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83 lines
2.3 KiB
83 lines
2.3 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 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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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 T2& 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 typename JacobianType::Scalar Scalar;
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typedef typename JacobianType::Index Index;
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typedef Matrix<Scalar,InputsAtCompileTime,1> DerivativeType;
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typedef AutoDiffScalar<DerivativeType> 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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eigen_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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for (Index j=0; j<jac.rows(); j++)
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av[j].derivatives().resize(this->inputs());
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for (Index i=0; i<jac.cols(); i++)
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ax[i].derivatives() = DerivativeType::Unit(this->inputs(),i);
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Functor::operator()(ax, &av);
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for (Index i=0; i<jac.rows(); i++)
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{
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(*v)[i] = av[i].value();
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jac.row(i) = av[i].derivatives();
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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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