Uses of Interface
com.bayesserver.data.EvidenceReaderCommand

Packages that use EvidenceReaderCommand

Package	Description
com.bayesserver.analysis
com.bayesserver.data
com.bayesserver.learning.parameters
com.bayesserver.learning.structure

Uses of EvidenceReaderCommand in com.bayesserver.analysis

Methods in com.bayesserver.analysis that return EvidenceReaderCommand

Modifier and Type	Method and Description
EvidenceReaderCommand	ClusterCountActions.createEvidenceReaderCommand(Network networkCopy, DataPartitioning partitioning) A user supplied function to create an evidence reader command based on a copy of the original network.

Methods in com.bayesserver.analysis with parameters of type EvidenceReaderCommand

Modifier and Type	Method and Description
void	ClusterCountActions.learn(Network networkCopy, EvidenceReaderCommand evidenceReaderCommand) A user supplied function to learn the paramters of a copy of the original network based on a training partition of the data.
void	InSampleAnomalyDetectionActions.learn(Network networkCopy, EvidenceReaderCommand evidenceReaderCommand) A user supplied function to learn the paramters of a copy of the original network based on a training partition of the data.

Uses of EvidenceReaderCommand in com.bayesserver.data

Classes in com.bayesserver.data that implement EvidenceReaderCommand

Modifier and Type	Class and Description
class	DefaultEvidenceReaderCommand Creates instances of EvidenceReader on demand.

Methods in com.bayesserver.data that return EvidenceReaderCommand

Modifier and Type	Method and Description
EvidenceReaderCommand	DataTableEvidenceReaderCommandFactory.create(Network network) Create an evidence reader command, based on a specific network which may be a copy of the original.
EvidenceReaderCommand	EvidenceReaderCommandFactory.create(Network network) Create an evidence reader command, based on a specific network which may be a copy of the original.
EvidenceReaderCommand	DataTableEvidenceReaderCommandFactory.createPartitioned(Network network, DataPartitioning dataPartitioning, int partitionCount) Create an evidence reader command on a partition, based on a specific network which may be a copy of the original.
EvidenceReaderCommand	EvidenceReaderCommandFactory.createPartitioned(Network network, DataPartitioning dataPartitioning, int partitionCount) Create an evidence reader command on a partition, based on a specific network which may be a copy of the original.

Methods in com.bayesserver.data with parameters of type EvidenceReaderCommand

Modifier and Type	Method and Description
static DataCountOutput	DataCount.count(EvidenceReaderCommand readerCommand, Evidence evidence) Counts the number of cases that match the current evidence configuration.

Uses of EvidenceReaderCommand in com.bayesserver.learning.parameters

Methods in com.bayesserver.learning.parameters with parameters of type EvidenceReaderCommand

Modifier and Type	Method and Description
ParameterLearningOutput	ParameterLearning.learn(EvidenceReaderCommand readerCommand, List<DistributionSpecification> distributionSpecifications, ParameterLearningOptions options) Learns the parameters of a Bayesian network or Dynamic Bayesian network, from data.
ParameterLearningOutput	ParameterLearning.learn(EvidenceReaderCommand readerCommand, ParameterLearningOptions options) Learns the parameters of a Bayesian network or Dynamic Bayesian network, from data.

Uses of EvidenceReaderCommand in com.bayesserver.learning.structure

Methods in com.bayesserver.learning.structure with parameters of type EvidenceReaderCommand

Modifier and Type	Method and Description
static FeatureSelectionOutput	FeatureSelection.detect(List<Variable> variables, EvidenceReaderCommand evidenceReaderCommand, Variable target, FeatureSelectionOptions options) Determines which variables are likely to be good features (predictors) of a target variable.
StructuralLearningOutput	ChowLiuStructuralLearning.learn(EvidenceReaderCommand readerCommand, List<Node> nodes, StructuralLearningOptions options) Learn the structure (links) of a Bayesian network.
StructuralLearningOutput	ClusteringStructuralLearning.learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options) Learn the structure (links) of a Bayesian network.
StructuralLearningOutput	HierarchicalStructuralLearning.learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options) Learn the structure (links) of a Bayesian network.
StructuralLearningOutput	PCStructuralLearning.learn(EvidenceReaderCommand readerCommand, List<Node> nodes, StructuralLearningOptions options) Learn the structure (links) of a Bayesian network.
StructuralLearningOutput	SearchStructuralLearning.learn(EvidenceReaderCommand readerCommand, List<Node> nodes, StructuralLearningOptions options) Learn the structure (links) of a Bayesian network.
StructuralLearningOutput	StructuralLearning.learn(EvidenceReaderCommand evidenceReaderCommand, List<Node> nodes, StructuralLearningOptions options) Learn the structure (links) of a Bayesian network.
StructuralLearningOutput	TANStructuralLearning.learn(EvidenceReaderCommand readerCommand, List<Node> nodes, StructuralLearningOptions options) Learn the structure (links) of a Bayesian network.