Examples of MarkovClassifier

Examples of statechum.analysis.learning.MarkovClassifier

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  @Test
  public void testUpdateMarkovSideways1a()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C","testUpdateMarkovSideways1",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertEquals(4,m.predictionsMatrix.size());
    Assert.assertEquals(4,m.occurrenceMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
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Examples of statechum.analysis.learning.MarkovClassifier

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  @Test
  public void testUpdateMarkovSideways1b()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C","testUpdateMarkovSideways1",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(6,m.predictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));
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Examples of statechum.analysis.learning.MarkovClassifier

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  public void testUpdateMarkovSideways1c()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C-a-#D / B-c-#D","testUpdateMarkovSideways1c",config, converter);

    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(9,m.predictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));
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Examples of statechum.analysis.learning.MarkovClassifier

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  @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);
    MarkovModel m = new MarkovModel(2,true,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(7,m.predictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.failure,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
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Examples of statechum.analysis.learning.MarkovClassifier

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

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  @Test
  public void testUpdateMarkovSideways3()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertEquals(9,m.predictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblC,lblA}),true)));
   
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Examples of statechum.analysis.learning.MarkovClassifier

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  @Test
  public void testUpdateMarkovSideways4()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(3,false,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertEquals(6,m.predictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB,lblA}),true)));
   
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblC,lblU,lblC}),true)));
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Examples of statechum.analysis.learning.MarkovClassifier

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  @Test
  public void testUpdateMarkovSideways5()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(4,false,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertTrue(m.predictionsMatrix.isEmpty());
  }
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Examples of statechum.analysis.learning.MarkovClassifier

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  @Test
  public void testPredictTransitionsSideways1()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel mSideways = new MarkovModel(2,false,true), mForward = new MarkovModel(2,true,true);
    new MarkovClassifier(mSideways,graph).updateMarkov(true);
    Assert.assertEquals(9,mSideways.predictionsMatrix.size());Assert.assertTrue(mForward.predictionsMatrix.isEmpty());
   
    List<List<Label>> interestingPaths = new LinkedList<List<Label>>();
    // nothing in Markov matrix hence no predictions.
    Assert.assertTrue(new MarkovClassifier(mForward,graph).predictTransitionsFromState(graph.findVertex("B"),null,2,interestingPaths).isEmpty());
    Assert.assertEquals(1,interestingPaths.size());Assert.assertEquals(Arrays.asList(new Label[]{lblA}),interestingPaths.get(0));
   
    interestingPaths.clear();
    Map<Label, MarkovOutcome> outcome1 = new MarkovClassifier(mSideways,graph).predictTransitionsFromState(graph.findVertex("B"),null,2,interestingPaths);
    Assert.assertEquals(1,interestingPaths.size());Assert.assertEquals(Arrays.asList(new Label[]{lblB}),interestingPaths.get(0));
    Assert.assertEquals(2,outcome1.size());
    Assert.assertEquals(MarkovOutcome.negative,outcome1.get(lblU));Assert.assertEquals(MarkovOutcome.positive,outcome1.get(lblB));
   
   
    interestingPaths.clear();
    outcome1 = new MarkovClassifier(mSideways,graph).predictTransitionsFromState(graph.findVertex("E"),null,2,interestingPaths);
    Assert.assertEquals(2,interestingPaths.size());Assert.assertEquals(Arrays.asList(new Label[]{lblC}),interestingPaths.get(0));Assert.assertEquals(Arrays.asList(new Label[]{lblU}),interestingPaths.get(1));
    Assert.assertEquals(3,outcome1.size());
    Assert.assertEquals(MarkovOutcome.positive,outcome1.get(lblU));Assert.assertEquals(MarkovOutcome.positive,outcome1.get(lblC));Assert.assertEquals(MarkovOutcome.positive,outcome1.get(lblA));
  }   
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Examples of statechum.analysis.learning.MarkovClassifier

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  @Test
  public void testPredictTransitionsSideways2()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-b->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
   
    Assert.assertEquals(8,m.predictionsMatrix.size());
   
    Assert.assertEquals(MarkovOutcome.failure,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblU}),true)));
   
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