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101 lines
3.1 KiB
C++
101 lines
3.1 KiB
C++
// 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 Gael Guennebaud <g.gael@free.fr>
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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_AUTODIFF_JACOBIAN_H
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#define EIGEN_AUTODIFF_JACOBIAN_H
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namespace Eigen
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{
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template<typename Functor> class AutoDiffJacobian : public Functor
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{
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public:
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AutoDiffJacobian() : Functor() {}
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AutoDiffJacobian(const Functor& f) : Functor(f) {}
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// forward constructors
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template<typename T0>
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AutoDiffJacobian(const T0& a0) : Functor(a0) {}
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template<typename T0, typename T1>
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AutoDiffJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
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template<typename T0, typename T1, typename T2>
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AutoDiffJacobian(const T0& a0, const T1& a1, const T1& a2) : Functor(a0, a1, a2) {}
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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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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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typedef AutoDiffScalar<Matrix<double,InputsAtCompileTime,1> > ActiveScalar;
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typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
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typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;
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void operator() (const InputType& x, ValueType* v, JacobianType* _jac=0) const
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{
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ei_assert(v!=0);
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if (!_jac)
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{
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Functor::operator()(x, v);
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return;
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}
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JacobianType& jac = *_jac;
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ActiveInput ax = x.template cast<ActiveScalar>();
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ActiveValue av(jac.rows());
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if(InputsAtCompileTime==Dynamic)
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{
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for (int j=0; j<jac.cols(); j++)
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ax[j].derivatives().resize(this->inputs());
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for (int j=0; j<jac.rows(); j++)
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av[j].derivatives().resize(this->inputs());
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}
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for (int j=0; j<jac.cols(); j++)
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for (int i=0; i<jac.cols(); i++)
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ax[i].derivatives().coeffRef(j) = i==j ? 1 : 0;
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Functor::operator()(ax, &av);
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for (int i=0; i<jac.rows(); i++)
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{
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(*v)[i] = av[i].value();
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for (int j=0; j<jac.cols(); j++)
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jac.coeffRef(i,j) = av[i].derivatives().coeff(j);
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}
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}
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protected:
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};
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}
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#endif // EIGEN_AUTODIFF_JACOBIAN_H
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