xmeans.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

				    Instances model   
				    ) {
    for (int i = 0; i < centers.numInstances(); i++) {
      double val;
      for (int j = 0; j < model.numAttributes(); j++) {
	val = meanOrMode(m_Instances, instOfCentIndexes[i], j);
	centers.instance(i).setValue(j, val);
      }
    }
  }

  /**
   * Computes Mean Or Mode of one attribute on a subset of m_Instances. 
   * The subset is defined by an index list.
   * @param instances all instances
   * @param instList the indexes of the instances the mean is computed from
   * @param attIndex the index of the attribute
   * @return mean value
   */
  private double meanOrMode(Instances instances, 
			    int [] instList, 
			    int attIndex) {
    double result, found;
    int [] counts;
    int numInst = instList.length;

    if (instances.attribute(attIndex).isNumeric()) {
      result = found = 0;
      for (int j = 0; j < numInst; j++) {
	Instance currInst = instances.instance(instList[j]);
	if (!currInst.isMissing(attIndex)) {
	  found += currInst.weight();
	  result += currInst.weight() * 
	    currInst.value(attIndex);
	}
      }
      if (Utils.eq(found, 0)) {
	return 0;
      } else {
	return result / found;
      }
    } else if (instances.attribute(attIndex).isNominal()) {
      counts = new int[instances.attribute(attIndex).numValues()];
      for (int j = 0; j < numInst; j++) {
	Instance currInst = instances.instance(instList[j]);
	if (!currInst.isMissing(attIndex)) {
	  counts[(int) currInst.value(attIndex)] += currInst.weight();
	}
      }
      return (double)Utils.maxIndex(counts);
    } else {
      return 0;
    }
  }

 
  /**
   * Assigns instances to centers.
   *
   * @param tree KDTree on all instances
   * @param centers all the input centers
   * @param instOfCent the instances to each center
   * @param allInstList list of all instances
   * @param assignments assignments of instances to centers
   * @param iterationCount the number of iteration 
   * @return true if converged
   * @throws Exception is something goes wrong
   */
  private boolean assignToCenters(KDTree tree,
                                  Instances centers, 
				  int [][] instOfCent, 
                                  int [] allInstList,
				  int [] assignments,
                                  int iterationCount) throws Exception {
    
    boolean converged = true;
    if (tree != null) {
      // using KDTree structure for assigning
      converged = assignToCenters(tree,
				  centers, 
				  instOfCent,
				  assignments,
				  iterationCount);
    } else {
      converged = assignToCenters(centers, 
				  instOfCent,
				  allInstList, 
				  assignments);
    }
    return converged;
  }

  /**
   * Assign instances to centers using KDtree.
   * First part of conventionell K-Means, returns true if new assignment
   * is the same as the last one.
   *
   * @param kdtree KDTree on all instances
   * @param centers all the input centers
   * @param instOfCent the instances to each center
   * @param assignments assignments of instances to centers
   * @param iterationCount the number of iteration 
   * @return true if converged
   * @throws Exception in case instances are not assigned to cluster
   */
  private boolean assignToCenters(KDTree kdtree,
				  Instances centers, 
				  int [][] instOfCent, 
				  int [] assignments,
                                  int iterationCount) throws Exception {

    int numCent = centers.numInstances();
    int numInst = m_Instances.numInstances(); 
    int [] oldAssignments = new int[numInst];
    
    // WARNING:  assignments is "input/output-parameter"
    // should not be null
    if (assignments == null) {
      assignments = new int[numInst];
      for (int i = 0; i < numInst; i++) {
	assignments[0] = -1;
      }
    }
    
    // WARNING:  instOfCent is "input/output-parameter"
    // should not be null
    if (instOfCent == null) {
      instOfCent = new int [numCent][];
    }
    
    // save old assignments
    for (int i = 0; i < assignments.length; i++) {
      oldAssignments[i] = assignments[i];
    }
    
    // use tree to get new assignments
    kdtree.centerInstances(centers, assignments,
			   Math.pow(.8, iterationCount));	
    boolean converged = true;
  
    // compare with previous assignment
    for (int i = 0; converged && (i < assignments.length); i++) {
      converged = (oldAssignments[i] == assignments[i]);
      if (assignments[i] == -1) 
	throw new Exception("Instance " + i + 
			    " has not been assigned to cluster.");
    }

    if (!converged) {
      int [] numInstOfCent = new int[numCent];
      for (int i = 0; i < numCent; i++)
	numInstOfCent[i] = 0;

      // count num of assignments per center
      for (int i = 0; i < numInst; i++)
	numInstOfCent[assignments[i]]++;
      
      // prepare instancelists per center
      for (int i = 0; i < numCent; i++){
	instOfCent[i] = new int[numInstOfCent[i]];
      }
      // write instance lists per center
      for (int i = 0; i < numCent; i++) {
	int index = -1;   
	for (int j = 0; j < numInstOfCent[i]; j++) {
	  index = nextAssignedOne(i, index, assignments);
	  instOfCent[i][j] = index;
	}
      }
    }
  
    return converged;
  }

  /**
   * Assign instances to centers.
   * Part of conventionell K-Means, returns true if new assignment
   * is the same as the last one.
   *
   * @param centers all the input centers
   * @param instOfCent the instances to each center
   * @param allInstList list of all indexes
   * @param assignments assignments of instances to centers
   * @return true if converged
   * @throws Exception if something goes wrong
   */
  private boolean assignToCenters(Instances centers, 
				  int [][] instOfCent,
				  int [] allInstList,
				  int [] assignments) 
    throws Exception {
    
    // todo: undecided situations
    boolean converged = true; // true if new assignment is the same 
                              // as the old one

    int numInst = allInstList.length; 
    int numCent = centers.numInstances();
    int [] numInstOfCent = new int [numCent];
    for (int i = 0; i < numCent; i++) numInstOfCent[i] = 0;

    // WARNING:  assignments is "input/output-parameter"
    // should not be null
    if (assignments == null) {
      assignments = new int[numInst];
      for (int i = 0; i < numInst; i++) {
	assignments[i] = -1;
      }
    }

    // WARNING: instOfCent is "input/output-parameter"
    // should not be null
    if (instOfCent == null) {
      instOfCent = new int [numCent][];
    }

    // set assignments
    for (int i = 0; i < numInst; i++) {
      Instance inst = m_Instances.instance(allInstList[i]);
      int newC = clusterProcessedInstance(inst, centers);
      
      if (converged && newC != assignments[i]) {
	converged = false;
      }

      numInstOfCent[newC]++; 
      if (!converged)
	assignments[i] = newC;
    }

    // the following is only done
    // if assignments are not the same, because too much effort
    if (!converged) {
      PFD(D_FOLLOWSPLIT, "assignToCenters -> it has NOT converged");
      for (int i = 0; i < numCent; i++) {
	instOfCent[i] = new int [numInstOfCent[i]];
      }

      for (int i = 0; i < numCent; i++) {
	int index = -1;   
	for (int j = 0; j < numInstOfCent[i]; j++) {
	  index = nextAssignedOne(i, index, assignments);
	  instOfCent[i][j] = allInstList[index];
	}
      }
    }
    else
      PFD(D_FOLLOWSPLIT, "assignToCenters -> it has converged");

    return converged;
  }

  /**
   * Searches along the assignment array for the next entry of the center 
   * in question.
   * @param cent index of the center 
   * @param lastIndex index to start searching
   * @param assignments assignments
   * @return index of the instance the center cent is assigned to
   */
  private int nextAssignedOne(int cent, int lastIndex, 
			      int [] assignments) {
    int len = assignments.length;
    int index = lastIndex + 1;
    while (index < len) {
      if (assignments[index] == cent) {
	return (index);
      }
      index++;
    }
    return (-1);
  }

  /**
   * Split centers in their region. Generates random vektor of 
   * length = variance and
   * adds and substractsx to cluster vektor to get two new clusters.
   * 
   * @param random random function
   * @param center the center that is split here
   * @param variance variance of the cluster 
   * @param model data model valid
   * @return a pair of new centers
   * @throws something in AlgVector goes wrong
   */
  private Instances splitCenter(Random random,
			        Instance center,
			        double variance,
			        Instances model) throws Exception {
    m_NumSplits++;
    AlgVector r = null;
    Instances children = new Instances(model, 2);

    if (m_DebugVektorsFile != null) {
      Instance nextVektor = getNextDebugVektorsInstance(model);
      PFD(D_RANDOMVEKTOR, "Random Vector from File " + nextVektor);
      r = new AlgVector(nextVektor);
    }
    else {
      // random vector of length = variance
      r = new AlgVector(model, random);
    }
    r.changeLength(Math.pow(variance, 0.5));
    PFD(D_RANDOMVEKTOR, "random vector *variance "+ r);
    
    // add random vector to center
    AlgVector c = new AlgVector(center);
    AlgVector c2 = (AlgVector) c.clone();
    c = c.add(r);
    Instance newCenter = c.getAsInstance(model, random);
    children.add(newCenter);
    PFD(D_FOLLOWSPLIT, "first child "+ newCenter);
    
    // substract random vector to center
    c2 = c2.substract(r);
    newCenter = c2.getAsInstance(model, random);
    children.add(newCenter);
    PFD(D_FOLLOWSPLIT, "second child "+ newCenter);

    return children;
  }

  /**
   * Split centers in their region.
   * (*Alternative version of splitCenter()*) 
   * 
   * @param random the random number generator
   * @param instances of the region
   * @param model 
   * @return a pair of new centers
   */
  private Instances splitCenters(Random random,
				 Instances instances,
				 Instances model) {
    Instances children = new Instances(model, 2);
    int instIndex = Math.abs(random.nextInt()) % 
      instances.numInstances();
    children.add(instances.instance(instIndex));
    int instIndex2 = instIndex;
    int count = 0;
    while ((instIndex2 == instIndex) && count < 10) {
      count++;
      instIndex2 = Math.abs(random.nextInt()) %
	instances.numInstances();
    }
    children.add(instances.instance(instIndex2));
    
    return children;
  }

  /**
   * Generates new centers randomly. Used for starting centers.
   *
   * @param random0 random number generator
   * @param model data model of the instances
   * @param numClusters number of clusters
   * @return new centers
   */
  private Instances makeCentersRandomly(Random random0,
					Instances model,
					int numClusters) {
    Instances clusterCenters = new Instances(model, numClusters);
    m_NumClusters = numClusters;

    // makes the new centers randomly
    for (int i = 0; i < numClusters; i++) {
      int instIndex = Math.abs(random0.nextInt()) % m_Instances.numInstances();
      clusterCenters.add(m_Instances.instance(instIndex));
    }
    return clusterCenters;
  }

  /**
   * Returns the BIC-value for the given center and instances.
   * @param instList The indices of the instances that belong to the center
   * @param center the center.
   * @param mle maximum likelihood
   * @param model the data model
   * @return the BIC value 
   */   
  private double calculateBIC(int [] instList, Instance center,
			      double mle, Instances model) {
    int [][] w1 = new int[1][instList.length];
    for (int i = 0; i < instList.length; i++) {
      w1[0][i] = instList[i];
    }
    double [] m = {mle};
    Instances w2 = new Instances(model, 1);
    w2.add(center);
    return calculateBIC(w1, w2, m);
    }
  
  /**
   * Calculates the BIC for the given set of centers and instances.
   * @param instOfCent The instances that belong to their respective centers
   * @param centers the centers
   * @param mle maximum likelihood
   * @return The BIC for the input.
   */
  private double calculateBIC(int [][] instOfCent, Instances centers,
			      double [] mle) {
    double loglike = 0.0;
    int numInstTotal = 0;
    int numCenters = centers.numInstances();
    int numDimensions = centers.numAttributes();
    int numParameters = (numCenters - 1) + //probabilities
      numCenters * numDimensions + //means
      numCenters; // variance params
    for (int i = 0; i < centers.numInstances(); i++) {
      loglike += logLikelihoodEstimate(instOfCent[i].length, centers.instance(i),
				       mle[i], centers.numInstances() * 2);
      numInstTotal += instOfCent[i].length;
    }
    /* diff
       thats how we did it
    loglike -= ((centers.numAttributes() + 1.0) * centers.numInstances() * 1)
      * Math.log(count);
      */
    loglike -= numInstTotal * Math.log(numInstTotal);
    //System.out.println ("numInstTotal " + numInstTotal +
    //                    "calculateBIC res " + loglike);
    loglike -= (numParameters / 2.0) * Math.log(numInstTotal);
    //System.out.println ("numParam " +
    //                     + numParameters +
    //			" calculateBIC res " + loglike);
    return loglike;
  }
  
  /**
   * Calculates the log-likelihood of the data for the given model, taken
   * at the maximum likelihood point.
   *