List of createMarkovLearner() Examples

Examples of 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.
statechum.analysis.learning.MarkovUniversalLearner.createMarkovLearner()
Constructs the tables used by the learner, from positive and negative traces. Only builds Markov in the forward direction. @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.MarkovModel.createMarkovLearner()

  @Test
  public void testCreateMarkovMatrix2()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter), minusStrings = new HashSet<List<Label>>();
    Map<Trace, MarkovOutcome> matrix = m.createMarkovLearner(plusStrings, minusStrings,false);
    Assert.assertEquals(3,matrix.size());
    
    Assert.assertSame(MarkovOutcome.positive, matrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblU}),true)));


    Assert.assertSame(MarkovOutcome.positive, matrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));

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

  @Test
  public void testCreateMarkovMatrix3a()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = new HashSet<List<Label>>(), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    Map<Trace, MarkovOutcome> matrix = m.createMarkovLearner(plusStrings, minusStrings,false);
    Assert.assertEquals(3,matrix.size());


    Assert.assertSame(MarkovOutcome.negative, matrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblU}),true)));


    Assert.assertSame(MarkovOutcome.positive, matrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));

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

  @Test
  public void testCreateMarkovMatrix3b()
  {
    MarkovModel m = new MarkovModel(3,true,true);
    Set<List<Label>> plusStrings = new HashSet<List<Label>>(), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    Map<Trace, MarkovOutcome> matrix = m.createMarkovLearner(plusStrings, minusStrings,false);
    Assert.assertEquals(3,matrix.size());


    Assert.assertSame(MarkovOutcome.negative, matrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblU}),true)));


    Assert.assertSame(MarkovOutcome.positive, matrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));

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

  @Test
  public void testCreateMarkovMatrix4()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = new HashSet<List<Label>>(), minusStrings = buildSet(new String[][] { new String[]{"u"} },config,converter);
    Map<Trace, MarkovOutcome> matrix = m.createMarkovLearner(plusStrings, minusStrings,false);
    Assert.assertEquals(1,matrix.size());


    Assert.assertSame(MarkovOutcome.negative, matrix.get(new Trace(Arrays.asList(new Label[]{lblU}),true)));
  }

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

  @Test
  public void testCreateMarkovMatrix5()
  {
    final MarkovModel m = new MarkovModel(2,true,true);
    final Set<List<Label>> plusStrings = new HashSet<List<Label>>(), minusStrings = buildSet(new String[][] { new String[]{},new String[]{"a","u"} },config,converter);
    Map<Trace, MarkovOutcome> matrix = m.createMarkovLearner(plusStrings, minusStrings,false);
    Assert.assertEquals(3,matrix.size());
    
    Assert.assertSame(MarkovOutcome.negative, matrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblU}),true)));


    Assert.assertSame(MarkovOutcome.positive, matrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));

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

    final Set<List<Label>> plusStrings = new HashSet<List<Label>>(), minusStrings = new HashSet<List<Label>>();
    Helper.checkForCorrectException(new whatToRun() {
      @Override
      public void run() throws NumberFormatException
      {
        m.createMarkovLearner(plusStrings, minusStrings,false);
      }
    }, IllegalArgumentException.class, "empty");
  }
  
  @Test

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

    final Set<List<Label>> plusStrings = new HashSet<List<Label>>(), minusStrings = buildSet(new String[][] { new String[]{},new String[]{} },config,converter);
    Helper.checkForCorrectException(new whatToRun() {
      @Override
      public void run() throws NumberFormatException
      {
        m.createMarkovLearner(plusStrings, minusStrings,false);
      }
    }, IllegalArgumentException.class, "empty");
  }
  
  /** Nothing to add because there not enough evidence. */

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

  @Test
  public void testConstructExtendedGraph1()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","p"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,false);
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-u->B-p->B","testConstructExtendedGraph1",config, converter);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    Assert.assertTrue(newTransitions.isEmpty());// not enough evidence to update, hence nothing should be recorded.
    final LearnerGraph expected = FsmParser.buildLearnerGraph("A-u->B-p->B","testConstructExtendedGraph1",config, converter);

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

  @Test
  public void testConstructExtendedGraph2()
  {
    MarkovModel m = new MarkovModel(2,true,true);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","p"} },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","testConstructExtendedGraph2",config, converter);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    Assert.assertTrue(newTransitions.isEmpty());// not enough evidence to update, hence nothing should be recorded.
    final LearnerGraph expected = FsmParser.buildLearnerGraph("A-a->B","testConstructExtendedGraph2",config, converter);

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

  @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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