Examples of weka.core.Instances.classIndex()

weka.core.Instances.classIndex()
Returns the class attribute's index. Returns negative number if it's undefined. @return the class index as an integer

    
    int numCl = data.numClasses();
    m_Root = new Ridor_node();
    m_Class = instances.classAttribute();     // The original class label
  
    int index = data.classIndex();
    m_Cover = data.sumOfWeights();


    m_Random = new Random(m_Seed);
  
    /* Create a binary attribute */

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      double total = Utils.sum(isPure);   
      
      if(m_IsMajority){
  defClass = (double)Utils.maxIndex(isPure);
  Instances data = new Instances(dataByClass[(int)defClass]);
  int index = data.classIndex();
    
  for(int j=0; j<data.numInstances(); j++)
    data.instance(j).setClassValue(1);       // Set one class as default
    
  for(int k=0; k < dataByClass.length; k++)    // Merge into one dataset

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  // Find default class
  for(int i=0; i < dataByClass.length; i++){
    if(isPure[i] >= m_Folds){
      Instances data = new Instances(dataByClass[i]);
      int index = data.classIndex();
      
      for(int j=0; j<data.numInstances(); j++)
        data.instance(j).setClassValue(1);       // Set one class as default
      
      for(int k=0; k < dataByClass.length; k++)    // Merge into one dataset

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      File currentFile = (File) getDatasets().elementAt(m_DatasetNumber);
      AbstractFileLoader loader = ConverterUtils.getLoaderForFile(currentFile);
      loader.setFile(currentFile);
      Instances data = new Instances(loader.getDataSet());
      // only set class attribute if not already done by loader
      if (data.classIndex() == -1) {
  if (m_ClassFirst) {
    data.setClassIndex(0);
  } else {
    data.setClassIndex(data.numAttributes() - 1);
  }

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  public void testFilteredClassifier() {
    try {
      Instances data = getFilteredClassifierData();


      for (int i = 0; i < data.numAttributes(); i++) {
  if (data.classIndex() == i)
    continue;
  if (data.attribute(i).isNominal()) {
    ((SwapValues) m_FilteredClassifier.getFilter()).setAttributeIndex(
        "" + (i + 1));
    break;

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  public void testFilteredClassifier() {
    try {
      Instances data = getFilteredClassifierData();


      for (int i = 0; i < data.numAttributes(); i++) {
  if (data.classIndex() == i)
    continue;
  if (data.attribute(i).isNominal()) {
    ((MergeTwoValues) m_FilteredClassifier.getFilter()).setAttributeIndex(
        "" + (i + 1));
    break;

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  public void testFilteredClassifier() {
    try {
      Instances data = getFilteredClassifierData();


      for (int i = 0; i < data.numAttributes(); i++) {
  if (data.classIndex() == i)
    continue;
  if (data.attribute(i).isNominal()) {
    ((AddValues) m_FilteredClassifier.getFilter()).setAttributeIndex(
        "" + (i + 1));
    break;

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  public void testFilteredClassifier() {
    try {
      Instances data = getFilteredClassifierData();


      for (int i = 0; i < data.numAttributes(); i++) {
  if (data.classIndex() == i)
    continue;
  if (data.attribute(i).isString()) {
    ((StringToNominal) m_FilteredClassifier.getFilter()).setAttributeRange(
        "" + (i + 1));
    break;

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      }


      //      Instances dataset = m_incrementalEvent.getInstance().dataset();
      Instances dataset = m_incrementalEvent.getStructure();
      // default to the last column if no class is set
      if (dataset.classIndex() < 0) {
        stop();
        String errorMessage = statusMessagePrefix()
            + Messages.getInstance().getString("Classifier_HandleIncrementalEvent_ErrorMessage_Text_First");
        if (m_log != null) {
          m_log.statusMessage(errorMessage);

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      return;
    }


    Instances testSet = e.getTestSet();
    if (testSet != null) {
      if (testSet.classIndex() < 0) {
//        testSet.setClassIndex(testSet.numAttributes() - 1);
        // stop all processing
        stop();
        String errorMessage = statusMessagePrefix()
            + Messages.getInstance().getString("Classifier_AcceptTestSet_ErrorMessage_Text_First");

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