unbbayes.simulation.montecarlo.sampling
Class MatrixMonteCarloSampling

java.lang.Object
  unbbayes.simulation.montecarlo.sampling.AMonteCarloSampling
      unbbayes.simulation.montecarlo.sampling.MatrixMonteCarloSampling

All Implemented Interfaces:

IMonteCarloSampling, ILongTaskProgressObservable

Direct Known Subclasses:

LikelihoodWeightingSampling

public class MatrixMonteCarloSampling
extends AMonteCarloSampling

Class that implements the Monte Carlo simulation. It uses forward sampling to calculate a RV's probability mass function. Based on its pmf, it then calculates the cumulative density function. Finally, a random number between 0 and 1 is generated and the sampled state is defined by the state the random number relates based on its cdf.

Author:

Danilo Custodio, Rommel Carvalho

Field Summary

Fields inherited from class unbbayes.simulation.montecarlo.sampling.AMonteCarloSampling

currentProgress, currentProgressStatus, factors, maxProgress, nTrials, pn, sampledStatesMap, sampledStatesMatrix, samplingNodeOrderQueue, timesSampled

Constructor Summary

MatrixMonteCarloSampling()

Method Summary

byte[][]	getSampledStatesCompactMatrix() Returns the generated compact sample matrix.
Map<Integer,Integer>	getSampledStatesMap() Not implemented.
byte[][]	getSampledStatesMatrix() Returns the generated sample matrix.
int[]	getStatesSetTimesSampled() Not implemented.
protected void	simulate(int nTrial) Responsible for simulating MC for sampling.
void	start(ProbabilisticNetwork pn, int nTrials) Generates the MC sample with the given size for the given probabilistic network.

Methods inherited from class unbbayes.simulation.montecarlo.sampling.AMonteCarloSampling

addToSamplingOrderQueue, computeFactors, createSamplingOrderQueue, getCumulativeDistributionFunction, getCurrentProgress, getCurrentProgressStatus, getIndexInQueue, getLinearCoord, getMaxProgress, getMultidimensionalCoord, getParentsIndexesInQueue, getPercentageDone, getProbabilityMassFunction, getSamplingNodeOrderQueue, getState, initSamplingOrderQueue, notityObservers, registerObserver, removeObserver, updateProgress, updateProgress

Methods inherited from class java.lang.Object

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

Constructor Detail

MatrixMonteCarloSampling

public MatrixMonteCarloSampling()

Method Detail

getSampledStatesCompactMatrix

public byte[][] getSampledStatesCompactMatrix()

Description copied from class: AMonteCarloSampling

Returns the generated compact sample matrix. The row represents the ith sampled state set and the column represents the jth node from the sampled order. The value matrix[i][j] represents the sampled state index (respecting the node's states order) for the jth node in the ith sampled state set. To get the number of times the ith set of states was sampled use getStatesSetTimesSampled().

Specified by:

getSampledStatesCompactMatrix in interface IMonteCarloSampling

Specified by:

getSampledStatesCompactMatrix in class AMonteCarloSampling

Returns:

The generated compact sample matrix.

getStatesSetTimesSampled

public int[] getStatesSetTimesSampled()

Not implemented.

Specified by:

getStatesSetTimesSampled in interface IMonteCarloSampling

Specified by:

getStatesSetTimesSampled in class AMonteCarloSampling

Returns:

null.

getSampledStatesMap

public Map<Integer,Integer> getSampledStatesMap()

Not implemented.

Specified by:

getSampledStatesMap in interface IMonteCarloSampling

Specified by:

getSampledStatesMap in class AMonteCarloSampling

Returns:

null.

getSampledStatesMatrix

public byte[][] getSampledStatesMatrix()

Returns the generated sample matrix.

Specified by:

getSampledStatesMatrix in interface IMonteCarloSampling

Specified by:

getSampledStatesMatrix in class AMonteCarloSampling

Returns:

The generated sample matrix.

start

public void start(ProbabilisticNetwork pn,
                  int nTrials)

Generates the MC sample with the given size for the given probabilistic network.

Specified by:

start in interface IMonteCarloSampling

Specified by:

start in class AMonteCarloSampling

Parameters:

pn - Probabilistic network that will be used for sampling.

nTrials - Number of trials to generate.

simulate

protected void simulate(int nTrial)

Responsible for simulating MC for sampling.

Parameters:

nTrial - The trial number to simulate.