Program: 50996
Project: 50996
Buggy source:
public static void main(String[] args){
  long[] seeds={11,21,31,41,51,61,71,81,91,101};
  double[] cs={0.001,0.01,0.1,1,10,100,1000};
  int[] depths={2};
  int[] depths2={1,2,3};
  int[] iterations={4};
  createAffiliationPredictionDataSet(1);
  boolean inference=true;
  List<EvaluationFunction> evalFuncs=new ArrayList<EvaluationFunction>();
  evalFuncs.add(new Accuracy());
  evalFuncs.add(new F1());
  List<Double> targets=EvaluationUtils.createTarget(labels);
  LibLINEARParameters linParms=new LibLINEARParameters(LibLINEARParameters.SVC_DUAL,cs);
  linParms.setEvalFunction(new Accuracy());
  linParms.setDoCrossValidation(true);
  linParms.setSplitFraction((float)0.8);
  linParms.setEps(0.1);
  Map<Double,Double> counts=EvaluationUtils.computeClassCounts(targets);
  int[] wLabels=new int[counts.size()];
  double[] weights=new double[counts.size()];
  for (  double label : counts.keySet()) {
    wLabels[(int)label - 1]=(int)label;
    weights[(int)label - 1]=1 / counts.get(label);
  }
  linParms.setWeightLabels(wLabels);
  linParms.setWeights(weights);
  ResultsTable resTable=new ResultsTable();
  resTable.setDigits(3);
  for (  int depth : depths) {
    resTable.newRow("String_Node_Str");
    for (    int it : iterations) {
      RDFLinearKernelExperiment exp=new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it,depth,inference,false),seeds,linParms,dataset,instances,targets,blackList,evalFuncs);
      System.out.println("String_Node_Str" + depth + "String_Node_Str"+ it);
      exp.setDoCV(true);
      exp.setDoTFIDF(true);
      exp.run();
      for (      Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
  }
  System.out.println(resTable);
  resTable.addCompResults(resTable.getBestResults());
  System.out.println(resTable);
}

Fixed version (ground truth):
public static void main(String[] args){
  long[] seeds={11,21,31,41,51,61,71,81,91,101};
  double[] cs={0.001,0.01,0.1,1,10,100,1000};
  int[] depths={2};
  int[] depths2={1,2,3};
  int[] iterations={4};
  createAffiliationPredictionDataSet(1);
  boolean inference=true;
  List<EvaluationFunction> evalFuncs=new ArrayList<EvaluationFunction>();
  evalFuncs.add(new Accuracy());
  evalFuncs.add(new F1());
  List<Double> targets=EvaluationUtils.createTarget(labels);
  LibLINEARParameters linParms=new LibLINEARParameters(LibLINEARParameters.SVC_DUAL,cs);
  linParms.setEvalFunction(new Accuracy());
  linParms.setDoCrossValidation(true);
  linParms.setSplitFraction((float)0.8);
  linParms.setEps(0.1);
  Map<Double,Double> counts=EvaluationUtils.computeClassCounts(targets);
  int[] wLabels=new int[counts.size()];
  double[] weights=new double[counts.size()];
  for (  double label : counts.keySet()) {
    wLabels[(int)label - 1]=(int)label;
    weights[(int)label - 1]=1 / counts.get(label);
  }
  linParms.setWeightLabels(wLabels);
  linParms.setWeights(weights);
  ResultsTable resTable=new ResultsTable();
  resTable.setDigits(3);
  for (  int depth : depths) {
    resTable.newRow("String_Node_Str");
    for (    int it : iterations) {
      RDFLinearKernelExperiment exp=new RDFLinearKernelExperiment(new RDFWLSubTreeKernel(it,depth,inference,false),seeds,linParms,dataset,instances,targets,blackList,evalFuncs);
      System.out.println("String_Node_Str" + depth + "String_Node_Str"+ it);
      exp.setDoTFIDF(true);
      exp.run();
      for (      Result res : exp.getResults()) {
        resTable.addResult(res);
      }
    }
  }
  System.out.println(resTable);
  resTable.addCompResults(resTable.getBestResults());
  System.out.println(resTable);
}