Examples of weka.core.Instance.classIndex()

weka.core.Instance.classIndex()
Returns the class attribute's index. @return the class index as an integer @throws UnassignedDatasetException if instance doesn't have access to a dataset

    m_min = update;
        }
      }
      
      m_ce.setLegendText(m_dataLegend);
      m_ce.setMin((inst.isMissing(inst.classIndex()) 
       ? m_min
       : 0)); 
      m_ce.setMax(m_max);
      m_ce.setDataPoint(m_dataPoint);
      m_ce.setReset(m_reset);

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    textTitle.substring(textTitle.lastIndexOf('.')+1,
            textTitle.length());
        String results = Messages.getInstance().getString("IncrementalClassifierEvaluator_AcceptClassifier_Result_Text_First") + textTitle 
    +  Messages.getInstance().getString("IncrementalClassifierEvaluator_AcceptClassifier_Result_Text_Second") + inst.dataset().relationName() + "\n\n"
    + m_eval.toSummaryString();
              if (inst.classIndex() >= 0 && 
                  inst.classAttribute().isNominal() &&
                  (m_outputInfoRetrievalStats)) {
                results += "\n" + m_eval.toClassDetailsString();
              }

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                  inst.classAttribute().isNominal() &&
                  (m_outputInfoRetrievalStats)) {
                results += "\n" + m_eval.toClassDetailsString();
              }


              if (inst.classIndex() >= 0 && 
                  inst.classAttribute().isNominal()) {
                results += "\n" + m_eval.toMatrixString();
              }
        textTitle = Messages.getInstance().getString("IncrementalClassifierEvaluator_AcceptClassifier_TextTitle_Text") + textTitle;
        TextEvent te =

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    int width = 10;
    int prec = 3;


    Instance withMissing = (Instance)inst.copy();
    withMissing.setDataset(inst.dataset());
    withMissing.setMissing(withMissing.classIndex());
    double predValue = classifier.classifyInstance(withMissing);


    // index
    result.append(Utils.padLeft("" + (instNum+1), 6));

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      // out of range?
      if (!m_Cols.isInRange(i))
  continue;
      
      // skip class?
      if ( (result.classIndex() == i) && (!m_IncludeClass) )
  continue;
      
      // too small?
      if (result.value(i) < m_MinThreshold) {
  if (getDebug())

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    instance = (Instance) enumInsts.nextElement();
    docClass = (int)instance.value(classIndex);
    //docsPerClass[docClass] += instance.weight();
    
    for(int a = 0; a<instance.numValues(); a++)
        if(instance.index(a) != instance.classIndex()) {
          if(!instance.isMissing(a)) {
            numOccurrences = instance.valueSparse(a) * instance.weight();
            if(numOccurrences < 0)
          throw new Exception("Numeric attribute"+
                                                  " values must all be greater"+

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    java.util.Enumeration enumInsts = instances.enumerateInstances();
    while (enumInsts.hasMoreElements()) 
      {
  instance = (Instance) enumInsts.nextElement();
  classIndex = (int)instance.value(instance.classIndex());
  docsPerClass[classIndex] += instance.weight();
    
  for(int a = 0; a<instance.numValues(); a++)
    if(instance.index(a) != instance.classIndex())
      {

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  instance = (Instance) enumInsts.nextElement();
  classIndex = (int)instance.value(instance.classIndex());
  docsPerClass[classIndex] += instance.weight();
    
  for(int a = 0; a<instance.numValues(); a++)
    if(instance.index(a) != instance.classIndex())
      {
        if(!instance.isMissing(a))
    {
      numOccurences = instance.valueSparse(a) * instance.weight();
      if(numOccurences < 0)

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    PMMLClassifier classifier = (PMMLClassifier)model;
    for (int i = 0; i < test.numInstances(); i++) {
      buff.append("Actual: ");
      Instance temp = test.instance(i);
      if (temp.classAttribute().isNumeric()) {
        buff.append(temp.value(temp.classIndex()) + " ");
      } else {
        buff.append(temp.classAttribute().value((int)temp.value(temp.classIndex())) + " ");
      }
      preds = classifier.distributionForInstance(temp);
      buff.append(" Predicted: ");

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      buff.append("Actual: ");
      Instance temp = test.instance(i);
      if (temp.classAttribute().isNumeric()) {
        buff.append(temp.value(temp.classIndex()) + " ");
      } else {
        buff.append(temp.classAttribute().value((int)temp.value(temp.classIndex())) + " ");
      }
      preds = classifier.distributionForInstance(temp);
      buff.append(" Predicted: ");
      for (int j = 0; j < preds.length; j++) {
        buff.append("" + preds[j] + " ");

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