Module es.upm.fi.cig.multictbnc

Package es.upm.fi.cig.multictbnc.models.submodels

Class Empty_maxK_MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node>

java.lang.Object

es.upm.fi.cig.multictbnc.models.AbstractPGM<Node>

es.upm.fi.cig.multictbnc.models.MultiCTBNC<NodeTypeBN,NodeTypeCTBN>

es.upm.fi.cig.multictbnc.models.submodels.Empty_maxK_MultiCTBNC<NodeTypeBN,NodeTypeCTBN>

Type Parameters:

NodeTypeBN - type of the nodes of the BN (class subgraph)

NodeTypeCTBN - type of the nodes of the CTBN (feature subgraph)

All Implemented Interfaces:

Classifier, PGM<Node>

public class Empty_maxK_MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node>
extends MultiCTBNC<NodeTypeBN,NodeTypeCTBN>

Implements a Multi-CTBNC with an empty class subgraph and a K-dependence continuous-time Bayesian network for the feature subgraph, i.e., the feature nodes are limited to having K parents (apart of the class variables).

Constructor Summary

Constructors

Constructor	Description
Empty_maxK_MultiCTBNC(BNLearningAlgorithms bnLearningAlgs, CTBNLearningAlgorithms ctbnLearningAlgs, int maxK, Class<NodeTypeBN> bnNodeClass, Class<NodeTypeCTBN> ctbnNodeClass)	Constructs a Empty_maxK_Multi-CTBNC by receiving the learning algorithms for Bayesian networks and continuous-time Bayesian networks and the maximum number of parents of the features (apart of the class variables).

Method Summary

Modifier and Type	Method	Description
Map<String,String>	getHyperparameters()	Returns the hyperparameters of the model the user sets.
String	getModelIdentifier()	Returns a String that identifies the model.
StructureConstraints	getStructureConstraintsBN()	Returns the structure constraints for the BN.
StructureConstraints	getStructureConstraintsCTBN()	Returns the structure constraints for the CTBN.
String	getType()	Provides the type of PGM.

Methods inherited from class es.upm.fi.cig.multictbnc.models.MultiCTBNC

areParametersEstimated, getBN, getCTBN, getInitialStructure, getLearningAlgsBN, getLearningAlgsCTBN, getNameFeatureVariables, getNodes, getNodesClassVariables, getNodesCTBN, getNodesCTBNInMarkovBlanketClassVariables, getNodesFeatureVariables, getNumClassVariables, getNumFeatureVariables, getTypeNodeClassVariable, getTypeNodeFeature, learn, learnParameters, learnParameters, predict, sample, sample, setBnLearningAlgs, setCtbnLearningAlgs, setInitialStructure, toString, update, updateBridgeAndFeatureSubgraph

Methods inherited from class es.upm.fi.cig.multictbnc.models.AbstractPGM

addNodes, computeSufficientStatistics, display, display, display, getAdjacencyMatrix, getDataset, getIndexNodes, getIndexOfNode, getNamesNodesByIndex, getNameVariables, getNodeByIndex, getNodeByName, getNodeClass, getNodeFactory, getNodeIndexer, getNumNodes, getParameterLearningAlg, getStructureConstraints, getStructureLearningAlg, initialiseModel, isStructureLegal, learn, learn, learn, learnParameters, learnParameters, learnParameters, learnParameters, removeAllEdges, removeAllNodes, saveGraph, setDataset, setNameVariables, setParameterLearningAlgorithm, setStructure, setStructure, setStructure, setStructureConstraints, setStructureLearningAlgorithm, setStructureModifiedNodes

Methods inherited from class java.lang.Object

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

Constructor Details

Empty_maxK_MultiCTBNC

public Empty_maxK_MultiCTBNC(BNLearningAlgorithms bnLearningAlgs,
 CTBNLearningAlgorithms ctbnLearningAlgs,
 int maxK,
 Class<NodeTypeBN> bnNodeClass,
 Class<NodeTypeCTBN> ctbnNodeClass)

Constructs a Empty_maxK_Multi-CTBNC by receiving the learning algorithms for Bayesian networks and continuous-time Bayesian networks and the maximum number of parents of the features (apart of the class variables).

Parameters:

bnLearningAlgs - parameter and structure learning algorithms for Bayesian networks

ctbnLearningAlgs - parameter and structure learning algorithms for continuous-time Bayesian networks

maxK - maximum number of parents the nodes of feature variables can have (without including nodes of class variables)

bnNodeClass - Bayesian network node type

ctbnNodeClass - continuous-time Bayesian network node type

Method Details

getHyperparameters

public Map<String,String> getHyperparameters()

Description copied from class: AbstractPGM

Returns the hyperparameters of the model the user sets.

Overrides:

getHyperparameters in class AbstractPGM<Node>

Returns:

a Map with the hyperparameters

getStructureConstraintsBN

public StructureConstraints getStructureConstraintsBN()

Description copied from class: MultiCTBNC

Returns the structure constraints for the BN.

Overrides:

getStructureConstraintsBN in class MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node>

Returns:

a StructureConstraint

getStructureConstraintsCTBN

public StructureConstraints getStructureConstraintsCTBN()

Description copied from class: MultiCTBNC

Returns the structure constraints for the CTBN.

Overrides:

getStructureConstraintsCTBN in class MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node>

Returns:

a StructureConstraint

getModelIdentifier

public String getModelIdentifier()

Description copied from interface: PGM

Returns a String that identifies the model.

Specified by:

getModelIdentifier in interface PGM<NodeTypeBN extends Node>

Overrides:

getModelIdentifier in class MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node>

Returns:

String that identifies the model

getType

public String getType()

Description copied from interface: PGM

Provides the type of PGM.

Specified by:

getType in interface PGM<NodeTypeBN extends Node>

Overrides:

getType in class MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node>

Returns:

string describing the type of PGM