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

     */
    private double computeDefAccu(Instances data){ 
      double defAccu=0;
      for(int i=0; i<data.numInstances(); i++){
  Instance inst = data.instance(i);
  if((int)inst.classValue() == (int)m_Consequent)
    defAccu += inst.weight();
      }
      return defAccu;
    }

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    Instance ins=newData.instance(y);
        
    if(antd.covers(ins)){   // Covered by this antecedent
      coverage[x] += ins.weight();
      data.add(ins);                 // Add to data for further pruning
      if((int)ins.classValue() == (int)m_Consequent) // Accurate prediction
        worthValue[x] += ins.weight();
    }
    else if(useWhole){ // Not covered
      if((int)ins.classValue() != (int)m_Consequent)
        tn += ins.weight();

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      data.add(ins);                 // Add to data for further pruning
      if((int)ins.classValue() == (int)m_Consequent) // Accurate prediction
        worthValue[x] += ins.weight();
    }
    else if(useWhole){ // Not covered
      if((int)ins.classValue() != (int)m_Consequent)
        tn += ins.weight();
    }      
  }
    
  if(useWhole){

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    // compute counts for all the values
    for (int i = 0; i < instances.numInstances(); i++) {
      Instance inst = instances.instance(i);
      if (!inst.isMissing(attIndex)) {
  distribution[1][(int)inst.classValue()] += inst.weight();
      } else {
  distribution[2][(int)inst.classValue()] += inst.weight();
  numMissing++;
      }
    }

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    for (int i = 0; i < instances.numInstances(); i++) {
      Instance inst = instances.instance(i);
      if (!inst.isMissing(attIndex)) {
  distribution[1][(int)inst.classValue()] += inst.weight();
      } else {
  distribution[2][(int)inst.classValue()] += inst.weight();
  numMissing++;
      }
    }


    // sort instances

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    new ReferenceInstances(m_trainInstances, m_negTrainInstances.numInstances());
  for (Enumeration e = clone.m_trainInstances.enumerateInstances();
       e.hasMoreElements(); ) {
    Instance inst = (Instance) e.nextElement();
    try { // ignore classValue() exception
      if ((int) inst.classValue() == 0)
        clone.m_negTrainInstances.addReference(inst); // belongs in negative class
      else
        clone.m_posTrainInstances.addReference(inst); // belongs in positive class
    } catch (Exception ignored) {} 
  }

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            // ... calculate the counts for this class
            Enumeration enu = ruleE.enumerateInstances();
            while (enu.hasMoreElements()) {
              Instance i = (Instance) enu.nextElement();
              covers[(int) i.value(attr)]++;
              if ((int) i.classValue() == cl) {
                correct[(int) i.value(attr)]++;
        }
            }


            // ... for each value of this attribute, see if this test is better

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        // Use training data
        for (int j = 0; j < insts.numInstances(); j++) {
          Instance inst = insts.instance(j);
          double[] vals = new double[2];
          vals[0] = SVMOutput(-1, inst);
          if (inst.classValue() == cl2) {
            vals[1] = 1;
          }
          data.add(new Instance(inst.weight(), vals));
        }
      } else {

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    break;
  }
    
  sndCover += inst.weight();
  if(m_ClassAttribute.isNominal()) // Nominal class
    snd[(int)inst.classValue()] += inst.weight();    
  else{                            // Numeric class
    sndWtSq += inst.weight() * inst.classValue() * inst.classValue();
    sndWtVl += inst.weight() * inst.classValue();
  }
      }

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  sndCover += inst.weight();
  if(m_ClassAttribute.isNominal()) // Nominal class
    snd[(int)inst.classValue()] += inst.weight();    
  else{                            // Numeric class
    sndWtSq += inst.weight() * inst.classValue() * inst.classValue();
    sndWtVl += inst.weight() * inst.classValue();
  }
      }

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