source: src/main/java/weka/core/MinkowskiDistance.java @ 5

/*
 *    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.
 */

/*
 *    MinkowskiDistance.java
 *    Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.core;

import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.neighboursearch.PerformanceStats;

import java.util.Enumeration;
import java.util.Vector;

/**
 <!-- globalinfo-start -->
 * Implementing Minkowski distance (or similarity) function.<br/>
 * <br/>
 * One object defines not one distance but the data model in which the distances between objects of that data model can be computed.<br/>
 * <br/>
 * Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.<br/>
 * <br/>
 * For more information, see:<br/>
 * <br/>
 * Wikipedia. Minkowski distance. URL http://en.wikipedia.org/wiki/Minkowski_distance.
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;misc{missing_id,
 *    author = {Wikipedia},
 *    title = {Minkowski distance},
 *    URL = {http://en.wikipedia.org/wiki/Minkowski_distance}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -P &lt;order&gt;
 *  The order 'p'. With '1' being the Manhattan distance and '2'
 *  the Euclidean distance.
 *  (default: 2)</pre>
 * 
 * <pre> -D
 *  Turns off the normalization of attribute 
 *  values in distance calculation.</pre>
 * 
 * <pre> -R &lt;col1,col2-col4,...&gt;
 *  Specifies list of columns to used in the calculation of the 
 *  distance. 'first' and 'last' are valid indices.
 *  (default: first-last)</pre>
 * 
 * <pre> -V
 *  Invert matching sense of column indices.</pre>
 * 
 <!-- options-end --> 
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 5953 $
 */
public class MinkowskiDistance
  extends NormalizableDistance
  implements Cloneable, TechnicalInformationHandler {

  /** for serialization. */
  private static final long serialVersionUID = -7446019339455453893L;
  
  /** the order of the minkowski distance. */
  protected double m_Order = 2;
  
  /**
   * Constructs an Minkowski Distance object, Instances must be still set.
   */
  public MinkowskiDistance() {
    super();
  }

  /**
   * Constructs an Minkowski Distance object and automatically initializes the
   * ranges.
   * 
   * @param data        the instances the distance function should work on
   */
  public MinkowskiDistance(Instances data) {
    super(data);
  }

  /**
   * Returns a string describing this object.
   * 
   * @return            a description of the evaluator suitable for
   *                    displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "Implementing Minkowski distance (or similarity) function.\n\n"
      + "One object defines not one distance but the data model in which "
      + "the distances between objects of that data model can be computed.\n\n"
      + "Attention: For efficiency reasons the use of consistency checks "
      + "(like are the data models of the two instances exactly the same), "
      + "is low.\n\n"
      + "For more information, see:\n\n"
      + getTechnicalInformation().toString();
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return            the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation        result;
    
    result = new TechnicalInformation(Type.MISC);
    result.setValue(Field.AUTHOR, "Wikipedia");
    result.setValue(Field.TITLE, "Minkowski distance");
    result.setValue(Field.URL, "http://en.wikipedia.org/wiki/Minkowski_distance");

    return result;
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return            an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    Vector<Option> result = new Vector<Option>();
    
    result.addElement(new Option(
        "\tThe order 'p'. With '1' being the Manhattan distance and '2'\n"
        + "\tthe Euclidean distance.\n"
        + "\t(default: 2)",
        "P", 1, "-P <order>"));
    
    Enumeration en = super.listOptions();
    while (en.hasMoreElements())
      result.addElement((Option)en.nextElement());
      
    return result.elements();
  }

  /**
   * Returns the tip text for this property.
   *
   * @return            tip text for this property suitable for
   *                    displaying in the explorer/experimenter gui
   */
  public String orderTipText() {
    return 
        "The order of the Minkowski distance ('1' is Manhattan distance and "
      + "'2' the Euclidean distance).";
  }

  /**
   * Sets the order.
   * 
   * @param value       the new order
   */
  public void setOrder(double value) {
    if (m_Order != 0.0) {
      m_Order = value;
      invalidate();
    }
    else {
      System.err.println("Order cannot be zero!");
    }
  }
  
  /**
   * Gets the order.
   * 
   * @return            the order
   */
  public double getOrder() {
    return m_Order;
  }   
  
  /**
   * Calculates the distance between two instances.
   * 
   * @param first       the first instance
   * @param second      the second instance
   * @return            the distance between the two given instances
   */
  public double distance(Instance first, Instance second) {
    return Math.pow(distance(first, second, Double.POSITIVE_INFINITY), 1/m_Order);
  }
  
  /**
   * Calculates the distance (or similarity) between two instances. Need to
   * pass this returned distance later on to postprocess method to set it on
   * correct scale. <br/>
   * P.S.: Please don't mix the use of this function with
   * distance(Instance first, Instance second), as that already does post
   * processing. Please consider passing Double.POSITIVE_INFINITY as the cutOffValue to
   * this function and then later on do the post processing on all the
   * distances.
   *
   * @param first       the first instance
   * @param second      the second instance
   * @param stats       the structure for storing performance statistics.
   * @return            the distance between the two given instances or 
   *                    Double.POSITIVE_INFINITY.
   */
  public double distance(Instance first, Instance second, PerformanceStats stats) { //debug method pls remove after use
    return Math.pow(distance(first, second, Double.POSITIVE_INFINITY, stats), 1/m_Order);
  }
  
  /**
   * 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) {
    double      result;
    
    result  = currDist;
    result += Math.pow(Math.abs(diff), m_Order);
    
    return result;
  }
  
  /**
   * Does post processing of the distances (if necessary) returned by
   * distance(distance(Instance first, Instance second, double cutOffValue). It
   * is necessary to do so to get the correct distances if
   * distance(distance(Instance first, Instance second, double cutOffValue) is
   * used. This is because that function actually returns the squared distance
   * to avoid inaccuracies arising from floating point comparison.
   * 
   * @param distances   the distances to post-process
   */
  public void postProcessDistances(double distances[]) {
    for(int i = 0; i < distances.length; i++) {
      distances[i] = Math.pow(distances[i], 1/m_Order);
    }
  }
  
  /**
   * Returns the revision string.
   * 
   * @return            the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 0$");
  }
}