List of updateMarkov() Examples

Examples of updateMarkov()

statechum.analysis.learning.MarkovClassifier.updateMarkov()
Updates Markov. This is useful where we have added something to the original PTA and need to update Markov. Crucial for learning of Markov for sideways inference, where we cannot learn from the original traces and have to delay Markov construction to the time where PTA is built.
Note that computing Markov using incoming/outgoing paths of length 0 is just a distribution of letters, tagged with pos/neg/fail. It is not based on earlier knowledge hence could be built either forwards or sideways in the same way. @param vert state to predict for @param alphabet alphabet of the graph of interest @param chunkLength how many steps to make a prediction for.
statechum.analysis.learning.MarkovUniversalLearner.updateMarkov()
Updates Markov based on predictions. This is useful where we have added something to the original PTA and need to update Markov. Crucial for learning of Markov for sideways inference, where we cannot learn from the original traces and have to delay Markov construction to the time where PTA is built.
Note that computing Markov using incoming/outgoing paths of length 0 is just a distribution of letters, tagged with pos/neg/fail. It is not based on earlier knowledge hence could be built either forwards or sideways in the same way. @param graph the graph to predict transitions for @param markovMatrix the matrix used for predictions @param Inverse_Graph graph used to make predictions @param predictForwardOrSideways whether to predict forward (true) or sideways (false). @param vert state to predict for @param alphabet alphabet of the graph of interest @param chunkLength how many steps to make a prediction for.

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

  @Test
  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState2()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    final MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.updateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B","testPredictTransitionsFromStatesWithPathBeyondCurrentState2",config, converter);
    final Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(false);

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

  @Test
  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState3()
  {
    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.updateMarkov(graph,true,true);
    Assert.assertEquals(4,m.getMarkov(true).size());
    Assert.assertTrue(m.getMarkov(false).isEmpty());
    final Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(true);
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);

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

  @Test
  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState4()
  {
    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.updateMarkov(graph,true,true);
    Assert.assertEquals(4,m.getMarkov(true).size());
    Assert.assertTrue(m.getMarkov(false).isEmpty());
    final Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(true);

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

  @Test
  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState5()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    final MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.updateMarkov(graph,true,true);
    Assert.assertEquals(4,m.getMarkov(true).size());
    Assert.assertTrue(m.getMarkov(false).isEmpty());
    final Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(true);

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

    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.updateMarkov(graph,true,true);
    Assert.assertEquals(m.getMarkov(true),mOther.getMarkov(true));Assert.assertTrue(m.getMarkov(false).isEmpty());
    Assert.assertEquals(m.getOccurrence(true),mOther.getOccurrence(true));Assert.assertTrue(m.getOccurrence(false).isEmpty());
  }
  
  @Test

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

  @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.updateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());Assert.assertEquals(4,m.getMarkov(false).size());
    Assert.assertTrue(m.getOccurrence(true).isEmpty());Assert.assertEquals(4,m.getOccurrence(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.getOccurrence(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(new UpdatablePairInteger(1, 0),m.getOccurrence(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(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));

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

  @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.updateMarkov(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(new UpdatablePairInteger(2, 0),m.getOccurrence(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(new UpdatablePairInteger(1, 0),m.getOccurrence(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(new UpdatablePairInteger(1, 0),m.getOccurrence(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)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));

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

  @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.updateMarkov(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.updateMarkov()

  @Test
  public void testUpdateMarkovSideways1d()
  {
    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.updateMarkov(graph,true,false);
    Assert.assertTrue(m.getMarkov(false).isEmpty());Assert.assertEquals(7,m.getMarkov(true).size());
    Assert.assertEquals(MarkovOutcome.failure,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));

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

  @Test
  public void testUpdateMarkovSideways2()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-c->C / B-b-#D","testUpdateMarkovSideways2",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.updateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(3,m.getMarkov(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblC}),true)));

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