//BackpropagationNode.h
//
//This file declares a BackpropagationNode class for use in training feedforward
//artificial neural networks using backpropagation of errors
//
//The BackpropagationNode class extends the FeedforwardNode abstract base class.
//
//
//Copyright 1998 Michael J. Wax
//You may use this code as is, or incorporate it in another program,
//as long as proper attribution is given.  However, I make no warranties,
//express or implied, regarding the performance of this code, its freedom from
//error, or its suitability for use in a given application.
//Questions or suggestions?  Contact me at neural@michaelwax.com
//

#ifndef BNODE_H
#define BNODE_H

#include "FeedforwardNode.cpp"

//class declarations

class BackpropagationNode : public FeedforwardNode {
public:
  BackpropagationNode ();

  //------------errror calculation functions--------------------
  //These functions calculate and return the error in the output value of the node.
  //Because they also alter the stored node error value, you can ignore the return value.

  //For all except output layer nodes:  calculate the error based on backpropagation of error from the output nodes.
  double calculateError ();

  //For output layer nodes:  calculate the error based on the difference between the calculated and actual values.
  double calculateOutputNodeError (double actualValue);

  //------------weight adjustment functions-------------------------
  //These adjust the input weights to minimize the error in the output, and save the change in weights.
  //Eta limits the amount of change in the weights during a given iteration, and has a default value of 0.5.
  //Alpha, the momentum parameter, determines how much of the last weight change
  // is added to the current calculated weight change, and has a default value of 0.5.

  //For all except first hidden layer nodes.
  //For output nodes, an eta of 0.3 might be better than the default of 0.5.
  void adjustWeight (double eta, double alpha);

  //For first hidden layer nodes when we have multiple input patterns not stored as input nodes.
  //If many hidden layers, etas higher than 0.5 might be better.
  void adjustFirstHiddenLayerNodeWeight (double *inputVector, double eta, double alpha);

};

#endif