Package statechum.analysis.learning.observers

Examples of statechum.analysis.learning.observers.DummyLearner

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    final LearnerEvaluationConfiguration bfLearnerInitConfiguration = new LearnerEvaluationConfiguration(evaluationConfiguration.config);
    bfLearnerInitConfiguration.ifthenSequences = evaluationConfiguration.ifthenSequences;
    bfLearnerInitConfiguration.setLabelConverter(evaluationConfiguration.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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    final LearnerGraph referenceA = new LearnerGraph(paper.learnerInitConfiguration.config);AbstractPersistence.loadGraph(outcomeName, referenceA, converter);
    final PairQualityLearner qualityLearner = new PairQualityLearner();
    qualityLearner.initWeka("wekaoutput2.arff",PairQualityLearner.largePTAFileName);
   
   
    new DummyLearner(new RPNIUniversalLearner(null, paper.learnerInitConfiguration))
    {
     
      @Override
      public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
      {// fast merger
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    public RPNIVariabilityExperiment(int choice)
    {
      learnerInitConfiguration.config.setGeneralisationThreshold(0);learnerInitConfiguration.config.setAskQuestions(false);pairChoice=choice;
      if (choice == pairchoiceMAX || choice == pairchoiceMIN) rnd = null;else rnd = new Random(choice);
     
      learner = new DummyLearner(new RPNIUniversalLearner(null, learnerInitConfiguration))
      {
        @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 super.MergeAndDeterminize(original, pair);
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      listOfPairsToWrite = lw;useOptimizedMerge = optimisedMerge;
      if (lc != null) listOfPairsToCheckAgainstIterator = lc.iterator();else listOfPairsToCheckAgainstIterator = null;
      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);
      learner = new DummyLearner(new RPNIUniversalLearner(null, bfLearnerInitConfiguration))
      {
        @Override
        public LearnerGraph MergeAndDeterminize(LearnerGraph original, StatePair pair)
        {
          if (useOptimizedMerge)
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    final LearnerEvaluationConfiguration bfLearnerInitConfiguration = new LearnerEvaluationConfiguration(evaluationConfiguration.config);
    bfLearnerInitConfiguration.ifthenSequences = evaluationConfiguration.ifthenSequences;
    bfLearnerInitConfiguration.setLabelConverter(evaluationConfiguration.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)
      {
View Full Code Here
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      final LearnerEvaluationConfiguration bfLearnerInitConfiguration = new LearnerEvaluationConfiguration(evaluationConfiguration.config);
      bfLearnerInitConfiguration.ifthenSequences = evaluationConfiguration.ifthenSequences;
      bfLearnerInitConfiguration.setLabelConverter(evaluationConfiguration.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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      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)
        {
View Full Code Here
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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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Related Classes of statechum.analysis.learning.observers.DummyLearner

  • statechum.analysis.learning.experiments.PairQualityLearner
  • statechum.analysis.learning.experiments.PaperUAS$RPNIBlueFringe
  • statechum.analysis.learning.experiments.PaperUAS$RPNIBlueFringeTestVariability
  • statechum.analysis.learning.experiments.PaperUAS$RPNIVariabilityExperiment
  • statechum.analysis.learning.RPNIBlueFringeVariability
  • statechum.analysis.learning.TestLearnerFromLargePTA$RPNIBlueFringeTestVariability
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