Package statechum.analysis.learning

Examples of statechum.analysis.learning.MarkovClassifier$ConsistencyCheckerForIdentificationOfUniques

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  @Test
  public void testCheckFanoutInconsistencySideways2()
  {
    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(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B-a->B-b->B","testCheckFanoutInconsistencySideways2",config, converter);
    graph2.transitionMatrix.get(graph2.getInit()).clear();// make it look like a graph has no transitions
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(), new MarkovClassifier.ConsistencyChecker(){

      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        Assert.fail("should not be called");
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  @Test
  public void testCheckFanoutInconsistencySideways3()
  {
    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(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B","testCheckFanoutInconsistencySideways3",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(), new MarkovClassifier.ConsistencyChecker(){

      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        counterA.addAndGet(1);
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    MarkovModel m = new MarkovModel(2,true,true);
    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);
   
    Assert.assertEquals(0,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }
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  @Test
  public void testCheckFanoutInconsistencySideways4()
  {
    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(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A","testCheckFanoutInconsistencySideways4",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(), new MarkovClassifier.ConsistencyChecker(){

      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        counterA.addAndGet(1);
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    MarkovModel m = new MarkovModel(2,true,true);
    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 / B-u->F / T-b->T-u->T","testCheckFanoutInconsistency1b1",config, converter);
   
    Assert.assertEquals(1,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }
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  @Test
  public void testCheckFanoutInconsistencySideways5()
  {
    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(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B","testCheckFanoutInconsistencySideways3",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(), new MarkovClassifier.ConsistencyChecker(){

      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        counterA.addAndGet(1);
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    MarkovModel m = new MarkovModel(2,true,true);
    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 / A-c->B / B-u->F / T-b->T-u->T","testCheckFanoutInconsistency1b2",config, converter);
   
    Assert.assertEquals(0,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()));
  }
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    MarkovModel m = new MarkovModel(2,true,true);
    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 / A-c->B / B-u->F / T-b->T-u->T","testCheckFanoutInconsistency1b2",config, converter);
   
    Assert.assertEquals(1,new MarkovClassifier(m,graph).checkFanoutInconsistency(graph.findVertex("B"),new MarkovClassifier.DifferentPredictionsInconsistency()
    {

      /**
       * @see statechum.analysis.learning.MarkovClassifier.DifferentPredictionsInconsistency#obtainAlphabet(statechum.analysis.learning.rpnicore.AbstractLearnerGraph, statechum.DeterministicDirectedSparseGraph.CmpVertex)
       */
 
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  @Test
  public void testCheckFanoutInconsistencySideways6()
  {
    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(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A","testCheckFanoutInconsistencySideways4",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(), new MarkovClassifier.ConsistencyChecker(){

      @SuppressWarnings("unused")
      @Override
      public boolean consistent(MarkovOutcome actual,MarkovOutcome predicted) {
        counterA.addAndGet(1);
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  {
    LearnerGraph gr=FsmParser.buildLearnerGraph("A-a->B / A-b->A / B-a->C-b->D-a->E / D-c->D / E-c->E","testBuildVerticesToMergeForPath1",config, converter);
    Collection<List<Label>> paths = new LinkedList<List<Label>>();paths.add(Arrays.asList(new Label[]{lblA}));paths.add(Arrays.asList(new Label[]{lblB}));paths.add(Arrays.asList(new Label[]{lblC}));
   
    //for(LearnerGraph g:grForPaths.values())  System.out.println(g.transitionMatrix);
    Collection<Set<CmpVertex>> collectionOfSets=new MarkovClassifier(new MarkovModel(2,true,true),gr).buildVerticesToMergeForPaths(paths);
    Assert.assertEquals(1,collectionOfSets.size());
    Assert.assertEquals(gr.transitionMatrix.keySet(), collectionOfSets.iterator().next());
  }
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