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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.
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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 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.
 */

/*
 *    AbstractStringDistanceFunction.java
 *    Copyright (C) 2008 Bruno Woltzenlogel Paleo (http://www.logic.at/people/bruno/ ; http://bruno-wp.blogspot.com/)
 *
 */

package weka.core;

import weka.core.neighboursearch.PerformanceStats;

/**
 * Represents the abstract ancestor for string-based distance functions, like
 * EditDistance.
 *
 * @author Bruno Woltzenlogel Paleo
 * @version $Revision: 1.1 $
 */
public abstract class AbstractStringDistanceFunction
    extends NormalizableDistance {
  
  /**
   * Constructor that doesn't set the data
   */
  public AbstractStringDistanceFunction() {
    super();
  }

  /**
   * Constructor that sets the data
   *
   * @param data the set of instances that will be used for
   * later distance comparisons
   */
  public AbstractStringDistanceFunction(Instances data) {
    super(data);
  }

    
  /**
   * Updates the current distance calculated so far with the new difference
   * between two attributes. The difference between the attributes was 
   * calculated with the difference(int,double,double) method.
   * 
   * @param currDist	the current distance calculated so far
   * @param diff	the difference between two new attributes
   * @return		the update distance
   * @see		#difference(int, double, double)
   */
  protected double updateDistance(double currDist, double diff) {
    return (currDist + (diff * diff));
  }

  /**
   * Computes the difference between two given attribute
   * values.
   * 
   * @param index	the attribute index
   * @param val1	the first value
   * @param val2	the second value
   * @return		the difference
   */
  protected double difference(int index, String string1, String string2) {
    switch (m_Data.attribute(index).type()) {
    case Attribute.STRING:
      double diff = stringDistance(string1, string2);
      if (m_DontNormalize == true) {
        return diff;
      }
      else {
        if (string1.length() > string2.length()) {
          return diff/((double) string1.length());  
        }
        else {
          return diff/((double) string2.length());    
        }
      }

    default:
      return 0;
    }
  }
  
  /**
   * Calculates the distance between two instances. Offers speed up (if the 
   * distance function class in use supports it) in nearest neighbour search by 
   * taking into account the cutOff or maximum distance. Depending on the 
   * distance function class, post processing of the distances by 
   * postProcessDistances(double []) may be required if this function is used.
   *
   * @param first 	the first instance
   * @param second 	the second instance
   * @param cutOffValue If the distance being calculated becomes larger than 
   *                    cutOffValue then the rest of the calculation is 
   *                    discarded.
   * @param stats 	the performance stats object
   * @return 		the distance between the two given instances or 
   * 			Double.POSITIVE_INFINITY if the distance being 
   * 			calculated becomes larger than cutOffValue. 
   */
  @Override
    public double distance(Instance first, Instance second, double cutOffValue, PerformanceStats stats) {
    double sqDistance = 0;
    int numAttributes = m_Data.numAttributes();
    
    validate();
    
    double diff;
    
    for (int i = 0; i < numAttributes; i++) {
      diff = 0;
      if (m_ActiveIndices[i]) {
        diff = difference(i, first.stringValue(i), second.stringValue(i));
      }
      sqDistance = updateDistance(sqDistance, diff);
      if (sqDistance > (cutOffValue * cutOffValue)) return Double.POSITIVE_INFINITY;
    }  
    double distance = Math.sqrt(sqDistance);
    return distance;
  }
  
  /**
   * Calculates the distance between two strings.
   * Must be implemented by any non-abstract StringDistance class
   *
   * @param stringA the first string
   * @param stringB the second string
   * @return the distance between the two given strings
   */
  abstract double stringDistance(String stringA, String stringB);

}