simplekmeans.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 *    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.
 */

/*
 *    SimpleKMeans.java
 *    Copyright (C) 2000 Mark Hall
 *
 */
package weka.clusterers;

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

import weka.classifiers.rules.DecisionTable;
import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;

/**
 * Simple k means clustering class.
 *
 * Valid options are:<p>
 *
 * -N <number of clusters> <br>
 * Specify the number of clusters to generate. <p>
 *
 * -S <seed> <br>
 * Specify random number seed. <p>
 *
 * @author Mark Hall ([e-mail])
 * @author Eibe Frank ([e-mail])
 * @version $Revision$
 * @see Clusterer
 * @see OptionHandler
 */
public class SimpleKMeans extends Clusterer 
  implements NumberOfClustersRequestable,
	     OptionHandler, WeightedInstancesHandler {

  /**
	 * 
	 */
	private static final long serialVersionUID = -3235809600124455376L;

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

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

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

  /**
   * Holds the standard deviations of the numeric attributes in each cluster
   */
  private Instances m_ClusterStdDevs;

  
  /**
   * For each cluster, holds the frequency counts for the values of each 
   * nominal attribute
   */
  private int [][][] m_ClusterNominalCounts;

  /**
   * The number of instances in each cluster
   */
  private int [] m_ClusterSizes;

  /**
   * random seed
   */
  private int m_Seed = 10;

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

  /**
   * Keep track of the number of iterations completed before convergence
   */
  private int m_Iterations = 0;

  private double [] m_squaredErrors;

  /**
   * 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 k means algorithm";
  }

  /**
   * 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 
   * @exception Exception if the clusterer has not been 
   * generated successfully
   */
  public void buildClusterer(Instances data) throws Exception {

    m_Iterations = 0;
    if (data.checkForStringAttributes()) {
      throw  new Exception("Can't handle string attributes!");
    }

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

    m_Min = new double [instances.numAttributes()];
    m_Max = new double [instances.numAttributes()];
    for (int i = 0; i < instances.numAttributes(); i++) {
      m_Min[i] = m_Max[i] = Double.NaN;
    }
    
    m_ClusterCentroids = new Instances(instances, m_NumClusters);
    int[] clusterAssignments = new int [instances.numInstances()];

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

    Random RandomO = new Random(m_Seed);
    boolean [] selected = new boolean[instances.numInstances()];
    int instIndex;
    HashMap initC = new HashMap();
    DecisionTable.hashKey hk = null;
    //    String hk = null;
    boolean centroidSearchBailOut = false;
    int i;
    for (i = 0; i < m_NumClusters; i++) {
      int centroidCount = 0;
      do {
	instIndex = RandomO.nextInt(instances.numInstances());
		hk = new DecisionTable.hashKey(instances.instance(instIndex), 
		instances.numAttributes());
	// hk = instances.instance(instIndex).toString();
	if (initC.containsKey(hk)) {
	//	if (initC.containsValue(instances.instance(instIndex))) {
	  if (!selected[instIndex]) {
	    centroidCount++;
	    selected[instIndex] = true;
	  }
	}
      } while (selected[instIndex] && centroidCount < instances.numInstances());

      if (centroidCount >= instances.numInstances()) {
	// bail out and set the number of requested clusters to i
	centroidSearchBailOut = true;
	break;
      }
      m_ClusterCentroids.add(instances.instance(instIndex));
      initC.put(hk, null);
      selected[instIndex] = true;
      centroidCount++;
    }
    selected = null;
    if (centroidSearchBailOut) {
      m_NumClusters = i;
    }

    boolean converged = false;
    int emptyClusterCount;
    Instances [] tempI = new Instances[m_NumClusters];
    m_squaredErrors = new double [m_NumClusters];
    m_ClusterNominalCounts = new int [m_NumClusters][instances.numAttributes()][0];
    while (!converged) {
      emptyClusterCount = 0;
      m_Iterations++;
      converged = true;
      for (i = 0; i < instances.numInstances(); i++) {
	Instance toCluster = instances.instance(i);
	int newC = clusterProcessedInstance(toCluster);
	if (newC != clusterAssignments[i]) {
	  converged = false;
	}
	clusterAssignments[i] = newC;
	//	System.out.println(newC);
      }
      
      // update centroids
      m_ClusterCentroids = new Instances(instances, m_NumClusters);
      for (i = 0; i < m_NumClusters; i++) {
	tempI[i] = new Instances(instances, 0);
      }
      for (i = 0; i < instances.numInstances(); i++) {
	tempI[clusterAssignments[i]].add(instances.instance(i));
      }
      for (i = 0; i < m_NumClusters; i++) {
	double [] vals = new double[instances.numAttributes()];
	if (tempI[i].numInstances() == 0) {
	  // empty cluster
	  emptyClusterCount++;
	} else {
	  for (int j = 0; j < instances.numAttributes(); j++) {
	    vals[j] = tempI[i].meanOrMode(j);
	    m_ClusterNominalCounts[i][j] = 
	      tempI[i].attributeStats(j).nominalCounts;
	  }
	  m_ClusterCentroids.add(new Instance(1.0, vals));
	}
      }

      if (emptyClusterCount > 0) {
	m_NumClusters -= emptyClusterCount;
	tempI = new Instances[m_NumClusters];
      }
      if (!converged) {
	m_squaredErrors = new double [m_NumClusters];
	m_ClusterNominalCounts = new int [m_NumClusters][instances.numAttributes()][0];
      }
    }
    m_ClusterStdDevs = new Instances(instances, m_NumClusters);
    m_ClusterSizes = new int [m_NumClusters];
    for (i = 0; i < m_NumClusters; i++) {
      double [] vals2 = new double[instances.numAttributes()];
      for (int j = 0; j < instances.numAttributes(); j++) {
	if (instances.attribute(j).isNumeric()) {
	  vals2[j] = Math.sqrt(tempI[i].variance(j));
	} else {
	  vals2[j] = Instance.missingValue();
	}	
      }
      m_ClusterStdDevs.add(new Instance(1.0, vals2));
      m_ClusterSizes[i] = tempI[i].numInstances();
    }
  }

  /**
   * clusters an instance that has been through the filters
   *
   * @param instance the instance to assign a cluster to
   * @return a cluster number
   */
  private int clusterProcessedInstance(Instance instance) {
    double minDist = Integer.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;
      }
    }
    m_squaredErrors[bestCluster] += minDist;
    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 interger
   * if the class is emerated, otherwise the predicted value
   * @exception 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 test the first instance
   * @param train the second instance
   * @return the distance between the two given instances, between 0 and 1
   */          
  private 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_ClusterCentroids.numAttributes();
      } else {
	firstI = first.index(p1); 
      }
      if (p2 >= second.numValues()) {
	secondI = m_ClusterCentroids.numAttributes();
      } else {
	secondI = second.index(p2);
      }
      if (firstI == m_ClusterCentroids.classIndex()) {
	p1++; continue;
      } 
      if (secondI == m_ClusterCentroids.classIndex()) {
	p2++; continue;
      } 
      double diff;
      if (firstI == secondI) {
	diff = difference(firstI, 
			  first.valueSparse(p1),