Minor updates, merging MK branch revs. 511:515

src/eva2/gui/GenericObjectEditor.java

@@ -320,9 +320,8 @@ public class GenericObjectEditor implements PropertyEditor {
 	 */
 	private void setObject(Object c) {
 		// This should really call equals() for comparison.
-		if (TRACE) System.out.println("setObject "+ c.getClass().getName());
+		if (TRACE) System.out.println("setObject "+ c.getClass().getName() + " " + c);
 		boolean trueChange = (c != getValue());
-		//System.err.println("Didn't even try to make a Object copy!! "+ "(using original)");
 
 		m_Backup = m_Object;
 		m_Object = c;

src/eva2/gui/JTextoutputFrame.java

@@ -153,7 +153,6 @@ Serializable {
 			try {
 				Thread.sleep(100);
 			} catch (InterruptedException e) {
-				// TODO Auto-generated catch block
 				e.printStackTrace();
 			}
 		}

src/eva2/gui/Plot.java

@@ -33,6 +33,8 @@ import java.io.IOException;
 import java.io.ObjectInputStream;
 import java.io.ObjectOutputStream;
 import java.io.Serializable;
+import java.util.Formatter;
+import java.util.Locale;
 
 import javax.imageio.ImageIO;
 import javax.swing.JButton;
@@ -260,22 +262,24 @@ public class Plot implements PlotInterface, Serializable {
 	 */
 	public void drawPopulation(String prefix, Population pop) {
 		for (int i = 0; i < pop.size(); i++) {
-			drawIndividual(1, 2, prefix, pop.getEAIndividual(i));
+			drawIndividual(0, 2, prefix, pop.getEAIndividual(i));
 		}
 	}
 
 	/**
-	 * Draw an individual to the Plot instance. It is annotated with the given
+	 * Draw an individual to the Plot instance. It is annotated with the
-	 * prefix and its fitness.
+	 * given prefix and its fitness with short scientific notation.
 	 * 
 	 * @param prefix
 	 * @param pop
 	 * @see FunctionArea.drawIcon
 	 */
-	public void drawIndividual(int iconType, int graphID, String prefix,
+	public void drawIndividual(int iconType, int graphID, String prefix, AbstractEAIndividual indy) {
-			AbstractEAIndividual indy) {
+		StringBuffer sb = new StringBuffer();
-		getFunctionArea().drawIcon(iconType, prefix + " " + indy.getFitness(0),
+		Formatter formatter = new Formatter(sb, Locale.US);
-				indy.getDoublePosition(), graphID);
+		formatter.format("%s %.3e", prefix, indy.getFitness(0));
+
+		getFunctionArea().drawIcon(iconType, sb.toString(), indy.getDoublePosition(), graphID);
 	}
 
 	public void setPreferredSize(Dimension prefSize) {

src/eva2/gui/PropertySheetPanel.java

@@ -912,7 +912,6 @@ public class PropertySheetPanel extends JPanel implements PropertyChangeListener
 				try {
 					val = getter.invoke(m_Target, (Object[])null);
 				} catch (Exception e) {
-					// TODO Auto-generated catch block
 					val = null;
 					e.printStackTrace();
 				}

src/eva2/server/go/enums/MutateESCrossoverTypeEnum.java

@@ -0,0 +1,5 @@
+package eva2.server.go.enums;
+
+public enum MutateESCrossoverTypeEnum {
+	none, intermediate, discrete;
+}

src/eva2/server/go/individuals/AbstractEAIndividual.java

@@ -3,6 +3,7 @@ package eva2.server.go.individuals;
 
 import java.util.ArrayList;
 import java.util.BitSet;
+import java.util.Formatter;
 import java.util.HashMap;
 import java.util.List;
 
@@ -805,7 +806,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     public double getCrossoverProbability() {
         return this.m_CrossoverProbability;
     }
-    public String crossoverProbalilityTipText() {
+    public String crossoverProbabilityTipText() {
         return "The chance that crossover occurs.";
     }
 
@@ -876,13 +877,13 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     public static String getDefaultStringRepresentation(AbstractEAIndividual individual) {
     	// Note that changing this method might change the hashcode of an individual 
     	// which might interfere with some functionality.
-        StringBuffer sb = new StringBuffer("Fit.: ");
+        StringBuffer sb = new StringBuffer("Fit.:\t");
         sb.append(BeanInspector.toString(individual.getFitness()));
         if (individual.isMarkedPenalized() || individual.violatesConstraint()) 
         	sb.append(", X"); 
         sb.append(", ID: ");
         sb.append(individual.getIndyID());
-        sb.append(", ");
+        sb.append(",\t");
         sb.append(getDefaultDataString(individual));
 
         if (individual.getParentIDs()!=null) {
@@ -902,7 +903,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     public static String getDefaultDataString(IndividualInterface individual) {
     	// Note that changing this method might change the hashcode of an individual 
     	// which might interfere with some functionality.
-    	return getDefaultDataString(individual, ", ");
+    	return getDefaultDataString(individual, ",");
     }
     
     /**
@@ -917,6 +918,7 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
     	// which might interfere with some functionality.
     	if (individual == null) return "null";
         StringBuffer sb = new StringBuffer("");
+        Formatter fm = new Formatter(sb);
         char left = '{';
         char right = '}';
         sb.append(left);
@@ -933,10 +935,12 @@ public abstract class AbstractEAIndividual implements IndividualInterface, java.
                 if ((i+1) < b.length) sb.append(separator);
             }
         } else if (individual instanceof InterfaceDataTypeDouble) {
-            double[] b = ((InterfaceDataTypeDouble)individual).getDoubleData();
+            double[] d = ((InterfaceDataTypeDouble)individual).getDoubleData();
-            for (int i = 0; i < b.length; i++) {
+            for (int i = 0; i < d.length; i++) {
-                sb.append(b[i]);
+//                sb.append(d[i]);
-                if ((i+1) < b.length) sb.append(separator);
+//                if ((i+1) < d.length) sb.append(separator);
+            	fm.format("% .3f", d[i]);
+            	if ((i+1) < d.length) sb.append(separator);
             }
         } else if (individual instanceof InterfaceDataTypePermutation) {
             int[] b = ((InterfaceDataTypePermutation)individual).getPermutationData()[0];

src/eva2/server/go/individuals/ESIndividualDoubleData.java

@@ -1,6 +1,7 @@
 package eva2.server.go.individuals;
 
 
+import eva2.server.go.enums.MutateESCrossoverTypeEnum;
 import eva2.server.go.operators.crossover.CrossoverESDefault;
 import eva2.server.go.operators.mutation.InterfaceMutation;
 import eva2.server.go.operators.mutation.MutateESGlobal;
@@ -25,7 +26,7 @@ public class ESIndividualDoubleData extends AbstractEAIndividual implements Inte
 
     public ESIndividualDoubleData() {
         this.m_MutationProbability  = 1.0;
-        this.m_MutationOperator     = new MutateESGlobal();
+        this.m_MutationOperator     = new MutateESGlobal(0.2, MutateESCrossoverTypeEnum.intermediate);
         this.m_CrossoverProbability = 0.5;
         this.m_CrossoverOperator    = new CrossoverESDefault();
         this.m_Genotype             = new double[1];

src/eva2/server/go/individuals/IndividualDistanceComparator.java

@@ -0,0 +1,40 @@
+package eva2.server.go.individuals;
+
+import java.util.Comparator;
+
+import eva2.server.go.operators.distancemetric.InterfaceDistanceMetric;
+
+/**
+ * Compare two AbstractEAIndividuals by their distance to a reference individual.
+ * Usable to sort by a distance.
+ **/
+public class IndividualDistanceComparator implements Comparator<Object> {
+
+	private AbstractEAIndividual refIndy=null;
+	private InterfaceDistanceMetric distMetric = null;
+	private boolean closerMeansLess = true;
+	
+	/**
+	 * Constructor with a reference individual to which the distance is measured and the corresponding desired metric.
+	 * Using closerIsLess it can be switched between increasing and decreasing order. 
+	 * 
+	 * @param referenceIndy
+	 * @param metric
+	 * @param closerIsLess
+	 */
+	public IndividualDistanceComparator(AbstractEAIndividual referenceIndy, InterfaceDistanceMetric metric, boolean closerIsLess) {
+		refIndy = referenceIndy;
+		distMetric = metric;
+		closerMeansLess = closerIsLess;
+	}
+
+	public int compare(Object o1, Object o2) {
+		double d1 = distMetric.distance((AbstractEAIndividual)o1, refIndy);
+		double d2 = distMetric.distance((AbstractEAIndividual)o2, refIndy);
+
+		if (d1==d2) return 0;
+		if (closerMeansLess) return ((d1<d2) ? -1 : 1);
+		else return ((d1<d2) ? 1 : -1);
+	}
+
+}

src/eva2/server/go/mocco/MOCCOProblemInitialization.java

@@ -60,7 +60,6 @@ public class MOCCOProblemInitialization extends MOCCOPhase implements InterfaceP
 		try {
 			altern = ReflectPackage.getAssignableClassesInPackage("eva2.server.go.problems", Class.forName("eva2.server.go.problems.InterfaceMultiObjectiveDeNovoProblem"), true, true);
 		} catch (ClassNotFoundException e) {
-			// TODO Auto-generated catch block
 			e.printStackTrace();
 		}
         this.m_ProblemChooser.setModel(new DefaultComboBoxModel(altern));

src/eva2/server/go/operators/archiving/ArchivingMaxiMin.java

@@ -53,7 +53,7 @@ public class ArchivingMaxiMin implements InterfaceArchiving, java.io.Serializabl
         Population tmpPop = new Population();
         tmpPop.addPopulation((Population)pop.getClone());
         tmpPop.addPopulation((Population)pop.getArchive().getClone());
-        tmpPop.removeDoubleInstancesUsingFitness();
+        tmpPop.removeRedundantIndiesUsingFitness();
 
         // Now calculate the MaxiMin Criterium
         this.m_MaxiMin.convertMultiObjective2SingleObjective(tmpPop);

src/eva2/server/go/operators/archiving/ArchivingNSGAII.java

@@ -65,7 +65,7 @@ public class ArchivingNSGAII extends ArchivingNSGA implements java.io.Serializab
         Population tmpPop = new Population();
         tmpPop.addPopulation((Population)pop.getClone());
         tmpPop.addPopulation((Population)pop.getArchive().getClone());
-        tmpPop.removeDoubleInstancesUsingFitness();
+        tmpPop.removeRedundantIndiesUsingFitness();
 
         // Now fetch the n pareto-fronts
         Population[] fronts = this.getNonDomiatedSortedFronts(tmpPop);

src/eva2/server/go/operators/archiving/ArchivingSPEAII.java

@@ -63,7 +63,7 @@ public class ArchivingSPEAII extends AbstractArchiving implements java.io.Serial
         Population tmpPop = new Population();
         tmpPop.addPopulation((Population)pop.getClone());
         tmpPop.addPopulation((Population)pop.getArchive().getClone());
-        tmpPop.removeDoubleInstancesUsingFitness();
+        tmpPop.removeRedundantIndiesUsingFitness();
 //        double[][] d = this.showMay(tmpPop);
 
         double[] RawFitness     = this.calculateRawFitness(tmpPop);

src/eva2/server/go/operators/constraint/GenericConstraint.java

@@ -84,10 +84,6 @@ public class GenericConstraint extends AbstractConstraint implements InterfaceDo
 		return 0.;
 	}
 
-//	private double evalLinearConstr(AbstractEAIndividual indy) {
-//		// TODO Auto-generated method stub
-//		return null;
-//	}
 	public String getConstraintString() {
 		return constraintString;
 	}

src/eva2/server/go/operators/crossover/CrossoverESDefault.java

@@ -48,6 +48,9 @@ public class CrossoverESDefault implements InterfaceCrossover, java.io.Serializa
         }
         //for (int i = 0; i < result.length; i++) System.out.println("Before Crossover: " +result[i].getSolutionRepresentationFor());
 
+        if (partners==null || (partners.size()==0)) {
+        	throw new RuntimeException("Error, empty partner population for crossover!");
+        }
         if ((indy1 instanceof InterfaceESIndividual) && (partners.get(0) instanceof InterfaceESIndividual)) {
             int     crossoverpoint = RNG.randomInt(0,((InterfaceESIndividual)indy1).getDGenotype().length-1);
             boolean switcher = RNG.randomBoolean();
@@ -75,7 +78,7 @@ public class CrossoverESDefault implements InterfaceCrossover, java.io.Serializa
             // write the result back
             for (int i = 0; i < result.length; i++) ((InterfaceESIndividual)result[i]).SetDGenotype(children[i]);
         }
-        //in case the crossover was successfull lets give the mutation operators a chance to mate the strategy parameters
+        //in case the crossover was successful lets give the mutation operators a chance to mate the strategy parameters
         for (int i = 0; i < result.length; i++) result[i].getMutationOperator().crossoverOnStrategyParameters(indy1, partners);
         //for (int i = 0; i < result.length; i++) System.out.println("After Crossover: " +result[i].getSolutionRepresentationFor());
         return result;

src/eva2/server/go/operators/distancemetric/EuclideanMetric.java

@@ -1,5 +1,7 @@
 package eva2.server.go.operators.distancemetric;
 
+import java.io.Serializable;
+
 import eva2.server.go.individuals.AbstractEAIndividual;
 
 /**
@@ -10,7 +12,7 @@ import eva2.server.go.individuals.AbstractEAIndividual;
  * @author mkron
  *
  */
-public class EuclideanMetric implements InterfaceDistanceMetric {
+public class EuclideanMetric implements InterfaceDistanceMetric, Serializable {
 
 	public Object clone() {
 		return (Object) new EuclideanMetric(this);

src/eva2/server/go/operators/distancemetric/IndividualDataMetric.java

@@ -24,7 +24,6 @@ public class IndividualDataMetric implements InterfaceDistanceMetric, Serializab
 	}
     
     public IndividualDataMetric(IndividualDataMetric pBestMetric) {
-		// TODO Auto-generated constructor stub
 	}
 
 	/** This method allows you to make a deep clone of

src/eva2/server/go/operators/migration/MOClusteringSeparation.java

@@ -390,12 +390,12 @@ public class MOClusteringSeparation implements InterfaceMigration, java.io.Seria
      * @return The distance type to use.
      */
     public boolean getReuseC() {
-        this.m_ReuseC = this.m_KMeans.m_ReuseC;
+        this.m_ReuseC = this.m_KMeans.getReuseC();
         return this.m_ReuseC;
     }
     public void setReuseC(boolean m){
         this.m_ReuseC = m;
-        this.m_KMeans.m_ReuseC = this.m_ReuseC;
+        this.m_KMeans.setReuseC(this.m_ReuseC);
     }
     public String reuseCTipText() {
         return "Toggel reuse of previously found cluster centroids.";

src/eva2/server/go/operators/moso/MOSOMaxiMin.java

@@ -108,7 +108,7 @@ public class MOSOMaxiMin implements InterfaceMOSOConverter, java.io.Serializable
 
         tmpFit          = indy.getFitness();
         indy.putData("MOFitness", tmpFit);
-        System.out.println("The MaxiMin MOSO can not be applied to single individuals! I default to random criterion.");
+        System.err.println("The MaxiMin MOSO can not be applied to single individuals! I default to random criterion.");
         resultFit[0]    = tmpFit[RNG.randomInt(0, tmpFit.length)];
         indy.SetFitness(resultFit);
     }

src/eva2/server/go/operators/mutation/MutateESGlobal.java

@@ -2,6 +2,7 @@ package eva2.server.go.operators.mutation;
 
 import java.util.ArrayList;
 
+import eva2.server.go.enums.MutateESCrossoverTypeEnum;
 import eva2.server.go.individuals.AbstractEAIndividual;
 import eva2.server.go.individuals.InterfaceESIndividual;
 import eva2.server.go.populations.Population;
@@ -21,26 +22,35 @@ public class MutateESGlobal implements InterfaceMutation, java.io.Serializable {
     protected double      m_MutationStepSize    = 0.2;
     protected double      m_Tau1                = 0.15;
     protected double      m_LowerLimitStepSize  = 0.0000005;
-    protected SelectedTag m_CrossoverType;
+    protected MutateESCrossoverTypeEnum m_CrossoverType = MutateESCrossoverTypeEnum.none;
 
     public MutateESGlobal() {
-        initTags();
     }
 
+    /**
+     * Use given mutation step size and no crossover on strategy params.
+     * 
+     * @param mutationStepSize
+     */
     public MutateESGlobal(double mutationStepSize) {
-        initTags();
+    	this(mutationStepSize, MutateESCrossoverTypeEnum.none);
+    }
+
+    /**
+     * Use given mutation step size and given crossover type on strategy params.
+     * 
+     * @param mutationStepSize
+     */
+    public MutateESGlobal(double mutationStepSize, MutateESCrossoverTypeEnum coType) {
         setMutationStepSize(mutationStepSize);
+        setCrossoverType(coType);
     }
     
     public MutateESGlobal(MutateESGlobal mutator) {
         this.m_MutationStepSize     = mutator.m_MutationStepSize;
         this.m_Tau1                 = mutator.m_Tau1;
         this.m_LowerLimitStepSize   = mutator.m_LowerLimitStepSize;
-        this.m_CrossoverType        = (SelectedTag)mutator.m_CrossoverType.clone();
+        this.m_CrossoverType        = mutator.m_CrossoverType;
-    }
-
-    protected void initTags() {
-        this.m_CrossoverType = new SelectedTag(new String[]{"None", "Intermediate", "Discrete"});
     }
 
     /** This method will enable you to clone a given mutation operator
@@ -100,29 +110,29 @@ public class MutateESGlobal implements InterfaceMutation, java.io.Serializable {
      * @param partners  The original partners
      */
     public void crossoverOnStrategyParameters(AbstractEAIndividual indy1, Population partners) {
-        ArrayList<Double> tmpList = new ArrayList<Double>();
+    	if (m_CrossoverType!=MutateESCrossoverTypeEnum.none) {
-        if (indy1.getMutationOperator() instanceof MutateESGlobal) tmpList.add(new Double(((MutateESGlobal)indy1.getMutationOperator()).m_MutationStepSize));
+    		ArrayList<Double> tmpList = new ArrayList<Double>();
-        for (int i = 0; i < partners.size(); i++) {
+    		if (indy1.getMutationOperator() instanceof MutateESGlobal) tmpList.add(new Double(((MutateESGlobal)indy1.getMutationOperator()).m_MutationStepSize));
-            if (((AbstractEAIndividual)partners.get(i)).getMutationOperator() instanceof MutateESGlobal) tmpList.add(new Double(((MutateESGlobal)((AbstractEAIndividual)partners.get(i)).getMutationOperator()).m_MutationStepSize));
+    		for (int i = 0; i < partners.size(); i++) {
-        }
+    			if (((AbstractEAIndividual)partners.get(i)).getMutationOperator() instanceof MutateESGlobal) tmpList.add(new Double(((MutateESGlobal)((AbstractEAIndividual)partners.get(i)).getMutationOperator()).m_MutationStepSize));
-        double[] list = new double[tmpList.size()];
+    		}
-        for (int i = 0; i < tmpList.size(); i++) list[i] = ((Double)tmpList.get(i)).doubleValue();
+    		double[] list = new double[tmpList.size()];
-        if (list.length <= 1) return;
+    		for (int i = 0; i < tmpList.size(); i++) list[i] = ((Double)tmpList.get(i)).doubleValue();
-        switch (this.m_CrossoverType.getSelectedTag().getID()) {
+    		if (list.length <= 1) return;
-            case 1 : {
+
-                this.m_MutationStepSize = 0;
+    		switch (this.m_CrossoverType) {
-                for (int i = 0; i < list.length; i++) this.m_MutationStepSize += list[i];
+    		case intermediate : 
-                this.m_MutationStepSize = this.m_MutationStepSize/(double)list.length;
+    			this.m_MutationStepSize = 0;
-                break;
+    			for (int i = 0; i < list.length; i++) this.m_MutationStepSize += list[i];
-            }
+    			this.m_MutationStepSize = this.m_MutationStepSize/(double)list.length;
-            case 2 : {
+    			break;
-                this.m_MutationStepSize = list[RNG.randomInt(0, list.length-1)];
+    		case discrete : 
-                break;
+    			this.m_MutationStepSize = list[RNG.randomInt(0, list.length-1)];
-            }
+    			break;
-            default : {
+    		case none : // do nothing
-                // do nothing
+    		break;
-            }
+    		}
-        }
+    	}
     }
 
     /** This method allows you to get a string representation of the mutation
@@ -195,10 +205,10 @@ public class MutateESGlobal implements InterfaceMutation, java.io.Serializable {
     /** Set the value for tau1 with this method.
      * @param d   The mutation operator.
      */
-    public void setCrossoverType(SelectedTag d) {
+    public void setCrossoverType(MutateESCrossoverTypeEnum d) {
         this.m_CrossoverType = d;
     }
-    public SelectedTag getCrossoverType() {
+    public MutateESCrossoverTypeEnum getCrossoverType() {
         return this.m_CrossoverType;
     }
     public String crossoverTypeTipText() {

src/eva2/server/go/operators/selection/SelectBestIndividuals.java

@@ -41,7 +41,8 @@ public class SelectBestIndividuals implements InterfaceSelection, java.io.Serial
         // nothing to prepare here
     }
 
-    /** This method will select one Individual from the given
+    /** 
+     * This method will select n best individuals from the given
      * Population.
      * @param population    The source population where to select from
      * @param size          The number of Individuals to select

src/eva2/server/go/operators/selection/SelectBestSingle.java

@@ -56,6 +56,7 @@ public class SelectBestSingle implements InterfaceSelection, java.io.Serializabl
         int                     currentCriteria = 0, critSize;
         double                  currentBestValue;
 
+        if (population.size()==0) return population; // trivial
         critSize = ((AbstractEAIndividual)population.get(0)).getFitness().length;
         result.setTargetSize(size);
         if (this.m_ObeyDebsConstViolationPrinciple) {
@@ -99,13 +100,21 @@ public class SelectBestSingle implements InterfaceSelection, java.io.Serializabl
 
     /** This method allows you to select >size< partners for a given Individual
      * @param dad               The already selected parent
-     * @param avaiablePartners  The mating pool.
+     * @param availablePartners  The mating pool.
      * @param size              The number of partners needed.
      * @return The selected partners.
      */
-    public Population findPartnerFor(AbstractEAIndividual dad, Population avaiablePartners, int size) {
+    public Population findPartnerFor(AbstractEAIndividual dad, Population availablePartners, int size) {
-    	if (excludeSelfAsPartner) return this.selectFrom(avaiablePartners.filter(new Population().addToPop(dad)), size);
+    	if (excludeSelfAsPartner) {
-    	else return this.selectFrom(avaiablePartners, size);
+    		Population newPartners = availablePartners.filter(new Population().addToPop(dad));
+    		if (newPartners.size()==0) { 
+    			// no problem if crossover is deactivated
+//    			EVAERROR.errorMsgOnce("Warning, no partners available when excluding self (SelectBestSingle.findPartnerFor)");
+//    			EVAERROR.errorMsgOnce("Partners where: " + availablePartners.getStringRepresentation());
+    			return newPartners;
+    		}
+    		return this.selectFrom(newPartners, size);
+    	} else return this.selectFrom(availablePartners, size);
     }
 /**********************************************************************************************************************
  * These are for GUI

src/eva2/server/go/operators/terminators/HistoryConvergenceTerminator.java

@@ -73,6 +73,8 @@ public class HistoryConvergenceTerminator implements InterfaceTerminator, Serial
             	  msg = "History did not improve" + (convergenceThreshold>0 ? (" by more than " + convergenceThreshold) : "") + " for " + haltingWindowLen + " iterations.";
               }
         	}
+        } else {
+        	if (haltingWindowLen > ((Population)pop).getMaxHistLength()) System.err.println("Warning, population history length not long enough for window length " + haltingWindowLen + " (HistoryConvergenceTerminator)");
         }
         return res;
 	}

src/eva2/server/go/populations/Population.java

@@ -2,8 +2,10 @@ package eva2.server.go.populations;
 
 import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.Comparator;
 import java.util.HashMap;
 import java.util.Iterator;
+import java.util.LinkedList;
 import java.util.List;
 import java.util.PriorityQueue;
 import java.util.Set;
@@ -22,6 +24,7 @@ import eva2.server.go.operators.postprocess.PostProcess;
 import eva2.server.go.operators.selection.probability.AbstractSelProb;
 import eva2.tools.EVAERROR;
 import eva2.tools.Pair;
+import eva2.tools.Serializer;
 import eva2.tools.math.Mathematics;
 import eva2.tools.math.RNG;
 import eva2.tools.math.StatisticUtils;
@@ -58,9 +61,10 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     protected int 			notifyEvalInterval	= 0;
     // additional data connected to the population
     protected HashMap<String, Object>		additionalPopData = null;
-    // historical best indidivuals may be traced
+    // historical best indidivuals may be traced for a given number of generations. Set to -1 to trace all, set to 0 to not trace at all
-    boolean useHistory						= false;
+    int historyMaxLen = 0;
-    private transient ArrayList<AbstractEAIndividual>  m_History       = new ArrayList<AbstractEAIndividual>();
+//    boolean useHistory						= false;
+    private transient LinkedList<AbstractEAIndividual>  m_History       = new LinkedList<AbstractEAIndividual>();
 
     // remember when the last sorted queue was prepared
     private int lastQModCount = -1;
@@ -78,7 +82,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     }
     
     /**
-     * Constructor setting initial capacity and population size to the given
+     * Constructor setting initial capacity and target population size to the given
      * integer value.
      * 
      * @param initialCapacity initial capacity and population size of the instance
@@ -123,7 +127,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 	public Population(InterfaceSolutionSet allSolutions) {
 		this(allSolutions.getCurrentPopulation().size()+allSolutions.getSolutions().size());
     	if (TRACE) System.err.println("TRACING POP");
-		addPopulation(allSolutions.getCurrentPopulation());
+		addPopulation(allSolutions.getCurrentPopulation(), false);
 		HashMap<Long, Integer> checkCols = new HashMap<Long, Integer>(size());
 		for (int i=0; i<size(); i++) {
 			checkCols.put(getEAIndividual(i).getIndyID(), 1);
@@ -139,9 +143,13 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		setInitMethod(getInitMethod());
 	}
 	
+	/**
+	 * Make (shallow) copies of the given instance within the current instance. 
+	 * @param population
+	 */
 	public void copyHistAndArchive(Population population) {
     	if (population.m_Archive != null) this.m_Archive = (Population)population.m_Archive.clone();
-    	if (population.m_History != null) this.m_History = (ArrayList<AbstractEAIndividual>)population.m_History.clone();
+    	if (population.m_History != null) this.m_History = (LinkedList<AbstractEAIndividual>)population.m_History.clone();
     	if (population.additionalPopData!=null) {
     		this.additionalPopData = (HashMap<String, Object>)additionalPopData.clone();
     		if (population.additionalPopData.size()>0) {
@@ -161,7 +169,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         this.m_Generation       = population.m_Generation;
         this.m_FunctionCalls    = population.m_FunctionCalls;
         this.m_TargetSize       = population.m_TargetSize;
-        this.useHistory 		= population.useHistory;
+        this.historyMaxLen 		= population.historyMaxLen;
         this.notifyEvalInterval = population.notifyEvalInterval;
         this.initMethod			= population.initMethod;
     	this.aroundDist			= population.aroundDist;
@@ -190,7 +198,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
     	if (this.m_Generation   != opop.m_Generation) return false;
     	if (this.m_FunctionCalls    != opop.m_FunctionCalls) return false;
     	if (this.m_TargetSize       != opop.m_TargetSize) return false;
-    	if (this.useHistory 		!= opop.useHistory) return false;
+    	if (this.historyMaxLen 		!= opop.historyMaxLen) return false;
     	if (this.notifyEvalInterval != opop.notifyEvalInterval) return false;
     	if (this.initMethod			!= opop.initMethod) return false;
     	if (this.aroundDist			!= opop.aroundDist) return false;
@@ -253,7 +261,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * have been inited by a problem
      */
     public void init() {
-        this.m_History = new ArrayList<AbstractEAIndividual>();
+        this.m_History = new LinkedList<AbstractEAIndividual>();
         this.m_Generation       = 0;
         this.m_FunctionCalls    = 0;
 //    	evaluationTimeHashes = null;
@@ -371,10 +379,19 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * @param useHist
      */
     public void setUseHistory(boolean useHist) {
-    	useHistory = useHist;
+    	if (useHist) setMaxHistoryLength(-1); // trace full history!
+    	else setMaxHistoryLength(0); // dont trace at all!
 //    	this.setUseHistory(useHist, (useHist ? (new AbstractEAIndividualComparator()) : null));
     }
-    
+    public boolean isUsingHistory() {
+    	return historyMaxLen!=0;
+    }
+    public void setMaxHistoryLength(int len) {
+    	historyMaxLen = len; 
+    }
+    public int getMaxHistLength() {
+    	return historyMaxLen;
+    }
 //    /**
 //     * Activate or deactivate the history tracking, which stores the best individual in every
 //     * generation in the incrGeneration() method.
@@ -388,15 +405,15 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 //    }
     
     public int getHistoryLength() {
-    	if (useHistory) return m_History.size();
+    	if (historyMaxLen!=0) return m_History.size();
     	else return 0;
     }
     
-    public ArrayList<AbstractEAIndividual> getHistory() {
+    public LinkedList<AbstractEAIndividual> getHistory() {
     	return m_History;
     }
     
-    public void SetHistory(ArrayList<AbstractEAIndividual> theHist) {
+    public void SetHistory(LinkedList<AbstractEAIndividual> theHist) {
     	m_History = theHist;
     }
     
@@ -485,8 +502,12 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * Stagnation measured etc. pp.
      */
     public void incrGeneration() {
-        if (useHistory && (this.size() >= 1)) {
+        if (isUsingHistory() && (this.size() >= 1)) {
-        	this.m_History.add((AbstractEAIndividual)this.getBestEAIndividual().clone());// TODO
+        	if (historyMaxLen>0 && (m_History.size()>=historyMaxLen)) {
+        		// oldest one must be replaced.. should be the one in front
+        		m_History.removeFirst();
+        	}
+        	this.m_History.add((AbstractEAIndividual)this.getBestEAIndividual().clone());
         }
         for (int i=0; i<size(); i++) ((AbstractEAIndividual)get(i)).incrAge(); 
         this.m_Generation++;
@@ -527,14 +548,32 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 
     /** This method allows you to add a complete population to the current population.
      * Note: After this operation the target population size may be exceeded.
+     * This method does not check for duplicate object pointers.
+     * 
      * @param pop   The population that is to be added.
      */
     public Population addPopulation(Population pop) {
+    	return this.addPopulation(pop, false);
+    }
+    
+    /**
+     * This method allows you to add a complete population to the current population.
+     * Note: After this operation the target population size may be exceeded.
+     * If indicated, this method checks for and avoids duplicate object pointers.
+     * 
+     * @param pop   The population that is to be added.
+     * @param avoidDuplicatePointers if true, duplicate object pointers are forbidden
+     */
+    public Population addPopulation(Population pop, boolean avoidDuplicatePointers) {
         if (pop != null) {
         	for (int i = 0; i < pop.size(); i++) {
         		AbstractEAIndividual indy = (AbstractEAIndividual)pop.get(i);
-        		if (indy != null) {
+        		if (avoidDuplicatePointers && this.contains(indy)) {
-        			this.add(indy);
+        			System.err.println("Warning, duplicate indy avoided in Population.addPopulation! Index of " + this.indexOf(indy));
+        		} else {
+        			if (indy != null) {
+        				this.add(indy);
+        			}
         		}
         	}
         }
@@ -1058,10 +1097,10 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * Sort the population returning a new ArrayList.
      * The returned array should not be modified!
      * 
-     * @param comp A comparator by which sorting is performed
+     * @param comp A comparator by which sorting is performed - it should work on AbstractEAIndividual instances.
      * @return
      */
-    public ArrayList<AbstractEAIndividual> getSorted(AbstractEAIndividualComparator comp) {
+    public ArrayList<AbstractEAIndividual> getSorted(Comparator<Object> comp) {
     	if (super.size()==0) return new ArrayList<AbstractEAIndividual>();
 		PriorityQueue<AbstractEAIndividual> sQueue = new PriorityQueue<AbstractEAIndividual>(super.size(), comp);
 		for (int i = 0; i < super.size(); i++) {
@@ -1206,7 +1245,7 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
      * This method relies on the implementation of the equals method
      * in the individuals.
      */
-    public void removeDoubleInstances() {
+    public void removeRedundantIndies() {
         for (int i = 0; i < this.size(); i++) {
             for (int j = i+1; j < this.size(); j++) {
                 if (((AbstractEAIndividual)this.get(i)).equals(this.get(j))) {
@@ -1217,9 +1256,34 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         }
     }
 
+    public Population removeRedundantIndiesAsNew() {
+    	Population pop = this.cloneShallowInds();
+    	pop.removeRedundantIndies();
+    	return pop;
+    }
+	/**
+	 * Return the number of individuals within this instance that are
+	 * seen as being equal by the equals relation of the individual.
+	 * 
+	 * @return the number of equal individuals
+	 * 
+	 */
+	public int getRedundancyCount() {
+		int redund = 0;
+		for (int i=0; i<size()-1; i++) {
+			for (int j=i+1; j<size(); j++) {
+				if (getEAIndividual(i).equals(getEAIndividual(j))) {
+					redund++;
+					break; // jump because the i-th is redundant and we dont want to count all redundant pairs
+				}
+			}
+		}
+		return redund;
+	}
+	
     /** This method will remove instances with equal fitness from the population.
      */
-    public void removeDoubleInstancesUsingFitness() {
+    public void removeRedundantIndiesUsingFitness() {
         for (int i = 0; i < this.size(); i++) {
             for (int j = i+1; j < this.size(); j++) {
                 if (((AbstractEAIndividual)this.get(i)).equalFitness((AbstractEAIndividual)this.get(j))) {
@@ -1477,9 +1541,17 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
         return false;
     }
 
-	public void removeMembers(Population tmpPop, boolean errorOnMissing) {
+    /**
-		for (int i=0; i<tmpPop.size(); i++) {
+     * Remove a set of individuals from the instance. If indicated by the errorOnMissing-flag
-			if (!removeMember(tmpPop.getEAIndividual(i))) {
+     * a RuntimeException is thrown as soon as one individual to be removed is not contained
+     * in the instance. Otherwise, this is ignored.
+     * 
+     * @param tmpPop
+     * @param errorOnMissing
+     */
+	public void removeMembers(Population popToRemove, boolean errorOnMissing) {
+		for (int i=0; i<popToRemove.size(); i++) {
+			if (!removeMember(popToRemove.getEAIndividual(i))) {
 				if (errorOnMissing) throw new RuntimeException("Error, member to be removed was missing (Population.removeMembers)!");
 			}
 		}
@@ -1584,6 +1656,42 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		return res;
 	}
 	
+	/**
+	 * Return the minimal, maximal, average, median, and variance of correlations between any two solutions.
+	 * @return
+	 */
+	public double[] getCorrelations() {
+		return Population.getCorrelations(this);
+	}
+	
+	/**
+	 * Return the minimal, maximal, average, median, and variance of correlations between any two solutions.
+	 * 
+	 * @param pop the population instance to look at
+	 * @return
+	 */
+	public static double[] getCorrelations(Population pop) {
+		double[] cors = new double[pop.size()*(pop.size()-1)/2]; 
+	
+		if (pop.size()<2) {
+			return new double[]{1.,1.,1.,1.};
+		}
+		int index=0;
+		double corsSum=0, minCor = 10., maxCor=-10.;
+        for (int i = 0; i < pop.size()-1; i++) {
+        	for (int j = i+1; j < pop.size(); j++) {
+        		double cor = StatisticUtils.correlation(pop.getEAIndividual(i).getDoublePosition(), pop.getEAIndividual(j).getDoublePosition());
+        		cors[index++] = cor; 
+        		corsSum += cor;
+        		if (cor>maxCor) maxCor=cor;
+        		if (cor<minCor) minCor=cor;
+        	}
+        }
+        double var = StatisticUtils.variance(cors, true);
+        double[] res = new double[] {minCor, maxCor, corsSum/cors.length, Mathematics.median(cors, false), var};
+        return res;
+	}
+	
     /**
      * Returns the average, minimal and maximal individual fitness and std dev. for the population in the given criterion.
      *
@@ -1673,11 +1781,13 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		 double dist = -1.;
 		 int sel=-1;
 		 for (int i = 0; i < pop.size(); i++) {
-			 double curDist = metric.distance(refIndy, pop.getEAIndividual(i));
+			 if (pop.getEAIndividual(i)!=null) {
-			 if ((dist<0) 	|| (!closestOrFarthest && (dist < curDist)) 
+				 double curDist = metric.distance(refIndy, pop.getEAIndividual(i));
-					 		|| (closestOrFarthest && (dist > curDist))) {
+				 if ((dist<0) 	|| (!closestOrFarthest && (dist < curDist)) 
-				 dist = curDist;
+						 || (closestOrFarthest && (dist > curDist))) {
-				 sel = i;
+					 dist = curDist;
+					 sel = i;
+				 }
 			 }
 		 }
 		 return new Pair<Integer,Double>(sel,dist);
@@ -1693,7 +1803,14 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 	  */
 	 public boolean isWithinPopDist(AbstractEAIndividual indy, double d, InterfaceDistanceMetric metric) {
 		 Pair<Integer,Double> closest = Population.getClosestFarthestIndy(indy, this, metric, true);
-		 return (closest.tail()<=d);
+		 if (closest.tail()<=d) {
+			 return true;
+		 } else {
+//			 if (size()==1) {
+//				 System.out.println("dist is " + metric.distance(getEAIndividual(0), indy));
+//			 }
+			 return false;
+		 }
 	 }
 	 
 	/**
@@ -1711,13 +1828,29 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		return centerPos;
 	}
 	
+	/**
+	 * Return an individual which is located at the center of the population.
+	 * The fitness will be invalid.
+	 * 
+	 * @return
+	 */
+	public IndividualInterface getCenterIndy() {
+		AbstractEAIndividual indy = (AbstractEAIndividual)getEAIndividual(0).clone();
+		double[] center = getCenter();
+		indy.setDoublePosition(indy, center);
+		indy.SetFitness(null);
+		return indy;
+	}
+	
 	/**
 	 * Calculate the weighted center position of the population. Weights must add up to one!
 	 * 
 	 * @return the average position of the population
 	 */
 	public double[] getCenterWeighted(double[] weights) {
-		if (size()==0 || (weights.length > size())) EVAERROR.errorMsgOnce("Invalid pop size in DistractingPopulation:getCenterWeighted!");
+		if (size()==0 || (weights.length > size()) || (weights.length==0)) {
+			EVAERROR.errorMsgOnce("Invalid pop size in DistractingPopulation:getCenterWeighted!");
+		}
 		double[] centerPos = AbstractEAIndividual.getDoublePosition(getEAIndividual(0));
 		Mathematics.svMult(weights[0], centerPos, centerPos);
 		for (int i=1; i<weights.length; i++) {
@@ -2005,6 +2138,56 @@ public class Population extends ArrayList implements PopulationInterface, Clonea
 		}
 	}
 
+	/**
+	 * Return true if the current instance is a subset of the given population, otherwise false.
+	 * 
+	 * @param offspring
+	 * @return
+	 */
+	public boolean isSubSet(Population set) {
+		Population filtered = this.filter(set); // if this is a subset of s, filtered must be empty. 
+		return (filtered.size()==0);
+	}
+
+	/**
+	 * Return the set of individuals which are members of both this and the other population.
+	 * Uses the individuals equals predicate.
+	 * 
+	 * @param other
+	 * @return
+	 */
+	public Population setCut(Population other) {
+		Population cut = new Population();
+		for (int i=0; i<size(); i++) {
+			if (other.indexOf(getEAIndividual(i))>=0) cut.add(getEAIndividual(i));
+		}
+		return cut;
+	}
+
+	/**
+	 * Copy all additional population data of the given instance
+	 * into the current instance (by references only).
+	 * 
+	 * @param pop
+	 */
+	public void copyHashData(Population pop) {
+		if (pop!=null && (pop.additionalPopData!=null)) {
+			for (String key : pop.additionalPopData.keySet()) {
+				Object origData = pop.getData(key);
+				Object maybeClone = Serializer.deepClone(origData);
+				if (maybeClone!=null) putData(key, maybeClone);
+				else {
+					System.err.println("Warning, additional pop data could not be cloned!");
+					putData(key, origData);
+				}
+			}
+		}
+	}
+
+	public void clearHistory() {
+		m_History.clear();
+	}
+
 //	/**
 //	 * Mark the population at the current state as evaluated. Changes to the modCount or hashes of individuals
 //	 * will invalidate the mark.

src/eva2/server/go/problems/AbstractOptimizationProblem.java

@@ -240,7 +240,7 @@ implements InterfaceOptimizationProblem /*, InterfaceParamControllable*/, Serial
      * @return String
      */
     public String[] getAdditionalFileStringHeader(PopulationInterface pop) {
-    	if (this instanceof InterfaceInterestingHistogram) return new String[]{"Solution","Histogram(c0)","Score"};
+    	if (this instanceof InterfaceInterestingHistogram) return new String[]{"Solution","Histogram","Score"};
     	else return new String[]{"Solution"};
     }
 

src/eva2/server/go/problems/AbstractProblemDouble.java

@@ -494,7 +494,7 @@ public abstract class AbstractProblemDouble extends AbstractOptimizationProblem
 	@Override
 	public String[] getAdditionalFileStringHeader(PopulationInterface pop) {
 		String[] superHeader = super.getAdditionalFileStringHeader(pop);
-		if (isWithConstraints()) return ToolBox.appendArrays(superHeader, new String[]{"RawFit.","Num.Viol.","Sum.Viol."});
+		if (isWithConstraints()) return ToolBox.appendArrays(superHeader, new String[]{"rawFit","numViol","sumViol"});
 		else return superHeader;
 	}
 

src/eva2/server/go/strategies/ClusterBasedNichingEA.java

@@ -2,6 +2,8 @@ package eva2.server.go.strategies;
 
 import java.util.ArrayList;
 import java.util.Collections;
+import java.util.LinkedList;
+import java.util.List;
 import java.util.PriorityQueue;
 
 import eva2.gui.BeanInspector;
@@ -17,7 +19,6 @@ import eva2.server.go.individuals.InterfaceDataTypeDouble;
 import eva2.server.go.operators.cluster.ClusteringDensityBased;
 import eva2.server.go.operators.cluster.InterfaceClustering;
 import eva2.server.go.operators.distancemetric.ObjectiveSpaceMetric;
-//import eva2.server.go.populations.Distraction;
 import eva2.server.go.populations.Population;
 import eva2.server.go.populations.SolutionSet;
 import eva2.server.go.problems.B1Problem;
@@ -327,7 +328,7 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
      * @return True if converged.
      */
     private boolean testSpeciesForConvergence(Population pop) {
-        ArrayList<AbstractEAIndividual> speciesHistory = pop.getHistory();
+        List<AbstractEAIndividual> speciesHistory = pop.getHistory();
         int     histLen = speciesHistory.size();
 
         if (histLen <= haltingWindow) {
@@ -780,10 +781,10 @@ public class ClusterBasedNichingEA implements InterfacePopulationChangedEventLis
 		newSp.setUseHistory(true);
     	if (startAtP1Gen) { // start explicitely as a child population of p1
     		newSp.setGenerationTo(parentSp.getGeneration());
-    		newSp.SetHistory((ArrayList<AbstractEAIndividual>) parentSp.getHistory().clone());
+    		newSp.SetHistory((LinkedList<AbstractEAIndividual>) parentSp.getHistory().clone());
     	} else { // start anew (from undiff)
         	newSp.setGenerationTo(0);
-            newSp.SetHistory(new ArrayList<AbstractEAIndividual>());
+            newSp.SetHistory(new LinkedList<AbstractEAIndividual>());
     	}
     	
         if (m_Optimizer instanceof InterfaceSpeciesAware) ((InterfaceSpeciesAware)m_Optimizer).splitFromFirst(parentSp, newSp);

src/eva2/server/modules/Processor.java

@@ -310,7 +310,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
 		else if (terminator instanceof EvaluationTerminator)
 			args = new Object[] {optimizer, optimizer.getPopulation(), optimizer.getPopulation().getFunctionCalls(), ((EvaluationTerminator)terminator).getFitnessCalls()};
 //			((InterfaceParameterControl)paramCtrl).updateParameters(optimizer, optimizer.getPopulation().getFunctionCalls(), ((EvaluationTerminator)terminator).getFitnessCalls());
-		else args = null;//new Object[]{optimizer, optimizer.getPopulation()};
+		else args = new Object[]{optimizer, optimizer.getPopulation()};
 //			((InterfaceParameterControl)paramCtrl).updateParameters(optimizer);
     	
 		if (args != null) { // only if iteration counting is available

src/eva2/server/stat/AbstractStatistics.java

@@ -305,6 +305,9 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 		} else {
 			if (printRunStoppedVerbosity()) printToTextListener(" NO feasible individual found.\n");
 		}
+		if (printRunStoppedVerbosity()) {
+			printToTextListener(" Solution correlations (min,max,avg,med,var): " + BeanInspector.toString(((Population)lastSols).getCorrelations((Population)lastSols)) + "\n");
+		}
 		if (bestOfRunFeasibleIndy != null) { 
 			runBestFeasibleList.add(bestOfRunFeasibleIndy);
 //			if (meanBestFeasibleFit==null) {
@@ -666,8 +669,8 @@ public abstract class AbstractStatistics implements InterfaceTextListener, Inter
 			case meanFit: ret[i] = (currentMeanFit==null) ? Double.NaN : currentMeanFit[defaultFitCriterion]; break;
 			case currentWorst: ret[i] = (currentWorstFit==null) ? Double.NaN : currentWorstFit[defaultFitCriterion]; break;
 			case runBest: ret[i] = bestOfRunIndy.getFitness()[defaultFitCriterion]; break;
-			case currentBestFeasible: ret[i] = currentBestFeasibleFit[defaultFitCriterion]; break;
+			case currentBestFeasible: ret[i] = (currentBestFeasibleFit==null) ? Double.NaN : currentBestFeasibleFit[defaultFitCriterion]; break;
-			case runBestFeasible: ret[i] = bestOfRunFeasibleIndy.getFitness()[defaultFitCriterion]; break;
+			case runBestFeasible: ret[i] = (bestOfRunFeasibleIndy==null) ? Double.NaN : bestOfRunFeasibleIndy.getFitness()[defaultFitCriterion]; break;
 			case avgPopDistance: ret[i] = currentAvgPopDist; break;
 			case maxPopDistance: ret[i] = currentMaxPopDist; break;
 			}

src/eva2/server/stat/StatisticsWithGUI.java

@@ -241,7 +241,10 @@ public class StatisticsWithGUI extends AbstractStatistics implements Serializabl
 				if (lastIsShowFull) colIndex = 1+graphDesc.get(i).tail;
 				// plot the column as indicated by the graph description
 				if (currentStatDoubleData[colIndex]!=null) plotFitnessPoint(0, subGraph++, functionCalls, currentStatDoubleData[colIndex]);
-				else EVAERROR.errorMsgOnce("Error, data field " + graphDesc.get(i).head + " does not contain primitive data and cannot be plotted.");
+				else {
+					EVAERROR.errorMsgOnce("Error, data field " + graphDesc.get(i).head + " does not contain primitive data and cannot be plotted.");
+					subGraph++; // increase index anyways or the name assignment gets inconsistent
+				}
 			}
 //		}
 	}

src/eva2/tools/Serializer.java

@@ -12,6 +12,8 @@ package eva2.tools;
 /*==========================================================================*
  * IMPORTS
  *==========================================================================*/
+import java.io.ByteArrayInputStream;
+import java.io.ByteArrayOutputStream;
 import java.io.File;
 import java.io.FileInputStream;
 import java.io.FileOutputStream;
@@ -68,6 +70,35 @@ public class Serializer {
     return ret;
   }
 
+  /**
+   * Returns a copy of the object, or null if the object cannot
+   * be serialized.
+   */
+  public static Object deepClone(Object orig) {
+      Object obj = null;
+      try {
+          // Write the object out to a byte array
+          ByteArrayOutputStream bos = new ByteArrayOutputStream();
+          ObjectOutputStream out = new ObjectOutputStream(bos);
+          out.writeObject(orig);
+          out.flush();
+          out.close();
+
+          // Make an input stream from the byte array and read
+          // a copy of the object back in.
+          ObjectInputStream in = new ObjectInputStream(
+              new ByteArrayInputStream(bos.toByteArray()));
+          obj = in.readObject();
+      }
+      catch(IOException e) {
+          e.printStackTrace();
+      }
+      catch(ClassNotFoundException cnfe) {
+          cnfe.printStackTrace();
+      }
+      return obj;
+  }
+
 
   /**
     * Use object serialization to make a "deep clone" of the object o.
@@ -77,35 +108,35 @@ public class Serializer {
     * usually implemented to produce a "shallow" clone that copies references
     * to other objects, instead of copying all referenced objects.
   **/
-  static public Object deepclone(final Serializable o) throws IOException, ClassNotFoundException {
+//  static public Object deepclone(final Serializable o) throws IOException, ClassNotFoundException {
-    // Create a connected pair of "piped" streams.
+//    // Create a connected pair of "piped" streams.
-    // We'll write bytes to one, and them from the other one.
+//    // We'll write bytes to one, and them from the other one.
-    final PipedOutputStream pipeout = new PipedOutputStream();
+//    final PipedOutputStream pipeout = new PipedOutputStream();
-    PipedInputStream pipein = new PipedInputStream(pipeout);
+//    PipedInputStream pipein = new PipedInputStream(pipeout);
-    // Now define an independent thread to serialize the object and write
+//    // Now define an independent thread to serialize the object and write
-    // its bytes to the PipedOutputStream
+//    // its bytes to the PipedOutputStream
-    Thread writer = new Thread() {
+//    Thread writer = new Thread() {
-      public void run() {
+//      public void run() {
-	ObjectOutputStream out = null;
+//	ObjectOutputStream out = null;
-	try {
+//	try {
-	  out = new ObjectOutputStream(pipeout);
+//	  out = new ObjectOutputStream(pipeout);
-	  out.writeObject(o); }
+//	  out.writeObject(o); }
-	catch(IOException e) {
+//	catch(IOException e) {
-	  System.out.println("ERROR in Serialization1"+ e.getMessage());
+//	  System.out.println("ERROR in Serialization1"+ e.getMessage());
-	}
+//	}
-	finally {
+//	finally {
-	  try { out.close(); } catch (Exception e) {
+//	  try { out.close(); } catch (Exception e) {
-	    System.out.println("ERROR in Serialization2"+ e.getMessage());
+//	    System.out.println("ERROR in Serialization2"+ e.getMessage());
-	  }
+//	  }
-	}
+//	}
-      }
+//      }
-    };
+//    };
-    writer.start();
+//    writer.start();
-    // Meanwhile, in this thread, read and deserialize from the piped
+//    // Meanwhile, in this thread, read and deserialize from the piped
-    // input stream.  The resulting object is a deep clone of the original.
+//    // input stream.  The resulting object is a deep clone of the original.
-    ObjectInputStream in = new ObjectInputStream(pipein);
+//    ObjectInputStream in = new ObjectInputStream(pipein);
-    return in.readObject();
+//    return in.readObject();
-  }
+//  }
   /**
      * This is a simple serializable data structure that we use below for
      * testing the methods above
@@ -123,8 +154,6 @@ public class Serializer {
     }
   }
 
-  /** This class defines a main() method for testing */
-  public static class Test {
     public static void main(String[] args) throws IOException, ClassNotFoundException {
       // Create a simple object graph
       DataStructure ds = new DataStructure();
@@ -144,11 +173,11 @@ public class Serializer {
       System.out.println("Read from the file: " + ds);
       // Create a deep clone and display that.  After making the copy
       // modify the original to prove that the clone is "deep".
-      DataStructure ds2 = (DataStructure) Serializer.deepclone(ds);
+      DataStructure ds2 = (DataStructure) Serializer.deepClone(ds);
       ds.other.message = null; ds.other.data = null; // Change original
       System.out.println("Deep clone: " + ds2);
     }
-  }
+
   /**
   * Serialize the string s and
   * store its serialized state in File with name Filename.

src/eva2/tools/math/Mathematics.java

@@ -485,7 +485,7 @@ public class Mathematics {
 	 * @return the median
 	 */
 	public static double[] median(List<double[]> dblArrList, boolean interpolate) {
-		java.util.Collections.sort(dblArrList, new DoubleArrayComparator());
+		java.util.Collections.sort(dblArrList, new DoubleArrayComparator()); // by default, the comparator uses pareto dominance
 			
 		int len = dblArrList.size();
 		if (len % 2 != 0) return dblArrList.get((len-1) / 2);
@@ -1081,6 +1081,21 @@ public class Mathematics {
 		vvAddOffs(v1, 0, v2, 0, res, 0, v1.length);
 	}
 
+	/**
+	 * Calculate r=1/2 * sqrt(sum(sqr(upperBound_i - lowerBound_i))).
+	 * 
+	 * @param range
+	 * @return the average length of the range intervals
+	 */
+    public static double getAvgRangeL2(double[][] range) {
+		double sum = 0.;
+		for (int i=0; i<range.length; i++) {
+			double d=(range[i][1]-range[i][0]);
+			sum+=(d*d);
+		}
+		return Math.sqrt(sum)/2.;
+	}
+    
     /**
 	 * Add vectors in place setting with an offset within the target
 	 * vector, meaning that res[resOffs+i]=v1[v1Offs+i]+v2[v2Offs+i] for i in length.
@@ -1159,4 +1174,17 @@ public class Mathematics {
 		Arrays.fill(result, 0, result.length-1, 0.);
 		return result;
 	}
+
+	/**
+	 * Create a double vector of length dim filled with value d.
+	 * 
+	 * @param d
+	 * @param dim
+	 * @return a double vector of length dim filled with value d
+	 */
+	public static double[] makeVector(double d, int dim) {
+		double[] ret = new double[dim];
+		Arrays.fill(ret, d);
+		return ret;
+	}
 }

src/eva2/tools/math/StatisticUtils.java

@@ -17,6 +17,8 @@ package eva2.tools.math;
 import java.util.ArrayList;
 import java.util.Collections;
 
+import eva2.gui.BeanInspector;
+import eva2.server.go.problems.AbstractProblemDouble;
 import eva2.tools.math.Jama.Matrix;
 
 /**
@@ -38,10 +40,12 @@ public class StatisticUtils
 	 * @param n the length of two double vectors
 	 * @return the correlation coefficient
 	 */
-	public final static double correlation(double y1[],double y2[],int n) {
+	public final static double correlation(double y1[],double y2[]) {
 
 		int i;
 		double av1 = 0.0, av2 = 0.0, y11 = 0.0, y22 = 0.0, y12 = 0.0, c;
+		int n=y1.length;
+		if (n!=y2.length) throw new RuntimeException("Error, mismatching vectors for correlation calculation in StatisticUtils.correlation(double[], double[])");
 
 		if (n <= 1) {
 			return 1.0;
@@ -230,21 +234,24 @@ public class StatisticUtils
 	 * Computes the variance for an array of doubles.
 	 *
 	 * @param vector the array
+	 * @param finiteSet if true, the vector is interpreted as complete data set, otherwise a set of samples of a larger set.
 	 * @return the variance
 	 */
-	public static double variance(double[] vector) {
+	public static double variance(double[] vector, boolean finiteSet) {
 
 		double sum = 0, sumSquared = 0;
-
+		int n=vector.length;
-		if (vector.length <= 1) {
+		if (n <= 1) {
 			return 0;
 		}
-		for (int i = 0; i < vector.length; i++) {
+		for (int i = 0; i < n; i++) {
 			sum += vector[i];
 			sumSquared += (vector[i] * vector[i]);
 		}
-		double result = (sumSquared - (sum * sum / (double) vector.length)) /
+		double denom;
-		(double) (vector.length - 1);
+		if (finiteSet) denom=n;
+		else denom=(n-1);
+		double result = (sumSquared - (sum * sum / (double) n)) / denom;
 
 		// We don't like negative variance
 		if (result < 0) {
@@ -371,8 +378,7 @@ public class StatisticUtils
 	 *
 	 * @author Joerg K. Wegner
 	 */
-	public static double getCorrelationCoefficient(double array1[], double array2[])
+	public static double getCorrelationCoefficient(double array1[], double array2[]) {
-	{
 		if ((array1 == null) || (array2 == null)) { return -2.0; }
 
 		double sumA  = 0;
@@ -406,48 +412,236 @@ public class StatisticUtils
 	 * Main method for testing this class.
 	 */
 	public static void main(String[] ops) {
+		double[][] vs = {{ 356.873,-498.563, 130.923,-223.684, 494.296, 380.739, 481.353, 344.571, 501.105,-166.109},
+				{ 199.116,-423.448, 487.898, 344.579, 140.709, 89.007,-259.310, 512.000, 152.069,-440.778},
+				{ 439.128, 112.370,-422.820,-43.119,-297.609,-438.940, 488.914,-512.000,-407.847, 386.611},
+				{ 395.772, 191.634,-511.999,-93.078,-282.853,-444.621, 491.291,-512.000,-407.620, 386.493}};
 		
-		//    System.out.println("test (0.5, 100): " +
+		vs[1]=Mathematics.rotate(vs[0], AbstractProblemDouble.initDefaultRotationMatrix(10, 10));
-		//		       StatisticUtils.test(100));
+		
+		for (int i=0; i<vs.length; i++) {
+			for (int j=0; j<vs.length; j++) {
+				System.out.print("\t" + correlation(vs[i], vs[j]));
+			}
+			System.out.println();
+		}
+//		vs[1]=Mathematics.rotate(vs[0], AbstractProblemDouble.initDefaultRotationMatrix(30, 10));
+//		vs[2]=Mathematics.rotate(vs[0], AbstractProblemDouble.initDefaultRotationMatrix(50, 10));
+//		vs[3]=Mathematics.rotate(vs[0], AbstractProblemDouble.initDefaultRotationMatrix(70, 10));
+		
+		vs = new double[][]{{0.1,0.9},
+							{0.8,0.2},
+//							{0.8,0.2},
+							{0.0,1.0}};
+		System.out.println("---");
+		
+//		for (int i=0; i<vs.length; i++) {
+//			Mathematics.svAdd(512, vs[i], vs[i]);
+//			Mathematics.normVect(vs[i], vs[i]);
+//			System.out.println(BeanInspector.toString(vs[i]));
+//		}
+		
+//		System.out.println("entropy cols: " + entropyOverColumns(vs));
+//		System.out.println("entropy rows: " + entropyOverRows(vs));
 	}
 	
-	// The following methods got mysteriously lost maybe during cvs-svn refactoring.
+    /**
-	// For the time being I add method thunks which give a warning when called. (mkron)
+     * Computes the entropy of the given array.
-	public static double quadratic_entropy(double[] ds) {
+     *
-		// TODO Auto-generated method stub
+     * @param array an array of double data
-		System.err.println("warning, not implemented!");
+     * @return the entropy
-		return 0;
+     */
-	}
+    public static double entropy(double[] array) {
+        double returnValue = 0;
+        double sum = 0;
 
-	public static double mutual_information(double[] ds, double[] ds2, int nbins) {
+        for (int i = 0; i < array.length; i++) {
-		// TODO Auto-generated method stub
+            returnValue -= lnFunc(array[i]);
-		System.err.println("warning, not implemented!");
+            sum += array[i];
-		return 0;
+        }
-	}
 
-	public static double quadratic_mutinf(double[] feature, double[] labels) {
+        if (StatisticUtils.eq(sum, 0)) {
-		// TODO Auto-generated method stub
+            return 0;
-		System.err.println("warning, not implemented!");
+        } else {
-		return 0;
+            return (returnValue + lnFunc(sum)) / (sum * log2);
-	}
+        }
+    }
 
-	public static double quadratic_mutinf(double[] feature, double[] labels, int[] classes) {
-		// TODO Auto-generated method stub
-		System.err.println("warning, not implemented!");
-		return 0;
-	}
     
-	public static double SUquadratic(double[] feature, double[] labels) {
+    // these came from ContingencyTables.java in the wsi package (mkron)
-		// TODO Auto-generated method stub
+//    /**
-		System.err.println("warning, not implemented!");
+//     * Computes conditional entropy of the rows given
-		return 0;
+//     * the columns.
-	}
+//     *
+//     * @param matrix the contingency table
+//     * @return the conditional entropy of the rows given the columns
+//     */
+//    public static double entropyConditionedOnColumns(double[][] matrix) {
+//        double ret = 0;
+//        double colSum;
+//        double total = 0;
+//
+//        for (int j = 0; j < matrix[0].length; j++) {
+//            colSum = 0;
+//
+//            for (int i = 0; i < matrix.length; i++) {
+//                ret = ret + lnFunc(matrix[i][j]);
+//                colSum += matrix[i][j];
+//            }
+//
+//            ret = ret - lnFunc(colSum);
+//            total += colSum;
+//        }
+//
+//        if (StatisticUtils.eq(total, 0)) {
+//            return 0;
+//        }
+//
+//        return -ret / (total * log2);
+//    }
 
-	public static double SUquadratic(double[] feature, double[] labels, int[] classes) {
+//    /**
-		// TODO Auto-generated method stub
+//     * Computes conditional entropy of the columns given
-		System.err.println("warning, not implemented!");
+//     * the rows.
-		return 0;
+//     *
-	}
+//     * @param matrix the contingency table
+//     * @return the conditional entropy of the columns given the rows
+//     */
+//    public static double entropyConditionedOnRows(double[][] matrix) {
+//        double returnValue = 0;
+//        double sumForRow;
+//        double total = 0;
+//
+//        for (int i = 0; i < matrix.length; i++) {
+//            sumForRow = 0;
+//
+//            for (int j = 0; j < matrix[0].length; j++) {
+//                returnValue = returnValue + lnFunc(matrix[i][j]);
+//                sumForRow += matrix[i][j];
+//            }
+//            returnValue = returnValue - lnFunc(sumForRow);
+//            total += sumForRow;
+//        }
+//
+//        if (StatisticUtils.eq(total, 0)) {
+//            return 0;
+//        }
+//
+//        return -returnValue / (total * log2);
+//    }
+
+//    /**
+//     * Computes the columns' entropy for the given contingency table.
+//     *
+//     * @param matrix the contingency table
+//     * @return the columns' entropy
+//     */
+//    public static double entropyOverColumns(double[][] matrix)
+//    {
+//        double returnValue = 0;
+//        double sumForColumn;
+//        double total = 0;
+//
+//        for (int j = 0; j < matrix[0].length; j++)
+//        {
+//            sumForColumn = 0;
+//
+//            for (int i = 0; i < matrix.length; i++)
+//            {
+//                sumForColumn += matrix[i][j];
+//            }
+//
+//            returnValue = returnValue - lnFunc(sumForColumn);
+//            total += sumForColumn;
+//        }
+//
+//        if (StatisticUtils.eq(total, 0))
+//        {
+//            return 0;
+//        }
+//
+//        return (returnValue + lnFunc(total)) / (total * log2);
+//    }
+
+//    /**
+//     * Computes the rows' entropy for the given contingency table.
+//     *
+//     * @param matrix the contingency table
+//     * @return the rows' entropy
+//     */
+//    public static double entropyOverRows(double[][] matrix)
+//    {
+//        double returnValue = 0;
+//        double sumForRow;
+//        double total = 0;
+//
+//        for (int i = 0; i < matrix.length; i++)
+//        {
+//            sumForRow = 0;
+//
+//            for (int j = 0; j < matrix[0].length; j++)
+//            {
+//                sumForRow += matrix[i][j];
+//            }
+//
+//            returnValue = returnValue - lnFunc(sumForRow);
+//            total += sumForRow;
+//        }
+//
+//        if (StatisticUtils.eq(total, 0))
+//        {
+//            return 0;
+//        }
+//
+//        return (returnValue + lnFunc(total)) / (total * log2);
+//    }
+    
+    private static double lnFunc(double num) {
+        // hard coded for efficiency reasons
+        if (num < 1e-7) {
+            return 0;
+        } else {
+            return num * Math.log(num);
+        }
+    }
+    
+//	// The following methods got mysteriously lost maybe during cvs-svn refactoring.
+//	// For the time being I add method thunks which give a warning when called. (mkron)
+//	public static double quadratic_entropy(double[] ds) {
+//		// TODO Auto-generated method stub
+//		System.err.println("warning, not implemented!");
+//		return 0;
+//	}
+//
+//	public static double mutual_information(double[] ds, double[] ds2, int nbins) {
+//		// TODO Auto-generated method stub
+//		System.err.println("warning, not implemented!");
+//		return 0;
+//	}
+//
+//	public static double quadratic_mutinf(double[] feature, double[] labels) {
+//		// TODO Auto-generated method stub
+//		System.err.println("warning, not implemented!");
+//		return 0;
+//	}
+//
+//	public static double quadratic_mutinf(double[] feature, double[] labels, int[] classes) {
+//		// TODO Auto-generated method stub
+//		System.err.println("warning, not implemented!");
+//		return 0;
+//	}
+//
+//	public static double SUquadratic(double[] feature, double[] labels) {
+//		// TODO Auto-generated method stub
+//		System.err.println("warning, not implemented!");
+//		return 0;
+//	}
+//
+//	public static double SUquadratic(double[] feature, double[] labels, int[] classes) {
+//		// TODO Auto-generated method stub
+//		System.err.println("warning, not implemented!");
+//		return 0;
+//	}
 
 	/**
 	 * Random Latin Hypercube Sampling within a given double range.