/*
* RapidMiner
*
* Copyright (C) 2001-2008 by Rapid-I and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapid-i.com
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License as published by
* the Free Software Foundation, either version 3 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 Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License
* along with this program. If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.clustering.clusterer;
import java.util.List;
import com.rapidminer.example.ExampleSet;
import com.rapidminer.operator.InputDescription;
import com.rapidminer.operator.OperatorDescription;
import com.rapidminer.operator.OperatorException;
import com.rapidminer.operator.learner.clustering.ClusterModel;
import com.rapidminer.operator.learner.clustering.FlatClusterModel;
import com.rapidminer.operator.learner.clustering.FlatCrispClusterModel;
import com.rapidminer.operator.learner.clustering.IdUtils;
import com.rapidminer.operator.similarity.DistanceSimilarityConverter;
import com.rapidminer.operator.similarity.SimilarityMeasure;
import com.rapidminer.operator.similarity.SimilarityUtil;
import com.rapidminer.parameter.ParameterType;
import com.rapidminer.parameter.ParameterTypeInt;
import com.rapidminer.tools.IterationArrayList;
/**
* Simple implementation of k-medoids.
*
* @author Michael Wurst, Ingo Mierswa
* @version $Id: KMedoids.java,v 1.7 2008/09/12 10:31:34 tobiasmalbrecht Exp $
*/
public class KMedoids extends AbstractKMethod {
/** The parameter name for "Use the given random seed instead of global random numbers (-1: use global)" */
public static final String PARAMETER_LOCAL_RANDOM_SEED = "local_random_seed";
private SimilarityMeasure sim;
private String[] medoids;
public KMedoids(OperatorDescription description) {
super(description);
}
public ClusterModel createClusterModel(ExampleSet es) throws OperatorException {
int maxK = getParameterAsInt(PARAMETER_K);
int maxOptimizationSteps = getParameterAsInt(PARAMETER_MAX_OPTIMIZATION_STEPS);
int maxRuns = getParameterAsInt(PARAMETER_MAX_RUNS);
sim = SimilarityUtil.resolveSimilarityMeasure(getParameters(), getInput(), es);
if (sim.isDistance())
sim = new DistanceSimilarityConverter(sim);
FlatClusterModel result = kmethod(es, maxK, maxOptimizationSteps, maxRuns);
return result;
}
protected void initKMethod(List<String> ids, int k) throws OperatorException {
medoids = new String[k];
List<String> randomList = IdUtils.getRandomIdList(ids, k, getParameterAsInt(PARAMETER_LOCAL_RANDOM_SEED));
for (int j = 0; j < k; j++) {
medoids[j] = randomList.get(j);
}
}
protected int bestIndex(String id, FlatCrispClusterModel cl, FlatCrispClusterModel cmOld) {
String d1 = id;
double max = Double.NEGATIVE_INFINITY;
int maxIndex = 0;
int j;
for (j = 0; (j < medoids.length) && !medoids[j].equals(d1); j++)
if (sim.isSimilarityDefined(d1, medoids[j]))
if (sim.similarity(d1, medoids[j]) > max) {
max = sim.similarity(d1, medoids[j]);
maxIndex = j;
}
if (j < medoids.length)
maxIndex = j;
return maxIndex;
}
protected void recalculateCentroids(FlatCrispClusterModel cl) {
for (int j = 0; j < medoids.length; j++) {
List<String> x = new IterationArrayList<String>(cl.getClusterAt(j).getObjects());
double max = Double.NEGATIVE_INFINITY;
String maxId = null;
for (int i1 = 0; i1 < x.size(); i1++) {
String d1 = x.get(i1);
double sum = 0.0;
for (int i2 = 0; i2 < x.size(); i2++) {
String d2 = x.get(i2);
if (sim.isSimilarityDefined(d1, d2))
sum = sum + sim.similarity(d1, d2);
}
if (sum > max) {
max = sum;
maxId = d1;
}
}
// For the case, that all similarities are undefined, take the first
// as mendoid
if (maxId == null)
maxId = x.get(0);
medoids[j] = maxId;
}
}
protected double evaluateClusterModel(FlatCrispClusterModel cm) {
double sum = 0.0;
int count = 0;
for (int i = 0; i < cm.getNumberOfClusters(); i++) {
List<String> objs = new IterationArrayList<String>(cm.getClusterAt(i).getObjects());
for (int j = 0; j < objs.size(); j++) {
String d1 = objs.get(j);
for (int l = j; l < objs.size(); l++) {
String d2 = objs.get(l);
if (sim.isSimilarityDefined(d1, d2)) {
sum = sum + sim.similarity(d1, d2);
count++;
}
}
}
}
return (sum / count);
}
public InputDescription getInputDescription(Class cls) {
if (SimilarityMeasure.class.isAssignableFrom(cls)) {
return new InputDescription(cls, false, true);
} else {
return super.getInputDescription(cls);
}
}
public List<ParameterType> getParameterTypes() {
List<ParameterType> types = super.getParameterTypes();
types.add(SimilarityUtil.generateSimilarityParameter());
types.add(new ParameterTypeInt(PARAMETER_LOCAL_RANDOM_SEED, "Use the given random seed instead of global random numbers (-1: use global)", -1,
Integer.MAX_VALUE, -1));
return types;
}
}