source: trunk/CrossPare/src/de/ugoe/cs/cpdp/dataselection/SetWiseEMClusterSelection.java @ 7

package de.ugoe.cs.cpdp.dataselection;

import java.util.LinkedList;
import java.util.List;

import org.apache.commons.collections4.list.SetUniqueList;

import weka.clusterers.EM;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Filter based on EM clustering after S. Herbold: Training data selection for cross-project defect prediction
 * @author Steffen Herbold
 */
public class SetWiseEMClusterSelection extends AbstractCharacteristicSelection {
        
        /**
         * @see de.ugoe.cs.cpdp.dataselection.SetWiseDataselectionStrategy#apply(weka.core.Instances, org.apache.commons.collections4.list.SetUniqueList)
         */
        @Override
        public void apply(Instances testdata, SetUniqueList<Instances> traindataSet) {
                final Instances data = normalizedCharacteristicInstances(testdata, traindataSet); 
                final Instance targetInstance = data.instance(0);
                final List<Instance> candidateInstances = new LinkedList<Instance>();
                for( int i=1; i<data.numInstances(); i++ ) {
                        candidateInstances.add(data.instance(i));
                }
                
                // cluster and select
                try {
                        final EM emeans = new EM();
                        boolean onlyTarget = true;
                        int targetCluster;
                        int maxNumClusters = candidateInstances.size();
                        do { // while(onlyTarget)
                                emeans.setMaximumNumberOfClusters(maxNumClusters);
                                emeans.buildClusterer(data);
                                                        
                                targetCluster = emeans.clusterInstance(targetInstance);
                                
                                // check if cluster only contains target project
                                for( int i=0 ; i<candidateInstances.size() && onlyTarget; i++ ) {
                                        onlyTarget &= !(emeans.clusterInstance(candidateInstances.get(i))==targetCluster);
                                }
                                maxNumClusters = emeans.numberOfClusters()-1;
                        } while(onlyTarget);
                        
                        int numRemoved = 0;
                        for( int i=0 ; i<candidateInstances.size() ; i++ ) {
                                if( emeans.clusterInstance(candidateInstances.get(i))!=targetCluster ) {
                                        traindataSet.remove(i-numRemoved++);
                                }
                        }
                } catch(Exception e) {
                        throw new RuntimeException("error applying setwise EM clustering training data selection", e);
                }
        }       
}