Examples of statechum.analysis.learning.MarkovModel

statechum.analysis.learning.MarkovModel
Describes a non-probabilistic Markov model, where for every path we know either that,
- the path was never encountered or
- the path was encountered and there is a specific set of elements of alphabet that followed it.
The idea is to use the supplied Markov matrix to predict transitions from a specific state, passed as an argument. The choice of direction is not a choice between predicting transitions leaving a state based on those surrounding that state v.s predicting transitions entering a state based on those surrounding it. It is rather a choice of classifier to make predictions, the one that looks at history and decides what is to follow and the one looking at surrounding transitions and making decisions based on that.
- 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.

            return !((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        });
        assert sPlus.size() > 0;
        assert sMinus.size() > 0;
        final MarkovModel m= new MarkovModel(chunkLen,true,true);
        m.createMarkovLearner(sPlus, sMinus,false);
        
        pta.clearColours();
        synchronized (AbstractLearnerGraph.syncObj) {
          //PaperUAS.computePTASize(selectionID+" attempt: "+attempt+" with unique: ", pta, referenceGraph);
        }

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    final Collection<List<Label>> pathsToMerge2=identifyPathsToMerge(merged,referenceGraph,m,!directionForwardOrInverse);
    /*
    m.updateMarkov(merged,predictForwardOrSideways,false);// now we construct sideways learner ...
    m.constructMarkovTentative(graph,predictForwardOrSideways);// ... and use it to add more transitions.
    */
    MarkovModel inverseModel = new MarkovModel(ptaClassifier.model.getChunkLen(),true,!ptaClassifier.model.directionForwardOrInverse);
    MarkovClassifier cl = new MarkovClassifier(inverseModel,ptaClassifier.graph);cl.updateMarkov(false);
    Collection<Set<CmpVertex>> verticesToMergeUsingSideways=cl.buildVerticesToMergeForPaths(pathsOfInterest);
    return verticesToMergeUsingSideways;
  }

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            return !((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        });
        assert sPlus.size() > 0;
        assert sMinus.size() > 0;
        final MarkovModel m= new MarkovModel(chunkLen,true,true);
        m.createMarkovLearner(sPlus, sMinus,false);
        
        pta.clearColours();


        if (!onlyUsePositives)
          assert pta.getStateNumber() > pta.getAcceptStateNumber() : "graph with only accept states but onlyUsePositives is not set";

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            return !((statechum.analysis.learning.rpnicore.RandomPathGenerator.StateName)name).accept;
          }
        });
        assert sPlus.size() > 0;
        assert sMinus.size() > 0;
        final MarkovModel m= new MarkovModel(chunkLen,true,true);
        m.createMarkovLearner(sPlus, sMinus,false);
        
        pta.clearColours();


        if (!onlyUsePositives)
          assert pta.getStateNumber() > pta.getAcceptStateNumber() : "graph with only accept states but onlyUsePositives is not set";

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                            LearnerGraph pta=new LearnerGraph(learnerInitConfiguration.config);
                            for(List<Label> seq:sPlus)
                              pta.paths.augmentPTA(seq,true,false,null);
                            for(List<Label> seq:sMinus)
                              pta.paths.augmentPTA(seq,false,false,null);
                            final MarkovModel m= new MarkovModel(3,true,true,false);


                            new MarkovClassifier(m, pta).updateMarkov(false);// construct Markov chain if asked for.
                            final ConsistencyChecker checker = new MarkovClassifier.DifferentPredictionsInconsistencyNoBlacklistingIncludeMissingPrefixes();
                          
                            pta.clearColours();

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  /** Tests that creating a model from PTA and from initial traces gives the same result. */
  @Test
  public void testMarkovUpdate1_longest()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","b"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,true);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);
    new MarkovClassifier(mOther,graph).updateMarkov(true);
    Assert.assertEquals(m.predictionsMatrix,mOther.predictionsMatrix);
    Assert.assertEquals(m.occurrenceMatrix,mOther.occurrenceMatrix);
  }

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   * can see that they exist but not the number of tails they lead to. This is left in because I do not use specific values occurrence counts. 
   */
  @Test
  public void testMarkovUpdate1_prefixclosed()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","b"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,false);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);
    new MarkovClassifier(mOther,graph).updateMarkov(false);
    Assert.assertEquals(m.predictionsMatrix,mOther.predictionsMatrix);
    
    // Workaround around a deficiency in the calculation of occurrences of prefixes by the PTA-based construction of Markov model.
    Assert.assertEquals(new UpdatablePairInteger(2, 0), m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));

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  /** Tests that creating a model from PTA and from initial traces give the same result. */
  @Test
  public void testMarkovUpdate2()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","b"},new String[]{"c","u"} },config,converter), minusStrings = buildSet(new String[][] {},config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,true);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);new MarkovClassifier(mOther,graph).updateMarkov(true);
    Assert.assertEquals(m.predictionsMatrix,mOther.predictionsMatrix);
    Assert.assertEquals(m.occurrenceMatrix,mOther.occurrenceMatrix);
  }

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  /** Tests that creating a model from PTA and from initial traces give the same result. */
  @Test
  public void testMarkovUpdate3()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] {},config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,true);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);new MarkovClassifier(mOther,graph).updateMarkov(true);
    Assert.assertEquals(m.predictionsMatrix,mOther.predictionsMatrix);
    Assert.assertEquals(m.occurrenceMatrix,mOther.occurrenceMatrix);
  }

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  @Test
  /** Tests that creating a model from PTA and from initial traces give the same result. */
  public void testMarkovUpdate4()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,true);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);new MarkovClassifier(mOther,graph).updateMarkov(true);
    Assert.assertEquals(m.predictionsMatrix,mOther.predictionsMatrix);
    Assert.assertEquals(m.occurrenceMatrix,mOther.occurrenceMatrix);
  }

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