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Update DPI clustering

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This commit is contained in:
Marcel Kronfeld 2011-05-03 12:30:16 +00:00
parent 0b5623ee9b
commit 315ef66d54
1 changed files with 98 additions and 15 deletions
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113
src/eva2/server/go/operators/cluster/ClusteringDynPeakIdent.java
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@ -1,8 +1,12 @@
package eva2.server.go.operators.cluster;
import java.util.ArrayList;
import eva2.gui.BeanInspector;
import eva2.server.go.individuals.AbstractEAIndividual;
import eva2.server.go.individuals.AbstractEAIndividualComparator;
import eva2.server.go.individuals.IndividualDistanceComparator;
import eva2.server.go.operators.distancemetric.EuclideanMetric;
import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
import eva2.server.go.operators.distancemetric.PhenotypeMetric;
import eva2.server.go.populations.Population;
@ -12,8 +16,11 @@ import eva2.tools.Pair;
* Clustering using the DPI mechanism (dynamic peak identification).
* Collect a number of peaks, which are the fittest individuals
* which are not dominated by other individuals within a certain distance.
* The remaining individuals are assigned to a peak if they have a distance
* smaller than rho to that peak.
* The remaining individuals are assigned to the closest peak up to a maximum
* niche count. If more individuals would be assigned to one peak, the best ones are
* chosen and remaining ones are assumed unclustered.
* For the strict radius case, individuals are only assigned to a peak if they
* have a distance smaller than the niche radius to that peak.
* The number of expected peaks (clusters) must be predefined.
* Note that the returned number of clusters may be smaller than q.
*
@ -21,21 +28,35 @@ import eva2.tools.Pair;
*
*/
public class ClusteringDynPeakIdent implements InterfaceClustering, java.io.Serializable {
private static final boolean TRACE=true;
private static final boolean TRACE=false;
private int numNiches;
private double nicheRadius;
private int maxNicheCount; // maximum number of individuals per peak
private boolean strictNicheRadius=true; // if false, all individuals are clustered to the closest niche, otherwise some remain unclustered (those which are further than the nicheRadius from any peak)
InterfaceDistanceMetric metric = new PhenotypeMetric();
public ClusteringDynPeakIdent(int numNs, double nicheRad) {
/**
* Uses the alternative metric if it is non null. In case it is null, the last metric is used or, if
* none was set, the default PhenotypeMetric() is used.
*
* @param numNs
* @param numIndiesPerPeak
* @param nicheRad
* @param strictRad
* @param alternativeMetric
*/
public ClusteringDynPeakIdent(int numNs, int numIndiesPerPeak, double nicheRad, boolean strictRad, InterfaceDistanceMetric alternativeMetric) {
this.numNiches = numNs;
this.maxNicheCount = numIndiesPerPeak;
this.nicheRadius = nicheRad;
this.strictNicheRadius = strictRad;
if (metric==null && (alternativeMetric==null)) metric=new PhenotypeMetric();
else if (alternativeMetric!=null) metric=alternativeMetric;
}
public ClusteringDynPeakIdent(ClusteringDynPeakIdent o) {
this(o.numNiches, o.nicheRadius);
metric = o.metric;
this.strictNicheRadius = o.strictNicheRadius;
this(o.numNiches, o.maxNicheCount, o.nicheRadius, o.strictNicheRadius, o.metric);
}
public Object clone() {
@ -73,15 +94,32 @@ public class ClusteringDynPeakIdent implements InterfaceClustering, java.io.Seri
}
public Population[] cluster(Population pop, Population referenceSet) {
// boolean TRACE_METH=false;
// if (TRACE_METH) System.out.println("A1 " + System.currentTimeMillis());
AbstractEAIndividualComparator eaComparator = new AbstractEAIndividualComparator(-1);
Population sorted = pop.getSortedBestFirst(eaComparator);
// if (TRACE_METH) System.out.println("A2 " + System.currentTimeMillis());
Population peaks = performDynPeakIdent(metric, sorted, numNiches, nicheRadius);
// System.out.println("peak measures: " + BeanInspector.toString(peaks.getPopulationMeasures()));
// if (TRACE_METH) System.out.println("A3 " + System.currentTimeMillis());
Population[] clusters = new Population[peaks.size()+1];
for (int i=0; i<clusters.length; i++) clusters[i]=new Population();
Population rest = pop.cloneShallowInds();
rest.filter(peaks);
for (int i=0; i<clusters.length; i++) {
clusters[i]=referenceSet.cloneWithoutInds();
if (i>0) clusters[i].add(peaks.getEAIndividual(i-1)); // add peaks to clusters!
}
// if (TRACE_METH) System.out.println("A4 " + System.currentTimeMillis());
Population rest = pop.filter(peaks);
// if (TRACE_METH) System.out.println("A4a " + System.currentTimeMillis());
if (pop.getRedundancyCount()>0) {
// happens e.g. on the bounds of the domain
System.err.println("warning, found redundant indies: " + pop.getRedundancyCount());
rest.removeRedundantIndies();
}
if ((rest.size()+peaks.size())+pop.getRedundancyCount()!=pop.size()) {
System.err.println("Warning, inconsistent filtering in ClusteringDynPeakIdent! Redundant: " + pop.getRedundancyCount() );
}
int[] assoc = assignLeaders(rest, peaks);
// if (TRACE_METH) System.out.println("A5 " + System.currentTimeMillis());
for (int i=0; i<assoc.length; i++) {
if (assoc[i]>=0) { // it can be assigned to a peak
@ -90,6 +128,30 @@ public class ClusteringDynPeakIdent implements InterfaceClustering, java.io.Seri
clusters[0].add(rest.getEAIndividual(i));
}
}
// if (TRACE_METH) System.out.println("A6 " + System.currentTimeMillis());
int cnt = clusters[0].size();
for (int i=1; i<clusters.length; i++) {
cnt += clusters[i].size();
if (clusters[i].size()==0) {
System.err.println("Warning!!!");
}
}
if (cnt!=(pop.size()-pop.getRedundancyCount())) {
System.err.println("Another warning!! " + cnt + " vs. " + pop.size());
}
if (maxNicheCount>0) { // check for too large species
for (int i=1; i<clusters.length; i++) {
if (clusters[i].size()>maxNicheCount) {
// Population overhead = clusters[i].getSortedNIndividuals(clusters[i].size()-maxNicheCount, false);
ArrayList<AbstractEAIndividual> overhd = clusters[i].getSorted(new IndividualDistanceComparator(peaks.getEAIndividual(i-1), new EuclideanMetric(), true));
Population overhead = new Population();
overhead.addAll(overhead.toTail(clusters[i].size()-maxNicheCount, overhd)); // add only the front maxNicheCount individuals
clusters[i].removeMembers(overhead, true);
clusters[0].addPopulation(overhead);
}
}
}
// if (TRACE_METH) System.out.println("-- " + System.currentTimeMillis());
return clusters;
}
@ -117,18 +179,39 @@ public class ClusteringDynPeakIdent implements InterfaceClustering, java.io.Seri
public static Population performDynPeakIdent(InterfaceDistanceMetric metric, Population sortedPop, int q, double rho) {
int i=0;
Population peaks = new Population(q);
if (TRACE) System.out.print("Adding peaks: ");
while (i<sortedPop.size() && (peaks.size() < q)) {
if ((peaks.size()==0) || (!peaks.isWithinPopDist((AbstractEAIndividual)sortedPop.get(i), rho, metric))) {
peaks.add(sortedPop.get(i));
System.out.println("Added peak " + sortedPop.get(i));
if (TRACE) System.out.print(" " + sortedPop.getEAIndividual(i).getFitness(0));
}
i++;
}
if (TRACE) {
System.out.println("Found " + peaks.size() + " peaks, ");
for (int k=0; k<peaks.size(); k++) System.out.println(" " + peaks.getEAIndividual(k));
System.out.println("Measures: " + BeanInspector.toString(peaks.getPopulationMeasures()));
System.out.println("\nFound " + peaks.size() + " peaks, ");
// for (int k=0; k<peaks.size(); k++) System.out.println(" " + peaks.getEAIndividual(k));
System.out.println("Measures: " + BeanInspector.toString(peaks.getPopulationMeasures(metric)));
}
return peaks;
}
public boolean isStrictNicheRadius() {
return strictNicheRadius;
}
public void setStrictNicheRadius(boolean strictNicheRadius) {
this.strictNicheRadius = strictNicheRadius;
}
public String strictNicheRadiusTipText() {
return "If false, any individual will be assigned its closest peak; if true, it must be within the niche radius to be assigend and remains unclustered otherwise.";
}
public void setNicheRadius(double r) {
nicheRadius = r;
}
public double getNicheRadius() {
return nicheRadius;
}
public String nicheRadiusTipText() {
return "Distance below which two individuals are assumed to belong to the same niche";
}
}