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