Examples of computePredictionMatrix()

Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testPredictTransitionsFromStatesSideways3()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertEquals(9,m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / T-a->T-u->T-b->T-c->T","testPredictTransitionsFromStatesSideways3",config, converter);
    LearnerGraph extendedGraph = new MarkovClassifier(m,graph2).constructMarkovTentative();
    Assert.assertNull(WMethod.checkM(FsmParser.buildLearnerGraph("A-a->B / A-c->F","testPredictTransitionsFromStatesForward3",config, converter), extendedGraph));// FSM comparison ignores unreachable states here
    Assert.assertTrue(extendedGraph.findVertex("T") != null);// extended graph starts as a replica of an original one.
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState1()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A / T-u->T-b->T","testCheckFanoutInconsistencySideways4",config, converter);// T is there to ensure that graph2's alphabet is the same as that of graph.
    Map<Label, MarkovOutcome> predictions = new MarkovClassifier(m, graph2).predictTransitionsFromState(graph2.getInit(),Arrays.asList(new Label[]{}),m.getChunkLen(),null);
   
    Assert.assertEquals(MarkovOutcome.positive,predictions.get(lblU));
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState2()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    final MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B","testPredictTransitionsFromStatesWithPathBeyondCurrentState2",config, converter);
   
    Helper.checkForCorrectException(new whatToRun() {
      @Override
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState3()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertEquals(4,m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Map<Label,MarkovOutcome> outgoing_labels_probabilities=new MarkovClassifier(m, graph2).predictTransitionsFromState(graph2.findVertex("B"),Arrays.asList(new Label[]{}),m.getChunkLen(),null);
    Assert.assertEquals(2,outgoing_labels_probabilities.size());
    Assert.assertEquals(MarkovOutcome.negative,outgoing_labels_probabilities.get(lblU));
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState4()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertEquals(4,m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-d->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Map<Label,MarkovOutcome> outgoing_labels_probabilities=new MarkovClassifier(m, graph2).predictTransitionsFromState(graph2.findVertex("B"),Arrays.asList(new Label[]{lblA}),m.getChunkLen(),null);
    Assert.assertEquals(2,outgoing_labels_probabilities.size());
    Assert.assertEquals(MarkovOutcome.negative,outgoing_labels_probabilities.get(lblU));
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testPredictTransitionsFromStatesWithPathBeyondCurrentState5()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    final MarkovModel m = new MarkovModel(2,true,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertEquals(4,m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-d->B / A-c->A/ T-u->T-b->T","testPredictTransitionsFromStatesForward2",config, converter);
    Helper.checkForCorrectException(new whatToRun() {
      @Override
      public void run() throws NumberFormatException
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testCheckFanoutInconsistencySideways1_s1()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-s->B","testCheckFanoutInconsistencySideways1",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(),new MarkovClassifier.ConsistencyChecker(){
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testCheckFanoutInconsistencySideways1_s2()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-s->B","testCheckFanoutInconsistencySideways1",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(),new MarkovClassifier.ConsistencyChecker(){
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testCheckFanoutInconsistencySideways1_a()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B","testCheckFanoutInconsistencySideways1",config, converter);
    final AtomicInteger counterA=new AtomicInteger(0),counterB=new AtomicInteger(0);
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(),new MarkovClassifier.ConsistencyChecker(){
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Examples of statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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  public void testCheckFanoutInconsistencySideways2()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-b->C / B-u-#D / A-c->E-u->F / E-c->G","testUpdateMarkovSideways3",config, converter);
    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(9+graph.getCache().getAlphabet().size(),m.computePredictionMatrix().size());
   
    final LearnerGraph graph2 = FsmParser.buildLearnerGraph("A-a->B-a->B-b->B","testCheckFanoutInconsistencySideways2",config, converter);
    graph2.transitionMatrix.get(graph2.getInit()).clear();// make it look like a graph has no transitions
    Assert.assertEquals(0,new MarkovClassifier(m, graph2).checkFanoutInconsistency(graph2.getInit(), new MarkovClassifier.ConsistencyChecker(){
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