD(sample_point,Deriv1::UnitX()); |
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res1 = tanh(val); |
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VERIFY_IS_APPROX(res1.derivatives().x(), Scalar(0.896629559604914)); |
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res2 = sinh(val); |
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VERIFY_IS_APPROX(res2.derivatives().x(), Scalar(1.056071867829939)); |
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res3 = cosh(val); |
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VERIFY_IS_APPROX(res3.derivatives().x(), Scalar(0.339540557256150)); |
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} |
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template <typename Scalar> |
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void check_limits_specialization() |
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{ |
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typedef Eigen::Matrix<Scalar, 1, 1> Deriv; |
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typedef Eigen::AutoDiffScalar<Deriv> AD; |
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typedef std::numeric_limits<AD> A; |
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typedef std::numeric_limits<Scalar> B; |
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// workaround "unused typedef" warning: |
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VERIFY(!bool(internal::is_same<B, A>::value)); |
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#if EIGEN_HAS_CXX11 |
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VERIFY(bool(std::is_base_of<B, A>::value)); |
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#endif |
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} |
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EIGEN_DECLARE_TEST(autodiff_scalar) |
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{ |
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for(int i = 0; i < g_repeat; i++) { |
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CALL_SUBTEST_1( check_atan2<float>() ); |
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CALL_SUBTEST_2( check_atan2<double>() ); |
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CALL_SUBTEST_3( check_hyperbolic_functions<float>() ); |
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CALL_SUBTEST_4( check_hyperbolic_functions<double>() ); |
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CALL_SUBTEST_5( check_limits_specialization<double>()); |
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} |
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}
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