Module es.upm.fi.cig.multictbnc

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

Class BN<NodeType extends Node>

java.lang.Object

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

es.upm.fi.cig.multictbnc.models.BN<NodeType>

Type Parameters:

NodeType - type of the nodes of the BN, e.g., nodes with conditional probability table (CPTNode)

All Implemented Interfaces:

PGM<NodeType>

public class BN<NodeType extends Node>
extends AbstractPGM<NodeType>

Implements a Bayesian network (BN).

Constructor Summary

Constructors

Constructor	Description
BN(Dataset dataset, List<String> nameVariables, BNLearningAlgorithms bnLearningAlgs, StructureConstraints structureConstraints, Class<NodeType> nodeClass)	Initialises a Bayesian network by receiving a dataset, a list of variables to use and the algorithms for parameter and structure learning.
BN(BN<NodeType> bn, boolean cloneStructure)	Constructor to clone a Bayesian network.
BN(List<NodeType> nodes)	Initialises a Bayesian network by receiving a list of nodes.
BN(List<NodeType> nodes, Dataset dataset)	Initialises a Bayesian network by receiving a list of nodes and a dataset.

Method Summary

Modifier and Type	Method	Description
List<NodeType>	getLearntNodes()	Returns the nodes with the learnt parameters.
String	getModelIdentifier()	Returns a String that identifies the model.
List<Node>	getTopologicalOrdering()	Obtains the topological ordering of the nodes with the Kahn's algorithm.
String	getType()	Provides the type of PGM.
String	toString()

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

addNodes, areParametersEstimated, computeSufficientStatistics, display, display, display, getAdjacencyMatrix, getDataset, getHyperparameters, getIndexNodes, getIndexOfNode, getNamesNodesByIndex, getNameVariables, getNodeByIndex, getNodeByName, getNodeClass, getNodeFactory, getNodeIndexer, getNodes, getNumNodes, getParameterLearningAlg, getStructureConstraints, getStructureLearningAlg, initialiseModel, isStructureLegal, learn, learn, learn, learn, learnParameters, learnParameters, 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

BN

public BN(List<NodeType> nodes)

Initialises a Bayesian network by receiving a list of nodes.

Parameters:

nodes - list of nodes

BN

public BN(List<NodeType> nodes,
 Dataset dataset)

Initialises a Bayesian network by receiving a list of nodes and a dataset. This constructor was thought to be used by MultiCTBNC.

Parameters:

nodes - list of nodes

dataset - dataset used to learn the Bayesian network

BN

public BN(Dataset dataset,
 List<String> nameVariables,
 BNLearningAlgorithms bnLearningAlgs,
 StructureConstraints structureConstraints,
 Class<NodeType> nodeClass)

Initialises a Bayesian network by receiving a dataset, a list of variables to use and the algorithms for parameter and structure learning. This constructor was thought to be used by MultiCTBNC.

Parameters:

dataset - dataset used to learn the Bayesian network

nameVariables - name of the variables

bnLearningAlgs - parameter and structure learning algorithms

structureConstraints - structure constrains to take into account during the learning of the Bayesian network

nodeClass - type of the BN nodes

BN

public BN(BN<NodeType> bn,
 boolean cloneStructure)

Constructor to clone a Bayesian network.

Parameters:

bn - Bayesian network to clone

cloneStructure - true if the structure of the BN should be clone, false to define the model nodes from a dataset contained in the BN, if any, without copying the structure.

Method Details

getLearntNodes

public List<NodeType> getLearntNodes()

Returns the nodes with the learnt parameters. This can be, for example, a list of CPTNode objects that store conditional probability tables.

Returns:

nodes with learnt parameters

getTopologicalOrdering

public List<Node> getTopologicalOrdering()

Obtains the topological ordering of the nodes with the Kahn's algorithm.

Returns:

sorted nodes

toString

public String toString()

Overrides:

toString in class Object

getType

public String getType()

Description copied from interface: PGM

Provides the type of PGM.

Returns:

string describing the type of PGM

getModelIdentifier

public String getModelIdentifier()

Description copied from interface: PGM

Returns a String that identifies the model.

Returns:

String that identifies the model