Modifier and Type | Method and Description |
---|---|
Network |
Network.copy()
Makes a copy of the network.
|
Network |
UnrollOutput.getDbn()
Gets the Dynamic Bayesian network before it was unrolled.
|
Network |
DecomposeOutput.getDecomposedNetwork()
Gets the network, which is the decomposed equivalent of the original network.
|
Network |
Link.getNetwork()
The
Network the link belongs to. |
Network |
NetworkLinkCollection.getNetwork()
Gets the
Network the collection belongs to. |
Network |
NetworkNodeCollection.getNetwork()
The
Network the collection belongs to. |
Network |
NetworkNodeGroupCollection.getNetwork()
Gets the network instance that these groups belong to.
|
Network |
NetworkVariableCollection.getNetwork()
The
Network the collection belongs to. |
Network |
Node.getNetwork()
The
Network the node belongs to. |
Network |
DecomposeOutput.getOriginalNetwork()
Gets the original network, containing nodes with multiple variables.
|
Network |
UnrollOutput.getUnrolled()
Gets the unrolled Dynamic Bayesian network.
|
Modifier and Type | Method and Description |
---|---|
static DecomposeOutput |
Decomposer.decompose(Network network,
DecomposeOptions options)
Decomposes a Bayesian network containing nodes with multiple variables into its single variable node equivalent.
|
static double |
ParameterCounter.getParameterCount(Network network)
Gets the number of parameters in a Bayesian network.
|
static double |
ParameterCounter.getParameterCount(Network network,
ParameterCountOptions options)
Gets the number of parameters in a Bayesian network.
|
static boolean |
Dag.isDag(Network network)
Determines if a network is a Directed Acyclic Graph (DAG).
|
static boolean |
Dag.isDag(Network network,
Iterable<Link> ignore,
Iterable<Link> extra)
Determines if a network is a DAG (Directed Acyclic Graph).
|
static Node[] |
TopologicalSort.sort(Network network)
Returns the nodes in a Bayesian network sorted in topological order.
|
static TopologicalSortNodeInfo[] |
TopologicalSort.sortWithDepth(Network network)
Returns the nodes in a Bayesian network sorted and grouped in topological order.
|
static UnrollOutput |
Unroller.unroll(Network network,
int sliceCount,
UnrollOptions options)
Unrolls the specified Dynamic Bayesian network into the equivalent Bayesian network.
|
Modifier and Type | Method and Description |
---|---|
static ImpactOutput |
Impact.calculate(Network network,
Distribution hypothesisQuery,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
|
static ImpactOutput |
Impact.calculate(Network network,
Distribution hypothesisQuery,
StateContext[] hypothesisCombination,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
|
static LogLikelihoodAnalysisOutput |
LogLikelihoodAnalysis.calculate(Network network,
Evidence evidence,
List<Variable> evidenceToAnalyse,
LogLikelihoodAnalysisOptions options)
Analyzes the log-likelihood based on subsets of evidence.
|
static DSeparationOutput |
DSeparation.calculate(Network network,
List<Node> sourceNodes,
List<Integer> sourceNodeTimes,
List<Node> testNodes,
List<Integer> testTimes,
Evidence evidence,
DSeparationOptions options)
Calculates whether sets of nodes are D-Separated, given any evidence, and associated times for any temporal nodes.
|
static DSeparationOutput |
DSeparation.calculate(Network network,
List<Node> sourceNodes,
List<Node> testNodes,
Evidence evidence,
DSeparationOptions options)
Calculates whether sets of nodes are D-Separated, given any evidence.
|
static ImpactOutput |
Impact.calculate(Network network,
Variable hypothesisVariable,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
|
static ImpactOutput |
Impact.calculate(Network network,
Variable hypothesisVariable,
State hypothesisState,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
|
static ImpactHypothesisOutput |
Impact.calculateStreamed(Network network,
Distribution hypothesisQuery,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactAction outputItem,
ImpactOptions options)
Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
|
static ImpactHypothesisOutput |
Impact.calculateStreamed(Network network,
Distribution hypothesisQuery,
StateContext[] hypothesisState,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactAction outputItem,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
|
static LogLikelihoodAnalysisBaselineOutput |
LogLikelihoodAnalysis.calculateStreamed(Network network,
Evidence evidence,
List<Variable> evidenceToAnalyse,
LogLikelihoodAnalysisAction outputItem,
LogLikelihoodAnalysisOptions options)
Analyzes the log-likelihood based on subsets of evidence.
|
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.
|
static ClusterCountOutput |
ClusterCount.detect(Network network,
Variable cluster,
List<Integer> clusterCounts,
ClusterCountActions actions,
ClusterCountOptions options)
Determine the number of clusters (discrete states of a latent variable) using cross validation.
|
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.
|
static InSampleAnomalyDetection |
InSampleAnomalyDetection.learn(Network network,
EvidenceReaderCommandFactory evidenceReaderCommandFactory,
InSampleAnomalyDetectionActions actions,
InSampleAnomalyDetectionOptions options)
Build the in-sample anomaly detector, which can be used to remove anomalous data from a training data set.
|
Constructor and Description |
---|
SensitivityToParameters(Network network,
InferenceFactory factory)
Initializes a new instance of the
SensitivityToParameters class . |
Modifier and Type | Method and Description |
---|---|
Network |
CrossValidationNetwork.getNetwork()
Gets the network learnt from the cross validation partitioning.
|
Network |
DefaultCrossValidationNetwork.getNetwork()
Gets the network learnt from a cross validation partitioning.
|
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.
|
void |
DefaultCrossValidationNetwork.setNetwork(Network value)
Sets the network learnt from a cross validation partitioning.
|
Constructor and Description |
---|
DefaultCrossValidationNetwork(Network network)
Initializes a new instance of the
DefaultCrossValidationNetwork class, with a network. |
Modifier and Type | Method and Description |
---|---|
Network |
DataSampler.getNetwork()
Gets the Bayesian network or Dynamic Bayesian network that was used in the constructor.
|
Constructor and Description |
---|
DataSampler(Network network)
Initializes a new instance of the
DataSampler class. |
DataSampler(Network network,
Evidence fixedData)
Initializes a new instance of the
DataSampler class. |
Modifier and Type | Method and Description |
---|---|
Network |
DefaultEvidence.getNetwork()
Gets the Bayesian network that is the the target of the evidence.
|
Network |
DefaultQueryDistributionCollection.getNetwork()
Gets the
Network that is the target for a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput) . |
Network |
DefaultQueryFunctionCollection.getNetwork()
Gets the
Network that is the target for a Inference.query(com.bayesserver.inference.QueryOptions, com.bayesserver.inference.QueryOutput) . |
Network |
Evidence.getNetwork()
Gets the Bayesian network that is the the target of the evidence.
|
Network |
Inference.getNetwork()
The target Bayesian network.
|
Network |
LikelihoodSamplingInference.getNetwork()
The target Bayesian network.
|
Network |
LoopyBeliefInference.getNetwork()
The target Bayesian network.
|
Network |
RelevanceTreeInference.getNetwork()
The target Bayesian network.
|
Network |
VariableEliminationInference.getNetwork()
The target Bayesian network.
|
Modifier and Type | Method and Description |
---|---|
Inference |
InferenceFactory.createInferenceEngine(Network network)
Creates an instance of an inference algorithm, with the [network] as it's target.
|
Inference |
LikelihoodSamplingInferenceFactory.createInferenceEngine(Network network)
Creates an instance of an inference algorithm, with the [network] as it's target.
|
Inference |
LoopyBeliefInferenceFactory.createInferenceEngine(Network network)
Creates an instance of an inference algorithm, with the [network] as it's target.
|
Inference |
RelevanceTreeInferenceFactory.createInferenceEngine(Network network)
Uses the factory design pattern to create inference related objects for the Relevance Tree algorithm.
|
Inference |
VariableEliminationInferenceFactory.createInferenceEngine(Network network)
Uses the factory design pattern to create inference related objects for the Variable elimination algorithm.
|
static TreeQueryOutput |
TreeQuery.query(Network network,
QueryDistributionCollection queryDistributions,
Evidence evidence,
TreeQueryOptions queryOptions)
Calculates properties of a Bayesian network or Dynamic Bayesian network when converted to a tree for inference.
|
Constructor and Description |
---|
DefaultEvidence(Network network)
Initializes a new instance of the
DefaultEvidence class, with the target Bayesian network. |
DefaultQueryDistributionCollection(Network network)
Initializes a new instance of the
DefaultQueryDistributionCollection class, passing the target Bayesian network as a parameter. |
DefaultQueryFunctionCollection(Network network)
Initializes a new instance of the
DefaultQueryFunctionCollection class, passing the target Bayesian network as a parameter. |
LikelihoodSamplingInference(Network network)
Initializes a new instance of the
LikelihoodSamplingInference class, with the target Bayesian network. |
LoopyBeliefInference(Network network)
Initializes a new instance of the
LoopyBeliefInference class, with the target Bayesian network. |
RelevanceTreeInference(Network network)
Initializes a new instance of the
RelevanceTreeInference class, with the target Bayesian network. |
VariableEliminationInference(Network network)
Initializes a new instance of the
VariableEliminationInference class, with the target Bayesian network. |
Modifier and Type | Method and Description |
---|---|
Network |
DistributedMapperContext.getNetwork()
Gets the
Network that is being learnt by the distributed process. |
Network |
ParameterLearning.getNetwork()
Returns the relevant network.
|
Modifier and Type | Method and Description |
---|---|
static ParameterLearningOutput |
ParameterLearning.learnDistributed(Network network,
List<DistributionSpecification> distributionSpecifications,
ParameterLearningOptions options,
Distributer<DistributerContext> distributer)
Learns the parameters of a Bayesian network or Dynamic Bayesian network from data, on a distributed platform.
|
static ParameterLearningOutput |
ParameterLearning.learnDistributed(Network network,
ParameterLearningOptions options,
Distributer<DistributerContext> distributer)
Learns the parameters of a Bayesian network or Dynamic Bayesian network from data, on a distributed platform.
|
Constructor and Description |
---|
OnlineLearning(Network network,
InferenceFactory factory)
Initializes a new instance of the
OnlineLearning class. |
ParameterLearning(Network network,
InferenceFactory factory)
Initializes a new instance of the
ParameterLearning class. |
Modifier and Type | Method and Description |
---|---|
OptimizerOutput |
GeneticOptimizer.optimize(Network network,
Objective objective,
List<DesignVariable> designVariables,
Evidence fixedEvidence,
OptimizerOptions options)
Perform optimization of an objective (target).
|
OptimizerOutput |
GeneticSimplification.optimize(Network network,
Objective objective,
List<DesignVariable> designVariables,
Evidence fixedEvidence,
OptimizerOptions options)
Perform optimization of an objective (target).
|
OptimizerOutput |
Optimizer.optimize(Network network,
Objective objective,
List<DesignVariable> designVariables,
Evidence fixedEvidence,
OptimizerOptions options)
Perform optimization of an objective (target).
|
Copyright © 2021. All rights reserved.