Bonjour,
je un problème dans cette code

import java.awt.*;
import java.awt.event.*;
import java.awt.image.*;
import java.io.*;
import java.lang.reflect.Method;
import javax.swing.*;
import javax.imageio.*;
import java.util.Vector;
import java.util.*;
import javax.swing.filechooser.FileFilter;
import java.awt.geom.AffineTransform;
import java.awt.Graphics;
import weka.filters.unsupervised.attribute.NominalToBinary;
import weka.classifiers.Classifier;
import weka.core.*;
import weka.classifiers.functions.*;
import weka.classifiers.functions.*;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.neural.NeuralConnection;
import weka.filters.Filter;
import weka.classifiers.lazy.IBk;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SelectedTag;
import weka.core.FastVector;
import weka.classifiers.bayes.NaiveBayesSimple;
import weka.classifiers.functions.neural.NeuralNode;
import java.io.BufferedReader;
import java.io.FileReader;
import java.lang.Object;
import weka.core.converters.ArffLoader.ArffReader;
import weka.core.converters.ConverterUtils.DataSource;
public class BoutonClassifier extends JButton implements ActionListener {
public ZoneImage zone;
public Method méthode;
// BufferedReader fichier;
Instances instances;
Fenêtre ff;
BoutonCalcul bc;
Histogramme hh;
public BoutonClassifier(String libellé, Icon icône, ZoneImage zone, String méthode,Fenêtre ff) {
super(libellé, icône);
this.zone = zone;
this.ff=ff;

try {
this.méthode = zone.getClass().getMethod(méthode);
}
catch (Exception ex) { }
addActionListener(this);
this.setPreferredSize(new Dimension(this.getWidth(), 40));
}
public void actionPerformed(ActionEvent e) {
try {

String ligne;
System.out.println("ce la bouton classifier");
FileReader reader = new FileReader("match.arff");
BufferedReader fichier=new BufferedReader(reader);fichier.read();
instances=new Instances(fichier);
instances.setClassIndex(instances.numAttributes()-1);
System.out.println("fethi");
Classifier base=new MultilayerPerceptron();
base.buildClassifier(instances);
Instance i=new Instance(4);
i.setDataset(instances);

i.setValue(0,ff.tdensit.getText());
i.setValue(1,ff.tmoyen.getText());
i.setValue(2,ff.tvariance.getText());
i.setValue(3,ff.thomogenete.getText());

MultilayerPerceptron mlp=new MultilayerPerceptron();
mlp.setDecay(false);
// System.out.println("flags= "+mlp.getDecay());
mlp.setReset(true);
// System.out.println("flags of reset "+mlp.getReset());
mlp.setNormalizeNumericClass(false);
mlp.setNormalizeAttributes(true);
mlp.setNominalToBinaryFilter(true);
mlp.setRandomSeed(1);

//validation du seuil
mlp.setValidationThreshold(1);
mlp.setLearningRate(1);
mlp.setMomentum(0);
mlp.setGUI(true);
mlp.setValidationSetSize(4);
mlp.setTrainingTime(2);
mlp.blocker(false);
//Calculater les probabilités d'appartenace de la nouvelle instance à chaque classe .
// double[] prob = base.distributionForInstance(i);
System.out.println("essai");
else
{
if(retour == cmp1)
valeur3 = "BENINE";
else
valeur3 = "MALINE";
}
valdecision.setText(valeur3);//dans noutre text fild

}


catch (Exception ex) { }
}
}


//while ((ligne=fichier.readLine())!=null )

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