PhaseIwrep.PII_maxParents = ((K2) ((BayesNet) currentClassifier).getSearchAlgorithm()).getMaxNrOfParents();
PhaseIwrep.res_graph_xml = ((BayesNet) eval_clas).graph();
ThresholdCurve tc = new ThresholdCurve();
Instances tcEachClass[] = new Instances[randData.numClasses()];
Instances tcResult2 = tc.getCurve(eval_validationE.predictions(),0);
tcResult2.delete();
double[] attValues = new double[tcResult2.numAttributes()];
for (int i = 0; i < attValues.length; i++) {attValues[i] = 1;}
tcResult2.add(new Instance(1, attValues)); // adds the point 1,1
for (int i = 0; i < attValues.length; i++) {attValues[i] = 0;}
try{
// vmc.setVisible(true);
// BufferedImage bi = new BufferedImage(300, 300, BufferedImage.TYPE_INT_RGB);
// ImageIO.write(bi, "png",new File("C:/users/aaron/desktop/cool44.png"));
// jf.setVisible(true);
}
catch (Exception ex)
{
System.out.println(ex.toString());
}
// PhaseIwrep.res_ROC_curves = new double[randData.numClasses()][2][tcEachClass.numInstances()];
Plot2D myPlot2Db = new Plot2D();
tc = new ThresholdCurve();
for (int class_index =0 ; class_index< randData.numClasses();class_index++){
tcEachClass[class_index] = tc.getCurve(eval_validationE.predictions(),class_index); // Uses Class 0-end
}
double[][] CL4 = new double[tcEachClass[0].numInstances()][randData.numClasses()];
double[][] CL5 = new double[tcEachClass[0].numInstances()][randData.numClasses()];