lio.probdistributions.bitchain
Class Tree

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

All Implemented Interfaces:

LiOResource, ProbDistribution

public class Tree
extends BivariateProbDistribution

Field Summary

(package private) double[]	entropies Entropy of each variable
(package private) double[]	marginalFqs Marginal frequencies
(package private) double[][]	mutualInformations Mutual information of the variables .
(package private) int	nRemNodes Number of remaining nodes.
(package private) int[]	remNodes Remaining nodes

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

Tree()

Method Summary

void	createStructures(int pSize) Creates the necessary structures for the search.
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

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

Field Detail

mutualInformations

double[][] mutualInformations

Mutual information of the variables .

marginalFqs

double[] marginalFqs

Marginal frequencies

entropies

double[] entropies

Entropy of each variable

remNodes

int[] remNodes

Remaining nodes

nRemNodes

int nRemNodes

Number of remaining nodes.

Constructor Detail

Tree

public Tree()

Method Detail

learn

public void learn(Individual[] individuals)

Description copied from interface: ProbDistribution

Learns a probability distribution from a set of data

Parameters:

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

createStructures

public void createStructures(int pSize)

Creates the necessary structures for the search.

Overrides:

createStructures in class BivariateProbDistribution

Parameters:

pSize - Size of the individuals from which the model will be learnt.

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.