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.
   */
  
  /*
   *    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("$Revision: 0$");
    }
  }
  