/*
* 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.
*/
/**
* EMGenerator.java
* Copyright (C) 2008 K.Hempstalk, University of Waikato, Hamilton, New Zealand.
*/
package weka.classifiers.meta.generators;
import weka.clusterers.EM;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.DenseInstance;
import weka.core.Instances;
import weka.core.Capabilities.Capability;
/**
<!-- globalinfo-start -->
* A generator that uses EM as an underlying model.
* <p/>
<!-- globalinfo-end -->
*
<!-- options-start -->
* Valid options are: <p/>
*
* <pre> -D
* If set, generator is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -S <seed>
* Sets the seed of the random number generator of the generator (default: 1)</pre>
*
<!-- options-end -->
*
* @author Kathryn Hempstalk (kah18 at cs.waikato.ac.nz)
* @version $Revision: 5987 $
*/
public class EMGenerator
extends RandomizableGenerator
implements InstanceHandler, NumericAttributeGenerator {
/** for serialization. */
private static final long serialVersionUID = 2769416817955024550L;
/**
* The underlying EM model.
*/
protected EM m_EMModel;
/**
* Returns a string describing this class' ability.
*
* @return A description of the class.
*/
public String globalInfo() {
return "A generator that uses EM as an underlying model.";
}
/**
* Returns the Capabilities of this object
*
* @return the capabilities of this object
* @see Capabilities
*/
public Capabilities getCapabilities() {
Capabilities result = new Capabilities(this);
// TODO: shouldn't that return EM's capabilities?
result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enableAllClasses();
result.enable(Capability.MISSING_CLASS_VALUES);
result.enable(Capability.NO_CLASS);
return result;
}
/**
* Builds the generator with a given set of instances.
*
* @param someinstances The instances that will be used to
* build up the probabilities for this generator.
* @throws Exception if data cannot be processed
*/
public void buildGenerator(Instances someinstances) throws Exception {
// can generator handle the data?
getCapabilities().testWithFail(someinstances);
someinstances = new Instances(someinstances);
someinstances.deleteWithMissing(0);
m_EMModel = new EM();
m_EMModel.setMaxIterations(10);
m_EMModel.buildClusterer(someinstances);
}
/**
* Generates a value that falls under this distribution.
*
* @return A generated value.
*/
public double generate() {
//one attribute
//get the cluster priors
double[] clusterProbabilities = m_EMModel.getClusterPriors();
double clusterPicked = m_Random.nextDouble();
//find the cluster we are going to generate data for
double sum = 0;
int clusterID = 0;
for(int i = 0; i < clusterProbabilities.length; i++) {
if(clusterPicked > sum &&
clusterPicked <= (sum + clusterProbabilities[i])) {
//it's this one
clusterID = i;
break;
} else {
sum = sum + clusterProbabilities[i];
clusterID = i;
}
}
//System.out.println("Selecting cluster: " + clusterID + " of " + numClusters);
//get the mean and standard deviation of this cluster
double[][][] normalDists = m_EMModel.getClusterModelsNumericAtts();
double mean = normalDists[clusterID][0][0];
double sd = normalDists[clusterID][0][1];
double gaussian = m_Random.nextGaussian();
double value = mean + (gaussian * sd);
return value;
}
/**
* Gets the probability that a value falls under
* this distribution.
*
*
* @param valuex The value to get the probability of.
* @return The probability of the given value.
*/
public double getProbabilityOf(double valuex) {
//find the cluster closest to the value of x
Instance inst = new DenseInstance(1);
inst.setValue(0, valuex);
try{
return Math.exp(m_EMModel.logDensityForInstance(inst));
}catch(Exception e) {
e.printStackTrace();
System.exit(-1);
}
return 0;
}
/**
* Gets the (natural) log of the probability of a given value.
*
* @param valuex The value to get the log probability of.
* @return The (natural) log of the probability.
*/
public double getLogProbabilityOf(double valuex) {
return Math.log(this.getProbabilityOf(valuex));
}
}