Examples of weka.classifiers.Classifier

weka.classifiers.Classifier
Abstract classifier. All schemes for numeric or nominal prediction in Weka extend this class. Note that a classifier MUST either implement distributionForInstance() or classifyInstance(). @author Eibe Frank (eibe@cs.waikato.ac.nz) @author Len Trigg (trigg@cs.waikato.ac.nz) @version $Revision: 5536 $

    //newWindowButton.setPreferredSize(new Dimension(120, m_addRemovePointsPanel.getHeight()));
    newWindowButton.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
    try {
      Instances newTrainingData = null;
      Classifier newClassifier = null;
      if (m_trainingInstances != null)
        newTrainingData = new Instances(m_trainingInstances);
      if (m_classifier != null)
        newClassifier = Classifier.makeCopy(m_classifier);
      createNewVisualizerWindow(newClassifier, newTrainingData);

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    double[] newDist = new double[inst.numClasses()];
    if (node.m_left == null) {
      newDist[node.getIndices()[0]] = 1.0;
      return newDist;
    } else {
      Classifier classifier = (Classifier)m_classifiers.get(node.m_left.getString() + "|" +
                  node.m_right.getString());
      double[] leftDist = distributionForInstance(inst, node.m_left);
      double[] rightDist = distributionForInstance(inst, node.m_right);
      double[] dist = classifier.distributionForInstance(inst);


      for (int i = 0; i < inst.numClasses(); i++) {
  if (node.m_right.contains(i)) {
    newDist[i] = dist[1] * rightDist[i];
  } else {

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      argsR = new String [args.length-2];
      for (int j = 2; j < args.length; j++) {
      argsR[j-2] = args[j];
      }
    }
    Classifier c = Classifier.forName(args[1], argsR);
    
    System.err.println("Loading instances from : "+args[0]);
    java.io.Reader r = new java.io.BufferedReader(
          new java.io.FileReader(args[0]));
    Instances i = new Instances(r);

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  argsR = new String [args.length-10];
  for (int j = 10; j < args.length; j++) {
    argsR[j-10] = args[j];
  }
      }
      Classifier c = AbstractClassifier.forName(args[9], argsR);
      KDDataGenerator dataGen = new KDDataGenerator();
      dataGen.setKernelBandwidth(bandWidth);
      bv.setDataGenerator(dataGen);
      bv.setNumSamplesPerRegion(loc);
      bv.setGeneratorSamplesBase(base);

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    repaint();
  }
      });
    ((GenericObjectEditor.GOEPanel) m_ClassifierEditor.getCustomEditor()).addOkListener(new ActionListener() {
  public void actionPerformed(ActionEvent e) {
    Classifier newCopy =
      (Classifier) copyObject(m_ClassifierEditor.getValue());
    addNewAlgorithm(newCopy);
  }
      });

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          try {
            File file = m_FileChooser.getSelectedFile();
            if (!file.getAbsolutePath().toLowerCase().endsWith(".xml"))
              file = new File(file.getAbsolutePath() + ".xml");
            XMLClassifier xmlcls = new XMLClassifier();
            Classifier c = (Classifier) xmlcls.read(file);
            m_AlgorithmListModel.setElementAt(c, m_List.getSelectedIndex());
            updateExperiment();
          }
          catch (Exception ex) {
            ex.printStackTrace();

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    delTransform.setAttributeIndicesArray(featArray);
    delTransform.setInputFormat(trainCopy);
    trainCopy = Filter.useFilter(trainCopy, delTransform);
    o_Evaluation = new Evaluation(trainCopy);
    String [] oneROpts = { "-B", ""+getMinimumBucketSize()};
    Classifier oneR = AbstractClassifier.forName("weka.classifiers.rules.OneR", oneROpts);
    if (m_evalUsingTrainingData) {
      oneR.buildClassifier(trainCopy);
      o_Evaluation.evaluateModel(oneR, trainCopy);
    } else {
      /*      o_Evaluation.crossValidateModel("weka.classifiers.rules.OneR", 
              trainCopy, 10, 
              null, new Random(m_randomSeed)); */

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      m_Exp.setRunLower(1);
      m_Exp.setRunUpper(m_numRepetitions);
    }


    SplitEvaluator se = null;
    Classifier sec = null;
    if (m_ExpClassificationRBut.isSelected()) {
      se = new ClassifierSplitEvaluator();
      sec = ((ClassifierSplitEvaluator)se).getClassifier();
    } else {
      se = new RegressionSplitEvaluator();

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  argsR = new String [args.length-10];
  for (int j = 10; j < args.length; j++) {
    argsR[j-10] = args[j];
  }
      }
      Classifier c = AbstractClassifier.forName(args[9], argsR);
      KDDataGenerator dataGen = new KDDataGenerator();
      dataGen.setKernelBandwidth(bandWidth);
      bv.setDataGenerator(dataGen);
      bv.setNumSamplesPerRegion(loc);
      bv.setGeneratorSamplesBase(base);

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    //newWindowButton.setPreferredSize(new Dimension(120, m_addRemovePointsPanel.getHeight()));
    newWindowButton.addActionListener(new ActionListener() {
      public void actionPerformed(ActionEvent e) {
    try {
      Instances newTrainingData = null;
      Classifier newClassifier = null;
      if (m_trainingInstances != null)
        newTrainingData = new Instances(m_trainingInstances);
      if (m_classifier != null)
        newClassifier = AbstractClassifier.makeCopy(m_classifier);
      createNewVisualizerWindow(newClassifier, newTrainingData);

View Full Code Here

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