lio.probdistributions.bitchain
Class Chain

java.lang.Object
  lio.probdistributions.bitchain.BinaryProbDistribution
      lio.probdistributions.bitchain.BivariateProbDistribution
          lio.probdistributions.bitchain.Chain

All Implemented Interfaces:

LiOResource, ProbDistribution

public class Chain
extends BivariateProbDistribution

Learns a bivariate model from bitChains where each node can have only one child. The learning method is the one used in MIMIC algorithm.

Field Summary

Fields inherited from class lio.probdistributions.bitchain.BivariateProbDistribution

conditionals, marginals, parents, variables

Fields inherited from class lio.probdistributions.bitchain.BinaryProbDistribution

data, laplace, size

Constructor Summary

Chain()

Method Summary

LiOResourceDefinition	getDefinition() Return a LiOResDescription object with the information of each object.
void	learn(Individual[] individuals) Learns a probability distribution from a set of data
static void	main(java.lang.String[] args) Main method to test the task.

Methods inherited from class lio.probdistributions.bitchain.BivariateProbDistribution

createStructures, sample

Methods inherited from class lio.probdistributions.bitchain.BinaryProbDistribution

isLaplace, setLaplace

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Chain

public Chain()

Method Detail

learn

public void learn(Individual[] individuals)

Learns a probability distribution from a set of data

Parameters:

individuals - Set of individiduals from which de distribution is estimated.

main

public static void main(java.lang.String[] args)

Main method to test the task.

getDefinition

public LiOResourceDefinition getDefinition()

Return a LiOResDescription object with the information of each object. That object holds the name and all parameters that describe the object

Returns:

A definition of the object.