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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 .
 */

/*
 * CoverTree.java
 * Copyright (C) 2006 Alina Beygelzimer and Sham Kakade and John Langford
 */

package weka.core.neighboursearch;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.Serializable;
import java.util.Collections;
import java.util.Enumeration;
import java.util.List;
import java.util.Vector;

import weka.core.DistanceFunction;
import weka.core.EuclideanDistance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.converters.CSVLoader;
import weka.core.neighboursearch.covertrees.Stack;

/**
 *  Class implementing the CoverTree datastructure.

 * The class is very much a translation of the c source code made available by
 * the authors.

 * 

 * For more information and original source code see:

 * 

 * Alina Beygelzimer, Sham Kakade, John Langford: Cover trees for nearest
 * neighbor. In: ICML'06: Proceedings of the 23rd international conference on
 * Machine learning, New York, NY, USA, 97-104, 2006.
 * 
 * 
 * 
 *  BibTeX:
 * 
 * 
 * @inproceedings{Beygelzimer2006,
 *    address = {New York, NY, USA},
 *    author = {Alina Beygelzimer and Sham Kakade and John Langford},
 *    booktitle = {ICML'06: Proceedings of the 23rd international conference on Machine learning},
 *    pages = {97-104},
 *    publisher = {ACM Press},
 *    title = {Cover trees for nearest neighbor},
 *    year = {2006},
 *    location = {Pittsburgh, Pennsylvania},
 *    HTTP = {http://hunch.net/\~jl/projects/cover_tree/cover_tree.html}
 * }
 * 
 * 
 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -B <value>
 *  Set base of the expansion constant
 *  (default = 1.3).
 * 
 * 
 * 
 * 
 * @author Alina Beygelzimer (original C++ code)
 * @author Sham Kakade (original C++ code)
 * @author John Langford (original C++ code)
 * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
 *         (Java port)
 * @version $Revision: 10203 $
 */
public class CoverTree extends NearestNeighbourSearch implements
  TechnicalInformationHandler {

  /** for serialization. */
  private static final long serialVersionUID = 7617412821497807586L;

  /**
   * class representing a node of the cover tree.
   * 
   * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
   * @version $Revision: 10203 $
   */
  public class CoverTreeNode implements Serializable, RevisionHandler {

    /** for serialization. */
    private static final long serialVersionUID = 1808760031169036512L;

    /** Index of the instance represented by this node in the index array. */
    private Integer idx;

    /** The distance of the furthest descendant of the node. */
    private double max_dist; // The maximum distance to any grandchild.

    /** The distance to the nodes parent. */
    private double parent_dist; // The distance to the parent.

    /** The children of the node. */
    private Stack children;

    /** The number of children node has. */
    private int num_children; // The number of children.

    /** The min i that makes base^i <= max_dist. */
    private int scale; // Essentially, an upper bound on the distance to any
                       // child.

    /** Constructor for the class. */
    public CoverTreeNode() {
    }

    /**
     * Constructor.
     * 
     * @param i The index of the Instance this node is associated with.
     * @param md The distance of the furthest descendant.
     * @param pd The distance of the node to its parent.
     * @param childs Children of the node in a stack.
     * @param numchilds The number of children of the node.
     * @param s The scale/level of the node in the tree.
     */
    public CoverTreeNode(Integer i, double md, double pd,
      Stack childs, int numchilds, int s) {
      idx = i;
      max_dist = md;
      parent_dist = pd;
      children = childs;
      num_children = numchilds;
      scale = s;
    }

    /**
     * Returns the instance represented by the node.
     * 
     * @return The instance represented by the node.
     */
    public Instance p() {
      return m_Instances.instance(idx);
    }

    /**
     * Returns whether if the node is a leaf or not.
     * 
     * @return true if the node is a leaf node.
     */
    public boolean isALeaf() {
      return num_children == 0;
    }

    /**
     * Returns the revision string.
     * 
     * @return the revision
     */
    @Override
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 10203 $");
    }
  }

  /**
   * Private class holding a point's distance to the current reference point p.
   * 
   * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
   * @version $Revision: 10203 $
   */
  private class DistanceNode implements RevisionHandler {

    /**
     * The last distance is to the current reference point (potential current
     * parent). The previous ones are to reference points that were previously
     * looked at (all potential ancestors).
     */
    Stack dist;

    /** The index of the instance represented by this node. */
    Integer idx;

    /**
     * Returns the instance represent by this DistanceNode.
     * 
     * @return The instance represented by this node.
     */
    public Instance q() {
      return m_Instances.instance(idx);
    }

    /**
     * Returns the revision string.
     * 
     * @return the revision
     */
    @Override
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 10203 $");
    }
  }

  /** The euclidean distance function to use. */
  protected EuclideanDistance m_EuclideanDistance;
  { // to make sure we have only one object of EuclideanDistance
    if (m_DistanceFunction instanceof EuclideanDistance) {
      m_EuclideanDistance = (EuclideanDistance) m_DistanceFunction;
    } else {
      m_DistanceFunction = m_EuclideanDistance = new EuclideanDistance();
    }
  }

  /** The root node. */
  protected CoverTreeNode m_Root;

  /**
   * Array holding the distances of the nearest neighbours. It is filled up both
   * by nearestNeighbour() and kNearestNeighbours().
   */
  protected double[] m_DistanceList;

  /** Number of nodes in the tree. */
  protected int m_NumNodes, m_NumLeaves, m_MaxDepth;

  /** Tree Stats variables. */
  protected TreePerformanceStats m_TreeStats = null;

  /**
   * The base of our expansion constant. In other words the 2 in 2^i used in
   * covering tree and separation invariants of a cover tree. P.S.: In paper
   * it's suggested the separation invariant is relaxed in batch construction.
   */
  protected double m_Base = 1.3;

  /**
   * if we have base 2 then this can be viewed as 1/ln(2), which can be used
   * later on to do il2*ln(d) instead of ln(d)/ln(2), to get log2(d), in
   * get_scale method.
   */
  protected double il2 = 1.0 / Math.log(m_Base);

  /**
   * default constructor.
   */
  public CoverTree() {
    super();
    if (getMeasurePerformance()) {
      m_Stats = m_TreeStats = new TreePerformanceStats();
    }
  }

  /**
   * Returns a string describing this nearest neighbour search algorithm.
   * 
   * @return a description of the algorithm for displaying in the
   *         explorer/experimenter gui
   */
  @Override
  public String globalInfo() {
    return "Class implementing the CoverTree datastructure.\n"
      + "The class is very much a translation of the c source code made "
      + "available by the authors.\n\n"
      + "For more information and original source code 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
   */
  @Override
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation result;

    result = new TechnicalInformation(Type.INPROCEEDINGS);
    result.setValue(Field.AUTHOR,
      "Alina Beygelzimer and Sham Kakade and John Langford");
    result.setValue(Field.TITLE, "Cover trees for nearest neighbor");
    result
      .setValue(Field.BOOKTITLE,
        "ICML'06: Proceedings of the 23rd international conference on Machine learning");
    result.setValue(Field.PAGES, "97-104");
    result.setValue(Field.YEAR, "2006");
    result.setValue(Field.PUBLISHER, "ACM Press");
    result.setValue(Field.ADDRESS, "New York, NY, USA");
    result.setValue(Field.LOCATION, "Pittsburgh, Pennsylvania");
    result.setValue(Field.HTTP,
      "http://hunch.net/~jl/projects/cover_tree/cover_tree.html");

    return result;
  }

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