Examples of statechum.analysis.learning.MarkovClassifier.checkFanoutInconsistency()

Class statechum.analysis.learning.MarkovClassifier

Examples of statechum.analysis.learning.MarkovClassifier.checkFanoutInconsistency()

statechum.analysis.learning.MarkovClassifier.checkFanoutInconsistency()
Uses the supplied Markov matrix to check if predicted transitions from specific states match those that actually exist.
- Where predictForwardOrSideways is true, we are predicting transitions based on paths leading to the state of interest. Parameter Inverse_Graph should be the (non-deterministic) inverse of graph.
- Where predictForwardOrSideways is false, we are predicting transitions based on paths leading from the state of interest (sideways predictions). Parameter Inverse_Graph should be the same as graph and pathBeyondCurrentState should be null because once we predicted one transition, there are no further transitions from that state, hence no further transitions can be predicted sideways.
Requires Markov matrix to contain prefix-closed set of traces, in order to check for paths that have not been seen at all and hence ignored (otherwise they will be counted as inconsistencies that is perhaps not right).
predictForwardOrSideways true if this is to predict forward (usual Markov) or false for sideways. directionForwardOrInverse whether to merge states identified with the supplied outgoing transitions or those that the supplied transitions lead into. For instance, one might frequently have a reset transition and all its target states could be merged together. @param checker Consistency checker to use for predictions, usually based on a static method from {@link MarkovOutcome}. @param vert state of interest

    ConsistencyChecker checker = new MarkovClassifier.InconsistencyNullVsPredicted();
    MarkovClassifier resultClassifier = new MarkovClassifier(cl.model,result);
    for(Entry<CmpVertex,Collection<Label>> entry:labelsAdded.entrySet())
      if (!entry.getValue().isEmpty())
      {
        double numberOfInconsistencies = resultClassifier.checkFanoutInconsistency(entry.getKey(),checker,false);
        tentativeScore-=numberOfInconsistencies;
      }


    return (long)tentativeScore;
  }

View Full Code Here

    ConsistencyChecker checker = new MarkovClassifier.InconsistencyNullVsPredicted();
    MarkovClassifier resultClassifier = new MarkovClassifier(cl.model,result);
    for(Entry<CmpVertex,Collection<Label>> entry:labelsAdded.entrySet())
      if (!entry.getValue().isEmpty())
      {
        double numberOfInconsistencies = resultClassifier.checkFanoutInconsistency(entry.getKey(),checker,false);
        tentativeScore-=numberOfInconsistencies;
      }


    return (long)tentativeScore;
  }

View Full Code Here

    ConsistencyChecker checker = new MarkovClassifier.InconsistencyNullVsPredicted();
    MarkovClassifier resultClassifier = new MarkovClassifier(cl.model,result);
    for(Entry<CmpVertex,Collection<Label>> entry:labelsAdded.entrySet())
      if (!entry.getValue().isEmpty())
      {
        double numberOfInconsistencies = resultClassifier.checkFanoutInconsistency(entry.getKey(),checker,false);
        tentativeScore-=numberOfInconsistencies;
      }


    return (long)tentativeScore;
  }

View Full Code Here

    ConsistencyChecker checker = new MarkovClassifier.InconsistencyNullVsPredicted();
    MarkovClassifier resultClassifier = new MarkovClassifier(cl.model,result);
    for(Entry<CmpVertex,Collection<Label>> entry:labelsAdded.entrySet())
      if (!entry.getValue().isEmpty())
      {
        double numberOfInconsistencies = resultClassifier.checkFanoutInconsistency(entry.getKey(),checker,false);
        tentativeScore-=numberOfInconsistencies;
      }


    return (long)tentativeScore;
  }

View Full Code Here

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