Package statechum.analysis.learning.MarkovModel.MarkovMatrixEngine

Examples of statechum.analysis.learning.MarkovModel.MarkovMatrixEngine.PredictionForSequence

478
479
480
481
482
483
484
485
486
487
488
      }

      @Override
      public void nodeEntered(PTAExplorationNode currentNode, LinkedList<PTAExplorationNode> pathToInit)
      {
        PredictionForSequence prediction = (PredictionForSequence)currentNode.getState();
        LinkedList<Label> path = new LinkedList<Label>();for(PTAExplorationNode elem:pathToInit) path.addFirst(elem.getInput());

        if (prediction.prediction != null)
          outcome.put(path, prediction.occurrence);
      }
View Full Code Here
71
72
73
74
75
76
77
    public static class PredictionStatePTAAutomaton extends PTASequenceSetAutomaton
    {
      @Override
      public Object getTheOnlyState()
      {
        return new PredictionForSequence();// it is important to return a new instance every time it is asked for, because otherwise we'll end up sharing instances that is not right.
      }
View Full Code Here
414
415
416
417
418
419
420
421
422
423
424
      }

      @Override
      public void nodeEntered(PTAExplorationNode currentNode, @SuppressWarnings("unused")  LinkedList<PTAExplorationNode> pathToInit)
      {
        PredictionForSequence prediction = (PredictionForSequence)currentNode.getState();
        if (prediction.occurrence.firstElem > 0 && prediction.occurrence.secondElem > 0)
          prediction.prediction = MarkovOutcome.failure;
        else
        if (prediction.occurrence.firstElem > 0)
          prediction.prediction = MarkovOutcome.positive;
View Full Code Here
453
454
455
456
457
458
459
460
461
462
463
      }

      @Override
      public void nodeEntered(PTAExplorationNode currentNode, LinkedList<PTAExplorationNode> pathToInit)
      {
        PredictionForSequence prediction = (PredictionForSequence)currentNode.getState();
        LinkedList<Label> path = new LinkedList<Label>();for(PTAExplorationNode elem:pathToInit) path.addFirst(elem.getInput());
        if (prediction.prediction != null)
          outcome.put(path, prediction.prediction);
      }
View Full Code Here
488
489
490
491
492
493
494
495
496
497
498
      }

      @Override
      public void nodeEntered(PTAExplorationNode currentNode, LinkedList<PTAExplorationNode> pathToInit)
      {
        PredictionForSequence prediction = (PredictionForSequence)currentNode.getState();
        LinkedList<Label> path = new LinkedList<Label>();for(PTAExplorationNode elem:pathToInit) path.addFirst(elem.getInput());

        if (prediction.prediction != null)
          outcome.put(path, prediction.occurrence);
      }
View Full Code Here
71
72
73
74
75
76
77
    public static class PredictionStatePTAAutomaton extends PTASequenceSetAutomaton
    {
      @Override
      public Object getTheOnlyState()
      {
        return new PredictionForSequence();// it is important to return a new instance every time it is asked for, because otherwise we'll end up sharing instances that is not right.
      }
View Full Code Here
659
660
661
662
663
664
665
666
667
668
669
              if (predictionGraphInverted)
                Collections.reverse(partOfTraceUsedInMarkovPredictions);
              Map<Label,PTASequenceEngine.Node> lastElementToPrediction = model.markovMatrix.getMapFromLabelsToPredictions(partOfTraceUsedInMarkovPredictions);
              for(Label label:allElementsOfAlphabet)
              {
                PredictionForSequence prediction = MarkovMatrixEngine.getPredictionIfExists(lastElementToPrediction,label);

                UpdatablePairInteger occurrence_of_label_predicted_form_Markov=prediction == null?null:prediction.occurrence;

                if(outgoing_labels_occurrences.containsKey(label))
                {
View Full Code Here
843
844
845
846
847
848
849
850
851
852
853
        {
          if (!failureLabels.contains(label))
          {// if the labels is not already recorded as being inconsistently predicted
            MarkovOutcome predictedFromEalierTrace = outgoing_labels_probabilities.get(label);
             
              PredictionForSequence prediction = MarkovMatrixEngine.getPredictionIfExists(lastElementToPrediction, label);
              MarkovOutcome predicted_from_Markov= prediction!=null?prediction.prediction:null;
            MarkovOutcome outcome = MarkovOutcome.reconcileOpinions_PosNeg_Overrides_Null(predictedFromEalierTrace, predicted_from_Markov);
            if (outcome != predictedFromEalierTrace)
            {// we learnt something new, be it a new value (or a non-null value) or a failure, record it
              if (outcome == MarkovOutcome.failure)
View Full Code Here
901
902
903
904
905
906
907
908
909
910
911
          }
         
          pathToUpdateInMarkov.add(lbl);
         
          MarkovOutcome newValue = null;
          PredictionForSequence prediction = model.markovMatrix.getPredictionAndCreateNewOneIfNecessary(pathToUpdateInMarkov);
         
          boolean foundAccept = false, foundReject = false;
          for(Object vObj:graphToCheckForConsistency.getTargets(targets))
          {
            if ( ((CmpVertex)vObj).isAccept() ) foundAccept = true;
View Full Code Here
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
            for(Label label:outgoingLabels)
            {
            MarkovOutcome labels_occurrence= outgoing_labels_value.get(label);
            if (labels_occurrence != MarkovOutcome.failure)
            {
                PredictionForSequence prediction = MarkovMatrixEngine.getPredictionIfExists(mapFromLastLabelToNodes, label);
                MarkovOutcome predicted_from_Markov=prediction == null?null:prediction.prediction;
                if (predicted_from_Markov != MarkovOutcome.failure)
                {// if training data does not lead to a consistent outcome for this label because chunk length is too small, not much we can do, but otherwise we are here and can make use of the data
                  if (!checker.consistent(labels_occurrence, predicted_from_Markov))
                  {
View Full Code Here
TOP

Related Classes of statechum.analysis.learning.MarkovModel.MarkovMatrixEngine.PredictionForSequence

  • statechum.analysis.learning.experiments.mutation.DiffExperiments
  • statechum.analysis.learning.MarkovClassifier
  • statechum.analysis.learning.MarkovModel
  • statechum.analysis.learning.MarkovModel$MarkovMatrixEngine$PredictionStatePTAAutomaton
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.