/**
 * @file reinforce_normal.hpp
 * @author Marcus Edel
 *
 * Definition of the ReinforceNormalLayer class, which implements the REINFORCE
 * algorithm for the normal distribution.
 *
 * mlpack is free software; you may redistribute it and/or modify it under the
 * terms of the 3-clause BSD license.  You should have received a copy of the
 * 3-clause BSD license along with mlpack.  If not, see
 * http://www.opensource.org/licenses/BSD-3-Clause for more information.
 */
#ifndef MLPACK_METHODS_ANN_LAYER_REINFORCE_NORMAL_HPP
#define MLPACK_METHODS_ANN_LAYER_REINFORCE_NORMAL_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace ann /** Artificial Neural Network. */ {

/**
 * Implementation of the reinforce normal layer. The reinforce normal layer
 * implements the REINFORCE algorithm for the normal distribution.
 *
 * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
 *         arma::sp_mat or arma::cube).
 * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
 *         arma::sp_mat or arma::cube).
 */
template <
    typename InputDataType = arma::mat,
    typename OutputDataType = arma::mat
>
class ReinforceNormal
{
 public:
  /**
   * Create the ReinforceNormal object.
   *
   * @param stdev Standard deviation used during the forward and backward pass.
   */
  ReinforceNormal(const double stdev = 1.0);

  /**
   * Ordinary feed forward pass of a neural network, evaluating the function
   * f(x) by propagating the activity forward through f.
   *
   * @param input Input data used for evaluating the specified function.
   * @param output Resulting output activation.
   */
  template<typename eT>
  void Forward(const arma::Mat<eT>& input, arma::Mat<eT>& output);

  /**
   * Ordinary feed backward pass of a neural network, calculating the function
   * f(x) by propagating x backwards through f. Using the results from the feed
   * forward pass.
   *
   * @param input The propagated input activation.
   * @param gy The backpropagated error.
   * @param g The calculated gradient.
   */
  template<typename DataType>
  void Backward(const DataType& input, const DataType& /* gy */, DataType& g);

  //! Get the output parameter.
  OutputDataType& OutputParameter() const { return outputParameter; }
  //! Modify the output parameter.
  OutputDataType& OutputParameter() { return outputParameter; }

  //! Get the delta.
  OutputDataType& Delta() const { return delta; }
  //! Modify the delta.
  OutputDataType& Delta() { return delta; }

  //! Get the value of the deterministic parameter.
  bool Deterministic() const { return deterministic; }
  //! Modify the value of the deterministic parameter.
  bool& Deterministic() { return deterministic; }

  //! Get the value of the reward parameter.
  double Reward() const { return reward; }
  //! Modify the value of the deterministic parameter.
  double& Reward() { return reward; }

  /**
   * Serialize the layer
   */
  template<typename Archive>
  void serialize(Archive& ar, const unsigned int /* version */);

 private:
  //! Standard deviation used during the forward and backward pass.
  double stdev;

  //! Locally-stored reward parameter.
  double reward;

  //! Locally-stored delta object.
  OutputDataType delta;

  //! Locally-stored output parameter object.
  OutputDataType outputParameter;

  //!  Locally-stored output module parameter parameters.
  std::vector<arma::mat> moduleInputParameter;

  //! If true use maximum a posteriori during the forward pass.
  bool deterministic;
}; // class ReinforceNormal

} // namespace ann
} // namespace mlpack

// Include implementation.
#include "reinforce_normal_impl.hpp"

#endif