Ajout de la gestion de moyennes dans le DistanceModel

src/mustic/gui/panels/classifier/ClassifierPanel.java

@@ -23,6 +23,9 @@ import jcl.data.attribute.AttributeSequence;
 import jcl.data.distance.Distance;
 import jcl.data.distance.DistanceParameter;
 import jcl.data.distance.EmptyDistanceParameter;
+import jcl.data.distance.average.AverageParameter;
+import jcl.data.distance.average.EmptyAverageParameter;
+import jcl.data.distance.average.ParameterDBAMean;
 import jcl.data.distance.sequential.DistanceDTW;
 import jcl.data.distance.sequential.DistanceDTWMD;
 import jcl.data.distance.sequential.ParameterDTW;
@@ -1022,23 +1025,29 @@ public class ClassifierPanel extends JPanel {
 				int nbIters = Integer.valueOf(((JTextField) comp[3]).getText()).intValue();
 				int nbThreads = 2;
 				DistanceParameter[][] distanceParameters = new DistanceParameter[nbThreads][model.getDistances().length];
+				AverageParameter[] averageParameters = new AverageParameter[model.getDistances().length];
 				for (int th = 0; th < nbThreads; th++) {
 					int i = 0;
 					for (Distance d : model.getDistances()) {
 						if(d instanceof DistanceDTWMD) {
 							AttributeMultiDimSequence seq = (AttributeMultiDimSequence) data.getOneDataObject().getAttribute(i);//need the corresponding attribute to get the sequence length
 							distanceParameters[th][i] = new ParameterDTW(new double[seq.getNbTuples()][seq.getNbTuples()]); //special attribute for DTW distances (requires a matrix to work in)
+							averageParameters[i] = new ParameterDBAMean(seq.getNbTuples(), 15);
 						}
 						else if(d instanceof DistanceDTW) {
 							AttributeSequence seq = (AttributeSequence) data.getOneDataObject().getAttribute(i);//need the corresponding attribute to get the sequence length
 							distanceParameters[th][i] = new ParameterDTW(new double[seq.getNbTuples()][seq.getNbTuples()]); //special attribute for DTW distances (requires a matrix to work in)
+							averageParameters[i] = new ParameterDBAMean(seq.getNbTuples(), 15);
 						}
-						else
+						else {
 							distanceParameters[th][i] = EmptyDistanceParameter.getInstance();//no parameter for a numerical euclidean distance
+							averageParameters[i] = EmptyAverageParameter.getInstance();
+
+						}
 						i++;
 					}
 				}
-				params = new ParametersKmeans(nbClusters, nbIters, nbThreads, weights, distanceParameters);
+				params = new ParametersKmeans(nbClusters, nbIters, nbThreads, weights, distanceParameters, averageParameters);
 				params.setModel(model);
 				break;
 			}