Package statechum.analysis.learning.experiments.mutation.DiffExperiments

Examples of statechum.analysis.learning.experiments.mutation.DiffExperiments.MachineGenerator.nextMachine()

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    {
      final int alphabet = 2*states;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);

      final LearnerGraph referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
     
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,states*alphabet);
     
      for(int attempt=0;attempt<2;++attempt)
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      final int tracesAlphabet = (int)(tracesAlphabetMultiplier*states);
     
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
     
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*tracesAlphabet));

      for(int attempt=0;attempt<2;++attempt)
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      final int tracesAlphabet = (int)(tracesAlphabetMultiplier*states);
     
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
     
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*tracesAlphabet));

      for(int attempt=0;attempt<2;++attempt)
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    {
      final int alphabet = (int)(alphabetMultiplier*states);
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
     
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*alphabet));

      for(int attempt=0;attempt<2;++attempt)
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      final int tracesAlphabet = (int)(tracesAlphabetMultiplier*states);
     
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
     
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*tracesAlphabet));

      for(int attempt=0;attempt<2;++attempt)
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    {
      final int alphabet = (int)(alphabetMultiplier*states);
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
     
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*alphabet));

      for(int attempt=0;attempt<2;++attempt)
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    Label uniqueFromInitial = null;
    final boolean pickUniqueFromInitial = true;
    MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
    do
    {
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
      if (pickUniqueFromInitial)
      {
        Map<Label,CmpVertex> uniques = PairQualityLearner.uniqueFromState(referenceGraph);
        if(!uniques.isEmpty())
        {
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      final int tracesAlphabet = (int)(tracesAlphabetMultiplier*states);
     
      LearnerGraph referenceGraph = null;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
      referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
     
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,makeEven(states*tracesAlphabet));

      for(int attempt=0;attempt<2;++attempt)
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    {
      final int alphabet = 2*states;
      ThreadResult outcome = new ThreadResult();
      MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);

      final LearnerGraph referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
     
      LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
      final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,states*alphabet);
     
      for(int attempt=0;attempt<2;++attempt)
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    final int alphabet = (int)(alphabetMultiplier*states);
    final int seed = traceQuantity;
   
    LearnerGraph referenceGraph = null;
    MachineGenerator mg = new MachineGenerator(states, 400 , (int)Math.round((double)states/5));mg.setGenerateConnected(true);
    referenceGraph = mg.nextMachine(alphabet,seed, config, converter).pathroutines.buildDeterministicGraph();// reference graph has no reject-states, because we assume that undefined transitions lead to reject states.
   
    LearnerEvaluationConfiguration learnerEval = new LearnerEvaluationConfiguration(config);learnerEval.setLabelConverter(converter);
    //final Collection<List<Label>> testSet = PaperUAS.computeEvaluationSet(referenceGraph,states*3,PairQualityLearner.makeEven(states*alphabet*traceMultiplier));

    final int attempt=0;
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