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starting work on a Numerical differenciation module
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unsupported/Eigen/NumericalDiff
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unsupported/Eigen/NumericalDiff
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// 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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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#ifndef EIGEN_NUMERICALDIFF_MODULE
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#define EIGEN_NUMERICALDIFF_MODULE
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#include <Eigen/Core>
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namespace Eigen {
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/** \ingroup Unsupported_modules
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* \defgroup NumericalDiff_Module Support for numerical differenciation.
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* See http://en.wikipedia.org/wiki/Numerical_differentiation
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*
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* \code
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* #include <unsupported/Eigen/NumericalDiff>
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* \endcode
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*/
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//@{
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}
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#include "src/NumericalDiff/NumericalDiff.h"
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namespace Eigen {
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//@}
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}
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#endif // EIGEN_NUMERICALDIFF_MODULE
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unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
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unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
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// 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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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#ifndef EIGEN_NUMERICAL_DIFF_H
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#define EIGEN_NUMERICAL_DIFF_H
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namespace Eigen
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{
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template<typename Functor> class NumericalDiff : public Functor
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{
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public:
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typedef typename Functor::Scalar Scalar;
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typedef typename Functor::InputType InputType;
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typedef typename Functor::ValueType ValueType;
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typedef typename Functor::JacobianType JacobianType;
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NumericalDiff(Scalar _epsfcn=0.) : Functor(), epsfcn(_epsfcn) {}
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NumericalDiff(const Functor& f, Scalar _epsfcn=0.) : Functor(f), epsfcn(_epsfcn) {}
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// forward constructors
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template<typename T0>
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NumericalDiff(const T0& a0) : Functor(a0), epsfcn(0) {}
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template<typename T0, typename T1>
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NumericalDiff(const T0& a0, const T1& a1) : Functor(a0, a1), epsfcn(0) {}
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template<typename T0, typename T1, typename T2>
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NumericalDiff(const T0& a0, const T1& a1, const T1& a2) : Functor(a0, a1, a2), epsfcn(0) {}
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enum {
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InputsAtCompileTime = Functor::InputsAtCompileTime,
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ValuesAtCompileTime = Functor::ValuesAtCompileTime
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};
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/**
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* return the number of evaluation of functor
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*/
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int df(const InputType& _x, JacobianType &jac) const
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{
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/* Local variables */
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Scalar h;
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int nfev=0;
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const int n = _x.size();
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const Scalar eps = ei_sqrt((std::max(epsfcn,epsilon<Scalar>() )));
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ValueType val, fx;
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InputType x = _x;
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// TODO : we should do this only if the size is not already known
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val.resize(Functor::values());
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fx.resize(Functor::values());
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// compute f(x)
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Functor::operator()(x, fx);
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/* Function Body */
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for (int j = 0; j < n; ++j) {
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h = eps * ei_abs(x[j]);
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if (h == 0.) {
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h = eps;
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}
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x[j] += h;
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Functor::operator()(x, val);
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nfev++;
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x[j] = _x[j];
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jac.col(j) = (val-fx)/h;
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}
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return nfev;
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}
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private:
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Scalar epsfcn;
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};
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} // namespace
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#endif // EIGEN_NUMERICAL_DIFF_H
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@ -16,6 +16,7 @@ else(ADOLC_FOUND)
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endif(ADOLC_FOUND)
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ei_add_test(NonLinear)
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ei_add_test(NumericalDiff)
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ei_add_test(autodiff)
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ei_add_test(BVH)
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#ei_add_test(matrixExponential)
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unsupported/test/NumericalDiff.cpp
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unsupported/test/NumericalDiff.cpp
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// 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 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.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_NumericalDiff()
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{
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CALL_SUBTEST(test_forward());
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}
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