List of predictTransitions() Examples

Examples of predictTransitions()

statechum.analysis.learning.MarkovClassifier.predictTransitions()
This function is predicts transitions from each state.
- 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.
@return a list of possible of outgoing transitions from each state
statechum.analysis.learning.MarkovUniversalLearner.predictTransitions()
This function is predicts transitions from each state.
- 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.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    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);
    LearnerGraph actual = cl.constructMarkovTentative();
    DifferentFSMException ex = WMethod.checkM(expected, actual);
    if (ex != null)

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Examples of statechum.analysis.learning.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    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);
    LearnerGraph actual = cl.constructMarkovTentative();
    DifferentFSMException ex = WMethod.checkM(expected, actual);
    if (ex != null)

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Examples of statechum.analysis.learning.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    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.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    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.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);// 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
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    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.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    
    Assert.assertEquals(1,newTransitions.size());


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

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Examples of statechum.analysis.learning.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    
    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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Examples of statechum.analysis.learning.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    
    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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Examples of statechum.analysis.learning.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    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);
    LearnerGraph actual = cl.constructMarkovTentative();
    DifferentFSMException ex = WMethod.checkM(expected, actual);
    if (ex != null)

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Examples of statechum.analysis.learning.MarkovClassifier.predictTransitions()

    MarkovModel m = new MarkovModel(2,true,true);
    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);
    MarkovClassifier cl = new MarkovClassifier(m,graph);
    Map<CmpVertex, Map<Label, MarkovOutcome>> newTransitions = cl.predictTransitions();
    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);
    LearnerGraph actual = cl.constructMarkovTentative();
    DifferentFSMException ex = WMethod.checkM(expected, actual);
    if (ex != null)

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