/*
* 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.
*/
/**
* RandomPerturbInitializer.java
*
* Initializer that perturbs the global centroid randomly to get
* initial clusters for K-Means
*
* Copyright (C) 2004 Sugato Basu, Misha Bilenko
*
*/
package weka.clusterers.initializers;
import java.io.*;
import java.util.*;
import weka.core.*;
import weka.clusterers.*;
public class RandomPerturbInitializer extends MPCKMeansInitializer {
/** Default perturbation */
protected double m_DefaultPerturb = 0.7;
/** Set default perturbation value
* @param p perturbation fraction
*/
public void setDefaultPerturb(double p) {
m_DefaultPerturb = p;
}
/** Get default perturbation value
* @return perturbation fraction
*/
public double getDefaultPerturb(){
return m_DefaultPerturb;
}
/** Default constructors */
public RandomPerturbInitializer() {
super();
}
/** Initialize with a clusterer */
public RandomPerturbInitializer (MPCKMeans clusterer) {
super(clusterer);
}
/** The main method for initializing cluster centroids
*
* @return the cluster centroids after initialization
*/
public Instances initialize() throws Exception {
System.out.println("Num clusters = " + m_numClusters);
Instances m_Instances = m_clusterer.getInstances();
boolean m_useTransitiveConstraints = m_clusterer.getUseTransitiveConstraints();
Instances m_ClusterCentroids = m_clusterer.getClusterCentroids();
boolean m_objFunDecreasing = m_clusterer.getMetric().isDistanceBased();
Random m_RandomNumberGenerator = m_clusterer.getRandomNumberGenerator();
boolean m_isSparseInstance = (m_Instances.instance(0) instanceof SparseInstance) ?
true: false;
// find global centroid
double [] globalValues = new double[m_Instances.numAttributes()];
if (m_isSparseInstance) {
globalValues = ClusterUtils.meanOrMode(m_Instances); // uses fast meanOrMode
} else {
for (int j = 0; j < m_Instances.numAttributes(); j++) {
globalValues[j] = m_Instances.meanOrMode(j); // uses usual meanOrMode
}
}
System.out.println("Done calculating global centroid");
// global centroid is dense in SPKMeans
Instance m_GlobalCentroid = new Instance(1.0, globalValues);
m_GlobalCentroid.setDataset(m_Instances);
if (!m_objFunDecreasing) {
ClusterUtils.normalizeInstance(m_GlobalCentroid);
}
for (int i=0; i<m_numClusters; i++) {
double [] values = new double[m_Instances.numAttributes()];
double normalizer = 0;
for (int j = 0; j < m_Instances.numAttributes(); j++) {
values[j] = m_GlobalCentroid.value(j) * (1 + m_DefaultPerturb * (m_RandomNumberGenerator.nextFloat() - 0.5));
normalizer += values[j] * values[j];
}
if (!m_objFunDecreasing) {
normalizer = Math.sqrt(normalizer);
for (int j = 0; j < m_Instances.numAttributes(); j++) {
values[j] /= normalizer;
}
}
if (m_isSparseInstance) {
m_ClusterCentroids.add(new SparseInstance(1.0, values)); // sparse for consistency with other cluster centroids
}
else {
m_ClusterCentroids.add(new Instance(1.0, values));
}
}
System.out.println("Initialized centroids by RandomPerturbGlobal");
// System.out.println("Centroids are: " + m_ClusterCentroids);
return m_ClusterCentroids;
}
public void setOptions (String[] options)
throws Exception {
// TODO
}
public Enumeration listOptions () {
// TODO
return null;
}
public String [] getOptions () {
String[] options = new String[10];
int current = 0;
options[current++] = "-N";
options[current++] = "" + m_numClusters;
while (current < options.length) {
options[current++] = "";
}
return options;
}
}