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.MarkovUniversalLearner.checkFanoutInconsistency()

    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / T-b->T-u->T","testCheckFanoutInconsistency1a",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)));
  }
  
  /** One from B with inconsistent predictions. */
  @Test
  public void testCheckFanoutInconsistency1b1()

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

    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-u->F / T-b->T-u->T","testCheckFanoutInconsistency1b1",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)));
  }
  
  /** One from B where Markov cannot make up its mind. */
  @Test
  public void testCheckFanoutInconsistency1b2()

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

    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-u->F / T-b->T-u->T","testCheckFanoutInconsistency1b2",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 positive but should be present as negative according to Markov. */
  @Test
  public void testCheckFanoutInconsistency1c()

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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","testCheckFanoutInconsistency1c",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 positive but should be absent according to Markov. */
  @Test
  public void testCheckFanoutInconsistency1d()

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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","testCheckFanoutInconsistency1d",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 b exists as negative but should be present as positive according to Markov. */
  @Test
  public void testCheckFanoutInconsistency1e()

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

    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-s->B","testCheckFanoutInconsistencySideways1",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,m.checkFanoutInconsistency(graph2,false,graph2,graph2.getInit(),m.getChunkLen(), new MarkovUniversalLearner.ConsistencyChecker(){


      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        Assert.assertEquals(MarkovOutcome.positive,actual);Assert.assertNull(predicted);
        counterA.addAndGet(1);

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

    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B-a->B-b->B","testCheckFanoutInconsistencySideways2",config, converter);
    graph2.transitionMatrix.get(graph2.getInit()).clear();// make it look like a graph has no transitions
    Assert.assertEquals(0,m.checkFanoutInconsistency(graph2,false,graph2,graph2.getInit(),m.getChunkLen(), new MarkovUniversalLearner.ConsistencyChecker(){


      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        Assert.fail("should not be called");

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

    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B","testCheckFanoutInconsistencySideways3",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,m.checkFanoutInconsistency(graph2,false,graph2,graph2.getInit(),m.getChunkLen(), new MarkovUniversalLearner.ConsistencyChecker(){


      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        counterA.addAndGet(1);

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

    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A","testCheckFanoutInconsistencySideways4",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,m.checkFanoutInconsistency(graph2,false,graph2,graph2.getInit(),m.getChunkLen(), new MarkovUniversalLearner.ConsistencyChecker(){


      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        counterA.addAndGet(1);

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

    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B","testCheckFanoutInconsistencySideways3",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,m.checkFanoutInconsistency(graph2,false,graph2,graph2.getInit(),m.getChunkLen(), new MarkovUniversalLearner.ConsistencyChecker(){


      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        counterA.addAndGet(1);

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