/*
* 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;
}
}