Examples of statechum.analysis.learning.MarkovModel.createMarkovLearner()

Class statechum.analysis.learning.MarkovModel

Examples of statechum.analysis.learning.MarkovModel.createMarkovLearner()

statechum.analysis.learning.MarkovModel.createMarkovLearner()
Constructs the tables used by the learner, from positive and negative traces. Only builds Markov model in the direction of traces. @param onlyLongest if set, only add traces of chunkLen to Markov matrix. Where false, all prefixes are added as well.

  @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);

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  @Test
  public void testCheckFanoutInconsistency1b3()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"a","u"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,false);
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-u->F / T-b->T-u->T","testCheckFanoutInconsistency1b2",config, converter);
    
    Assert.assertEquals(1,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()
    {

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  @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);

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  @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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  @Test
  public void testCheckFanoutInconsistency1c()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","u"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"},new String[]{"a","d"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,false);
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-d->F / T-b->T-u->T-d->T","testCheckFanoutInconsistency1c",config, converter);
    
    Assert.assertEquals(1,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }

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  @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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  /** 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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  @Test
  public void testCheckFanoutInconsistency1d()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","u"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"}},config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,false);
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-d->F / T-b->T-u->T-d->T","testCheckFanoutInconsistency1d",config, converter);
    
    Assert.assertEquals(1,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }

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    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblC}),true)));
    
    Set<List<Label>> plusStrings = buildSet(new String[][] {},config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","a","a"},new String[]{"a","b","a"},new String[]{"a","c"} },config,converter);
    MarkovModel another = new MarkovModel(2,true,true);
    another.createMarkovLearner(plusStrings, minusStrings, false);


    Assert.assertEquals(7,another.predictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.failure,another.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,another.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,another.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));

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  @Test
  public void testCheckFanoutInconsistency1e()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","u"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"}},config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,false);
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-b-#F / T-b->T-u->T-d->T","testCheckFanoutInconsistency1e",config, converter);
    
    Assert.assertEquals(1,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }

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