Package statechum

Examples of statechum.Trace

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    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)));

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

    Assert.assertSame(MarkovOutcome.negative, matrix.get(new Trace(Arrays.asList(new Label[]{lblU}),true)));
  }
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    MarkovModel mOther = new MarkovModel(2,true,true);
    new MarkovClassifier(mOther,graph).updateMarkov(false);
    Assert.assertEquals(m.predictionsMatrix,mOther.predictionsMatrix);
   
    // Workaround around a deficiency in the calculation of occurrences of prefixes by the PTA-based construction of Markov model.
    Assert.assertEquals(new UpdatablePairInteger(2, 0), m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
    Assert.assertEquals(new UpdatablePairInteger(1, 0), mOther.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
   
    m.occurrenceMatrix.remove(new Trace(Arrays.asList(new Label[]{lblA}),true));mOther.occurrenceMatrix.remove(new Trace(Arrays.asList(new Label[]{lblA}),true));
    Assert.assertEquals(m.occurrenceMatrix,mOther.occurrenceMatrix);
  }
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    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)));
    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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  {
    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)));

    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
  }
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  {
    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)));
    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,lblC}),true)));

    Assert.assertEquals(MarkovOutcome.failure,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblC}),true)));
  }
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  {
    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)));

    Assert.assertEquals(MarkovOutcome.failure,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.predictionsMatrix.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);
    MarkovModel another = new MarkovModel(2,true,true);
    another.createMarkovLearner(plusStrings, minusStrings, false);

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

    Assert.assertEquals(MarkovOutcome.failure,another.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,another.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,another.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblC}),true)));
  }
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  {
    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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  {
    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)));
   
    Assert.assertEquals(MarkovOutcome.negative,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblU}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));
   
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblC,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblU,lblU}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblC,lblU}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblU,lblC}),true)));
  }
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Related Classes of statechum.Trace

  • statechum.analysis.learning.ErlangOracleLearner
  • statechum.analysis.learning.ErlangOracleVisualiser
  • statechum.analysis.learning.experiments.PairSelection.MarkovPassivePairSelection
  • statechum.analysis.learning.experiments.PairSelection.TestMarkovLearner
  • statechum.analysis.learning.MarkovClassifier
  • statechum.analysis.learning.MarkovModel
  • statechum.analysis.learning.MarkovUniversalLearner
  • statechum.analysis.learning.RPNILearner
  • statechum.apps.ErlangQSMOracle
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