Examples of weka.core.Instance.classValue()

weka.core.Instance.classValue()
Returns an instance's class value in internal format. (ie. as a floating-point number) @return the corresponding value as a double (If the corresponding attribute is nominal (or a string) then it returns the value's index as a double). @throws UnassignedClassException if the class is not set or the instance doesn'thave access to a dataset

        Instance instance = (Instance)enu.nextElement();
        if( instance.classIsMissing() ) {
          instancesOfClass[numClasses].add( instance );
  }
  else {
          int c = (int)instance.classValue();
          instancesOfClass[c].add( instance );
        }
      }
      // If there are not instances with a missing class, we do not need to
      // consider them

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      double classYWeights = 0, totalWeights = 0;
      for(int j=0; j < data.numInstances(); j++){
    Instance datum = data.instance(j);
    totalWeights += datum.weight();
    if((int)datum.classValue() == y){
        classYWeights += datum.weight();
    }            
      }  
          
      // DL of default rule, no theory DL, only data DL

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         m_Variance[x][y] = m_ZERO;
         m_Change[x][y] = 1.0;
         }  */  


      data.add(example);
      m_Class[x] = example.classValue();
      m_Weights[x] = example.weight();  
    }


    for(int z=0; z < numegs; z++)
      findWeights(z, m_Mean);

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    for (int i = 0; i < insts.numClasses(); i++) {
      subsets[i] = new Instances(insts, insts.numInstances());
    }
    for (int j = 0; j < insts.numInstances(); j++) {
      Instance inst = insts.instance(j);
      subsets[(int)inst.classValue()].add(inst);
    }
    for (int i = 0; i < insts.numClasses(); i++) {
      subsets[i].compactify();
    }

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    i = 0;
    while (source.hasMoreElements(instances)) {
      instance = source.nextElement(instances);
      if (m_clusterAssignments[i] >= 0) {
        counts[(int)m_clusterAssignments[i]][(int)instance.classValue()]++;
        clusterTotals[(int)m_clusterAssignments[i]]++;
      }
      i++;
    }
    numInstances = i;

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  classDstr[1][j] = 0;
      }  
      if(m_ClassAttribute.isNominal()){      
  for(int i=0; i < growData.numInstances(); i++){
    Instance datum = growData.instance(i);
    classDstr[0][(int)datum.classValue()] += datum.weight();
  }
  defInfo = ContingencyTables.entropy(classDstr[0]);    
      }
      else{
  for(int i=0; i < growData.numInstances(); i++){

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  defInfo = ContingencyTables.entropy(classDstr[0]);    
      }
      else{
  for(int i=0; i < growData.numInstances(); i++){
    Instance datum = growData.instance(i);
    classDstr[0][0] += datum.weight() * datum.classValue();
  }
    
  // No need to be divided by the denomitor because
  // it's always the same
  double defMean = (classDstr[0][0] / whole);

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  growData = coverData;// Grow data size is shrinking       
    
  for(int x=0; x < uncoverData.numInstances(); x++){
    Instance datum = uncoverData.instance(x);
    if(m_ClassAttribute.isNumeric()){
      uncoveredWtSq += datum.weight() * datum.classValue() * datum.classValue();
      uncoveredWtVl += datum.weight() * datum.classValue();
      uncoveredWts += datum.weight();
      classDstr[0][0] -= datum.weight() * datum.classValue();
      classDstr[1][0] += datum.weight() * datum.classValue();
    }

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  growData = coverData;// Grow data size is shrinking       
    
  for(int x=0; x < uncoverData.numInstances(); x++){
    Instance datum = uncoverData.instance(x);
    if(m_ClassAttribute.isNumeric()){
      uncoveredWtSq += datum.weight() * datum.classValue() * datum.classValue();
      uncoveredWtVl += datum.weight() * datum.classValue();
      uncoveredWts += datum.weight();
      classDstr[0][0] -= datum.weight() * datum.classValue();
      classDstr[1][0] += datum.weight() * datum.classValue();
    }

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  for(int x=0; x < uncoverData.numInstances(); x++){
    Instance datum = uncoverData.instance(x);
    if(m_ClassAttribute.isNumeric()){
      uncoveredWtSq += datum.weight() * datum.classValue() * datum.classValue();
      uncoveredWtVl += datum.weight() * datum.classValue();
      uncoveredWts += datum.weight();
      classDstr[0][0] -= datum.weight() * datum.classValue();
      classDstr[1][0] += datum.weight() * datum.classValue();
    }
    else{

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