content: /*
   *    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 2 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, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   *    LinearUnit.java
   *    Copyright (C) 2001 University of Waikato, Hamilton, New Zealand
   */
  
  package weka.classifiers.functions.neural;
  
  import weka.core.RevisionHandler;
  import weka.core.RevisionUtils;
  
  /**
   * This can be used by the 
   * neuralnode to perform all it's computations (as a Linear unit).
   *
   * @author Malcolm Ware (mfw4@cs.waikato.ac.nz)
   * @version $Revision: 5928 $
   */
  public class LinearUnit
    implements NeuralMethod, RevisionHandler {
  
    /** for serialization */
    private static final long serialVersionUID = 8572152807755673630L;
    
    /**
     * This function calculates what the output value should be.
     * @param node The node to calculate the value for.
     * @return The value.
     */
    public double outputValue(NeuralNode node) {
      double[] weights = node.getWeights();
      NeuralConnection[] inputs = node.getInputs();
      double value = weights[0];
      for (int noa = 0; noa < node.getNumInputs(); noa++) {
        
        value += inputs[noa].outputValue(true) 
  	* weights[noa+1];
      }
       
      return value;
    }
    
    /**
     * This function calculates what the error value should be.
     * @param node The node to calculate the error for.
     * @return The error.
     */
    public double errorValue(NeuralNode node) {
      //then calculate the error.
      
      NeuralConnection[] outputs = node.getOutputs();
      int[] oNums = node.getOutputNums();
      double error = 0;
   
      for (int noa = 0; noa < node.getNumOutputs(); noa++) {
        error += outputs[noa].errorValue(true) 
  	* outputs[noa].weightValue(oNums[noa]);
      }
      return error;
    }
  
    /**
     * 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.
     */
    public void updateWeights(NeuralNode node, double learn, double momentum) {
  
      NeuralConnection[] inputs = node.getInputs();
      double[] cWeights = node.getChangeInWeights();
      double[] weights = node.getWeights();
      
      double learnTimesError = 0;
      learnTimesError = learn * node.errorValue(false);
      
      double c = learnTimesError + momentum * cWeights[0];
      weights[0] += c;
      cWeights[0] = c;
        
      int stopValue = node.getNumInputs() + 1;
      for (int noa = 1; noa < stopValue; noa++) {
        
        c = learnTimesError * inputs[noa-1].outputValue(false);
        c += momentum * cWeights[noa];
        
        weights[noa] += c;
        cWeights[noa] = c; 
      }
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5928 $");
    }
  }
  