Examples of HiddenMarkovModel

Examples of aima.core.probability.hmm.HiddenMarkovModel

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  public static void forwardBackWardDemo() {

    System.out.println("\nForward BackWard Demo\n");

    HiddenMarkovModel rainmanHmm = HMMFactory.createRainmanHMM();
    System.out
        .println("Creating a Hdden Markov Model to represent the model in Fig 15.5 ");
    List<String> perceptions = new ArrayList<String>();
    perceptions.add(HmmConstants.SEE_UMBRELLA);
    perceptions.add(HmmConstants.SEE_UMBRELLA);

    List<VarDistribution> results = rainmanHmm
        .forward_backward(perceptions);

    VarDistribution smoothedDayOne = results.get(1);
    System.out.println("Smoothed Probability Of Raining on Day One = "
        + smoothedDayOne.getProbabilityOf(HmmConstants.RAINING));
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Examples of aima.core.probability.hmm.HiddenMarkovModel

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  }

  public static void particleFilterinfDemo() {
    System.out.println("\nParticle Filtering Demo\n");
    HiddenMarkovModel rainman = HMMFactory.createRainmanHMM();
    Randomizer r = new JavaRandomizer();
    ParticleSet starting = rainman.prior().toParticleSet(rainman, r, 1000);
    System.out.println("at the beginning, "
        + starting.numberOfParticlesWithState(HmmConstants.RAINING)
        + " particles 0f 1000 indicate status == 'raining' ");
    System.out.println("at the beginning, "
        + starting.numberOfParticlesWithState(HmmConstants.NOT_RAINING)
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Examples of com.aliasi.hmm.HiddenMarkovModel

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      //*-- read the POS Tagger model and generate the decoder
      String modelFile = MUSTRU_HOME + File.separator + "data" + File.separator + "training" + File.separator + "pos" + File.separator + "pos_tagger";
      logger.info("Reading POS tagger model from " + modelFile);
      long startReadTime = new Date().getTime();
      ObjectInputStream oi = new ObjectInputStream( new FileInputStream(modelFile) );
      HiddenMarkovModel hmm = (HiddenMarkovModel) oi.readObject();
      oi.close();
      long readTime = new Date().getTime() - startReadTime;
      System.out.println("Time to read model " + readTime + " msecs.");
      HmmDecoder decoder = new HmmDecoder(hmm);
     
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Examples of com.aliasi.hmm.HiddenMarkovModel

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   {
    logger.info("Reading POS tagger model from " + Constants.POS_TAGGER_MODEL);
    ObjectInputStream oi = null;
    try
    { oi = new ObjectInputStream( new FileInputStream(Constants.POS_TAGGER_MODEL) );
    HiddenMarkovModel hmm = (HiddenMarkovModel) oi.readObject();
    decoder = new HmmDecoder(hmm);
    setTagPosXref();
    }
    catch (IOException ie ) { logger.error("setforPOS IO Error : could not read " + ie.getMessage() ); }
    catch (ClassNotFoundException ce) { logger.error("setforPOS Class Error : " + ce.getMessage() ); }
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Examples of com.aliasi.hmm.HiddenMarkovModel

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   * @throws IOException The file was not found or could not be read.
   */
  private void setTaggerModelFile(File taggerModelFile) throws IOException {

    FileInputStream fileIn;
    HiddenMarkovModel hmm;

    try {
      fileIn = new FileInputStream(taggerModelFile);
      ObjectInputStream objIn = new ObjectInputStream(fileIn);
      hmm = (HiddenMarkovModel) objIn.readObject();
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Examples of org.encog.ml.hmm.HiddenMarkovModel

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    double [][] covariance1 = { {1, 2}, {1, 4} };
   
    double [] mean2 = {0.5, 0.25};
    double [][] covariance2 = { {4, 2}, {3, 4} };
   
    HiddenMarkovModel hmm = new HiddenMarkovModel(2);
   
    hmm.setPi(0, 0.8);
    hmm.setPi(1, 0.2);
   
    hmm.setStateDistribution(0, new ContinousDistribution(mean1,covariance1));
    hmm.setStateDistribution(1, new ContinousDistribution(mean2,covariance2));
   
    hmm.setTransitionProbability(0, 1, 0.05);
    hmm.setTransitionProbability(0, 0, 0.95);
    hmm.setTransitionProbability(1, 0, 0.10);
    hmm.setTransitionProbability(1, 1, 0.90);
   
    return hmm;
  }
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Examples of org.encog.ml.hmm.HiddenMarkovModel

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    return hmm;
  }
 
  static HiddenMarkovModel buildDiscHMM()
  { 
    HiddenMarkovModel hmm =
      new HiddenMarkovModel(2, 2);
   
    hmm.setPi(0, 0.95);
    hmm.setPi(1, 0.05);
   
    hmm.setStateDistribution(0, new DiscreteDistribution(new double[][] { { 0.95, 0.05 } }));
    hmm.setStateDistribution(1, new DiscreteDistribution(new double[][] { { 0.20, 0.80 } }));
   
    hmm.setTransitionProbability(0, 1, 0.05);
    hmm.setTransitionProbability(0, 0, 0.95);
    hmm.setTransitionProbability(1, 0, 0.10);
    hmm.setTransitionProbability(1, 1, 0.90);
   
    return hmm;
  }
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Examples of org.encog.ml.hmm.HiddenMarkovModel

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      Assert.assertTrue(e<0.01);
  }
 
  public void testDiscPersistEG()
  {
    HiddenMarkovModel sourceHMM = buildDiscHMM();

    EncogDirectoryPersistence.saveObject(EG_FILENAME, sourceHMM);
    HiddenMarkovModel resultHMM = (HiddenMarkovModel)EncogDirectoryPersistence.loadObject(EG_FILENAME);

    validate(resultHMM,sourceHMM);
  }
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Examples of org.encog.ml.hmm.HiddenMarkovModel

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    validate(resultHMM,sourceHMM);
  }
 
  public void testDiscPersistSerial() throws IOException, ClassNotFoundException
  {
    HiddenMarkovModel sourceHMM = buildDiscHMM();
   
    SerializeObject.save(SERIAL_FILENAME, sourceHMM);
    HiddenMarkovModel resultHMM = (HiddenMarkovModel)SerializeObject.load(SERIAL_FILENAME);
       
    validate(resultHMM,sourceHMM);
  }
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Examples of org.encog.ml.hmm.HiddenMarkovModel

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    validate(resultHMM,sourceHMM);
  }
 
  public void testContPersistEG()
  {
    HiddenMarkovModel sourceHMM = buildContHMM();

    EncogDirectoryPersistence.saveObject(EG_FILENAME, sourceHMM);
    HiddenMarkovModel resultHMM = (HiddenMarkovModel)EncogDirectoryPersistence.loadObject(EG_FILENAME);

    validate(resultHMM,sourceHMM);
  }
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