Package statechum

Examples of statechum.Trace

635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C-a-#D / B-c-#D","testUpdateMarkovSideways1c",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,true,false);
    Assert.assertTrue(m.getMarkov(false).isEmpty());Assert.assertEquals(7,m.getMarkov(true).size());
    Assert.assertEquals(MarkovOutcome.failure,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));

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

    Assert.assertEquals(MarkovOutcome.failure,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
    Assert.assertEquals(MarkovOutcome.negative,another.getMarkov(true).get(new Trace(Arrays.asList(new Label[]{lblC}),true)));
  }
View Full Code Here
667
668
669
670
671
672
673
674
675
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-c->C / B-b-#D","testUpdateMarkovSideways2",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(3,m.getMarkov(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.negative,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblC}),true)));
  }
View Full Code Here
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
    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());
    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[]{lblA,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblA}),true)));
   
    Assert.assertEquals(MarkovOutcome.negative,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[]{lblB,lblB}),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,lblU}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblU}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblU,lblC}),true)));
  }
View Full Code Here
701
702
703
704
705
706
707
708
709
710
711
712
713
714
    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(3);
    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(6,m.getMarkov(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB,lblA}),true)));
   
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblU,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblU,lblA}),true)));
   
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblC,lblC}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblC,lblC,lblA}),true)));
  }
View Full Code Here
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
    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)));
  }
View Full Code Here
55
56
57
58
59
60
61
62
    trainingGraphForClosures = FsmParser.buildLearnerGraph("A-a->B-b->C-a->D-b->E / C-c->C / B-a->C / D-a->E","testComputeClosure",config, converter);
  }
 
  @Test
  public void testGetChunks1() {
    List<Trace> l = MarkovModel.splitTrace(new Trace(Arrays.asList(new Label[]{}),true),1);
    Assert.assertTrue(l.isEmpty());
  }
View Full Code Here
61
62
63
64
65
66
67
68
    Assert.assertTrue(l.isEmpty());
  }

  @Test
  public void testGetChunks2a() {
    List<Trace> l = MarkovModel.splitTrace(new Trace(Arrays.asList(new Label[]{lblA,lblB,lblC}),true),4);
    Assert.assertTrue(l.isEmpty());
  }
View Full Code Here
67
68
69
70
71
72
73
74
    Assert.assertTrue(l.isEmpty());
  }

  @Test
  public void testGetChunks2b() {
    List<Trace> l = MarkovModel.splitTrace(new Trace(Arrays.asList(new Label[]{lblA,lblB,lblC}),true),10);
    Assert.assertTrue(l.isEmpty());
  }
View Full Code Here
73
74
75
76
77
78
79
80
81
82
83
    Assert.assertTrue(l.isEmpty());
  }

  @Test
  public void testGetChunks3() {
    List<Trace> l = MarkovModel.splitTrace(new Trace(Arrays.asList(new Label[]{lblA,lblB,lblC}),true),1);
    Assert.assertEquals(3,l.size());
    Assert.assertEquals(new Trace(Arrays.asList(new Label[]{lblA}),true),l.get(0));
    Assert.assertEquals(new Trace(Arrays.asList(new Label[]{lblB}),true),l.get(1));
    Assert.assertEquals(new Trace(Arrays.asList(new Label[]{lblC}),true),l.get(2));
  }
View Full Code Here
82
83
84
85
86
87
88
89
90
91
    Assert.assertEquals(new Trace(Arrays.asList(new Label[]{lblC}),true),l.get(2));
  }

  @Test
  public void testGetChunks4() {
    List<Trace> l = MarkovModel.splitTrace(new Trace(Arrays.asList(new Label[]{lblA,lblB,lblC}),true),2);
    Assert.assertEquals(2,l.size());
    Assert.assertEquals(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true),l.get(0));
    Assert.assertEquals(new Trace(Arrays.asList(new Label[]{lblB,lblC}),true),l.get(1));
  }
View Full Code Here
TOP

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
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.