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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/Eigen/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);
}
EIGEN_DECLARE_TEST(NumericalDiff)
{
CALL_SUBTEST(test_forward());
CALL_SUBTEST(test_central());
}
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