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 $

      values[idx] = instance.value(i);
      idx++;
  }
    }


    Classifier classifier = getClassifier(k);


    if (m_BaseFormat.classAttribute().isNumeric()) {
      throw new Exception("Class Attribute must not be numeric!");
    } else {
      double[] dist = classifier.distributionForInstance(instance);
      
      maxIdx=0;
      maxVal=dist[0];
      for (int j = 1; j < dist.length; j++) {
  if (dist[j]>maxVal) {

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      Instances inst = new Instances(new java.io.InputStreamReader(System.in));
      
      inst.setClassIndex(inst.numAttributes() - 1);
      CostCurve cc = new CostCurve();
      EvaluationUtils eu = new EvaluationUtils();
      Classifier classifier = new weka.classifiers.functions.Logistic();
      FastVector predictions = new FastVector();
      for (int i = 0; i < 2; i++) { // Do two runs.
  eu.setSeed(i);
  predictions.appendElements(eu.getCVPredictions(classifier, inst, 10));
  //System.out.println("\n\n\n");

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      int total = models.size();
      int invalid = 0;
      
      for (int i = 0; i < models.size(); i++) {
  Classifier classifier = (Classifier) models.get(i);
  
  //This method will invoke the classifier's setOptions
  //method to see if the current set of options was 
  //valid.

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        System.out.println( ThresholdCurve.getNPointPrecision( inst, 11 ) );
      } else {
        inst.setClassIndex( inst.numAttributes() - 1 );
        ThresholdCurve tc = new ThresholdCurve();
        EvaluationUtils eu = new EvaluationUtils();
        Classifier classifier = new weka.classifiers.functions.Logistic();
        FastVector predictions = new FastVector();
        for( int i = 0; i < 2; i++ ) { // Do two runs.


          eu.setSeed( i );
          predictions.appendElements( eu.getCVPredictions( classifier, inst, 10 ) );

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   *
   * @return the classifier string.
   */
  protected String getClassifierSpec() {
    
    Classifier c = getClassifier();
    if (c instanceof OptionHandler) {
      return c.getClass().getName() + " "
  + Utils.joinOptions(((OptionHandler)c).getOptions());
    }
    return c.getClass().getName();
  }

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   *
   * @return the classifier string.
   */
  protected String getClassifierSpec() {
    
    Classifier c = getClassifier();
    return c.getClass().getName() + " "
      + Utils.joinOptions(((OptionHandler)c).getOptions());
  }

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  if(artSize==0) artSize=1;//atleast add one random example
  computeStats(data);//Compute training data stats for creating artificial examples
  
  //initialize new committee
  m_Committee = new Vector();
  Classifier newClassifier = m_Classifier;
  newClassifier.buildClassifier(divData);
  m_Committee.add(newClassifier);
  double eComm = computeError(divData);//compute ensemble error
  if(m_Debug) System.out.println("Initialize:\tClassifier "+i+" added to ensemble. Ensemble error = "+eComm);
  
  //repeat till desired committee size is reached OR the max number of iterations is exceeded 
  while(i<m_DesiredSize && numTrials<m_NumIterations){
      //Generate artificial training examples
      artData = generateArtificialData(artSize, data);
      
      //Label artificial examples
      labelData(artData);
      addInstances(divData, artData);//Add new artificial data
      
      //Build new classifier
      Classifier tmp[] = Classifier.makeCopies(m_Classifier,1);
      newClassifier = tmp[0]; 
      newClassifier.buildClassifier(divData);
      //Remove all the artificial data
      removeInstances(divData, artSize);

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  public double[] distributionForInstance(Instance instance) throws Exception {
      if (instance.classAttribute().isNumeric()) {
    throw new UnsupportedClassTypeException("Decorate can't handle a numeric class!");
      }
      double [] sums = new double [instance.numClasses()], newProbs; 
      Classifier curr;
      
      for (int i = 0; i < m_Committee.size(); i++) {
    curr = (Classifier) m_Committee.get(i);
    newProbs = curr.distributionForInstance(instance);
    for (int j = 0; j < newProbs.length; j++)
      sums[j] += newProbs[j];
      }
      if (Utils.eq(Utils.sum(sums), 0)) {
    return sums;

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    setLayout(new BorderLayout());
    add(m_ClassifierEditor, BorderLayout.CENTER);
  }
  
  private void checkOnClassifierType() {
    Classifier editedC = m_dsClassifier.getClassifier();
    if (editedC instanceof weka.classifiers.UpdateableClassifier && 
  m_dsClassifier.hasIncomingStreamInstances()) {
      if (!m_panelVisible) {
  add(m_incrementalPanel, BorderLayout.SOUTH);
  m_panelVisible = true;

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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 = Classifier.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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