1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. 3 // 4 // Copyright (C) 2009 Gael Guennebaud <gael.guennebaud (at) inria.fr> 5 // 6 // This Source Code Form is subject to the terms of the Mozilla 7 // Public License v. 2.0. If a copy of the MPL was not distributed 8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9 10 #ifndef EIGEN_AUTODIFF_JACOBIAN_H 11 #define EIGEN_AUTODIFF_JACOBIAN_H 12 13 namespace Eigen 14 { 15 16 template<typename Functor> class AutoDiffJacobian : public Functor 17 { 18 public: 19 AutoDiffJacobian() : Functor() {} 20 AutoDiffJacobian(const Functor& f) : Functor(f) {} 21 22 // forward constructors 23 template<typename T0> 24 AutoDiffJacobian(const T0& a0) : Functor(a0) {} 25 template<typename T0, typename T1> 26 AutoDiffJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {} 27 template<typename T0, typename T1, typename T2> 28 AutoDiffJacobian(const T0& a0, const T1& a1, const T2& a2) : Functor(a0, a1, a2) {} 29 30 enum { 31 InputsAtCompileTime = Functor::InputsAtCompileTime, 32 ValuesAtCompileTime = Functor::ValuesAtCompileTime 33 }; 34 35 typedef typename Functor::InputType InputType; 36 typedef typename Functor::ValueType ValueType; 37 typedef typename Functor::JacobianType JacobianType; 38 typedef typename JacobianType::Scalar Scalar; 39 typedef typename JacobianType::Index Index; 40 41 typedef Matrix<Scalar,InputsAtCompileTime,1> DerivativeType; 42 typedef AutoDiffScalar<DerivativeType> ActiveScalar; 43 44 45 typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput; 46 typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue; 47 48 void operator() (const InputType& x, ValueType* v, JacobianType* _jac=0) const 49 { 50 eigen_assert(v!=0); 51 if (!_jac) 52 { 53 Functor::operator()(x, v); 54 return; 55 } 56 57 JacobianType& jac = *_jac; 58 59 ActiveInput ax = x.template cast<ActiveScalar>(); 60 ActiveValue av(jac.rows()); 61 62 if(InputsAtCompileTime==Dynamic) 63 for (Index j=0; j<jac.rows(); j++) 64 av[j].derivatives().resize(this->inputs()); 65 66 for (Index i=0; i<jac.cols(); i++) 67 ax[i].derivatives() = DerivativeType::Unit(this->inputs(),i); 68 69 Functor::operator()(ax, &av); 70 71 for (Index i=0; i<jac.rows(); i++) 72 { 73 (*v)[i] = av[i].value(); 74 jac.row(i) = av[i].derivatives(); 75 } 76 } 77 protected: 78 79 }; 80 81 } 82 83 #endif // EIGEN_AUTODIFF_JACOBIAN_H 84
