Examples of statechum.analysis.learning.rpnicore.LearnerGraphND

Package statechum.analysis.learning.rpnicore

Examples of statechum.analysis.learning.rpnicore.LearnerGraphND

statechum.analysis.learning.rpnicore.LearnerGraphND
This is a non-deterministic graph. Strictly speaking, all the methods here are applicable to the generalised graph too and thus could have been placed in AbstractPathRoutines. The reason they are here is that there does not seem to be much use in running such methods on deterministic graphs. In case one needs to do this, the relevant routines can be moved. @author kirill

  /** Tests non-deterministic case of {@link MarkovClassifier#tracePath}, deterministic case is tested with {@link TestPathTracing}. */
  @Test
  public void testTracePath3()
  {
    final LearnerGraph fsm = FsmParser.buildLearnerGraph("A-a->B-b->C", "testTracePath1",config,converter);
    LearnerGraphND ndFSM = new LearnerGraphND(fsm,config);
    synchronized(AbstractLearnerGraph.syncObj)
    {
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("A"), true, lblA),true,lblA),true,lblA),true,lblA);
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("A"), true, lblA),true,lblA),true,lblA);
    }
    Assert.assertEquals(10,ndFSM.getStateNumber());
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","b"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertFalse(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"a","a","a","a","a"}),config,converter),fsm.findVertex("A")));

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  /** Tests non-deterministic case of {@link MarkovClassifier#tracePath}, deterministic case is tested with {@link TestPathTracing}. */
  @Test
  public void testTracePath4()
  {
    final LearnerGraph fsm = FsmParser.buildLearnerGraph("A-t->B-b->C", "testTracePath1",config,converter);
    LearnerGraphND ndFSM = new LearnerGraphND(fsm,config);
    synchronized(AbstractLearnerGraph.syncObj)
    {
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),true,lblA),true,lblA),true,lblA);
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),true,lblA),true,lblC);
    }
    Assert.assertEquals(10,ndFSM.getStateNumber());
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","b"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","c"}),config,converter),fsm.findVertex("A")));

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  /** Tests non-deterministic case of {@link MarkovClassifier#tracePath}, deterministic case is tested with {@link TestPathTracing}. */
  @Test
  public void testTracePath5()
  {
    final LearnerGraph fsm = FsmParser.buildLearnerGraph("A-t->B-b->C", "testTracePath1",config,converter);
    LearnerGraphND ndFSM = new LearnerGraphND(fsm,config);
    CmpVertex Brej=null;
    synchronized(AbstractLearnerGraph.syncObj)
    {
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),true,lblA),true,lblA),true,lblA);
      ndFSM.addVertex(ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),true,lblA),false,lblC);
      Brej=ndFSM.addVertex(ndFSM.addVertex(fsm.findVertex("B"), true, lblA),false,lblB);
      ndFSM.addVertex(Brej,false,lblC);
    }
    Assert.assertEquals(13,ndFSM.getStateNumber());
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","b"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertTrue(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","a","a"}),config,converter),fsm.findVertex("A")));
    Assert.assertFalse(MarkovClassifier.tracePath(ndFSM,AbstractLearnerGraph.buildList(Arrays.asList(new String[]{"t","a","a","c"}),config,converter),fsm.findVertex("A")));

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    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);
    
    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(0,m.checkFanoutInconsistency(Inverse_Graph,true,graph,graph.findVertex("B"),m.getChunkLen(), new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)));
  }

View Full Code Here

    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);
    
    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

    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);
    
    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

    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)));
  }

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    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)));
  }

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    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

    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","testCheckFanoutInconsistency1f",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)));
    
    Assert.assertEquals(4.,MarkovUniversalLearner.computeInconsistency(graph, true, m, new MarkovUniversalLearner.DifferentPredictionsInconsistency(graph)),Configuration.fpAccuracy);// inconsistencies detected are mostly due to state T
  }

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