Package statechum.analysis.learning

Examples of statechum.analysis.learning.MarkovUniversalLearner$UpdatableOutcome

425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
 
  /** Transition d exists as positive but should be present as negative according to Markov. */
  @Test
  public void testCheckFanoutInconsistency1c()
  {
    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"},new String[]{"a","d"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,false);
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B / A-c->B / B-d->F / T-b->T-u->T-d->T","testCheckFanoutInconsistency1c",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
    Assert.assertEquals(1,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));
  }
View Full Code Here
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
 
  /** Transition d exists as positive but should be absent according to Markov. */
  @Test
  public void testCheckFanoutInconsistency1d()
  {
    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 / B-d->F / T-b->T-u->T-d->T","testCheckFanoutInconsistency1d",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
    Assert.assertEquals(1,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));
  }
View Full Code Here
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
 
  /** Transition b exists as negative but should be present as positive according to Markov. */
  @Test
  public void testCheckFanoutInconsistency1e()
  {
    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 / B-b-#F / T-b->T-u->T-d->T","testCheckFanoutInconsistency1e",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
    Assert.assertEquals(1,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));
  }
View Full Code Here
91
92
93
94
95
96
97
98
99
    Helper.checkForCorrectException(new whatToRun() {
      @SuppressWarnings("unused")
      @Override
      public void run() throws NumberFormatException
      {
        new MarkovUniversalLearner(1);
      }
    }, IllegalArgumentException.class, "chunkLen");
  }
View Full Code Here
99
100
101
102
103
104
105
106
107
108
109
110
111
  }
 
  @Test
  public void testCreateMarkovMatrix1()
  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b","c"}, new String[]{"a","b"}, new String[]{"a","d","c"}},config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","b","c","d"}, new String[]{"a","u"} },config,converter);
    Map<Trace, MarkovOutcome> matrix = m.createMarkovLearner(plusStrings, minusStrings,false);
    Assert.assertEquals(11,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[]{lblD,lblC}),true)));
View Full Code Here
632
633
634
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
  /** This one is similar to the {@link #testUpdateMarkovSideways1b}, except that there are a few additional negative transitions and the computation is forward. */
  @Test
  public void testUpdateMarkovSideways1d()
  {
    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
663
664
665
666
667
668
669
670
671
672
673
674
675
  @Test
  public void testUpdateMarkovSideways2()
  {
    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
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
 
  @Test
  public void testUpdateMarkovSideways3()
  {
    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
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
 
  @Test
  public void testUpdateMarkovSideways4()
  {
    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
715
716
717
718
719
720
721
722
723
 
  @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.predictTransitionsAndUpdateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());Assert.assertTrue(m.getMarkov(false).isEmpty());
  }
View Full Code Here
TOP

Related Classes of statechum.analysis.learning.MarkovUniversalLearner$UpdatableOutcome

  • 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
    • 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.