Add agent constrained samarah

src/jcl/clustering/Cluster.java

@@ -6,6 +6,7 @@ import java.io.Serializable;
 import jcl.data.Data;
 import jcl.data.DataObject;
 import jcl.weights.Weights;
+import multiCube.tools.util.exceptions.MethodNotImplementedException;
 
 /**
  * <p>
@@ -28,7 +29,7 @@ abstract public class Cluster implements Serializable {
 	protected Color color = new Color(0);
 
 	/** The cluster's father node color*/
-	protected Color couleur_pere = new Color(0);
+	protected Color couleur_pere = null;
 	
 	/** L'index qui correspond a la place du cluster dans result */
 	protected int id = -1;
@@ -204,17 +205,21 @@ abstract public class Cluster implements Serializable {
 	 * 
 	 * @param o
 	 *            l'index de l'objet
-	 * @return le degre d'appartenance du o-ieme objet e la classe
+	 * @return le degre d'appartenance du o-ieme objet e la classe	 * 
+	 * @throws MethodNotImplementedException
+	 *             si la methode n'implante pas cet operateur
 	 */
-	abstract public double getMembership(final int o);
+	abstract public double getMembership(final int o) throws MethodNotImplementedException;
 	/**
 	 * <p>
 	 * Probabilite de la classe.
 	 * </p>
 	 * 
-	 * @return la probabilite de la classe
+	 * @return la probabilite de la classe	 * 
+	 * @throws MethodNotImplementedException
+	 *             si la methode n'implante pas cet operateur
 	 */
-	abstract public double getMembershipProbabilityEM();
+	abstract public double getMembershipProbabilityEM() throws MethodNotImplementedException;
 
 	/**
 	 * <p>

src/jcl/clustering/LightCluster.java

@@ -7,6 +7,7 @@ import jcl.data.mask.IntArrayMask.IntMaskIterator;
 import jcl.data.mask.Mask;
 import jcl.weights.Weights;
 import jj2000.j2k.NotImplementedError;
+import multiCube.tools.util.exceptions.MethodNotImplementedException;
 
 public class LightCluster extends Cluster {
 
@@ -147,13 +148,13 @@ public class LightCluster extends Cluster {
 	
 
 	@Override
-	public double getMembership(int o) {
-		throw new NotImplementedError("method not Implemented for LighCluster class");
+	public double getMembership(int o) throws MethodNotImplementedException{
+		throw new MethodNotImplementedException("method not Implemented for LighCluster class");
 	}
 
 	@Override
-	public double getMembershipProbabilityEM() {
-		throw new NotImplementedError("method not Implemented for LighCluster class");
+	public double getMembershipProbabilityEM() throws MethodNotImplementedException{
+		throw new MethodNotImplementedException("method not Implemented for LighCluster class");
 	}
 
 	@Override

src/jcl/clustering/SimpleCluster.java

@@ -6,6 +6,7 @@ import jcl.data.Data;
 import jcl.data.DataObject;
 import jcl.data.SimpleData;
 import jcl.weights.Weights;
+import multiCube.tools.util.exceptions.MethodNotImplementedException;
 
 /**
  * <p>
@@ -44,7 +45,11 @@ public class SimpleCluster extends Cluster {
 		}	
 		this.membershipDegree = new double[cluster.membershipDegree.length];
 		for (int i = 0; i < this.membershipDegree.length; i++) {
-			this.membershipDegree[i] = cluster.getMembership(i);
+			try {
+				this.membershipDegree[i] = cluster.getMembership(i);
+			} catch (MethodNotImplementedException e) {
+				e.printStackTrace();
+			}
 		}	
 		this.membershipProbabilityEM = cluster.membershipProbabilityEM;
 	}
@@ -230,18 +235,22 @@ public class SimpleCluster extends Cluster {
 			this.data = (Data) cluster.getData(fromSample).clone();
 		}
 		
-		if(cluster instanceof SimpleCluster) {
-			this.membershipDegree = new double[((SimpleCluster)cluster).membershipDegree.length];
-			for (int i = 0; i < this.membershipDegree.length; i++) {
-				this.membershipDegree[i] = cluster.getMembership(i);
-			}
-			this.membershipProbabilityEM = ((SimpleCluster)cluster).membershipProbabilityEM;
-		
-		} else {
-			this.membershipDegree = new double[cluster.getCard()];
-			for (int i = 0; i < this.membershipDegree.length; i++) {
-				this.membershipDegree[i] = cluster.getMembership(i);
+		try {
+			if(cluster instanceof SimpleCluster) {
+				this.membershipDegree = new double[((SimpleCluster)cluster).membershipDegree.length];
+				for (int i = 0; i < this.membershipDegree.length; i++) {
+						this.membershipDegree[i] = cluster.getMembership(i);
+				}
+				this.membershipProbabilityEM = ((SimpleCluster)cluster).membershipProbabilityEM;
+			
+			} else {
+				this.membershipDegree = new double[cluster.getCard()];
+				for (int i = 0; i < this.membershipDegree.length; i++) {
+					this.membershipDegree[i] = cluster.getMembership(i);
+				}
 			}
+		} catch (MethodNotImplementedException e) {
+			e.printStackTrace();
 		}
 		
 		if (this.clusterCenter == null)
@@ -322,7 +331,7 @@ public class SimpleCluster extends Cluster {
 	 *            l'index de l'objet
 	 * @return le degre d'appartenance du o-ieme objet e la classe
 	 */
-	public double getMembership(final int o) {
+	public double getMembership(final int o) throws MethodNotImplementedException {
 		double degree = 0;
 		if ((o >= 0) && (o < this.membershipDegree.length)) {
 			degree = this.membershipDegree[o];
@@ -337,7 +346,7 @@ public class SimpleCluster extends Cluster {
 	 * 
 	 * @return la probabilite de la classe
 	 */
-	public double getMembershipProbabilityEM() {
+	public double getMembershipProbabilityEM() throws MethodNotImplementedException {
 		return this.membershipProbabilityEM;
 	}
 

src/jcl/data/attribute/AttributeMultiDimSequence.java

@@ -853,7 +853,7 @@ public class AttributeMultiDimSequence extends Attribute {
 	}
 
 	public static int ArgMin3(final double a, final double b, final double c) {
-		return (a < b) ? ((a < c) ? 0 : 2) : (b < c) ? 1 : 2;
+		return (a <= b) ? ((a <= c) ? 0 : 2) : (b <= c) ? 1 : 2;
 	}
 
 	public static int getMode() {
@@ -3351,8 +3351,11 @@ public class AttributeMultiDimSequence extends Attribute {
 		// AttributeMultiDimSequence seq1 = new AttributeMultiDimSequence(new double[][] { { 1.0 }, { 10.0 }, { 10.0 }, { 10.0 }, { 1.0 }, { 1.0 }, {
 		// 1.0 } });
 		// AttributeMultiDimSequence seq2 = new AttributeMultiDimSequence(new double[][] { { 1.0 }, { 1.0 }, { 1.0 }, { 10.0 }, { 1.0 } });
-		AttributeMultiDimSequence seq1 = new AttributeMultiDimSequence(new double[][] { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } });
-		AttributeMultiDimSequence seq2 = new AttributeMultiDimSequence(new double[][] { { 0.0 }, { 0.0 }, { 1.0 }, { 0.0 }, { 0.0 } });
+		AttributeMultiDimSequence seq1 = new AttributeMultiDimSequence(new double[][] { { 0.0 }, { 0.0 }, { 0.0 }, { 3.0 }, { 1.0 } });
+		AttributeMultiDimSequence seq2 = new AttributeMultiDimSequence(new double[][] { { 0.0 }, { 0.0 }, { 3.0 }, { 1.0 }, { 0.0 } });
+		AttributeMultiDimSequence seq3 = new AttributeMultiDimSequence(new double[][] { { 0.0 }, { 0.0 }, { 3.0 }, { 3.0 }, { 1.0 } });
+		AttributeMultiDimSequence seq4 = new AttributeMultiDimSequence(new double[][] { { 0.0 }, { 1.0 }, { 1.0 }, { 1.0 }, { 0.0 } });
+		AttributeMultiDimSequence seq5 = new AttributeMultiDimSequence(new double[][] { { 0.0 }, { 0.0 }, { 0.0 }, { 3.0 }, { 0.0 } });
 		//		System.out.println(DTWPageRank(seq1, seq2));
 		//		System.out.println(DTWDistanceToDiagonal(seq1, seq2));
 		//		System.out.println(DTWDistanceToDiagonal(seq2, seq1));
@@ -3365,7 +3368,12 @@ public class AttributeMultiDimSequence extends Attribute {
 		//		System.out.println(DTWDatedTimeToGo(seq1, new short[] { 0, 2, 3, 4 }, seq2, new short[] { 0, 1, 2, 3, 4 }));
 		//		System.out.println(DTWDatedMeanSpeed(seq1, new short[] { 0, 2, 3, 4 }, seq2, new short[] { 0, 1, 2, 3, 4 }));
 		AttributeMultiDimSequence.setMode(DTW_BARYCENTRE);
-		System.out.println(seq1.distance(seq2));
+//		System.out.println(seq1.distance(seq2));
+		AttributeMultiDimSequence[] tabSequence = { seq1, seq2, seq3, seq4, seq5 };
+		
+		AttributeMultiDimSequence avg = DBAMean(seq2, tabSequence);
+		
+		System.out.println("DBA mean : " +avg);
 
 	}
 

src/jcl/evaluation/clustering/ClusteringEvaluation.java

@@ -13,6 +13,7 @@ import jcl.data.DataObject;
 import jcl.evaluation.QualityIndex;
 import jcl.utils.MathTools;
 import jcl.weights.Weights;
+import multiCube.tools.util.exceptions.MethodNotImplementedException;
 
 /**
  * @author Alexandre BLANSCHE
@@ -544,7 +545,11 @@ public class ClusteringEvaluation {
 		int i = 0;
 		while(iter.hasNext()) {
 			final double distance = cluster.distance(iter.next());
-			sum += distance * distance * cluster.getMembership(i);
+			try {
+				sum += distance * distance * cluster.getMembership(i);
+			} catch (MethodNotImplementedException e) {
+				e.printStackTrace();
+			}
 			i++;
 		}
 
@@ -608,9 +613,13 @@ public class ClusteringEvaluation {
 		for (int i = 0; i < clusteringResult.getNbObjects(); i++) {
 			double proba = 0;
 			for (int k = 0; k < clusteringResult.getNbClusters(); k++) {
-				proba += clusteringResult.getCluster(k)
-						.getMembershipProbabilityEM()
-						* clusteringResult.getCluster(k).getMembership(i);
+				try {
+					proba += clusteringResult.getCluster(k)
+							.getMembershipProbabilityEM()
+							* clusteringResult.getCluster(k).getMembership(i);
+				} catch (MethodNotImplementedException e) {
+					e.printStackTrace();
+				}
 			}
 			sum += Math.log(proba);
 		}
@@ -757,7 +766,11 @@ public class ClusteringEvaluation {
 		for (int i = 0; i < clusteringResult.getNbObjects(); i++) {
 			double sum = 0;
 			for (int k = 0; k < membership.length; k++) {
-				membership[k] = clusteringResult.getCluster(k).getMembership(i);
+				try {
+					membership[k] = clusteringResult.getCluster(k).getMembership(i);
+				} catch (MethodNotImplementedException e) {
+					e.printStackTrace();
+				}
 				sum += membership[k];
 			}
 			for (int k = 0; k < membership.length; k++) {
@@ -1016,7 +1029,11 @@ public class ClusteringEvaluation {
 		for (int i = 0; i < clusteringResult.getNbObjects(); i++) {
 			double sum = 0;
 			for (int k = 0; k < membership.length; k++) {
-				membership[k] = clusteringResult.getCluster(k).getMembership(i);
+				try {
+					membership[k] = clusteringResult.getCluster(k).getMembership(i);
+				} catch (MethodNotImplementedException e) {
+					e.printStackTrace();
+				}
 				sum += membership[k];
 			}
 			for (int k = 0; k < membership.length; k++) {

src/jcl/examples/Kmeans.java

@@ -52,36 +52,35 @@ public class Kmeans {
 		Random randGenerator = new Random();
 		for (int i = 0; i < NB_OBJECTS; i++) { //~ Initialization of objects (random)
 
-			Attribute[] attributes = new Attribute[2];
+			Attribute[] attributes = new Attribute[1];
 
 			if (randGenerator.nextDouble() < 0.5) { // first cluster
-				attributes[0] = new AttributeNumerical(randGenerator.nextGaussian() + .5);
+//				attributes[0] = new AttributeNumerical(randGenerator.nextGaussian() + .5);
 
 				double[] sequence = new double[SEQUENCE_LENGTH];
 				for (int l = 0; l < sequence.length; l++) {
 					double p = l + randGenerator.nextGaussian();
 					sequence[l] = cos(p / SEQUENCE_LENGTH * 2 * PI);
 				}
-				attributes[1] = new AttributeSequence(sequence);
+				attributes[0] = new AttributeSequence(sequence);
 
 			} else {
-				attributes[0] = new AttributeNumerical(randGenerator.nextGaussian() - .5);
+//				attributes[0] = new AttributeNumerical(randGenerator.nextGaussian() - .5);
 
 				double[] sequence = new double[SEQUENCE_LENGTH];
 				for (int l = 0; l < sequence.length; l++) {
 					double p = l + randGenerator.nextGaussian();
 					sequence[l] = sin(p / SEQUENCE_LENGTH * 2 * PI);
 				}
-				attributes[1] = new AttributeSequence(sequence);
+				attributes[0] = new AttributeSequence(sequence);
 
 			}
 			objects.add(new DataObject(attributes));
 		}
-
 		//~ In order to create a dataset, a model of comparison has to be set
-		Distance[] distances = new Distance[2]; // a distance is set for every attribute
-		distances[0] = NumericalEuclideanDistance.getInstance(); // first attribute compared with an euclidean distance between numericals
-		distances[1] = jcl.data.distance.sequential.DistanceDTW.getInstance(); // second attribute (sequential) compared with the DTW distance 
+		Distance[] distances = new Distance[1]; // a distance is set for every attribute
+//		distances[0] = NumericalEuclideanDistance.getInstance(); // first attribute compared with an euclidean distance between numericals
+		distances[0] = jcl.data.distance.sequential.DistanceDTW.getInstance(); // second attribute (sequential) compared with the DTW distance 
 		MetaDistance metaDistance = MetaDistanceEuclidean.getInstance(); // defines the way the two scores are combined (possibility to weight) 
 		Model model = new Model(distances, metaDistance);
 
@@ -94,8 +93,8 @@ public class Kmeans {
 
 		DistanceParameter[][] distanceParameters = new DistanceParameter[NB_THREADS][2];
 		for (int th = 0; th < NB_THREADS; th++) {
-			distanceParameters[th][0] = EmptyDistanceParameter.getInstance(); //no parameter for a numerical euclidean distance
-			distanceParameters[th][1] = new ParameterDTW(new double[SEQUENCE_LENGTH][SEQUENCE_LENGTH]); //but yes for DTW (requires a matrix to work in)
+//			distanceParameters[th][0] = EmptyDistanceParameter.getInstance(); //no parameter for a numerical euclidean distance
+			distanceParameters[th][0] = new ParameterDTW(new double[SEQUENCE_LENGTH][SEQUENCE_LENGTH]); //but yes for DTW (requires a matrix to work in)
 		}
 		param = new ParametersKmeans(NB_CLUSTERS, NB_TURNS, NB_THREADS, new GlobalWeights(dataset), distanceParameters);
 		

src/jcl/learning/LearningMethod.java

@@ -8,6 +8,7 @@ import java.util.Vector;
 import javax.swing.JPanel;
 
 import jcl.clustering.ClusteringResult;
+import jcl.clustering.constraints.Constraint;
 import jcl.data.Data;
 import jcl.jcld.RmiServer;
 import jcl.jcld.database.SSHParameters;
@@ -277,60 +278,103 @@ public abstract class LearningMethod implements Progressable, Cloneable,
 
 	/**
 	 * <p>
-	 * Methode de fusion d'un ensemble de classes.
+	 * Method to merge a set of clusters
 	 * </p>
 	 * 
 	 * @param learningResult
-	 *            resultat d'apprentissage
-	 * 
+	 *            the learned model
 	 * @param result
-	 *            le resultat e modifier
+	 *            the result to modify
 	 * @param data
-	 *            les donnees d'apprentrissage
+	 *            the data to train the model on
 	 * @param cr
-	 *            l'ensemble des classes representatives e fusionner
-	 * @return le resultat modifie
+	 *            the set of clusters to merge
+	 * @return the modified result
 	 * @throws MethodNotImplementedException
-	 *             si la methode n'implante pas cet operateur
+	 *             if the LearningMethod does not implement this method
 	 */
 	public abstract ClusteringResult merge(LearningResult learningResult,
 			ClusteringResult result, Data data, Vector cr)
 			throws MethodNotImplementedException;
 
 	/**
-	 * affichage dans la console, deux cas possible: execution local, algorithme
-	 * effetue par le client, affichage direct. execution deporte, renvoie du
-	 * resultat par RMI 
+	 * <p>
+	 * Method to split a cluster
+	 * </p>
 	 * 
-	 * @param s
+	 * @param learningResult
+	 *            the learned model
+	 * @param result
+	 *            the result to modify
+	 * @param data
+	 *            the data to train the model on
+	 * @param c
+	 *            the cluster to remove id
+	 * @param n
+	 *            the number of cluster to split the cluster into
+	 * @return the modified result
+	 * @throws MethodNotImplementedException
+	 *             if the LearningMethod does not implement this method
 	 */
-	public void print(final String s) {
-		for (int i = 0; i < this.viewers.size(); i++) {
-			this.viewers.get(i).print(s);
-		}
-	}
+	public abstract ClusteringResult split(LearningResult learningResult,
+			ClusteringResult result, Data data, int c, int n)
+			throws MethodNotImplementedException;
 
 	/**
 	 * <p>
-	 * Methode de suppresion d'une classe.
+	 * Method to remove a cluster a classify its element 
+	 * in the other ones
 	 * </p>
 	 * 
 	 * @param learningResult
-	 *            resultat d'apprentissage
-	 * 
+	 *            the learned model
 	 * @param result
-	 *            le resultat a modifier
+	 *            the result to modify
 	 * @param data
-	 *            les donnees d'apprentrissage
+	 *            the data to train the model on
 	 * @param c
-	 *            le numero de la classe a supprimer
-	 * @return le resultat modifie
+	 *            the cluster to remove id
+	 * @return the modified result
 	 * @throws MethodNotImplementedException
-	 *             si la methode d'apprentissage n'implante pas cet operateur
+	 *             if the LearningMethod does not implement this method
 	 */
 	public abstract ClusteringResult reclass(LearningResult learningResult,
 			ClusteringResult result, Data data, int c)
 			throws MethodNotImplementedException;
+	
+	/**
+	 * <p>
+	 * Method to compute a new clustering from the existing one that respect the constraints
+	 * </p>
+	 * 
+	 * @param learningResult
+	 *            the learned model
+	 * @param result
+	 *            the result to modify
+	 * @param data
+	 *            the data to train the model on
+	 * @param constraints
+	 *            the set of Constraints to use
+	 * @return the modified result
+	 * @throws MethodNotImplementedException
+	 *             if the LearningMethod does not implement this method
+	 */
+	public abstract ClusteringResult injectConstraints(LearningResult learningResult,
+			ClusteringResult result, Data data, Vector<Constraint> constraints)
+			throws MethodNotImplementedException;
+	
+	/**
+	 * affichage dans la console, deux cas possible: execution local, algorithme
+	 * effetue par le client, affichage direct. execution deporte, renvoie du
+	 * resultat par RMI 
+	 * 
+	 * @param s
+	 */
+	public void print(final String s) {
+		for (int i = 0; i < this.viewers.size(); i++) {
+			this.viewers.get(i).print(s);
+		}
+	}
 
 	/**
 	 * <p>
@@ -381,30 +425,6 @@ public abstract class LearningMethod implements Progressable, Cloneable,
 		return statusName;
 	}
 
-
-	/**
-	 * <p>
-	 * Methode de decoupage d'une classe en sous-classes.
-	 * </p>
-	 * 
-	 * @param learningResult
-	 *            le resultat d'apprentissage
-	 * @param result
-	 *            le resultat a modifier
-	 * @param data
-	 *            les donnees d'apprentissage
-	 * @param c
-	 *            le numero de la classe a scinder
-	 * @param n
-	 *            le nombre de sous-classes desire
-	 * @return le resultat modifie
-	 * @throws MethodNotImplementedException
-	 *             si la methode d'apprentissage n'implante pas cet operateur
-	 */
-	public abstract ClusteringResult split(LearningResult learningResult,
-			ClusteringResult result, Data data, int c, int n)
-			throws MethodNotImplementedException;
-
 	@Override
 	public String toString() {
 		return this.getStringParams();

src/jcl/learning/methods/ClassifierUnified.java

@@ -3,10 +3,12 @@ package jcl.learning.methods;
 import java.util.Vector;
 
 import jcl.clustering.ClusteringResult;
+import jcl.clustering.constraints.Constraint;
 import jcl.data.Data;
 import jcl.learning.LearningMethod;
 import jcl.learning.LearningParameters;
 import jcl.learning.LearningResult;
+import multiCube.tools.util.exceptions.MethodNotImplementedException;
 
 /**
  * <p>
@@ -165,4 +167,10 @@ public class ClassifierUnified extends LearningMethod implements Cloneable {
 				"Method split() not yet implemented.");
 	}
 
+	@Override
+	public ClusteringResult injectConstraints(LearningResult learningResult, ClusteringResult result, Data data,
+			Vector<Constraint> constraints) throws MethodNotImplementedException {
+		throw new MethodNotImplementedException();
+	}
+
 }

src/jcl/learning/methods/ClassifierUtils.java

@@ -87,6 +87,11 @@ public class ClassifierUtils {
 	 */
 	public final static int	       	SAMARAHV4	           				= 121;
 
+	/**
+	 * Identification of the SMARAH algorithm where constraints are handled at agent level
+	 */
+	public final static int	       	SAMARAH_CONSTRAINED           		= 122;
+
 	public final static int	       	MACLAW	               				= 125;
 	/**
 	 * Constante representant un classifieur SAMARAH : Ne pas modifier car correspond à l'indice de

src/jcl/learning/methods/monostrategy/SingleClassification.java

@@ -7,7 +7,6 @@ import java.io.IOException;
 import java.io.ObjectInputStream;
 import java.io.ObjectOutputStream;
 import java.util.ArrayList;
-import java.util.Date;
 import java.util.Vector;
 
 import javax.naming.Context;
@@ -17,6 +16,7 @@ import javax.swing.JOptionPane;
 import jcl.Classification;
 import jcl.clustering.Cluster;
 import jcl.clustering.ClusteringResult;
+import jcl.clustering.constraints.Constraint;
 import jcl.data.Data;
 import jcl.data.SimpleData;
 import jcl.jcld.interJcld;
@@ -27,7 +27,6 @@ import jcl.learning.methods.ClassifierUtils;
 import jcl.learning.methods.monostrategy.ahc.LearningResultAHC;
 import jcl.learning.methods.monostrategy.ahc.ParametersAHC;
 import jcl.learning.methods.monostrategy.kmeans.LearningResultKmeans;
-import jcl.utils.Progressable;
 import jcl.utils.exceptions.JCLFormatException;
 import jcl.weights.ClassificationWeights;
 import multiCube.tools.util.exceptions.MethodNotImplementedException;
@@ -490,6 +489,22 @@ public class SingleClassification extends Classification {
 		}
 
 	}
+	
+	/**
+	 * Compute a new clustering from the existing one that respect the constraints
+	 * @param constraints
+	 * 			the set of Constraints to use
+	 * @throws MethodNotImplementedException
+	 *             if the LearningMethod does not implement this method
+	 */
+	public void injectConstraints(Vector<Constraint> constraints) throws MethodNotImplementedException {
+		if (this.getClusteringResult() != null) {
+			this.clusteringResult = this.getLearningMethod().injectConstraints(
+					this.getLearningResult(), this.getClusteringResult(),
+					this.classificationData, constraints);
+		}
+
+	}
 
 	@Override
 	public void save(final String file) throws IOException {

src/jcl/learning/methods/monostrategy/ahc/ClassifierAHC.java

@@ -4,6 +4,7 @@ import java.util.Iterator;
 import java.util.Vector;
 
 import jcl.clustering.ClusteringResult;
+import jcl.clustering.constraints.Constraint;
 import jcl.data.Data;
 import jcl.data.DataObject;
 import jcl.learning.LearningMethod;
@@ -94,24 +95,27 @@ public class ClassifierAHC extends LearningMethod {
 	public ClusteringResult merge(final LearningResult learningResult,
 			final ClusteringResult result, final Data data, final Vector cr)
 			throws MethodNotImplementedException {
-		// TODO Auto-generated method stub
-		return null;
+		throw new MethodNotImplementedException();
 	}
 
 	@Override
 	public ClusteringResult reclass(final LearningResult learningResult,
 			final ClusteringResult result, final Data data, final int c)
 			throws MethodNotImplementedException {
-		// TODO Auto-generated method stub
-		return null;
+		throw new MethodNotImplementedException();
 	}
 
 	@Override
 	public ClusteringResult split(final LearningResult learningResult,
 			final ClusteringResult result, final Data data, final int c,
 			final int n) throws MethodNotImplementedException {
-		// TODO Auto-generated method stub
-		return null;
+		throw new MethodNotImplementedException();
+	}
+
+	@Override
+	public ClusteringResult injectConstraints(LearningResult learningResult, ClusteringResult result, Data data,
+			Vector<Constraint> constraints) throws MethodNotImplementedException {
+		throw new MethodNotImplementedException();
 	}
 
 }

src/jcl/learning/methods/monostrategy/cobweb/ClassifierCobweb.java

@@ -5,11 +5,13 @@ import java.util.Iterator;
 import java.util.Vector;
 
 import jcl.clustering.ClusteringResult;
+import jcl.clustering.constraints.Constraint;
 import jcl.data.Data;
 import jcl.data.DataObject;
 import jcl.learning.LearningMethod;
 import jcl.learning.LearningResult;
 import jcl.learning.methods.ClassifierUtils;
+import multiCube.tools.util.exceptions.MethodNotImplementedException;
 
 /**
  * <p>
@@ -569,6 +571,12 @@ public class ClassifierCobweb extends LearningMethod implements Cloneable,
 		return resultP;
 	}
 
+	@Override
+	public ClusteringResult injectConstraints(LearningResult learningResult, ClusteringResult result, Data data,
+			Vector<Constraint> constraints) throws MethodNotImplementedException {
+		throw new MethodNotImplementedException();
+	}
+
 	/**