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82f0ce2726
This provide several advantages: - more flexibility in designing unit tests - unit tests can be glued to speed up compilation - unit tests are compiled with same predefined macros, which is a requirement for zapcc
102 lines
2.9 KiB
C++
102 lines
2.9 KiB
C++
// 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 http://mozilla.org/MPL/2.0/.
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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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