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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Thomas Capricelli <orzel@freehackers.org>
#include <stdio.h>
#include "main.h"
#include <unsupported/StormEigen/NumericalDiff>
// Generic functor
template<typename _Scalar, int NX=Dynamic, int NY=Dynamic> struct Functor { typedef _Scalar Scalar; enum { InputsAtCompileTime = NX, ValuesAtCompileTime = NY }; typedef Matrix<Scalar,InputsAtCompileTime,1> InputType; typedef Matrix<Scalar,ValuesAtCompileTime,1> ValueType; typedef Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType; int m_inputs, m_values; Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {} Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {} int inputs() const { return m_inputs; } int values() const { return m_values; }
};
struct my_functor : Functor<double> { my_functor(void): Functor<double>(3,15) {} int operator()(const VectorXd &x, VectorXd &fvec) const { double tmp1, tmp2, tmp3; double y[15] = {1.4e-1, 1.8e-1, 2.2e-1, 2.5e-1, 2.9e-1, 3.2e-1, 3.5e-1, 3.9e-1, 3.7e-1, 5.8e-1, 7.3e-1, 9.6e-1, 1.34, 2.1, 4.39};
for (int i = 0; i < values(); i++) { tmp1 = i+1; tmp2 = 16 - i - 1; tmp3 = (i>=8)? tmp2 : tmp1; fvec[i] = y[i] - (x[0] + tmp1/(x[1]*tmp2 + x[2]*tmp3)); } return 0; }
int actual_df(const VectorXd &x, MatrixXd &fjac) const { double tmp1, tmp2, tmp3, tmp4; for (int i = 0; i < values(); i++) { tmp1 = i+1; tmp2 = 16 - i - 1; tmp3 = (i>=8)? tmp2 : tmp1; tmp4 = (x[1]*tmp2 + x[2]*tmp3); tmp4 = tmp4*tmp4; fjac(i,0) = -1; fjac(i,1) = tmp1*tmp2/tmp4; fjac(i,2) = tmp1*tmp3/tmp4; } return 0; } };
void test_forward() { VectorXd x(3); MatrixXd jac(15,3); MatrixXd actual_jac(15,3); my_functor functor;
x << 0.082, 1.13, 2.35;
// real one
functor.actual_df(x, actual_jac); // std::cout << actual_jac << std::endl << std::endl;
// using NumericalDiff
NumericalDiff<my_functor> numDiff(functor); numDiff.df(x, jac); // std::cout << jac << std::endl;
VERIFY_IS_APPROX(jac, actual_jac); }
void test_central() { VectorXd x(3); MatrixXd jac(15,3); MatrixXd actual_jac(15,3); my_functor functor;
x << 0.082, 1.13, 2.35;
// real one
functor.actual_df(x, actual_jac);
// using NumericalDiff
NumericalDiff<my_functor,Central> numDiff(functor); numDiff.df(x, jac);
VERIFY_IS_APPROX(jac, actual_jac); }
void test_NumericalDiff() { CALL_SUBTEST(test_forward()); CALL_SUBTEST(test_central()); }
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