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

      m_removeFilter.setAttributeIndicesArray(finalAttsToDelete);
      m_removeFilter.setInvertSelection(false);
      m_removeFilter.setInputFormat(toFilter);
      
      for (int i = 0; i < toFilter.numInstances(); i++) {
  m_removeFilter.input(toFilter.instance(i));
      }
      m_removeFilter.batchFinished();


      Instance processed;
      Instances outputDataset = m_removeFilter.getOutputFormat();

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      // The dataset may be large, so to make things easier we'll
      // output an instance at a time (rather than having to convert
      // the entire dataset to one large string)
      System.out.println(new Instances(aha, 0));
      for (int i = 0; i < aha.numInstances(); i++) {
  System.out.println(aha.instance(i));
      }
    } catch(Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }

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          if(!m_DataBaseConnection.isConnected())
              connectToDatabase();
          setWriteMode(WRITE);
          writeStructure();
          for(int i = 0; i < instances.numInstances(); i++){
            writeInstance(instances.instance(i));
          }
          m_DataBaseConnection.disconnectFromDatabase();
          setWriteMode(WAIT);
          resetStructure();
          m_count = 1;

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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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    current.buildClassifier(train);
    Instances test = inst.testCV(numFolds, fold);
    m_Log.statusMessage("Evaluating model for fold "
            + (fold + 1) + "...");
    for (int jj=0;jj<test.numInstances();jj++) {
      plotInstances.process(test.instance(jj), current, eval);
      if (outputPredictionsText) {
        classificationOutput.printClassification(current, test.instance(jj), jj);
      }
    }
        }

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