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7b0c4102fa
construction of generic expressions working for both dense and sparse matrix. A nicer solution would be to use CwiseBinaryOp for any kind of matrix. To this end we either need to change the overall design so that the base class(es) depends on the kind of matrix, or we could add a template parameter to each expression type (e.g., int Kind = ei_traits<MatrixType>::Kind) allowing to specialize each expression for each kind of matrix. * Extend AutoDiffScalar to work with sparse vector expression for the derivatives.
98 lines
3.0 KiB
C++
98 lines
3.0 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 typename JacobianType::Scalar Scalar;
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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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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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for (int j=0; j<jac.rows(); j++)
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av[j].derivatives().resize(this->inputs());
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for (int 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 (int 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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