Package statechum.analysis.learning

Examples of statechum.analysis.learning.MarkovUniversalLearner$InconsistencyNullVsPredicted

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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);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
   
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(false);
   
    List<List<Label>> interestingPaths = new LinkedList<List<Label>>();
    // nothing in Markov matrix hence no predictions.
    Assert.assertTrue(MarkovUniversalLearner.predictTransitionsFromState(m.getMarkov(true),graph,true,graph.findVertex("B"),graph.pathroutines.computeAlphabet(),null,2,interestingPaths).isEmpty());
    Assert.assertEquals(1,interestingPaths.size());Assert.assertEquals(Arrays.asList(new Label[]{lblB}),interestingPaths.get(0));
   
    interestingPaths.clear();
    Map<Label, MarkovOutcome> outcome1 = MarkovUniversalLearner.predictTransitionsFromState(markovMatrix,graph,false,graph.findVertex("B"),graph.pathroutines.computeAlphabet(),null,2,interestingPaths);
    Assert.assertEquals(1,interestingPaths.size());Assert.assertEquals(Arrays.asList(new Label[]{lblB}),interestingPaths.get(0));
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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);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
   
    Assert.assertEquals(8,m.getMarkov(false).size());
   
    Assert.assertEquals(MarkovOutcome.failure,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblU}),true)));
   
    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[]{lblC,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblC}),true)));
   
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblU,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblU,lblU}),true)));
  }
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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);
    MarkovUniversalLearner m = new MarkovUniversalLearner(4);
    m.updateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());Assert.assertTrue(m.getMarkov(false).isEmpty());
  }
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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);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.updateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
   
    Configuration shallowCopy = graph.config.copy();shallowCopy.setLearnerCloneGraph(false);
    Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(false);
   
    List<List<Label>> interestingPaths = new LinkedList<List<Label>>();
    // nothing in Markov matrix hence no predictions.
    Assert.assertTrue(MarkovUniversalLearner.predictTransitionsFromState(m.getMarkov(true),graph,true,graph.findVertex("B"),graph.pathroutines.computeAlphabet(),null,2,interestingPaths).isEmpty());
    Assert.assertEquals(1,interestingPaths.size());Assert.assertEquals(Arrays.asList(new Label[]{lblB}),interestingPaths.get(0));
   
    interestingPaths.clear();
    Map<Label, MarkovOutcome> outcome1 = MarkovUniversalLearner.predictTransitionsFromState(markovMatrix,graph,false,graph.findVertex("B"),graph.pathroutines.computeAlphabet(),null,2,interestingPaths);
    Assert.assertEquals(1,interestingPaths.size());Assert.assertEquals(Arrays.asList(new Label[]{lblB}),interestingPaths.get(0));
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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);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.updateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
   
    Assert.assertEquals(8,m.getMarkov(false).size());
   
    Assert.assertEquals(MarkovOutcome.failure,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblU}),true)));
   
    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[]{lblC,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblC}),true)));
   
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblU,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblU,lblU}),true)));
  }
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  /** Tests that upon a label labelled as invalid, subsequent inconsistency checks are stopped. It is hence equivalent to a single incoming path. */
  @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.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 / A-c->A","testCheckFanoutInconsistencySideways4",config, converter);
    Map<Trace, MarkovOutcome> markovMatrix = m.getMarkov(false);
    Map<Label, MarkovOutcome> predictions = MarkovUniversalLearner.predictTransitionsFromState(markovMatrix,graph2,false,graph2.getInit(),graph.pathroutines.computeAlphabet(),null,m.getChunkLen(),null);
   
    Assert.assertEquals(MarkovOutcome.positive,predictions.get(lblU));
    Assert.assertEquals(MarkovOutcome.positive,predictions.get(lblC));
    Assert.assertEquals(MarkovOutcome.positive,predictions.get(lblA));
  }
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  @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.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 / A-c->A","testCheckFanoutInconsistencySideways4",config, converter);
    Map<CmpVertex, Map<Label, MarkovOutcome>> predictions = m.predictTransitions(graph2,true);
    Assert.assertTrue(predictions.isEmpty());// empty Markov means no predictions.
  }
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  @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.updateMarkov(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);
    Assert.assertEquals(2,predictions.size());Assert.assertEquals(2,predictions.get(graph2.findVertex("A")).size());Assert.assertEquals(2,predictions.get(graph2.findVertex("B")).size());
    Assert.assertEquals(MarkovOutcome.positive,predictions.get(graph2.findVertex("A")).get(lblU));// because c is looping in the A state
    Assert.assertEquals(MarkovOutcome.positive,predictions.get(graph2.findVertex("A")).get(lblC));
    Assert.assertEquals(MarkovOutcome.negative,predictions.get(graph2.findVertex("B")).get(lblU));
    Assert.assertEquals(MarkovOutcome.positive,predictions.get(graph2.findVertex("B")).get(lblB));
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  /** Very similar to {@link #testPredictTransitionsFromStatesForward2a()} except that the graph to predict from has a single state and even that reject. */
  @Test
  public void testPredictTransitionsFromStatesForward2b()
  {
    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 LearnerGraph graph2 = new LearnerGraph(config);graph2.getInit().setAccept(false);
    Map<CmpVertex, Map<Label, MarkovOutcome>> predictions = m.predictTransitions(graph2,true);
    Assert.assertTrue(predictions.isEmpty());
    predictions = m.predictTransitions(graph2,false);
    Assert.assertTrue(predictions.isEmpty());
  }
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  @Test
  public void testPredictTransitionsFromStatesForward3()
  {
    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 LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A/ T-a->T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    m.constructMarkovTentative(graph2, true);
    Assert.assertNull(WMethod.checkM(FsmParser.buildLearnerGraph("A-a->B / A-c->A / A-u->E / B-b->C / B-u-#D","testPredictTransitionsFromStatesForward3",config, converter), m.get_extension_model()));
    Assert.assertTrue(m.get_extension_model().findVertex("T") != null);// extended graph starts as a replica of an original one.
  }
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Related Classes of statechum.analysis.learning.MarkovUniversalLearner$InconsistencyNullVsPredicted

  • statechum.analysis.learning.experiments.PairSelection.MarkovPassivePairSelection$LearnerRunner
  • statechum.analysis.learning.experiments.PairSelection.TestMarkovLearner
  • statechum.analysis.learning.rpnicore.AbstractLearnerGraph
  • statechum.analysis.learning.rpnicore.LearnerGraph
  • statechum.analysis.learning.rpnicore.LearnerGraphND
  • statechum.Configuration
  • statechum.DeterministicDirectedSparseGraph.CmpVertex
  • statechum.Label
  • statechum.Trace
  • java.util.Map
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