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

                  (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 = 
    new TextEvent(this,

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       m_Classifier.buildClassifier(trainData);
       m_BestClassifierOptions = m_InitOptions;
       return;
    }


    if (trainData.classAttribute().isNominal()) {
      trainData.stratify(m_NumFolds);
    }
    m_BestClassifierOptions = null;
    
    // Set up m_ClassifierOptions -- take getOptions() and remove

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    data = Filter.useFilter(instances, m_Filter);
  
    if(data == null)
      throw new Exception(" Unable to randomize the class orders.");
    
    m_Class = data.classAttribute();  
    m_Ruleset = new FastVector();
    m_RulesetStats = new FastVector();
    m_Distributions = new FastVector();


    // Sort by classes frequency

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      source = new DataSource(test);
    testRaw = source.getStructure(test.classIndex());
    
    // If class is set then do class based evaluation as well
    if (hasClass) {
      if (testRaw.classAttribute().isNumeric())
  throw new Exception("ClusterEvaluation: Class must be nominal!");


      filter = new Remove();
      ((Remove) filter).setAttributeIndices("" + (testRaw.classIndex() + 1));
      ((Remove) filter).setInvertSelection(false);

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    data.deleteWithMissingClass();
    
    if(data.numInstances() < m_Folds)
      throw new Exception("Not enough data for REP.");


    m_ClassAttribute = data.classAttribute();
    if(m_ClassAttribute.isNominal())
      m_NumClasses = m_ClassAttribute.numValues();
    else
      m_NumClasses = 1;

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    m_BaseFormat = new Instances(data, 0);
    newData.deleteWithMissingClass();


    Random random = new Random(m_Seed);
    newData.randomize(random);
    if (newData.classAttribute().isNominal()) {
      newData.stratify(m_NumFolds);
    }


    // Create meta level
    generateMetaLevel(newData, random);

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      FastVector newClassVals = new FastVector(numNonEmptyClasses);
      int[] oldIndexToNewIndex = new int[newTrain.numClasses()];
      for (int i = 0; i < newTrain.numClasses(); i++) {
        if (notEmptyClass[i]) {
          oldIndexToNewIndex[i] = newClassVals.size();
          newClassVals.addElement(newTrain.classAttribute().value(i));
        }
      }
      
      // Compute new header information
      Attribute newClass = new Attribute(newTrain.classAttribute().name(),

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          newClassVals.addElement(newTrain.classAttribute().value(i));
        }
      }
      
      // Compute new header information
      Attribute newClass = new Attribute(newTrain.classAttribute().name(), 
                                         newClassVals);
      FastVector newAttributes = new FastVector(newTrain.numAttributes());
      for (int i = 0; i < newTrain.numAttributes(); i++) {
        if (i != newTrain.classIndex()) {
          newAttributes.addElement(newTrain.attribute(i).copy());

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        TargetMetaInfo targetData = m_miningSchema.getTargetMetaData();
        if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
          preds[0] = targetData.getDefaultValue();
        } else {
          Instances miningSchemaI = m_miningSchema.getFieldsAsInstances();
          for (int i = 0; i < miningSchemaI.classAttribute().numValues(); i++) {
            preds[i] = targetData.getPriorProbability(miningSchemaI.classAttribute().value(i));
          }
        }
        return preds;
      }

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        if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
          preds[0] = targetData.getDefaultValue();
        } else {
          Instances miningSchemaI = m_miningSchema.getFieldsAsInstances();
          for (int i = 0; i < miningSchemaI.classAttribute().numValues(); i++) {
            preds[i] = targetData.getPriorProbability(miningSchemaI.classAttribute().value(i));
          }
        }
        return preds;
      }
    } else {

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