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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 .
 */

/*
 *    NormalizableDistance.java
 *    Copyright (C) 2007-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.core;

import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.neighboursearch.PerformanceStats;

/**
 * Represents the abstract ancestor for normalizable distance functions, like
 * Euclidean or Manhattan distance.
 * 
 * @author Fracpete (fracpete at waikato dot ac dot nz)
 * @author Gabi Schmidberger ([email protected]) -- original code from
 *         weka.core.EuclideanDistance
 * @author Ashraf M. Kibriya ([email protected]) -- original code from
 *         weka.core.EuclideanDistance
 * @version $Revision: 10535 $
 */
public abstract class NormalizableDistance implements DistanceFunction,
  OptionHandler, Serializable, RevisionHandler {

  /** Serial version id to avoid warning */
  private static final long serialVersionUID = -2806520224161351708L;

  /** Index in ranges for MIN. */
  public static final int R_MIN = 0;

  /** Index in ranges for MAX. */

  public static final int R_MAX = 1;

  /** Index in ranges for WIDTH. */
  public static final int R_WIDTH = 2;

  /** the instances used internally. */
  protected Instances m_Data = null;

  /** True if normalization is turned off (default false). */
  protected boolean m_DontNormalize = false;

  /** The range of the attributes. */
  protected double[][] m_Ranges;

  /** The range of attributes to use for calculating the distance. */
  protected Range m_AttributeIndices = new Range("first-last");

  /** The boolean flags, whether an attribute will be used or not. */
  protected boolean[] m_ActiveIndices;

  /** Whether all the necessary preparations have been done. */
  protected boolean m_Validated;

  /**
   * Invalidates the distance function, Instances must be still set.
   */
  public NormalizableDistance() {
    invalidate();
  }

  /**
   * Initializes the distance function and automatically initializes the ranges.
   * 
   * @param data the instances the distance function should work on
   */
  public NormalizableDistance(Instances data) {
    setInstances(data);
  }

  /**
   * Returns a string describing this object.
   * 
   * @return a description of the evaluator suitable for displaying in the
   *         explorer/experimenter gui
   */
  public abstract String globalInfo();

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  @Override
  public Enumeration




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