Examples of UpdatablePairInteger

Examples of statechum.analysis.learning.MarkovModel.UpdatablePairInteger

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    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Map<List<Label>,UpdatablePairInteger> mOccurrenceMatrix = m.computeOccurrenceMatrix();Map<List<Label>,MarkovOutcome> mPredictionsMatrix = m.computePredictionMatrix();
    Assert.assertEquals(4,mPredictionsMatrix.size());
    Assert.assertEquals(4,mOccurrenceMatrix.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})));
  }
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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);
    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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    MarkovModel mOther = new MarkovModel(2,true,true);
    new MarkovClassifier(mOther,graph).updateMarkov(false);
    Assert.assertEquals(m.predictionsMatrix,mOther.predictionsMatrix);
   
    // Workaround around a deficiency in the calculation of occurrences of prefixes by the PTA-based construction of Markov model.
    Assert.assertEquals(new UpdatablePairInteger(2, 0), m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
    Assert.assertEquals(new UpdatablePairInteger(1, 0), mOther.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA}),true)));
   
    m.occurrenceMatrix.remove(new Trace(Arrays.asList(new Label[]{lblA}),true));mOther.occurrenceMatrix.remove(new Trace(Arrays.asList(new Label[]{lblA}),true));
    Assert.assertEquals(m.occurrenceMatrix,mOther.occurrenceMatrix);
  }
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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);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Assert.assertEquals(4,m.predictionsMatrix.size());
    Assert.assertEquals(4,m.occurrenceMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));
  }
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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);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Assert.assertEquals(6,m.predictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(2, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblA,lblB}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblA}),true)));
    Assert.assertEquals(MarkovOutcome.positive,m.predictionsMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));Assert.assertEquals(new UpdatablePairInteger(1, 0),m.occurrenceMatrix.get(new Trace(Arrays.asList(new Label[]{lblB,lblB}),true)));

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

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    System.out.println("m is "+m.computePredictionMatrix());
    System.out.println("other is "+mOther.computePredictionMatrix());
    Assert.assertEquals(m.computePredictionMatrix(),mOther.computePredictionMatrix());
   
    // Workaround around a deficiency in the calculation of occurrences of prefixes by the PTA-based construction of Markov model.
    Assert.assertEquals(new UpdatablePairInteger(2, 0), m.computeOccurrenceMatrix().get(Arrays.asList(new Label[]{lblA})));
    Assert.assertEquals(new UpdatablePairInteger(1, 0), mOther.computeOccurrenceMatrix().get(Arrays.asList(new Label[]{lblA})));

    Map<List<Label>,UpdatablePairInteger> mOccurrenceMatrix = m.computeOccurrenceMatrix(), mOtherOccurrenceMatrix = mOther.computeOccurrenceMatrix();
    mOccurrenceMatrix.remove(Arrays.asList(new Label[]{lblA}));mOtherOccurrenceMatrix.remove(Arrays.asList(new Label[]{lblA}));
    Assert.assertEquals(mOccurrenceMatrix,mOtherOccurrenceMatrix);
  }
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Examples of statechum.analysis.learning.MarkovModel.UpdatablePairInteger

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    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Map<List<Label>,UpdatablePairInteger> mOccurrenceMatrix = m.computeOccurrenceMatrix();Map<List<Label>,MarkovOutcome> mPredictionsMatrix = m.computePredictionMatrix();
    Assert.assertEquals(4,mPredictionsMatrix.size());
    Assert.assertEquals(4,mOccurrenceMatrix.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})));
  }
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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);
    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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    MarkovModel mOther = new MarkovModel(2,true,true,markovPTAUseMatrix);
    new MarkovClassifier(mOther,graph).updateMarkov(false);
    Assert.assertEquals(m.computePredictionMatrix(),mOther.computePredictionMatrix());
   
    // Workaround around a deficiency in the calculation of occurrences of prefixes by the PTA-based construction of Markov model.
    Assert.assertEquals(new UpdatablePairInteger(2, 0), m.computeOccurrenceMatrix().get(Arrays.asList(new Label[]{lblA})));
    Assert.assertEquals(new UpdatablePairInteger(1, 0), mOther.computeOccurrenceMatrix().get(Arrays.asList(new Label[]{lblA})));

    Map<List<Label>,UpdatablePairInteger> mOccurrenceMatrix = m.computeOccurrenceMatrix(), mOtherOccurrenceMatrix = mOther.computeOccurrenceMatrix();
    mOccurrenceMatrix.remove(Arrays.asList(new Label[]{lblA}));mOtherOccurrenceMatrix.remove(Arrays.asList(new Label[]{lblA}));
    Assert.assertEquals(mOccurrenceMatrix,mOtherOccurrenceMatrix);
  }
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Examples of statechum.analysis.learning.MarkovModel.UpdatablePairInteger

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    MarkovModel m = new MarkovModel(2,false,true,markovPTAUseMatrix);
    new MarkovClassifier(m,graph).updateMarkov(true);
    Map<List<Label>,UpdatablePairInteger> mOccurrenceMatrix = m.computeOccurrenceMatrix();Map<List<Label>,MarkovOutcome> mPredictionsMatrix = m.computePredictionMatrix();
    Assert.assertEquals(4,mPredictionsMatrix.size());
    Assert.assertEquals(4,mOccurrenceMatrix.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})));
  }
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