source: src/main/java/weka/clusterers/FarthestFirst.java @ 21

/*
 *    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.
 */

/*
 *    FarthestFirst.java
 *    Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
 *
 */
package weka.clusterers;

import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;

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

/**
 <!-- globalinfo-start -->
 * Cluster data using the FarthestFirst algorithm.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * Hochbaum, Shmoys (1985). A best possible heuristic for the k-center problem. Mathematics of Operations Research. 10(2):180-184.<br/>
 * <br/>
 * Sanjoy Dasgupta: Performance Guarantees for Hierarchical Clustering. In: 15th Annual Conference on Computational Learning Theory, 351-363, 2002.<br/>
 * <br/>
 * Notes:<br/>
 * - works as a fast simple approximate clusterer<br/>
 * - modelled after SimpleKMeans, might be a useful initializer for it
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;article{Hochbaum1985,
 *    author = {Hochbaum and Shmoys},
 *    journal = {Mathematics of Operations Research},
 *    number = {2},
 *    pages = {180-184},
 *    title = {A best possible heuristic for the k-center problem},
 *    volume = {10},
 *    year = {1985}
 * }
 * 
 * &#64;inproceedings{Dasgupta2002,
 *    author = {Sanjoy Dasgupta},
 *    booktitle = {15th Annual Conference on Computational Learning Theory},
 *    pages = {351-363},
 *    publisher = {Springer},
 *    title = {Performance Guarantees for Hierarchical Clustering},
 *    year = {2002}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -N &lt;num&gt;
 *  number of clusters. (default = 2).</pre>
 * 
 * <pre> -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)</pre>
 * 
 <!-- options-end -->
 *
 * @author Bernhard Pfahringer (bernhard@cs.waikato.ac.nz)
 * @version $Revision: 5987 $
 * @see RandomizableClusterer
 */
public class FarthestFirst 
  extends RandomizableClusterer 
  implements TechnicalInformationHandler {

  //Todo: rewrite to be fully incremental
  //      cleanup, like deleting m_instances 

  /** for serialization */
  static final long serialVersionUID = 7499838100631329509L;
  
  /**
   * training instances, not necessary to keep, 
   * could be replaced by m_ClusterCentroids where needed for header info
   */
  protected Instances m_instances;

  /**
   * replace missing values in training instances
   */
  protected ReplaceMissingValues m_ReplaceMissingFilter;

  /**
   * number of clusters to generate
   */
  protected int m_NumClusters = 2;

  /**
   * holds the cluster centroids
   */
  protected Instances m_ClusterCentroids;

  /**
   * attribute min values
   */
  private double [] m_Min;
  
  /**
   * attribute max values
   */
  private double [] m_Max;

  /**
   * Returns a string describing this clusterer
   * @return a description of the evaluator suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "Cluster data using the FarthestFirst algorithm.\n\n"
      + "For more information see:\n\n"
      + getTechnicalInformation().toString() + "\n\n"
      + "Notes:\n"
      + "- works as a fast simple approximate clusterer\n"
      + "- modelled after SimpleKMeans, might be a useful initializer for it";
  }

  /**
   * 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;
    TechnicalInformation        additional;
    
    result = new TechnicalInformation(Type.ARTICLE);
    result.setValue(Field.AUTHOR, "Hochbaum and Shmoys");
    result.setValue(Field.YEAR, "1985");
    result.setValue(Field.TITLE, "A best possible heuristic for the k-center problem");
    result.setValue(Field.JOURNAL, "Mathematics of Operations Research");
    result.setValue(Field.VOLUME, "10");
    result.setValue(Field.NUMBER, "2");
    result.setValue(Field.PAGES, "180-184");
    
    additional = result.add(Type.INPROCEEDINGS);
    additional.setValue(Field.AUTHOR, "Sanjoy Dasgupta");
    additional.setValue(Field.TITLE, "Performance Guarantees for Hierarchical Clustering");
    additional.setValue(Field.BOOKTITLE, "15th Annual Conference on Computational Learning Theory");
    additional.setValue(Field.YEAR, "2002");
    additional.setValue(Field.PAGES, "351-363");
    additional.setValue(Field.PUBLISHER, "Springer");
    
    return result;
  }

  /**
   * Returns default capabilities of the clusterer.
   *
   * @return      the capabilities of this clusterer
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();
    result.enable(Capability.NO_CLASS);

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    return result;
  }

  /**
   * Generates a clusterer. Has to initialize all fields of the clusterer
   * that are not being set via options.
   *
   * @param data set of instances serving as training data 
   * @throws Exception if the clusterer has not been 
   * generated successfully
   */
  public void buildClusterer(Instances data) throws Exception {

    // can clusterer handle the data?
    getCapabilities().testWithFail(data);

    //long start = System.currentTimeMillis();

    m_ReplaceMissingFilter = new ReplaceMissingValues();
    m_ReplaceMissingFilter.setInputFormat(data);
    m_instances = Filter.useFilter(data, m_ReplaceMissingFilter);

    initMinMax(m_instances);

    m_ClusterCentroids = new Instances(m_instances, m_NumClusters);

    int n = m_instances.numInstances();
    Random r = new Random(getSeed());
    boolean[] selected = new boolean[n];
    double[] minDistance = new double[n];

    for(int i = 0; i<n; i++) minDistance[i] = Double.MAX_VALUE;

    int firstI = r.nextInt(n);
    m_ClusterCentroids.add(m_instances.instance(firstI));
    selected[firstI] = true;

    updateMinDistance(minDistance,selected,m_instances,m_instances.instance(firstI));

    if (m_NumClusters > n) m_NumClusters = n;

    for(int i = 1; i < m_NumClusters; i++) {
      int nextI =  farthestAway(minDistance, selected);
      m_ClusterCentroids.add(m_instances.instance(nextI));
      selected[nextI] = true;
      updateMinDistance(minDistance,selected,m_instances,m_instances.instance(nextI));
    }

    m_instances = new Instances(m_instances,0);
    //long end = System.currentTimeMillis();
    //System.out.println("Clustering Time = " + (end-start));
  }


  protected void updateMinDistance(double[] minDistance, boolean[] selected, 
                                   Instances data, Instance center) {
    for(int i = 0; i<selected.length; i++) 
      if (!selected[i]) {
        double d = distance(center,data.instance(i));
        if (d<minDistance[i]) 
          minDistance[i] = d;
      }
  }

  protected int farthestAway(double[] minDistance, boolean[] selected) {
    double maxDistance = -1.0;
    int maxI = -1;
    for(int i = 0; i<selected.length; i++) 
      if (!selected[i]) 
        if (maxDistance < minDistance[i]) {
          maxDistance = minDistance[i];
          maxI = i;
        }
    return maxI;
  }

  protected void initMinMax(Instances data) {
    m_Min = new double [data.numAttributes()];
    m_Max = new double [data.numAttributes()];
    for (int i = 0; i < data.numAttributes(); i++) {
      m_Min[i] = m_Max[i] = Double.NaN;
    }

    for (int i = 0; i < data.numInstances(); i++) {
      updateMinMax(data.instance(i));
    }
  }


  /**
   * Updates the minimum and maximum values for all the attributes
   * based on a new instance.
   *
   * @param instance the new instance
   */
  private void updateMinMax(Instance instance) {  

    for (int j = 0;j < instance.numAttributes(); j++) {
      if (Double.isNaN(m_Min[j])) {
        m_Min[j] = instance.value(j);
        m_Max[j] = instance.value(j);
      } else {
        if (instance.value(j) < m_Min[j]) {
          m_Min[j] = instance.value(j);
        } else {
          if (instance.value(j) > m_Max[j]) {
            m_Max[j] = instance.value(j);
          }
        }
      }
    }
  }


  /**
   * clusters an instance that has been through the filters
   *
   * @param instance the instance to assign a cluster to
   * @return a cluster number
   */
  protected int clusterProcessedInstance(Instance instance) {
    double minDist = Double.MAX_VALUE;
    int bestCluster = 0;
    for (int i = 0; i < m_NumClusters; i++) {
      double dist = distance(instance, m_ClusterCentroids.instance(i));
      if (dist < minDist) {
        minDist = dist;
        bestCluster = i;
      }
    }
    return bestCluster;
  }

  /**
   * Classifies a given instance.
   *
   * @param instance the instance to be assigned to a cluster
   * @return the number of the assigned cluster as an integer
   * if the class is enumerated, otherwise the predicted value
   * @throws Exception if instance could not be classified
   * successfully
   */
  public int clusterInstance(Instance instance) throws Exception {
    m_ReplaceMissingFilter.input(instance);
    m_ReplaceMissingFilter.batchFinished();
    Instance inst = m_ReplaceMissingFilter.output();

    return clusterProcessedInstance(inst);
  }

  /**
   * Calculates the distance between two instances
   *
   * @param first the first instance
   * @param second the second instance
   * @return the distance between the two given instances, between 0 and 1
   */          
  protected double distance(Instance first, Instance second) {  

    double distance = 0;
    int firstI, secondI;

    for (int p1 = 0, p2 = 0; 
         p1 < first.numValues() || p2 < second.numValues();) {
      if (p1 >= first.numValues()) {
        firstI = m_instances.numAttributes();
      } else {
        firstI = first.index(p1); 
      }
      if (p2 >= second.numValues()) {
        secondI = m_instances.numAttributes();
      } else {
        secondI = second.index(p2);
      }
      if (firstI == m_instances.classIndex()) {
        p1++; continue;
      } 
      if (secondI == m_instances.classIndex()) {
        p2++; continue;
      } 
      double diff;
      if (firstI == secondI) {
        diff = difference(firstI, 
                          first.valueSparse(p1),
                          second.valueSparse(p2));
        p1++; p2++;
      } else if (firstI > secondI) {
        diff = difference(secondI, 
                          0, second.valueSparse(p2));
        p2++;
      } else {
        diff = difference(firstI, 
                          first.valueSparse(p1), 0);
        p1++;
      }
      distance += diff * diff;
    }
    
    return Math.sqrt(distance / m_instances.numAttributes());
  }

  /**
   * Computes the difference between two given attribute
   * values.
   */
  protected double difference(int index, double val1, double val2) {

    switch (m_instances.attribute(index).type()) {
    case Attribute.NOMINAL:
      
      // If attribute is nominal
      if (Utils.isMissingValue(val1) || 
          Utils.isMissingValue(val2) ||
          ((int)val1 != (int)val2)) {
        return 1;
      } else {
        return 0;
      }
    case Attribute.NUMERIC:

      // If attribute is numeric
      if (Utils.isMissingValue(val1) || 
          Utils.isMissingValue(val2)) {
        if (Utils.isMissingValue(val1) && 
            Utils.isMissingValue(val2)) {
          return 1;
        } else {
          double diff;
          if (Utils.isMissingValue(val2)) {
            diff = norm(val1, index);
          } else {
            diff = norm(val2, index);
          }
          if (diff < 0.5) {
            diff = 1.0 - diff;
          }
          return diff;
        }
      } else {
        return norm(val1, index) - norm(val2, index);
      }
    default:
      return 0;
    }
  }

  /**
   * Normalizes a given value of a numeric attribute.
   *
   * @param x the value to be normalized
   * @param i the attribute's index
   * @return the normalized value
   */
  protected double norm(double x, int i) {

    if (Double.isNaN(m_Min[i]) || Utils.eq(m_Max[i],m_Min[i])) {
      return 0;
    } else {
      return (x - m_Min[i]) / (m_Max[i] - m_Min[i]);
    }
  }

  /**
   * Returns the number of clusters.
   *
   * @return the number of clusters generated for a training dataset.
   * @throws Exception if number of clusters could not be returned
   * successfully
   */
  public int numberOfClusters() throws Exception {
    return m_NumClusters;
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions () {
    Vector result = new Vector();
    
    result.addElement(new Option(
        "\tnumber of clusters. (default = 2).", 
        "N", 1, "-N <num>"));
    
    Enumeration en = super.listOptions();
    while (en.hasMoreElements())
      result.addElement(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 numClustersTipText() {
    return "set number of clusters";
  }

  /**
   * set the number of clusters to generate
   *
   * @param n the number of clusters to generate
   * @throws Exception if number of clusters is negative
   */
  public void setNumClusters(int n) throws Exception {
    if (n < 0) {
      throw new Exception("Number of clusters must be > 0");
    }
    m_NumClusters = n;
  }

  /**
   * gets the number of clusters to generate
   *
   * @return the number of clusters to generate
   */
  public int getNumClusters() {
    return m_NumClusters;
  }

  /**
   * Parses a given list of options. <p/>
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -N &lt;num&gt;
   *  number of clusters. (default = 2).</pre>
   * 
   * <pre> -S &lt;num&gt;
   *  Random number seed.
   *  (default 1)</pre>
   * 
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  public void setOptions (String[] options)
    throws Exception {

    String optionString = Utils.getOption('N', options);

    if (optionString.length() != 0) {
      setNumClusters(Integer.parseInt(optionString));
    }
    
    super.setOptions(options);
  }

  /**
   * Gets the current settings of FarthestFirst
   *
   * @return an array of strings suitable for passing to setOptions()
   */
  public String[] getOptions () {
    int         i;
    Vector      result;
    String[]    options;

    result = new Vector();

    result.add("-N");
    result.add("" + getNumClusters());

    options = super.getOptions();
    for (i = 0; i < options.length; i++)
      result.add(options[i]);

    return (String[]) result.toArray(new String[result.size()]);          
  }

  /**
   * return a string describing this clusterer
   *
   * @return a description of the clusterer as a string
   */
  public String toString() {
    StringBuffer temp = new StringBuffer();

    temp.append("\n FarthestFirst\n==============\n");

    temp.append("\nCluster centroids:\n");
    for (int i = 0; i < m_NumClusters; i++) {
      temp.append("\nCluster "+i+"\n\t");
      for (int j = 0; j < m_ClusterCentroids.numAttributes(); j++) {
        if (m_ClusterCentroids.attribute(j).isNominal()) {
          temp.append(" "+m_ClusterCentroids.attribute(j).
                      value((int)m_ClusterCentroids.instance(i).value(j)));
        } else {
          temp.append(" "+m_ClusterCentroids.instance(i).value(j));
        }
      }
    }
    temp.append("\n\n");
    return temp.toString();
  }
  
  /**
   * Returns the revision string.
   * 
   * @return            the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 5987 $");
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain the following arguments: <p>
   * -t training file [-N number of clusters]
   */
  public static void main (String[] argv) {
    runClusterer(new FarthestFirst(), argv);
  }
}