content: /*
   *    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.
   */
  
  /*
   *    AbstractDensityBasedClusterer.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.clusterers;
  
  import weka.core.Instance;
  import weka.core.SerializedObject;
  import weka.core.Utils;
  
  /** 
   * Abstract clustering model that produces (for each test instance)
   * an estimate of the membership in each cluster 
   * (ie. a probability distribution).
   *
   * @author   Mark Hall (mhall@cs.waikato.ac.nz)
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @version  $Revision: 1.1 $
   */
  public abstract class AbstractDensityBasedClusterer
    extends AbstractClusterer implements DensityBasedClusterer {
  
    /** for serialization. */
    private static final long serialVersionUID = -5950728041704213845L;
  
    // ===============
    // Public methods.
    // ===============
  
    /**
     * Returns the prior probability of each cluster.
     *
     * @return the prior probability for each cluster
     * @exception Exception if priors could not be 
     * returned successfully
     */
    public abstract double[] clusterPriors() 
      throws Exception;
  
    /**
     * Computes the log of the conditional density (per cluster) for a given instance.
     * 
     * @param instance the instance to compute the density for
     * @return an array containing the estimated densities
     * @exception Exception if the density could not be computed
     * successfully
     */
    public abstract double[] logDensityPerClusterForInstance(Instance instance) 
      throws Exception;
  
    /**
     * Computes the density for a given instance.
     * 
     * @param instance the instance to compute the density for
     * @return the density.
     * @exception Exception if the density could not be computed successfully
     */
    public double logDensityForInstance(Instance instance) throws Exception {
  
      double[] a = logJointDensitiesForInstance(instance);
      double max = a[Utils.maxIndex(a)];
      double sum = 0.0;
  
      for(int i = 0; i < a.length; i++) {
        sum += Math.exp(a[i] - max);
      }
  
      return max + Math.log(sum);
    }
  
    /**
     * Returns the cluster probability distribution for an instance.
     *
     * @param instance the instance to be clustered
     * @return the probability distribution
     * @throws Exception if computation fails
     */  
    public double[] distributionForInstance(Instance instance) throws Exception {
      
      return Utils.logs2probs(logJointDensitiesForInstance(instance));
    }
  
    /** 
     * Returns the logs of the joint densities for a given instance.
     *
     * @param inst the instance 
     * @return the array of values
     * @exception Exception if values could not be computed
     */
    public double[] logJointDensitiesForInstance(Instance inst)
      throws Exception {
  
      double[] weights = logDensityPerClusterForInstance(inst);
      double[] priors = clusterPriors();
  
      for (int i = 0; i < weights.length; i++) {
        if (priors[i] > 0) {
  	weights[i] += Math.log(priors[i]);
        } else {
  	throw new IllegalArgumentException("Cluster empty!");
        }
      }
      return weights;
    }
  
    /**
     * Creates copies of the current clusterer. Note that this method
     * now uses Serialization to perform a deep copy, so the Clusterer
     * object must be fully Serializable. Any currently built model will
     * now be copied as well.
     *
     * @param model an example clusterer to copy
     * @param num the number of clusterer copies to create.
     * @return an array of clusterers.
     * @exception Exception if an error occurs 
     */
    public static DensityBasedClusterer [] makeCopies(DensityBasedClusterer model,
  						    int num) throws Exception {
       if (model == null) {
        throw new Exception("No model clusterer set");
      }
      DensityBasedClusterer [] clusterers = new DensityBasedClusterer [num];
      SerializedObject so = new SerializedObject(model);
      for(int i = 0; i < clusterers.length; i++) {
        clusterers[i] = (DensityBasedClusterer) so.getObject();
      }
      return clusterers;
    }
  }
  