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

Class statechum.analysis.learning.MarkovUniversalLearner

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

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.

    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblC}),true)));
    
    Set<List<Label>> plusStrings = buildSet(new String[][] {},config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","a","a"},new String[]{"a","b","a"},new String[]{"a","c"} },config,converter);
    MarkovUniversalLearner another = new MarkovUniversalLearner(2);
    another.createMarkovLearner(plusStrings, minusStrings, false);


    Assert.assertTrue(another.getMarkov(false).isEmpty());Assert.assertEquals(7,another.getMarkov(true).size());
    Assert.assertEquals(MarkovOutcome.failure,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));

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  @Test
  public void testMarkovUpdate4()
  {
    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());

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    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblC}),true)));
    
    Set<List<Label>> plusStrings = buildSet(new String[][] {},config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","a","a"},new String[]{"a","b","a"},new String[]{"a","c"} },config,converter);
    MarkovUniversalLearner another = new MarkovUniversalLearner(2);
    another.createMarkovLearner(plusStrings, minusStrings, false);


    Assert.assertTrue(another.getMarkov(false).isEmpty());Assert.assertEquals(7,another.getMarkov(true).size());
    Assert.assertEquals(MarkovOutcome.failure,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));

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  @Test
  public void testConstructExtendedGraph2()
  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    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);
    DifferentFSMException ex = WMethod.checkM(expected, m.get_extension_model());

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


    Assert.assertSame(MarkovOutcome.negative, newTransitions.get(graph.findVertex("B")).get(lblU));

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  @Test
  public void testConstructExtendedGraph4()
  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    Assert.assertEquals(1,newTransitions.size());// not enough evidence to update, hence nothing should be recorded.


    Assert.assertFalse(newTransitions.get(graph.findVertex("B")).containsKey(lblU));// failure ignored

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  @Test
  public void testConstructExtendedGraph5()
  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);// 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
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    Assert.assertEquals(1,newTransitions.size());


    Assert.assertEquals(1,newTransitions.get(graph.findVertex("B")).size());

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  @Test
  public void testConstructExtendedGraph6()
  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    
    Assert.assertEquals(1,newTransitions.size());

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  @Test
  public void testConstructExtendedGraph7()
  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    
    Assert.assertEquals(2,newTransitions.size());

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  @Test
  public void testCheckFanoutInconsistency1a()
  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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);
    
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    LearnerGraphND Inverse_Graph = new LearnerGraphND(shallowCopy);
    AbstractPathRoutines.buildInverse(graph,LearnerGraphND.ignoreNone,Inverse_Graph);  // do the inverse to the tentative graph

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