Examples of UpdatablePairInteger

Examples of statechum.analysis.learning.MarkovModel.UpdatablePairInteger

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    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C","testUpdateMarkovSideways1",config, converter);
    MarkovModel m = new MarkovModel(2,false,true,markovPTAUseMatrix);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Map<List<Label>,UpdatablePairInteger> mOccurrenceMatrix = m.computeOccurrenceMatrix();Map<List<Label>,MarkovOutcome> mPredictionsMatrix = m.computePredictionMatrix();
    Assert.assertEquals(6,mPredictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblA,lblA})));Assert.assertEquals(new UpdatablePairInteger(2, 0),mOccurrenceMatrix.get(Arrays.asList(new Label[]{lblA,lblA})));
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblA,lblB})));Assert.assertEquals(new UpdatablePairInteger(1, 0),mOccurrenceMatrix.get(Arrays.asList(new Label[]{lblA,lblB})));
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblB,lblA})));Assert.assertEquals(new UpdatablePairInteger(1, 0),mOccurrenceMatrix.get(Arrays.asList(new Label[]{lblB,lblA})));
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblB,lblB})));Assert.assertEquals(new UpdatablePairInteger(1, 0),mOccurrenceMatrix.get(Arrays.asList(new Label[]{lblB,lblB})));

    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblA})));Assert.assertEquals(new UpdatablePairInteger(2, 0),mOccurrenceMatrix.get(Arrays.asList(new Label[]{lblA})));
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblB})));Assert.assertEquals(new UpdatablePairInteger(1, 0),mOccurrenceMatrix.get(Arrays.asList(new Label[]{lblB})));
  }
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Examples of statechum.analysis.learning.MarkovModel.UpdatablePairInteger

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      {
        if(vert.isAccept() )
            {
            final Map<Label,UpdatablePairDouble> outgoing_labels_probabilities=new HashMap<Label,UpdatablePairDouble>();
            final Map<Label,UpdatablePairInteger> outgoing_labels_occurrences=new HashMap<Label,UpdatablePairInteger>();
            final UpdatablePairInteger sum=new UpdatablePairInteger(0,0);
            WalkThroughAllPathsOfSpecificLength(graphToUseForPrediction,vert,model.getPredictionLen(),new ForEachCollectionOfPaths()
            {
          @Override
          public void handlePath(List<Label> pathToNewState)
          {
              List<Label> partOfTraceUsedInMarkovPredictions=new ArrayList<Label>(pathToNewState.size());partOfTraceUsedInMarkovPredictions.addAll(pathToNewState);
              if (predictionGraphInverted)
                Collections.reverse(partOfTraceUsedInMarkovPredictions);
              Map<Label,PTASequenceEngine.Node> lastElementToPrediction = model.markovMatrix.getMapFromLabelsToPredictions(partOfTraceUsedInMarkovPredictions);
              for(Label label:allElementsOfAlphabet)
              {
                PredictionForSequence prediction = MarkovMatrixEngine.getPredictionIfExists(lastElementToPrediction,label);

                UpdatablePairInteger occurrence_of_label_predicted_form_Markov=prediction == null?null:prediction.occurrence;

                if(outgoing_labels_occurrences.containsKey(label))
                {
                  UpdatablePairInteger labels_occurence= outgoing_labels_occurrences.get(label);
                  sum.add(labels_occurence);
                  labels_occurence.add(occurrence_of_label_predicted_form_Markov);                      
                }
                else
                {
                  outgoing_labels_occurrences.put(label, occurrence_of_label_predicted_form_Markov);
                  sum.add(occurrence_of_label_predicted_form_Markov);
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Examples of statechum.analysis.learning.MarkovUniversalLearner.UpdatablePairInteger

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    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C","testUpdateMarkovSideways1",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.updateMarkov(graph,false,true);
    Assert.assertTrue(m.getMarkov(true).isEmpty());Assert.assertEquals(4,m.getMarkov(false).size());
    Assert.assertTrue(m.getOccurrence(true).isEmpty());Assert.assertEquals(4,m.getOccurrence(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));
  }
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Examples of statechum.analysis.learning.MarkovUniversalLearner.UpdatablePairInteger

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  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C","testUpdateMarkovSideways1",config, converter);
    MarkovUniversalLearner m = new MarkovUniversalLearner(2);
    m.updateMarkov(graph,false,false);
    Assert.assertTrue(m.getMarkov(true).isEmpty());Assert.assertEquals(6,m.getMarkov(false).size());
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));

    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.getMarkov(false).get(new Trace(Arrays.asList(new Label[]{lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.getOccurrence(false).get(new Trace(Arrays.asList(new Label[]{lblB}),true)));
  }
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