Package com.bayesserver.learning.structure

Class ClusteringStructuralLearning

java.lang.Object

com.bayesserver.learning.structure.ClusteringStructuralLearning

All Implemented Interfaces:

StructuralLearning

public final class ClusteringStructuralLearning
extends Object
implements StructuralLearning

A structural learning algorithm for a cluster model (a.k.a mixture model).

Constructor Summary

Constructors

Constructor	Description
ClusteringStructuralLearning()

Method Summary

Modifier and Type	Method	Description
StructuralLearningOutput	learn(EvidenceReaderCommandFactory readerCommandFactory, List<Node> nodes, StructuralLearningOptions options)	Learn a cluster / mixture model.
StructuralLearningOutput	learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options)	Learn the structure (links) of a Bayesian network.

Methods inherited from class java.lang.Object

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

Constructor Detail

ClusteringStructuralLearning

public ClusteringStructuralLearning()

Method Detail

learn

public StructuralLearningOutput learn(EvidenceReaderCommand evidenceReaderCommand,
                                      List<Node> nodes,
                                      StructuralLearningOptions options)

Learn the structure (links) of a Bayesian network.

Specified by:

learn in interface StructuralLearning

Parameters:

evidenceReaderCommand - Can create a reader which iterates round the data used to learn the network.

nodes - The nodes to be considered for links.

options - Options for the structural learning algorithm.

Returns:

The output generated by the structural learning algorithm.

learn

public StructuralLearningOutput learn(EvidenceReaderCommandFactory readerCommandFactory,
                                      List<Node> nodes,
                                      StructuralLearningOptions options)

Learn a cluster / mixture model.

Specified by:

learn in interface StructuralLearning

Parameters:

readerCommandFactory - Creates new evidence reader commands, which may be partitioned for use in cross validation.

nodes - The nodes to be considered for links.

options - Options for the structural learning algorithm.

Returns:

The output generated by the structural learning algorithm.