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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
142 lines
3.7 KiB
C++
142 lines
3.7 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) 2008 Gael Guennebaud <g.gael@free.fr>
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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 <Eigen/Dense>
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#define NUMBER_DIRECTIONS 16
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#include <unsupported/Eigen/AdolcForward>
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template<typename Vector>
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EIGEN_DONT_INLINE typename Vector::Scalar foo(const Vector& p)
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{
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typedef typename Vector::Scalar Scalar;
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return (p-Vector(Scalar(-1),Scalar(1.))).norm() + (p.array().sqrt().abs() * p.array().sin()).sum() + p.dot(p);
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}
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template<typename _Scalar, int NX=Dynamic, int NY=Dynamic>
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struct TestFunc1
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{
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typedef _Scalar Scalar;
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enum {
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InputsAtCompileTime = NX,
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ValuesAtCompileTime = NY
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};
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typedef Matrix<Scalar,InputsAtCompileTime,1> InputType;
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typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
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typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
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int m_inputs, m_values;
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TestFunc1() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
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TestFunc1(int inputs, int values) : m_inputs(inputs), m_values(values) {}
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int inputs() const { return m_inputs; }
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int values() const { return m_values; }
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template<typename T>
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void operator() (const Matrix<T,InputsAtCompileTime,1>& x, Matrix<T,ValuesAtCompileTime,1>* _v) const
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{
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Matrix<T,ValuesAtCompileTime,1>& v = *_v;
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v[0] = 2 * x[0] * x[0] + x[0] * x[1];
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v[1] = 3 * x[1] * x[0] + 0.5 * x[1] * x[1];
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if(inputs()>2)
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{
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v[0] += 0.5 * x[2];
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v[1] += x[2];
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}
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if(values()>2)
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{
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v[2] = 3 * x[1] * x[0] * x[0];
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}
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if (inputs()>2 && values()>2)
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v[2] *= x[2];
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}
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void operator() (const InputType& x, ValueType* v, JacobianType* _j) const
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{
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(*this)(x, v);
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if(_j)
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{
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JacobianType& j = *_j;
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j(0,0) = 4 * x[0] + x[1];
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j(1,0) = 3 * x[1];
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j(0,1) = x[0];
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j(1,1) = 3 * x[0] + 2 * 0.5 * x[1];
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if (inputs()>2)
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{
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j(0,2) = 0.5;
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j(1,2) = 1;
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}
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if(values()>2)
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{
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j(2,0) = 3 * x[1] * 2 * x[0];
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j(2,1) = 3 * x[0] * x[0];
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}
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if (inputs()>2 && values()>2)
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{
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j(2,0) *= x[2];
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j(2,1) *= x[2];
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j(2,2) = 3 * x[1] * x[0] * x[0];
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j(2,2) = 3 * x[1] * x[0] * x[0];
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}
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}
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}
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};
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template<typename Func> void adolc_forward_jacobian(const Func& f)
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{
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typename Func::InputType x = Func::InputType::Random(f.inputs());
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typename Func::ValueType y(f.values()), yref(f.values());
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typename Func::JacobianType j(f.values(),f.inputs()), jref(f.values(),f.inputs());
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jref.setZero();
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yref.setZero();
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f(x,&yref,&jref);
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// std::cerr << y.transpose() << "\n\n";;
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// std::cerr << j << "\n\n";;
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j.setZero();
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y.setZero();
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AdolcForwardJacobian<Func> autoj(f);
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autoj(x, &y, &j);
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// std::cerr << y.transpose() << "\n\n";;
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// std::cerr << j << "\n\n";;
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VERIFY_IS_APPROX(y, yref);
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VERIFY_IS_APPROX(j, jref);
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}
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EIGEN_DECLARE_TEST(forward_adolc)
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{
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adtl::setNumDir(NUMBER_DIRECTIONS);
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,2>()) ));
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CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,2,3>()) ));
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CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,2>()) ));
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CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double,3,3>()) ));
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CALL_SUBTEST(( adolc_forward_jacobian(TestFunc1<double>(3,3)) ));
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}
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{
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// simple instantiation tests
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Matrix<adtl::adouble,2,1> x;
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foo(x);
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Matrix<adtl::adouble,Dynamic,Dynamic> A(4,4);;
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A.selfadjointView<Lower>().eigenvalues();
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}
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}
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