List of constructMarkovTentative() Examples

Examples of constructMarkovTentative()

statechum.analysis.learning.MarkovClassifier.constructMarkovTentative()
This function is predicts transitions from each state and then adds them to the supplied graph. Uses predictions from the model to add transitions without a second thought.
Can only be used to predict transition forward since inverse expects disconnected states to be added and QSM is not expected to deal with such states. We could certainly attempt to merge them somewhere but this seems to make little sense.
- Where predictForward is true, we are predicting transitions based on paths leading to the state of interest. Parameter Inverse_Graph should be the (non-deterministic) inverse of graph.
- Where predictForward is false, we are predicting transitions based on paths leading from the state of interest (sideways predictions). Parameter Inverse_Graph should be the same as graph.
directionForwardOrInverse determines whether to merge states identified with the supplied outgoing transitions or those that the supplied transitions lead into. For instance, one might frequently have a reset transition and all its target states could be merged together. @return the graph with predicted transitions added to it.
statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()
This function is predicts transitions from each state and then adds them to the supplied graph.
- Where predictForward is true, we are predicting transitions based on paths leading to the state of interest. Parameter Inverse_Graph should be the (non-deterministic) inverse of graph.
- Where predictForward is false, we are predicting transitions based on paths leading from the state of interest (sideways predictions). Parameter Inverse_Graph should be the same as graph.
@param tentativeAutomaton tentative Automaton @return a list of possible of outgoing transitions from each state

Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

    m.predictTransitionsAndUpdateMarkov(graph,true,true);
    Assert.assertEquals(4,m.getMarkov(true).size());
    Assert.assertTrue(m.getMarkov(false).isEmpty());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A/ T-a->T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    m.constructMarkovTentative(graph2, true);
    Assert.assertNull(WMethod.checkM(FsmParser.buildLearnerGraph("A-a->B / A-c->A / A-u->E / B-b->C / B-u-#D","testPredictTransitionsFromStatesForward3",config, converter), m.get_extension_model()));
    Assert.assertTrue(m.get_extension_model().findVertex("T") != null);// extended graph starts as a replica of an original one.
  }


  /** Here the alphabet is limited to what is an the tentative automaton, hence nothing is added. */

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B","testCheckFanoutInconsistencySideways4",config, converter);
    m.constructMarkovTentative(graph2, false);
    Assert.assertNull(WMethod.checkM(graph2,m.get_extension_model()));
  }
  
  /** Tests that upon a label labelled as invalid, subsequent inconsistency checks are stopped. It is hence equivalent to a single incoming path. */
  @Test

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

    m.predictTransitionsAndUpdateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());
    Assert.assertEquals(9,m.getMarkov(false).size());
    
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / T-a->T-u->T-b->T-c->T","testPredictTransitionsFromStatesSideways3",config, converter);
    m.constructMarkovTentative(graph2, false);
    Assert.assertNull(WMethod.checkM(FsmParser.buildLearnerGraph("A-a->B / A-c->F","testPredictTransitionsFromStatesForward3",config, converter), m.get_extension_model()));// FSM comparison ignores unreachable states here
    Assert.assertTrue(m.get_extension_model().findVertex("T") != null);// extended graph starts as a replica of an original one.
  }


  /** Same as {@link #testPredictTransitionsFromStatesSideways1()}, except that the path beyond is empty rather than null. */

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","p"} },config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,false);
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-u->B-p->B","testConstructExtendedGraph1",config, converter);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    Assert.assertTrue(newTransitions.isEmpty());// not enough evidence to update, hence nothing should be recorded.
    final LearnerGraph expected = FsmParser.buildLearnerGraph("A-u->B-p->B","testConstructExtendedGraph1",config, converter);
    DifferentFSMException ex = WMethod.checkM(expected, m.get_extension_model());
    if (ex != null)
      throw ex;

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","p"} },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","testConstructExtendedGraph2",config, converter);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    Assert.assertTrue(newTransitions.isEmpty());// not enough evidence to update, hence nothing should be recorded.
    final LearnerGraph expected = FsmParser.buildLearnerGraph("A-a->B","testConstructExtendedGraph2",config, converter);
    DifferentFSMException ex = WMethod.checkM(expected, m.get_extension_model());
    if (ex != null)
      throw ex;

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"} },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 / T-b->T-u->T","testConstructExtendedGraph3a",config, converter);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    Assert.assertEquals(1,newTransitions.size());// not enough evidence to update, hence nothing should be recorded.


    Assert.assertSame(MarkovOutcome.negative, newTransitions.get(graph.findVertex("B")).get(lblU));
    
    Assert.assertSame(MarkovOutcome.positive, newTransitions.get(graph.findVertex("B")).get(lblB));

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    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 / T-b->T-u->T","testConstructExtendedGraph3a",config, converter);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    Assert.assertEquals(1,newTransitions.size());// not enough evidence to update, hence nothing should be recorded.


    Assert.assertFalse(newTransitions.get(graph.findVertex("B")).containsKey(lblU));// failure ignored
    
    Assert.assertSame(MarkovOutcome.positive, newTransitions.get(graph.findVertex("B")).get(lblB));

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

  {
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);// w below is to ensure that all elements of the alphabet are included in traces.
    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","u"},new String[]{"w"} },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-w->M-c->B / T-b->T-u->T","testConstructExtendedGraph5a",config, converter);// the purpose of the w-transition is to ensure transition c is taken into account in graph comparison
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    Assert.assertEquals(1,newTransitions.size());


    Assert.assertEquals(1,newTransitions.get(graph.findVertex("B")).size());
    
    Assert.assertSame(MarkovOutcome.positive,newTransitions.get(graph.findVertex("B")).get(lblB));

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

  {
    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 / T-b->T-u->T","testConstructExtendedGraph6a",config, converter);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    
    Assert.assertEquals(1,newTransitions.size());


    Assert.assertEquals(1,newTransitions.get(graph.findVertex("B")).size());

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Examples of statechum.analysis.learning.MarkovUniversalLearner.constructMarkovTentative()

  {
    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-c->Z / T-b->T-u->T","testConstructExtendedGraph7a",config, converter);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = m.constructMarkovTentative(graph,true);
    
    Assert.assertEquals(2,newTransitions.size());


    Assert.assertEquals(1,newTransitions.get(graph.findVertex("B")).size());
    Assert.assertEquals(1,newTransitions.get(graph.findVertex("Z")).size());

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