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

  {
    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 constructMarkovTentative()

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