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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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EIGEN_DECLARE_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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