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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

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/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

/*
 *    NeuralMethod.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 */


package weka.classifiers.functions.neural;

import java.io.Serializable;

/**
 * This is an interface used to create classes that can be used by the 
 * neuralnode to perform all it's computations.
 *
 * @author Malcolm Ware ([email protected])
 * @version $Revision: 8034 $
 */
public interface NeuralMethod extends Serializable {
  
  /**
   * This function calculates what the output value should be.
   * @param node The node to calculate the value for.
   * @return The value.
   */
  double outputValue(NeuralNode node);

  /**
   * This function calculates what the error value should be.
   * @param node The node to calculate the error for.
   * @return The error.
   */
  double errorValue(NeuralNode node);

  /**
   * This function will calculate what the change in weights should be
   * and also update them.
   * @param node The node to update the weights for.
   * @param learn The learning rate to use.
   * @param momentum The momentum to use.
   */
  void updateWeights(NeuralNode node, double learn, double momentum);

}