Examples of DiscreteDistribution

edu.cmu.graphchi.walks.distributions.DiscreteDistribution
Presents a map from integers to frequencies. Special distributions for avoidance can be used to exclude certain ids from the distributions in merges. @author Aapo Kyrola, akyrola@cs.cmu.edu
org.encog.ml.hmm.distributions.DiscreteDistribution
A discrete distribution is a distribution with a finite set of states that it can be in.
stallone.stat.DiscreteDistribution
@author noe
weka.classifiers.misc.monotone.DiscreteDistribution
This class represents a discrete probability distribution over a finite number of values.
In the present implementation, objects of type DiscreteDistribution are in fact immutable, so all one can do is create objects and retrieve information, such as median and mean, from them.

This implementation is part of the master's thesis: "Studie en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd rangschikken", Stijn Lievens, Ghent University, 2004.
@author Stijn Lievens (stijn.lievens@ugent.be) @version $Revision: 1.2 $

Examples of stallone.stat.DiscreteDistribution

    public MarkovChain(IDoubleArray _startingDistribution, IDoubleArray _T)
    {
        this.T = _T;
        dd = new DiscreteDistributions(_T);
        this.p0 = _startingDistribution;
        p0dist = new DiscreteDistribution(p0);
    }

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Examples of stallone.stat.DiscreteDistribution

     */
    public void setStartingDistribution(IDoubleArray _p0)
    {
        fixedStartingState = false;
        p0 = _p0;
        p0dist = new DiscreteDistribution(p0);
    }

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Examples of stallone.stat.DiscreteDistribution

        else
        {
            if (p0 == null)
            {
                p0 = msm.stationaryDistribution(T);
                p0dist = new DiscreteDistribution(p0);
            }
            return p0dist.sample();
        }
    }

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Examples of stallone.stat.DiscreteDistribution


        // save memory?
        boolean saveMemory = false;


        // output model and parametrizer
        DiscreteDistribution dd = new DiscreteDistribution(initialParameters.getOutputParameters(0));
        dd.setPrior(prior);
        EM em = new EM(_obs, eventBased, initialParameters.getNStates(), initialParameters.isReversible(), dd, dd, saveMemory);
        em.setInitialParameters(initialParameters);


        return em;
    }

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Examples of stallone.stat.DiscreteDistribution


        // save memory?
        boolean saveMemory = false;


        // output model and parametrizer
        DiscreteDistribution dd = new DiscreteDistribution(initialParameters.getOutputParameters(0));
        EM em = new EM(_obs, eventBased, initialParameters.getNStates(), initialParameters.isReversible(), dd, dd, saveMemory);
        em.setInitialParameters(initialParameters);


        return em;
    }

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Examples of weka.classifiers.misc.monotone.DiscreteDistribution

      DiscreteEstimator df = 
  (DiscreteEstimator) m_estimatedDistributions.get(cc);
      cc.getValues(attValues);
      switch(m_atype) {
  case AT_MEAN:
    attValues[attValues.length - 1] = (new DiscreteDistribution(df)).mean();
    break;
  case AT_MEDIAN:
    attValues[attValues.length - 1] = (new DiscreteDistribution(df)).median();
    break;
  case AT_MAXPROB:
    attValues[attValues.length - 1] = (new DiscreteDistribution(df)).modes()[0];
    break;
  default: 
    throw new IllegalStateException("Not a valid averaging type");
      }

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