List of predictTransitionsAndUpdateMarkov() Examples

Examples of predictTransitionsAndUpdateMarkov()

statechum.analysis.learning.MarkovUniversalLearner.predictTransitionsAndUpdateMarkov()
This function predicts transitions from each state and then adds predictions to the Markov model. @param tentativeAutomaton tentative Automaton @param predictForward whether to update a forward or a sideways Markov matrix. @param onlyLongest if set, only add traces of chunkLen to Markov matrix. Where false, all prefixes are added as well.

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

    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);
    MarkovUniversalLearner mOther = new MarkovUniversalLearner(2);
    mOther.predictTransitionsAndUpdateMarkov(graph,true,true);
    Assert.assertEquals(m.getMarkov(true),mOther.getMarkov(true));Assert.assertTrue(m.getMarkov(false).isEmpty());
  }
  
  @Test
  public void testMarkovUpdate2()

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

    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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);
    MarkovUniversalLearner mOther = new MarkovUniversalLearner(2);mOther.predictTransitionsAndUpdateMarkov(graph,true,true);
    Assert.assertEquals(m.getMarkov(true),mOther.getMarkov(true));Assert.assertTrue(m.getMarkov(false).isEmpty());
  }
  
  @Test
  public void testMarkovUpdate3()

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

    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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);
    MarkovUniversalLearner mOther = new MarkovUniversalLearner(2);mOther.predictTransitionsAndUpdateMarkov(graph,true,true);
    Assert.assertEquals(m.getMarkov(true),mOther.getMarkov(true));Assert.assertTrue(m.getMarkov(false).isEmpty());
  }
  
  @Test
  public void testMarkovUpdate4()

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

    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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);
    MarkovUniversalLearner mOther = new MarkovUniversalLearner(2);mOther.predictTransitionsAndUpdateMarkov(graph,true,true);
    Assert.assertEquals(m.getMarkov(true),mOther.getMarkov(true));Assert.assertTrue(m.getMarkov(false).isEmpty());
  }
  
  @Test
  public void testUpdateMarkovSideways1a()

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

  @Test
  public void testUpdateMarkovSideways1a()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C","testUpdateMarkovSideways1",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());Assert.assertEquals(4,m.getMarkov(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));

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

  @Test
  public void testUpdateMarkovSideways1b()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C","testUpdateMarkovSideways1",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,false,false);
    Assert.assertTrue(m.getMarkov(true).isEmpty());Assert.assertEquals(6,m.getMarkov(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));

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

  @Test
  public void testUpdateMarkovSideways1c()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C-a-#D / B-c-#D","testUpdateMarkovSideways1c",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,false,false);
    Assert.assertTrue(m.getMarkov(true).isEmpty());Assert.assertEquals(9,m.getMarkov(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));

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

  @Test
  public void testPredictTransitionsFromStatesSideways1()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    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);
    Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(false);

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

  @Test
  public void testPredictTransitionsFromStatesForward1()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    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);
    Map<CmpVertex, Map<Label, MarkovOutcome>> predictions = m.predictTransitions(graph2,true);

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

  @Test
  public void testPredictTransitionsFromStatesForward2a()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,true,true);
    Assert.assertEquals(4,m.getMarkov(true).size());
    Assert.assertTrue(m.getMarkov(false).isEmpty());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Map<CmpVertex, Map<Label, MarkovOutcome>> predictions = m.predictTransitions(graph2,true);

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