lio.probdistributions.bitchain
Class MarginalProbabilityVector

java.lang.Object
  lio.probdistributions.bitchain.BinaryProbDistribution
      lio.probdistributions.bitchain.MarginalProbabilityVector

All Implemented Interfaces:

LiOResource, ProbDistribution

public class MarginalProbabilityVector
extends BinaryProbDistribution

This class implements a probability distribution for binary elements assuming that all variables are independents. Thus, it uses a vector to store the marginal probabilities. *

Field Summary

Fields inherited from class lio.probdistributions.bitchain.BinaryProbDistribution

data, laplace, size

Constructor Summary

MarginalProbabilityVector()

Method Summary

void	convexCombination(double[] nMarginals, double alpha) Updates the probability vector by doing the convex combination with the one recevied a parameter.
void	convexCombination(MarginalProbabilityVector model, double alpha) Updates the probability vector by doing the convex combination with the one recevied a parameter.
LiOResourceDefinition	getDefinition() Return a LiOResDescription object with the information of each object.
double[]	getMarginals() Getter for property proVector.
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.
static Individual[]	sample(double[] pMarginals, int nIndividuals) Samples a population from a vector of marginal probabilities
Individual[]	sample(int nIndividuals) Samples some individuals from a probability distribution

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

MarginalProbabilityVector

public MarginalProbabilityVector()

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.

sample

public Individual[] sample(int nIndividuals)

Samples some individuals from a probability distribution

Parameters:

nIndividuals: - Number of individuals that must be sampled.

Returns:

the sampled individuals.

sample

public static Individual[] sample(double[] pMarginals,
                                  int nIndividuals)

Samples a population from a vector of marginal probabilities

Returns:

the specified number of samples.

convexCombination

public void convexCombination(double[] nMarginals,
                              double alpha)

Updates the probability vector by doing the convex combination with the one recevied a parameter.

Parameters:

newProbVector - New probability vector.

alpha - Learning Rate.

convexCombination

public void convexCombination(MarginalProbabilityVector model,
                              double alpha)

Updates the probability vector by doing the convex combination with the one recevied a parameter.

Parameters:

model - Marginal Probabilty Vector used to update the current one.

alpha - Learning Rate.

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.

main

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

Main method to test the task.

getMarginals

public double[] getMarginals()

Getter for property proVector.

Returns:

Value of property proVector.