Module es.upm.fi.cig.multictbnc

Package es.upm.fi.cig.multictbnc.learning.structure.hybrid

Class CTBNHybridStructureLearningAlgorithm

java.lang.Object

es.upm.fi.cig.multictbnc.learning.structure.hybrid.CTBNHybridStructureLearningAlgorithm

All Implemented Interfaces:

StructureLearningAlgorithm

public class CTBNHybridStructureLearningAlgorithm
extends Object
implements StructureLearningAlgorithm

Implements the hybrid structure learning algorithm for continuous-time Bayesian networks. This class was designed to learn the bridge and feature subgraphs of a Multi-CTBNC.

Constructor Summary

Constructors

Constructor	Description
CTBNHybridStructureLearningAlgorithm(CTBNScoreFunction scoreFunction, int maxSizeSepSet, double sigTimeTransitionHypothesis, double sigStateToStateTransitionHypothesis)	Initialises the hybrid structure learning algorithm receiving significance values, a score function and the maximum size of the separating sets.

Method Summary

Modifier and Type	Method	Description
String	getIdentifier()	Returns a unique identifier for the structure learning algorithm.
Map<String,String>	getParametersAlgorithm()	Returns the parameters that are used by the algorithm.
void	learn(PGM<? extends Node> pgm)	Learns the structure of a certain PGM.
void	learn(PGM<? extends Node> pgm, int idxNode)	Learn the local structure of a certain node of a PGM.
void	learn(PGM<? extends Node> pgm, List<Integer> idxNodes)	Learns the local structure of certain nodes of a PGM.

Methods inherited from class java.lang.Object

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

Constructor Details

CTBNHybridStructureLearningAlgorithm

public CTBNHybridStructureLearningAlgorithm(CTBNScoreFunction scoreFunction,
 int maxSizeSepSet,
 double sigTimeTransitionHypothesis,
 double sigStateToStateTransitionHypothesis)

Initialises the hybrid structure learning algorithm receiving significance values, a score function and the maximum size of the separating sets.

Parameters:

scoreFunction - score function for the maximisation phase

maxSizeSepSet - maximum separating size for the restriction phase

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

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

Method Details

getIdentifier

public String getIdentifier()

Description copied from interface: StructureLearningAlgorithm

Returns a unique identifier for the structure learning algorithm.

Specified by:

getIdentifier in interface StructureLearningAlgorithm

Returns:

unique identifier for the structure learning algorithm

getParametersAlgorithm

public Map<String,String> getParametersAlgorithm()

Description copied from interface: StructureLearningAlgorithm

Returns the parameters that are used by the algorithm.

Specified by:

getParametersAlgorithm in interface StructureLearningAlgorithm

Returns:

a Map with the parameters used by the algorithm

learn

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

Description copied from interface: StructureLearningAlgorithm

Learns the local structure of certain nodes of a PGM.

Specified by:

learn in interface StructureLearningAlgorithm

Parameters:

pgm - a probabilistic graphical model

idxNodes - node indexes

learn

public void learn(PGM<? extends Node> pgm)
           throws ErroneousValueException

Description copied from interface: StructureLearningAlgorithm

Learns the structure of a certain PGM.

Specified by:

learn in interface StructureLearningAlgorithm

Parameters:

pgm - a probabilistic graphical model

Throws:

ErroneousValueException - if a parameter provided is invalid for the requested task

learn

public void learn(PGM<? extends Node> pgm,
 int idxNode)

Description copied from interface: StructureLearningAlgorithm

Learn the local structure of a certain node of a PGM.

Specified by:

learn in interface StructureLearningAlgorithm

Parameters:

pgm - a probabilistic graphical model

idxNode - node index