Program: 51052
Project: 51052
Buggy source:
public void run(){
  double[] accScores=new double[seeds.length];
  double[] fScores=new double[seeds.length];
  double[][] matrix=new double[1][1];
  double[] target;
  if (maxClassSize == 0) {
    matrix=kernel.compute(dataSet.getGraphs());
  }
  for (int i=0; i < seeds.length; i++) {
    if (maxClassSize == 0) {
      matrix=kernel.shuffle(matrix,seeds[i]);
      dataSet.shuffle(seeds[i]);
      target=LibSVM.createTargets(dataSet.getLabels());
    }
 else {
      PropertyPredictionDataSet subset=dataSet.getSubSet(maxClassSize,seeds[i]);
      matrix=kernel.compute(subset.getGraphs());
      target=LibSVM.createTargets(subset.getLabels());
    }
    double[] prediction=LibSVM.crossValidate(matrix,target,10,cs);
    accScores[i]=LibSVM.computeAccuracy(target,prediction);
    fScores[i]=LibSVM.computeF1(target,prediction);
  }
  Result accRes=results.getAccuracy();
  Result fRes=results.getF1();
  accRes.setLabel("String_Node_Str");
  fRes.setLabel("String_Node_Str");
  accRes.setScores(accScores);
  fRes.setScores(fScores);
  output.println(dataSet.getLabel());
  output.println(kernel.getLabel() + "String_Node_Str" + Arrays.toString(seeds)+ "String_Node_Str"+ Arrays.toString(cs));
  output.print("String_Node_Str" + accRes.getScore());
  output.print("String_Node_Str" + fRes.getScore());
  output.println("String_Node_Str");
  output.print("String_Node_Str" + Arrays.toString(accScores));
  output.print("String_Node_Str" + Arrays.toString(fScores));
  output.println("String_Node_Str");
  output.flush();
  results.setLabel(dataSet.getLabel() + "String_Node_Str" + Arrays.toString(seeds)+ "String_Node_Str"+ Arrays.toString(cs)+ "String_Node_Str"+ kernel.getLabel());
  results.setAccuracy(accRes);
  results.setF1(fRes);
}

Fixed version (ground truth):
public void run(){
  double[] accScores=new double[seeds.length];
  double[] fScores=new double[seeds.length];
  double[][] matrix=new double[1][1];
  double[] target;
  if (maxClassSize == 0) {
    matrix=kernel.compute(dataSet.getGraphs());
  }
  for (int i=0; i < seeds.length; i++) {
    if (maxClassSize == 0) {
      matrix=kernel.shuffle(matrix,seeds[i]);
      dataSet.shuffle(seeds[i]);
      target=LibSVM.createTargets(dataSet.getLabels());
    }
 else {
      PropertyPredictionDataSet subset=dataSet.getSubSet(maxClassSize,seeds[i]);
      subset.shuffle(seeds[i]);
      matrix=kernel.compute(subset.getGraphs());
      target=LibSVM.createTargets(subset.getLabels());
    }
    double[] prediction=LibSVM.crossValidate(matrix,target,10,cs);
    accScores[i]=LibSVM.computeAccuracy(target,prediction);
    fScores[i]=LibSVM.computeF1(target,prediction);
  }
  Result accRes=results.getAccuracy();
  Result fRes=results.getF1();
  accRes.setLabel("String_Node_Str");
  fRes.setLabel("String_Node_Str");
  accRes.setScores(accScores);
  fRes.setScores(fScores);
  output.println(dataSet.getLabel());
  output.println(kernel.getLabel() + "String_Node_Str" + Arrays.toString(seeds)+ "String_Node_Str"+ Arrays.toString(cs));
  output.print("String_Node_Str" + accRes.getScore());
  output.print("String_Node_Str" + fRes.getScore());
  output.println("String_Node_Str");
  output.print("String_Node_Str" + Arrays.toString(accScores));
  output.print("String_Node_Str" + Arrays.toString(fScores));
  output.println("String_Node_Str");
  output.flush();
  results.setLabel(dataSet.getLabel() + "String_Node_Str" + Arrays.toString(seeds)+ "String_Node_Str"+ Arrays.toString(cs)+ "String_Node_Str"+ kernel.getLabel());
  results.setAccuracy(accRes);
  results.setF1(fRes);
}