Examples of weka.core.Instances.stratify()

weka.core.Instances.stratify()
Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed). @param numFolds the number of folds in the cross-validation @throws UnassignedClassException if the class is not set

        m_Log.statusMessage("Randomizing instances...");
        Random random = new Random(seed);
        inst.randomize(random);
        if (inst.attribute(classIndex).isNominal()) {
    m_Log.statusMessage("Stratifying instances...");
    inst.stratify(numFolds);
        }
        for (int fold = 0; fold < numFolds;fold++) {
    m_Log.statusMessage("Creating splits for fold "
            + (fold + 1) + "...");
    Instances train = inst.trainCV(numFolds, fold, random);

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  //Best iteration is selected only from these.
  int completedIterations = m_maxIterations;
  
  Instances allData = new Instances(m_train);
  
  allData.stratify(m_numFoldsBoosting);        


  double[] error = new double[m_maxIterations + 1];  
  
  for (int i = 0; i < m_numFoldsBoosting; i++) {
      //split into training/test data in fold

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          dataSet.randomize(random);
        }
        if (dataSet.classIndex() >= 0 && 
      dataSet.attribute(dataSet.classIndex()).isNominal() &&
      !m_preserveOrder) {
    dataSet.stratify(getFolds());
    if (m_logger != null) {
      m_logger.logMessage("[" + getCustomName() + "] "
              +"stratifying data");
    }
        }

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