Examples of KullbackLeiblerDistanceCalculator

be.ac.ulg.montefiore.run.jahmm.toolbox.KullbackLeiblerDistanceCalculator
Computes the distance between HMMs.
The distance metric is similar to the Kullback-Leibler distance defined on distributions. More information can be found in A Probabilistic Distance Measure For HMMs by Rabiner and Juang (AT&T Technical Journal, vol. 64, Feb. 1985, pages 391-408).
This distance measure is not symetric: distance(hmm1, hmm2) is not necessary equal to distance(hmm2, hmm1). To get a symetric distance definition, compute (distance(hmm1, hmm2) + distance(hmm2, hmm1)) / 2.
org.encog.ml.hmm.alog.KullbackLeiblerDistanceCalculator
This class produces a Kullback-Leibler estimation of the distance between two HMMs. This allows the similarity of two different HMM's to be evaluated. ^ Kullback, S.; Leibler, R.A. (1951). "On Information and Sufficiency". Annals of Mathematical Statistics 22 (1): 79-86. doi:10.1214/aoms/1177729694. MR39968.

Examples of be.ac.ulg.montefiore.run.jahmm.toolbox.KullbackLeiblerDistanceCalculator

    
    sequences = new ArrayList<List<ObservationInteger>>();    
    for (int i = 0; i < 100; i++)
      sequences.add(mg.observationSequence(100));
    
    klc = new KullbackLeiblerDistanceCalculator();
  }

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Examples of be.ac.ulg.montefiore.run.jahmm.toolbox.KullbackLeiblerDistanceCalculator

    BaumWelchLearner bwl = new BaumWelchLearner();
    
    Hmm<ObservationDiscrete<Packet>> learntHmm = buildInitHmm();
    
    // This object measures the distance between two HMMs
    KullbackLeiblerDistanceCalculator klc = 
      new KullbackLeiblerDistanceCalculator();
    
    // Incrementally improve the solution
    for (int i = 0; i < 10; i++) {
      System.out.println("Distance at iteration " + i + ": " +
          klc.distance(learntHmm, hmm));
      learntHmm = bwl.iterate(learntHmm, sequences);
    }
    
    System.out.println("Resulting HMM:\n" + learntHmm);

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Examples of be.ac.ulg.montefiore.run.jahmm.toolbox.KullbackLeiblerDistanceCalculator

  throws IOException, FileFormatException
  {
    Hmm<O> hmm1 = HmmReader.read(reader1, relatedObjs.opdfReader());
    Hmm<O> hmm2 = HmmReader.read(reader2, relatedObjs.opdfReader());
    
    KullbackLeiblerDistanceCalculator kl = 
      new KullbackLeiblerDistanceCalculator();
    System.out.println(kl.distance(hmm1, hmm2));
  }

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Examples of org.encog.ml.hmm.alog.KullbackLeiblerDistanceCalculator

    return hmm;
  }
  
  public void validate(HiddenMarkovModel result, HiddenMarkovModel source)
  {
    KullbackLeiblerDistanceCalculator klc = 
        new KullbackLeiblerDistanceCalculator();
          
      double e = klc.distance(result, source);
      Assert.assertTrue(e<0.01);
  }

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Examples of org.encog.ml.hmm.alog.KullbackLeiblerDistanceCalculator

    MarkovGenerator mg = new MarkovGenerator(hmm);
    MLSequenceSet training = mg.generateSequences(200,100);
    
    TrainBaumWelch bwl = new TrainBaumWelch(learntHmm,training);
    
    KullbackLeiblerDistanceCalculator klc = 
      new KullbackLeiblerDistanceCalculator();
    
    bwl.iteration(5);
    
    learntHmm = (HiddenMarkovModel)bwl.getMethod();
    
    double e = klc.distance(learntHmm, hmm);
    Assert.assertTrue(e<0.01);
  }

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Examples of org.encog.ml.hmm.alog.KullbackLeiblerDistanceCalculator

    MarkovGenerator mg = new MarkovGenerator(hmm);
    MLSequenceSet training = mg.generateSequences(200,100);
    
    TrainBaumWelch bwl = new TrainBaumWelch(learntHmm,training);
    
    KullbackLeiblerDistanceCalculator klc = 
      new KullbackLeiblerDistanceCalculator();
    
    bwl.iteration(5);
    learntHmm = (HiddenMarkovModel)bwl.getMethod();
    
    double e = klc.distance(learntHmm, hmm);
    Assert.assertTrue(e<0.01);
  }

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Examples of org.encog.ml.hmm.alog.KullbackLeiblerDistanceCalculator

    
    TrainKMeans trainer = new TrainKMeans(hmm,sequences);
    
    HiddenMarkovModel learntHmm = buildDiscInitHMM();
    
    KullbackLeiblerDistanceCalculator klc = 
      new KullbackLeiblerDistanceCalculator();
    
    trainer.iteration(5);
    learntHmm = (HiddenMarkovModel)trainer.getMethod();
    double e = klc.distance(learntHmm, hmm);
    Assert.assertTrue(e<0.05);
  }

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