List of computePredictionMatrix() Examples

Examples of computePredictionMatrix()

statechum.analysis.learning.MarkovModel.computePredictionMatrix()

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

    m.createMarkovLearner(plusStrings, minusStrings,true);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);
    new MarkovClassifier(mOther,graph).updateMarkov(true);
    Assert.assertEquals(m.computePredictionMatrix(),mOther.computePredictionMatrix());
    Assert.assertEquals(m.computeOccurrenceMatrix(),mOther.computeOccurrenceMatrix());
  }
    
  /** Tests that creating a model from PTA and from initial traces gives almost the same result. The difference is in PTA-based construction mis-counting the number of times shorter traces occur since it
   * can see that they exist but not the number of tails they lead to. This is left in because I do not use specific values occurrence counts.

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


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);
    new MarkovClassifier(mOther,graph).updateMarkov(false);
    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})));

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

    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);
    new MarkovClassifier(mOther,graph).updateMarkov(false);
    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})));

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

    Set<List<Label>> plusStrings = buildSet(new String[][] { new String[]{"a","b"},new String[]{"c","b"},new String[]{"c","u"} },config,converter), minusStrings = buildSet(new String[][] {},config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,true);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);new MarkovClassifier(mOther,graph).updateMarkov(true);
    Assert.assertEquals(m.computePredictionMatrix(),mOther.computePredictionMatrix());
    Assert.assertEquals(m.computeOccurrenceMatrix(),mOther.computeOccurrenceMatrix());
  }
  
  /** Tests that creating a model from PTA and from initial traces give the same result. */
  @Test

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

    Set<List<Label>> plusStrings = buildSet(new String[][] {},config,converter), minusStrings = buildSet(new String[][] { new String[]{"a","u"} },config,converter);
    m.createMarkovLearner(plusStrings, minusStrings,true);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);new MarkovClassifier(mOther,graph).updateMarkov(true);
    Assert.assertEquals(m.computePredictionMatrix(),mOther.computePredictionMatrix());
    Assert.assertEquals(m.computeOccurrenceMatrix(),mOther.computeOccurrenceMatrix());
  }
  
  @Test
  /** Tests that creating a model from PTA and from initial traces give the same result. */

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

    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,true);


    final LearnerGraph graph = new LearnerGraph(config);graph.paths.augmentPTA(plusStrings, true, false);graph.paths.augmentPTA(minusStrings, false, false);
    MarkovModel mOther = new MarkovModel(2,true,true);new MarkovClassifier(mOther,graph).updateMarkov(true);
    Assert.assertEquals(m.computePredictionMatrix(),mOther.computePredictionMatrix());
    Assert.assertEquals(m.computeOccurrenceMatrix(),mOther.computeOccurrenceMatrix());
  }
  
  @Test
  public void testUpdateMarkovSideways1a()

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

  public void testUpdateMarkovSideways1a()
  {
    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);
    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})));

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

  public void testUpdateMarkovSideways1b()
  {
    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})));

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

  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C-a-#D / B-c-#D","testUpdateMarkovSideways1c",config, converter);


    MarkovModel m = new MarkovModel(2,false,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Map<List<Label>,MarkovOutcome> mPredictionsMatrix = m.computePredictionMatrix();
    Assert.assertEquals(9,mPredictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblA,lblA})));
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblA,lblB})));
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblB,lblA})));
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblB,lblB})));

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

  public void testUpdateMarkovSideways1d()
  {
    final LearnerGraph graph = FsmParser.buildLearnerGraph("A-a->B-a->C / B-b->C-a-#D / B-c-#D","testUpdateMarkovSideways1c",config, converter);
    MarkovModel m = new MarkovModel(2,true,true);
    new MarkovClassifier(m,graph).updateMarkov(false);
    Map<List<Label>,MarkovOutcome> mPredictionsMatrix = m.computePredictionMatrix();
    Assert.assertEquals(7,mPredictionsMatrix.size());
    Assert.assertEquals(MarkovOutcome.failure,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblA,lblA})));
    Assert.assertEquals(MarkovOutcome.positive,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblA,lblB})));
    Assert.assertEquals(MarkovOutcome.negative,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblA,lblC})));
    Assert.assertEquals(MarkovOutcome.negative,mPredictionsMatrix.get(Arrays.asList(new Label[]{lblB,lblA})));

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