Package statechum

Examples of statechum.ProgressIndicator

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            learnerRunner.setSelectionID(selection);
            learnerRunner.setPresetLearningParameters(presetForBestResults);
            runner.submit(learnerRunner);
            ++numberOfTasks;
          }
        ProgressIndicator progress = new ProgressIndicator(new Date()+" evaluating "+numberOfTasks+" tasks for "+selection+" trace num: "+traceNum, numberOfTasks);
        for(int count=0;count < numberOfTasks;++count)
        {
          ThreadResult result = runner.take().get();// this will throw an exception if any of the tasks failed.
         
          for(SampleData sample:result.samples)
          {
            if (sample.referenceLearner.differenceBCR.getValue() > 0)
            {
              // we'll generate both positives and negatives; in the considered experiments, only positives are used hence half the number of sequences are actually being learnt from.
              gr_BCRImprovementForDifferentNrOfTracesWithNegatives.add(traceNum,sample.actualLearner.differenceBCR.getValue()/sample.referenceLearner.differenceBCR.getValue());
              gr_BCRForDifferentNrOfTracesWithNegatives.add(traceNum,sample.actualLearner.differenceBCR.getValue());
            }
            if (sample.referenceLearner.differenceStructural.getValue() > 0)
            {
              gr_StructuralImprovementForDifferentNrOfTracesWithNegatives.add(traceNum,sample.actualLearner.differenceStructural.getValue()/sample.referenceLearner.differenceStructural.getValue());
              gr_StructuralForDifferentNrOfTracesWithNegatives.add(traceNum,sample.actualLearner.differenceStructural.getValue());
            }
          }
          progress.next();
        }
        gr_BCRForDifferentNrOfTracesWithNegatives.drawInteractive(gr);gr_StructuralForDifferentNrOfTracesWithNegatives.drawInteractive(gr);
      }
      catch(Exception ex)
      {
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  private ErlangModule(Configuration config) throws IOException
  {
    final File f = config.getErlangSourceFile();
    name = ErlangRunner.getName(f, ERL.MOD,config.getErlangCompileIntoBeamDirectory());
    sourceFolder = f.getParentFile();
    ProgressIndicator progress = new ProgressIndicator(name, 7);
    // launch Erlang by calling a test method.
    ErlangRunner erlangRunner = ErlangRunner.getRunner(config.getErlangMboxName());
    erlangRunner.call(
        new OtpErlangObject[] { new OtpErlangAtom("echo2Tuple"),
            new OtpErlangAtom("aaa") }, "echo2Tuple");
    progress.next();// 1

    // Compile and typecheck the module...
    ErlangRunner.compileErl(f, erlangRunner,config.getErlangCompileIntoBeamDirectory());
    progress.next();// 2
    sigs = new TreeMap<String, FuncSignature>();ignoredFunctions = new TreeSet<String>();ignoredBehaviours = new TreeSet<String>();

    File pltFile = new File(ErlangRunner.getName(f, ERL.PLT,config.getErlangCompileIntoBeamDirectory()));

    // Almost the same arguments for dialyzer and typer, the first argument
    // determines which of the two to run.
    OtpErlangObject otpArgs[] = new OtpErlangObject[] {
        null, // either Dialyzer or typer

        new OtpErlangList(new OtpErlangObject[] { new OtpErlangString(
            ErlangRunner.getName(f, ERL.BEAM,config.getErlangCompileIntoBeamDirectory())) }),
        new OtpErlangString(ErlangRunner.getName(f, ERL.PLT,config.getErlangCompileIntoBeamDirectory())),
        new OtpErlangList(new OtpErlangObject[] { new OtpErlangString(
            ErlangRunner.getName(f, ERL.ERL,false))}),
        new OtpErlangAtom("types") };

    if (!pltFile.canRead() || f.lastModified() > pltFile.lastModified()) {// rebuild the PLT file since the source was modified or the plt file does not exist
      pltFile.delete();
      otpArgs[0] = new OtpErlangAtom("dialyzer");
      erlangRunner.call(otpArgs, "Could not run dialyzer");
    }
    progress.next();// 3

    // Typer always has to be run
    otpArgs[0] = new OtpErlangAtom("typer");
    OtpErlangTuple response = null;
    try
    {
      response = erlangRunner.call(otpArgs,"Could not run typer");
     
      progress.next();// 4
      progress.next();// 5
    }
    catch(ErlangThrownException ex)
    {
      pltFile.delete();
      otpArgs[0] = new OtpErlangAtom("dialyzer");
      progress.next();// 4
      erlangRunner.call(otpArgs, "Could not run dialyzer");
      otpArgs[0] = new OtpErlangAtom("typer");
      progress.next();// 5
      response = erlangRunner.call(otpArgs,"Could not run typer for the second time");     
    }
    progress.next();// 6

    sigTypes = new TreeMap<String,OtpErlangTuple>();
    OtpErlangList analysisResults = (OtpErlangList) response.elementAt(1);
    Assert.assertEquals(1, analysisResults.arity());
    OtpErlangTuple fileDetails = (OtpErlangTuple) analysisResults.elementAt(0);
    OtpErlangList typeInformation = (OtpErlangList) fileDetails.elementAt(3);
    for (int i = 0; i < typeInformation.arity(); ++i) {
      OtpErlangTuple functionDescr = (OtpErlangTuple) typeInformation.elementAt(i);
      if (functionDescr.arity() > 3)
      {
        FuncSignature s = new FuncSignature(config,functionDescr, null);
        sigTypes.put(s.getQualifiedName(), functionDescr);
        //sigs.put(s.getQualifiedName(), s);
      }
      else
      {// if not a function signature, it is an error message. The first two elements are function name and arity, we add this module name.
        String fullName = FuncSignature.qualifiedNameFromFunction(getName(),
            ((OtpErlangAtom)functionDescr.elementAt(0)).atomValue(),
            ((OtpErlangLong)functionDescr.elementAt(1)).longValue());
        ignoredFunctions.add( fullName );
        System.out.println("Ignoring: "+fullName+", "+functionDescr.elementAt(2));
      }
    }
    rebuildSigs(config, Collections.<String,OtpErlangTuple>emptyMap());
    progress.next();// 7
  }
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                  learnerRunner.setTraceLengthMultiplier(traceLengthMultiplierMax);learnerRunner.setChunkLen(chunkSize);
                  learnerRunner.setSelectionID(branch+"_states"+states+"_sample"+sample);
                  runner.submit(learnerRunner);
                  ++numberOfTasks;
                }
              ProgressIndicator progress = new ProgressIndicator(new Date()+" evaluating "+numberOfTasks+" tasks for learning whole graphs", numberOfTasks);
              for(int count=0;count < numberOfTasks;++count)
              {
                ThreadResult result = runner.take().get();// this will throw an exception if any of the tasks failed.
                for(SampleData sample:result.samples)
                  gr_StructuralDiff.add(sample.referenceLearner.differenceStructural.getValue(),sample.actualLearner.differenceStructural.getValue());
             
                for(SampleData sample:result.samples)
                {
                  gr_BCR.add(sample.referenceLearner.differenceBCR.getValue(),sample.actualLearner.differenceBCR.getValue());
                  gr_BCR_singletons.add((double)sample.fractionOfStatesIdentifiedBySingletons,sample.actualLearner.differenceBCR.getValue());
                  gr_BCR_states.add((double)sample.stateNumber,sample.actualLearner.differenceBCR.getValue());
                  if (sample.referenceLearner.differenceBCR.getValue() > 0)
                  gr_ImprovementPerState.add((double)sample.stateNumber,sample.actualLearner.differenceBCR.getValue()/sample.referenceLearner.differenceBCR.getValue());
                }
                progress.next();
              }
              gr_StructuralDiff.drawInteractive(gr);gr_BCR.drawInteractive(gr);gr_BCR_singletons.drawInteractive(gr);gr_BCR_states.drawInteractive(gr);gr_ImprovementPerState.drawInteractive(gr);
            }
            catch(Exception ex)
            {
              IllegalArgumentException e = new IllegalArgumentException("failed to compute, the problem is: "+ex);e.initCause(ex);
              if (executorService != null) { executorService.shutdownNow();executorService = null; }
              throw e;
            }
      }
      if (gr_StructuralDiff != null) gr_StructuralDiff.drawPdf(gr);
      if (gr_BCR != null) gr_BCR.drawPdf(gr);
      if (gr_BCR_singletons != null) gr_BCR_singletons.drawPdf(gr);
      if (gr_BCR_states != null) gr_BCR_states.drawPdf(gr);
      if (gr_ImprovementPerState != null) gr_ImprovementPerState.drawPdf(gr);

    final RBoxPlot<String> gr_BCRImprovementForDifferentAlphabetSize = new RBoxPlot<String>("alphabet multiplier","improvement, BCR",new File(branch+"BCR_vs_alphabet.pdf"));
    final RBoxPlot<String> gr_BCRForDifferentAlphabetSize = new RBoxPlot<String>("alphabet multiplier","BCR",new File(branch+"BCR_absolute_vs_alphabet.pdf"));
    final RBoxPlot<String> gr_StructuralImprovementForDifferentAlphabetSize = new RBoxPlot<String>("alphabet multiplier","improvement, structural",new File(branch+"structural_vs_alphabet.pdf"));
    final RBoxPlot<String> gr_StructuralForDifferentAlphabetSize = new RBoxPlot<String>("alphabet multiplier","structural",new File(branch+"structural_absolute_vs_alphabet.pdf"));
    final RBoxPlot<String> gr_MarkovAccuracyForDifferentAlphabetSize = new RBoxPlot<String>("alphabet multiplier","Markov accuracy",new File(branch+"markov_accuracy_vs_alphabet.pdf"));

    // Same experiment but with different alphabet size
    for(final boolean onlyPositives:new boolean[]{true})
      for(final double alphabetMultiplier:new double[]{alphabetMultiplierMax/16,alphabetMultiplierMax/8,alphabetMultiplierMax/4,alphabetMultiplierMax/2,alphabetMultiplierMax,alphabetMultiplierMax*2,alphabetMultiplierMax*4})
      {
        String selection;
          selection = branch+";EVALUATION;"+
              ";onlyPositives="+onlyPositives+";";

        final int totalTaskNumber = traceQuantity;
        try
        {
          int numberOfTasks = 0;
          for(int states=minStateNumber;states < minStateNumber+rangeOfStateNumbers;states+=stateNumberIncrement)
            for(int sample=0;sample<samplesPerFSM;++sample)
            {
              LearnerRunner learnerRunner = new LearnerRunner(states,sample,totalTaskNumber+numberOfTasks,traceQuantity, config, converter);
              learnerRunner.setOnlyUsePositives(onlyPositives);
              learnerRunner.setTracesAlphabetMultiplier(alphabetMultiplierMax);
              learnerRunner.setAlphabetMultiplier(alphabetMultiplier);
              learnerRunner.setTraceLengthMultiplier(traceLengthMultiplierMax);learnerRunner.setChunkLen(chunkSize);
              learnerRunner.setSelectionID(selection+"_states"+states+"_sample"+sample);
              runner.submit(learnerRunner);
              ++numberOfTasks;
            }
          ProgressIndicator progress = new ProgressIndicator(new Date()+" evaluating "+numberOfTasks+" tasks for "+selection+" alphabet multiplier: "+alphabetMultiplier, numberOfTasks);
          for(int count=0;count < numberOfTasks;++count)
          {
            ThreadResult result = runner.take().get();// this will throw an exception if any of the tasks failed.

            for(SampleData sample:result.samples)
            {
              if (sample.referenceLearner.differenceBCR.getValue() > 0)
              {
                gr_BCRImprovementForDifferentAlphabetSize.add(String.format("%.2f",alphabetMultiplier),sample.actualLearner.differenceBCR.getValue()/sample.referenceLearner.differenceBCR.getValue());
                gr_BCRForDifferentAlphabetSize.add(String.format("%.2f",alphabetMultiplier),sample.actualLearner.differenceBCR.getValue());
              }
              if (sample.referenceLearner.differenceStructural.getValue() > 0)
              {
                gr_StructuralImprovementForDifferentAlphabetSize.add(String.format("%.2f",alphabetMultiplier),sample.actualLearner.differenceStructural.getValue()/sample.referenceLearner.differenceStructural.getValue());
                gr_StructuralForDifferentAlphabetSize.add(String.format("%.2f",alphabetMultiplier),sample.actualLearner.differenceStructural.getValue());
              }
              gr_MarkovAccuracyForDifferentAlphabetSize.add(String.format("%.2f,P",alphabetMultiplier),(double)sample.markovPrecision,"green");
              gr_MarkovAccuracyForDifferentAlphabetSize.add(String.format("%.2f,R",alphabetMultiplier),(double)sample.markovRecall,"blue");
            }
            progress.next();
          }
          gr_BCRImprovementForDifferentAlphabetSize.drawInteractive(gr);gr_BCRForDifferentAlphabetSize.drawInteractive(gr);gr_MarkovAccuracyForDifferentAlphabetSize.drawInteractive(gr);
          gr_StructuralImprovementForDifferentAlphabetSize.drawInteractive(gr);gr_StructuralForDifferentAlphabetSize.drawInteractive(gr);
        }
        catch(Exception ex)
        {
          IllegalArgumentException e = new IllegalArgumentException("failed to compute, the problem is: "+ex);e.initCause(ex);
          if (executorService != null) { executorService.shutdownNow();executorService = null; }
          throw e;
        }
      }
      if (gr_BCRImprovementForDifferentAlphabetSize != null) gr_BCRImprovementForDifferentAlphabetSize.drawPdf(gr);
      if (gr_StructuralImprovementForDifferentAlphabetSize != null) gr_StructuralImprovementForDifferentAlphabetSize.drawPdf(gr);
      if (gr_BCRForDifferentAlphabetSize != null) gr_BCRForDifferentAlphabetSize.drawPdf(gr);
      if (gr_StructuralForDifferentAlphabetSize != null) gr_StructuralForDifferentAlphabetSize.drawPdf(gr);
      if (gr_MarkovAccuracyForDifferentAlphabetSize != null) gr_MarkovAccuracyForDifferentAlphabetSize.drawPdf(gr);

      // Same experiment but with different number of sequences.
      final RBoxPlot<Integer> gr_BCRImprovementForDifferentNrOfTraces = new RBoxPlot<Integer>("nr of traces","improvement, BCR",new File(branch+"BCR_vs_tracenumber.pdf"));
      final RBoxPlot<Integer> gr_BCRForDifferentNrOfTraces = new RBoxPlot<Integer>("nr of traces","BCR",new File(branch+"BCR_absolute_vs_tracenumber.pdf"));
      final RBoxPlot<Integer> gr_StructuralImprovementForDifferentNrOfTraces = new RBoxPlot<Integer>("nr of traces","improvement, structural",new File(branch+"structural_vs_tracenumber.pdf"));
      final RBoxPlot<Integer> gr_StructuralForDifferentNrOfTraces = new RBoxPlot<Integer>("nr of traces","structural",new File(branch+"structural_absolute_vs_tracenumber.pdf"));
      for(final boolean onlyPositives:new boolean[]{true})
        for(final double alphabetMultiplier:new double[]{alphabetMultiplierMax})
          for(int traceNum=2;traceNum<traceQuantity*3;traceNum+=2)
          {
            String selection;
              selection = branch+";EVALUATION;"+
                  ";onlyPositives="+onlyPositives+";";

            final int totalTaskNumber = traceNum;
            try
            {
              int numberOfTasks = 0;
              for(int states=minStateNumber;states < minStateNumber+rangeOfStateNumbers;states+=stateNumberIncrement)
                for(int sample=0;sample<samplesPerFSM;++sample)
                {
                  LearnerRunner learnerRunner = new LearnerRunner(states,sample,totalTaskNumber+numberOfTasks,traceNum, config, converter);
                  learnerRunner.setOnlyUsePositives(onlyPositives);
                  learnerRunner.setAlphabetMultiplier(alphabetMultiplier);
                  learnerRunner.setTraceLengthMultiplier(traceLengthMultiplierMax);learnerRunner.setChunkLen(chunkSize);
                  learnerRunner.setSelectionID(selection+"_states"+states+"_sample"+sample);
                  runner.submit(learnerRunner);
                  ++numberOfTasks;
                }
              ProgressIndicator progress = new ProgressIndicator(new Date()+" evaluating "+numberOfTasks+" tasks for "+selection+" trace num: "+traceNum, numberOfTasks);
              for(int count=0;count < numberOfTasks;++count)
              {
                ThreadResult result = runner.take().get();// this will throw an exception if any of the tasks failed.
               
                for(SampleData sample:result.samples)
                {
                  if (sample.referenceLearner.differenceBCR.getValue() > 0)
                  {
                    // we'll generate both positives and negatives; in the considered experiments, only positives are used hence half the number of sequences are actually being learnt from.
                    gr_BCRImprovementForDifferentNrOfTraces.add(traceNum/2,sample.actualLearner.differenceBCR.getValue()/sample.referenceLearner.differenceBCR.getValue());
                    gr_BCRForDifferentNrOfTraces.add(traceNum/2,sample.actualLearner.differenceBCR.getValue());
                  }
                  if (sample.referenceLearner.differenceStructural.getValue() > 0)
                  {
                    gr_StructuralImprovementForDifferentNrOfTraces.add(traceNum/2,sample.actualLearner.differenceStructural.getValue()/sample.referenceLearner.differenceStructural.getValue());
                    gr_StructuralForDifferentNrOfTraces.add(traceNum/2,sample.actualLearner.differenceStructural.getValue());
                  }
                }
                progress.next();
              }
              gr_BCRForDifferentNrOfTraces.drawInteractive(gr);gr_StructuralForDifferentNrOfTraces.drawInteractive(gr);
            }
            catch(Exception ex)
            {
              IllegalArgumentException e = new IllegalArgumentException("failed to compute, the problem is: "+ex);e.initCause(ex);
              if (executorService != null) { executorService.shutdownNow();executorService = null; }
              throw e;
            }
          }
      if (gr_BCRImprovementForDifferentNrOfTraces != null) gr_BCRImprovementForDifferentNrOfTraces.drawPdf(gr);
      if (gr_BCRForDifferentNrOfTraces != null) gr_BCRForDifferentNrOfTraces.drawPdf(gr);
      if (gr_StructuralImprovementForDifferentNrOfTraces != null) gr_StructuralImprovementForDifferentNrOfTraces.drawPdf(gr);
      if (gr_StructuralForDifferentNrOfTraces != null) gr_StructuralForDifferentNrOfTraces.drawPdf(gr);

      // Same experiment but with different number of sequences, both positive and negative.
      final RBoxPlot<Integer> gr_BCRImprovementForDifferentNrOfTracesPosNeg = new RBoxPlot<Integer>("nr of traces","improvement, BCR",new File(branch+"BCR_vs_tracenumber_posneg.pdf"));
      final RBoxPlot<Integer> gr_BCRForDifferentNrOfTracesPosNeg = new RBoxPlot<Integer>("nr of traces","BCR",new File(branch+"BCR_absolute_vs_tracenumber_posneg.pdf"));
      final RBoxPlot<Integer> gr_StructuralImprovementForDifferentNrOfTracesPosNeg = new RBoxPlot<Integer>("nr of traces","improvement, structural",new File(branch+"structural_vs_tracenumber_posneg.pdf"));
      final RBoxPlot<Integer> gr_StructuralForDifferentNrOfTracesPosNeg = new RBoxPlot<Integer>("nr of traces","structural",new File(branch+"structural_absolute_vs_tracenumber_posneg.pdf"));
      for(final boolean onlyPositives:new boolean[]{false})
        for(final double alphabetMultiplier:new double[]{alphabetMultiplierMax})
          for(int traceNum=2;traceNum<traceQuantity*3;traceNum+=2)
          {
            String selection;
            selection = branch+";EVALUATION;"+
                ";onlyPositives="+onlyPositives+";";

            final int totalTaskNumber = traceNum;
            try
            {
              int numberOfTasks = 0;
              for(int states=minStateNumber;states < minStateNumber+rangeOfStateNumbers;states+=stateNumberIncrement)
                for(int sample=0;sample<samplesPerFSM;++sample)
                {
                  LearnerRunner learnerRunner = new LearnerRunner(states,sample,totalTaskNumber+numberOfTasks,traceNum, config, converter);
                  learnerRunner.setOnlyUsePositives(onlyPositives);
                  learnerRunner.setAlphabetMultiplier(alphabetMultiplier);
                  learnerRunner.setTraceLengthMultiplier(traceLengthMultiplierMax);learnerRunner.setChunkLen(chunkSize);
                  learnerRunner.setSelectionID(selection+"_states"+states+"_sample"+sample);
                  runner.submit(learnerRunner);
                  ++numberOfTasks;
                }
              ProgressIndicator progress = new ProgressIndicator(new Date()+" evaluating "+numberOfTasks+" tasks for "+selection+" trace num: "+traceNum, numberOfTasks);
              for(int count=0;count < numberOfTasks;++count)
              {
                ThreadResult result = runner.take().get();// this will throw an exception if any of the tasks failed.
               
                for(SampleData sample:result.samples)
                {
                  if (sample.referenceLearner.differenceBCR.getValue() > 0)
                  {
                    gr_BCRImprovementForDifferentNrOfTracesPosNeg.add(traceNum,sample.actualLearner.differenceBCR.getValue()/sample.referenceLearner.differenceBCR.getValue());
                    gr_BCRForDifferentNrOfTracesPosNeg.add(traceNum,sample.actualLearner.differenceBCR.getValue());
                  }
                  if (sample.referenceLearner.differenceStructural.getValue() > 0)
                  {
                    gr_StructuralImprovementForDifferentNrOfTracesPosNeg.add(traceNum,sample.actualLearner.differenceStructural.getValue()/sample.referenceLearner.differenceStructural.getValue());
                    gr_StructuralForDifferentNrOfTracesPosNeg.add(traceNum,sample.actualLearner.differenceStructural.getValue());
                  }
                }
                progress.next();
              }
              gr_BCRForDifferentNrOfTracesPosNeg.drawInteractive(gr);gr_StructuralForDifferentNrOfTracesPosNeg.drawInteractive(gr);
            }
            catch(Exception ex)
            {
              IllegalArgumentException e = new IllegalArgumentException("failed to compute, the problem is: "+ex);e.initCause(ex);
              if (executorService != null) { executorService.shutdownNow();executorService = null; }
              throw e;
            }
          }
    if (gr_BCRImprovementForDifferentNrOfTracesPosNeg != null) gr_BCRImprovementForDifferentNrOfTracesPosNeg.drawPdf(gr);
    if (gr_BCRForDifferentNrOfTracesPosNeg != null) gr_BCRForDifferentNrOfTracesPosNeg.drawPdf(gr);
    if (gr_StructuralImprovementForDifferentNrOfTracesPosNeg != null) gr_StructuralImprovementForDifferentNrOfTracesPosNeg.drawPdf(gr);
    if (gr_StructuralForDifferentNrOfTracesPosNeg != null) gr_StructuralForDifferentNrOfTracesPosNeg.drawPdf(gr);

    // Same experiment but with different trace length but the same number of sequences
    final RBoxPlot<Double> gr_BCRImprovementForDifferentLengthOfTraces = new RBoxPlot<Double>("trace length multiplier","improvement, BCR",new File(branch+"BCR_vs_tracelength.pdf"));
    final RBoxPlot<Double> gr_BCRForDifferentLengthOfTraces = new RBoxPlot<Double>("trace length multiplier","BCR",new File(branch+"BCR_absolute_vs_tracelength.pdf"));
    final RBoxPlot<Double> gr_StructuralImprovementForDifferentLengthOfTraces = new RBoxPlot<Double>("trace length multiplier","improvement, structural",new File(branch+"structural_vs_tracelength.pdf"));
    final RBoxPlot<Double> gr_StructuralForDifferentLengthOfTraces = new RBoxPlot<Double>("trace length multiplier","structural",new File(branch+"structural_absolute_vs_tracelength.pdf"));
    final RBoxPlot<Double> gr_TransitionCoverageForDifferentLengthOfTraces = new RBoxPlot<Double>("trace length multiplier","transition coverage",new File(branch+"transitionCoverage_vs_tracelength.pdf"));
    for(final boolean onlyPositives:new boolean[]{true})
      for(double traceMultiplier=0.125;traceMultiplier<10;traceMultiplier*=2.)
        {
          String selection;
            selection = branch+";EVALUATION;"+
                ";onlyPositives="+onlyPositives+";";

          final int totalTaskNumber = traceQuantity;
          try
          {
            int numberOfTasks = 0;
            for(int states=minStateNumber;states < minStateNumber+rangeOfStateNumbers;states+=stateNumberIncrement)
              for(int sample=0;sample<samplesPerFSM;++sample)
              {
                LearnerRunner learnerRunner = new LearnerRunner(states,sample,totalTaskNumber+numberOfTasks,traceQuantity, config, converter);
                learnerRunner.setAlphabetMultiplier(alphabetMultiplierMax);
                learnerRunner.setOnlyUsePositives(onlyPositives);
                learnerRunner.setTraceLengthMultiplier(traceMultiplier);learnerRunner.setChunkLen(chunkSize);
                learnerRunner.setSelectionID(selection+"_states"+states+"_sample"+sample);
                runner.submit(learnerRunner);
                ++numberOfTasks;
              }
            ProgressIndicator progress = new ProgressIndicator(new Date()+" evaluating "+numberOfTasks+" tasks for "+selection+" trace length multiplier "+traceMultiplier, numberOfTasks);
            for(int count=0;count < numberOfTasks;++count)
            {
              ThreadResult result = runner.take().get();// this will throw an exception if any of the tasks failed.
             
              for(SampleData sample:result.samples)
              {
                if (sample.referenceLearner.differenceBCR.getValue() > 0)
                {
                  gr_BCRImprovementForDifferentLengthOfTraces.add(traceMultiplier,sample.actualLearner.differenceBCR.getValue()/sample.referenceLearner.differenceBCR.getValue());
                  gr_BCRForDifferentLengthOfTraces.add(traceMultiplier,sample.actualLearner.differenceBCR.getValue());
                }
                if (sample.referenceLearner.differenceStructural.getValue() > 0)
                {
                  gr_StructuralImprovementForDifferentLengthOfTraces.add(traceMultiplier,sample.actualLearner.differenceStructural.getValue()/sample.referenceLearner.differenceStructural.getValue());
                  gr_StructuralForDifferentLengthOfTraces.add(traceMultiplier,sample.actualLearner.differenceStructural.getValue());
                }
                gr_TransitionCoverageForDifferentLengthOfTraces.add(traceMultiplier,(double)sample.transitionsSampled);
              }
              progress.next();
            }
           
            gr_BCRForDifferentLengthOfTraces.drawInteractive(gr);gr_StructuralForDifferentLengthOfTraces.drawInteractive(gr);gr_TransitionCoverageForDifferentLengthOfTraces.drawInteractive(gr);
          }
          catch(Exception ex)
          {
            IllegalArgumentException e = new IllegalArgumentException("failed to compute, the problem is: "+ex);e.initCause(ex);
            if (executorService != null) { executorService.shutdownNow();executorService = null; }
            throw e;
          }
        }
    if (gr_BCRImprovementForDifferentLengthOfTraces != null) gr_BCRImprovementForDifferentLengthOfTraces.drawPdf(gr);
    if (gr_BCRForDifferentLengthOfTraces != null) gr_BCRForDifferentLengthOfTraces.drawPdf(gr);
    if (gr_StructuralImprovementForDifferentLengthOfTraces != null) gr_StructuralImprovementForDifferentLengthOfTraces.drawPdf(gr);
    if (gr_StructuralForDifferentLengthOfTraces != null) gr_StructuralForDifferentLengthOfTraces.drawPdf(gr);
    if (gr_TransitionCoverageForDifferentLengthOfTraces != null) gr_TransitionCoverageForDifferentLengthOfTraces.drawPdf(gr);

    // Same experiment but with different prefix length but the same number of sequences and their length
    final RBoxPlot<Integer> gr_BCRImprovementForDifferentPrefixlen = new RBoxPlot<Integer>("length of prefix","improvement, BCR",new File(branch+"BCR_vs_prefixLength.pdf"));
    final RBoxPlot<Integer> gr_BCRForDifferentPrefixlen = new RBoxPlot<Integer>("length of prefix","BCR",new File(branch+"BCR_absolute_vs_prefixLength.pdf"));
    final RBoxPlot<Integer> gr_StructuralImprovementForDifferentPrefixlen = new RBoxPlot<Integer>("length of prefix","improvement, structural",new File(branch+"structural_vs_prefixLength.pdf"));
    final RBoxPlot<Integer> gr_StructuralForDifferentPrefixlen = new RBoxPlot<Integer>("length of prefix","structural",new File(branch+"structural_absolute_vs_prefixLength.pdf"));
    final RBoxPlot<String> gr_MarkovAccuracyForDifferentPrefixlen = new RBoxPlot<String>("length of prefix","Markov accuracy",new File(branch+"markov_accuracy_vs_prefixLength.pdf"));
    for(final boolean onlyPositives:new boolean[]{true})
      for(int prefixLength=1;prefixLength<6;++prefixLength)
        {
          String selection;
            selection = branch+";EVALUATION;"+
                ";onlyPositives="+onlyPositives+";";

            final int totalTaskNumber = traceQuantity;
            try
            {
              int numberOfTasks = 0;
              for(int states=minStateNumber;states < minStateNumber+rangeOfStateNumbers;states+=stateNumberIncrement)
                for(int sample=0;sample<samplesPerFSM;++sample)
                {
                  LearnerRunner learnerRunner = new LearnerRunner(states,sample,totalTaskNumber+numberOfTasks,traceQuantity, config, converter);
                  learnerRunner.setAlphabetMultiplier(alphabetMultiplierMax);
                  learnerRunner.setOnlyUsePositives(onlyPositives);
                  learnerRunner.setTraceLengthMultiplier(traceLengthMultiplierMax);learnerRunner.setChunkLen(prefixLength+1);
                  learnerRunner.setSelectionID(selection+"_states"+states+"_sample"+sample);
                  runner.submit(learnerRunner);
                  ++numberOfTasks;
                }
              ProgressIndicator progress = new ProgressIndicator(new Date()+" evaluating "+numberOfTasks+" tasks for "+selection+" using prefix length "+prefixLength, numberOfTasks);
              for(int count=0;count < numberOfTasks;++count)
              {
                ThreadResult result = runner.take().get();// this will throw an exception if any of the tasks failed.
               
                for(SampleData sample:result.samples)
                {
                  if (sample.referenceLearner.differenceBCR.getValue() > 0)
                  {
                    gr_BCRImprovementForDifferentPrefixlen.add(prefixLength,sample.actualLearner.differenceBCR.getValue()/sample.referenceLearner.differenceBCR.getValue());
                    gr_BCRForDifferentPrefixlen.add(prefixLength,sample.actualLearner.differenceBCR.getValue());
                  }
                  if (sample.referenceLearner.differenceStructural.getValue() > 0)
                  {
                    gr_StructuralImprovementForDifferentPrefixlen.add(prefixLength,sample.actualLearner.differenceStructural.getValue()/sample.referenceLearner.differenceStructural.getValue());
                    gr_StructuralForDifferentPrefixlen.add(prefixLength,sample.actualLearner.differenceStructural.getValue());
                  }
                  gr_MarkovAccuracyForDifferentPrefixlen.add(""+prefixLength+",P",(double)sample.markovPrecision,"green");
                  gr_MarkovAccuracyForDifferentPrefixlen.add(""+prefixLength+",R",(double)sample.markovRecall,"blue");
                }
                progress.next();
              }
              gr_BCRForDifferentPrefixlen.drawInteractive(gr);gr_StructuralForDifferentPrefixlen.drawInteractive(gr);gr_MarkovAccuracyForDifferentPrefixlen.drawInteractive(gr);
            }
            catch(Exception ex)
            {
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      int alphabet = states/2;
     
      MachineGenerator mg = new MachineGenerator(states, 40, states/10);
      int mutationsPerStage = (states/2) / 2;
      //System.out.print("\n"+states+": ");
      ProgressIndicator progress = new ProgressIndicator(""+states, mutationStages*experimentsPerMutationCategory);
      for(int mutationStage = 0;mutationStage<mutationStages;mutationStage++)
      {
        for(int experiment=0;experiment<experimentsPerMutationCategory;experiment++)
        {
          ExperimentResult outcome = new ExperimentResult();
          while(!outcome.experimentValid)
          {
            int mutations = mutationsPerStage * (mutationStage+1);
            LearnerGraphND origGraph = mg.nextMachine(alphabet, experiment,config);
            GraphMutator<List<CmpVertex>,LearnerGraphNDCachedData> mutator = new GraphMutator<List<CmpVertex>,LearnerGraphNDCachedData>(origGraph,r);
            mutator.mutate(mutations);
            LearnerGraphND origAfterRenaming = new LearnerGraphND(origGraph.config);
            Map<CmpVertex,CmpVertex> origToNew = copyStatesAndTransitions(origGraph,origAfterRenaming);
            LearnerGraphND mutated = (LearnerGraphND)mutator.getMutated();
            Set<Transition> appliedMutations = new HashSet<Transition>();
            for(Transition tr:mutator.getDiff())
            {
              CmpVertex renamedFrom = origToNew.get(tr.getFrom());if (renamedFrom == null) renamedFrom = tr.getFrom();
              CmpVertex renamedTo = origToNew.get(tr.getTo());if (renamedTo == null) renamedTo = tr.getTo();
              appliedMutations.add(new Transition(renamedFrom,renamedTo,tr.getLabel()));
            }
           
            final double perfectLowToHigh=0.7,perfectThreshold=0.5;
           
            // These experiments are only run for the maximal number of states
            if (graphComplexity == graphComplexityMax-1)
            {
              for(double k:new double[]{0,.2,.4,.6,.8,.95})
              {
                config.setAttenuationK(k);
                config.setGdKeyPairThreshold(0.5);
                config.setGdLowToHighRatio(perfectLowToHigh);
                config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
                linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
               
                gr_Diff_k.add(k, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
               
              }

              for(double threshold:new double[]{0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95})
              {
                config.setGdKeyPairThreshold(threshold);
                config.setGdLowToHighRatio(perfectLowToHigh);
                config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
                linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
               
                gr_Diff_threshold.add(threshold, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
              }

              for(double lowtohigh:new double[]{0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95})
              {
                config.setGdKeyPairThreshold(perfectThreshold);
                config.setGdLowToHighRatio(lowtohigh);
                config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
                linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
               
                gr_Diff_lowtohigh.add(lowtohigh, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
              }
             
             
              for(int i=0;i<pairThreshold.length;++i)
                for(double ratio:lowToHigh)
                {
                  config.setGdKeyPairThreshold(pairThreshold[i]);
                  config.setGdLowToHighRatio(ratio);
                  config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
                  linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
                 
                  gr_Diff_thresholdslowhigh.add(new Pair<Double,Double>(ratio,pairThreshold[i]), outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
                }
            }
            config.setAttenuationK(0.5);
            config.setGdKeyPairThreshold(perfectThreshold);
            config.setGdLowToHighRatio(perfectLowToHigh);
            config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
            linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
           
            if (!skip)
            {
              LearnerGraph fromDet = null, toDet = null;
              try {
                fromDet = mergeAndDeterminize(origAfterRenaming);
                toDet = mergeAndDeterminize(mutated);
              } catch (IncompatibleStatesException e) {
                Helper.throwUnchecked("failed to build a deterministic graph from a nondet one", e);
              }
              languageDiff(fromDet,toDet,states, graphComplexity,outcome);
            }
            outcome.experimentValid = true;
            progress.next();
            gr_Diff_States.add(states, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
            gr_W_States.add(states,outcome.getValue(DOUBLE_V.ACCURACY_W));
            Pair<Integer,Integer> mutations_states = new Pair<Integer,Integer>(mutationStage+1,states);
            Pair<Integer,Integer> states_mutations = new Pair<Integer,Integer>(states,mutationStage+1);
            gr_DiffGD_StatesLevel.add(mutations_states, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
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      for(int graphComplexity=0;graphComplexity < graphComplexityMax;graphComplexity++)
      {
        int states=initStates+graphComplexity*20;
        int alphabet = states/2;
        MachineGenerator mg = new MachineGenerator(states, 40, states/10);
        ProgressIndicator progress = new ProgressIndicator(""+states, experimentsPerCategory);
       
        for(int experiment=0;experiment<experimentsPerCategory;experiment++)
        {
          LearnerGraphND generatedFSM = mg.nextMachine(alphabet, experiment,config);
          if (generatedFSM.getInit() != generatedFSM.transitionMatrix.entrySet().iterator().next().getKey())
            throw new RuntimeException("first state is not the initial state");
         
          //Visualiser.updateFrame(origGraph, null);
          randomFSM = new FileWriter("resources/randomFSM/fsm_"+states+"_"+experiment+".x_");
          for(Entry<CmpVertex,Map<Label,List<CmpVertex>>> entry:generatedFSM.transitionMatrix.entrySet())
            for(Entry<Label,List<CmpVertex>> targets:entry.getValue().entrySet())
              for(CmpVertex targetState:generatedFSM.getTargets(targets.getValue()))
              {
                randomFSM.write(entry.getKey().getStringId());randomFSM.write(' ');
                randomFSM.write(targetState.getStringId());randomFSM.write(' ');
                randomFSM.write(targets.getKey().toString());randomFSM.write('\n');
              }
          randomFSM.close();randomFSM = null;
          progress.next();
        }
      }
    }
    catch(Exception ex)
    {
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      uas_Diff=new RBoxPlot<String>("Time","Diff-measure",new File("time_"+name+"_Diff.pdf"));
    SquareBagPlot gr_diff_to_f = new SquareBagPlot("f-measure","diff-based measure",new File("diff-to-f.pdf"),0,1,true);

    Set<Integer> allFrames = collectionOfTraces.get(UAVAllSeeds).tracesForUAVandFrame.get(UAVAllSeeds).keySet();
    Classifier classifiers[] = loadClassifierFromArff(arffName);
    ProgressIndicator progress = new ProgressIndicator("UAS", allFrames.size()*classifiers.length);
    LearnerEvaluationConfiguration initConfiguration = new LearnerEvaluationConfiguration(learnerInitConfiguration.config);
    initConfiguration.setLabelConverter(learnerInitConfiguration.getLabelConverter());// we do not copy if-then automata here because we do not wish to augment from if-then on every iteration because our properties are pairwise and this permits augmentation to be carried out first thing and not any more.
    initConfiguration.config.setUseConstraints(false);// do not use if-then during learning (refer to the explanation above)
   
    LearnerGraph [] ifthenAutomata = Transform.buildIfThenAutomata(learnerInitConfiguration.ifthenSequences, null, referenceGraph, learnerInitConfiguration.config, learnerInitConfiguration.getLabelConverter()).toArray(new LearnerGraph[0]);
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        };
          outcomes.add(executorService.submit(interactiveRunner));
      }
*/
      ProgressIndicator progress = new ProgressIndicator("running concurrent experiment",outcomes.size());
      for(Future<?> task:outcomes) { task.get();progress.next(); }// wait for termination of all tasks
    }
    catch(Exception ex)
    {
      Helper.throwUnchecked("failed to run experiment", ex);
    }
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                  learnerRunner.setOnlyUsePositives(onlyPositives);learnerRunner.setLengthMultiplier(lengthMultiplier);
                  learnerRunner.setSelectionID(selection+"_states"+states+"_sample"+sample);
                  runner.submit(learnerRunner);
                  ++numberOfTasks;
                }
              ProgressIndicator progress = new ProgressIndicator(new Date()+" evaluating "+numberOfTasks+" tasks for "+selection, numberOfTasks);
              for(int count=0;count < numberOfTasks;++count)
              {
                ThreadResult result = runner.take().get();// this will throw an exception if any of the tasks failed.
                if (gr_NewToOrig != null)
                {
                  for(SampleData sample:result.samples)
                    gr_NewToOrig.add(sample.referenceLearner.getValue(),sample.actualLearner.getValue());
                }
               
                for(SampleData sample:result.samples)
                  if (sample.referenceLearner.getValue() > 0)
                    gr_QualityForNumberOfTraces.add(traceQuantity+"",sample.actualLearner.getValue()/sample.referenceLearner.getValue());
                progress.next();
              }
              if (gr_PairQuality != null)
              {
                synchronized(pairQualityCounter)
                {
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      for(int graphComplexity=0;graphComplexity < graphComplexityMax;graphComplexity++)
      {
        int states=initStates+graphComplexity*20;
        int alphabet = states/2;
        MachineGenerator mg = new MachineGenerator(states, 40, states/10);
        ProgressIndicator progress = new ProgressIndicator(""+states, experimentsPerCategory);
       
        for(int experiment=0;experiment<experimentsPerCategory;experiment++)
        {
          LearnerGraphND generatedFSM = mg.nextMachine(alphabet, experiment,config, converter);
          if (generatedFSM.getInit() != generatedFSM.transitionMatrix.entrySet().iterator().next().getKey())
            throw new RuntimeException("first state is not the initial state");
         
          //Visualiser.updateFrame(origGraph, null);
          randomFSM = new FileWriter("resources/randomFSM/fsm_"+states+"_"+experiment+".x_");
          for(Entry<CmpVertex,Map<Label,List<CmpVertex>>> entry:generatedFSM.transitionMatrix.entrySet())
            for(Entry<Label,List<CmpVertex>> targets:entry.getValue().entrySet())
              for(CmpVertex targetState:generatedFSM.getTargets(targets.getValue()))
              {
                randomFSM.write(entry.getKey().getStringId());randomFSM.write(' ');
                randomFSM.write(targetState.getStringId());randomFSM.write(' ');
                randomFSM.write(targets.getKey().toString());randomFSM.write('\n');
              }
          randomFSM.close();randomFSM = null;
          progress.next();
        }
      }
    }
    catch(Exception ex)
    {
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      int alphabet = states/2;
     
      MachineGenerator mg = new MachineGenerator(states, 40, states/10);
      int mutationsPerStage = (states/2) / 2;
      //System.out.print("\n"+states+": ");
      ProgressIndicator progress = new ProgressIndicator(""+states, mutationStages*experimentsPerMutationCategory);
      for(int mutationStage = 0;mutationStage<mutationStages;mutationStage++)
      {
        for(int experiment=0;experiment<experimentsPerMutationCategory;experiment++)
        {
          ExperimentResult outcome = new ExperimentResult();
          while(!outcome.experimentValid)
          {
            int mutations = mutationsPerStage * (mutationStage+1);
            LearnerGraphND origGraph = mg.nextMachine(alphabet, experiment,config,converter);
            GraphMutator<List<CmpVertex>,LearnerGraphNDCachedData> mutator = new GraphMutator<List<CmpVertex>,LearnerGraphNDCachedData>(origGraph,r);
            mutator.mutate(mutations);
            LearnerGraphND origAfterRenaming = new LearnerGraphND(origGraph.config);
            Map<CmpVertex,CmpVertex> origToNew = copyStatesAndTransitions(origGraph,origAfterRenaming);
            LearnerGraphND mutated = (LearnerGraphND)mutator.getMutated();
            Set<Transition> appliedMutations = new HashSet<Transition>();
            for(Transition tr:mutator.getDiff())
            {
              CmpVertex renamedFrom = origToNew.get(tr.getFrom());if (renamedFrom == null) renamedFrom = tr.getFrom();
              CmpVertex renamedTo = origToNew.get(tr.getTo());if (renamedTo == null) renamedTo = tr.getTo();
              appliedMutations.add(new Transition(renamedFrom,renamedTo,tr.getLabel()));
            }
           
            final double perfectLowToHigh=0.7,perfectThreshold=0.5;
           
            // These experiments are only run for the maximal number of states
            if (graphComplexity == graphComplexityMax-1)
            {
              for(double k:new double[]{0,.2,.4,.6,.8,.95})
              {
                config.setAttenuationK(k);
                config.setGdKeyPairThreshold(0.5);
                config.setGdLowToHighRatio(perfectLowToHigh);
                config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
                linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
               
                gr_Diff_k.add(k, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
               
              }

              for(double threshold:new double[]{0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95})
              {
                config.setGdKeyPairThreshold(threshold);
                config.setGdLowToHighRatio(perfectLowToHigh);
                config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
                linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
               
                gr_Diff_threshold.add(threshold, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
              }

              for(double lowtohigh:new double[]{0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.95})
              {
                config.setGdKeyPairThreshold(perfectThreshold);
                config.setGdLowToHighRatio(lowtohigh);
                config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
                linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
               
                gr_Diff_lowtohigh.add(lowtohigh, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
              }
             
             
              for(int i=0;i<pairThreshold.length;++i)
                for(double ratio:lowToHigh)
                {
                  config.setGdKeyPairThreshold(pairThreshold[i]);
                  config.setGdLowToHighRatio(ratio);
                  config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
                  linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
                 
                  gr_Diff_thresholdslowhigh.add(new Pair<Double,Double>(ratio,pairThreshold[i]), outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
                }
            }
            config.setAttenuationK(0.5);
            config.setGdKeyPairThreshold(perfectThreshold);
            config.setGdLowToHighRatio(perfectLowToHigh);
            config.setGdPropagateDet(false);// this is to ensure that if we removed a transition 0 from to a state and then added one from that state to a different one, det-propagation will not force the two very different states into a key-pair relation.
            linearDiff(origAfterRenaming,mutated, appliedMutations,outcome);
           
            if (!skip)
            {
              LearnerGraph fromDet = null, toDet = null;
              try {
                fromDet = mergeAndDeterminize(origAfterRenaming);
                toDet = mergeAndDeterminize(mutated);
              } catch (IncompatibleStatesException e) {
                Helper.throwUnchecked("failed to build a deterministic graph from a nondet one", e);
              }
              languageDiff(fromDet,toDet,states, graphComplexity,outcome);
            }
            outcome.experimentValid = true;
            progress.next();
            gr_Diff_States.add(states, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
            gr_W_States.add(states,outcome.getValue(DOUBLE_V.ACCURACY_W));
            Pair<Integer,Integer> mutations_states = new Pair<Integer,Integer>(mutationStage+1,states);
            Pair<Integer,Integer> states_mutations = new Pair<Integer,Integer>(states,mutationStage+1);
            gr_DiffGD_StatesLevel.add(mutations_states, outcome.getValue(DOUBLE_V.OBTAINED_TO_EXPECTED));
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