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 testConstructExtendedGraph3()
  {
    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,false);
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / T-b->T-u->T","testConstructExtendedGraph3a",config, converter);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    Assert.assertEquals(1,newTransitions.size());// not enough evidence to update, hence nothing should be recorded.

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  @Test
  public void testConstructExtendedGraph7()
  {
    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-c->Z / T-b->T-u->T","testConstructExtendedGraph7a",config, converter);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    
    Assert.assertEquals(2,newTransitions.size());

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  @Test
  public void testConstructExtendedGraph4()
  {
    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 / T-b->T-u->T","testConstructExtendedGraph3a",config, converter);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    Assert.assertEquals(1,newTransitions.size());// not enough evidence to update, hence nothing should be recorded.

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  @Test
  public void testCheckFanoutInconsistency1a()
  {
    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 / T-b->T-u->T","testCheckFanoutInconsistency1a",config, converter);
    
    Assert.assertEquals(0,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }

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  @Test
  public void testCheckFanoutInconsistency1b1()
  {
    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-u->F / T-b->T-u->T","testCheckFanoutInconsistency1b1",config, converter);
    
    Assert.assertEquals(1,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }

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  @Test
  public void testConstructExtendedGraph5()
  {
    MarkovModel m = new MarkovModel(2,true,true);// w below is to ensure that all elements of the alphabet are included in traces.
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","u"},new String[]{"w"} },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-w->M-c->B / T-b->T-u->T","testConstructExtendedGraph5a",config, converter);// the purpose of the w-transition is to ensure transition c is taken into account in graph comparison
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    Assert.assertEquals(1,newTransitions.size());

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  @Test
  public void testCheckFanoutInconsistency1b2()
  {
    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(0,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }

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  @Test
  public void testConstructExtendedGraph6()
  {
    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 / T-b->T-u->T","testConstructExtendedGraph6a",config, converter);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    
    Assert.assertEquals(1,newTransitions.size());

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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 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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