Package statechum.analysis.learning

Examples of statechum.analysis.learning.RPNIUniversalLearner

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    public static void main(String[] args) {
        QSMTool tool = new QSMTool();
        tool.loadConfig(args[0]);
        if (tool.showLTL) {
            Learner l = new RPNIUniversalLearner(null, tool.learnerInitConfiguration);
            LTL_to_ba ba = new LTL_to_ba(tool.learnerInitConfiguration.config, tool.learnerInitConfiguration.getLabelConverter());
            if (ba.ltlToBA(tool.learnerInitConfiguration.ifthenSequences, l.init(tool.sPlus, tool.sMinus), true,
                    GlobalConfiguration.getConfiguration().getProperty(GlobalConfiguration.G_PROPERTIES.LTL2BA))) {
                try {
                    LearnerGraph graph = Transform.ltlToIfThenAutomaton(ba.getLTLgraph().pathroutines.buildDeterministicGraph());
                    DirectedSparseGraph gr = graph.pathroutines.getGraph();
                    PathRoutines.convertPairAssociationsToTransitions(gr, graph, tool.learnerInitConfiguration.config,tool.learnerInitConfiguration.getLabelConverter());
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  public static void main(String[] args)
  {
    QSMTool tool = new QSMTool();tool.loadConfig(args[0]);
    if (tool.showLTL)
    {
      Learner l = new RPNIUniversalLearner(null,tool.learnerInitConfiguration);
      LTL_to_ba ba = new LTL_to_ba(tool.learnerInitConfiguration.config);ba.ltlToBA(tool.learnerInitConfiguration.ltlSequences, l.init(tool.sPlus, tool.sMinus));
      Visualiser.updateFrame(ba.augmentGraph(new LearnerGraph(tool.learnerInitConfiguration.config)), null);
    }
    else
      tool.runExperiment();
  }
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      final LearnerEvaluationConfiguration bfLearnerInitConfiguration = new LearnerEvaluationConfiguration(conf);bfLearnerInitConfiguration.ifthenSequences = PaperUAS.this.learnerInitConfiguration.ifthenSequences;
      config = bfLearnerInitConfiguration.config;// our config is a copy of the one supplied as an argument.
      config.setAskQuestions(false);
      if (choice == pairchoiceMAX || choice == pairchoiceMIN) rnd = null;else rnd = new Random(choice);
     
      learner = new DummyLearner(new RPNIUniversalLearner(null, bfLearnerInitConfiguration))
      {
        @Override
        public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
        {// Use the old and limited version to compute the merge because the general one is too slow on large graphs and we do not need either to merge arbitrary states or to handle "incompatibles".
          return MergeStates.mergeAndDeterminize(original, pair);
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      if (lc != null) listOfPairsToCheckAgainstIterator = lc.iterator();else listOfPairsToCheckAgainstIterator = null;
      final LearnerEvaluationConfiguration bfLearnerInitConfiguration = new LearnerEvaluationConfiguration(conf);bfLearnerInitConfiguration.ifthenSequences = PaperUAS.this.learnerInitConfiguration.ifthenSequences;
      bfLearnerInitConfiguration.setLabelConverter(PaperUAS.this.learnerInitConfiguration.getLabelConverter());
      config = bfLearnerInitConfiguration.config;// our config is a copy of the one supplied as an argument.
      config.setAskQuestions(false);
      learner = new DummyLearner(new RPNIUniversalLearner(null, bfLearnerInitConfiguration))
      {
       
        @Override
        public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
        {
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    //Visualiser.updateFrame(new LearnerGraph(FsmParser.buildGraph("A-e->A-c->B-b->C-p->G-e->A\nB-a->D-a->E-a->D\nE-b->F-p->G\n"+
    //"B-e->A\nC-e->A\nD-e->A\nE-e->A\nF-e->A\nG-e->A\n","testAutoAnswers0"),Configuration.getDefaultConfiguration()),null);
    config.setGdFailOnDuplicateNames(false);
    config.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);

    RPNILearner learner = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,config,null,null))
    {
      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          List<Label> question, @SuppressWarnings("unused") int responseForNoRestart,
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    final AutoAnswers semiUser = new AutoAnswers(null);
    semiUser.loadAnswers(new StringReader(
        partA
    ),config);
   
    RPNILearner learner = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,config,null,null))
    {
      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          List<Label> question, @SuppressWarnings("unused") int responseForNoRestart,
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  public void testAuto2()
  {
    config.setGdFailOnDuplicateNames(false);
    config.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);

    RPNILearner learner = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,config,null,null))
    {
      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          @SuppressWarnings("unused")  List<Label> question, @SuppressWarnings("unused") int responseForNoRestart,
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  public void testAuto3()
  {
    config.setGdFailOnDuplicateNames(false);
    config.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);

    RPNILearner learner = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,config,null,null))
    {
      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          @SuppressWarnings("unused")  List<Label> question, @SuppressWarnings("unused") int responseForNoRestart,
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  public void testAuto4()
  {
    config.setGdFailOnDuplicateNames(false);
    config.setLearnerIdMode(IDMode.POSITIVE_NEGATIVE);

    RPNILearner learner = new RPNIUniversalLearner(null,new LearnerEvaluationConfiguration(null,null,config,null,null))
    {
      @Override
      public Pair<Integer,String> CheckWithEndUser(
          @SuppressWarnings("unused")  LearnerGraph model,
          @SuppressWarnings("unused")  List<Label> question, @SuppressWarnings("unused") int responseForNoRestart,
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      final LearnerEvaluationConfiguration bfLearnerInitConfiguration = new LearnerEvaluationConfiguration(conf);bfLearnerInitConfiguration.ifthenSequences = PaperUAS.this.learnerInitConfiguration.ifthenSequences;
      config = bfLearnerInitConfiguration.config;// our config is a copy of the one supplied as an argument.
      config.setAskQuestions(false);
      if (choice == pairchoiceMAX || choice == pairchoiceMIN) rnd = null;else rnd = new Random(choice);
     
      learner = new DummyLearner(new RPNIUniversalLearner(null, bfLearnerInitConfiguration))
      {
        @Override
        public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
        {// Use the old and limited version to compute the merge because the general one is too slow on large graphs and we do not need either to merge arbitrary states or to handle "incompatibles".
          return MergeStates.mergeAndDeterminize(original, pair);
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Related Classes of statechum.analysis.learning.RPNIUniversalLearner

  • statechum.analysis.Erlang.Synapse$StatechumProcess
  • statechum.analysis.learning.experiments.IncrementalAccuracyAndQuestionsExperiment$RPNIEvaluator
  • statechum.analysis.learning.experiments.PairQualityLearner
  • statechum.analysis.learning.experiments.PaperUAS$RPNIBlueFringe
  • statechum.analysis.learning.experiments.PaperUAS$RPNIBlueFringeTestVariability
  • statechum.analysis.learning.experiments.PaperUAS$RPNIVariabilityExperiment
  • statechum.analysis.learning.observers.TestAutoAnswers
  • statechum.analysis.learning.observers.TestRecorderIntegration
  • statechum.analysis.learning.rpnicore.LearnerGraph
  • statechum.analysis.learning.rpnicore.LTL_to_ba
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