List of checkFanoutInconsistency() Examples

Examples of 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
statechum.analysis.learning.MarkovUniversalLearner.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.
@param Inverse_Graph graph used to compute all paths of specific length leading to a state of interest @param predictForwardOrSideways true if this is to predict forward (usual Markov) or false for sideways. @param vert state of interest @param chunkLength length of paths to consider (before the pathBeyondCurrentState component). @param checker consistency checker, usually based on a static method from {@link MarkovOutcome}.

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

    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;
  }

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

    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;
  }

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

    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;
  }

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

    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;
  }

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

    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-b-#F / T-b->T-u->T-d->T","testCheckFanoutInconsistency1e",config, converter);
    
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraphND Inverse_Graph = new LearnerGraphND(shallowCopy);
    AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,Inverse_Graph);  // do the inverse to the tentative graph 
    Assert.assertEquals(1,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));
  }
  
  /** Transition d exists as negative but should be absent according to Markov. */
  @Test
  public void testCheckFanoutInconsistency1f()

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

    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-d-#F / T-b->T-u->T-d->T","testCheckFanoutInconsistency1f",config, converter);
    
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraphND Inverse_Graph = new LearnerGraphND(shallowCopy);
    AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,Inverse_Graph);  // do the inverse to the tentative graph 
    Assert.assertEquals(1,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));
    
    Assert.assertEquals(4.,MarkovUniversalLearner.computeInconsistency(graph, true, m, new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)),Configuration.fpAccuracy);// inconsistencies detected are mostly due to state T
  }
  
  /** Two inconsistencies, transition u and transition b which should not exist after c. */

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

    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B-b->C / B-u->F / T-b->T-u->T","testCheckFanoutInconsistency2",config, converter);
    
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraphND Inverse_Graph = new LearnerGraphND(shallowCopy);
    AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,Inverse_Graph);  // do the inverse to the tentative graph 
    Assert.assertEquals(2,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));
  }
  
  /** One inconsistency: transition u. */
  @Test
  public void testCheckFanoutInconsistency3()

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

    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B-u->C / T-b->T-u->T","testCheckFanoutInconsistency3",config, converter);
    
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraphND Inverse_Graph = new LearnerGraphND(shallowCopy);
    AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,Inverse_Graph);  // do the inverse to the tentative graph 
    Assert.assertEquals(1,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));
  }
  
  /** No inconsistencies since there are very few paths. */
  @Test
  public void testCheckFanoutInconsistency4()

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

    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->D-b->C / A-c->B-b->C / B-u->E / T-b->T-u->T","testCheckFanoutInconsistency4",config, converter);
    
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraphND Inverse_Graph = new LearnerGraphND(shallowCopy);
    AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,Inverse_Graph);  // do the inverse to the tentative graph 
    Assert.assertEquals(0,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));// everything as expected.
    Assert.assertEquals(0,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("D"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));// missing reject-transition with label u is ignored because we are only considering actual outgoing transitions
  }


  @Test
  public void testMarkovUpdate1()

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

    
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraphND Inverse_Graph = new LearnerGraphND(shallowCopy);
    AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,Inverse_Graph);  // do the inverse to the tentative graph 
    Assert.assertEquals(0,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));// everything as expected.
    Assert.assertEquals(0,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("D"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));// missing reject-transition with label u is ignored because we are only considering actual outgoing transitions
  }


  @Test
  public void testMarkovUpdate1()
  {

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