/*
* 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.
*/
/*
* DistributionClusterer.java
* Copyright (C) 1999 Mark Hall
*
*/
package weka.clusterers;
import weka.core.*;
/**
* 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)
* @version $Revision: 1.1.1.1 $
*/
public abstract class DistributionClusterer extends Clusterer {
// ===============
// Public methods.
// ===============
/**
* 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 abstract double densityForInstance(Instance instance)
throws Exception;
/**
* Predicts the cluster memberships for a given instance.
*
* @param instance the instance to be assigned a cluster.
* @return an array containing the estimated membership
* probabilities of the test instance in each cluster (this
* should sum to at most 1)
* @exception Exception if distribution could not be
* computed successfully
*/
public abstract double[] distributionForInstance(Instance instance)
throws Exception;
/**
* Assigns an instance to a Cluster.
*
* @param instance the instance to be classified
* @return the predicted most likely cluster for the instance.
* @exception Exception if an error occurred during the prediction
*/
public int clusterInstance(Instance instance) throws Exception {
double [] dist = distributionForInstance(instance);
if (dist == null) {
throw new Exception("Null distribution predicted");
}
if (Utils.sum(dist) <= 0) {
throw new Exception("Unable to cluster instance");
}
return Utils.maxIndex(dist);
}
}