import weka.classifiers.evaluation.Prediction; //导入依赖的package包/类
public static void evaluateResults(Evaluation evaluation) {

        for (Prediction p : evaluation.predictions()) {
            System.out.println(p.actual() + " " + p.predicted());
        }
        System.out.println(evaluation.toSummaryString("\nResults\n======\n", true));
        //  System.out.println(evaluation.toSummaryString(evaluation.correlationCoefficient() + " " + evaluation.errorRate() + " " + evaluation.meanAbsoluteError() + " ");

    }
 

import weka.classifiers.evaluation.Prediction; //导入依赖的package包/类
public double computeAccuracyAndRecordPrediction(BayesianNetwork bn, DataOnMemory<DataInstance> data){


        double correctPredictions = 0;
        Variable classVariable = bn.getVariables().getVariableById(nb_.getClassIndex());

        VMP vmp = new VMP();
        vmp.setModel(bn);
        for (DataInstance instance : data) {
            double realValue = instance.getValue(classVariable);
            instance.setValue(classVariable, Utils.missingValue());
            vmp.setEvidence(instance);
            vmp.runInference();
            Multinomial posterior = vmp.getPosterior(classVariable);

            if (Utils.maxIndex(posterior.getProbabilities())==realValue)
                correctPredictions++;
            Prediction prediction = new NominalPrediction(realValue, posterior.getProbabilities());
            predictions.add(prediction);

            instance.setValue(classVariable, realValue);
        }

        return correctPredictions/data.getNumberOfDataInstances();

    }
 

import weka.classifiers.evaluation.Prediction; //导入依赖的package包/类
/**
 * Returns a string containing all the predictions.
 * 
 * @param predictions a <code>FastVector</code> containing the predictions
 * @return a <code>String</code> representing the vector of predictions.
 */
public static String predictionsToString(ArrayList<Prediction> predictions) {
  StringBuffer sb = new StringBuffer();
  sb.append(predictions.size()).append(" predictions\n");
  for (int i = 0; i < predictions.size(); i++) {
    sb.append(predictions.get(i)).append('\n');
  }
  return sb.toString();
}
 

import weka.classifiers.evaluation.Prediction; //导入依赖的package包/类
/**
 * Returns a string containing all the predictions.
 * 
 * @param predictions a <code>FastVector</code> containing the predictions
 * @return a <code>String</code> representing the vector of predictions.
 */
protected String predictionsToString(ArrayList<Prediction> predictions) {
  StringBuffer sb = new StringBuffer();
  sb.append(predictions.size()).append(" predictions\n");
  for (int i = 0; i < predictions.size(); i++) {
    sb.append(predictions.get(i)).append('\n');
  }
  return sb.toString();
}
 

import weka.classifiers.evaluation.Prediction; //导入依赖的package包/类
public static void evaluateResults(Evaluation evaluation){

        for(Prediction p: evaluation.predictions()){
            System.out.println(p.actual() + " " + p.predicted() );
        }


        System.out.println(evaluation.toSummaryString("\nResults\n======\n", true));
        //  System.out.println(evaluation.toSummaryString(evaluation.correlationCoefficient() + " " + evaluation.errorRate() + " " + evaluation.meanAbsoluteError() + " ");


    }
 

import weka.classifiers.evaluation.Prediction; //导入依赖的package包/类
public static void main(String[] args) {
  try {
    Instances train = new Instances(new java.io.BufferedReader(
      new java.io.FileReader(args[0])));
    train.setClassIndex(train.numAttributes() - 1);
    weka.classifiers.evaluation.ThresholdCurve tc = new weka.classifiers.evaluation.ThresholdCurve();
    weka.classifiers.evaluation.EvaluationUtils eu = new weka.classifiers.evaluation.EvaluationUtils();
    // weka.classifiers.Classifier classifier = new
    // weka.classifiers.functions.Logistic();
    weka.classifiers.Classifier classifier = new weka.classifiers.bayes.NaiveBayes();
    ArrayList<Prediction> predictions = new ArrayList<Prediction>();
    eu.setSeed(1);
    predictions.addAll(eu.getCVPredictions(classifier, train, 10));
    Instances result = tc.getCurve(predictions, 0);
    PlotData2D pd = new PlotData2D(result);
    pd.m_alwaysDisplayPointsOfThisSize = 10;

    boolean[] connectPoints = new boolean[result.numInstances()];
    for (int i = 1; i < connectPoints.length; i++) {
      connectPoints[i] = true;
    }
    pd.setConnectPoints(connectPoints);
    final javax.swing.JFrame jf = new javax.swing.JFrame("CostBenefitTest");
    jf.setSize(1000, 600);
    // jf.pack();
    jf.getContentPane().setLayout(new BorderLayout());
    final CostBenefitAnalysis.AnalysisPanel analysisPanel = new CostBenefitAnalysis.AnalysisPanel();

    jf.getContentPane().add(analysisPanel, BorderLayout.CENTER);
    jf.addWindowListener(new java.awt.event.WindowAdapter() {
      @Override
      public void windowClosing(java.awt.event.WindowEvent e) {
        jf.dispose();
        System.exit(0);
      }
    });

    jf.setVisible(true);

    analysisPanel.setDataSet(pd, train.classAttribute());

  } catch (Exception ex) {
    ex.printStackTrace();
  }

}
 

import weka.classifiers.evaluation.Prediction; //导入依赖的package包/类
/**
 * Returns the predictions that have been collected.
 * 
 * @return a reference to the FastVector containing the predictions that have
 *         been collected. This should be null if no predictions have been
 *         collected.
 */
public ArrayList<Prediction> predictions() {
  return m_delegate.predictions();
}
 

import weka.classifiers.evaluation.Prediction; //导入依赖的package包/类
/**
 * Get a JMenu or JMenuItem which contain action listeners that perform the
 * visualization, using some but not necessarily all of the data. Exceptions
 * thrown because of changes in Weka since compilation need to be caught by
 * the implementer.
 * 
 * @see NoClassDefFoundError
 * @see IncompatibleClassChangeError
 * 
 * @param preds predictions
 * @param classAtt class attribute
 * @return menuitem for opening visualization(s), or null to indicate no
 *         visualization is applicable for the input
 */
public JMenuItem getVisualizeMenuItem(ArrayList<Prediction> preds,
  Attribute classAtt);