Examples of org.encog.ml.hmm.distributions.StateDistribution

org.encog.ml.hmm.distributions.StateDistribution
This class represents a "state distribution". This is the means by which the probabilities between the states and observations are mapped. Currently two are supported. Use ContinousDistribution to use a Gaussian-based continuous distribution. Use DiscreteDistribution for a item-based distribution.

    out.writeProperty(HiddenMarkovModel.TAG_PI,net.getPi());
    out.writeProperty(HiddenMarkovModel.TAG_TRANSITION,new Matrix(net.getTransitionProbability()));
    
    for( int i=0; i<net.getStateCount();i++) {
      out.addSubSection("DISTRIBUTION-"+i);  
      StateDistribution sd = net.getStateDistribution(i);
      out.writeProperty(HiddenMarkovModel.TAG_DIST_TYPE, sd.getClass().getSimpleName());
      
      if( sd instanceof ContinousDistribution ) {
        ContinousDistribution cDist = (ContinousDistribution)sd;
        out.writeProperty(HiddenMarkovModel.TAG_MEAN, cDist.getMean());
        out.writeProperty(HiddenMarkovModel.TAG_COVARIANCE, cDist.getCovariance());

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      for (j--; j >= 0; j--) {
        weights[j] /= sum;
      }


      final StateDistribution opdf = nhmm.getStateDistribution(i);
      opdf.fit(this.training, weights);
    }


    this.method = nhmm;
  }

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    for (int i = 0; i < hmm.getStateCount(); i++) {
      final Collection<MLDataPair> clusterObservations = this.clusters
          .cluster(i);


      if (clusterObservations.size() < 1) {
        final StateDistribution o = this.modelHMM
            .createNewDistribution();
        hmm.setStateDistribution(i, o);
      } else {
        final MLDataSet temp = new BasicMLDataSet();
        for (final MLDataPair pair : clusterObservations) {

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Related Classes of org.encog.ml.hmm.distributions.StateDistribution

org.encog.ml.hmm.PersistHMM

org.encog.ml.hmm.train.bw.BaseBaumWelch

org.encog.ml.hmm.train.kmeans.TrainKMeans

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