Package weka.core

Examples of weka.core.Instances.classIndex()

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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).isDate()) {
    ((ChangeDateFormat) 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()) {
    ((MakeIndicator) m_FilteredClassifier.getFilter()).setAttributeIndex(
        "" + (i + 1));
    break;
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  protected Instance mergeInstances(Instance source, Instance dest) {

    Instances outputFormat = outputFormatPeek();
    double[] vals = new double[outputFormat.numAttributes()];
    for(int i = 0; i < vals.length; i++) {
      if ((i != outputFormat.classIndex()) && (m_SelectedCols.isInRange(i))) {
        if (source != null) {
          vals[i] = source.value(i);
        } else {
          vals[i] = Instance.missingValue();
        }
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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).isNumeric()) {
    ((TimeSeriesTranslate) m_FilteredClassifier.getFilter()).setAttributeIndices("" + (i + 1));
    ((TimeSeriesTranslate) m_FilteredClassifier.getFilter()).setFillWithMissing(true);
    break;
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  // rename attributes
  processed = renameAttributes(processed, "filtered-" + i + "-");

  // add attributes
  for (n = 0; n < processed.numAttributes(); n++) {
    if (n == processed.classIndex())
      continue;
    atts.addElement((Attribute) processed.attribute(n).copy());
  }
      }
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    if (m_Means == null) {
      Instances input = getInputFormat();
      m_Means = new double[input.numAttributes()];
      for (int i = 0; i < input.numAttributes(); i++) {
  if (input.attribute(i).isNumeric() &&
      (input.classIndex() != i)) {
    m_Means[i] = input.meanOrMode(i);
  }
      }

      // Convert pending input instances
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      for (int j = 0; j < input.numInstances(); j++) {
  double[] value = input.instance(j).toDoubleArray();
  for (int i = 0; i < input.numAttributes(); i++) {
    if (input.attribute(i).isNumeric() &&
        (input.classIndex() != i)) {
      if (!Instance.isMissingValue(value[i])) {
        if (Double.isNaN(m_MinArray[i])) {
    m_MinArray[i] = m_MaxArray[i] = value[i];
        }
        else {
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      m_attStats = new AttributeStats [input.numAttributes()];
     
      for (int i = 0; i < input.numAttributes(); i++) {
  if (input.attribute(i).isNumeric() &&
      (input.classIndex() != i)) {
    m_attStats[i] = input.attributeStats(i);
  }
      }

      // Convert pending input instances
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  protected Instance mergeInstances(Instance source, Instance dest) {

    Instances outputFormat = outputFormatPeek();
    double[] vals = new double[outputFormat.numAttributes()];
    for(int i = 0; i < vals.length; i++) {
      if ((i != outputFormat.classIndex()) && (m_SelectedCols.isInRange(i))) {
        if ((source != null) && !source.isMissing(i) && !dest.isMissing(i)) {
          vals[i] = dest.value(i) - source.value(i);
        } else {
          vals[i] = Instance.missingValue();
        }
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                            + "Target element in PMML does not have any discrete values "
                            + "defined!");
      }

      // Finally, convert the class in the mining schema to nominal
      m_miningSchema.convertNumericAttToNominal(miningSchemaI.classIndex(), targetVals);
    }
   
    // allocate space for the matrix
    m_paramMatrix =
        new PCell[(classAtt.isNumeric())
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