Examples of weka.core.Instances.instance()

weka.core.Instances.instance()
Returns the instance at the given position. @param index the instance's index (index starts with 0) @return the instance at the given position

      double[] vals = new double[2];
      vals[0] = smo.SVMOutput(-1, test.instance(j));
      if (test.instance(j).classValue() == cl2) {
        vals[1] = 1;
      }
      data.add(new Instance(test.instance(j).weight(), vals));
    }
  }
      }


      // Build logistic regression model

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      Instances cBCurve = m_costBenefit.getPlotInstances();
      int maxMinIndex = 0;
      
      for (int i = 0; i < cBCurve.numInstances(); i++) {
        Instance current = cBCurve.instance(i);
        if (max) {
          if (current.value(1) > maxMin) {
            maxMin = current.value(1);
            maxMinIndex = i;
          }

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      int indexOfThreshold =
        m_masterPlot.getPlotInstances().attribute(ThresholdCurve.THRESHOLD_NAME).index();
      
      // update labels
      m_percPopLab.setText(Utils.
          doubleToString(100.0 * plotInstances.instance(index).value(indexOfSampleSize), 4));
      m_percOfTargetLab.setText(Utils.doubleToString(
          100.0 * plotInstances.instance(index).value(indexOfPercOfTarget), 4));
      m_thresholdLab.setText(Utils.doubleToString(plotInstances.instance(index).value(indexOfThreshold), 4));
      /*if (m_percPop.isSelected()) {
        m_percPopLab.setText(Utils.doubleToString(100.0 * value, 4));

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      // update labels
      m_percPopLab.setText(Utils.
          doubleToString(100.0 * plotInstances.instance(index).value(indexOfSampleSize), 4));
      m_percOfTargetLab.setText(Utils.doubleToString(
          100.0 * plotInstances.instance(index).value(indexOfPercOfTarget), 4));
      m_thresholdLab.setText(Utils.doubleToString(plotInstances.instance(index).value(indexOfThreshold), 4));
      /*if (m_percPop.isSelected()) {
        m_percPopLab.setText(Utils.doubleToString(100.0 * value, 4));
      } else if (m_percOfTarget.isSelected()) {
        m_percOfTargetLab.setText(Utils.doubleToString(100.0 * value, 4));

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      // update labels
      m_percPopLab.setText(Utils.
          doubleToString(100.0 * plotInstances.instance(index).value(indexOfSampleSize), 4));
      m_percOfTargetLab.setText(Utils.doubleToString(
          100.0 * plotInstances.instance(index).value(indexOfPercOfTarget), 4));
      m_thresholdLab.setText(Utils.doubleToString(plotInstances.instance(index).value(indexOfThreshold), 4));
      /*if (m_percPop.isSelected()) {
        m_percPopLab.setText(Utils.doubleToString(100.0 * value, 4));
      } else if (m_percOfTarget.isSelected()) {
        m_percOfTargetLab.setText(Utils.doubleToString(100.0 * value, 4));
      } else {

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//      double totalInstances = 
      int tp = plotInstances.attribute(ThresholdCurve.TRUE_POS_NAME).index();
      int fp = plotInstances.attribute(ThresholdCurve.FALSE_POS_NAME).index();
      int tn = plotInstances.attribute(ThresholdCurve.TRUE_NEG_NAME).index();
      int fn = plotInstances.attribute(ThresholdCurve.FALSE_NEG_NAME).index();
      Instance temp = plotInstances.instance(index);
      double totalInstances = temp.value(tp) + temp.value(fp) + temp.value(tn) + temp.value(fn);
      // get the value out of the total pop field (if possible)
      double requestedPopSize = totalInstances;
      try {
        requestedPopSize = Double.parseDouble(m_totalPopField.getText());

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      // process the performance data to make this curve
      Instances performanceI = m_masterPlot.getPlotInstances();
      
      for (int i = 0; i < performanceI.numInstances(); i++) {
        Instance current = performanceI.instance(i);
        
        double[] vals = new double[2];
        vals[0] = current.value(10); // sample size
        vals[1] = (current.value(0) * tpCost
            + current.value(1) * fnCost

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    // the main loop to handle computing nearest neighbors and generating SMOTE
    // examples from each instance in the original minority class data
    Instance[] nnArray = new Instance[nearestNeighbors];
    for (int i = 0; i < sample.numInstances(); i++) {
      Instance instanceI = sample.instance(i);
      // find k nearest neighbors for each instance
      List distanceToInstance = new LinkedList();
      for (int j = 0; j < sample.numInstances(); j++) {
  Instance instanceJ = sample.instance(j);
  if (i != j) {

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    for (int i = 0; i < sample.numInstances(); i++) {
      Instance instanceI = sample.instance(i);
      // find k nearest neighbors for each instance
      List distanceToInstance = new LinkedList();
      for (int j = 0; j < sample.numInstances(); j++) {
  Instance instanceJ = sample.instance(j);
  if (i != j) {
    double distance = 0;
    attrEnum = getInputFormat().enumerateAttributes();
    while(attrEnum.hasMoreElements()) {
      Attribute attr = (Attribute) attrEnum.nextElement();

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    }
    
    flushInput();


    for (int i = 0; i < instances.numInstances(); i++) {
      push(instances.instance(i));
    }


    m_NewBatch = true;
    m_FirstBatchDone = true;
    return (numPendingOutput() != 0);

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