modification of samarah

src/jcl/clustering/LightCluster.java

@@ -5,6 +5,7 @@ import jcl.data.DataObject;
 import jcl.data.mask.IntArrayMask;
 import jcl.data.mask.IntArrayMask.IntMaskIterator;
 import jcl.data.mask.Mask;
+import jcl.weights.Weights;
 import jj2000.j2k.NotImplementedError;
 
 public class LightCluster extends Cluster {
@@ -48,6 +49,7 @@ public class LightCluster extends Cluster {
 		this.id = id;
 		this.clusterCenter = center;
 		this.cardinal = card;
+		this.weights = new Weights(center.getNbAttributes());
 	}
 	
 	/**
@@ -68,6 +70,8 @@ public class LightCluster extends Cluster {
 		this.result = result;
 		this.id = id;
 		this.clusterCenter = center;
+		this.cardinal = IntArrayMask.objectInMask(result.getClusterMap(), id);
+		this.weights = new Weights(center.getNbAttributes());
 	}
 	
 	/**
@@ -85,6 +89,8 @@ public class LightCluster extends Cluster {
 	public LightCluster(final ClusteringResult result, int id) {
 		this.result = result;
 		this.id = id;
+		this.cardinal = IntArrayMask.objectInMask(result.getClusterMap(), id);
+		this.weights = new Weights(result.getData().getOneDataObject().getNbAttributes());
 	}
 	
 	/**
@@ -101,6 +107,7 @@ public class LightCluster extends Cluster {
 		this.result = cluster.getResult();
 		this.id = cluster.getId();
 		this.clusterCenter = cluster.getCenter();
+		this.cardinal = cluster.cardinal;
 	}
 	
 	

src/jcl/clustering/constraints/CannotLinkConstraint.java

 package jcl.clustering.constraints;
 
+import jcl.clustering.ClusteringResult;
+
 /**
  * Class that represent a cannot-link constraint.
  * It specified that two objects shouldn't be assigned to the same class.
@@ -54,4 +56,12 @@ public class CannotLinkConstraint extends Constraint {
 	public int getType() {
 		return Constraint.CANNOT_LINK_TYPE;
 	}
+
+	@Override
+	public double evaluate(ClusteringResult result) {
+		if(result.getClusterMap()[firstIndex] == result.getClusterMap()[secondIndex]) {
+			return 0;
+		}
+		return 1;
+	}
 }

src/jcl/clustering/constraints/ClusterDiameterConstraint.java

 package jcl.clustering.constraints;
 
+import jcl.clustering.ClusteringResult;
+
 /**
  * Class that represent the maximum cluster diameter constraint
  * It specified the maximum diameter possible for a cluster
@@ -42,4 +44,9 @@ public class ClusterDiameterConstraint extends Constraint {
 		return Constraint.CLUSTER_DIAMETER_TYPE;
 	}
 
+	@Override
+	public double evaluate(ClusteringResult result) {
+		return 0;
+	}
+
 }

src/jcl/clustering/constraints/Constraint.java

 package jcl.clustering.constraints;
 
+import jcl.clustering.ClusteringResult;
+
 /**
  * Class that represent a generic constraint.
  * 
@@ -26,11 +28,33 @@ public abstract class Constraint {
 	@Override
 	public abstract String toString();
 	
+	/**
+	 * Return the type of the constraint
+	 * 
+	 * @return the type of constraints (i.e. Constraint.LABEL_TYPE)
+	 */
 	public abstract int getType();
 	
-	public boolean isPixelRelated() {
+	/**
+	 * Evaluate a clustering result
+	 * 
+	 * @param result 
+	 * 			the result to evaluate
+	 * @return a value between 0 and 1, 
+	 * 			1 being the result fully satisfy the constraint, 
+	 * 			-1 if it cannot be evaluate
+	 */
+	public abstract double evaluate(ClusteringResult result);
+	
+	/**
+	 * Indicates if the constraint is about internal or external cluster quality 
+	 * 
+	 * @return true if internal, false if external
+	 */
+	public boolean isInternal() {
 		if (getType() > 100)
 			return false;
 		return true;
 	}
+	
 }

src/jcl/clustering/constraints/LabelConstraint.java

 package jcl.clustering.constraints;
 
+import java.util.Vector;
+
+import jcl.clustering.ClusteringResult;
+
 /**
  * Class that represent a label constraint.
  * It indicates that an object should be assigned to a specified class.
@@ -48,4 +52,55 @@ public class LabelConstraint extends Constraint {
 		return Constraint.LABEL_TYPE;
 	}
 
+	@Override
+	public double evaluate(ClusteringResult result) {
+		// this constraint cannot be evaluate individually 
+		return -1;
+	}
+	
+	/**
+	 * Compute the Rand index between two set, the first set is composed 
+	 * of a clustering result, the second is constructed from a set of labelized indexes
+	 * @param constraints
+	 * 			the set of constraints to construct the second set
+	 * @param weights
+	 * 			the set of weights associated to the labels
+	 * @param result
+	 * 			the clustering result for the second set
+	 * @return the quality evaluation, between 1 and 0
+	 */
+	public static double computeRandIndex(Vector<LabelConstraint> constraints, 
+			Vector<Double> weights, ClusteringResult result) {
+		double totalWeights = 0.0;
+		
+		for(Double d : weights) {
+			totalWeights += d;
+		}
+		
+		return computeRandIndex(constraints, weights, totalWeights, result);
+	}
+	
+
+	/**
+	 * Compute the Rand index between two set, the first set is composed 
+	 * of a clustering result, the second is constructed from a set of labelized indexes
+	 * @param constraints
+	 * 			the set of constraints to construct the second set
+	 * @param weights
+	 * 			the set of weights associated to the labels
+	 * @param totalWeights
+	 * 			sum of the associated weights
+	 * @param result
+	 * 			the clustering result for the second set
+	 * @return the quality evaluation, between 1 and 0
+	 */
+	public static double computeRandIndex(Vector<LabelConstraint> constraints, 
+			Vector<Double> weights, double totalWeights, ClusteringResult result) {
+		double quality = 0.0;
+		
+		// First, we construct the set from labels
+		// we group indexes by labels
+		return quality;
+	}
+
 }

src/jcl/clustering/constraints/MustLinkConstraint.java

 package jcl.clustering.constraints;
 
+import jcl.clustering.ClusteringResult;
+
 /**
  * Class that represent a must-link constraint.
  * It specified that two objects shouldn't be assigned to the same class.
@@ -54,4 +56,12 @@ public class MustLinkConstraint extends Constraint {
 	public int getType() {
 		return Constraint.MUST_LINK_TYPE;
 	}
+
+	@Override
+	public double evaluate(ClusteringResult result) {
+		if(result.getClusterMap()[firstIndex] == result.getClusterMap()[secondIndex]) {
+			return 1;
+		}
+		return 0;
+	}
 }

src/jcl/clustering/constraints/NbClusterConstraint.java

 package jcl.clustering.constraints;
 
+import jcl.clustering.ClusteringResult;
+
 /**
  * Class that represent a number of cluster constraint
  * It specified that the number of cluster should be in a specific range
@@ -57,4 +59,11 @@ public class NbClusterConstraint extends Constraint {
 		return Constraint.NB_CLUSTER_TYPE;
 	}
 
+	@Override
+	public double evaluate(ClusteringResult result) {
+		return (maxBoundry - minBoundry) / (
+					Math.abs(result.getNbClusters() - minBoundry) +
+					Math.abs(maxBoundry - result.getNbClusters()));
+	}
+
 }

src/jcl/data/Data.java

@@ -38,7 +38,7 @@ public abstract class Data implements Cloneable, Serializable, Iterable<DataObje
 	private static final long 		serialVersionUID	 		= 1L;
 	
 	/** fichiers correspondant aux donnees */
-	protected Vector<String> 		dataFilesNames 				= null;
+	protected Vector<String> 		dataFilesNames 				= new Vector<String>();
 
 	// Description de la donnee et des attributs
 	/** ensemble des donnees dans la vue courante */
@@ -96,11 +96,15 @@ public abstract class Data implements Cloneable, Serializable, Iterable<DataObje
 
 	/** a mask to filter the Data */
 	private Mask 					mask 						= null;
+
+	/** the ponderation weight of each constraints */
+	private Vector<Double>			constraintsWeights			= null;
 	
 	/** types to define the source represented by the Data  */
 	static public int 				DEFAULT_TYPE 				= 0;
 	static public int 				NOT_IMAGE_FILE_TYPE 		= 1;
 	static public int 				IMAGE_TYPE 					= 2;
+	
 
 	/**
 	 * <p>
@@ -1761,5 +1765,37 @@ public abstract class Data implements Cloneable, Serializable, Iterable<DataObje
 	 * @return the set of constraints
 	 */
 	public abstract Vector<Constraint> getConstraints();
+
+	/**
+	 * Return the weights associated to the constraints
+	 * 
+	 * @return the constraintsWeights, null if all constraints have the same weight
+	 */
+	public Vector<Double> getConstraintsWeights() {
+		return constraintsWeights;
+	}
+
+	/**
+	 * Return the weight associate to a constraint
+	 * 
+	 * @return the constraint weight, 1 is returned if no weight is set
+	 */
+	public double getConstraintWeight(int index) {
+		try {
+			return constraintsWeights.get(index);
+		} catch (Exception e) {
+			return 1;
+		}
+	}
+	
+	/**
+	 * Set the weights associated to the constraints
+	 * 
+	 * @param constraintsWeights 
+	 * 			the constraints weights to set, null to disable weights
+	 */
+	public void setConstraintsWeights(Vector<Double> constraintsWeights) {
+		this.constraintsWeights = constraintsWeights;
+	}
 	
 }

src/jcl/data/SimpleData.java

@@ -27,7 +27,7 @@ import jcl.weights.Weights;
 /**
  * <p>
  * Basic implementation of the abstract class Data
- * It is performent in access time but has a very high memory consumption
+ * It is performant in access time but has a very high memory consumption
  * <p>
  * 
  * @author Baptiste Lafabregue
@@ -70,6 +70,7 @@ public class SimpleData extends Data {
 		for (int i = 0; i < data.getNbObjects(); i++) {
 			this.currentView.add((DataObject) data.getDataObject(i).clone());
 		}
+		this.wholeDataNbObjects = data.getNbObjects();
 
 		if (data.locked != null) {
 			this.locked = new boolean[data.locked.length];
@@ -86,9 +87,11 @@ public class SimpleData extends Data {
 		}
 		this.inertia = data.inertia;
 
-		this.attributesNames = new String[data.attributesNames.length];
-		for (int i = 0; i < data.attributesNames.length; i++) {
-			this.attributesNames[i] = new String(data.attributesNames[i]);
+		if (attributesNames != null) {
+			this.attributesNames = new String[data.attributesNames.length];
+			for (int i = 0; i < data.attributesNames.length; i++) {
+				this.attributesNames[i] = new String(data.attributesNames[i]);
+			}
 		}
 		this.comments = new String(data.comments);
 		this.dataName = new String(data.dataName);
@@ -123,6 +126,7 @@ public class SimpleData extends Data {
 	 */
 	public SimpleData(final Data data, List<DataObject> newView) {
 		this.currentView = newView;
+		this.wholeDataNbObjects = newView.size();
 
 		if (data.locked != null) {
 			this.locked = new boolean[data.locked.length];
@@ -139,9 +143,11 @@ public class SimpleData extends Data {
 		}
 		this.inertia = data.inertia;
 
-		this.attributesNames = new String[data.attributesNames.length];
-		for (int i = 0; i < data.attributesNames.length; i++) {
-			this.attributesNames[i] = new String(data.attributesNames[i]);
+		if (data.attributesNames != null) {
+			this.attributesNames = new String[data.attributesNames.length];
+			for (int i = 0; i < data.attributesNames.length; i++) {
+				this.attributesNames[i] = new String(data.attributesNames[i]);
+			}
 		}
 		this.comments = new String(data.comments);
 		this.dataName = new String(data.dataName);
@@ -176,6 +182,7 @@ public class SimpleData extends Data {
 			nbObjects += data[i].getNbObjects();
 		}
 		this.currentView = new ArrayList<DataObject>(nbObjects);
+		this.wholeDataNbObjects = nbObjects;
 
 		for (int i = 0; i < data.length; i++) {
 			for (int j = 0; j < data[i].getNbObjects(); j++) {
@@ -237,6 +244,7 @@ public class SimpleData extends Data {
 		} else {
 			data.locked = null;
 		}
+		this.wholeDataNbObjects = currentViewSize;
 
 		final boolean done[] = new boolean[data.getNbObjects()];
 		for (int i = 0; i < done.length; i++) {
@@ -293,6 +301,7 @@ public class SimpleData extends Data {
 				nbAttributes++;
 			}
 		}
+		this.wholeDataNbObjects = data.getWholeDataNbObjects();
 		this.currentView = new ArrayList<DataObject>(data.getNbObjects());
 		for (int i = 0; i < data.getNbObjects(); i++) {
 			this.currentView.add(new DataObject(nbAttributes));
@@ -343,6 +352,7 @@ public class SimpleData extends Data {
 	 */
 	public SimpleData(final List<DataObject> data) {
 		this.currentView = data;
+		this.wholeDataNbObjects = data.size();
 	}
 	
 	/**
@@ -353,6 +363,7 @@ public class SimpleData extends Data {
 	public SimpleData(final List<DataObject> data,Model model) {
 		this.currentView = data;
 		this.model = model;
+		this.wholeDataNbObjects = data.size();
 	}
 
 	/**
@@ -366,6 +377,7 @@ public class SimpleData extends Data {
 		this.currentView = data;
 		this.knownResult = ClusteringResult.gerenerateDefaultClusteringResult(null, knownResult,
 				new GlobalWeights(this), nbClusters, this, null);
+		this.wholeDataNbObjects = data.size();
 	}
 	
 	/**
@@ -379,6 +391,7 @@ public class SimpleData extends Data {
 		this.knownResult = ClusteringResult.gerenerateDefaultClusteringResult(null, knownResult,
 				new GlobalWeights(this), nbClusters, this, null);
 		this.model = model;
+		this.wholeDataNbObjects = data.size();
 	}
 
 	/**
@@ -389,6 +402,7 @@ public class SimpleData extends Data {
 	public SimpleData(final int nbObjects) {
 		this.currentView = new ArrayList<DataObject>(nbObjects);
 		this.locked = new boolean[nbObjects];
+		this.wholeDataNbObjects = nbObjects;
 	}
 	
 	/**
@@ -400,6 +414,7 @@ public class SimpleData extends Data {
 		this.currentView = new ArrayList<DataObject>(nbObjects);
 		this.locked = new boolean[nbObjects];
 		this.model = model;
+		this.wholeDataNbObjects = nbObjects;
 	}
 
 	@Override
@@ -410,10 +425,10 @@ public class SimpleData extends Data {
 	@Override
 	public Data getMaskedData(Mask mask, boolean fromSample) {
 		SimpleData data = null;
-		if (fromSample & getSampler() != null) {
-			if(mask.getOverallCarinality() != getSampler().getSizeByCount()) {
-				throw new IndexOutOfBoundsException("The Mask does not match the underlying Data");
-			}	
+		if (fromSample || getSampler() != null) {
+//			if(mask.getOverallCarinality() != getSampler().getSizeByCount()) {
+//				throw new IndexOutOfBoundsException("The Mask does not match the underlying Data");
+//			}	
 			data = new SimpleData(this, getDataObjects(mask));
 		} else {
 			if(mask.getOverallCarinality() != this.wholeDataNbObjects) {

src/jcl/data/attribute/AttributeMultiDimSequence.java

@@ -80,7 +80,7 @@ public class AttributeMultiDimSequence extends Attribute {
 	/**
 	 * ces attributs et la methode distopt sont déclarés static pour des raisons d'optimisation (on évite des réallocations de mémoire à chaque appel)
 	 */
-	private final static int MAX_SEQ_LENGTH = 100;
+	private final static int MAX_SEQ_LENGTH = 300;
 
 	private static double[][][] matriceW = new double[NB_THREADS][AttributeMultiDimSequence.MAX_SEQ_LENGTH][AttributeMultiDimSequence.MAX_SEQ_LENGTH];
 

src/jcl/data/mask/DummyMask.java

+package jcl.data.mask;
+
+import java.util.Iterator;
+
+public class DummyMask implements Mask {
+
+	private int cardinality;
+	
+	public DummyMask(int cardinality) {
+		this.cardinality = cardinality;
+	}
+
+	@Override
+	public boolean isMasked(int index) {
+		return false;
+	}
+
+	@Override
+	public Iterator<Integer> getIncludedIndexes() {
+		return new DummyMaskIterator(cardinality);
+	}
+
+	@Override
+	public Iterator<Integer> getIncludedIndexes(int start, int end) {
+		return new DummyMaskIterator(start, end);
+	}
+
+	@Override
+	public int getCarinality() {
+		return this.cardinality;
+	}
+
+	@Override
+	public int getOverallCarinality() {
+		return this.cardinality;
+	}
+
+	@Override
+	public int getLastIndex(int start, int end) {
+		return end;
+	}
+
+	@Override
+	public int getCardinalityInInterval(int start, int end) {
+		return end - start;
+	}
+	
+	public static final class DummyMaskIterator implements Iterator<Integer> {
+		private int cursor;
+		private final int end;
+
+		public DummyMaskIterator(final int cardinality) {
+			this.cursor = 0;
+			this.end = cardinality;
+		}
+		
+		public DummyMaskIterator(final int start, final int end) {
+			this.cursor = start;
+			this.end = end;
+		}
+		
+		
+		public boolean hasNext() {
+			return this.cursor < end;
+		}
+		
+		public Integer next() {
+			int next = -1;
+			if(cursor <= end) {
+				next = cursor;
+				cursor++;
+			}
+			return next;
+		}
+	}
+
+}

src/jcl/data/sampling/ImageSampler.java

@@ -508,8 +508,8 @@ public class ImageSampler extends Sampler {
 			} else {
 				index = getIndex(i);
 			}
-			x = getStartX() + ((int) index) % (imageWidth+1);
-			y = getStartY() + ((int) index) / (imageWidth+1);
+			x = getStartX() + ((int) index) % imageWidth;
+			y = getStartY() + ((int) index) / imageWidth;
 			result.add(new DataObject(1));
 
 			List<double[]> values = new ArrayList<double[]>();

src/jcl/evaluation/clustering/ClusteringEvaluation.java

 package jcl.evaluation.clustering;
 
 import java.util.Iterator;
+import java.util.Vector;
 
 import jcl.Classification;
 import jcl.clustering.Cluster;
 import jcl.clustering.ClusteringResult;
+import jcl.clustering.constraints.Constraint;
+import jcl.clustering.constraints.LabelConstraint;
 import jcl.data.Data;
 import jcl.data.DataObject;
 import jcl.evaluation.QualityIndex;
+import jcl.utils.MathTools;
 import jcl.weights.Weights;
 
 /**
@@ -64,12 +68,15 @@ public class ClusteringEvaluation {
 
 	/** Constante pour le critere de Jarque-Bera */
 	public static final int JARQUE_BERA = 15;
+	
+	/** Constante pour le critere de Jarque-Bera */
+	public static final int BACKGROUND_KNOWLEDGE = 16;
 
 	/** Ensemble des noms des criteres de qualite */
 	public static final String NAME[] = { "WG", "Square error", "Compactness",
 			"Fuzzy square error", "Predictivity", "Log-Likelihood Sum", "XB",
 			"Fuzzy XB", "Dunn", "Bezdek", "DB", "PE", "Modified Hubert",
-			"Tradeoff", "Rand from knowledge", "Jarque-Bera" };
+			"Tradeoff", "Rand from knowledge", "Jarque-Bera", "Background Knowledge"};
 
 	/**
 	 * <p>
@@ -164,6 +171,10 @@ public class ClusteringEvaluation {
 			quality = ClusteringEvaluation.getJarqueBera(clusteringResult);
 			optimization = QualityIndex.MIN;
 			break;
+		case ClusteringEvaluation.BACKGROUND_KNOWLEDGE:
+			quality = ClusteringEvaluation.getBackgroundKnowledge(clusteringResult, data);
+			optimization = QualityIndex.MAX;
+			break;
 		}
 
 		return new QualityIndex(name, quality, optimization);
@@ -925,6 +936,60 @@ public class ClusteringEvaluation {
 		return 1 - compare.quality[ResultsComparison.RAND];
 	}
 
+	/**
+	 * Compute an index that denote the quality of the clustering regarding 
+	 * the set of constraints associated to the data
+	 * 
+	 * @param clusteringResult
+	 * 			the clustering result to evaluate
+	 * @param data
+	 * 			the associated data
+	 * @return the index value between 0 and 1, 1 being all constraints are fulfilled
+	 */
+	private static double getBackgroundKnowledge(ClusteringResult clusteringResult, Data data) {
+
+		double labelGlobalWeight = 0.0;		
+		double totalWeight = 0.0;
+		double qualityValue = 0.0;
+		
+		Vector<LabelConstraint> labelConstraints = new Vector<LabelConstraint>();
+		Vector<Double> labelConstraintsWeights = new Vector<Double>();
+		
+		
+		for (int i = 0 ; i < data.getConstraints().size() ; i++) {
+			// Label constraints are evaluated separately because they are
+			// used are evaluated as a similarity between and label classification
+			// we use the Rand index
+			if (data.getConstraints().get(i) instanceof LabelConstraint) {
+				labelConstraints.add((LabelConstraint) data.getConstraints().get(i));
+				labelConstraintsWeights.add(data.getConstraintWeight(i));
+				labelGlobalWeight += data.getConstraintWeight(i);
+			} else {
+			// other constraints are evaluated normally
+				
+				// we ignore if the constraints can't be evaluated (negative value)
+				if (qualityValue >= 0) {
+					totalWeight += data.getConstraintWeight(i);
+					qualityValue += data.getConstraints().get(i).evaluate(clusteringResult);
+				}  
+			}
+		}
+		
+		// compute Rand index for label constraints
+		//labelGlobalWeight += 
+		
+		qualityValue /= totalWeight;
+		
+		// we reajust the index to ignore the first 0.3
+		if (qualityValue < 0.3) {
+			qualityValue = 0.0;
+		} 
+		qualityValue -= 0.3;
+		qualityValue = qualityValue / 0.7 ;
+		
+		return qualityValue;
+	}
+
 	/**
 	 * <p>
 	 * Calcul de l'indice .
@@ -1027,24 +1092,45 @@ public class ClusteringEvaluation {
 			final ClusteringResult clusteringResult, final int nInf,
 			final int nSup) {
 		double qe = 1.0;
-		final double inM = ((((double) (nSup - nInf)) / 2) + nInf);
+		final double inTaille = (nSup - nInf) / 2.0;
+		final double inM = inTaille + nInf;
 
 		if (nInf > 0 || nSup > 0) {
-			final int inTaille = nSup - nInf;
-			int diff = 0;
+			// old version
+//			int diff = 0;
+//			if ((nInf < 0) || (nSup < 0)) {
+//				qe = 1;
+//			} else if (inTaille == 0) {
+//				diff = Math.abs(clusteringResult.getNbClusters() - nInf);
+//				qe = 1 - 0.10 * diff;
+//			} else {
+//				diff = (int) Math.abs(clusteringResult.getNbClusters() - inM);
+//				if (diff <= inTaille / 2) {
+//					qe = 1 - 0.05 * diff;
+//				} else {
+//					qe = 1 - 0.05 * (inTaille / 2);
+//					diff = diff - (inTaille / 2);
+//					qe = qe - 0.1 * diff;
+//				}
+//			}
+			double diff = 0;
 			if ((nInf < 0) || (nSup < 0)) {
-				qe = 1;
-			} else if (inTaille == 0) {
-				diff = Math.abs(clusteringResult.getNbClusters() - nInf);
-				qe = 1 - 0.10 * diff;
+				qe = 1.0;
 			} else {
-				diff = (int) Math.abs(clusteringResult.getNbClusters() - inM);
-				if (diff <= inTaille / 2) {
-					qe = 1 - 0.05 * diff;
+				diff = Math.abs(clusteringResult.getNbClusters() - inM);
+				if (diff <= inTaille) {
+					qe = 1;
 				} else {
-					qe = 1 - 0.05 * (inTaille / 2);
-					diff = diff - (inTaille / 2);
-					qe = qe - 0.1 * diff;
+					diff = diff - inTaille;
+					double variance = inTaille;
+					if (variance == 0) {
+						variance = nInf * 0.05;
+					}
+					// we want a probability under 10% when out of variance 
+					double z = diff/variance/2;
+					if (z < 0)
+						z = 1.0;
+					qe =  1 - MathTools.normalApproximation(z);
 				}
 			}
 		}
@@ -1148,4 +1234,6 @@ public class ClusteringEvaluation {
 
 		return qualities;
 	}
+
+	
 }

src/jcl/io/results/CSVResultWriter.java

+package jcl.io.results;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.PrintWriter;
+
+import jcl.Classification;