- generate(DataReaderCommand, List<VariableDefinition>, VariableGeneratorOptions) - Static method in class com.bayesserver.data.discovery.VariableGenerator
-
Generates variables from a data source.
- GeneticOptimizer - Class in com.bayesserver.optimization
-
A genetic algorithm optimizer.
- GeneticOptimizer() - Constructor for class com.bayesserver.optimization.GeneticOptimizer
-
- GeneticOptimizerOptions - Class in com.bayesserver.optimization
-
Options governing the behaviour of the com.bayesserver.optimization.genetic.GeneticOptimizer
algorithm.
- GeneticOptimizerOptions() - Constructor for class com.bayesserver.optimization.GeneticOptimizerOptions
-
- GeneticOptimizerOutput - Class in com.bayesserver.optimization
-
Contains the results from the genetic optimization algorithm.
- GeneticOptimizerProgressInfo - Class in com.bayesserver.optimization
-
Contains progress information sent from the genetic optimization algorithm.
- GeneticOptionsBase - Class in com.bayesserver.optimization
-
Base class for common Genetic algorithm options.
- GeneticSimplification - Class in com.bayesserver.optimization
-
An algorithm that attempts to simply the evidence found by an optimizer.
- GeneticSimplification() - Constructor for class com.bayesserver.optimization.GeneticSimplification
-
- GeneticSimplificationOptions - Class in com.bayesserver.optimization
-
Options for the genetic simplifcation algorithm.
- GeneticSimplificationOptions() - Constructor for class com.bayesserver.optimization.GeneticSimplificationOptions
-
- GeneticSimplificationOutput - Class in com.bayesserver.optimization
-
Contains the results from the genetic simplifcation algorithm.
- GeneticTerminationOptions - Class in com.bayesserver.optimization
-
Termination options for the genetic optimization algorithm.
- GeneticTerminationOptions() - Constructor for class com.bayesserver.optimization.GeneticTerminationOptions
-
- get(int) - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection
-
- get(int) - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection
-
- get(int) - Method in class com.bayesserver.analysis.DSeparationTestResultCollection
-
- get(String) - Method in class com.bayesserver.CustomPropertyCollection
-
Gets the
CustomProperty
with the specified name, from the collection, or returns null if not found.
- get(int) - Method in class com.bayesserver.CustomPropertyCollection
-
- get(String) - Method in class com.bayesserver.data.DataColumnCollection
-
Gets the column with the specified name, or null if the name if not found.
- get(int) - Method in class com.bayesserver.data.DataColumnCollection
-
Gets the DataColumn at the given index.
- get(int) - Method in class com.bayesserver.data.DataRow
-
Gets the value at the specified index.
- get(int) - Method in class com.bayesserver.data.DataRowCollection
-
Gets the row at the given index.
- get(int) - Method in class com.bayesserver.data.sampling.ExcludedVariables
-
- get(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
-
- get(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the evidence for a discrete variable at the specified time.
- get(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the evidence for a node with a single variable at the specified time.
- get(Variable, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the evidence for a temporal variable.
- get(Node, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the evidence for a node's single temporal variable.
- get(Node) - Method in class com.bayesserver.inference.DefaultEvidence
-
- get(int) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
-
- get(int) - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
-
- get(Variable) - Method in interface com.bayesserver.inference.Evidence
-
- get(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Gets the evidence for a discrete variable at the specified time.
- get(Variable, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence
-
Gets the evidence for a temporal variable.
- get(Node, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence
-
Gets the evidence for a node's single temporal variable.
- get(Node) - Method in interface com.bayesserver.inference.Evidence
-
- get(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Gets the evidence for a node with a single variable at the specified time.
- get(int) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
-
- get(int) - Method in class com.bayesserver.NetworkLinkCollection
-
Gets the
Link
object at the specified index.
- get(int) - Method in class com.bayesserver.NetworkNodeCollection
-
Gets the
Node
object at the specified index.
- get(String) - Method in class com.bayesserver.NetworkNodeCollection
-
Performs a case sensitive lookup.
- get(String, boolean) - Method in class com.bayesserver.NetworkNodeCollection
-
Performs a case sensitive lookup.
- get(String) - Method in class com.bayesserver.NetworkNodeGroupCollection
-
Gets the
NodeGroup
with the specified name, from the collection, or returns null if not found.
- get(int) - Method in class com.bayesserver.NetworkNodeGroupCollection
-
- get(String) - Method in class com.bayesserver.NetworkVariableCollection
-
Performs a case sensitive lookup.
- get(String, boolean) - Method in class com.bayesserver.NetworkVariableCollection
-
Performs a case sensitive lookup.
- get(int) - Method in class com.bayesserver.NetworkVariableCollection
-
Gets the
Variable
object at the specified index.
- get(int) - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets a distribution expression at a particular temporal order.
- get(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets a distribution expression with particular properties, such as temporal order.
- get(NodeDistributionKind) - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets a particular kind of distribution expression on the node.
- get(NodeDistributionKey, NodeDistributionKind, ExpressionDistribution) - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets a distribution expression with particular properties, such as temporal order.
- get(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets a distribution expression with particular properties, such as temporal order.
- get(int) - Method in class com.bayesserver.NodeDistributions
-
Gets a distribution at a particular temporal order.
- get(NodeDistributionKey) - Method in class com.bayesserver.NodeDistributions
-
Gets a distribution with particular properties, such as temporal order.
- get(NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions
-
Gets a particular kind of distribution on the node.
- get(NodeDistributionKey, NodeDistributionKind) - Method in class com.bayesserver.NodeDistributions
-
Gets a distribution with particular properties, such as temporal order.
- get(int) - Method in class com.bayesserver.NodeGroupCollection
-
Gets the group at the specified index.
- get(int) - Method in class com.bayesserver.NodeLinkCollection
-
- get(int) - Method in class com.bayesserver.NodeVariableCollection
-
Gets the
Variable
object at the specified index.
- get(String) - Method in class com.bayesserver.NodeVariableCollection
-
Performs a case sensitive lookup.
- get(String, boolean) - Method in class com.bayesserver.NodeVariableCollection
-
Performs a case sensitive lookup.
- get(String) - Method in class com.bayesserver.StateCollection
-
Performs a case sensitive lookup.
- get(String, boolean) - Method in class com.bayesserver.StateCollection
-
Performs a case sensitive lookup.
- get(int) - Method in class com.bayesserver.StateCollection
-
Gets the
State
at the specified index.
- get(State...) - Method in class com.bayesserver.Table
-
Gets the table value corresponding to the given states.
- get(StateContext...) - Method in class com.bayesserver.Table
-
Gets the table value corresponding to the given states and associated times.
- get(int) - Method in class com.bayesserver.Table
-
Gets the
Table
value at the specified index into the 1-dimensional array.
- get(int[]) - Method in class com.bayesserver.TableAccessor
-
Gets the underlying
Table
value, using states corresponding to the order of variables in the
TableAccessor
.
- get(int) - Method in class com.bayesserver.TableAccessor
-
Gets the underlying
Table
value, specified i.
- get(int) - Method in class com.bayesserver.VariableContextCollection
-
Gets the
Variable
object at the specified index.
- get(int) - Method in class com.bayesserver.VariableMap
-
Maps between the custom order and the sorted collection.
- getA() - Method in class com.bayesserver.learning.structure.LinkConstraint
-
Gets the first node involved in the constraint.
- getAccuracy() - Method in class com.bayesserver.analysis.ConfusionMatrix
-
- getActual() - Method in class com.bayesserver.analysis.LiftChart
-
Gets the name of the data column containing the actual classification.
- getAddNodeGroups() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets a value which determines whether network node groups are added for each group in a level.
- getAdjustmentSet() - Method in class com.bayesserver.causal.BackdoorValidationOptions
-
Gets the adjustment set to be validated.
- getAdjustmentSet() - Method in class com.bayesserver.causal.DisjunctiveCauseQueryOptions
-
Gets the adjustment set, which must include all nodes that are causes of either treatments (X) or outcomes (Y) or both, except those with evidence set.
- getAdjustmentSet() - Method in class com.bayesserver.causal.DisjunctiveCauseValidationOptions
-
Gets the adjustment set to be validated.
- getAdjustmentSetOverride() - Method in class com.bayesserver.causal.BackdoorQueryOptions
-
Gets an adjustment set to use during estimation, instead of the algorithm generating it automatically.
- getAdjustmentSetXZ() - Method in class com.bayesserver.causal.FrontDoorValidationOptions
-
Gets the adjustment set for the adjustment between treatments (X) and front-door nodes (Z).
- getAdjustmentSetXZOverride() - Method in class com.bayesserver.causal.FrontDoorQueryOptions
-
Gets the 'adjustment set' for adjusting between treatments (X) and front-door nodes (Z).
- getAdjustmentSetZY() - Method in class com.bayesserver.causal.FrontDoorValidationOptions
-
Gets the adjustment set for the adjustment between front-door nodes (Z) and outcomes (Y).
- getAdjustmentSetZYOverride() - Method in class com.bayesserver.causal.FrontDoorQueryOptions
-
Gets the 'adjustment set' for adjusting between the front-door nodes (Z) and the outcomes (Y).
- getAllowMissing() - Method in class com.bayesserver.optimization.DesignVariable
-
Determines whether the optimizer can consider missing values (evidence not set) on this variable.
- getAllowNullDistributions() - Method in class com.bayesserver.ValidationOptions
-
Determines whether validation should succeed even if the required distribution(s) have not been assigned to a node.
- getAllowNullFunctions() - Method in class com.bayesserver.ValidationOptions
-
Determines whether validation should succeed even if a function has not been assigned to a functiomn variable.
- getAlpha() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
-
Gets Alpha from the sensitivity function.
- getAlpha1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets Alpha1 from the sensitivity function.
- getAlpha2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets Alpha2 from the sensitivity function.
- getAnomalyScore() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOutput
-
Gets a value between [0, 1] with values closer to 0 being more likely to be anomalous.
- getAutoCommit() - Method in class com.bayesserver.data.DatabaseDataReaderCommand
-
Gets the auto commit value to be set on each connection created.
- getAutoDetectDiscreteLimit() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Gets the distinct value count, which when exceeded changes a variable from discrete to continuous.
- getAutoReadTemporal() - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Determines whether any temporal data is read automatically.
- getB() - Method in class com.bayesserver.learning.structure.LinkConstraint
-
Gets the second node involved in the constraint.
- getBaseEvidence() - Method in class com.bayesserver.causal.CausalInferenceBase
-
Optional evidence which can be used to calculate the lift of queries.
- getBaseEvidence() - Method in interface com.bayesserver.inference.Inference
-
Optional evidence which can be used to calculate the lift of queries.
- getBaseEvidence() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Optional evidence which can be used to calculate the lift of queries.
- getBaseEvidence() - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Optional evidence which can be used to calculate the lift of queries.
- getBaseEvidence() - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Optional evidence which can be used to calculate the lift of queries.
- getBaseEvidence() - Method in class com.bayesserver.inference.VariableEliminationInference
-
Optional evidence which can be used to calculate the lift of queries.
- getBaseline() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutput
-
Gets baseline log-likelihood values.
- getBestToWorst() - Method in class com.bayesserver.analysis.ClusterCountOutput
-
A list of scores, sorted from best to worst.
- getBeta() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
-
Gets Beta from the sensitivity function.
- getBeta1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets Beta1 from the sensitivity function.
- getBeta2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets Beta2 from the sensitivity function.
- getBIC() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
-
Gets the Bayesian Information Criterion (BIC) for the final learnt
Network
based on the learning data.
- getBoolean(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getBoolean(int) - Method in interface com.bayesserver.data.DataRecord
-
Gets a boolean value for the specified column.
- getBoolean(int) - Method in class com.bayesserver.data.DataTableReader
-
- getBoolean(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets a boolean value for the specified column.
- getBounds() - Method in class com.bayesserver.Node
-
Gets the size and location of the node.
- getCalculateStatistics() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets a value indicating whether to calculate summary statistics in an extra iteration at the end of learning.
- getCancel() - Method in interface com.bayesserver.Cancellation
-
When set to true
attempts to cancel a long running operation.
- getCancel() - Method in class com.bayesserver.DefaultCancellation
-
When set to true
attempts to cancel a long running operation.
- getCancellation() - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Allows cancellation of a query.
- getCancellation() - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions
-
Gets of sets an instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Gets of sets an instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Allows cancellation of a query.
- getCancellation() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Allows cancellation of a query.
- getCancellation() - Method in interface com.bayesserver.inference.QueryOptions
-
Allows cancellation of a query.
- getCancellation() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Allows cancellation of a query.
- getCancellation() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Allows cancellation of a query.
- getCancellation() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Allows cancellation of a query.
- getCancellation() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- getCancellation() - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
-
Used to cancel a long running operation.
- getCanStop() - Method in interface com.bayesserver.Stop
-
When true
, indicates that the algorithm supports early stopping.
- getCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
-
Gets the number of cases (records) in the learning data.
- getCaseId() - Method in class com.bayesserver.data.ReadInfo
-
The current case id.
- getCaseIdColumn() - Method in class com.bayesserver.data.NestedDataReader
-
The name of the case identifier column, which links to the case table.
- getCaseIdColumn() - Method in class com.bayesserver.data.ReaderOptions
-
The name of the case identifier column, if one is present.
- getCaseIdColumn() - Method in class com.bayesserver.data.TemporalReaderOptions
-
The name of the temporal case identifier column, if one is present.
- getCategory() - Method in class com.bayesserver.analysis.DSeparationTestResult
-
The test result.
- getCausalEffectKind() - Method in class com.bayesserver.causal.BackdoorCriterionOptions
-
The type of causal effect, such as Total or Direct.
- getCausalEffectKind() - Method in class com.bayesserver.causal.BackdoorValidationOptions
-
The type of causal effect, such as Total or Direct.
- getCausalEffectKind() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Gets the kind of effect to calculate.
- getCausalEffectKind() - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOptions
-
The type of causal effect, such as Total or Direct.
- getCausalEffectKind() - Method in class com.bayesserver.causal.DisjunctiveCauseValidationOptions
-
The type of causal effect, such as Total or Direct.
- getCausalEffectKind() - Method in class com.bayesserver.causal.FrontDoorCriterionOptions
-
The type of causal effect, such as Total or Direct.
- getCausalEffectKind() - Method in class com.bayesserver.causal.FrontDoorValidationOptions
-
The type of causal effect, such as Total or Direct.
- getCausalEffectKind() - Method in interface com.bayesserver.causal.IdentificationOptions
-
The type of causal effect, such as Total or Direct.
- getCausalEffectKind() - Method in interface com.bayesserver.causal.ValidationOptions
-
The type of causal effect, such as Total or Direct.
- getCausalEffectKind() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Gets the kind of effect to calculate.
- getCausalEffectKind() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Gets the kind of effect to calculate.
- getCausalEffectKind() - Method in interface com.bayesserver.inference.QueryOptions
-
Gets the kind of effect to calculate.
- getCausalEffectKind() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Gets the kind of effect to calculate.
- getCausalEffectKind() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Gets the kind of effect to calculate.
- getCausalEffectKind() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Gets the kind of effect to calculate.
- getCausalEffectKind() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Gets the kind of causal effect to optimize.
- getCausalEffectKind() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Gets the kind of causal effect to optimize.
- getCausalInferenceFactory() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- getCausalInferenceFactory() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- getCausalInferenceFactory() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- getCausalInferenceFactory() - Method in interface com.bayesserver.inference.QueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- getCausalInferenceFactory() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- getCausalInferenceFactory() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- getCausalInferenceFactory() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- getCausalObservability() - Method in class com.bayesserver.Node
-
- getCausesOfTreatmentsOrOutcomes() - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOptions
-
Gets a list of nodes which must include all causes of treatments (X) or causes of outcomes (Y) or causes of both.
- getCausesOfTreatmentsOrOutcomes() - Method in class com.bayesserver.causal.DisjunctiveCauseQueryOptions
-
Gets the list of all nodes that are either causes of treatments (X) or outcomes (Y) or both.
- getCell(Comparable, Comparable) - Method in class com.bayesserver.analysis.ConfusionMatrix
-
Gets information about a cell in a confusion matrix.
- getCleared() - Method in class com.bayesserver.data.DefaultReadOptions
-
- getCleared() - Method in interface com.bayesserver.data.ReadOptions
-
- getClusterCount() - Method in class com.bayesserver.analysis.ClusterScore
-
The number of clusters used to generate this score.
- getClusterVariableName() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets the name of the cluster/latent node/variable created when more than 1 hidden state is detected.
- getColumn() - Method in class com.bayesserver.data.VariableReference
-
Gets the name of the relevant column in the data source.
- getColumnCount() - Method in class com.bayesserver.data.DataReaderFiltered
-
- getColumnCount() - Method in interface com.bayesserver.data.DataRecord
-
Gets the number of columns (fields) in the data.
- getColumnCount() - Method in class com.bayesserver.data.DataTableReader
-
- getColumnCount() - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets the number of columns (fields) in the data.
- getColumnIndex(String) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getColumnIndex(String) - Method in interface com.bayesserver.data.DataRecord
-
Gets the zero based column index for a column name.
- getColumnIndex(String) - Method in class com.bayesserver.data.DataTableReader
-
- getColumnIndex(String) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets the zero based column index for a column name.
- getColumnName() - Method in class com.bayesserver.data.DataColumn
-
- getColumnName(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getColumnName(int) - Method in interface com.bayesserver.data.DataRecord
-
Gets the name of the column at the specified index.
- getColumnName(int) - Method in class com.bayesserver.data.DataTableReader
-
- getColumnName() - Method in class com.bayesserver.data.discovery.DiscretizationColumn
-
Gets the name of the column of data to be discretized.
- getColumnName(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets the name of the column at the specified index.
- getColumns() - Method in class com.bayesserver.data.DataTable
-
Gets the columns in the table.
- getColumnType(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getColumnType(int) - Method in interface com.bayesserver.data.DataRecord
-
Get the data type for the specified column.
- getColumnType(int) - Method in class com.bayesserver.data.DataTableReader
-
- getColumnType(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Get the data type for the specified column.
- getColumnValueType() - Method in class com.bayesserver.data.VariableReference
-
Gets the type of value in the bound data column.
- getComparison() - Method in class com.bayesserver.inference.QueryDistribution
-
Gets a value indicating whether queried values should be adjusted to show how they compare to the same query with no evidence, or base evidence.
- getConfiguration() - Method in interface com.bayesserver.Distributer
-
Gets configuration name value pairs which must be made available to the distributed workers.
- getConflict() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
- getConflict() - Method in class com.bayesserver.causal.CausalQueryOutputBase
-
Gets the conflict measure.
- getConflict() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
- getConflict() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
-
Gets the conflict measure.
- getConflict() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
- getConflict() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
-
Gets the conflict measure.
- getConflict() - Method in interface com.bayesserver.inference.QueryOptions
-
- getConflict() - Method in interface com.bayesserver.inference.QueryOutput
-
Gets the conflict measure.
- getConflict() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
- getConflict() - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
-
Gets the conflict measure.
- getConflict() - Method in class com.bayesserver.inference.TreeQueryOptions
-
- getConflict() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
- getConflict() - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
-
Gets the conflict measure.
- getContinuous() - Method in class com.bayesserver.learning.parameters.Priors
-
Gets the amount continuous distributions are adjusted during learning.
- getContinuousTargetInterval() - Method in class com.bayesserver.analysis.AutoInsightOutput
-
Gets the target interval (if any).
- getConverged() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
-
Gets a value indicating whether this parameter learning converged.
- getConvergenceMethod() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets the method used to determine convergence of the learning algorithm.
- getCorrectCount() - Method in class com.bayesserver.analysis.ConfusionMatrix
-
Gets the total number of correct predictions.
- getCounts() - Method in class com.bayesserver.data.discovery.VariableInfo
-
Gets counts such as missing and non-missing data for the variable.
- getCovariance(int, int, int) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution at the specified [index] in the
Table
of discrete combinations.
- getCovariance(Variable, Variable, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCovariance(Variable, Variable) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
- getCovariance(Variable, Integer, Variable, Integer) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
- getCovariance(Variable, Variable, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCovariance(Variable, Integer, Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCovariance(Variable, Integer, Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCovariance(VariableContext, VariableContext, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCovariance(VariableContext, VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCovariance(Variable, Variable, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCovariance(Variable, Integer, Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCovariance(VariableContext, VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- getCrossoverProbability() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
The probability of parents being crossed.
- getCustomProperties() - Method in class com.bayesserver.Link
-
Gets custom properties associated with this instance.
- getCustomProperties() - Method in class com.bayesserver.Network
-
Gets custom properties associated with this instance.
- getCustomProperties() - Method in class com.bayesserver.Node
-
Gets custom properties associated with this instance.
- getCustomProperties() - Method in class com.bayesserver.NodeGroup
-
Gets custom properties associated with this instance.
- getCustomProperties() - Method in class com.bayesserver.State
-
Gets custom properties associated with this instance.
- getCustomProperties() - Method in class com.bayesserver.Variable
-
Gets custom properties associated with this instance.
- getDataColumn() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
The name of the data column, containing the data used to generate the new variable.
- getDataProgress() - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Gets the instance used to report progress on the number of cases read.
- getDataProgress() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Reports progress on the number of cases read.
- getDataProgressInterval() - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Gets a value which determines how often progress events are raised.
- getDataReader() - Method in class com.bayesserver.data.NestedDataReader
-
Gets the nested data reader.
- getDataType() - Method in class com.bayesserver.data.DataColumn
-
Gets the type of data the column contains.
- getDbn() - Method in class com.bayesserver.UnrollOutput
-
Gets the Dynamic Bayesian network before it was unrolled.
- getDbnNode(Node) - Method in class com.bayesserver.UnrollOutput
-
Maps from a node in the unrolled network to the corresponding node in the original Dynamic Bayesian network.
- getDbnVariable(Variable) - Method in class com.bayesserver.UnrollOutput
-
Maps from a variable in the unrolled network to the corresponding variable in the original Dynamic Bayesian network.
- getDecisionAlgorithm() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Gets the algorithm to use when a network contains Decision nodes.
- getDecisionAlgorithm() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Gets the algorithm to use when a network contains Decision nodes.
- getDecisionAlgorithm() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Gets the algorithm to use when a network contains Decision nodes.
- getDecisionAlgorithm() - Method in interface com.bayesserver.inference.QueryOptions
-
Gets the algorithm to use when a network contains Decision nodes.
- getDecisionAlgorithm() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Gets the algorithm to use when a network contains Decision nodes.
- getDecisionAlgorithm() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Gets the algorithm to use when a network contains Decision nodes.
- getDecisionAlgorithm() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Gets the algorithm to use when a network contains Decision nodes.
- getDecisionAlgorithm() - Method in class com.bayesserver.learning.parameters.OnlineLearningOptions
-
Gets the algorithm to use for adaption of decision graphs.
- getDecisionPostProcessing() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets the post processing method for decision nodes.
- getDecomposedNetwork() - Method in class com.bayesserver.DecomposeOutput
-
Gets the network, which is the decomposed equivalent of the original network.
- getDecomposedVariable(Variable) - Method in class com.bayesserver.DecomposeOutput
-
Maps a variable in the original network to the equivalent variable in the decomposed network.
- getDefault() - Static method in class com.bayesserver.NodeDistributionKey
-
Gets a default instance, which is equivalent to constructing a new instance with the default constructor.
- getDelta() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
-
Gets Delta from the sensitivity function.
- getDelta() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
-
Gets the relative change in parameters used to determine convergence.
- getDelta1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets Delta1 from the sensitivity function.
- getDelta2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets Delta2 from the sensitivity function.
- getDescription() - Method in class com.bayesserver.CustomProperty
-
An optional description for the custom property.
- getDescription() - Method in class com.bayesserver.Link
-
Optional description for the link.
- getDescription() - Method in class com.bayesserver.Network
-
An optional description for the Bayesian network.
- getDescription() - Method in class com.bayesserver.Node
-
An optional description for the node.
- getDescription() - Method in class com.bayesserver.NodeGroup
-
An optional description for the custom property.
- getDescription() - Method in class com.bayesserver.State
-
Gets an optional description for the state.
- getDescription() - Method in class com.bayesserver.Variable
-
An optional description for the variable.
- getDesignStates() - Method in class com.bayesserver.optimization.DesignVariable
-
Gets the design states, one for each state in the variable.
- getDetectIntegralFloats() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Gets a value, which when true tests floating point column data to see if the data is an integral type, which would then become a candidate to be a discrete variable when VariableValueType is not specified.
- getDifference() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
-
Gets the difference between the probability of this state given the target state and the probability of this state excluding the target state.
- getDiscrete() - Method in class com.bayesserver.learning.parameters.Priors
-
Gets the amount distributions containing discrete variables are adjusted during learning.
- getDiscretePriorMethod() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
-
Gets the type of discrete prior to use for this distribution.
- getDiscretePriorMethod() - Method in class com.bayesserver.learning.parameters.Priors
-
The default discrete prior to use for discrete distributions during parameter learning.
- getDiscretizationMethod() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
Gets the method (algorithm) to use for discretization, if any.
- getDiscretizationOptions() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
- getDistance() - Method in class com.bayesserver.inference.QueryDistribution
-
The distance between this query calculated with base evidence or no evidence, and when calculated with evidence.
- getDistribution() - Method in class com.bayesserver.inference.QueryDistribution
-
Gets the distribution to query.
- getDistribution() - Method in class com.bayesserver.Node
-
Returns the distribution currently associated with the
Node
.
- getDistribution() - Method in class com.bayesserver.NodeDistributions.DistributionOrder
-
Gets the distribution.
- getDistributionMonitoring() - Method in interface com.bayesserver.learning.parameters.ParameterLearningProgress
-
Gets information about the current state of distributions being monitored.
- getDistributionOptions() - Method in class com.bayesserver.Node
-
Options that apply to all distributions of this instance.
- getDistributions() - Method in class com.bayesserver.Node
-
Returns the distributions associated with this instance with NodeDistributionKind = Probability.
- getDouble(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getDouble(int) - Method in interface com.bayesserver.data.DataRecord
-
Gets a double value for the specified column.
- getDouble(int) - Method in class com.bayesserver.data.DataTableReader
-
- getDouble(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets a double value for the specified column.
- getEmptyStringAction() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
Determines the action to take if an empty string is encountered.
- getEmptyStringAction() - Method in class com.bayesserver.data.VariableReference
-
Determines the action to take if an empty string is encountered.
- getEnsureTestWithoutCluster() - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Gets a value which indicates whether a test must be included which excludes the cluster variable altogether.
- getEntropyX() - Method in class com.bayesserver.analysis.AssociationPairOutput
-
Gets the entropy for X.
- getEntropyY() - Method in class com.bayesserver.analysis.AssociationPairOutput
-
Gets the entropy for Y.
- getEnumerateAllMissing() - Method in class com.bayesserver.analysis.CombinationOptions
-
Gets a value which indicates whether the combination where all states are null/missing should be included in the enumeration.
- getEnumerateMissing() - Method in class com.bayesserver.analysis.CombinationOptions
-
Gets a value which indicates whether null/missing values should be enumerated in addition to each state.
- getEvidence() - Method in class com.bayesserver.causal.CausalInferenceBase
-
Represents the evidence, or case data (e.g.
- getEvidence() - Method in interface com.bayesserver.inference.Inference
-
Represents the evidence, or case data (e.g.
- getEvidence() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Represents the evidence, or case data (e.g.
- getEvidence() - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Represents the evidence, or case data (e.g.
- getEvidence() - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Represents the evidence, or case data (e.g.
- getEvidence() - Method in class com.bayesserver.inference.VariableEliminationInference
-
Gets the evidence (case data, e.g.
- getEvidence() - Method in class com.bayesserver.learning.parameters.OnlineLearning
-
Gets the evidence used internally.
- getEvidence() - Method in class com.bayesserver.optimization.GeneticOptimizerOutput
-
The evidence required to produce the optimized objective value.
- getEvidence() - Method in class com.bayesserver.optimization.GeneticOptimizerProgressInfo
-
Gets the evidence for the objective value.
- getEvidence() - Method in class com.bayesserver.optimization.GeneticSimplificationOutput
-
The evidence required to produce the optimized objective value.
- getEvidence() - Method in interface com.bayesserver.optimization.OptimizerOutput
-
The evidence required to produce the optimized objective value.
- getEvidence() - Method in interface com.bayesserver.optimization.OptimizerProgressInfo
-
Gets the evidence for the objective value.
- getEvidenceFlags() - Method in class com.bayesserver.analysis.ImpactOutputItem
-
Gets a list of values each of which indicate which of the evidence being analyzed is set.
- getEvidenceFlags() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutputItem
-
Gets a list of values each of which indicate which of the evidence being analyzed is set.
- getEvidenceKind() - Method in class com.bayesserver.optimization.DesignVariable
-
Determines whether the optimizer uses hard or soft/virtual evidence for this variable.
- getEvidenceReader() - Method in class com.bayesserver.data.EvidenceReaderEventArgs
-
Gets the reader created by a reader command.
- getEvidenceToSimplify() - Method in class com.bayesserver.optimization.GeneticSimplificationOptions
-
The evidence from a previous optimization.
- getEvidenceType(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
-
Returns the type of evidence currently set for a variable (if any).
- getEvidenceType(Node) - Method in class com.bayesserver.inference.DefaultEvidence
-
Returns the type of evidence currently set for a node with a single variable.
- getEvidenceType(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Returns the type of evidence currently set for a node with a single variable at a given time.
- getEvidenceType(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Returns the type of evidence currently set for a variable at a given time.
- getEvidenceType(Variable) - Method in interface com.bayesserver.inference.Evidence
-
Returns the type of evidence currently set for a variable (if any).
- getEvidenceType(Node) - Method in interface com.bayesserver.inference.Evidence
-
Returns the type of evidence currently set for a node with a single variable.
- getEvidenceType(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Returns the type of evidence currently set for a node with a single variable at a given time.
- getEvidenceType(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Returns the type of evidence currently set for a variable at a given time.
- getEvidenceType() - Method in class com.bayesserver.inference.EvidenceTypes
-
- getEvidenceTypes(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
- getEvidenceTypes(Node) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
- getEvidenceTypes(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
- getEvidenceTypes(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
- getEvidenceTypes(Variable) - Method in interface com.bayesserver.inference.Evidence
-
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
- getEvidenceTypes(Node) - Method in interface com.bayesserver.inference.Evidence
-
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
- getEvidenceTypes(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
- getEvidenceTypes(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
- getExcludeNullDistributions() - Method in class com.bayesserver.ParameterCountOptions
-
Gets a value indicating whether null distributions are excluded from the parameter count.
- getExpression() - Method in class com.bayesserver.NodeDistributionExpressions.DistributionExpressionOrder
-
Gets the expression.
- getExpressionAlias() - Method in class com.bayesserver.Variable
-
Gets a c-style name for a variable that can be used as an alias in expressions.
- getExpressions() - Method in class com.bayesserver.NodeDistributions
-
Gets any expressions associated with a node, that are used to generate distributions.
- getFactory() - Method in class com.bayesserver.analysis.ImpactOptions
-
Gets the inference factory which is used to create inference engines during an impact analysis.
- getFactory() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
-
Gets the inference factory which is used to create inference engines during a Log-Likelihood analysis.
- getFailureMode() - Method in class com.bayesserver.learning.structure.LinkConstraint
-
Gets the action to take when this link constraint is violated.
- getFetchSize() - Method in class com.bayesserver.data.DatabaseDataReaderCommand
-
Gets the fetch size to be set on each statement created.
- getFloat(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getFloat(int) - Method in interface com.bayesserver.data.DataRecord
-
Gets a float value for the specified column.
- getFloat(int) - Method in class com.bayesserver.data.DataTableReader
-
- getFloat(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets a float value for the specified column.
- getFrom() - Method in class com.bayesserver.Link
-
The parent node of the directed link.
- getFrontDoorNodes() - Method in class com.bayesserver.causal.FrontDoorValidationOptions
-
Gets the front-door nodes to use during validation.
- getFrontDoorNodesOverride() - Method in class com.bayesserver.causal.FrontDoorQueryOptions
-
Gets the set of front-door nodes (Z) used by the front-door adjustment.
- getFunction() - Method in class com.bayesserver.Variable
-
Gets an expression, which is evaluated during a query, and can be based on other queries and expressions.
- getFunctionOutput() - Method in class com.bayesserver.inference.QueryFunction
-
Gets the function to evaluate.
- getGamma() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
-
Gets Gamma from the sensitivity function.
- getGamma1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets Gamma1 from the sensitivity function.
- getGamma2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets Gamma2 from the sensitivity function.
- getGap() - Method in class com.bayesserver.DecomposeOptions
-
The gap between decomposed nodes, used when laying out new nodes.
- getGroups() - Method in class com.bayesserver.Node
-
Gets the groups this node belongs to.
- getHasTemporalReader() - Method in class com.bayesserver.data.DefaultDataReader
-
Gets a value indicating whether the reader includes temporal data.
- getHasZeroIntercepts() - Method in class com.bayesserver.NodeDistributionOptions
-
Determines whether
CLGaussian
intercept terms are fixed to zero.
- getHeadTail() - Method in class com.bayesserver.VariableContext
-
Specifies whether the variable is marked as Head or Tail.
- getHeight() - Method in class com.bayesserver.Bounds
-
Gets the height of the element.
- getHypothesis() - Method in class com.bayesserver.analysis.ImpactOutput
-
Gets output information for the hypothesis variable/state.
- getHypothesisImprovement() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput
-
Gets the improvement between the hypothesis statistic when we do not have knowledge about this test variable and when we do.
- getHypothesisStatistic() - Method in class com.bayesserver.analysis.ValueOfInformationOutput
-
Gets the statistic associated with the hypothesis before any test variables have evidence set.
- getHypothesisStatistic() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput
-
Gets the statistic for the hypothesis given knowledge on this test variable.
- getIdeal() - Method in class com.bayesserver.analysis.LiftChart
-
Gets the population probability value at which the target reaches 100 %.
- getIncludeGlobalCovariance() - Method in class com.bayesserver.learning.parameters.Priors
-
When Gaussian distributions are adjusted according to the
Priors.getContinuous()
prior, this property determines whether the global covariance should be included in the adjustment, as well as the global variance.
- getInconsistentEvidenceMode() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
- getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
- getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
- getInconsistentEvidenceMode() - Method in interface com.bayesserver.inference.QueryOptions
-
- getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
- getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.TreeQueryOptions
-
- getInconsistentEvidenceMode() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
- getIndependence() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Gets options controlling how the independence tests are carried out.
- getIndex() - Method in class com.bayesserver.Link
-
The Index of this instance in the collection of links belonging to a network, or -1 if the link does not belong to a network.
- getIndex() - Method in class com.bayesserver.Node
-
The Index of this instance in the collection of nodes belonging to a network, or -1 if the node does not belong to a network.
- getIndex() - Method in class com.bayesserver.State
-
- getIndex() - Method in class com.bayesserver.Table.MaxValue
-
- getIndex() - Method in class com.bayesserver.Variable
-
The Index of this instance in the collection of variables belonging to a network, or -1 if the variable does not belong to a node and hence a network.
- getInference() - Method in interface com.bayesserver.inference.QueryLifecycleBegin
-
The current inference engine.
- getInference() - Method in class com.bayesserver.inference.QueryLifecycleBeginBase
-
The current inference engine.
- getInference() - Method in interface com.bayesserver.inference.QueryLifecycleEnd
-
The current inference engine.
- getInference() - Method in class com.bayesserver.inference.QueryLifecycleEndBase
-
The current inference engine.
- getInferenceFactory() - Method in class com.bayesserver.analysis.AssociationOptions
-
Gets the inference factory used for link strength calculations.
- getInferenceFactory() - Method in class com.bayesserver.analysis.AutoInsightOptions
-
Gets the inference factory used for link strength calculations.
- getInferenceFactory() - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Gets the factory which is used to create inference engines during the cluster count tests.
- getInferenceFactory() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
-
Gets the factory which is used to create inference engines during the in-sample anomaly detection process.
- getInferenceFactory() - Method in class com.bayesserver.causal.AbductionOptions
-
Used to create an inference engine, to determine the values for the characterstic variables.
- getInferenceFactory() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets the inference factory used during scoring.
- getInferenceFactory() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets the inference factory used during scoring.
- getInferenceFactory() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets the inference factory used during scoring.
- getInferenceFactory() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Used to create one or more inference engines, used by the algorithm to determine the fitness of possible solutions.
- getInferenceFactory() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Creates one or more inference engines used by the optimization algorithm.
- getInfiniteExtremes() - Method in class com.bayesserver.data.discovery.DiscretizationOptions
-
Gets a value indicating whether the first and last intervals extend to negative and positive infinity respectively.
- getInitialization() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Options for initialization.
- getInitialize() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
-
Gets a flag indicating whether the distribution should be initialized.
- getInitializeDistributions() - Method in class com.bayesserver.learning.parameters.InitializationOptions
-
Indicates whether or not to initialize distributions by default.
- getInnerMessage() - Method in class com.bayesserver.data.discovery.VariableGeneratorProgressInfo
-
Gets an inner progress message.
- getInt(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getInt(int) - Method in interface com.bayesserver.data.DataRecord
-
Gets an integer value for the specified column.
- getInt(int) - Method in class com.bayesserver.data.DataTableReader
-
- getInt(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets an integer value for the specified column.
- getInterval() - Method in class com.bayesserver.analysis.ParameterTuningOneWay
-
Gets the interval for the parameter which satisfies the constraint used in parameter tuning.
- getIntervals() - Method in class com.bayesserver.data.discovery.DiscretizationInfo
-
Gets the intervals generated by a discretization algorithm for a column of data.
- getInterventionColumn() - Method in class com.bayesserver.data.VariableReference
-
Gets the optional name of a column in the data source that identifies whether this is an intervention (Do evidence) or not.
- getInterventionType() - Method in class com.bayesserver.inference.EvidenceTypes
-
- getInterventionType() - Method in class com.bayesserver.optimization.DesignVariable
-
Determines the evidence intervention type for this variable.
- getIsApproximate() - Method in class com.bayesserver.analysis.AutoInsightOutput
-
Gets a value which when true indicates that the auto-insight calculations were approximated using sampling.
- getIsConstant() - Method in class com.bayesserver.data.discovery.VariableInfo
-
Gets a value which when true indicates that the variable has a constant value.
- getIsEnabled() - Method in class com.bayesserver.inference.QueryDistribution
-
Gets a value indicating whether the distribution should be queried.
- getIsEnabled() - Method in class com.bayesserver.inference.QueryFunction
-
Gets a value indicating whether the function should be evaluated.
- getIsImpliedEvidenceEnabled() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Gets a value indicating whether to detect implied evidence during the calculation.
- getIsInternal() - Method in class com.bayesserver.Network
-
For internal use only.
- getIsProper() - Method in class com.bayesserver.causal.BackdoorGraphOptions
-
Gets a value which determines whether a 'proper Backdoor graph' is constructed.
- getIsReadOnly() - Method in class com.bayesserver.StateCollection
-
Gets a value indicating whether or not the collection is read-only.
- getIsValid() - Static method in class com.bayesserver.License
-
Gets a value indicating whether a license has been successfully validated or not.
- getItems() - Method in class com.bayesserver.analysis.ImpactOutput
-
Gets the output for each combination.
- getItems() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutput
-
Gets the output for each combination.
- getItems() - Method in class com.bayesserver.causal.EffectsAnalysisOutput
-
A result for each treatment value.
- getIterationCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
-
Gets the number of iterations performed during learning.
- getIterationCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
-
Gets the current iteration count.
- getIterations() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
-
Gets the number of iterations performed.
- getJSDivergence() - Method in class com.bayesserver.analysis.AutoInsightOptions
-
Gets a value which determines the type of Jensen Shannon divergence calculations to perform, if any.
- getJSDivergenceBits() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
-
Gets the Jensen Shannon divergence for the test distribution, measured in BITS.
- getKeepEvidenceNotAnalyzed() - Method in class com.bayesserver.analysis.ImpactOptions
-
Gets a value which when true retains evidence not being analysed, or when false ignores it.
- getKeepEvidenceNotAnalyzed() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
-
Gets a value which when true retains evidence not being analysed, or when false ignores it.
- getKey() - Method in class com.bayesserver.analysis.ParameterReference
-
Gets the of the node's distribution being referenced.
- getKey() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
-
Gets the order/related node of the distribution.
- getKeys() - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets the collection of node distribution keys that require distributions.
- getKeys() - Method in class com.bayesserver.NodeDistributions
-
Gets the collection of node distribution keys that require distributions.
- getKind() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput
-
Gets the type of Value of information statistic calculated.
- getKind() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
- getKind() - Method in class com.bayesserver.optimization.Objective
-
Gets the kind of optimization to carry out.
- getKind() - Method in class com.bayesserver.Variable
-
Gets the kind of variable, such as Probability, Decision, Utility or Function.
- getKLDivergence() - Method in class com.bayesserver.analysis.AutoInsightOptions
-
Gets a value which determines the type of KL divergence calculations to perform, if any.
- getKLDivergence() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
-
Gets the Kullback-Leibler divergence for the test distribution, and tells us how much the test variance changes with the hypothesis.
- getKLDivergenceFromNone() - Method in class com.bayesserver.analysis.ImpactOutputItem
-
Gets the Kullback-Leibler divergence D(P||Q) from the hypothesis query without evidence to analyze set (Q) to the current combination (P).
- getKLDivergenceToAll() - Method in class com.bayesserver.analysis.ImpactOutputItem
-
Gets the Kullback-Leibler divergence D(P||Q) from the hypothesis query with the current subset of evidence (Q) to all evidence to analyze set (P).
- getLift() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
-
Gets the ratio of the probability of this state given the target state over the probability of this state excluding the target state.
- getLink() - Method in class com.bayesserver.learning.structure.ChowLiuLinkOutput
-
Gets the new link.
- getLink() - Method in class com.bayesserver.learning.structure.ClusteringLinkOutput
-
Gets the new link.
- getLink() - Method in class com.bayesserver.learning.structure.HierarchicalLinkOutput
-
Gets the new link.
- getLink() - Method in interface com.bayesserver.learning.structure.LinkOutput
-
Gets the new link.
- getLink() - Method in class com.bayesserver.learning.structure.PCLinkOutput
-
Gets the new link.
- getLink() - Method in class com.bayesserver.learning.structure.SearchLinkOutput
-
Gets the new link.
- getLink() - Method in class com.bayesserver.learning.structure.TANLinkOutput
-
Gets the new link.
- getLinkConstraints() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Gets any link constraints to use during structural learning.
- getLinkConstraints() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets any link constraints to use during structural learning.
- getLinkConstraints() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets any link constraints to use during structural learning.
- getLinkConstraints() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Gets any link constraints to use during structural learning.
- getLinkConstraints() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets any link constraints to use during structural learning.
- getLinkConstraints() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
-
Gets any link constraints to use during structural learning.
- getLinkConstraints() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Gets any link constraints to use during structural learning.
- getLinkOutputs() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOutput
-
Gets information about any new links added during the learning process.
- getLinkOutputs() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOutput
-
Gets information about any new links added during the learning process.
- getLinkOutputs() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOutput
-
Gets information about any new links added during the learning process.
- getLinkOutputs() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOutput
-
Gets information about any new links added during the learning process.
- getLinkOutputs() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOutput
-
Gets information about any new links added during the learning process.
- getLinkOutputs() - Method in interface com.bayesserver.learning.structure.StructuralLearningOutput
-
Gets information about any new links added during the learning process.
- getLinkOutputs() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOutput
-
Gets information about any new links added during the learning process.
- getLinks() - Method in class com.bayesserver.Network
-
The collection of links in the Bayesian network.
- getLinks() - Method in class com.bayesserver.Node
-
Collection of both incoming and outgoing links (parent and child nodes).
- getLinksIn() - Method in class com.bayesserver.Node
-
Collection of incoming links (linking to parent nodes).
- getLinksOut() - Method in class com.bayesserver.Node
-
Collection of outgoing links (linking to child nodes).
- getLocked() - Method in class com.bayesserver.CLGaussian
-
Locks or unlocks a distribution.
- getLocked() - Method in interface com.bayesserver.Distribution
-
Locks or unlocks a distribution.
- getLocked() - Method in class com.bayesserver.Table
-
Locks or unlocks a distribution.
- getLogarithmBase() - Method in class com.bayesserver.analysis.ValueOfInformationOptions
-
- getLogLikelihood() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOutputItem
-
Gets the log-likelihood for this output item evidence.
- getLogLikelihood() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
- getLogLikelihood() - Method in class com.bayesserver.causal.CausalQueryOutputBase
-
Gets the log-likelihood value.
- getLogLikelihood() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
- getLogLikelihood() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
-
Gets the log-likelihood value.
- getLogLikelihood() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
- getLogLikelihood() - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
-
Gets the log-likelihood value.
- getLogLikelihood() - Method in class com.bayesserver.inference.QueryDistribution
-
The log-likelihood specific to the evidence used to calculate this query.
- getLogLikelihood() - Method in interface com.bayesserver.inference.QueryOptions
-
- getLogLikelihood() - Method in interface com.bayesserver.inference.QueryOutput
-
Gets the log-likelihood value.
- getLogLikelihood() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
- getLogLikelihood() - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
-
Gets the log-likelihood value.
- getLogLikelihood() - Method in class com.bayesserver.inference.TreeQueryOptions
-
- getLogLikelihood() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
- getLogLikelihood() - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
-
Gets the log-likelihood value.
- getLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
-
Gets the log likelihood of the learning data with the final learnt
Network
.
- getLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
-
Gets the current log likelihood value, if calculated
- getLogLikelihoodAll() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisBaselineOutput
-
Gets the log-likelihood with all evidence to analyze set.
- getLogLikelihoodNone() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisBaselineOutput
-
Gets the log-likelihood with no evidence to analyze set.
- getLogWeight() - Method in class com.bayesserver.inference.DefaultEvidence
-
- getLogWeight() - Method in interface com.bayesserver.inference.Evidence
-
- getLong(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getLong(int) - Method in interface com.bayesserver.data.DataRecord
-
Gets a long value for the specified column.
- getLong(int) - Method in class com.bayesserver.data.DataTableReader
-
- getLong(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets a long value for the specified column.
- getLowerBound() - Method in class com.bayesserver.optimization.DesignState
-
The minimum value allowed for this variable/state during the optimization process.
- getMaxDepth() - Method in class com.bayesserver.TopologicalSortNodeInfo
-
Gets the maximum number of links from a root node to this node.
- getMaxEvidenceSubsetSize() - Method in class com.bayesserver.analysis.ImpactOptions
-
Gets the maximum size of evidence subsets to consider.
- getMaxEvidenceSubsetSize() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
-
Gets the maximum size of evidence subsets to consider.
- getMaximum() - Method in class com.bayesserver.Interval
-
Gets the maximum interval value.
- getMaximumAdjustmentSets() - Method in class com.bayesserver.causal.BackdoorCriterionOptions
-
Limits the number of adjustment sets generated.
- getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
- getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
- getMaximumBatchSize() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Gets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
- getMaximumClusterCount() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets the maximum number of clusters generated.
- getMaximumClustersPerGroup() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets the maximum number of clusters generated for each group.
- getMaximumConcurrency() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets the maximum number of inference engines used during learning.
- getMaximumConcurrency() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Gets the maximum number of inference engines used during optimization.
- getMaximumConcurrency() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Gets the maximum number of inference engines used during optimization.
- getMaximumConditional() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Gets the maximum number of conditional variables to consider during independence testing.
- getMaximumEndPoint() - Method in class com.bayesserver.Interval
-
Gets the end point type for the maximum value of the interval.
- getMaximumGroupsPerLevel() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets the maximum number of groups created per level.
- getMaximumIterations() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets the maximum number of iterations that parameter learning will perform.
- getMaximumIterations() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets the maximum number of iterations used by parameter learning to score each configuration.
- getMaximumIterations() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets the maximum number of iterations used by parameter learning to score each configuration.
- getMaximumIterations() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets the optional maximum number of iterations (moves) made during the search procedure.
- getMaximumLevels() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets the maximum number of levels generated by the algorithm.
- getMaximumSupport() - Method in class com.bayesserver.learning.parameters.InitializationOptions
-
Limits the amount of support each distribution is given during initialization.
- getMaximumTemporalOrder() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Gets the maximum order of temporal links that are considered during learning.
- getMaxTemporalOrder() - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets the current maximum temporal order.
- getMaxTemporalOrder() - Method in class com.bayesserver.NodeDistributions
-
Gets the current maximum temporal order.
- getMaxTime(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the maximum time containing evidence for a variable.
- getMaxTime() - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the maximum time containing evidence.
- getMaxTime(Variable) - Method in interface com.bayesserver.inference.Evidence
-
Gets the maximum time containing evidence for a variable.
- getMaxTime() - Method in interface com.bayesserver.inference.Evidence
-
Gets the maximum time containing evidence.
- getMaxValue() - Method in class com.bayesserver.Table
-
Gets the maximum table value, and the index at which it occurs.
- getMean(int, int) - Method in class com.bayesserver.CLGaussian
-
Gets the mean of the Gaussian distribution at the specified [index] in the
Table
of discrete combinations.
- getMean(Variable, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- getMean(Variable) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
- getMean(Variable, Integer) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable and time.
- getMean(Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- getMean(VariableContext, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- getMean(Variable, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- getMean(Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- getMean(VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- getMean(Variable, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
- getMean(Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
- getMean(VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
- getMean() - Method in class com.bayesserver.statistics.IntervalStatistics
-
Gets the mean of the discretized variable.
- getMeanAbsoluteError() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the mean absolute error (MAE), which is a common measure used to determine how close predictions are to the actual values.
- getMeanActual() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the mean of the actual column.
- getMeanActual() - Method in class com.bayesserver.data.R2CrossValidationTestResult
-
Gets the mean of the actual column values (as opposed to the predicted values).
- getMeanSquaredError() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the mean squared error (MSE), which is a common measure used to determine how close predictions are to the actual values.
- getMessage() - Method in interface com.bayesserver.data.discovery.DiscretizeProgressInfo
-
Gets a progress message.
- getMessage() - Method in class com.bayesserver.data.discovery.VariableGeneratorProgressInfo
-
Gets a progress message.
- getMessage() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningProgressInfo
-
Gets a progress message.
- getMessage() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningProgressInfo
-
Gets a progress message.
- getMessage() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningProgressInfo
-
Gets a progress message.
- getMessage() - Method in class com.bayesserver.learning.structure.PCStructuralLearningProgressInfo
-
Gets a progress message.
- getMessage() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningProgressInfo
-
Gets a progress message.
- getMessage() - Method in interface com.bayesserver.learning.structure.StructuralLearningProgressInfo
-
Gets a progress message.
- getMessage() - Method in class com.bayesserver.learning.structure.TANStructuralLearningProgressInfo
-
Gets a progress message.
- getMessage() - Method in class com.bayesserver.optimization.OptimizationWarning
-
Gets the warning message.
- getMethod() - Method in class com.bayesserver.causal.BackdoorCriterionOptions
- getMethod() - Method in class com.bayesserver.data.DataPartitioning
-
Gets the partitioning method.
- getMethod() - Method in class com.bayesserver.learning.parameters.InitializationOptions
-
Determines the algorithm used for initialization.
- getMethod() - Method in class com.bayesserver.learning.structure.LinkConstraint
-
- getMinDepth() - Method in class com.bayesserver.TopologicalSortNodeInfo
-
Gets the minimum number of links from a root node to this node.
- getMinimum() - Method in class com.bayesserver.Interval
-
Gets the minimum interval value.
- getMinimumEndPoint() - Method in class com.bayesserver.Interval
-
Gets the end point type for the minimum value of the interval.
- getMissing() - Method in class com.bayesserver.data.discovery.VariableInfoCounts
-
Gets the count of missing/null values.
- getMissingDataExclusions() - Method in class com.bayesserver.data.sampling.DataSamplingOptions
-
Variables can be added, to indicate that they should not generate missing values.
- getMissingDataProbability() - Method in class com.bayesserver.data.sampling.DataSamplingOptions
-
- getMissingDataProbabilityMin() - Method in class com.bayesserver.data.sampling.DataSamplingOptions
-
- getMissingProbability() - Method in class com.bayesserver.data.discovery.VariableInfo
-
Gets weighted and unweighted values between 0 and 1 indicating the percentage of data that is missing for this variable.
- getMonitoredDistribution(Node) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
-
Gets a copy of the current distribution assigned to the [node].
- getMonitoredDistribution(Node, Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
-
Gets a copy of the current distribution assigned to the [node] at the requested order.
- getMonitoredDistribution(Node, NodeDistributionKey) - Method in class com.bayesserver.learning.parameters.ParameterLearningProgressInfo
-
Gets a copy of the current distribution assigned to the [node] at the requested order.
- getMonitorLogLikelihood() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Calculates the log likelihood at each iteration.
- getMutationProbability() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
The probability of genes being mutated.
- getMutualInformation() - Method in class com.bayesserver.analysis.AssociationPairOutput
-
Gets the mutual information between X and Y, denoted I(X;Y).
- getMutualInformation() - Method in class com.bayesserver.learning.structure.FeatureSelectionOptions
-
Gets a value which when true calculates the mutual information between each target and test.
- getMutualInformation() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
-
Gets the mutual information between the target and the test in NATS.
- getName() - Method in class com.bayesserver.CustomProperty
-
- getName() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
Gets the name for the new variable.
- getName() - Method in class com.bayesserver.learning.parameters.DistributerContext
-
Gets the name of the process/iteration being distributed.
- getName() - Method in class com.bayesserver.Network
-
An optional name for the Bayesian network.
- getName() - Method in class com.bayesserver.Node
-
The name of the node.
- getName() - Method in class com.bayesserver.NodeGroup
-
- getName() - Method in class com.bayesserver.State
-
Gets the name of the state.
- getName() - Method in class com.bayesserver.Variable
-
Gets the name of the variable.
- getNestedTableCount() - Method in class com.bayesserver.data.DefaultDataReader
-
Gets the number of nested tables.
- getNetwork() - Method in class com.bayesserver.causal.BackdoorCriterion
-
The Bayesian network on which the identification is based.
- getNetwork() - Method in class com.bayesserver.causal.CausalInferenceBase
-
The target Bayesian network.
- getNetwork() - Method in class com.bayesserver.causal.DisjunctiveCauseCriterion
-
The Bayesian network on which the identification is based.
- getNetwork() - Method in class com.bayesserver.causal.FrontDoorCriterion
-
The Bayesian network on which the identification is based.
- getNetwork() - Method in interface com.bayesserver.causal.Identification
-
The Bayesian network on which the identification is based.
- getNetwork() - Method in interface com.bayesserver.data.CrossValidationNetwork
-
Gets the network learnt from the cross validation partitioning.
- getNetwork() - Method in class com.bayesserver.data.DefaultCrossValidationNetwork
-
Gets the network learnt from a cross validation partitioning.
- getNetwork() - Method in class com.bayesserver.data.sampling.DataSampler
-
Gets the Bayesian network or Dynamic Bayesian network that was used in the constructor.
- getNetwork() - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the Bayesian network that is the the target of the evidence.
- getNetwork() - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
-
- getNetwork() - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
-
- getNetwork() - Method in interface com.bayesserver.inference.Evidence
-
Gets the Bayesian network that is the the target of the evidence.
- getNetwork() - Method in interface com.bayesserver.inference.Inference
-
The target Bayesian network.
- getNetwork() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
The target Bayesian network.
- getNetwork() - Method in class com.bayesserver.inference.LoopyBeliefInference
-
The target Bayesian network.
- getNetwork() - Method in class com.bayesserver.inference.RelevanceTreeInference
-
The target Bayesian network.
- getNetwork() - Method in class com.bayesserver.inference.VariableEliminationInference
-
The target Bayesian network.
- getNetwork() - Method in class com.bayesserver.learning.parameters.DistributedMapperContext
-
Gets the
Network
that is being learnt by the distributed process.
- getNetwork() - Method in class com.bayesserver.learning.parameters.ParameterLearning
-
Returns the relevant network.
- getNetwork() - Method in class com.bayesserver.Link
-
- getNetwork() - Method in class com.bayesserver.NetworkLinkCollection
-
Gets the
Network
the collection belongs to.
- getNetwork() - Method in class com.bayesserver.NetworkNodeCollection
-
The
Network
the collection belongs to.
- getNetwork() - Method in class com.bayesserver.NetworkNodeGroupCollection
-
Gets the network instance that these groups belong to.
- getNetwork() - Method in class com.bayesserver.NetworkVariableCollection
-
The
Network
the collection belongs to.
- getNetwork() - Method in class com.bayesserver.Node
-
- getNode() - Method in class com.bayesserver.analysis.DSeparationTestResult
-
The test node.
- getNode() - Method in class com.bayesserver.analysis.ParameterReference
-
Gets the node whose distribution parameter is being referenced.
- getNode() - Method in class com.bayesserver.causal.AdjustmentSetNode
-
Gets the node.
- getNode() - Method in class com.bayesserver.causal.CausalNode
-
Gets the Bayesian network node.
- getNode() - Method in class com.bayesserver.causal.DisjunctiveCauseSetNode
-
Gets the node.
- getNode() - Method in class com.bayesserver.causal.FrontDoorSetNode
-
Gets the node.
- getNode() - Method in interface com.bayesserver.causal.NodeSetItem
-
Gets the node.
- getNode() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
-
Gets the
Node
this distribution specification refers to.
- getNode() - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets the node that this instance belongs to.
- getNode() - Method in class com.bayesserver.NodeDistributionOptions
-
The node this instance belongs to.
- getNode() - Method in class com.bayesserver.NodeDistributions
-
Gets the node that this instance belongs to.
- getNode() - Method in class com.bayesserver.NodeGroupCollection
-
The
Node
the collection belongs to.
- getNode() - Method in class com.bayesserver.NodeLinkCollection
-
Gets the
Node
to which the collection belongs to.
- getNode() - Method in class com.bayesserver.NodeVariableCollection
-
The
Node
the collection belongs to.
- getNode() - Method in class com.bayesserver.TopologicalSortNodeInfo
-
Gets the node in the network.
- getNode() - Method in class com.bayesserver.UnrollOutput.NodeTime
-
Gets the node.
- getNode() - Method in class com.bayesserver.Variable
-
Gets the
Node
this instance belongs to, if any.
- getNodeGroups() - Method in class com.bayesserver.Network
-
Gets groups which nodes can belong to.
- getNodes() - Method in class com.bayesserver.causal.AdjustmentSet
-
Gets the adjustment set nodes.
- getNodes() - Method in class com.bayesserver.causal.DisjunctiveCauseSet
-
Gets the nodes in the set.
- getNodes() - Method in class com.bayesserver.causal.FrontDoorSet
-
Gets the front-door nodes used by the front-door adjustment, and can be identified using the front-door criterion.
- getNodes() - Method in interface com.bayesserver.causal.NodeSet
-
Gets the list of nodes in the set.
- getNodes() - Method in class com.bayesserver.Network
-
The collection of nodes in the Bayesian network.
- getNodeWidthOverride() - Method in class com.bayesserver.DecomposeOptions
-
Gets a value that can be used to override the width of nodes, used when laying out new nodes.
- getNodeWidthOverride() - Method in class com.bayesserver.UnrollOptions
-
Gets a value that can be used to override the width of nodes, used when laying out nodes.
- getNoisyOrder() - Method in class com.bayesserver.Link
-
Gets a value which determines the nature of the effect between the parent node (from) and a noisy child node (to).
- getNoisyType() - Method in class com.bayesserver.NodeDistributionOptions
-
Gets a value which identifies this node as a noisy node or not.
- getNormalization() - Method in class com.bayesserver.TableExpression
-
Gets of sets the normalization method, if any, to use once the Table values have been generated, but before assignment to a node.
- getNotMissing() - Method in class com.bayesserver.data.discovery.VariableInfoCounts
-
Gets the counts of values that are not missing/null values.
- getObject(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getObject(int) - Method in interface com.bayesserver.data.DataRecord
-
Gets an Object representation for the value at the specified column.
- getObject(int) - Method in class com.bayesserver.data.DataTableReader
-
- getObject(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets an Object representation for the value at the specified column.
- getObjectiveValue() - Method in class com.bayesserver.optimization.GeneticOptimizerOutput
-
The objective value.
- getObjectiveValue() - Method in class com.bayesserver.optimization.GeneticOptimizerProgressInfo
-
Gets the optimized objective (target) value.
- getObjectiveValue() - Method in class com.bayesserver.optimization.GeneticSimplificationOutput
-
The objective value.
- getObjectiveValue() - Method in interface com.bayesserver.optimization.OptimizerOutput
-
The objective value.
- getObjectiveValue() - Method in interface com.bayesserver.optimization.OptimizerProgressInfo
-
Gets the optimized objective (target) value.
- getOneMinusPValue() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
-
Gets a value which equals one minus the p-value returned from the statical independence test.
- getOnExecuteReader() - Method in class com.bayesserver.data.DefaultEvidenceReaderCommand
-
Gets a function that is called when a new reader is created.
- getOptions() - Method in class com.bayesserver.data.discovery.DiscretizationColumn
-
Gets the discretization options for this column of data.
- getOrder() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
-
Gets the order of the distribution, for temporal nodes.
- getOrder() - Method in class com.bayesserver.NodeDistributionKey
-
Gets the temporal order of the related node distribution.
- getOriginalNetwork() - Method in class com.bayesserver.DecomposeOutput
-
Gets the original network, containing nodes with multiple variables.
- getOriginalVariable(Variable) - Method in class com.bayesserver.DecomposeOutput
-
Maps a variable in the decomposed network to the equivalent variable in the original network.
- getOutcome() - Method in class com.bayesserver.causal.EffectsAnalysisOutput
-
Gets the outome (target) variable on which effects are being measured.
- getOutcomeDistribution() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem
-
Gets P(Outcome|Do(Treatment=TreatmentState)) for discrete treatments and P(Outcome|Do(Treatment=TreatmentValue)) for continuous treatments.
- getOutcomeFunctionOutput() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem
-
Gets the function output when the outcome is a function.
- getOutcomeMean() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem
-
Gets the mean of the outcome for this treatment (cause).
- getOutcomeVariance() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem
-
Gets the variance of the outcome for this treatment (cause).
- getOuter() - Method in class com.bayesserver.CLGaussian
-
- getOuter() - Method in interface com.bayesserver.Distribution
-
Returns the parent distribution, if this instance is aggregated by another distribution.
- getOuter() - Method in class com.bayesserver.Table
-
- getOwner() - Method in class com.bayesserver.CLGaussian
-
Gets the current owner, if assigned to a node.
- getOwner() - Method in class com.bayesserver.CustomPropertyCollection
-
Gets the instance that these custom properties belong to.
- getOwner() - Method in interface com.bayesserver.Distribution
-
Gets the current owner, if assigned to a node.
- getOwner() - Method in interface com.bayesserver.DistributionExpression
-
Gets the current owner, if assigned to a node.
- getOwner() - Method in class com.bayesserver.FunctionVariableExpression
-
Gets the current owner, if assigned to a variable.
- getOwner() - Method in interface com.bayesserver.QueryExpression
-
Gets the current owner, if assigned to a variable.
- getOwner() - Method in class com.bayesserver.Table
-
Gets the current owner, if assigned to a node.
- getOwner() - Method in class com.bayesserver.TableExpression
-
Gets the current owner, if assigned to a node.
- getPair() - Method in class com.bayesserver.analysis.AssociationPairOutput
-
Gets the pair (X, Y) that these association results are calculated for.
- getPairOutputs() - Method in class com.bayesserver.analysis.AssociationOutput
-
Gets the output for each pair.
- getParameterCount(Network) - Static method in class com.bayesserver.ParameterCounter
-
Gets the number of parameters in a Bayesian network.
- getParameterCount(Network, ParameterCountOptions) - Static method in class com.bayesserver.ParameterCounter
-
Gets the number of parameters in a Bayesian network.
- getParameterCount(Node, int) - Static method in class com.bayesserver.ParameterCounter
-
Gets the parameter count for an individual node distribution.
- getParameterCount(Node, NodeDistributionKey) - Static method in class com.bayesserver.ParameterCounter
-
Gets the parameter count for an individual node distribution.
- getParameterValue() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
-
Gets the original value of the parameter being analyzed.
- getParameterValue1() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets the original value of the first parameter being analyzed (t1).
- getParameterValue2() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets the original value of the second parameter being analyzed (t2).
- getParent() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
-
Gets the parent collection.
- getParent() - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection
-
Gets the parent variable output.
- getParent() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
-
Gets the parent collection.
- getParent() - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection
-
Gets the main output.
- getParent() - Method in class com.bayesserver.CustomProperty
-
Gets the parent collection, if set, otherwise null.
- getParent() - Method in class com.bayesserver.NodeGroup
-
Gets the parent collection, if set, otherwise null.
- getPartition() - Method in class com.bayesserver.data.CrossValidationOutput
-
Gets the zero based index of the partition.
- getPartitionCount() - Method in class com.bayesserver.data.DataPartitioning
-
Gets the total number of partitions.
- getPartitionCount() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets the number of partitions used by scoring functions that use cross validation.
- getPartitionNumber() - Method in class com.bayesserver.data.DataPartitioning
-
Gets the zero based partition number.
- getPartitions() - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Gets the number of cross validation partitions to use.
- getPartitions() - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
-
Gets the number of cross validation partitions to use.
- getPartitions() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets the number of cross validation partitions to use when scoring each cluster count.
- getPartitions() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets the number of cross validation partitions to use when scoring each cluster count.
- getPoints() - Method in class com.bayesserver.analysis.LiftChart
-
Gets the xy points that make up the lift chart.
- getPopulationSize() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Gets the number of chromosomes in each generation.
- getPredictedProbability() - Method in class com.bayesserver.analysis.LiftChart
-
Gets the name of the data column which contains the predicted probability generated by an inference query.
- getPriors() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Contains parameters used to avoid boundary conditions during learning.
- getProbability() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
-
Gets the probability of this state with no evidence set on the target state.
- getProbability() - Method in class com.bayesserver.analysis.ConfusionMatrixCell
-
Gets the overall probability for this cell.
- getProbabilityGivenActual() - Method in class com.bayesserver.analysis.ConfusionMatrixCell
-
Gets the probability for this cell, conditional on the actual counts.
- getProbabilityGivenPredicted() - Method in class com.bayesserver.analysis.ConfusionMatrixCell
-
Gets the probability for this cell, conditional on the predicted counts.
- getProbabilityGivenTarget() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
-
Gets the probability of this state given the target state.
- getProbabilityGivenTarget() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
-
Gets the distribution of this variable given the target.
- getProbabilityHypothesisGivenEvidence() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
-
Gets P(h|e).
- getProbabilityHypothesisGivenEvidence() - Method in class com.bayesserver.analysis.SensitivityFunctionTwoWay
-
Gets P(h|e).
- getProbabilityTargetGivenThis() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
-
Gets the probability of the target state given this state.
- getProficiencyXGivenY() - Method in class com.bayesserver.analysis.AssociationPairOutput
-
Gets the proficiency (uncertainty coefficient) U(X|Y) = I(X;Y) / H(X).
- getProficiencyYGivenX() - Method in class com.bayesserver.analysis.AssociationPairOutput
-
Gets the proficiency (uncertainty coefficient) U(Y|X) = I(X;Y) / H(Y).
- getProgress() - Method in class com.bayesserver.data.discovery.Clustering
-
Gets an instance that receive progress notifications.
- getProgress() - Method in interface com.bayesserver.data.discovery.Discretize
-
Gets an instance that receive progress notifications.
- getProgress() - Method in class com.bayesserver.data.discovery.EqualFrequencies
-
Gets an instance that receive progress notifications.
- getProgress() - Method in class com.bayesserver.data.discovery.EqualIntervals
-
Gets an instance that receive progress notifications.
- getProgress() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
- getProgress() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
- getProgress() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
- getProgress() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
- getProgress() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
- getProgress() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
- getProgress() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
- getProgress() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
-
- getProgress() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
- getProgress() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Gets of sets the instance implementing
OptimizerProgress
, used for progress notifications.
- getProgress() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Gets of sets the instance implementing
OptimizerProgress
, used for progress notifications.
- getPropagation() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Gets the propagation method to be used during inference.
- getPropagation() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Gets the propagation method to be used during inference.
- getPropagation() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Gets the propagation method to be used during inference.
- getPropagation() - Method in interface com.bayesserver.inference.QueryOptions
-
Gets the propagation method to be used during inference.
- getPropagation() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Gets the propagation method to be used during inference.
- getPropagation() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Gets the propagation method to be used during inference.
- getPropagation() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Gets the propagation method to be used during inference.
- getPropagation() - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
-
Gets the propagation method to use during marginalization.
- getQueryCount() - Method in class com.bayesserver.optimization.GeneticOptimizerOutput
-
Gets the number of call made to the inference engine(s) during optimization.
- getQueryCount() - Method in class com.bayesserver.optimization.GeneticOptimizerProgressInfo
-
Gets the number of calls made to inference engines during optimization.
- getQueryCount() - Method in class com.bayesserver.optimization.GeneticSimplificationOutput
-
Gets the number of call made to the inference engine(s) during optimization.
- getQueryCount() - Method in interface com.bayesserver.optimization.OptimizerOutput
-
The number of queries to inference engines performed during optimization.
- getQueryCount() - Method in interface com.bayesserver.optimization.OptimizerProgressInfo
-
Gets the number of calls made to inference engines during optimization.
- getQueryDistance() - Method in class com.bayesserver.inference.QueryDistribution
-
Gets a value indicating whether the distance should be calculated between the query calculated with base evidence (or no evidence), and the same query calculated with evidence.
- getQueryDistributions() - Method in class com.bayesserver.causal.CausalInferenceBase
-
Gets the collection of distributions to calculate.
- getQueryDistributions() - Method in class com.bayesserver.causal.EffectsAnalysisOptions
-
Determines which additional queries, if any, should be calculated by the inference engine.
- getQueryDistributions() - Method in interface com.bayesserver.inference.Inference
-
Gets the collection of distributions to calculate.
- getQueryDistributions() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Gets the collection of distributions to calculate.
- getQueryDistributions() - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Gets the collection of distributions to calculate.
- getQueryDistributions() - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Gets the collection of distributions to calculate.
- getQueryDistributions() - Method in class com.bayesserver.inference.VariableEliminationInference
-
- getQueryDistributions() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Determines which additional queries, if any, should be calculated by the inference engine when evaluating the fitness of a solution.
- getQueryDistributions() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Determines which additional queries, if any, should be calculated by the inference engine when evaluating the fitness of a solution.
- getQueryEvidenceMode() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Determines whether evidence is retracted for each query.
- getQueryEvidenceMode() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Determines whether evidence is retracted for each query.
- getQueryEvidenceMode() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Determines whether evidence is retracted for each query.
- getQueryEvidenceMode() - Method in interface com.bayesserver.inference.QueryOptions
-
Determines whether evidence is retracted for each query.
- getQueryEvidenceMode() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Determines whether evidence is retracted for each query.
- getQueryEvidenceMode() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Determines whether evidence is retracted for each query.
- getQueryEvidenceMode() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Determines whether evidence is retracted for each query.
- getQueryFunctions() - Method in class com.bayesserver.causal.CausalInferenceBase
-
Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
- getQueryFunctions() - Method in class com.bayesserver.causal.EffectsAnalysisOptions
-
Determines which additional functions, if any, should be calculated by the inference engine.
- getQueryFunctions() - Method in interface com.bayesserver.inference.Inference
-
Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
- getQueryFunctions() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
- getQueryFunctions() - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
- getQueryFunctions() - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
- getQueryFunctions() - Method in class com.bayesserver.inference.VariableEliminationInference
-
Gets the collection of functions to evaluate, after QueryDistributions have been calculated.
- getQueryFunctions() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Determines which additional functions, if any, should be calculated by the inference engine when evaluating the fitness of a solution.
- getQueryFunctions() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Determines which additional functions, if any, should be calculated by the inference engine when evaluating the fitness of a solution.
- getQueryLifecycle() - Method in class com.bayesserver.causal.CausalInferenceBase
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- getQueryLifecycle() - Method in class com.bayesserver.causal.DisjunctiveCauseInferenceFactory
-
Gets a query lifecycle instance.
- getQueryLifecycle() - Method in interface com.bayesserver.inference.Inference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- getQueryLifecycle() - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- getQueryLifecycle() - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- getQueryLifecycle() - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- getQueryLifecycle() - Method in class com.bayesserver.inference.VariableEliminationInference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- getQueryLogLikelihood() - Method in class com.bayesserver.inference.QueryDistribution
-
- getQueryLogLikelihood() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Determines whether the log-likelihood should be calculated by the inference engine when evaluating the fitness of a solution.
- getQueryLogLikelihood() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Determines whether the log-likelihood should be calculated by the inference engine when evaluating the fitness of a solution.
- getQueryOptions() - Method in interface com.bayesserver.inference.QueryLifecycleBegin
-
The query options instance being used in the query.
- getQueryOptions() - Method in class com.bayesserver.inference.QueryLifecycleBeginBase
-
The query options instance being used in the query.
- getQueryOptions() - Method in interface com.bayesserver.inference.QueryLifecycleEnd
-
The query options instance being used in the query.
- getQueryOptions() - Method in class com.bayesserver.inference.QueryLifecycleEndBase
-
The query options instance being used in the query.
- getQueryOutput() - Method in interface com.bayesserver.inference.QueryLifecycleEnd
-
The query output.
- getQueryOutput() - Method in class com.bayesserver.inference.QueryLifecycleEndBase
-
The query output.
- getQueryTimeout() - Method in class com.bayesserver.data.DatabaseDataReaderCommand
-
Gets the timeout to be used when statements are executed.
- getReadInfo() - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Provides information about the last read of non temporal data.
- getRecord() - Method in class com.bayesserver.data.NestedReadInfo
-
The current nested table record.
- getRecord() - Method in class com.bayesserver.data.ReadInfo
-
The current record.
- getRecord() - Method in class com.bayesserver.data.TemporalReadInfo
-
The current temporal record.
- getRelatedNode() - Method in class com.bayesserver.learning.parameters.DistributionSpecification
-
Gets the related node (if any) of the distribution.
- getRelatedNode() - Method in class com.bayesserver.NodeDistributionKey
-
Gets the parent of the noisy node this distribution refers to, or the noisy node itself to identify the leak distribution.
- getRemoveAbductionEvidence() - Method in class com.bayesserver.causal.AbductionOptions
-
Gets a value which when true
removes the abduction evidence, after updating the characteristic variables.
- getReturnType() - Method in interface com.bayesserver.Expression
-
Gets the return type of the expression.
- getReturnType() - Method in class com.bayesserver.FunctionVariableExpression
-
Gets the return type of the expression.
- getReturnType() - Method in class com.bayesserver.TableExpression
-
Gets the return type of the expression.
- getRMSE() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the root mean squared error (RMSE).
- getRoot() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Gets the root of the Chow-Liu tree.
- getRoot() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Gets the root of the TAN tree.
- getRow(int[]) - Method in class com.bayesserver.TableAccessor
-
- getRow() - Method in class com.bayesserver.TableIterator
-
Gets the current position of the iterator.
- getRows() - Method in class com.bayesserver.data.DataTable
-
Gets the rows of data in the table.
- getRSquared() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the R squared value (Coefficient of determination).
- getRunsPerConfiguration() - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Gets of sets the number of times training is re-run for each network structure tested.
- getRunsPerConfiguration() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets the number of times training is re-run for each network structure tested.
- getRunsPerConfiguration() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets the number of times training is re-run for each network structure tested.
- getSampleCount() - Method in class com.bayesserver.analysis.AutoInsightSamplingOptions
-
The number of samples used to approximate sufficient statistics, when exact inference is not possible.
- getSampleCount() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Gets a value indicating how many samples cases to generate in order to approximate the current query.
- getSampleCount() - Method in interface com.bayesserver.inference.QuerySamplingOptions
-
Gets a value indicating how many samples cases to generate in order to approximate the current query.
- getSampling() - Method in class com.bayesserver.analysis.AutoInsightOptions
-
Options affecting sampling, when approximate inference is required.
- getSamplingProbability() - Method in class com.bayesserver.learning.parameters.InitializationOptions
-
A value between 0 and 1 (inclusive) indicating what probability of cases to use for initialization.
- getSaveHyperparameters() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets a value indicating whether hyperparameters (e.g.
- getScore() - Method in class com.bayesserver.analysis.ClusterScore
-
The score achieved for this number of clusters.
- getScore() - Method in class com.bayesserver.analysis.LiftChart
-
Gets the overall score, which is a positive or negative probability between 0 and 1 indicating the classification performance of the network.
- getScore() - Method in interface com.bayesserver.data.CrossValidationScore
-
Gets the combined score over each cross validation partitioning.
- getScore() - Method in class com.bayesserver.data.DefaultCrossValidationScore
-
Gets the combined score over each cross validation partitioning.
- getScoreMethod() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets the scoring method used to evaluate search moves.
- getScores() - Method in class com.bayesserver.analysis.ClusterCountOutput
-
A list of scores, one for each cluster count in the same order passed to
ClusterCount
.
- getSeed() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Gets an optional seed for the random number generator.
- getSeed() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
-
Gets the seed used by the random number generator.
- getSeed() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets the seed used to generate random numbers for initialization.
- getSeed() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
-
Gets the seed used to generate random numbers for initialization.
- getSeed() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
The seed for the random number generator used by the Genetic Algorithm.
- getSensitivityValue() - Method in class com.bayesserver.analysis.SensitivityFunctionOneWay
-
Gets the sensitivity value which is the derivative of the sensitivity function evaluated at t.
- getSequenceLength() - Method in class com.bayesserver.data.sampling.DataSamplingOptions
-
The sequence length generated for each sample from networks with temporal nodes.
- getSets() - Method in class com.bayesserver.causal.BackdoorCriterionOutput
-
Gets a list of identified 'adjustment sets'.
- getSets() - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOutput
-
Gets an adjustment set which includes all causes of treatments (X) or causes of outcomes (Y) or causes of both.
- getSets() - Method in class com.bayesserver.causal.FrontDoorCriterionOutput
-
Gets a list of front-door node sets.
- getShift() - Method in class com.bayesserver.data.timeseries.WindowOptions
-
Gets the number of records between successive windows.
- getSignificanceLevel() - Method in class com.bayesserver.learning.structure.IndependenceOptions
-
Gets the significance level used to accept or reject (conditional) independence tests.
- getSimpleVariance() - Method in class com.bayesserver.learning.parameters.Priors
-
Used to make a fixed adjustment to all covariance matrices during learning, by increasing each diagonal (variance) entry.
- getSimplifyTolerance() - Method in class com.bayesserver.optimization.GeneticSimplificationOptions
-
This is a non negative number which determines whether a simplified solution is close enough to the best found.
- getSliceCount() - Method in class com.bayesserver.UnrollOutput
-
Gets the slice count of the unrolled network.
- getSliceGap() - Method in class com.bayesserver.UnrollOptions
-
Gets the gap between time slices.
- getSortedContinuousHead() - Method in class com.bayesserver.CLGaussian
-
Gets the collection of continuous head variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
- getSortedContinuousTail() - Method in class com.bayesserver.CLGaussian
-
Gets the collection of continuous tail variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
- getSortedIndex(State...) - Method in class com.bayesserver.Table
-
Gets the index of the table element that corresponds to a particular combination of states.
- getSortedIndex(StateContext...) - Method in class com.bayesserver.Table
-
Gets the index of the table element that corresponds to a particular combination of states and their times.
- getSortedUniqueValues() - Method in class com.bayesserver.analysis.ConfusionMatrix
-
Gets a sorted list of unique values which is the union of the different actual and predicted values found.
- getSortedVariables() - Method in class com.bayesserver.CLGaussian
-
Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
- getSortedVariables() - Method in interface com.bayesserver.Distribution
-
Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
- getSortedVariables() - Method in class com.bayesserver.Table
-
Gets the collection of variables in the distribution, sorted by time (which may be null) and the order in which variables were created.
- getSortOrder() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
Gets the sort order for states of a new discrete variable.
- getStagnationCount() - Method in class com.bayesserver.optimization.GeneticTerminationOptions
-
Gets the number of generations with equal objective values that are evaluated before the optimizer terminates.
- getState() - Method in class com.bayesserver.analysis.AutoInsightStateOutput
-
Gets the state this insight refers to.
- getState(Variable) - Method in class com.bayesserver.inference.DefaultEvidence
-
- getState(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
- getState(Node) - Method in class com.bayesserver.inference.DefaultEvidence
-
- getState(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
- getState(Variable) - Method in interface com.bayesserver.inference.Evidence
-
- getState(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
-
- getState(Node) - Method in interface com.bayesserver.inference.Evidence
-
- getState(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
-
- getState() - Method in class com.bayesserver.optimization.DesignState
-
Gets the state these options refer to.
- getState() - Method in class com.bayesserver.optimization.Objective
-
Gets the state being optimized.
- getState() - Method in class com.bayesserver.StateContext
-
Gets the State.
- getState(int, int) - Method in class com.bayesserver.TableAccessor
-
Gets the state at the given position [i] for the node given by [node].
- getState(int) - Method in class com.bayesserver.TableIterator
-
Gets the state for an individual node indexed by the order of nodes in the
TableIterator
.
- getStateAllDiffThis() - Method in class com.bayesserver.analysis.ImpactOutputItem
-
Gets the probability of the hypothesis state (if specified) with all evidence to analyze minus the state probability for this evidence configuration.
- getStateAllLiftThis() - Method in class com.bayesserver.analysis.ImpactOutputItem
-
Gets the probability of the hypothesis state (if specified) when all evidence to analyze is set relative to when this evidence configuration is set.
- getStateNotFoundAction() - Method in class com.bayesserver.data.VariableReference
-
Determines the action to take if the name or value from the data cannot be matched to a particular state for this reference variable.
- getStateOutputs() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
-
Gets the insight for each state of this test variable.
- getStateProbability() - Method in class com.bayesserver.analysis.ImpactOutputItem
-
Gets the probability of the hypothesis state (if specified) for this output item evidence.
- getStateProbabilityAll() - Method in class com.bayesserver.analysis.ImpactHypothesisOutput
-
Gets the probability of the hypothesis state (if any) with all evidence to analyze set.
- getStateProbabilityNone() - Method in class com.bayesserver.analysis.ImpactHypothesisOutput
-
Gets the probability of the hypothesis state (if any) with no evidence to analyze set.
- getStates() - Method in class com.bayesserver.analysis.ParameterReference
-
Gets the states which together locate a specific parameter in the node's distribution.
- getStates(Variable, double[]) - Method in class com.bayesserver.inference.DefaultEvidence
-
Fills out a buffer containing the soft evidence for a particular variable.
- getStates(Node, double[]) - Method in class com.bayesserver.inference.DefaultEvidence
-
Fills out a buffer containing the soft evidence for a node with a single variable.
- getStates(Table) - Method in class com.bayesserver.inference.DefaultEvidence
-
Fills out a table containing the soft evidence for a particular variable.
- getStates(Node, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Fills out a buffer containing the soft evidence for a node with a single variable at a specified time.
- getStates(Variable, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Fills out a buffer containing the soft evidence for a particular variable at a specified time.
- getStates(Variable, double[]) - Method in interface com.bayesserver.inference.Evidence
-
Fills out a buffer containing the soft evidence for a particular variable.
- getStates(Node, double[]) - Method in interface com.bayesserver.inference.Evidence
-
Fills out a buffer containing the soft evidence for a node with a single variable.
- getStates(Variable, double[], Integer) - Method in interface com.bayesserver.inference.Evidence
-
Fills out a buffer containing the soft evidence for a particular variable at a specified time.
- getStates(Node, double[], Integer) - Method in interface com.bayesserver.inference.Evidence
-
Fills out a buffer containing the soft evidence for a node with a single variable at a specified time.
- getStates(Table) - Method in interface com.bayesserver.inference.Evidence
-
Fills out a table containing the soft evidence for a particular variable.
- getStates() - Method in class com.bayesserver.State
-
- getStates(int, int[]) - Method in class com.bayesserver.TableAccessor
-
Gets the states at the given position [i].
- getStates(int[]) - Method in class com.bayesserver.TableIterator
-
Gets the states of all nodes, based on the order of nodes in the
TableIterator
not the underlying
Table
.
- getStates() - Method in class com.bayesserver.Variable
-
Returns the collection of states belonging to the variable.
- getStateThisDiffNone() - Method in class com.bayesserver.analysis.ImpactOutputItem
-
Gets the probability of the hypothesis state (if specified) for this evidence configuration minus the state probability with no evidence to analyze.
- getStateThisLiftNone() - Method in class com.bayesserver.analysis.ImpactOutputItem
-
Gets the probability of the hypothesis state (if specified) for this evidence configuration relative to when no evidence to analyze is set.
- getStateValueType() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
- getStateValueType() - Method in class com.bayesserver.Variable
-
Gets the type of value that states belonging to this variable can represent.
- getStop() - Method in interface com.bayesserver.Stop
-
When true
, indicates to the algorithm to complete early.
- getStopping() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Gets the instance implementing
Stop
used for early stopping.
- getStopping() - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Gets the instance implementing
Stop
used for early stopping.
- getString(int) - Method in class com.bayesserver.data.DataReaderFiltered
-
- getString(int) - Method in interface com.bayesserver.data.DataRecord
-
Gets a string value for the specified column.
- getString(int) - Method in class com.bayesserver.data.DataTableReader
-
- getString(int) - Method in class com.bayesserver.data.timeseries.WindowDataReader
-
Gets a string value for the specified column.
- getSubsetMethod() - Method in class com.bayesserver.analysis.ImpactOptions
-
Gets a value which determines whether evidence subsets are included, excluded or both.
- getSubsetMethod() - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
-
Gets a value which determines whether evidence subsets are included, excluded or both.
- getSuggestedBinCount() - Method in class com.bayesserver.analysis.HistogramDensityOptions
-
Gets the approximate number of bins to use to represent the approximate density function.
- getSuggestedBinCount() - Method in class com.bayesserver.data.discovery.DiscretizationOptions
-
Gets the number of suggested bins to use during discretization.
- getSumAbsoluteError() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the sum absolute error (SAE).
- getSumSquaredError() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the sum of squared errors (SSE), which is a common measure used to determine how close predictions are to the actual values.
- getSupport() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the support/weight for the values used to calculate the statistics.
- getSymmetricMutualInformation() - Method in class com.bayesserver.analysis.AssociationPairOutput
-
Gets a normalized version of the mutual information called the 'symmetric uncertainty' between X and Y.
- getSyncNodeVariableName() - Static method in class com.bayesserver.Network
-
When
true
synchronizes
Variable
names with their containing
Node
.
- getTable() - Method in class com.bayesserver.CLGaussian
-
Gets the
Table
which specifies the distribution over any discrete variables.
- getTable() - Method in class com.bayesserver.data.DataColumn
-
- getTable() - Method in class com.bayesserver.data.DataRow
-
- getTable() - Method in interface com.bayesserver.Distribution
-
Gets the
Table
which specifies the distribution over any discrete variables.
- getTable() - Method in class com.bayesserver.Table
-
- getTable() - Method in class com.bayesserver.TableAccessor
-
Gets the underlying
Table
.
- getTable() - Method in class com.bayesserver.TableIterator
-
Gets the underlying
Table
.
- getTableIndex(int) - Method in class com.bayesserver.TableAccessor
-
Gets the equivalent index in the underlying table that corresponds to the index in the accessor.
- getTableRow() - Method in class com.bayesserver.TableIterator
-
Gets the position of the iterator in the underlying
Table
.
- getTarget() - Method in class com.bayesserver.analysis.AutoInsightOutput
-
Gets the target state used to calculate the insight.
- getTarget() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
-
Gets the variable that was the target of the feature selection test.
- getTarget() - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Gets the target of the TAN tree.
- getTargetProbability() - Method in class com.bayesserver.analysis.AutoInsightOutput
-
Gets the probability of the target state, given any optional background evidence.
- getTargetValue() - Method in class com.bayesserver.analysis.LiftChart
-
Gets the target value which we are interested in.
- getTemporalOrder() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
-
Gets the temporal order (if any) used to test the variables.
- getTemporalOrder() - Method in class com.bayesserver.learning.structure.LinkConstraint
-
Gets the temporal order of the constraint.
- getTemporalOrder() - Method in class com.bayesserver.Link
-
Gets the temporal order of the link.
- getTemporalOrder() - Method in class com.bayesserver.NodeDistributionExpressions.DistributionExpressionOrder
-
Gets the temporal order of the distribution expression.
- getTemporalOrder() - Method in class com.bayesserver.NodeDistributions.DistributionOrder
-
Gets the temporal order of the distribution.
- getTemporalReadInfo() - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Provides information about the last read of temporal data.
- getTemporalType() - Method in class com.bayesserver.Node
-
- getTerminalTime() - Method in class com.bayesserver.analysis.DSeparationOptions
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTerminalTime() - Method in class com.bayesserver.analysis.ValueOfInformationOptions
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTerminalTime() - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTerminalTime() - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTerminalTime() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTerminalTime() - Method in interface com.bayesserver.inference.QueryOptions
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTerminalTime() - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTerminalTime() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTerminalTime() - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Gets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- getTermination() - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Termination options.
- getTestIndependence() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets a value which when true uses independence tests to reduce the search space.
- getTestOutputs() - Method in class com.bayesserver.analysis.ValueOfInformationOutput
-
Gets the result of tests carried out on the test variables.
- getTestPartitioning() - Method in class com.bayesserver.data.CrossValidationOutput
-
Gets the test DataPartitioning associated with this partition.
- getTestResults() - Method in class com.bayesserver.analysis.DSeparationOutput
-
The collection of test results.
- getTests() - Method in class com.bayesserver.learning.structure.FeatureSelectionOutput
-
Gets the tests carried out for each variable against the target.
- getTestSingleCluster() - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets a value which determines whether a test is performed for a single cluster (i.e.
- getText() - Method in interface com.bayesserver.Expression
-
Gets the expression text.
- getText() - Method in class com.bayesserver.FunctionVariableExpression
-
Gets the expression text.
- getText() - Method in class com.bayesserver.TableExpression
-
Gets the expression text, which is run for each cell in the table.
- getTime() - Method in class com.bayesserver.analysis.DSeparationTestResult
-
The zero based time at which the test was performed, or null if the node is not temporal.
- getTime() - Method in class com.bayesserver.causal.AdjustmentSetNode
-
Gets the node time, for temporal nodes.
- getTime() - Method in class com.bayesserver.causal.CausalNode
-
Gets the optional time, required for temporal nodes.
- getTime() - Method in class com.bayesserver.causal.DisjunctiveCauseSetNode
-
Gets the node time, for temporal nodes.
- getTime() - Method in class com.bayesserver.causal.FrontDoorSetNode
-
Gets the node time, for temporal nodes.
- getTime() - Method in interface com.bayesserver.causal.NodeSetItem
-
Gets the node time, for temporal nodes.
- getTime() - Method in class com.bayesserver.StateContext
-
Gets the zero based time associated with the state if the state belongs to a temporal node, or null otherwise.
- getTime() - Method in class com.bayesserver.UnrollOutput.NodeTime
-
Gets the time of the node, or null if a time is not appropriate for the temporal type of the node.
- getTime() - Method in class com.bayesserver.UnrollOutput.VariableTime
-
Gets the time of the variable, or null if a time is not appropriate for the temporal type of the variable.
- getTime() - Method in class com.bayesserver.VariableContext
-
Gets the time associated with the variable if it belongs to a temporal node.
- getTimeColumn() - Method in class com.bayesserver.data.TemporalReaderOptions
-
The name of the time column in the temporal data, if temporal data is present.
- getTimeIndex() - Method in class com.bayesserver.data.TemporalReadInfo
-
Gets the zero based time index (e.g.
- getTimes() - Method in class com.bayesserver.data.timeseries.WindowOptions
-
Gets the times to include in the window.
- getTimeSeriesMode() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets the mode in which time series distributions are learned.
- getTimeValue() - Method in class com.bayesserver.data.TemporalReadInfo
-
Gets the current value in the time column.
- getTimeValueType() - Method in class com.bayesserver.data.TemporalReaderOptions
-
The type of values contained in the time column.
- getTo() - Method in class com.bayesserver.Link
-
The child node of the directed link.
- getTolerance() - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Gets the tolerance used to determine whether or not the approximate inference process has converged.
- getTolerance() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets the tolerance used to determine whether or not parameter learning has converged.
- getTolerance() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets the tolerance used to determine whether or not a search move is a significant improvement.
- getToleranceOrDefault() - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
If Tolerance is null, this returns the default tolerance for the given convergence method, otherwise Tolerance is returned.
- getToleranceOrDefault() - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
If Tolerance is null, this returns the default tolerance for the given scoring method, otherwise Tolerance is returned.
- getTotalCount() - Method in class com.bayesserver.analysis.ConfusionMatrix
-
Gets a count of the number of predictions, whether they were correct or not.
- getTrainingPartioning() - Method in class com.bayesserver.data.CrossValidationOutput
-
Gets the training DataPartitioning associated with this partition.
- getTreatment() - Method in class com.bayesserver.causal.EffectsAnalysisOutput
-
Gets the treatment variable which is being varied.
- getTreatmentState() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem
-
Gets the treatment state used to measure the causal effect on the treatment.
- getTreatmentValue() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem
-
Gets the treatment value used to measure the causal effect on the treatment.
- getTreatmentValues() - Method in class com.bayesserver.causal.EffectsAnalysisOptions
-
A list of treatment values to evaluate the causal effect on the outcome for.
- getTreatmentVariable() - Method in class com.bayesserver.causal.EffectsAnalysisOutputItem
-
Gets the treatment variable used to measure the causal effect on the treatment.
- getTreeWidth() - Method in class com.bayesserver.inference.TreeQueryOptions
-
Gets a value indicating whether or not to calculate the tree width.
- getTreeWidth() - Method in class com.bayesserver.inference.TreeQueryOutput
-
Gets the tree width, if requested.
- getUnrolled() - Method in class com.bayesserver.UnrollOutput
-
Gets the unrolled Dynamic Bayesian network.
- getUnrolledNode(Node, Integer) - Method in class com.bayesserver.UnrollOutput
-
Maps between a node in the original Dynamic Bayesian network, and the corresponding node in the unrolled network.
- getUnrolledVariable(Variable, Integer) - Method in class com.bayesserver.UnrollOutput
-
Maps between a variable in the original Dynamic Bayesian network, and the corresponding variable in the unrolled network.
- getUnweighted() - Method in class com.bayesserver.data.discovery.VariableInfoCount
-
The number of records.
- getUnweighted() - Method in class com.bayesserver.data.discovery.VariableInfoValue
-
Gets the unweighted value.
- getUnweightedCaseCount() - Method in class com.bayesserver.data.DataProgressEventArgs
-
Gets the number of cases read so far.
- getUnweightedCaseCount() - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Gets the number of cases (unweighted) read so far.
- getUnweightedCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
-
Gets the unweighted case count in the learning data.
- getUnweightedTemporalCount() - Method in class com.bayesserver.data.DataProgressEventArgs
-
Gets the number of temporal rows read so far for all cases.
- getUpperBound() - Method in class com.bayesserver.optimization.DesignState
-
The maximum value allowed for this variable/state during the optimization process.
- getValue() - Method in class com.bayesserver.CustomProperty
-
The custom property value.
- getValue() - Method in interface com.bayesserver.data.CrossValidationTestResult
-
Gets the test result value for this test partitioning.
- getValue() - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
-
Gets the test result value for this test partitioning.
- getValue() - Method in class com.bayesserver.data.discovery.WeightedValue
-
Gets the value, which can be null.
- getValue() - Method in class com.bayesserver.data.R2CrossValidationTestResult
-
Gets the test result value for this test partitioning.
- getValue() - Method in class com.bayesserver.inference.QueryFunctionOutput
-
Holds the result of a function evaluation at query time.
- getValue() - Method in class com.bayesserver.optimization.Objective
-
Gets the objective target value.
- getValue() - Method in class com.bayesserver.State
-
Gets an optional value for a state, such as an interval for discretized variables.
- getValue() - Method in class com.bayesserver.Table.MaxValue
-
- getValue() - Method in class com.bayesserver.TableIterator
-
Gets the underlying
Table
value at the current position of the iterator.
- getValueType() - Method in class com.bayesserver.data.discovery.VariableDefinition
-
- getValueType() - Method in class com.bayesserver.Variable
-
Gets the variable's value type, e.g.
- getVariable() - Method in class com.bayesserver.analysis.AutoInsightVariableOutput
-
Gets the test variable.
- getVariable() - Method in class com.bayesserver.analysis.ValueOfInformationTestOutput
-
Gets the variable that was tested.
- getVariable() - Method in class com.bayesserver.data.discovery.VariableInfo
-
- getVariable() - Method in class com.bayesserver.data.VariableReference
-
Gets the variable.
- getVariable() - Method in class com.bayesserver.inference.QueryFunctionOutput
-
The function variable to evaluate.
- getVariable() - Method in class com.bayesserver.learning.structure.FeatureSelectionTest
-
- getVariable() - Method in class com.bayesserver.optimization.DesignVariable
-
Gets the variable these options refer to.
- getVariable() - Method in class com.bayesserver.optimization.Objective
-
Gets the variable being optimized.
- getVariable() - Method in class com.bayesserver.State
-
Gets the
Variable
the state belongs to, if any.
- getVariable() - Method in class com.bayesserver.StateCollection
-
Gets the
Variable
this collection belongs to.
- getVariable() - Method in class com.bayesserver.UnrollOutput.VariableTime
-
Gets the variable.
- getVariable() - Method in class com.bayesserver.VariableContext
-
Gets the variable.
- getVariableOutputs() - Method in class com.bayesserver.analysis.AutoInsightOutput
-
Contains the insights from each test variable.
- getVariables(Variable[]) - Method in class com.bayesserver.inference.DefaultEvidence
-
Fills out a buffer with all variables that have either hard or soft evidence.
- getVariables(Variable[]) - Method in interface com.bayesserver.inference.Evidence
-
Fills out a buffer with all variables that have either hard or soft evidence.
- getVariables() - Method in class com.bayesserver.Network
-
The collection of variables in the Bayesian network.
- getVariables() - Method in class com.bayesserver.Node
-
Collection of variables represented by the node.
- getVariance(int, int) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution at the specified [index] in the
Table
of discrete combinations.
- getVariance(Variable, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance(Variable) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
- getVariance(Variable, Integer) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
- getVariance(Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance(VariableContext, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance(Variable, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance(Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance(VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance(Variable, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance(Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance(VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- getVariance() - Method in class com.bayesserver.statistics.IntervalStatistics
-
Gets the variance of the discretized variable.
- getVarianceActual() - Method in class com.bayesserver.analysis.RegressionStatistics
-
Gets the variance of the actual column.
- getVarianceActual() - Method in class com.bayesserver.data.R2CrossValidationTestResult
-
Gets the variance of the actual column values (as opposed to the predicted values).
- getWarnings() - Method in class com.bayesserver.optimization.GeneticOptimizerOutput
-
Contains any warnings generated by optimization algorithms.
- getWarnings() - Method in class com.bayesserver.optimization.GeneticSimplificationOutput
-
Contains any warnings generated by optimization algorithms.
- getWarnings() - Method in interface com.bayesserver.optimization.OptimizerOutput
-
Contains any warnings generated by optimization algorithms.
- getWeight(int, int, int) - Method in class com.bayesserver.CLGaussian
-
Gets the weight (regression coefficient) of the Gaussian distribution at the specified [index] in the
Table
of discrete combinations.
- getWeight(Variable, Variable, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight(Variable, Integer, Variable, Integer, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight(VariableContext, VariableContext, State...) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight(Variable, Variable, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight(Variable, Variable) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
- getWeight(Variable, Integer, Variable, Integer, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight(Variable, Integer, Variable, Integer) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
- getWeight(VariableContext, VariableContext, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight(Variable, Variable, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight(Variable, Integer, Variable, Integer, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight(VariableContext, VariableContext, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- getWeight() - Method in class com.bayesserver.data.discovery.WeightedValue
-
- getWeight() - Method in class com.bayesserver.data.ReadInfo
-
The case weight.
- getWeight() - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets a weight that can be applied to the evidence.
- getWeight() - Method in interface com.bayesserver.inference.Evidence
-
Gets a weight that can be applied to the evidence.
- getWeightColumn() - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions
-
Gets a column that contains case weights for each record.
- getWeightColumn() - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Gets the name of a column which contains a weight (support) for each case.
- getWeightColumn() - Method in class com.bayesserver.data.ReaderOptions
-
The name of the case weight column, if one is present.
- getWeighted() - Method in class com.bayesserver.data.discovery.VariableInfoCount
-
The sum of record weights.
- getWeighted() - Method in class com.bayesserver.data.discovery.VariableInfoValue
-
Gets the weighted value.
- getWeightedCaseCount() - Method in interface com.bayesserver.data.CrossValidationTestResult
-
Gets the number of records in the test partitioning.
- getWeightedCaseCount() - Method in class com.bayesserver.data.DataProgressEventArgs
-
Gets the number of cases read so far.
- getWeightedCaseCount() - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
-
Gets the number of records in the test partitioning.
- getWeightedCaseCount() - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Gets the number of cases (weighted) read so far.
- getWeightedCaseCount() - Method in class com.bayesserver.data.R2CrossValidationTestResult
-
Gets the number of records in the test partitioning.
- getWeightedCaseCount() - Method in class com.bayesserver.learning.parameters.ParameterLearningOutput
-
Gets the weighted case count in the learning data.
- getWidth() - Method in class com.bayesserver.Bounds
-
Gets the width of the element.
- getWindowColumnName() - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions
-
Gets the name of the column which will contain the window identifier.
- getWindowTimeColumnName() - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions
-
Gets the name of the column which will contain the window time.
- getX() - Method in class com.bayesserver.analysis.AssociationPair
-
Gets the variable contexts in the first set.
- getX() - Method in class com.bayesserver.analysis.LiftChartPoint
-
Gets the value on the x-axis.
- getX() - Method in class com.bayesserver.Bounds
-
Gets the x-axis value of the left side of the element.
- getY() - Method in class com.bayesserver.analysis.AssociationPair
-
Gets the varible contexts in the second set.
- getY() - Method in class com.bayesserver.analysis.LiftChartPoint
-
Gets the value on the y-axis.
- getY() - Method in class com.bayesserver.Bounds
-
Gets the y-axis value of the top side of the element.
- save(OutputStream) - Method in class com.bayesserver.inference.DefaultEvidence
-
Saves the evidence to the specified stream.
- save(String) - Method in class com.bayesserver.inference.DefaultEvidence
-
Saves the specified to the specified file.
- save(OutputStream) - Method in interface com.bayesserver.inference.Evidence
-
Saves the evidence to the specified stream.
- save(String) - Method in interface com.bayesserver.inference.Evidence
-
Saves the specified to the specified file.
- save(OutputStream) - Method in class com.bayesserver.Network
-
- save(String) - Method in class com.bayesserver.Network
-
Saves this
Network
to the specified [path] overwriting the file if it already exists.
- saveToString(String) - Method in class com.bayesserver.inference.DefaultEvidence
-
Saves evidence to a string, with the specified encoding.
- saveToString() - Method in class com.bayesserver.inference.DefaultEvidence
-
Saves evidence to a string, with UTF-8 encoding.
- saveToString(String) - Method in interface com.bayesserver.inference.Evidence
-
Saves evidence to a string, with the specified encoding.
- saveToString() - Method in interface com.bayesserver.inference.Evidence
-
Saves evidemce to a string, with UTF-8 encoding.
- saveToString(String) - Method in class com.bayesserver.Network
-
Saves the network to a string, with the specified encoding.
- saveToString() - Method in class com.bayesserver.Network
-
Saves the network to a string, with UTF-8 encoding.
- ScoreMethod - Enum in com.bayesserver.learning.structure
-
The scoring mechanism used to evaluate different Bayesian network structures during a search.
- SearchLinkOutput - Class in com.bayesserver.learning.structure
-
Contains information about a new link learnt using the com.bayesserver.learning.structure.search.SearchStructuralLearning
algorithm.
- SearchStructuralLearning - Class in com.bayesserver.learning.structure
-
A structural learning algorithm for Bayesian networks based on Search and Score.
- SearchStructuralLearning() - Constructor for class com.bayesserver.learning.structure.SearchStructuralLearning
-
- SearchStructuralLearningOptions - Class in com.bayesserver.learning.structure
-
Options for structural learning with the com.bayesserver.learning.structure.search.SearchStructuralLearning
class.
- SearchStructuralLearningOptions() - Constructor for class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
- SearchStructuralLearningOutput - Class in com.bayesserver.learning.structure
-
Contains information returned from the com.bayesserver.learning.structure.search.SearchStructuralLearning
algorithm.
- SearchStructuralLearningProgressInfo - Class in com.bayesserver.learning.structure
-
Progress information returned from the Search based structural learning algorithm.
- SensitivityFunctionOneWay - Class in com.bayesserver.analysis
-
Represents the result on a one-way sensitivity to parameters analysis.
- SensitivityFunctionTwoWay - Class in com.bayesserver.analysis
-
Represents the result on a two-way sensitivity to parameters analysis.
- SensitivityToParameters - Class in com.bayesserver.analysis
-
Calculates the affect of one or more parameters on the value of a hypothesis.
- SensitivityToParameters(Network, InferenceFactory) - Constructor for class com.bayesserver.analysis.SensitivityToParameters
-
- set(int, CustomProperty) - Method in class com.bayesserver.CustomPropertyCollection
-
- set(int, Object) - Method in class com.bayesserver.data.DataRow
-
Sets the value at the specified index.
- set(int, Variable) - Method in class com.bayesserver.data.sampling.ExcludedVariables
- set(Variable, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets temporal evidence on a variable.
- set(Node, Double[], int, int, int) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets temporal evidence on a node with a single variable.
- set(Variable, Double) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets a variable to a particular value (hard evidence).
- set(Node, Double, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets evidence on a node's single variable at a specified time.
- set(Variable, Double, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets evidence on a variable at a specified time.
- set(Variable, Double, Integer, InterventionType) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets evidence on the variable, in the form of an intervention (do-operator).
- set(Node, Double) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets a node's variable to a particular value (hard evidence).
- set(int, QueryDistribution) - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
- set(int, QueryFunction) - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
- set(Variable, Double, Integer, InterventionType) - Method in interface com.bayesserver.inference.Evidence
-
Sets evidence on the variable, in the form of an intervention (do-operator).
- set(Variable, Double) - Method in interface com.bayesserver.inference.Evidence
-
Sets a variable to a particular value (hard evidence).
- set(Variable, Double, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets evidence on a variable at a specified time.
- set(Node, Double, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets evidence on a node's single variable at a specified time.
- set(Variable, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence
-
Sets temporal evidence on a variable.
- set(Node, Double[], int, int, int) - Method in interface com.bayesserver.inference.Evidence
-
Sets temporal evidence on a node with a single variable.
- set(Node, Double) - Method in interface com.bayesserver.inference.Evidence
-
Sets a node's variable to a particular value (hard evidence).
- set(int, LinkConstraint) - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
-
- set(int, Link) - Method in class com.bayesserver.NetworkLinkCollection
-
Sets the
Link
object at the specified index.
- set(int, Node) - Method in class com.bayesserver.NetworkNodeCollection
-
Sets the
Node
object at the specified index.
- set(int, NodeGroup) - Method in class com.bayesserver.NetworkNodeGroupCollection
-
- set(int, Variable) - Method in class com.bayesserver.NetworkVariableCollection
-
Gets the
Variable
object at the specified index.
- set(int, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions
-
Sets a distribution expression at a particular temporal order.
- set(NodeDistributionKey, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions
-
Sets a distribution expression with particular properties, such as temporal order.
- set(NodeDistributionKind, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions
-
Sets a particular kind of distribution expression on the node.
- set(NodeDistributionKey, NodeDistributionKind, ExpressionDistribution, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions
-
Sets a distribution expression with particular properties, such as temporal order.
- set(NodeDistributionKey, NodeDistributionKind, DistributionExpression) - Method in class com.bayesserver.NodeDistributionExpressions
-
Sets a distribution expression with particular properties, such as temporal order.
- set(int, Distribution) - Method in class com.bayesserver.NodeDistributions
-
Sets a distribution at a particular temporal order.
- set(NodeDistributionKey, Distribution) - Method in class com.bayesserver.NodeDistributions
-
Sets a distribution with particular properties, such as temporal order.
- set(NodeDistributionKind, Distribution) - Method in class com.bayesserver.NodeDistributions
-
Sets a particular kind of distribution on the node.
- set(NodeDistributionKey, NodeDistributionKind, Distribution) - Method in class com.bayesserver.NodeDistributions
-
Sets a distribution with particular properties, such as temporal order.
- set(int, String) - Method in class com.bayesserver.NodeGroupCollection
-
Sets the group at the specified index.
- set(int, Variable) - Method in class com.bayesserver.NodeVariableCollection
-
Sets the
Variable
object at the specified index.
- set(int, State) - Method in class com.bayesserver.StateCollection
-
Sets the
State
at the specified index.
- set(double, State...) - Method in class com.bayesserver.Table
-
Sets the table value corresponding to the given states.
- set(double, StateContext...) - Method in class com.bayesserver.Table
-
Sets the table value corresponding to the given states and associated times.
- set(int, double) - Method in class com.bayesserver.Table
-
Sets the
Table
value at the specified index into the 1-dimensional array.
- set(int[], double) - Method in class com.bayesserver.TableAccessor
-
Sets the underlying
Table
value, using states corresponding to the order of variables in the
TableAccessor
.
- set(int, double) - Method in class com.bayesserver.TableAccessor
-
Sets the underlying
Table
value, specified i.
- set(int, VariableContext) - Method in class com.bayesserver.VariableContextCollection
-
Gets the
Variable
object at the specified index.
- setAddNodeGroups(boolean) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets a value which determines whether network node groups are added for each group in a level.
- setAdjustmentSet(AdjustmentSet) - Method in class com.bayesserver.causal.DisjunctiveCauseQueryOptions
-
Sets the adjustment set, which must include all nodes that are causes of either treatments (X) or outcomes (Y) or both, except those with evidence set.
- setAdjustmentSetOverride(AdjustmentSet) - Method in class com.bayesserver.causal.BackdoorQueryOptions
-
Gets an adjustment set to use during estimation, instead of the algorithm generating it automatically.
- setAdjustmentSetXZOverride(AdjustmentSet) - Method in class com.bayesserver.causal.FrontDoorQueryOptions
-
Sets the 'adjustment set' for adjusting between treatments (X) and front-door nodes (Z).
- setAdjustmentSetZYOverride(AdjustmentSet) - Method in class com.bayesserver.causal.FrontDoorQueryOptions
-
Sets the 'adjustment set' for adjusting between the front-door nodes (Z) and the outcomes (Y).
- setAll(double) - Method in class com.bayesserver.Table
-
Sets all values in the
Table
to a specified value.
- setAllowMissing(boolean) - Method in class com.bayesserver.optimization.DesignVariable
-
Determines whether the optimizer can consider missing values (evidence not set) on this variable.
- setAllowNullDistributions(boolean) - Method in class com.bayesserver.ValidationOptions
-
Determines whether validation should succeed even if the required distribution(s) have not been assigned to a node.
- setAllowNullFunctions(boolean) - Method in class com.bayesserver.ValidationOptions
-
Determines whether validation should succeed even if a function has not been assigned to a functiomn variable.
- setAutoCommit(boolean) - Method in class com.bayesserver.data.DatabaseDataReaderCommand
-
Sets the auto commit value to be set on each connection created.
- setAutoDetectDiscreteLimit(int) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Sets the distinct value count, which when exceeded changes a variable from discrete to continuous.
- setAutoReadTemporal(boolean) - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Determines whether any temporal data is read automatically.
- setBaseEvidence(Evidence) - Method in class com.bayesserver.causal.CausalInferenceBase
-
Optional evidence which can be used to calculate the lift of queries.
- setBaseEvidence(Evidence) - Method in interface com.bayesserver.inference.Inference
-
Optional evidence which can be used to calculate the lift of queries.
- setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Optional evidence which can be used to calculate the lift of queries.
- setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Optional evidence which can be used to calculate the lift of queries.
- setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Optional evidence which can be used to calculate the lift of queries.
- setBaseEvidence(Evidence) - Method in class com.bayesserver.inference.VariableEliminationInference
-
Optional evidence which can be used to calculate the lift of queries.
- setBounds(Bounds) - Method in class com.bayesserver.Node
-
Sets the size and location of the node.
- setCalculateStatistics(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets a value indicating whether to calculate summary statistics in an extra iteration at the end of learning.
- setCancel(boolean) - Method in interface com.bayesserver.Cancellation
-
When set to true
attempts to cancel a long running operation.
- setCancel(boolean) - Method in class com.bayesserver.DefaultCancellation
-
When set to true
attempts to cancel a long running operation.
- setCancellation(Cancellation) - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Allows cancellation of a query.
- setCancellation(Cancellation) - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions
-
Gets of sets an instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Gets of sets an instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Allows cancellation of a query.
- setCancellation(Cancellation) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Allows cancellation of a query.
- setCancellation(Cancellation) - Method in interface com.bayesserver.inference.QueryOptions
-
Allows cancellation of a query.
- setCancellation(Cancellation) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Allows cancellation of a query.
- setCancellation(Cancellation) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Allows cancellation of a query.
- setCancellation(Cancellation) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Allows cancellation of a query.
- setCancellation(Cancellation) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Gets of sets the instance implementing
Cancellation
, used for cancellation.
- setCancellation(Cancellation) - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
-
Used to cancel a long running operation.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.BackdoorCriterionOptions
-
The type of causal effect, such as Total or Direct.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.BackdoorValidationOptions
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Sets the kind of effect to calculate.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOptions
-
The type of causal effect, such as Total or Direct.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.DisjunctiveCauseValidationOptions
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.FrontDoorCriterionOptions
-
The type of causal effect, such as Total or Direct.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.causal.FrontDoorValidationOptions
- setCausalEffectKind(CausalEffectKind) - Method in interface com.bayesserver.causal.IdentificationOptions
-
The type of causal effect, such as Total or Direct.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Sets the kind of effect to calculate.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Sets the kind of effect to calculate.
- setCausalEffectKind(CausalEffectKind) - Method in interface com.bayesserver.inference.QueryOptions
-
Sets the kind of effect to calculate.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Sets the kind of effect to calculate.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Sets the kind of effect to calculate.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Sets the kind of effect to calculate.
- setCausalEffectKind(CausalEffectKind) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Sets the kind of causal effect to optimize.
- setCausalEffectKind(CausalEffectKind) - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Sets the kind of causal effect to optimize.
- setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- setCausalInferenceFactory(InferenceFactory) - Method in interface com.bayesserver.inference.QueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- setCausalInferenceFactory(InferenceFactory) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Factory that can create inference engines, used by estimation (adjustment) algorithms to perform queries on the Bayesian network.
- setCausalObservability(CausalObservability) - Method in class com.bayesserver.Node
-
- setCausesOfTreatmentsOrOutcomes(DisjunctiveCauseSet) - Method in class com.bayesserver.causal.DisjunctiveCauseCriterionOptions
-
Sets a list of nodes which must include all causes of treatments (X) or causes of outcomes (Y) or causes of both.
- setCausesOfTreatmentsOrOutcomes(DisjunctiveCauseSet) - Method in class com.bayesserver.causal.DisjunctiveCauseQueryOptions
-
Sets the list of all nodes that are either causes of treatments (X) or outcomes (Y) or both.
- setCleared(boolean) - Method in class com.bayesserver.data.DefaultReadOptions
-
- setClusterVariableName(String) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Sets the name of the cluster/latent node/variable created when more than 1 hidden state is detected.
- setColumnName(String) - Method in class com.bayesserver.data.discovery.DiscretizationColumn
-
Sets the name of the column of data to be discretized.
- setComparison(QueryComparison) - Method in class com.bayesserver.inference.QueryDistribution
-
Sets a value indicating whether queried values should be adjusted to show how they compare to the same query with no evidence, or base evidence.
- setConflict(boolean) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
- setConflict(Double) - Method in class com.bayesserver.causal.CausalQueryOutputBase
-
Sets the conflict measure.
- setConflict(boolean) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
- setConflict(Double) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
-
Sets the conflict measure.
- setConflict(boolean) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
- setConflict(Double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
-
Sets the conflict measure.
- setConflict(boolean) - Method in interface com.bayesserver.inference.QueryOptions
-
- setConflict(Double) - Method in interface com.bayesserver.inference.QueryOutput
-
Sets the conflict measure.
- setConflict(boolean) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
- setConflict(Double) - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
-
Sets the conflict measure.
- setConflict(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions
-
- setConflict(boolean) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
- setConflict(Double) - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
-
Sets the conflict measure.
- setContinuous(double) - Method in class com.bayesserver.learning.parameters.Priors
-
Sets the amount continuous distributions are adjusted during learning.
- setContinuousTargetInterval(Interval<Double>) - Method in class com.bayesserver.analysis.AutoInsightOutput
-
Gets the target interval (if any).
- setConvergenceMethod(ConvergenceMethod) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets the method used to determine convergence of the learning algorithm.
- setCovariance(int, int, int, double) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance value of the Gaussian distribution at the specified [index] in the
Table
of discrete combinations.
- setCovariance(Variable, Variable, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCovariance(Variable, Integer, Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCovariance(VariableContext, VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCovariance(Variable, Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCovariance(Variable, Integer, Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCovariance(Variable, Integer, Variable, Integer, double) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
- setCovariance(Variable, Variable, double) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
- setCovariance(VariableContext, VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCovariance(VariableContext, VariableContext, double) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
- setCovariance(Variable, Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCovariance(Variable, Integer, Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCovariance(VariableContext, VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
- setCrossoverProbability(double) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
The probability of parents being crossed.
- setDataColumn(String) - Method in class com.bayesserver.data.discovery.VariableDefinition
-
The name of the data column, containing the data used to generate the new variable.
- setDataProgress(DataProgress) - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Gets the instance used to report progress on the number of cases read.
- setDataProgress(DataProgress) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Reports progress on the number of cases read.
- setDataProgressInterval(int) - Method in class com.bayesserver.data.DefaultEvidenceReader
-
Sets a value which determines how often progress events are raised.
- setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Sets the algorithm to use when a network contains Decision nodes.
- setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Sets the algorithm to use when a network contains Decision nodes.
- setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Sets the algorithm to use when a network contains Decision nodes.
- setDecisionAlgorithm(DecisionAlgorithm) - Method in interface com.bayesserver.inference.QueryOptions
-
Sets the algorithm to use when a network contains Decision nodes.
- setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Sets the algorithm to use when a network contains Decision nodes.
- setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Sets the algorithm to use when a network contains Decision nodes.
- setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Sets the algorithm to use when a network contains Decision nodes.
- setDecisionAlgorithm(DecisionAlgorithm) - Method in class com.bayesserver.learning.parameters.OnlineLearningOptions
-
Sets the algorithm to use for adaption of decision graphs.
- setDecisionPostProcessing(DecisionPostProcessingMethod) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets the post processing method for decision nodes.
- setDescription(String) - Method in class com.bayesserver.CustomProperty
-
An optional description for the custom property.
- setDescription(String) - Method in class com.bayesserver.Link
-
Optional description for the link.
- setDescription(String) - Method in class com.bayesserver.Network
-
An optional description for the Bayesian network.
- setDescription(String) - Method in class com.bayesserver.Node
-
An optional description for the node.
- setDescription(String) - Method in class com.bayesserver.NodeGroup
-
An optional description for the custom property.
- setDescription(String) - Method in class com.bayesserver.State
-
Sets an optional description for the state.
- setDescription(String) - Method in class com.bayesserver.Variable
-
An optional description for the variable.
- setDetectIntegralFloats(boolean) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Sets a value, which when true tests floating point column data to see if the data is an integral type, which would then become a candidate to be a discrete variable when VariableValueType is not specified.
- setDiscrete(double) - Method in class com.bayesserver.learning.parameters.Priors
-
Sets the amount distributions containing discrete variables are adjusted during learning.
- setDiscretePriorMethod(DiscretePriorMethod) - Method in class com.bayesserver.learning.parameters.DistributionSpecification
-
Sets the type of discrete prior to use for this distribution.
- setDiscretePriorMethod(DiscretePriorMethod) - Method in class com.bayesserver.learning.parameters.Priors
-
The default discrete prior to use for discrete distributions during parameter learning.
- setDiscretizationMethod(DiscretizationMethod) - Method in class com.bayesserver.data.discovery.VariableDefinition
-
Sets the method (algorithm) to use for discretization, if any.
- setDistance(Double) - Method in class com.bayesserver.inference.QueryDistribution
-
The distance between this query calculated with base evidence or no evidence, and when calculated with evidence.
- setDistribution(Distribution) - Method in class com.bayesserver.Node
-
Returns the distribution currently associated with the
Node
.
- setEmptyStringAction(EmptyStringAction) - Method in class com.bayesserver.data.discovery.VariableDefinition
-
Determines the action to take if an empty string is encountered.
- setEnsureTestWithoutCluster(boolean) - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Sets a value which indicates whether a test must be included which excludes the cluster variable altogether.
- setEnumerateAllMissing(boolean) - Method in class com.bayesserver.analysis.CombinationOptions
-
Sets a value which indicates whether the combination where all states are null/missing should be included in the enumeration.
- setEnumerateMissing(boolean) - Method in class com.bayesserver.analysis.CombinationOptions
-
Sets a value which indicates whether null/missing values should be enumerated in addition to each state.
- setEvidence(Evidence) - Method in class com.bayesserver.causal.CausalInferenceBase
-
Represents the evidence, or case data (e.g.
- setEvidence(Evidence) - Method in interface com.bayesserver.inference.Inference
-
Represents the evidence, or case data (e.g.
- setEvidence(Evidence) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Represents the evidence, or case data (e.g.
- setEvidence(Evidence) - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Represents the evidence, or case data (e.g.
- setEvidence(Evidence) - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Represents the evidence, or case data (e.g.
- setEvidence(Evidence) - Method in class com.bayesserver.inference.VariableEliminationInference
-
Sets the evidence (case data, e.g.
- setEvidenceKind(DesignEvidenceKind) - Method in class com.bayesserver.optimization.DesignVariable
-
Determines whether the optimizer uses hard or soft/virtual evidence for this variable.
- setEvidenceToSimplify(Evidence) - Method in class com.bayesserver.optimization.GeneticSimplificationOptions
-
The evidence from a previous optimization.
- setEvidenceType(EvidenceType) - Method in class com.bayesserver.inference.EvidenceTypes
-
- setExcludeNullDistributions(boolean) - Method in class com.bayesserver.ParameterCountOptions
-
Sets a value indicating whether null distributions are excluded from the parameter count.
- setExpressionAlias(String) - Method in class com.bayesserver.Variable
-
Sets a c-style name for a variable that can be used as an alias in expressions.
- setFactory(InferenceFactory) - Method in class com.bayesserver.analysis.ImpactOptions
-
Sets the inference factory which is used to create inference engines during an impact analysis.
- setFactory(InferenceFactory) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
-
Sets the inference factory which is used to create inference engines during a Log-Likelihood analysis.
- setFetchSize(int) - Method in class com.bayesserver.data.DatabaseDataReaderCommand
-
Sets the fetch size to be set on each statement created.
- setFixedData(Evidence) - Method in class com.bayesserver.data.sampling.DataSampler
-
Sets any evidence that should be fixed for each sample.
- setFrontDoorNodesOverride(FrontDoorSet) - Method in class com.bayesserver.causal.FrontDoorQueryOptions
-
Sets the set of front-door nodes (Z) used by the front-door adjustment.
- setFunction(QueryExpression) - Method in class com.bayesserver.Variable
-
Sets an expression, which is evaluated during a query, and can be based on other queries and expressions.
- setGap(double) - Method in class com.bayesserver.DecomposeOptions
-
The gap between decomposed nodes, used when laying out new nodes.
- setHasZeroIntercepts(boolean) - Method in class com.bayesserver.NodeDistributionOptions
-
Determines whether
CLGaussian
intercept terms are fixed to zero.
- setIncludeGlobalCovariance(boolean) - Method in class com.bayesserver.learning.parameters.Priors
-
When Gaussian distributions are adjusted according to the
Priors.getContinuous()
prior, this property determines whether the global covariance should be included in the adjustment, as well as the global variance.
- setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
- setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
- setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
- setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in interface com.bayesserver.inference.QueryOptions
-
- setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
- setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.TreeQueryOptions
-
- setInconsistentEvidenceMode(InconsistentEvidenceMode) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.AssociationOptions
-
Sets the inference factory used for link strength calculations.
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.AutoInsightOptions
-
Sets the inference factory used for link strength calculations.
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Sets the factory which is used to create inference engines during the cluster count tests.
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
-
Sets the factory which is used to create inference engines during the in-sample anomaly detection process.
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.causal.AbductionOptions
-
Used to create an inference engine, to determine the values for the characterstic variables.
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Sets the inference factory used during scoring.
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets the inference factory used during scoring.
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Sets the inference factory used during scoring.
- setInferenceFactory(InferenceFactory) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Used to create one or more inference engines, used by the algorithm to determine the fitness of possible solutions.
- setInferenceFactory(InferenceFactory) - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Creates one or more inference engines used by the optimization algorithm.
- setInfiniteExtremes(boolean) - Method in class com.bayesserver.data.discovery.DiscretizationOptions
-
Sets a value indicating whether the first and last intervals extend to negative and positive infinity respectively.
- setInitialize(Boolean) - Method in class com.bayesserver.learning.parameters.DistributionSpecification
-
Sets a flag indicating whether the distribution should be initialized.
- setInitializeDistributions(boolean) - Method in class com.bayesserver.learning.parameters.InitializationOptions
-
Indicates whether or not to initialize distributions by default.
- setInterventionType(InterventionType) - Method in class com.bayesserver.inference.EvidenceTypes
-
- setInterventionType(InterventionType) - Method in class com.bayesserver.optimization.DesignVariable
-
Determines the evidence intervention type for this variable.
- setIsApproximate(boolean) - Method in class com.bayesserver.analysis.AutoInsightOutput
-
Gets a value which when true indicates that the auto-insight calculations were approximated using sampling.
- setIsEnabled(boolean) - Method in class com.bayesserver.inference.QueryDistribution
-
Sets a value indicating whether the distribution should be queried.
- setIsEnabled(boolean) - Method in class com.bayesserver.inference.QueryFunction
-
Sets a value indicating whether the function should be evaluated.
- setIsImpliedEvidenceEnabled(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Sets a value indicating whether to detect implied evidence during the calculation.
- setIsInternal(boolean) - Method in class com.bayesserver.Network
-
For internal use only.
- setIsProper(boolean) - Method in class com.bayesserver.causal.BackdoorGraphOptions
-
Sets a value which determines whether a 'proper Backdoor graph' is constructed.
- setJSDivergence(AutoInsightJSDivergence) - Method in class com.bayesserver.analysis.AutoInsightOptions
-
Sets a value which determines the type of Jensen Shannon divergence calculations to perform, if any.
- setKeepEvidenceNotAnalyzed(boolean) - Method in class com.bayesserver.analysis.ImpactOptions
-
Sets a value which when true retains evidence not being analysed, or when false ignores it.
- setKeepEvidenceNotAnalyzed(boolean) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
-
Sets a value which when true retains evidence not being analysed, or when false ignores it.
- setKind(VariableKind) - Method in class com.bayesserver.data.discovery.VariableDefinition
-
- setKLDivergence(AutoInsightKLDivergence) - Method in class com.bayesserver.analysis.AutoInsightOptions
-
Sets a value which determines the type of KL divergence calculations to perform, if any.
- setLocked(boolean) - Method in class com.bayesserver.CLGaussian
-
Locks or unlocks a distribution.
- setLocked(boolean) - Method in interface com.bayesserver.Distribution
-
Locks or unlocks a distribution.
- setLocked(boolean) - Method in class com.bayesserver.Table
-
Locks or unlocks a distribution.
- setLogarithmBase(LogarithmBase) - Method in class com.bayesserver.analysis.ValueOfInformationOptions
-
- setLogLikelihood(boolean) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
- setLogLikelihood(Double) - Method in class com.bayesserver.causal.CausalQueryOutputBase
-
Sets the log-likelihood value.
- setLogLikelihood(boolean) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
- setLogLikelihood(Double) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
-
Sets the log-likelihood value.
- setLogLikelihood(boolean) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
- setLogLikelihood(Double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOutput
-
Sets the log-likelihood value.
- setLogLikelihood(Double) - Method in class com.bayesserver.inference.QueryDistribution
-
The log-likelihood specific to the evidence used to calculate this query.
- setLogLikelihood(boolean) - Method in interface com.bayesserver.inference.QueryOptions
-
- setLogLikelihood(Double) - Method in interface com.bayesserver.inference.QueryOutput
-
Sets the log-likelihood value.
- setLogLikelihood(boolean) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
- setLogLikelihood(Double) - Method in class com.bayesserver.inference.RelevanceTreeQueryOutput
-
Sets the log-likelihood value.
- setLogLikelihood(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions
-
- setLogLikelihood(boolean) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
- setLogLikelihood(Double) - Method in class com.bayesserver.inference.VariableEliminationQueryOutput
-
Sets the log-likelihood value.
- setLogWeight(double) - Method in class com.bayesserver.inference.DefaultEvidence
-
- setLogWeight(double) - Method in interface com.bayesserver.inference.Evidence
-
- setLowerBound(Double) - Method in class com.bayesserver.optimization.DesignState
-
The minimum value allowed for this variable/state during the optimization process.
- setMaxEvidenceSubsetSize(int) - Method in class com.bayesserver.analysis.ImpactOptions
-
Sets the maximum size of evidence subsets to consider.
- setMaxEvidenceSubsetSize(int) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
-
Sets the maximum size of evidence subsets to consider.
- setMaximum(T) - Method in class com.bayesserver.Interval
-
Sets the maximum interval value.
- setMaximumAdjustmentSets(Integer) - Method in class com.bayesserver.causal.BackdoorCriterionOptions
-
Limits the number of adjustment sets generated.
- setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
- setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
- setMaximumBatchSize(long) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Sets the maximum number of tests that are buffered in memory for processing in a single iteration of the data.
- setMaximumClusterCount(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Sets the maximum number of clusters generated.
- setMaximumClustersPerGroup(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets the maximum number of clusters generated for each group.
- setMaximumConcurrency(Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets the maximum number of inference engines used during learning.
- setMaximumConcurrency(Integer) - Method in class com.bayesserver.optimization.GeneticOptionsBase
- setMaximumConditional(int) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Sets the maximum number of conditional variables to consider during independence testing.
- setMaximumEndPoint(IntervalEndPoint) - Method in class com.bayesserver.Interval
-
Sets the end point type for the maximum value of the interval.
- setMaximumGroupsPerLevel(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets the maximum number of groups created per level.
- setMaximumIterations(int) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets the maximum number of iterations that parameter learning will perform.
- setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Sets the maximum number of iterations used by parameter learning to score each configuration.
- setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets the maximum number of iterations used by parameter learning to score each configuration.
- setMaximumIterations(Integer) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Sets the optional maximum number of iterations (moves) made during the search procedure.
- setMaximumLevels(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets the maximum number of levels generated by the algorithm.
- setMaximumSupport(int) - Method in class com.bayesserver.learning.parameters.InitializationOptions
-
Limits the amount of support each distribution is given during initialization.
- setMaximumTemporalOrder(int) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Sets the maximum order of temporal links that are considered during learning.
- setMean(int, int, double) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution at the specified [index] in the
Table
of discrete combinations.
- setMean(Variable, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- setMean(Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- setMean(VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- setMean(Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- setMean(Variable, double) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
- setMean(Variable, Integer, double) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
- setMean(Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- setMean(VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
- setMean(Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
- setMean(Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
- setMean(VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
- setMethod(BackdoorMethod) - Method in class com.bayesserver.causal.BackdoorCriterionOptions
- setMethod(InitializationMethod) - Method in class com.bayesserver.learning.parameters.InitializationOptions
-
Determines the algorithm used for initialization.
- setMinimum(T) - Method in class com.bayesserver.Interval
-
Sets the minimum interval value.
- setMinimumEndPoint(IntervalEndPoint) - Method in class com.bayesserver.Interval
-
Sets the end point type for the minimum value of the interval.
- setMissingDataProbability(double) - Method in class com.bayesserver.data.sampling.DataSamplingOptions
-
- setMissingDataProbabilityMin(Double) - Method in class com.bayesserver.data.sampling.DataSamplingOptions
-
- setMonitorLogLikelihood(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Calculates the log likelihood at each iteration.
- setMutationProbability(double) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
The probability of genes being mutated.
- setMutualInformation(boolean) - Method in class com.bayesserver.learning.structure.FeatureSelectionOptions
-
Sets a value which when true calculates the mutual information between each target and test.
- setName(String) - Method in class com.bayesserver.data.discovery.VariableDefinition
-
Sets the name for the new variable.
- setName(String) - Method in class com.bayesserver.Network
-
An optional name for the Bayesian network.
- setName(String) - Method in class com.bayesserver.Node
-
The name of the node.
- setName(String) - Method in class com.bayesserver.NodeGroup
-
- setName(String) - Method in class com.bayesserver.State
-
Sets the name of the state.
- setName(String) - Method in class com.bayesserver.Variable
-
Sets the name of the variable.
- setNetwork(Network) - Method in class com.bayesserver.causal.CausalInferenceBase
-
- setNetwork(Network) - Method in class com.bayesserver.data.DefaultCrossValidationNetwork
-
Sets the network learnt from a cross validation partitioning.
- setNodeWidthOverride(Double) - Method in class com.bayesserver.DecomposeOptions
-
Sets a value that can be used to override the width of nodes, used when laying out new nodes.
- setNodeWidthOverride(Double) - Method in class com.bayesserver.UnrollOptions
-
Sets a value that can be used to override the width of nodes, used when laying out nodes.
- setNoisyOrder(NoisyOrder) - Method in class com.bayesserver.Link
-
Sets a value which determines the nature of the effect between the parent node (from) and a noisy child node (to).
- setNoisyType(NoisyType) - Method in class com.bayesserver.NodeDistributionOptions
-
Sets a value which identifies this node as a noisy node or not.
- setNormalization(TableExpressionNormalization) - Method in class com.bayesserver.TableExpression
-
Gets of sets the normalization method, if any, to use once the Table values have been generated, but before assignment to a node.
- setOnExecuteReader(ExecuteEvidenceReader) - Method in class com.bayesserver.data.DefaultEvidenceReaderCommand
-
Sets a function that is called when a new reader is created.
- setPartitionCount(int) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Sets the number of partitions used by scoring functions that use cross validation.
- setPartitions(int) - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Sets the number of cross validation partitions to use.
- setPartitions(int) - Method in class com.bayesserver.analysis.InSampleAnomalyDetectionOptions
-
Sets the number of cross validation partitions to use.
- setPartitions(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Sets the number of cross validation partitions to use when scoring each cluster count.
- setPartitions(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets the number of cross validation partitions to use when scoring each cluster count.
- setPopulationSize(int) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Sets the number of chromosomes in each generation.
- setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.Clustering
-
Gets an instance that receive progress notifications.
- setProgress(DiscretizeProgress) - Method in interface com.bayesserver.data.discovery.Discretize
-
Gets an instance that receive progress notifications.
- setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.EqualFrequencies
-
Gets an instance that receive progress notifications.
- setProgress(DiscretizeProgress) - Method in class com.bayesserver.data.discovery.EqualIntervals
-
Gets an instance that receive progress notifications.
- setProgress(VariableGeneratorProgress) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
- setProgress(ParameterLearningProgress) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
- setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
- setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
- setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
- setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
- setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
- setProgress(StructuralLearningProgress) - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
-
- setProgress(StructuralLearningProgress) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
- setProgress(OptimizerProgress) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Gets of sets the instance implementing
OptimizerProgress
, used for progress notifications.
- setProgress(OptimizerProgress) - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Gets of sets the instance implementing
OptimizerProgress
, used for progress notifications.
- setPropagation(PropagationMethod) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Sets the propagation method to be used during inference.
- setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Sets the propagation method to be used during inference.
- setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Sets the propagation method to be used during inference.
- setPropagation(PropagationMethod) - Method in interface com.bayesserver.inference.QueryOptions
-
Sets the propagation method to be used during inference.
- setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Sets the propagation method to be used during inference.
- setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Sets the propagation method to be used during inference.
- setPropagation(PropagationMethod) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Sets the propagation method to be used during inference.
- setPropagation(PropagationMethod) - Method in class com.bayesserver.Table.MarginalizeLowMemoryOptions
-
Sets the propagation method to use during marginalization.
- setQueryDistance(QueryDistance) - Method in class com.bayesserver.inference.QueryDistribution
-
Sets a value indicating whether the distance should be calculated between the query calculated with base evidence (or no evidence), and the same query calculated with evidence.
- setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.causal.CausalInferenceBase
-
Sets the collection of distributions to calculate.
- setQueryDistributions(QueryDistributionCollection) - Method in interface com.bayesserver.inference.Inference
-
Sets the collection of distributions to calculate.
- setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Sets the collection of distributions to calculate.
- setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Sets the collection of distributions to calculate.
- setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Sets the collection of distributions to calculate.
- setQueryDistributions(QueryDistributionCollection) - Method in class com.bayesserver.inference.VariableEliminationInference
-
- setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Determines whether evidence is retracted for each query.
- setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Determines whether evidence is retracted for each query.
- setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Determines whether evidence is retracted for each query.
- setQueryEvidenceMode(QueryEvidenceMode) - Method in interface com.bayesserver.inference.QueryOptions
-
Determines whether evidence is retracted for each query.
- setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Determines whether evidence is retracted for each query.
- setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Determines whether evidence is retracted for each query.
- setQueryEvidenceMode(QueryEvidenceMode) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Determines whether evidence is retracted for each query.
- setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.causal.CausalInferenceBase
-
Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
- setQueryFunctions(QueryFunctionCollection) - Method in interface com.bayesserver.inference.Inference
-
Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
- setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
- setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
- setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
- setQueryFunctions(QueryFunctionCollection) - Method in class com.bayesserver.inference.VariableEliminationInference
-
Sets the collection of functions to evaluate, after QueryDistributions have been calculated.
- setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.causal.CausalInferenceBase
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.causal.DisjunctiveCauseInferenceFactory
-
Sets a query lifecycle instance.
- setQueryLifecycle(QueryLifecycle) - Method in interface com.bayesserver.inference.Inference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.inference.LikelihoodSamplingInference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.inference.LoopyBeliefInference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.inference.RelevanceTreeInference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- setQueryLifecycle(QueryLifecycle) - Method in class com.bayesserver.inference.VariableEliminationInference
-
Optional, allowing callers to hook into query lifecycle events, such as begin query and end query.
- setQueryLogLikelihood(boolean) - Method in class com.bayesserver.inference.QueryDistribution
-
- setQueryLogLikelihood(Boolean) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Determines whether the log-likelihood should be calculated by the inference engine when evaluating the fitness of a solution.
- setQueryLogLikelihood(Boolean) - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Determines whether the log-likelihood should be calculated by the inference engine when evaluating the fitness of a solution.
- setQueryTimeout(int) - Method in class com.bayesserver.data.DatabaseDataReaderCommand
-
Sets the timeout to be used when statements are executed.
- setRemoveAbductionEvidence(boolean) - Method in class com.bayesserver.causal.AbductionOptions
-
Sets a value which when true
removes the abduction evidence, after updating the characteristic variables.
- setReturnType(ExpressionReturnType) - Method in class com.bayesserver.FunctionVariableExpression
- setReturnType(ExpressionReturnType) - Method in class com.bayesserver.TableExpression
- setRoot(Node) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Sets the root of the Chow-Liu tree.
- setRoot(Node) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Sets the root of the TAN tree.
- setRunsPerConfiguration(int) - Method in class com.bayesserver.analysis.ClusterCountOptions
-
Gets of sets the number of times training is re-run for each network structure tested.
- setRunsPerConfiguration(Integer) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Sets the number of times training is re-run for each network structure tested.
- setRunsPerConfiguration(Integer) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets the number of times training is re-run for each network structure tested.
- setSampleCount(Integer) - Method in class com.bayesserver.analysis.AutoInsightSamplingOptions
-
The number of samples used to approximate sufficient statistics, when exact inference is not possible.
- setSampleCount(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Sets a value indicating how many samples cases to generate in order to approximate the current query.
- setSampleCount(Integer) - Method in interface com.bayesserver.inference.QuerySamplingOptions
-
Sets a value indicating how many samples cases to generate in order to approximate the current query.
- setSamplingProbability(double) - Method in class com.bayesserver.learning.parameters.InitializationOptions
-
A value between 0 and 1 (inclusive) indicating what probability of cases to use for initialization.
- setSaveHyperparameters(boolean) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets a value indicating whether hyperparameters (e.g.
- setScoreMethod(ScoreMethod) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Sets the scoring method used to evaluate search moves.
- setSeed(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Sets an optional seed for the random number generator.
- setSeed(int) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOutput
-
Sets the seed used by the random number generator.
- setSeed(Integer) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets the seed used to generate random numbers for initialization.
- setSeed(Integer) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
The seed for the random number generator used by the Genetic Algorithm.
- setSequenceLength(Integer) - Method in class com.bayesserver.data.sampling.DataSamplingOptions
-
The sequence length generated for each sample from networks with temporal nodes.
- setShift(int) - Method in class com.bayesserver.data.timeseries.WindowOptions
-
Sets the number of records between successive windows.
- setSignificanceLevel(double) - Method in class com.bayesserver.learning.structure.IndependenceOptions
-
Sets the significance level used to accept or reject (conditional) independence tests.
- setSimpleVariance(double) - Method in class com.bayesserver.learning.parameters.Priors
-
Used to make a fixed adjustment to all covariance matrices during learning, by increasing each diagonal (variance) entry.
- setSimplifyTolerance(double) - Method in class com.bayesserver.optimization.GeneticSimplificationOptions
-
This is a non negative number which determines whether a simplified solution is close enough to the best found.
- setSliceGap(double) - Method in class com.bayesserver.UnrollOptions
-
Sets the gap between time slices.
- setSortOrder(SortOrder) - Method in class com.bayesserver.data.discovery.VariableDefinition
-
Sets the sort order for states of a new discrete variable.
- setStagnationCount(Integer) - Method in class com.bayesserver.optimization.GeneticTerminationOptions
-
Sets the number of generations with equal objective values that are evaluated before the optimizer terminates.
- setState(Variable, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets a discrete variable to a particular state (hard evidence).
- setState(Variable, Integer, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets a discrete variable to a particular state (hard evidence), specifiying a time if the state belongs to a variable whose node is temporal.
- setState(State) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets evidence on a discrete state (hard evidence).
- setState(State, Integer, InterventionType) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets evidence on a discrete state (hard evidence), in the form of an intervention (do-operator).
- setState(State, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets evidence on a discrete state (hard evidence) at a particular time (zero based).
- setState(Node, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets evidence on a node with a single discrete variable to a particular state (hard evidence).
- setState(Node, Integer, Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets evidence on a node with a single discrete variable to a particular state (hard evidence) specifiying a time if the node is temporal.
- setState(Variable, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets a discrete variable to a particular state (hard evidence).
- setState(Variable, Integer, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets a discrete variable to a particular state (hard evidence), specifiying a time if the state belongs to a variable whose node is temporal.
- setState(State) - Method in interface com.bayesserver.inference.Evidence
-
Sets evidence on a discrete state (hard evidence).
- setState(State, Integer, InterventionType) - Method in interface com.bayesserver.inference.Evidence
-
Sets evidence on a discrete state (hard evidence), in the form of an intervention (do-operator).
- setState(State, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets evidence on a discrete state (hard evidence) at a particular time (zero based).
- setState(Node, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets evidence on a node with a single discrete variable to a particular state (hard evidence).
- setState(Node, Integer, Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets evidence on a node with a single discrete variable to a particular state (hard evidence) specifiying a time if the node is temporal.
- setStates(Variable, double[]) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets soft evidence for a particular discrete variable.
- setStates(Node, double[]) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets soft evidence for a discrete node with a single variable.
- setStates(Node, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets soft evidence for a discrete node with a single variable, at a specified time.
- setStates(Variable, double[], Integer) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets soft evidence for a particular discrete variable at a specified time.
- setStates(Variable, double[]) - Method in interface com.bayesserver.inference.Evidence
-
Sets soft evidence for a particular discrete variable.
- setStates(Node, double[]) - Method in interface com.bayesserver.inference.Evidence
-
Sets soft evidence for a discrete node with a single variable.
- setStates(Variable, double[], Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets soft evidence for a particular discrete variable at a specified time.
- setStates(Node, double[], Integer) - Method in interface com.bayesserver.inference.Evidence
-
Sets soft evidence for a discrete node with a single variable, at a specified time.
- setStateValueType(StateValueType) - Method in class com.bayesserver.data.discovery.VariableDefinition
-
- setStateValueType(StateValueType) - Method in class com.bayesserver.Variable
-
Sets the type of value that states belonging to this variable can represent.
- setStop(boolean) - Method in interface com.bayesserver.Stop
-
When true
, indicates to the algorithm to complete early.
- setStopping(Stop) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in class com.bayesserver.learning.structure.ChowLiuStructuralLearningOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in class com.bayesserver.learning.structure.HierarchicalStructuralLearningOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in class com.bayesserver.learning.structure.PCStructuralLearningOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in interface com.bayesserver.learning.structure.StructuralLearningOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in class com.bayesserver.optimization.GeneticOptionsBase
-
Sets the instance implementing
Stop
used for early stopping.
- setStopping(Stop) - Method in interface com.bayesserver.optimization.OptimizerOptions
-
Sets the instance implementing
Stop
used for early stopping.
- setSubsetMethod(ImpactSubsetMethod) - Method in class com.bayesserver.analysis.ImpactOptions
-
Sets a value which determines whether evidence subsets are included, excluded or both.
- setSubsetMethod(LogLikelihoodAnalysisSubsetMethod) - Method in class com.bayesserver.analysis.LogLikelihoodAnalysisOptions
-
Sets a value which determines whether evidence subsets are included, excluded or both.
- setSuggestedBinCount(int) - Method in class com.bayesserver.analysis.HistogramDensityOptions
-
Sets the approximate number of bins to use to represent the approximate density function.
- setSuggestedBinCount(int) - Method in class com.bayesserver.data.discovery.DiscretizationOptions
-
Sets the number of suggested bins to use during discretization.
- setSyncNodeVariableName(boolean) - Static method in class com.bayesserver.Network
-
When
true
synchronizes
Variable
names with their containing
Node
.
- setTarget(Node) - Method in class com.bayesserver.learning.structure.TANStructuralLearningOptions
-
Sets the target of the TAN tree.
- setTemporalType(TemporalType) - Method in class com.bayesserver.Node
-
- setTerminalTime(Integer) - Method in class com.bayesserver.analysis.DSeparationOptions
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTerminalTime(Integer) - Method in class com.bayesserver.analysis.ValueOfInformationOptions
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTerminalTime(Integer) - Method in class com.bayesserver.causal.CausalQueryOptionsBase
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTerminalTime(Integer) - Method in class com.bayesserver.inference.LikelihoodSamplingQueryOptions
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTerminalTime(Integer) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTerminalTime(Integer) - Method in interface com.bayesserver.inference.QueryOptions
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTerminalTime(Integer) - Method in class com.bayesserver.inference.RelevanceTreeQueryOptions
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTerminalTime(Integer) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTerminalTime(Integer) - Method in class com.bayesserver.inference.VariableEliminationQueryOptions
-
Sets the terminal time, which is the time at which any terminal nodes in a Dynamic Bayesian Network connect to temporal nodes.
- setTestIndependence(boolean) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Sets a value which when true uses independence tests to reduce the search space.
- setTestSingleCluster(boolean) - Method in class com.bayesserver.learning.structure.ClusteringStructuralLearningOptions
-
Sets a value which determines whether a test is performed for a single cluster (i.e.
- setText(String) - Method in interface com.bayesserver.Expression
-
Sets the expression text.
- setText(String) - Method in class com.bayesserver.FunctionVariableExpression
-
Sets the expression text.
- setText(String) - Method in class com.bayesserver.TableExpression
-
Sets the expression text, which is run for each cell in the table.
- setTimes(int[]) - Method in class com.bayesserver.data.timeseries.WindowOptions
-
Sets the times to include in the window.
- setTimeSeriesMode(TimeSeriesMode) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets the mode in which time series distributions are learned.
- setTolerance(double) - Method in class com.bayesserver.inference.LoopyBeliefQueryOptions
-
Sets the tolerance used to determine whether or not the approximate inference process has converged.
- setTolerance(Double) - Method in class com.bayesserver.learning.parameters.ParameterLearningOptions
-
Sets the tolerance used to determine whether or not parameter learning has converged.
- setTolerance(Double) - Method in class com.bayesserver.learning.structure.SearchStructuralLearningOptions
-
Sets the tolerance used to determine whether or not a search move is a significant improvement.
- setTreatmentValues(List<Double>) - Method in class com.bayesserver.causal.EffectsAnalysisOptions
-
A list of treatment values to evaluate the causal effect on the outcome for.
- setTreeWidth(boolean) - Method in class com.bayesserver.inference.TreeQueryOptions
-
Sets a value indicating whether or not to calculate the tree width.
- setUnweightedCaseCount(long) - Method in class com.bayesserver.data.DataProgressEventArgs
-
Gets the number of cases read so far.
- setUnweightedTemporalCount(Long) - Method in class com.bayesserver.data.DataProgressEventArgs
-
Gets the number of temporal rows read so far for all cases.
- setUpperBound(Double) - Method in class com.bayesserver.optimization.DesignState
-
The maximum value allowed for this variable/state during the optimization process.
- setValue(String) - Method in class com.bayesserver.CustomProperty
-
The custom property value.
- setValue(Object) - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
- setValue(Double) - Method in class com.bayesserver.data.discovery.WeightedValue
-
Sets the value, which can be null.
- setValue(Object) - Method in class com.bayesserver.inference.QueryFunctionOutput
-
Holds the result of a function evaluation at query time.
- setValue(Object) - Method in class com.bayesserver.State
-
Sets an optional value for a state, such as an interval for discretized variables.
- setValue(double) - Method in class com.bayesserver.TableIterator
-
Sets the underlying
Table
value at the current position of the iterator.
- setValueType(VariableValueType) - Method in class com.bayesserver.data.discovery.VariableDefinition
-
- setVariance(int, int, double) - Method in class com.bayesserver.CLGaussian
-
Sets the variance value of the Gaussian distribution at the specified [index] in the
Table
of discrete combinations.
- setVariance(Variable, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setVariance(Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setVariance(VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setVariance(Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setVariance(Variable, double) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
- setVariance(Variable, Integer, double) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
- setVariance(Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setVariance(VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setVariance(Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setVariance(Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setVariance(VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
- setWeight(int, int, int, double) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution at the specified [index] in the
Table
of discrete combinations.
- setWeight(Variable, Variable, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(Variable, Integer, Variable, Integer, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(VariableContext, VariableContext, double, State...) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(Variable, Variable, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(Variable, Variable, double) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
- setWeight(Variable, Integer, Variable, Integer, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(Variable, Integer, Variable, Integer, double) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
- setWeight(VariableContext, VariableContext, double, StateContext...) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(Variable, Variable, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(Variable, Integer, Variable, Integer, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(VariableContext, VariableContext, double, TableIterator) - Method in class com.bayesserver.CLGaussian
-
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
- setWeight(double) - Method in class com.bayesserver.data.discovery.WeightedValue
-
- setWeight(double) - Method in class com.bayesserver.inference.DefaultEvidence
-
Sets a weight that can be applied to the evidence.
- setWeight(double) - Method in interface com.bayesserver.inference.Evidence
-
Sets a weight that can be applied to the evidence.
- setWeightColumn(String) - Method in class com.bayesserver.data.discovery.DiscretizationAlgoOptions
-
Sets a column that contains case weights for each record.
- setWeightColumn(String) - Method in class com.bayesserver.data.discovery.VariableGeneratorOptions
-
Sets the name of a column which contains a weight (support) for each case.
- setWeightedCaseCount(double) - Method in class com.bayesserver.data.DataProgressEventArgs
-
Gets the number of cases read so far.
- setWeightedCaseCount(double) - Method in class com.bayesserver.data.DefaultCrossValidationTestResult
- setWindowColumnName(String) - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions
-
Sets the name of the column which will contain the window identifier.
- setWindowTimeColumnName(String) - Method in class com.bayesserver.data.timeseries.WindowDataReaderOptions
-
Sets the name of the column which will contain the window time.
- size() - Method in class com.bayesserver.analysis.AutoInsightStateOutputCollection
-
- size() - Method in class com.bayesserver.analysis.AutoInsightVariableOutputCollection
-
- size() - Method in class com.bayesserver.analysis.ConfusionMatrixCell
-
Gets the count (support) for this cell.
- size() - Method in class com.bayesserver.analysis.DSeparationTestResultCollection
-
- size() - Method in class com.bayesserver.CustomPropertyCollection
-
- size() - Method in class com.bayesserver.data.DataColumnCollection
-
Gets the number of columns in the collection.
- size() - Method in class com.bayesserver.data.DataRowCollection
-
Gets the number of rows in the collection.
- size() - Method in class com.bayesserver.data.sampling.ExcludedVariables
-
- size() - Method in class com.bayesserver.inference.DefaultEvidence
-
Gets the count of variables with either hard, soft or temporal evidence set.
- size() - Method in class com.bayesserver.inference.DefaultQueryDistributionCollection
-
- size() - Method in class com.bayesserver.inference.DefaultQueryFunctionCollection
-
- size() - Method in interface com.bayesserver.inference.Evidence
-
Gets the count of variables with either hard, soft or temporal evidence set.
- size() - Method in class com.bayesserver.learning.structure.LinkConstraintCollection
-
- size() - Method in class com.bayesserver.NetworkLinkCollection
-
- size() - Method in class com.bayesserver.NetworkNodeCollection
-
- size() - Method in class com.bayesserver.NetworkNodeGroupCollection
-
- size() - Method in class com.bayesserver.NetworkVariableCollection
-
- size() - Method in class com.bayesserver.NodeDistributionExpressions
-
Gets the number of distributions in the container.
- size() - Method in class com.bayesserver.NodeDistributions
-
Gets the number of distributions in the container.
- size() - Method in class com.bayesserver.NodeGroupCollection
-
- size() - Method in class com.bayesserver.NodeLinkCollection
-
- size() - Method in class com.bayesserver.NodeVariableCollection
-
- size() - Method in class com.bayesserver.StateCollection
- size() - Method in class com.bayesserver.Table
-
The data count in the
Table
.
- size() - Method in class com.bayesserver.TableAccessor
-
Gets the count of values in the underlying
Table
.
- size() - Method in class com.bayesserver.TableIterator
-
Gets the count of values in the underlying
Table
.
- size() - Method in class com.bayesserver.VariableContextCollection
-
Gets the number of elements contained in the collection.
- SoftEvidence - Class in com.bayesserver.inference
-
Helper methods for manipulating soft/virtual evidence.
- sort(Network) - Static method in class com.bayesserver.TopologicalSort
-
Returns the nodes in a Bayesian network sorted in topological order.
- SortOrder - Enum in com.bayesserver.data.discovery
-
The sort order of states for new discrete variables.
- sortWithDepth(Network) - Static method in class com.bayesserver.TopologicalSort
-
Returns the nodes in a Bayesian network sorted and grouped in topological order.
- State - Class in com.bayesserver
-
Represents a state of a variable.
- State() - Constructor for class com.bayesserver.State
-
Initializes a new instance of the
State
class.
- State(String) - Constructor for class com.bayesserver.State
-
Initializes a new instance of the
State
class with the specified [name].
- State(String, Object) - Constructor for class com.bayesserver.State
-
Initializes a new instance of the
State
class with the specified [name] and [value].
- StateCollection - Class in com.bayesserver
-
Represents a collection of states belonging to a
Variable
.
- StateContext - Class in com.bayesserver
-
Identifies a
State
and contextual information such as the time (zero based).
- StateContext(State, Integer) - Constructor for class com.bayesserver.StateContext
-
- stateCount(int) - Method in class com.bayesserver.Table
-
Gets the number of states of a variable at the time this instance was constructed.
- StateNotFoundAction - Enum in com.bayesserver.data
-
Determines the action to take when a state name or value cannot be matched to a variable state.
- stateRepeat(int) - Method in class com.bayesserver.Table
-
Gets the number of times each state is repeated for a
Variable
in the
Table
layout.
- statesCollectionChange(Variable, int, State, State, CollectionAction, boolean) - Method in interface com.bayesserver.NetworkMonitor
-
For internal use.
- StateValueType - Enum in com.bayesserver
-
The type of value represented by a
State
.
- Stop - Interface in com.bayesserver
-
Interface to allow early completion of a long running task.
- StructuralLearning - Interface in com.bayesserver.learning.structure
-
Defines methods for learning the structure (links) of a Bayesian network.
- StructuralLearningOptions - Interface in com.bayesserver.learning.structure
-
Options governing a structural learning algorithm.
- StructuralLearningOutput - Interface in com.bayesserver.learning.structure
-
Contains information returned from a structural learning algorithm.
- StructuralLearningProgress - Interface in com.bayesserver.learning.structure
-
Interface to provide progress information during structural learning.
- StructuralLearningProgressInfo - Interface in com.bayesserver.learning.structure
-
Interface to provide progress information during structural learning.
- sum() - Method in class com.bayesserver.Table
-
Calculates the sum of all values in the
Table
.