/****************************************************************************************/
/*                                                                                      */
/*  Project     :  1D-BRNN                                                              */
/*  Release     :  3.3                                                                  */
/*                                                                                      */
/*  File        :  Layer.h                                                              */
/*  Description :  Single layer of a neural network                                     */
/*                                                                                      */
/*  Author(s)   :  Christophe Magnan  (2013) - New generic version                      */
/*              :  Jianlin Cheng      (2003) - New custom version for SCRATCH           */
/*              :  Gianluca Pollastri (2001) - Customized version for SCRATCH           */
/*              :  Paolo Frasconi     (1997) - Initial generic version                  */
/*                                                                                      */
/*  Copyright   :  Institute for Genomics and Bioinformatics                            */
/*                 University of California, Irvine                                     */
/*                                                                                      */
/*  Modified    :  2015/07/01                                                           */
/*                                                                                      */
/****************************************************************************************/

#include "Import.h"

class Layer
{

  public:

  // Layer parameters

  int Num_Nodes;             // Number of nodes in the layer
  int Num_Inputs;            // Number of features in input of the layer
  int IsOutputLay;           // Flag for the output layer of the BRNN

  // Layer data

  float* Bias;               // Bias value associated with each node in the layer
  float** Weights;           // Matrix of weights associated with each node in the layer

  // Propagation data

  float* Direct_Outputs;     // Outputs of the layer after the last propagation
  float* SigApp_Outputs;     // Sigmoid approximation of the direct outputs

  // Back-Propagation data

  float* Gradient;           // Gradient contributions computed during the last back-propagation
  float* Back_Prop;          // Next values to back-propagate in lower layers
  float* dBias;              // Gradients of the Bias
  float** dWeights;          // Gradients of the Weights

  // Interface

  Layer();
  ~Layer();
  void write(ostream& os);
  void load(istream& is);
  void initialize(int nodes,int inputs);
  void alloc_backpropagation();
  void reset_gradient();
  void propagate(float* inputs);
  void back_propagate(float* target);
  void update_gradient(float* inputs);
  void update_weights(float epsilon);
  void check_viability();

};