List of evaluateCorrectnessOfMarkov() Examples

Examples of evaluateCorrectnessOfMarkov()

statechum.analysis.learning.MarkovClassifier.evaluateCorrectnessOfMarkov()
Given that this classified is instantiate with a reference graph, determines the ration of correct predictions by this classifier to the total number of predictions. @return fraction of Markov's predictions that are correct.

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

  public void testMarkovPerformance1()
  {
    final LearnerGraph trainingGraph = 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);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    statechum.Pair<Double,Double> pairTraining = cl.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(2./3,pairTraining.firstElem,Configuration.fpAccuracy);// reflects that transitions u and c from G are not present but predicted
    Assert.assertEquals(2./3.,pairTraining.secondElem,Configuration.fpAccuracy);// reflects that transitions a and c are not predicted but present.
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-t->B","testMarkovPerformance1a",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();

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

    statechum.Pair<Double,Double> pairTraining = cl.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(2./3,pairTraining.firstElem,Configuration.fpAccuracy);// reflects that transitions u and c from G are not present but predicted
    Assert.assertEquals(2./3.,pairTraining.secondElem,Configuration.fpAccuracy);// reflects that transitions a and c are not predicted but present.
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-t->B","testMarkovPerformance1a",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(0,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0,pair.secondElem,Configuration.fpAccuracy);


    MarkovClassifier evalB = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B","testMarkovPerformance1b",config, converter));
    statechum.Pair<Double,Double> pairB = evalB.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(0,pairB.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0,pairB.secondElem,Configuration.fpAccuracy);

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

    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-t->B","testMarkovPerformance1a",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(0,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0,pair.secondElem,Configuration.fpAccuracy);


    MarkovClassifier evalB = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B","testMarkovPerformance1b",config, converter));
    statechum.Pair<Double,Double> pairB = evalB.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(0,pairB.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0,pairB.secondElem,Configuration.fpAccuracy);
  }
  
  @Test
  public void testMarkovPerformance2()

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

    final LearnerGraph trainingGraph = 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);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-u-#D / B-b->G","testMarkovPerformance2",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(1,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(2./3,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted
  }
  
  @Test
  public void testMarkovPerformance3()

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

    final LearnerGraph trainingGraph = 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);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-u-#D / B-b->G / B-e->Z","testMarkovPerformance3",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(1,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0.5,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted
  }
  
  @Test
  public void testMarkovPerformance4()

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

    final LearnerGraph trainingGraph = 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);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-b->C-c->D-u->E","testMarkovPerformance4",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(3./4,pair.firstElem,Configuration.fpAccuracy);// u is predicted as negative and is indeed missing, b is correctly predicted as a positive; u after c is correctly predicted as positive and c after c is not correctly predicted.
    Assert.assertEquals(0.5,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted
  }
  
  @Test

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

    final LearnerGraph trainingGraph = 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);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B-b->G","testMarkovPerformance5",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(1,pair.firstElem,Configuration.fpAccuracy);// u is predicted as negative and is indeed missing, b is correctly predicted as a positive
    Assert.assertEquals(0.5,pair.secondElem,Configuration.fpAccuracy);// transition a is not predicted
  }
  
}

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

  public void testMarkovPerformance1()
  {
    final LearnerGraph trainingGraph = 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);
    MarkovClassifier cl=new MarkovClassifier(m,trainingGraph);cl.updateMarkov(false);
    statechum.Pair<Double,Double> pairTraining = cl.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(2./3,pairTraining.firstElem,Configuration.fpAccuracy);// reflects that transitions u and c from G are not present but predicted
    Assert.assertEquals(2./3.,pairTraining.secondElem,Configuration.fpAccuracy);// reflects that transitions a and c are not predicted but present.
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-t->B","testMarkovPerformance1a",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();

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

    statechum.Pair<Double,Double> pairTraining = cl.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(2./3,pairTraining.firstElem,Configuration.fpAccuracy);// reflects that transitions u and c from G are not present but predicted
    Assert.assertEquals(2./3.,pairTraining.secondElem,Configuration.fpAccuracy);// reflects that transitions a and c are not predicted but present.
    
    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-t->B","testMarkovPerformance1a",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(0,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0,pair.secondElem,Configuration.fpAccuracy);


    MarkovClassifier evalB = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B","testMarkovPerformance1b",config, converter));
    statechum.Pair<Double,Double> pairB = evalB.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(0,pairB.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0,pairB.secondElem,Configuration.fpAccuracy);

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

    MarkovClassifier eval = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-t->B","testMarkovPerformance1a",config, converter));
    statechum.Pair<Double,Double> pair = eval.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(0,pair.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0,pair.secondElem,Configuration.fpAccuracy);


    MarkovClassifier evalB = new MarkovClassifier(m,FsmParser.buildLearnerGraph("A-a->B","testMarkovPerformance1b",config, converter));
    statechum.Pair<Double,Double> pairB = evalB.evaluateCorrectnessOfMarkov();
    Assert.assertEquals(0,pairB.firstElem,Configuration.fpAccuracy);Assert.assertEquals(0,pairB.secondElem,Configuration.fpAccuracy);
  }
  
  @Test
  public void testMarkovPerformance2()

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