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Minor cosmetics.

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This commit is contained in:
Marcel Kronfeld 2009-10-21 13:44:16 +00:00
parent 0416188dd6
commit 87b8877d5d
2 changed files with 67 additions and 18 deletions
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73
src/eva2/server/go/operators/cluster/ClusteringHierarchical.java
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@ -25,26 +25,34 @@ import eva2.server.go.populations.Population;
*
*/
public class ClusteringHierarchical implements InterfaceClustering, Serializable {
private InterfaceDistanceMetric metric = new PhenotypeMetric();
private static final long serialVersionUID = 1L;
private InterfaceDistanceMetric metric = new PhenotypeMetric();
private double absoluteDistThreshold = 0.5;
private boolean thresholdMultipleOfMeanDist = false;
private double meanDistFactor = 2.; // recommended setting
private double currentMeanDistance = -1.;
private int minimumGroupSize = 3;
private boolean testConvergingSpeciesOnBestOnly = true; // if two species are tested for convergence, only the best indies may be compared regarding the distance threshold
private int[] uplink;
private double[] uplinkDist;
private AbstractEAIndividualComparator comparator = new AbstractEAIndividualComparator();
private Vector<Integer>[] children;
// private boolean[] clustered;
// private boolean m_TestConvergingSpeciesOnBestOnly = true;
private boolean TRACE = false;
public ClusteringHierarchical() {
private static boolean TRACE = false;
public ClusteringHierarchical() {
}
public ClusteringHierarchical(ClusteringHierarchical clusteringHierarchical) {
public ClusteringHierarchical(ClusteringHierarchical o) {
this.metric = o.metric;
this.absoluteDistThreshold = o.absoluteDistThreshold;
this.thresholdMultipleOfMeanDist = o.thresholdMultipleOfMeanDist;
this.meanDistFactor = o.meanDistFactor;
this.currentMeanDistance = o.currentMeanDistance;
this.minimumGroupSize = o.minimumGroupSize;
this.comparator = (AbstractEAIndividualComparator)o.comparator.clone();
this.testConvergingSpeciesOnBestOnly = o.testConvergingSpeciesOnBestOnly;
}
public void hideHideable() {
@ -154,15 +162,34 @@ public class ClusteringHierarchical implements InterfaceClustering, Serializable
else return absoluteDistThreshold;
}
public boolean convergingSpecies(Population species1, Population species2) {
// TODO Auto-generated method stub
return false;
}
/** This method allows you to decide if two species converge.
* @param species1 The first species.
* @param species2 The second species.
* @return True if species converge, else False.
*/
public boolean convergingSpecies(Population species1, Population species2) {
if (testConvergingSpeciesOnBestOnly) {
if (this.metric.distance(species1.getBestEAIndividual(), species2.getBestEAIndividual()) < this.currentDistThreshold()) return true;
else return false;
} else {
Population tmpPop = new Population(species1.size()+species2.size());
tmpPop.addPopulation(species1);
tmpPop.addPopulation(species2);
if (this.cluster(tmpPop).length <= 2) return true;
else return false;
}
}
public String globalInfo() {
return "A tree is produced by assigning each individual the closest individual with better fitness. Connections with a distance above a certain threshold are cut. After that, each interconnected subtree forms a cluster.";
}
public String metricTipText() {
return "The metric to use during clustering.";
}
public InterfaceDistanceMetric getMetric() {
return metric;
}
public void setMetric(InterfaceDistanceMetric metric) {
this.metric = metric;
}
@ -193,9 +220,9 @@ public class ClusteringHierarchical implements InterfaceClustering, Serializable
public AbstractEAIndividualComparator getComparator() {
return comparator;
}
public void setComparator(AbstractEAIndividualComparator comparator) {
this.comparator = comparator;
}
// public void setComparator(AbstractEAIndividualComparator comparator) {
// this.comparator = comparator;
// }
public String adaptiveThresholdTipText() {
return "Activate adaptive threshold which is calculated from mean distance in the population and a constant factor.";
@ -205,8 +232,8 @@ public class ClusteringHierarchical implements InterfaceClustering, Serializable
}
public void setAdaptiveThreshold(boolean thresholdMultipleOfMeanDist) {
this.thresholdMultipleOfMeanDist = thresholdMultipleOfMeanDist;
GenericObjectEditor.setHideProperty(this.getClass(), "meanDistFactor", thresholdMultipleOfMeanDist);
GenericObjectEditor.setHideProperty(this.getClass(), "distThreshold", !thresholdMultipleOfMeanDist);
GenericObjectEditor.setHideProperty(this.getClass(), "meanDistFactor", !thresholdMultipleOfMeanDist);
GenericObjectEditor.setHideProperty(this.getClass(), "distThreshold", thresholdMultipleOfMeanDist);
}
public String meanDistFactorTipText() {
@ -218,4 +245,16 @@ public class ClusteringHierarchical implements InterfaceClustering, Serializable
public void setMeanDistFactor(double meanDistFactor) {
this.meanDistFactor = meanDistFactor;
}
public String testConvergingSpeciesOnBestOnlyTipText() {
return "Only the best individuals may be compared when testing whether to merge two species.";
}
public boolean isTestConvergingSpeciesOnBestOnly() {
return testConvergingSpeciesOnBestOnly;
}
public void SetTestConvergingSpeciesOnBestOnly(
boolean testConvergingSpeciesOnBestOnly) {
this.testConvergingSpeciesOnBestOnly = testConvergingSpeciesOnBestOnly;
}
}

12
src/eva2/tools/math/Jama/Matrix.java
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@ -7,6 +7,7 @@ import java.io.StreamTokenizer;
import java.text.DecimalFormat;
import java.text.DecimalFormatSymbols;
import java.text.NumberFormat;
import java.util.Arrays;
import java.util.Locale;
import eva2.gui.BeanInspector;
@ -448,7 +449,16 @@ public class Matrix implements Cloneable, java.io.Serializable {
public void set (int i, int j, double s) {
A[i][j] = s;
}
/**
* Set all matrix values to the given value. Overwrites the Matrix completely.
*
* @param v
*/
public void fill(double v) {
for (int i=0; i<A.length; i++) Arrays.fill(A[i], v);
}
/** Set a submatrix.
@param i0 Initial row index
@param i1 Final row index