mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
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
115 lines
2.8 KiB
C++
115 lines
2.8 KiB
C++
// 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());
|
|
}
|