Partial migration to DistanceModel utilisation

src/mustic/gui/MainFrame.java

@@ -26,11 +26,14 @@ import jcl.clustering.Cluster;
 import jcl.clustering.ClusteringResult;
 import jcl.data.Data;
 import jcl.data.DataObject;
+import jcl.data.DistanceModel;
 import jcl.data.SimpleData;
 import jcl.data.attribute.AttributeMultiDimSequence;
+import jcl.data.distance.DistanceParameter;
 import jcl.learning.LearningMethod;
 import jcl.learning.methods.monostrategy.SingleClassification;
 import jcl.weights.ClassificationWeights;
+import jcl.weights.GlobalWeights;
 import jsl.Segmentation;
 import mustic.gui.dialog.*;
 import mustic.gui.dialog.arff.ArffLoadDialog;
@@ -2989,7 +2992,13 @@ public class MainFrame extends JFrame {
 						result = ClusteringResult.gerenerateDefaultClusteringResult(clusters);
 						result.setClusterMap(clusterMap);
 						result.setWeights(weights);
-						Classification classif = new SingleClassification(name, new SimpleData(dataObjects), method, result);
+						Classification classif = new SingleClassification(name, 
+								new SimpleData(dataObjects, null, null), method, result);
+						DistanceModel model = DistanceModel.generateDefaultModel(dataObjects.get(0), 
+								new GlobalWeights(dataObjects.get(0)));
+						DistanceParameter[][] parameters = DistanceModel.generateDefaultDistanceParameters(1, 
+								model, classif.getData());
+						classif.getData().setDistanceModel(model, parameters);
 
 						// fichierImage = new File(fileName);
 						if (MainFrame.DEBUG_MODE)
@@ -3065,7 +3074,12 @@ public class MainFrame extends JFrame {
 
 						}
 						else {
-							Data data = new SimpleData(dataObjects);
+							Data data = new SimpleData(dataObjects, null, null);
+							DistanceModel distModel = DistanceModel.generateDefaultModel(dataObjects.get(0), 
+									new GlobalWeights(dataObjects.get(0)));
+							DistanceParameter[][] distParameters = DistanceModel
+									.generateDefaultDistanceParameters(1, model, data);
+							data.setDistanceModel(distModel, distParameters);
 							data.setDataName(file.getName());
 							
 							MainFrame.getInstance().createDataSession(data);
@@ -3086,7 +3100,8 @@ public class MainFrame extends JFrame {
 							// classif.setClusteringResult(result);
 							ci.addResult(classif, name);
 							// System.out.println("Resultat ajoute a la classif");
-							ImageResultPanel irp = (ImageResultPanel) ci.tabbedPane.getTabAt(ci.tabbedPane.getTabCount() - 1).getContentComponent();
+							ImageResultPanel irp = (ImageResultPanel) ci.tabbedPane
+									.getTabAt(ci.tabbedPane.getTabCount() - 1).getContentComponent();
 							if (MainFrame.DEBUG_MODE)
 								System.out.println(qualities);
 							irp.setQualityPanel(new QualityPanel(classif, qualities));

src/mustic/gui/dialog/arff/ArffToDataDialog.java

@@ -138,7 +138,7 @@ public class ArffToDataDialog extends JInternalFrame implements ToUpdateObject {
 		}
 	}
 
-	@SuppressWarnings({ "unchecked", "deprecation" })
+	@SuppressWarnings({ "unchecked" })
 	protected void buttonOk_actionPerformed() {
 		try {
 			Object[] result = DataArffExchange.arffToData(this.textFile.getText());

src/mustic/gui/dialog/classifier/ConstraintsSelectionFrame.java

@@ -56,6 +56,9 @@ import jcl.clustering.constraints.LabelConstraint;
 import jcl.clustering.constraints.MustLinkConstraint;
 import jcl.clustering.constraints.NbClusterConstraint;
 import jcl.data.Data;
+import jcl.data.DistanceModel;
+import jcl.data.distance.DistanceParameter;
+import jcl.weights.GlobalWeights;
 import mustic.utils.image.ImageAddOn;
 import mustic.utils.image.ImageDisplayer;
 import mustic.utils.tools.ProgressPropertyChangeListener;
@@ -161,11 +164,15 @@ public class ConstraintsSelectionFrame extends JFrame implements ImageAddOn {
 		this.imageDisplayer = imgDisplayer;
 		
 		this.constraintsContainer = constraintsContainer;
-
+		
 		if (data != null) {
 			// case of Data provided
-			minDistance = data.getEstimatedMinDistance();
-			maxDistance = data.getEstimatedMaxDistance();
+			DistanceModel model = DistanceModel.generateDefaultModel(data.getOneDataObject(), 
+					new GlobalWeights(data.getOneDataObject()));
+			DistanceParameter[][] parameters = DistanceModel.generateDefaultDistanceParameters(1,model, data);
+			
+			minDistance = data.getEstimatedMinDistance(model, parameters[0]);
+			maxDistance = data.getEstimatedMaxDistance(model, parameters[0]);
 			this.data = data;
 		} else {
 			// otherwise we base our estimation on the image

src/mustic/gui/dialog/csv/CSVToDataDialog.java

@@ -22,8 +22,11 @@ import javax.swing.JTextField;
 
 import jcl.data.Data;
 import jcl.data.DataObject;
+import jcl.data.DistanceModel;
 import jcl.data.SimpleData;
 import jcl.data.attribute.AttributeMultiDimSequence;
+import jcl.data.distance.DistanceParameter;
+import jcl.weights.GlobalWeights;
 import mustic.gui.MainFrame;
 import mustic.gui.dialog.Messages;
 import mustic.utils.documentFilter.ToUpdateObject;
@@ -140,7 +143,6 @@ public class CSVToDataDialog extends JInternalFrame implements ToUpdateObject {
 		}
 	}
 
-	@SuppressWarnings({"deprecation"})
 	protected void buttonOk_actionPerformed() {
 		try {
 			List<DataObject> result = new ArrayList<DataObject>();
@@ -160,7 +162,11 @@ public class CSVToDataDialog extends JInternalFrame implements ToUpdateObject {
 			}
 			
 			if(result != null) {
-					data = new SimpleData(result);
+				data = new SimpleData(result, null, null);
+				DistanceModel model = DistanceModel.generateDefaultModel(data.getOneDataObject(), 
+						new GlobalWeights(data.getOneDataObject()));
+				DistanceParameter[][] parameters = DistanceModel.generateDefaultDistanceParameters(1,model, data);
+				data.setDistanceModel(model, parameters);
 				
 				if (this.textData.getText().length() == 0) {
 					this.textData.setText(ImageHelper.extractFileName(

src/mustic/gui/panels/ImageResultPanel.java

@@ -4,8 +4,6 @@ import java.awt.*;
 import java.awt.event.*;
 import java.awt.image.*;
 import java.io.*;
-import java.text.SimpleDateFormat;
-import java.util.Date;
 import java.util.Observable;
 import java.util.Observer;
 import java.util.Vector;
@@ -949,10 +947,11 @@ public class ImageResultPanel extends ResultPanel implements TreeSelectionListen
 	 */
 	public void hClustering_actionPerformed(boolean newResult) {
 		try {
-			LearningParameters learningParameters = new ParametersAHC(null);
+			LearningParameters learningParameters = new ParametersAHC(imgData.getDistanceModel(), 
+					imgData.getDistanceParameters());
 			LearningMethod learningMethod = learningParameters.getLearningMethodInstance();
-			@SuppressWarnings("deprecation")
-			Data classificationData = new SimpleData(this.result.getClusterCenters());
+			Data classificationData = new SimpleData(this.result.getClusterCenters(), 
+					imgData.getDistanceModel(), imgData.getDistanceParameters());
 			LearningResult learningResult = learningMethod.learn(classificationData);
 			this.result.hierarchy = ((LearningResultAHC) learningResult).getHierarchy();
 			Classification classif = new SingleClassification("", classificationData, learningMethod, this.result);

src/mustic/gui/panels/classifier/components/HybridClassificationPanel.java

@@ -10,6 +10,7 @@ import javax.swing.*;
 import javax.swing.border.TitledBorder;
 
 import jcl.data.Data;
+import jcl.data.DistanceModel;
 import jcl.data.attribute.Attribute;
 import jcl.data.attribute.AttributeMultiDimSequence;
 import jcl.data.distance.Distance;
@@ -17,6 +18,7 @@ import jcl.data.distance.DistanceParameter;
 import jcl.data.distance.sequential.DistanceEuclidean;
 import jcl.learning.LearningParameters;
 import jcl.learning.methods.multistrategy.samarah.*;
+import jcl.weights.GlobalWeights;
 import mustic.gui.MainFrame;
 import mustic.gui.dialog.classifier.AgentDialog;
 import mustic.gui.panels.classifier.*;
@@ -78,7 +80,7 @@ public class HybridClassificationPanel extends JPanel implements AgentListManage
 		this.title = this.title + "." + HybridClassificationPanel.nbSamarah;
 		HybridClassificationPanel.nbSamarah++;
 		// this.samarah = new HybridClassification("TEST", data, null);
-		this.samarah = new HybridClassification();
+		this.samarah = new HybridClassification(null, null);
 		if (MainFrame.DEBUG_MODE)
 			System.out.println(" in HybridClassificationParametersPanel() ");
 		this.samarah.config.printInfo();
@@ -102,7 +104,11 @@ public class HybridClassificationPanel extends JPanel implements AgentListManage
 		jbInit();
 		HybridClassificationPanel.nbSamarah++;
 		// this.samarah = new HybridClassification("TEST", data, null);
-		this.samarah = new HybridClassification();
+
+		DistanceModel model = DistanceModel.generateDefaultModel(data.getOneDataObject(), 
+				new GlobalWeights(data.getOneDataObject()));
+		DistanceParameter[][] parameters = DistanceModel.generateDefaultDistanceParameters(1,model, data);
+		this.samarah = new HybridClassification(model, parameters);
 	}
 
 	@Override
@@ -155,16 +161,17 @@ public class HybridClassificationPanel extends JPanel implements AgentListManage
 			else {
 				file += "\t\t<isSimplifySequence>true</isSimplifySequence>\n";
 			}
-			for (Distance<Attribute, DistanceParameter> d : agent.getParams().getModel().getDistances()) {
-				if(!(d instanceof DistanceEuclidean)) 
-				
-			}
-			if (AttributeMultiDimSequence.getMode() == AttributeMultiDimSequence.EUCLIDIEN && agent.getParams().getModel()) {
-				file += "\t\t<isUseNaiveDist>true</isUseNaiveDist>\n";
-			}
-			else {
-				file += "\t\t<isUseNaiveDist>false</isUseNaiveDist>\n";
-			}
+			//TODO try to match with the new distanceModel 
+//			for (Distance<Attribute, DistanceParameter> d : agent.getParams().getModel().getDistances()) {
+//				if(!(d instanceof DistanceEuclidean)) 
+//				
+//			}
+//			if (AttributeMultiDimSequence.getMode() == AttributeMultiDimSequence.EUCLIDIEN && agent.getParams().getModel()) {
+//				file += "\t\t<isUseNaiveDist>true</isUseNaiveDist>\n";
+//			}
+//			else {
+//				file += "\t\t<isUseNaiveDist>false</isUseNaiveDist>\n";
+//			}
 			if (agent.getParams().weights != null) {
 				file += "\t\t<weight>";
 				double weights[] = agent.getParams().weights.getGlobalWeights().getWeights();

src/mustic/gui/panels/data/DataConstructPanel.java

@@ -16,8 +16,10 @@ import com.l2fprod.common.swing.JTaskPane;
 import com.l2fprod.common.swing.JTaskPaneGroup;
 
 import jcl.data.DistanceModel;
+import jcl.data.distance.DistanceParameter;
 import jcl.data.mask.Mask;
 import jcl.data.sampling.Sampler;
+import jcl.weights.GlobalWeights;
 import mustic.gui.ImageDesktopFrame;
 import mustic.gui.ImageSession;
 import mustic.gui.MainFrame;
@@ -147,12 +149,16 @@ public class DataConstructPanel extends JPanel implements ToUpdateObject {
 				} 
 				
 				if (filesPaths.size() > 1) {
-					data = new ImageData(sample, filesPaths, null);
-//					data.setModel(Model.generateDefaultModel(data.getOneDataObject()));
+					data = new ImageData(sample, filesPaths, null, null, null);
 				} else {
-					data = new ImageData(sample, filesPaths.get(0), null);
-//					data.setModel(Model.generateDefaultModel(data.getOneDataObject()));
+					data = new ImageData(sample, filesPaths.get(0), null, null);
 				}
+				DistanceModel distModel = DistanceModel.generateDefaultModel(data.getOneDataObject(), 
+						new GlobalWeights(data.getOneDataObject()));
+				DistanceParameter[][] distParameters = DistanceModel
+						.generateDefaultDistanceParameters(1, distModel, data);
+				data.setDistanceModel(distModel, distParameters);
+				
 				MainFrame.getInstance().createDataSession(data);
 				
 				DataConstructFrame.getInstance().updateDataConstructPanel();

src/mustic/io/ImageData.java

@@ -7,8 +7,8 @@ import java.util.Vector;
 import jcl.data.DataObject;
 import jcl.data.DistanceModel;
 import jcl.data.SimpleData;
-import jcl.data.attribute.AttributeMultiDimSequence;
 import jcl.data.attribute.AttributeNumerical;
+import jcl.data.distance.DistanceParameter;
 import jcl.data.mask.Mask;
 import jcl.data.sampling.Sampler;
 import jcl.utils.exceptions.MethodNotImplementedException;
@@ -59,7 +59,8 @@ public class ImageData extends SimpleData implements Serializable {
 	 * @param image the image to convert
 	 */
 	@Deprecated
-	public ImageData(RawImage image) {
+	public ImageData(RawImage image, DistanceModel model, DistanceParameter[][] parameters) {
+		super(model, parameters);
 		if (MainFrame.DEBUG_MODE) {
 			System.out.println(">> ImageData(RawImage) <<");
 		}
@@ -168,7 +169,8 @@ public class ImageData extends SimpleData implements Serializable {
 	 * @param percent le pourcentage de pixels a choisir parmi l'image
 	 */
 	@Deprecated
-	public ImageData(RawImage image, double percent) {
+	public ImageData(RawImage image, double percent, DistanceModel model, DistanceParameter[][] parameters) {
+		super(model, parameters);
 		if (MainFrame.DEBUG_MODE)
 			System.out.println(">>  ImageData(RawImage, double)  <<");
 
@@ -240,7 +242,9 @@ public class ImageData extends SimpleData implements Serializable {
 	 * @param model
 	 * 			the model to use with the data
 	 */
-	public ImageData(Sampler sampler, RawImage path, Mask mask, DistanceModel model) {
+	public ImageData(Sampler sampler, RawImage path, Mask mask, 
+			DistanceModel model, DistanceParameter[][] parameters) {
+		super(model, parameters);
 		if (MainFrame.DEBUG_MODE) {
 			System.out.println(">> ImageData(Sampler,String) <<");
 		}
@@ -292,9 +296,6 @@ public class ImageData extends SimpleData implements Serializable {
 			long endTime = System.currentTimeMillis();
 			System.out.println("___________________Total elapsed time in execution of loading image is :" + ((endTime - startTime)));
 		}
-		
-		setModel(model);
-
 	}
 	
 	/**
@@ -310,8 +311,8 @@ public class ImageData extends SimpleData implements Serializable {
 	 * @param model
 	 * 			the model to use with the data
 	 */
-	public ImageData(Sampler sampler, RawImage path, DistanceModel model) {
-		this(sampler, path, null,model);
+	public ImageData(Sampler sampler, RawImage path, DistanceModel model, DistanceParameter[][] parameters) {
+		this(sampler, path, null, model, parameters);
 	}
 	
 	/**
@@ -329,8 +330,9 @@ public class ImageData extends SimpleData implements Serializable {
 	 * @param model
 	 * 			the model to use with the data
 	 */
-	public ImageData(Sampler sampler, Vector<RawImage> images, Mask mask, DistanceModel model) {
-
+	public ImageData(Sampler sampler, Vector<RawImage> images, Mask mask, 
+			DistanceModel model, DistanceParameter[][] parameters) {
+		super(model, parameters);
 		for(RawImage s : images) {
 			this.rawImages.add(s);
 		}
@@ -389,9 +391,6 @@ public class ImageData extends SimpleData implements Serializable {
 			System.out.println("___________________Total elapsed time in execution of loading image is :" 
 			+ ((endTime - startTime)));
 		}
-		
-		setModel(model);
-
 	}
 	
 	/**
@@ -407,8 +406,9 @@ public class ImageData extends SimpleData implements Serializable {
 	 * @param model
 	 * 			the model to use with the data
 	 */
-	public ImageData(Sampler sampler, Vector<RawImage> images, DistanceModel model) {
-		this(sampler, images, null, model);
+	public ImageData(Sampler sampler, Vector<RawImage> images, 
+			DistanceModel model, DistanceParameter[][] parameters) {
+		this(sampler, images, null, model, parameters);
 
 	}
 

src/mustic/models/gui/dialogs/FilterDataDialog.java

@@ -146,7 +146,8 @@ public class FilterDataDialog extends JDialog {
 
 	private void jButtonOK_actionPerformed() {
 		setVisible(false);
-		Filter filter = FilterUtils.createFilter(getParameters());
+		Filter filter = FilterUtils.createFilter(getParameters(), 
+				data.getDistanceModel(), data.getDistanceParameters());
 		new FilterThread(filter, this.data, MainFrame.getProgressBar(), this)
 				.start();
 	}

src/mustic/models/gui/panels/ClassConfigPanel.java

@@ -13,6 +13,8 @@ import javax.swing.tree.*;
 import jcl.Classification;
 import jcl.clustering.Cluster;
 import jcl.clustering.ClusteringResult;
+import jcl.data.DistanceModel;
+import jcl.data.distance.DistanceParameter;
 import jcl.learning.IterativeClassification;
 import jcl.learning.methods.ClassifierUtils;
 import jcl.learning.methods.multistrategy.samarah.HybridClassification;
@@ -107,15 +109,20 @@ public class ClassConfigPanel extends JPanel implements TreeSelectionListener, A
 	 * Constructeur par defaut.
 	 * </p>
 	 * @param classification les classes e afficher
+	 * @param model 
+	 * 			the model used to compute the distance
+	 * @param parameters
+	 * 			the list of parameters used by the distance model
 	 * @param resultPanel le panel de resultat e rafraichir en cas de modification. peut etre null
 	 */
-	public ClassConfigPanel(Classification classification, ResultPanel resultPanel) {
+	public ClassConfigPanel(Classification classification, ResultPanel resultPanel, 
+			DistanceModel model, DistanceParameter[] parameters) {
 		super();
 		this.classification_ = classification;
 		this.resultPanel_ = resultPanel;
 		this.setLayout(new BorderLayout());
 		this.treeScrollPane = new JScrollPane();
-		this.treeScrollPane.setViewportView(getClusterTree());
+		this.treeScrollPane.setViewportView(getClusterTree(model, parameters));
 		this.treeScrollPane.setBorder(BorderFactory.createTitledBorder("Clusters"));
 
 		JPanel panel = null;
@@ -911,12 +918,17 @@ public class ClassConfigPanel extends JPanel implements TreeSelectionListener, A
 	 * <p>
 	 * This method initializes clusterTree.
 	 * </p>
+	 * 
+	 * @param model 
+	 * 			the model used to compute the distance
+	 * @param parameters
+	 * 			the list of parameters used by the distance model
 	 * @return le tree configure
 	 */
-	private JTree getClusterTree() {
+	private JTree getClusterTree(DistanceModel model, DistanceParameter[] parameters) {
 
 		ClusteringResult result = this.classification_.getClusteringResult();
-		this.clusterTree = new JTree(result.getRoot());
+		this.clusterTree = new JTree(result.getRoot(model, parameters));
 		this.clusterTree.setRootVisible(true);
 		this.clusterTree.setSelectionRow(0);
 		this.clusterTree.addTreeSelectionListener(this);

src/mustic/models/gui/panels/HybridClassificationParametersPanel.java

@@ -109,7 +109,7 @@ public class HybridClassificationParametersPanel extends JPanel {
 		this.jSliderPcr.setValue(20);
 		// this.samarah = new HybridClassification("TEST", data, null);
 		this.data = null;
-		this.samarah = new HybridClassification();
+		this.samarah = new HybridClassification(null, null);
 		System.out.println(" in HybridClassificationParametersPanel() ");
 		this.samarah.config.printInfo();
 	}
@@ -128,7 +128,7 @@ public class HybridClassificationParametersPanel extends JPanel {
 		this.jSliderPcr.setValue(20);
 		// this.samarah = new HybridClassification("TEST", data, null);
 		this.data = data;
-		this.samarah = new HybridClassification();
+		this.samarah = new HybridClassification(data.getDistanceModel(), data.getDistanceParameters());
 	}
 
 	/**

src/mustic/models/gui/panels/QualityPanel.java

@@ -51,7 +51,8 @@ public class QualityPanel extends JPanel implements Serializable {
 			this.qualities = qualities;
 		} else {
 			this.qualities = ClusteringEvaluation
-					.getAllQualities(classification);
+					.getAllQualities(classification, classification.getData().getDistanceModel(),
+							classification.getData().getDistanceParameters()[0]);
 		}
 		int nbIndices = this.qualities.length;
 

src/mustic/models/gui/panels/ResultPanel.java

@@ -194,7 +194,9 @@ public class ResultPanel extends JPanel {
 
 			/* On garde une trace de la classification */
 			this.classification = classification;
-			this.classPanel = new ClassConfigPanel(this.classification, this);
+			this.classPanel = new ClassConfigPanel(this.classification, this,
+					classification.getData().getDistanceModel(), 
+					classification.getData().getDistanceParameters()[0]);
 			this.classFrame = frame;
 			setLayout(new BorderLayout());
 			panel = new JSplitPane(JSplitPane.HORIZONTAL_SPLIT,

src/mustic/utils/io/WriteDistancesPath.java

@@ -37,7 +37,7 @@ public class WriteDistancesPath {
 				//System.out.println("\t\t  " + i + " ===> j =  " + (i+1+j));
 				dataObject2 = dataObjectIter_j.next();
 				//distance = DistanceDTWMD.getInstance().compute(attMultiDimSequence1, attMultiDimSequence2, parameterDTW);
-				distance = dataObject1.distance(dataObject2);
+				distance = dataObject1.distance(dataObject2, data.getDistanceModel(), data.getDistanceParameters()[0]);
 				line += (distance);
 				if (dataObjectIter_j.hasNext())
 					line += ",";

src/mustic/utils/transform/RegionDataBuilder.java

@@ -10,10 +10,8 @@ import jcl.data.DataObject;
 import jcl.data.DistanceModel;
 import jcl.data.SimpleData;
 import jcl.data.attribute.AttributeNumerical;
-import jcl.data.distance.Distance;
-import jcl.data.distance.MetaDistance;
-import jcl.data.distance.MetaDistanceEuclidean;
-import jcl.data.distance.NumericalEuclideanDistance;
+import jcl.data.distance.DistanceParameter;
+import jcl.weights.GlobalWeights;
 import jsl.Segmentation;
 import jsl.io.Descripteur;
 import jsl.io.ObjetImage;
@@ -58,7 +56,7 @@ public class RegionDataBuilder {
 		int index = 0;
 
 		for (ObjetImage io : s.getResultat().getObjectsImage()) {
-			List listDescr = io.getListe_objetsDescripteur();
+			List<Descripteur> listDescr = io.getListe_objetsDescripteur();
 			current = new DataObject(listDescr.size());
 			int in = 0;
 			
@@ -76,19 +74,18 @@ public class RegionDataBuilder {
 			index++;
 		}
 
-		Distance[] distances = new Distance[listDesc.size()]; // a distance is set for every attribute
-		for (int i = 0; i < listDesc.size(); i++) {
-			distances[i] = NumericalEuclideanDistance.getInstance(); // first attribute compared with an euclidean distance between numericals
-		}
-		MetaDistance metaDistance = MetaDistanceEuclidean.getInstance(); // defines the way the two scores are combined (possibility to weight) 
-		DistanceModel model = new DistanceModel(distances, metaDistance);
-		this.data = new SimpleData(allObjects, model);
+		this.data = new SimpleData(allObjects, null, null);
 		this.data.setAttributesNames(names);
 		this.data.setComments("% Donnees de regions");
 		this.data.setDataName("Regions");
 		Vector<String> filesName = new Vector<String>();
 		filesName.add("not assigned");
 		this.data.setDataFilesName(filesName);
+		
+		DistanceModel model = DistanceModel.generateDefaultModel(this.data.getOneDataObject(), 
+				new GlobalWeights(this.data.getOneDataObject()));
+		DistanceParameter[][] parameters = DistanceModel.generateDefaultDistanceParameters(1,model, this.data);
+		this.data.setDistanceModel(model, parameters);
 	}
 
 	public Data getData() {

src/test/TestA2CNES.java

@@ -88,7 +88,7 @@ public class TestA2CNES {
 		classification.setAdvancedParameters(degradation, classRatio, solutionType, kIntern, 
 				kExtern, unificationType, criterion, constraintsWgt);
 		
-		ClassificationWeights weights = new GlobalWeights(dataTrain);
+		ClassificationWeights weights = new GlobalWeights(dataTrain.getOneDataObject());
 		
 		final Vector<Thread> threadList = new Vector<Thread>();
 		final Vector<Classification> classifList = new Vector<Classification>();

src/test/TestA2CNESIterative.java

@@ -80,7 +80,7 @@ public class TestA2CNESIterative {
 		classification.setAdvancedParameters(degradation, classRatio, solutionType, kIntern, 
 				kExtern, unificationType, criterion, constraintsWgt);
 		
-		ClassificationWeights weights = new GlobalWeights(dataTest);
+		ClassificationWeights weights = new GlobalWeights(dataTest.getOneDataObject());
 		
 		final Vector<Thread> threadList = new Vector<Thread>();
 		final Vector<Classification> classifList = new Vector<Classification>();