unsupported/Eigen/src/AutoDiff/AutoDiffJacobian.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_AUTODIFF_JACOBIAN_H
#define EIGEN_AUTODIFF_JACOBIAN_H

// IWYU pragma: private
#include "./InternalHeaderCheck.h"

namespace Eigen {

template <typename Functor>
class AutoDiffJacobian : public Functor {
 public:
  AutoDiffJacobian() : Functor() {}
  AutoDiffJacobian(const Functor& f) : Functor(f) {}

  // forward constructors
  template <typename... T>
  AutoDiffJacobian(const T&... Values) : Functor(Values...) {}

  typedef typename Functor::InputType InputType;
  typedef typename Functor::ValueType ValueType;
  typedef typename ValueType::Scalar Scalar;

  enum { InputsAtCompileTime = InputType::RowsAtCompileTime, ValuesAtCompileTime = ValueType::RowsAtCompileTime };

  typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
  typedef typename JacobianType::Index Index;

  typedef Matrix<Scalar, InputsAtCompileTime, 1> DerivativeType;
  typedef AutoDiffScalar<DerivativeType> ActiveScalar;

  typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
  typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;

  // Some compilers don't accept variadic parameters after a default parameter,
  // i.e., we can't just write _jac=0 but we need to overload operator():
  EIGEN_STRONG_INLINE void operator()(const InputType& x, ValueType* v) const { this->operator()(x, v, 0); }
  template <typename... ParamsType>
  void operator()(const InputType& x, ValueType* v, JacobianType* _jac, const ParamsType&... Params) const {
    eigen_assert(v != 0);

    if (!_jac) {
      Functor::operator()(x, v, Params...);
      return;
    }

    JacobianType& jac = *_jac;

    ActiveInput ax = x.template cast<ActiveScalar>();
    ActiveValue av(jac.rows());

    if (InputsAtCompileTime == Dynamic)
      for (Index j = 0; j < jac.rows(); j++) av[j].derivatives().resize(x.rows());

    for (Index i = 0; i < jac.cols(); i++) ax[i].derivatives() = DerivativeType::Unit(x.rows(), i);

    Functor::operator()(ax, &av, Params...);

    for (Index i = 0; i < jac.rows(); i++) {
      (*v)[i] = av[i].value();
      jac.row(i) = av[i].derivatives();
    }
  }
};

}  // namespace Eigen

#endif  // EIGEN_AUTODIFF_JACOBIAN_H