Examples of weka.core.Instances.classAttribute()

weka.core.Instances.classAttribute()
Returns the class attribute. @return the class attribute @throws UnassignedClassException if the class is not set

              } else {
          if (classifierToUse instanceof PMMLClassifier) {
            // set the class based on information in the mining schema
            Instances miningSchemaStructure = 
              ((PMMLClassifier)classifierToUse).getMiningSchema().getMiningSchemaAsInstances();
            String className = miningSchemaStructure.classAttribute().name();
            Attribute classMatch = userTestStructure.attribute(className);
            if (classMatch == null) {
              throw new Exception("Can't find a match for the PMML target field " 
            + className + " in the "
            + "test instances!");

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      J48 cls = (J48)weka.core.SerializationHelper.read(modelfile);
      cls.setOptions(options);
      for(int i = 0; i < data.numInstances(); i++){
        double pred = cls.classifyInstance(data.instance(i));
        ClusterClass clusClass = ClusterClass.valueOf(
            data.classAttribute().value((int)pred).toUpperCase());
        if(!retval.containsKey(clusClass)){
          retval.put(clusClass, new ArrayList<StoredDomainCluster>());
        }
        retval.get(clusClass).add(clusters.get(i));
      }

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    discretizedDataset.setDataset(data);
    
    /*
     * build J48 classifier
     */
    if(data.classAttribute().numValues()<2)
      return null;
    
    //System.out.println("dataset class index "+data.classIndex() + " "+data.classAttribute());
    
    classifier.buildClassifier(data);

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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++) {

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      }
      
      // 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()) {
    newVec.addElement(new Attribute(data.classAttribute().name(), values,

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  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()) {
    newVec.addElement(new Attribute(data.classAttribute().name(), values, 
            data.classAttribute().getMetadata()));
  } else {
    newVec.addElement(data.attribute(i));
  }
      }

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      }
      FastVector newVec = new FastVector(data.numAttributes());
      for (int i = 0; i < data.numAttributes(); i++) {
  if (i == data.classIndex()) {
    newVec.addElement(new Attribute(data.classAttribute().name(), values, 
            data.classAttribute().getMetadata()));
  } else {
    newVec.addElement(data.attribute(i));
  }
      }
      Instances newInsts = new Instances(data.relationName(), newVec, 0);

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  }
      }


      // 3. error flag?
      if (getOutputErrorFlag()) {
  if (result.classAttribute().isNominal()) {
    if (oldInstance.classValue() == m_ActualClassifier.classifyInstance(oldInstance))
      newValues[start] = 0;
    else
      newValues[start] = 1;
  }

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      //String relationNameModifier = oldStructure.relationName()
  //+"_with predictions";
      String relationNameModifier = "_with predictions";
  //+"_with predictions";
       if (!m_appendProbabilities 
     || oldStructure.classAttribute().isNumeric()) {
   try {
     m_format = makeDataSetClass(oldStructure, classifier,
                 relationNameModifier);
   } catch (Exception ex) {
     ex.printStackTrace();

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            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();
              }


              if (inst.classIndex() >= 0 &&

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