Examples of weka.core.Instances.numClasses()

weka.core.Instances.numClasses()
Returns the number of class labels. @return the number of class labels as an integer if the classattribute is nominal, 1 otherwise. @throws UnassignedClassException if the class is not set

      Instances[] toFilterIgnoringAttributes;


      // Make subsets if class is nominal
      if ((toFilter.classIndex() >= 0) && toFilter.classAttribute().isNominal()) {
  toFilterIgnoringAttributes = new Instances[toFilter.numClasses()];
  for (int i = 0; i < toFilter.numClasses(); i++) {
    toFilterIgnoringAttributes[i] = new Instances(toFilter, toFilter.numInstances());
  }
  for (int i = 0; i < toFilter.numInstances(); i++) {
    toFilterIgnoringAttributes[(int)toFilter.instance(i).classValue()].add(toFilter.instance(i));
  }

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    toFilterIgnoringAttributes[i] = new Instances(toFilter, toFilter.numInstances());
  }
  for (int i = 0; i < toFilter.numInstances(); i++) {
    toFilterIgnoringAttributes[(int)toFilter.instance(i).classValue()].add(toFilter.instance(i));
  }
  m_priors = new double[toFilter.numClasses()];
  for (int i = 0; i < toFilter.numClasses(); i++) {
    toFilterIgnoringAttributes[i].compactify();
    m_priors[i] = toFilterIgnoringAttributes[i].sumOfWeights();
  }
  Utils.normalize(m_priors);

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  }
  for (int i = 0; i < toFilter.numInstances(); i++) {
    toFilterIgnoringAttributes[(int)toFilter.instance(i).classValue()].add(toFilter.instance(i));
  }
  m_priors = new double[toFilter.numClasses()];
  for (int i = 0; i < toFilter.numClasses(); i++) {
    toFilterIgnoringAttributes[i].compactify();
    m_priors[i] = toFilterIgnoringAttributes[i].sumOfWeights();
  }
  Utils.normalize(m_priors);
      } else {

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          Instance ni = new DenseInstance(1.0, vals);
          newInsts.add(ni);
        }
        
        // predicted class attribute is always actualClassIndex - 1
        Instances[] classes = new Instances[newInsts.numClasses()];
        for (int i = 0; i < newInsts.numClasses(); i++) {
          classes[i] = new Instances(newInsts, 0);
          classes[i].setRelationName(newInsts.classAttribute().value(i));
        }
        Instances errors = new Instances(newInsts, 0);

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          newInsts.add(ni);
        }
        
        // predicted class attribute is always actualClassIndex - 1
        Instances[] classes = new Instances[newInsts.numClasses()];
        for (int i = 0; i < newInsts.numClasses(); i++) {
          classes[i] = new Instances(newInsts, 0);
          classes[i].setRelationName(newInsts.classAttribute().value(i));
        }
        Instances errors = new Instances(newInsts, 0);
        int actualClass = newInsts.classIndex();

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      m_offscreenPlotData = new ArrayList<Instances>();      
      Instances predictedI = e.getDataSet();
      if (predictedI.classIndex() >= 0 && predictedI.classAttribute().isNominal()) {
        // set up multiple series - one for each class
        Instances[] classes = new Instances[predictedI.numClasses()];
        for (int i = 0; i < predictedI.numClasses(); i++) {
          classes[i] = new Instances(predictedI, 0);
          classes[i].setRelationName(predictedI.classAttribute().value(i));
        }
        for (int i = 0; i < predictedI.numInstances(); i++) {

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      m_offscreenPlotData = new ArrayList<Instances>();      
      Instances predictedI = e.getDataSet();
      if (predictedI.classIndex() >= 0 && predictedI.classAttribute().isNominal()) {
        // set up multiple series - one for each class
        Instances[] classes = new Instances[predictedI.numClasses()];
        for (int i = 0; i < predictedI.numClasses(); i++) {
          classes[i] = new Instances(predictedI, 0);
          classes[i].setRelationName(predictedI.classAttribute().value(i));
        }
        for (int i = 0; i < predictedI.numInstances(); i++) {
          Instance current = predictedI.instance(i);

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  m_ClassCounts = tmp2;
      }
      
      // Change the class values
      FastVector values = new FastVector(data.classAttribute().numValues());
      for (int i = 0; i < data.numClasses(); i++) {
  values.addElement(data.classAttribute().value(m_Converter[i]));
      }
      FastVector newVec = new FastVector(data.numAttributes());
      for (int i = 0; i < data.numAttributes(); i++) {
  if (i == data.classIndex()) {

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        long[] xx = null;
        if (ValidateFile(jjField.getText())){
        Instances theData = new Instances(new BufferedReader(new FileReader(jjField.getText())));
        theData.setClassIndex(theData.numAttributes()-1);
        xx = new long[theData.numClasses()];
//        double[] theClasses = new double[theData.numClasses()];
        for (int xx_counter = 0;xx_counter<theData.numClasses();xx_counter++ ){
            xx[xx_counter] = 0;
            for (int inst_counter = 0 ;inst_counter<theData.numInstances();inst_counter++ ){
                if (theData.instance(inst_counter).stringValue(theData.classIndex()).equals(theData.classAttribute().value(xx_counter))){

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        if (ValidateFile(jjField.getText())){
        Instances theData = new Instances(new BufferedReader(new FileReader(jjField.getText())));
        theData.setClassIndex(theData.numAttributes()-1);
        xx = new long[theData.numClasses()];
//        double[] theClasses = new double[theData.numClasses()];
        for (int xx_counter = 0;xx_counter<theData.numClasses();xx_counter++ ){
            xx[xx_counter] = 0;
            for (int inst_counter = 0 ;inst_counter<theData.numInstances();inst_counter++ ){
                if (theData.instance(inst_counter).stringValue(theData.classIndex()).equals(theData.classAttribute().value(xx_counter))){
                    xx[xx_counter] = xx[xx_counter] +1;
                }

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