Module es.upm.fi.cig.multictbnc

Package es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC

Class OnlineMarkovBlanketCTPC

java.lang.Object

es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.MarkovBlanketCTPC

es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.OnlineMarkovBlanketCTPC

All Implemented Interfaces:

StructureLearningAlgorithm

public class OnlineMarkovBlanketCTPC
extends MarkovBlanketCTPC

This class extends the MB-CTPC algorithm to an online learning context, allowing for dynamic updates to the learned model based on new data.

Constructor Summary

Constructors

Constructor	Description
OnlineMarkovBlanketCTPC(double sigTimeTransitionHypothesis, double sigStateToStateTransitionHypothesis)	Initialises the Online-MB-CTPC algorithm by providing the significances to be used.

Method Summary

Modifier and Type	Method	Description
protected List<Integer>	getIdxFeatureVariables(PGM<? extends Node> pgm, List<Integer> idxNodes)	Retrieves the index nodes representing feature variables from a given list of index nodes.
void	learn(PGM<? extends Node> pgm, List<Integer> idxNodesToUpdate)	Learns the local structure of certain nodes of a PGM.

Methods inherited from class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.MarkovBlanketCTPC

learn

Methods inherited from class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

addSepSetAndNodeAsParents, addSepSetAsParents, buildCompleteStructure, getIdentifier, getIdxFeatureVariables, getIdxParentsNode, getParametersAlgorithm, learn, learnParentSetNode, retrieveParametersAndSuffStatistics, testNullStateToStateTransitionHypForGivenSepSet, testNullTimeToTransitionHypForGivenSepSet

Methods inherited from class java.lang.Object

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

Constructor Details

OnlineMarkovBlanketCTPC

public OnlineMarkovBlanketCTPC(double sigTimeTransitionHypothesis,
 double sigStateToStateTransitionHypothesis)

Initialises the Online-MB-CTPC algorithm by providing the significances to be used.

Parameters:

sigTimeTransitionHypothesis - significance level used for the null time to transition hypothesis

sigStateToStateTransitionHypothesis - significance level used for the null state-to-state transition hypothesis

Method Details

learn

public void learn(PGM<? extends Node> pgm,
 List<Integer> idxNodesToUpdate)

Description copied from interface: StructureLearningAlgorithm

Learns the local structure of certain nodes of a PGM.

Specified by:

learn in interface StructureLearningAlgorithm

Overrides:

learn in class CTPC

Parameters:

pgm - a probabilistic graphical model

idxNodesToUpdate - node indexes

getIdxFeatureVariables

protected List<Integer> getIdxFeatureVariables(PGM<? extends Node> pgm,
 List<Integer> idxNodes)

Retrieves the index nodes representing feature variables from a given list of index nodes.

Parameters:

pgm - the PGM containing the nodes

idxNodes - the list of index nodes

Returns:

the list of index nodes representing feature variables