Index

A B C D E F G H I K L M N O P R S T U V W X
All Classes and Interfaces|All Packages|Serialized Form

A

AbstractCSVReader - Class in es.upm.fi.cig.multictbnc.data.reader

Common attributes and methods for dataset readers.

AbstractCSVReader(String) - Constructor for class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

Receives the path to the dataset folder and initialises the reader as out-of-date.

AbstractExperiment - Class in es.upm.fi.cig.multictbnc.experiments

Abstract class for defining experiments.

AbstractExperiment(String[]) - Constructor for class es.upm.fi.cig.multictbnc.experiments.AbstractExperiment

This constructor initializes an experiment with the provided configuration parameters.

AbstractLikelihood - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores

Abstract class defining common variables and methods for likelihood-based scores.

AbstractLikelihood(String) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.AbstractLikelihood

Receives the name of the penalisation function for the structure complexity.

AbstractNode - Class in es.upm.fi.cig.multictbnc.nodes

Abstract class defining common variables and methods for any kind of node.

AbstractNode(String) - Constructor for class es.upm.fi.cig.multictbnc.nodes.AbstractNode

Common initialisation for nodes.

AbstractNode(String, boolean) - Constructor for class es.upm.fi.cig.multictbnc.nodes.AbstractNode

Common initialisation for nodes.

AbstractPGM<NodeType extends Node> - Class in es.upm.fi.cig.multictbnc.models

Contains common attributes and methods for PGM.

AbstractPGM() - Constructor for class es.upm.fi.cig.multictbnc.models.AbstractPGM

Default constructor.

AbstractPGM(List<NodeType>) - Constructor for class es.upm.fi.cig.multictbnc.models.AbstractPGM

Common initialisation for PGMs.

AbstractPGM(List<NodeType>, Dataset) - Constructor for class es.upm.fi.cig.multictbnc.models.AbstractPGM

Common initialisation for PGMs.

AbstractStructureConstraints - Class in es.upm.fi.cig.multictbnc.learning.structure.constraints

Contains common attributes and methods for classes that determine the structure constraints of PFG.

AbstractStructureConstraints() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraints.AbstractStructureConstraints

adaptModel(MultiCTBNC<CPTNode, CIMNode>, Dataset) - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftAdaptiveMethod

Abstract method for adapting the model based on the provided data batch.

adaptModel(MultiCTBNC<CPTNode, CIMNode>, Dataset) - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftGloballyAdaptiveMethod

Adapts the provided MultiCTBNC model based on the new data batch.

adaptModel(MultiCTBNC<CPTNode, CIMNode>, Dataset) - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftLocallyAdaptiveMethod

Adapts the provided MultiCTBNC model based on the new data batch.

add(T) - Method in class es.upm.fi.cig.multictbnc.data.representation.SlidingWindow

Adds a new object to the sliding window.

addEvent(String, String) - Method in class es.upm.fi.cig.multictbnc.data.representation.State

Adds an event (a variable taking a certain value) to the state.

addEvents(Map<String, String>) - Method in class es.upm.fi.cig.multictbnc.data.representation.State

Adds events to the state.

addFeatureVariable(String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Registers the name of a feature in the dataset to allow the inclusion of sequences that contain it.

addFeatureVariable(String, Dataset) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Add a new feature variable to the dataset given the sequences containing the transitions of the variable.

addFeatureVariable(String, Sequence) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Adds a new feature variable to the sequence by copying the values from another sequence with the same number of observations.

addHorizontalValueMarker(double) - Method in class es.upm.fi.cig.multictbnc.gui.XYLineChart

Adds a horizontal value marker to the chart.

addNodes(List<NodeType>, boolean) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

addNodes(List<NodeType>, boolean) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Adds the provided nodes to the PGM.

addSepSetAndNodeAsParents(PGM<? extends Node>, CIMNode, List<Integer>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Defines a node, which is being studied as a possible parent node, and a separating set as parents of an evaluated node.

addSepSetAsParents(PGM<? extends Node>, CIMNode, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Defines a separating set as parents of an evaluated node.

addSequence(Sequence) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Receives a Sequence to add it to the dataset.

addSequence(List<String[]>) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Receives a list of Strings (a sequence) from which a Sequence is created and adds it to the dataset.

addSequence(List<String[]>, String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Receives a list of Strings (a sequence) and the path of the file from which it was extracted.

addSeries(String...) - Method in class es.upm.fi.cig.multictbnc.gui.XYLineChart

Adds new series to the chart.

addVerticalValueMarker(double) - Method in class es.upm.fi.cig.multictbnc.gui.XYLineChart

Adds a vertical value marker to the chart.

areParametersEstimated() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

areParametersEstimated() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

areParametersEstimated() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns true if all the parameters were estimated.

areParametersEstimated() - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

areParametersEstimated() - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

areParametersEstimated() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Returns true if the parameters of the node were estimated.

arrayToQueue(String[]) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Converts an array of strings into a queue of strings.

AverageLocalLogLikelihood - Class in es.upm.fi.cig.multictbnc.conceptdriftdetection

This class provides a method to compute the average local log-likelihood of each node of a Multi-CTBNC.

AverageLocalLogLikelihood(String) - Constructor for class es.upm.fi.cig.multictbnc.conceptdriftdetection.AverageLocalLogLikelihood

Constructs an instance of AverageLocalLogLikelihood with a specified penalisation function.

B

BN<NodeType extends Node> - Class in es.upm.fi.cig.multictbnc.models

Implements a Bayesian network (BN).

BN(Dataset, List<String>, BNLearningAlgorithms, StructureConstraints, Class<NodeType>) - Constructor for class es.upm.fi.cig.multictbnc.models.BN

Initialises a Bayesian network by receiving a dataset, a list of variables to use and the algorithms for parameter and structure learning.

BN(BN<NodeType>, boolean) - Constructor for class es.upm.fi.cig.multictbnc.models.BN

Constructor to clone a Bayesian network.

BN(List<NodeType>) - Constructor for class es.upm.fi.cig.multictbnc.models.BN

Initialises a Bayesian network by receiving a list of nodes.

BN(List<NodeType>, Dataset) - Constructor for class es.upm.fi.cig.multictbnc.models.BN

Initialises a Bayesian network by receiving a list of nodes and a dataset.

BNBayesianEstimation - Class in es.upm.fi.cig.multictbnc.learning.parameters.bn

Implements the Bayesian estimation to estimate the parameters of a BN.

BNBayesianEstimation(double) - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNBayesianEstimation

Receives the hyperparameter of the Dirichlet prior distribution over the parameters (i.e. imaginary counts).

BNBayesianScore - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn

Implements the Bayesian Dirichlet equivalence metric for Bayesian networks with nodes that have CPTs (Heckerman et al., 1995).

BNBayesianScore() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNBayesianScore

BNHillClimbing - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation

Implements hill climbing algorithm for BNs.

BNHillClimbing(BNScoreFunction) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

Constructor that receives the score function to optimise.

BNHillClimbingHybridAlgorithm - Class in es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing

Implements the maximisation phase (hill climbing algorithm) of the hybrid structure learning algorithm for Bayesian networks.

BNHillClimbingHybridAlgorithm(BNScoreFunction, boolean[][]) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing.BNHillClimbingHybridAlgorithm

Initialises the algorithm by proving a score function and a skeleton of the Bayesian network.

BNHybridStructureLearningAlgorithm - Class in es.upm.fi.cig.multictbnc.learning.structure.hybrid

Implements the hybrid structure learning algorithm for Bayesian networks.

BNHybridStructureLearningAlgorithm(BNScoreFunction, double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.hybrid.BNHybridStructureLearningAlgorithm

Initialises the hybrid structure learning algorithm receiving a significance value and a score function.

BNLearningAlgorithms - Class in es.upm.fi.cig.multictbnc.learning

Stores the parameter and structure learning algorithms for a Bayesian network.

BNLearningAlgorithms(BNParameterLearningAlgorithm, StructureLearningAlgorithm) - Constructor for class es.upm.fi.cig.multictbnc.learning.BNLearningAlgorithms

Receives the learning algorithms for the parameters and the structure.

BNLogLikelihood - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn

Implements the log-likelihood score for Bayesian networks with nodes that have CPTs.

BNLogLikelihood(String) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNLogLikelihood

Receives the name of the penalisation function for the structure complexity.

BNMaximumLikelihoodEstimation - Class in es.upm.fi.cig.multictbnc.learning.parameters.bn

Maximum likelihood estimation of BN parameters.

BNMaximumLikelihoodEstimation() - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNMaximumLikelihoodEstimation

BNParameterLearningAlgorithm - Class in es.upm.fi.cig.multictbnc.learning.parameters.bn

Defines methods for parameter learning algorithms of discrete Bayesian networks.

BNParameterLearningAlgorithm() - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNParameterLearningAlgorithm

BNParameterLearningAlgorithmFactory - Class in es.upm.fi.cig.multictbnc.learning.parameters.bn

Builds the specified parameter learning algorithm for a BN.

BNParameterLearningAlgorithmFactory() - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNParameterLearningAlgorithmFactory

BNScoreFunction - Interface in es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn

Interface used to define scores for Bayesian networks.

BNSufficientStatistics - Class in es.upm.fi.cig.multictbnc.learning.parameters.bn

Compute and store the sufficient statistics of a discrete BN node.

BNSufficientStatistics(double) - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNSufficientStatistics

Constructs a BNSufficientStatistics by receiving the hyperparameter of the Dirichlet prior distribution.

BNTabuSearch - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch

Implements the tabu search algorithm for Bayesian networks.

BNTabuSearch(BNScoreFunction, int, int) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.BNTabuSearch

Initialises the tabu search algorithm by proving a score function and a tabu list size.

buildCompleteStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Returns the adjacency matrix of a PGM with a complete structure.

buildCompleteStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

Returns the adjacency matrix of a PGM with a complete structure.

C

call() - Method in class es.upm.fi.cig.multictbnc.tasks.ClassificationTask

call() - Method in class es.upm.fi.cig.multictbnc.tasks.EvaluationTask

call() - Method in class es.upm.fi.cig.multictbnc.tasks.TrainingTask

cartesianProduct(List<List<State>>) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Given a list of lists with the possible states of some variables, this method returns the Cartesian product between each of the possible states of each variable.

changeDatasetReader() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

A dataset reader was selected in the comboBox.

changeDatasetReaderClassification() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

A dataset reader for the classification dataset was selected in the comboBox.

changeDatasetReaderClassificationStrategy() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

An strategy for the extraction of sequences to classify was selected.

changeDatasetReaderStrategy() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

An strategy for the extraction of sequences was selected.

changeModel() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

A model was selected in the comboBox.

changeParameterLearningAlgBN() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

A parameter learning algorithm for BNs was selected in the comboBox.

changeParameterLearningAlgCTBN() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

A parameter learning algorithm for CTBNs was selected in the comboBox.

changeScoreFunction() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

A score function was selected in the comboBox.

changeStructureLearningAlg() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

A structure learning algorithm was selected in the comboBox.

checkVarianceFeatures(boolean) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Removes from the dataset those feature variables with zero variance.

CIMNode - Class in es.upm.fi.cig.multictbnc.nodes

Extends the DiscreteNode class to store a CIM and the sufficient statistics for a CTBN.

CIMNode(CIMNode) - Constructor for class es.upm.fi.cig.multictbnc.nodes.CIMNode

Constructor to clone a CIM node.

CIMNode(String, List<String>) - Constructor for class es.upm.fi.cig.multictbnc.nodes.CIMNode

Constructs a CIMNode given its name and possible states.

CIMNode(String, List<String>, boolean) - Constructor for class es.upm.fi.cig.multictbnc.nodes.CIMNode

Initialises a CIMNode given its name, possible states and if it is a class variable.

ClassificationService - Class in es.upm.fi.cig.multictbnc.services

Service that creates and manages a ClassificaionTask.

ClassificationService() - Constructor for class es.upm.fi.cig.multictbnc.services.ClassificationService

ClassificationTask - Class in es.upm.fi.cig.multictbnc.tasks

Task that allows executing the classification of sequences in a background thread.

ClassificationTask(MultiCTBNC<?, ?>, DatasetReader, boolean) - Constructor for class es.upm.fi.cig.multictbnc.tasks.ClassificationTask

Constructs a ClassificationTask that receives an MultiCTBNC model and a datasetReader.

Classifier - Interface in es.upm.fi.cig.multictbnc.classification

Interface representing classification models.

ClassifierFactory - Class in es.upm.fi.cig.multictbnc.classification

Provides static methods for the creation of classifiers.

ClassifierFactory() - Constructor for class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

classify() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Performs classification over a provided dataset with a previously trained model.

clearParentAndChildrenSets() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

clearParentAndChildrenSets() - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

clearParentAndChildrenSets() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Remove the set of parents and children of the node.

clone2DArray(double[][]) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Creates a deep copy of a two-dimensional double array.

clone3DArray(double[][][]) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Creates a deep copy of a three-dimensional double array.

close() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamImplementationExperiment

Closes the resources used for the experiment.

close() - Method in class es.upm.fi.cig.multictbnc.writers.performance.ExcelExperimentsWriter

close() - Method in class es.upm.fi.cig.multictbnc.writers.performance.MetricsWriter

Closes the writer.

combination(List<type>, int) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns all possible combinations of size 'k' of a given list of elements.

compute(BN<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNBayesianScore

compute(BN<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNLogLikelihood

Computes the (penalised) log-likelihood score for a discrete Bayesian network.

compute(BN<? extends Node>) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNScoreFunction

Computes the score of a Bayesian network.

compute(CTBN<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNBayesianScore

compute(CTBN<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNConditionalLogLikelihood

Computes the (penalised) conditional log-likelihood score of a discrete continuous-time Bayesian network.

compute(CTBN<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNLogLikelihood

compute(CTBN<? extends Node>) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNScoreFunction

Computes the score for a continuous-time Bayesian network.

compute(CTBN<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNBayesianScore

compute(CTBN<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNConditionalLogLikelihood

compute(CTBN<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNLogLikelihood

Computes the (penalised) log-likelihood score at a given node of a discrete continuous-time Bayesian network.

compute(CTBN<? extends Node>, int) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNScoreFunction

Computes the score of a continuous-time Bayesian network at a given node.

compute(MultiCTBNC<CPTNode, CIMNode>, Dataset, List<String>) - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.AverageLocalLogLikelihood

compute(MultiCTBNC<CPTNode, CIMNode>, Dataset, List<String>) - Method in interface es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftScore

Compute the score over each node of the provided MultiCTBNC.

compute(Map<String, Double>) - Method in interface es.upm.fi.cig.multictbnc.performance.Metric

Computes the value of the evaluation metric given a Map containing a confusion matrix.

computeScore(BN<? extends Node>, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

Computes the score of a structure given an adjacency matrix.

computeScore(CTBN<? extends Node>, int, boolean[][], Map<Long, Double>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

Computes the score at a certain node given an adjacency matrix.

computeSufficientStatistics(DiscreteStateNode, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNSufficientStatistics

Computes the sufficient statistics of a node in a BN.

computeSufficientStatistics(DiscreteStateNode, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Computes the sufficient statistics of a CTBN node.

computeSufficientStatistics(DiscreteStateNode, Dataset) - Method in interface es.upm.fi.cig.multictbnc.learning.parameters.SufficientStatistics

Computes the sufficient statistics of a discrete node.

computeSufficientStatistics(List<Integer>) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

computeSufficientStatistics(List<Integer>) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Computes the sufficient statistics for the nodes whose indexes are specified.

ConceptDriftAdaptiveMethod - Class in es.upm.fi.cig.multictbnc.conceptdriftdetection

Abstract class representing a concept drift adaptive method.

ConceptDriftAdaptiveMethod(ConceptDriftScore, List<String>, double, boolean, String) - Constructor for class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftAdaptiveMethod

Initializes the concept drift adaptive method with the specified parameters.

ConceptDriftGloballyAdaptiveMethod - Class in es.upm.fi.cig.multictbnc.conceptdriftdetection

This class implements a concept drift adaptive method that operates globally on a MultiCTBNC model.

ConceptDriftGloballyAdaptiveMethod(List<String>, ConceptDriftScore, double, double, boolean, int, boolean, String) - Constructor for class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftGloballyAdaptiveMethod

Initializes the globally adaptive concept drift method with specified parameters.

ConceptDriftLocallyAdaptiveMethod - Class in es.upm.fi.cig.multictbnc.conceptdriftdetection

This class implements a concept drift adaptive method that operates locally on each node of a MultiCTBNC model.

ConceptDriftLocallyAdaptiveMethod(List<String>, ConceptDriftScore, double, double, boolean, int, boolean, String) - Constructor for class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftLocallyAdaptiveMethod

Initializes the locally adaptive concept drift method with specified parameters.

ConceptDriftScore - Interface in es.upm.fi.cig.multictbnc.conceptdriftdetection

Interface representing scores that can be computed locally on each node of a MultiCTBNC and used to detect concept drifts in a given data batch.

conditionalIndependenceTest(CIMNode, CPTNode, List<CIMNode>) - Static method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Tests whether a feature and a class variables are conditionally independent given a certain separating set.

ConInd - Class in es.upm.fi.cig.multictbnc.fss

This class implements the ConInd online feature subset selection algorithm from Yu et al. 2018.

ConInd(List<String>, ParameterLearningAlgorithm, int, double, double) - Constructor for class es.upm.fi.cig.multictbnc.fss.ConInd

Constructs a ConInd object.

ConsoleExperimentsWriter - Class in es.upm.fi.cig.multictbnc.writers.performance

Allows writing the results of the experiments through the standard output stream.

ConsoleExperimentsWriter() - Constructor for class es.upm.fi.cig.multictbnc.writers.performance.ConsoleExperimentsWriter

containsFeature(String) - Method in class es.upm.fi.cig.multictbnc.fss.SubsetSelectedFeatures

Checks if a specific feature is included in the selected subset.

Controller - Class in es.upm.fi.cig.multictbnc.gui

Controller used to initialise the elements of the GUI and allow the interaction between the logic of the application and the GUI.

Controller() - Constructor for class es.upm.fi.cig.multictbnc.gui.Controller

ControllerUtil - Class in es.upm.fi.cig.multictbnc.util

Utility class with methods related to controlling the UI behaviour.

CPTNode - Class in es.upm.fi.cig.multictbnc.nodes

Extends the DiscreteNode class to store a CPT and the sufficient statistics for a BN.

CPTNode(CPTNode) - Constructor for class es.upm.fi.cig.multictbnc.nodes.CPTNode

Constructor that receives a CPTNode and clones it.

CPTNode(String, List<String>) - Constructor for class es.upm.fi.cig.multictbnc.nodes.CPTNode

Constructor that receives the name of the variable and its possible states.

CPTNode(String, List<String>, boolean) - Constructor for class es.upm.fi.cig.multictbnc.nodes.CPTNode

Constructor that receives the name of the variable, a list of strings with its possible states and if it is a class variable.

createEmptyNode(Node) - Method in class es.upm.fi.cig.multictbnc.nodes.NodeFactory

Creates a node from a given one without storing its parameters or sufficient statistics.

createFactory(Class<NodeType>) - Static method in class es.upm.fi.cig.multictbnc.nodes.NodeFactory

Constructs a NodeFactory for nodes whose Class is passed as a parameter.

createNode(Node) - Method in class es.upm.fi.cig.multictbnc.nodes.NodeFactory

Creates a node from a given one.

createNode(String, Dataset) - Method in class es.upm.fi.cig.multictbnc.nodes.NodeFactory

Creates a node given the name of its variable and the dataset where it appears.

createNodes(List<NodeType>) - Method in class es.upm.fi.cig.multictbnc.nodes.NodeFactory

Creates a list of nodes with the same attributes as those provided.

createTask() - Method in class es.upm.fi.cig.multictbnc.services.ClassificationService

createTask() - Method in class es.upm.fi.cig.multictbnc.services.EvaluationService

createTask() - Method in class es.upm.fi.cig.multictbnc.services.TrainingService

createXYLineChart(String, String, String, int[], String...) - Static method in class es.upm.fi.cig.multictbnc.util.UserInterfaceUtil

Creates an XY line chart with the specified parameters.

CrossValidationBinaryRelevanceMethod - Class in es.upm.fi.cig.multictbnc.performance

Implements a cross-validation method used to learn one CTBNC for each class variable and merge the results.

CrossValidationBinaryRelevanceMethod(DatasetReader, int, boolean, boolean, long) - Constructor for class es.upm.fi.cig.multictbnc.performance.CrossValidationBinaryRelevanceMethod

Constructor for cross-validation method.

CrossValidationMethod - Class in es.upm.fi.cig.multictbnc.performance

Implements cross-validation method.

CrossValidationMethod(DatasetReader, int, boolean, boolean, Long) - Constructor for class es.upm.fi.cig.multictbnc.performance.CrossValidationMethod

Constructor for cross-validation method.

CTBN<NodeType extends Node> - Class in es.upm.fi.cig.multictbnc.models

Implements a continuous-time Bayesian network (CTBN).

CTBN(Dataset, List<String>, CTBNLearningAlgorithms, StructureConstraints, BN<? extends Node>, Class<NodeType>) - Constructor for class es.upm.fi.cig.multictbnc.models.CTBN

Initialises a continuous-time Bayesian network given a dataset, the list of variables to use and the algorithms for parameter and structure learning.

CTBN(Dataset, List<String>, CTBNLearningAlgorithms, StructureConstraints, Class<NodeType>) - Constructor for class es.upm.fi.cig.multictbnc.models.CTBN

Initialises a continuous-time Bayesian network given a dataset, the list of variables to use and the algorithms for parameter and structure learning.

CTBN(CTBN<NodeType>, boolean) - Constructor for class es.upm.fi.cig.multictbnc.models.CTBN

Constructor to clone a continuous-time Bayesian network.

CTBN(CTBN<NodeType>, Dataset) - Constructor for class es.upm.fi.cig.multictbnc.models.CTBN

Constructor to clone a continuous-time Bayesian network.

CTBN(List<NodeType>, BN<? extends Node>) - Constructor for class es.upm.fi.cig.multictbnc.models.CTBN

Initialises a continuous-time Bayesian network by receiving a list of nodes and a Bayesian network modelling the class subgraph of a Multi-CTBNC.

CTBN(List<NodeType>, BN<? extends Node>, Dataset) - Constructor for class es.upm.fi.cig.multictbnc.models.CTBN

Initialises a continuous-time Bayesian network by receiving a list of nodes, a Bayesian network modelling the class subgraph of a Multi-CTBNC and a training dataset.

CTBNBayesianEstimation - Class in es.upm.fi.cig.multictbnc.learning.parameters.ctbn

Bayesian parameter estimation for a discrete CTBN.

CTBNBayesianEstimation(double, double) - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNBayesianEstimation

Constructs a CTBNBayesianEstimation for the Bayesian estimation of the parameters of a discrete CTBN.

CTBNBayesianScore - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn

Implements the Bayesian Dirichlet equivalence metric for CTBNs with nodes that have CIMs (Nodelman et al., 2003).

CTBNBayesianScore() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNBayesianScore

CTBNConditionalLogLikelihood - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn

Implements the conditional log-likelihood score for Multi-CTBNCs with nodes that have CPTs and CIMs to define its bridge and feature subgraph (represented by a CTBN).

CTBNConditionalLogLikelihood(String) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNConditionalLogLikelihood

Receives the name of the penalisation function for the structure complexity.

CTBNHillClimbing - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation

Implements hill climbing algorithm for CTBNs.

CTBNHillClimbing(CTBNScoreFunction) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbing

Constructor that receives the score function to optimise.

CTBNHillClimbingHybridAlgorithm - Class in es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing

Implements the maximisation phase (hill climbing algorithm) of the hybrid structure learning algorithm for continuous-time Bayesian networks.

CTBNHillClimbingHybridAlgorithm(CTBNScoreFunction, boolean[][]) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing.CTBNHillClimbingHybridAlgorithm

Initialises the algorithm by proving a score function and an initial adjacency matrix for the continuous-time Bayesian network.

CTBNHillClimbingIndividual - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation

Implements hill climbing algorithm for CTBNs.

CTBNHillClimbingIndividual(CTBNScoreFunction) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

Constructor that receives the score function to optimise.

CTBNHybridStructureLearningAlgorithm - Class in es.upm.fi.cig.multictbnc.learning.structure.hybrid

Implements the hybrid structure learning algorithm for continuous-time Bayesian networks.

CTBNHybridStructureLearningAlgorithm(CTBNScoreFunction, int, double, double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.hybrid.CTBNHybridStructureLearningAlgorithm

Initialises the hybrid structure learning algorithm receiving significance values, a score function and the maximum size of the separating sets.

CTBNLearningAlgorithms - Class in es.upm.fi.cig.multictbnc.learning

Stores the parameter and structure learning algorithms for a continuous-time Bayesian network.

CTBNLearningAlgorithms(CTBNParameterLearningAlgorithm, StructureLearningAlgorithm) - Constructor for class es.upm.fi.cig.multictbnc.learning.CTBNLearningAlgorithms

Receives the learning algorithms for the parameters and the structure.

CTBNLogLikelihood - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn

Implements the log-likelihood score for CTBNs with nodes that have CIMs.

CTBNLogLikelihood(String) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNLogLikelihood

Receives the name of the penalisation function for the structure complexity.

CTBNMaximumLikelihoodEstimation - Class in es.upm.fi.cig.multictbnc.learning.parameters.ctbn

Maximum likelihood estimation of CTBN parameters.

CTBNMaximumLikelihoodEstimation() - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNMaximumLikelihoodEstimation

CTBNParameterLearningAlgorithm - Class in es.upm.fi.cig.multictbnc.learning.parameters.ctbn

Define methods for parameter learning algorithms of continuous-time Bayesian networks.

CTBNParameterLearningAlgorithm() - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithm

CTBNParameterLearningAlgorithmFactory - Class in es.upm.fi.cig.multictbnc.learning.parameters.ctbn

Builds the specified parameter learning algorithm for a CTBN.

CTBNParameterLearningAlgorithmFactory() - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithmFactory

CTBNScoreFunction - Interface in es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn

Interface used to define scores for continuous-time Bayesian networks.

CTBNSufficientStatistics - Class in es.upm.fi.cig.multictbnc.learning.parameters.ctbn

Computes and stores the sufficient statistics of a discrete CTBN node.

CTBNSufficientStatistics(double, double) - Constructor for class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Receives the hyperparameters of the Dirichlet prior distribution over the parameters that are necessary for Bayesian estimation.

CTBNTabuSearchIndividual - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch

Implements the tabu search algorithm for continuous-time Bayesian networks.

CTBNTabuSearchIndividual(CTBNScoreFunction, int, int) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.CTBNTabuSearchIndividual

Initialises the tabu search algorithm by proving a score function and a tabu list size.

CTPC - Class in es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC

Implementation of the CTPC algorithm for Multi-CTBNCs.

CTPC(double, double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Initialises the CTPC algorithm by providing the significance levels to be used.

CTPCHybridAlgorithm - Class in es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC

Implements the restriction phase (CTPC algorithm) of the hybrid structure learning algorithm.

CTPCHybridAlgorithm(int, double, double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC.CTPCHybridAlgorithm

Initialises the algorithm by proving a significance level.

D

DAG - Class in es.upm.fi.cig.multictbnc.learning.structure.constraints.BN

Defines the restrictions of a general directed acyclic graph.

DAG() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraints.BN.DAG

DAG_maxK_MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node> - Class in es.upm.fi.cig.multictbnc.models.submodels

Implements a Multi-CTBNC where the class subgraph is formed by a Bayesian network, while the feature subgraph is a K-dependence continuous-time Bayesian network, i.e., the feature nodes are limited to have K parents (apart of the class variables).

DAG_maxK_MultiCTBNC(BNLearningAlgorithms, CTBNLearningAlgorithms, int, Class<NodeTypeBN>, Class<NodeTypeCTBN>) - Constructor for class es.upm.fi.cig.multictbnc.models.submodels.DAG_maxK_MultiCTBNC

Constructs a DAG_maxK_MultiCTBNC by receiving the learning algorithms for Bayesian networks and continuous-time Bayesian networks and the maximum number of parents of the feature variables (apart from the class variables).

DataSampler - Class in es.upm.fi.cig.multictbnc.sampling

Implements methods for the generation and writing of datasets sampled from Multi-CTBNCs.

DataSampler() - Constructor for class es.upm.fi.cig.multictbnc.sampling.DataSampler

Dataset - Class in es.upm.fi.cig.multictbnc.data.representation

Represents a time series dataset, which stores sequences and provides methods to access and modify their information.

Dataset(String) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.Dataset

Creates an empty dataset with the name of the time variable.

Dataset(String, List<String>) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.Dataset

Creates an empty dataset with the names of the time variable and class variables.

Dataset(List<Sequence>) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.Dataset

Creates a dataset with a list of sequences.

datasetClassificationModified() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

The information of the dataset used for classification was modified, so its DatasetReader is warned.

datasetModified() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

The information of the dataset for training/evaluation was modified, so its DatasetReader is warned.

DatasetReader - Interface in es.upm.fi.cig.multictbnc.data.reader

Interface for classes that read datasets.

DatasetReaderFactory - Class in es.upm.fi.cig.multictbnc.data.reader

Creates dataset readers.

DatasetReaderFactory() - Constructor for class es.upm.fi.cig.multictbnc.data.reader.DatasetReaderFactory

DataStreamExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments

Represents an experiment for evaluating continuous-time Bayesian network classifiers on streaming data.

DataStreamExperiment() - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

DataStreamMultipleCSVReader - Class in es.upm.fi.cig.multictbnc.data.reader

The class is designed for reading and processing streaming data from multiple CSV files.

DataStreamMultipleCSVReader(String, String, List<String>) - Constructor for class es.upm.fi.cig.multictbnc.data.reader.DataStreamMultipleCSVReader

This constructor prepares the reader to process CSV files from the specified folder.

detectChange(double) - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.PageHinkleyTest

Detects concept drifts based on a new observation.

detectNewFeatureVariables(File) - Method in class es.upm.fi.cig.multictbnc.data.reader.DataStreamMultipleCSVReader

Extracts the names of the variables from a CSV file.

Digraph - Class in es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC

Specifies the structure restrictions of a CTBN.

Digraph() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC.Digraph

DiscreteStateNode - Class in es.upm.fi.cig.multictbnc.nodes

Abstract class defining common variables and methods for discrete nodes.

DiscreteStateNode(String, List<String>) - Constructor for class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Initialises a discrete node given a list of states.

DiscreteStateNode(String, List<String>, boolean) - Constructor for class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Initialises a discrete node specifying if the node is for a class variable or a feature.

display() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

display() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Displays the probabilistic graphical model.

display(String) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

display(String) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Displays the probabilistic graphical model.

display(String, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

display(String, List<Integer>) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Displays the probabilistic graphical model.

displayModel(MultiCTBNC<?, ?>) - Method in class es.upm.fi.cig.multictbnc.performance.ValidationMethod

Displays the model obtained with the validation method.

displayResults(Map<String, Double>) - Method in class es.upm.fi.cig.multictbnc.performance.ValidationMethod

Displays the results obtained with the validation method.

E

Empty_digraph_MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node> - Class in es.upm.fi.cig.multictbnc.models.submodels

Implements a Multi-CTBNC with an empty class subgraph.

Empty_digraph_MultiCTBNC(BNLearningAlgorithms, CTBNLearningAlgorithms, Class<NodeTypeBN>, Class<NodeTypeCTBN>) - Constructor for class es.upm.fi.cig.multictbnc.models.submodels.Empty_digraph_MultiCTBNC

Constructs a Empty_digraph_MultiCTBNC by receiving the learning algorithms for Bayesian networks and continuous-time Bayesian networks.

Empty_maxK_MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node> - Class in es.upm.fi.cig.multictbnc.models.submodels

Implements a Multi-CTBNC with an empty class subgraph and a K-dependence continuous-time Bayesian network for the feature subgraph, i.e., the feature nodes are limited to having K parents (apart of the class variables).

Empty_maxK_MultiCTBNC(BNLearningAlgorithms, CTBNLearningAlgorithms, int, Class<NodeTypeBN>, Class<NodeTypeCTBN>) - Constructor for class es.upm.fi.cig.multictbnc.models.submodels.Empty_maxK_MultiCTBNC

Constructs a Empty_maxK_Multi-CTBNC by receiving the learning algorithms for Bayesian networks and continuous-time Bayesian networks and the maximum number of parents of the features (apart of the class variables).

EmptyBN - Class in es.upm.fi.cig.multictbnc.learning.structure.constraints.BN

It only allows the creation of empty BNs.

EmptyBN() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraints.BN.EmptyBN

equals(Object) - Method in class es.upm.fi.cig.multictbnc.data.representation.State

equals(Object) - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

ErroneousSequenceException - Exception in es.upm.fi.cig.multictbnc.exceptions

Thrown when a valid sequence could not be created with the provided data.

ErroneousSequenceException(String) - Constructor for exception es.upm.fi.cig.multictbnc.exceptions.ErroneousSequenceException

Constructs a ErroneousSequenceException with the specified detail message.

ErroneousValueException - Exception in es.upm.fi.cig.multictbnc.exceptions

Thrown when an error occurs due to an incorrect value provided by the user.

ErroneousValueException(String) - Constructor for exception es.upm.fi.cig.multictbnc.exceptions.ErroneousValueException

Constructs a ErroneousValue with the specified detail message.

es.upm.fi.cig.multictbnc - module es.upm.fi.cig.multictbnc

es.upm.fi.cig.multictbnc - package es.upm.fi.cig.multictbnc

es.upm.fi.cig.multictbnc.classification - package es.upm.fi.cig.multictbnc.classification

es.upm.fi.cig.multictbnc.conceptdriftdetection - package es.upm.fi.cig.multictbnc.conceptdriftdetection

es.upm.fi.cig.multictbnc.data.reader - package es.upm.fi.cig.multictbnc.data.reader

es.upm.fi.cig.multictbnc.data.representation - package es.upm.fi.cig.multictbnc.data.representation

es.upm.fi.cig.multictbnc.data.writer - package es.upm.fi.cig.multictbnc.data.writer

es.upm.fi.cig.multictbnc.exceptions - package es.upm.fi.cig.multictbnc.exceptions

es.upm.fi.cig.multictbnc.experiments - package es.upm.fi.cig.multictbnc.experiments

es.upm.fi.cig.multictbnc.experiments.implementationsexperiments - package es.upm.fi.cig.multictbnc.experiments.implementationsexperiments

es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments - package es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments

es.upm.fi.cig.multictbnc.fss - package es.upm.fi.cig.multictbnc.fss

es.upm.fi.cig.multictbnc.gui - package es.upm.fi.cig.multictbnc.gui

es.upm.fi.cig.multictbnc.learning - package es.upm.fi.cig.multictbnc.learning

es.upm.fi.cig.multictbnc.learning.parameters - package es.upm.fi.cig.multictbnc.learning.parameters

es.upm.fi.cig.multictbnc.learning.parameters.bn - package es.upm.fi.cig.multictbnc.learning.parameters.bn

es.upm.fi.cig.multictbnc.learning.parameters.ctbn - package es.upm.fi.cig.multictbnc.learning.parameters.ctbn

es.upm.fi.cig.multictbnc.learning.structure - package es.upm.fi.cig.multictbnc.learning.structure

es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC - package es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC

es.upm.fi.cig.multictbnc.learning.structure.constraints - package es.upm.fi.cig.multictbnc.learning.structure.constraints

es.upm.fi.cig.multictbnc.learning.structure.constraints.BN - package es.upm.fi.cig.multictbnc.learning.structure.constraints.BN

es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC - package es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC

es.upm.fi.cig.multictbnc.learning.structure.hybrid - package es.upm.fi.cig.multictbnc.learning.structure.hybrid

es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing - package es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing

es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC - package es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC

es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing - package es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing

es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation - package es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation

es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores - package es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores

es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn - package es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn

es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn - package es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn

es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch - package es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch

es.upm.fi.cig.multictbnc.models - package es.upm.fi.cig.multictbnc.models

es.upm.fi.cig.multictbnc.models.submodels - package es.upm.fi.cig.multictbnc.models.submodels

es.upm.fi.cig.multictbnc.nodes - package es.upm.fi.cig.multictbnc.nodes

es.upm.fi.cig.multictbnc.performance - package es.upm.fi.cig.multictbnc.performance

es.upm.fi.cig.multictbnc.sampling - package es.upm.fi.cig.multictbnc.sampling

es.upm.fi.cig.multictbnc.services - package es.upm.fi.cig.multictbnc.services

es.upm.fi.cig.multictbnc.tasks - package es.upm.fi.cig.multictbnc.tasks

es.upm.fi.cig.multictbnc.util - package es.upm.fi.cig.multictbnc.util

es.upm.fi.cig.multictbnc.writers.classification - package es.upm.fi.cig.multictbnc.writers.classification

es.upm.fi.cig.multictbnc.writers.performance - package es.upm.fi.cig.multictbnc.writers.performance

estimateLogLikelihood() - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

estimateLogLikelihood() - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

estimateLogLikelihood() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Returns the local log-likelihood for the node.

estimateParameters(CIMNode) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithm

Estimates the parameters for a given node from its computed sufficient statistics.

evaluate() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Evaluates the selected model.

evaluate(Prediction[], Dataset) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Uses different performance metrics to evaluate how good the given predictions are.

evaluate(MultiCTBNC<?, ?>) - Method in class es.upm.fi.cig.multictbnc.performance.CrossValidationBinaryRelevanceMethod

Evaluates the performance of the specified model using cross-validation.

evaluate(MultiCTBNC<?, ?>) - Method in class es.upm.fi.cig.multictbnc.performance.CrossValidationMethod

Evaluates the performance of the specified model using cross-validation.

evaluate(MultiCTBNC<?, ?>) - Method in class es.upm.fi.cig.multictbnc.performance.HoldOutMethod

Evaluates the performance of the specified model using hold-out validation.

evaluate(MultiCTBNC<?, ?>) - Method in class es.upm.fi.cig.multictbnc.performance.TestDatasetBinaryRelevanceMethod

evaluate(MultiCTBNC<?, ?>) - Method in class es.upm.fi.cig.multictbnc.performance.TestDatasetMethod

evaluate(MultiCTBNC<?, ?>) - Method in class es.upm.fi.cig.multictbnc.performance.ValidationMethod

Evaluates the performance of the specified model and returns the results.

evaluate(MultiCTBNC<?, ?>, double) - Method in class es.upm.fi.cig.multictbnc.performance.CrossValidationBinaryRelevanceMethod

evaluate(MultiCTBNC<?, ?>, double) - Method in class es.upm.fi.cig.multictbnc.performance.CrossValidationMethod

evaluate(MultiCTBNC<?, ?>, double) - Method in class es.upm.fi.cig.multictbnc.performance.HoldOutMethod

evaluate(MultiCTBNC<?, ?>, double) - Method in class es.upm.fi.cig.multictbnc.performance.TestDatasetBinaryRelevanceMethod

evaluate(MultiCTBNC<?, ?>, double) - Method in class es.upm.fi.cig.multictbnc.performance.TestDatasetMethod

evaluate(MultiCTBNC<?, ?>, double) - Method in class es.upm.fi.cig.multictbnc.performance.ValidationMethod

Evaluates the performance of the specified model and returns the results.

EvaluationService - Class in es.upm.fi.cig.multictbnc.services

Service that creates and manages an EvaluationTask.

EvaluationService() - Constructor for class es.upm.fi.cig.multictbnc.services.EvaluationService

EvaluationTask - Class in es.upm.fi.cig.multictbnc.tasks

Task that allows executing the training and evaluation of a model in a background thread.

EvaluationTask(ValidationMethod, MultiCTBNC<?, ?>) - Constructor for class es.upm.fi.cig.multictbnc.tasks.EvaluationTask

Constructs an EvaluationTask that receives a ValidationMethod and an MultiCTBNC model.

ExcelExperimentsWriter - Class in es.upm.fi.cig.multictbnc.writers.performance

Allows writing the results of the experiments in an Excel file.

ExcelExperimentsWriter(List<String>, List<String>, List<String>, List<String>, BNParameterLearningAlgorithm, CTBNParameterLearningAlgorithm, String, double, double, double, List<Long>, String) - Constructor for class es.upm.fi.cig.multictbnc.writers.performance.ExcelExperimentsWriter

Initialises the writer.

ExcelExperimentsWriter(List<String>, List<String>, List<String>, List<String>, List<String>, BNParameterLearningAlgorithm, CTBNParameterLearningAlgorithm, String, String, List<Long>, String) - Constructor for class es.upm.fi.cig.multictbnc.writers.performance.ExcelExperimentsWriter

Initialises the writer.

execute() - Method in interface es.upm.fi.cig.multictbnc.experiments.Experiment

Executes the experiment.

execute() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.LearningStreamExperiment

execute() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.ModelComparisonExperiment

execute() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.StructureLearningAlgorithmsComparisonExperiment

execute(MultiCTBNC<CPTNode, CIMNode>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamAsStaticDatasetExperiment

execute(MultiCTBNC<CPTNode, CIMNode>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamImplementationExperiment

Executes the experiment.

execute(MultiCTBNC<CPTNode, CIMNode>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithFSSExperiment

execute(MultiCTBNC<CPTNode, CIMNode>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithFSSWithoutUpdatingExperiment

execute(MultiCTBNC<CPTNode, CIMNode>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithoutFSSExperiment

execute(String, Dataset) - Method in class es.upm.fi.cig.multictbnc.fss.ConInd

execute(String, Dataset) - Method in interface es.upm.fi.cig.multictbnc.fss.OnlineFeatureSubsetSelection

Executes the feature subset selection algorithm for a newly arrived feature variable in a given data batch.

executeExperiment(String, double) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Executes a single data stream experiment for a specified path and detection threshold.

executeExperiment(String, String) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.FeatureStreamExperiment

Executes a single feature stream experiment for a specified path.

executeExperiment(String, String, double, double) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.FeatureStreamExperiment

Executes a single feature stream experiment.

Experiment - Interface in es.upm.fi.cig.multictbnc.experiments

Represents an experiment that can be executed.

ExperimentFactory - Class in es.upm.fi.cig.multictbnc.experiments

A factory class for creating instances of experiments based on provided arguments.

ExperimentFactory() - Constructor for class es.upm.fi.cig.multictbnc.experiments.ExperimentFactory

extractDecimal(String, double) - Static method in class es.upm.fi.cig.multictbnc.util.ControllerUtil

Receives an String and tries to convert it to a double.

extractFirstLong(String) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns the first long in a String.

extractFixedSequences(Dataset, List<String[]>) - Method in class es.upm.fi.cig.multictbnc.data.reader.SingleCSVReader

Extracts sequences that have the same maximum length and add them to the specified dataset.

extractFixedSequencesSameCC(Dataset, List<String[]>) - Method in class es.upm.fi.cig.multictbnc.data.reader.SingleCSVReader

Extracts sequences that have the same maximum length and add them to the specified dataset.

extractInt(String) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns an int from a String.

extractInteger(String, int) - Static method in class es.upm.fi.cig.multictbnc.util.ControllerUtil

Receives an String and tries to convert it to an Integer.

extractLong(String) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns a long from a String.

extractPathExperimentDatasets(String) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns the path to the datasets given the folder of a experiment.

extractVariableNames(File) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

Extracts the names of the variables given in some CSV files.

extremeProbability() - Static method in class es.upm.fi.cig.multictbnc.util.ProbabilityUtil

Returns a probability between 0 or 0.3, or between 0.7 and 1.

F

f1score(Map<String, Double>) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Compute the F1 score from a Map containing a confusion matrix.

failed() - Method in class es.upm.fi.cig.multictbnc.tasks.ClassificationTask

failed() - Method in class es.upm.fi.cig.multictbnc.tasks.EvaluationTask

failed() - Method in class es.upm.fi.cig.multictbnc.tasks.TrainingTask

FeatureStreamAsStaticDatasetExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments

Implements an experiment where a feature stream is treated as a static dataset.

FeatureStreamAsStaticDatasetExperiment(String, String, List<String>, DatasetReader, DatasetReader, int, int, boolean) - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamAsStaticDatasetExperiment

Constructs a FeatureStreamAsStaticDatasetExperiment with the specified parameters.

FeatureStreamExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments

Represents an experiment for evaluating continuous-time Bayesian network classifiers on feature streams.

FeatureStreamExperiment() - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.FeatureStreamExperiment

FeatureStreamExperimentFactory - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments

Factory class for creating specific types of feature stream experiments.

FeatureStreamExperimentFactory() - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamExperimentFactory

FeatureStreamImplementationExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments

Abstract class representing an implementation of an experiment with feature streams.

FeatureStreamImplementationExperiment(String, String, List<String>, DatasetReader, DatasetReader, int, int, boolean) - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamImplementationExperiment

Constructs a FeatureStreamImplementationExperiment with the specified parameters.

FeatureStreamMultipleCSVReader - Class in es.upm.fi.cig.multictbnc.data.reader

Class responsible for reading multiple CSV files representing a feature stream.

FeatureStreamMultipleCSVReader(String, Dataset, String) - Constructor for class es.upm.fi.cig.multictbnc.data.reader.FeatureStreamMultipleCSVReader

Constructs a FeatureStreamMultipleCSVReader with the specified path to the feature stream, a current dataset and the name of the time variable.

FeatureStreamWithFSSExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments

Represents an experiment that processes a feature stream with online feature subset selection using a MultiCTBNC.

FeatureStreamWithFSSExperiment(String, String, List<String>, DatasetReader, DatasetReader, CTBNParameterLearningAlgorithm, int, int, int, boolean, double, double) - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithFSSExperiment

Initializes a FeatureStreamWithFSSExperiment with the provided configuration parameters.

FeatureStreamWithFSSWithoutUpdatingExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments

Represents an experiment for processing a feature stream dataset with online feature subset selection without updating the model.

FeatureStreamWithFSSWithoutUpdatingExperiment(String, String, List<String>, DatasetReader, DatasetReader, CTBNParameterLearningAlgorithm, int, int, int, boolean, double, double) - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithFSSWithoutUpdatingExperiment

Initializes a FeatureStreamWithFSSWithoutUpdatingExperiment with the provided configuration parameters.

FeatureStreamWithoutFSSExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments

Represents an experiment for processing a feature stream dataset without online feature subset selection but with model updates.

FeatureStreamWithoutFSSExperiment(String, String, List<String>, DatasetReader, DatasetReader, int, int, boolean) - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithoutFSSExperiment

Initializes a FeatureStreamWithoutFSSExperiment with the provided configuration parameters.

featureSubsetSelectionGivenClassVariable(String, Dataset) - Method in class es.upm.fi.cig.multictbnc.fss.ConInd

Performs feature subset selection given a new feature variable.

fill2dArray(double[][], double) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Fills a two-dimensional double array with the provided double.

fill3dArray(double[][][], double) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Fills a three-dimensional double array with the provided double.

filter(List<T>, List<T>) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns the elements of a list "a" except those in "b".

filter(List<T>, T) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns the elements of a list "a" except "b".

findBestNeighbor(BN<? extends Node>, HillClimbingSolution, double[], boolean[][][], String) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing.BNHillClimbingHybridAlgorithm

findBestNeighbor(BN<? extends Node>, HillClimbingSolution, double[], boolean[][][], String) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

Finds the best neighbour of the adjacency matrix "bestStructure" given an operation to perform (addition, deletion or reversal of arcs).

findBestNeighbor(BN<? extends Node>, HillClimbingSolution, double[], boolean[][][], String) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.BNTabuSearch

findBestNeighbor(CTBN<? extends Node>, int, boolean[][], Map<Long, Double>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing.CTBNHillClimbingHybridAlgorithm

findBestNeighbor(CTBN<? extends Node>, int, boolean[][], Map<Long, Double>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

Finds the best neighbour for a CTBN node.

findBestNeighbor(CTBN<? extends Node>, int, boolean[][], Map<Long, Double>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.CTBNTabuSearchIndividual

findStructure() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.FirstChoiceHillClimbing

findStructure() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

Performs greedy Hill climbing to find a better structure than the initial one.

findStructure() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.RandomRestartHillClimbing

findStructure(int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.FirstChoiceHillClimbing

findStructure(int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

Performs greedy Hill climbing to find a better local structure for a given node.

findStructure(int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.RandomRestartHillClimbing

findStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.hillclimbing.BNHillClimbingHybridAlgorithm

findStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

findStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbing

findStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

findStructure(PGM<? extends Node>) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.HillClimbingImplementation

Finds a structure for a given PGM.

findStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.BNTabuSearch

findStructure(PGM<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

findStructure(PGM<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbing

findStructure(PGM<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

findStructure(PGM<? extends Node>, int) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.HillClimbingImplementation

Finds the local structure of a given node of a PGM.

findStructure(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

findStructure(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbing

findStructure(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

findStructure(PGM<? extends Node>, List<Integer>) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.HillClimbingImplementation

Finds the local structure of some given nodes of a PGM.

findStructure(List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.FirstChoiceHillClimbing

findStructure(List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

Performs greedy Hill climbing to find a better local structure for some given nodes.

findStructure(List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.RandomRestartHillClimbing

findStructureNode(CTBN<? extends Node>, int, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

Optimises the function score to find the parent set of a given node.

findStructureNode(CTBN<? extends Node>, int, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.CTBNTabuSearchIndividual

FirstChoiceHillClimbing - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing

Implements first-choice Hill Climbing.

FirstChoiceHillClimbing(HillClimbingImplementation) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.FirstChoiceHillClimbing

Constructs a FirstChoiceHillClimbing by receiving the implementation of the hill climbing algorithm (for a Bayesian network, continuous-time Bayesian network...).

G

generateDataset(MultiCTBNC<CPTNode, CIMNode>, int, double, boolean, String) - Static method in class es.upm.fi.cig.multictbnc.sampling.DataSampler

Sample a dataset from the provided model.

generateDataset(MultiCTBNC<CPTNode, CIMNode>, int, double, double, double, String) - Static method in class es.upm.fi.cig.multictbnc.sampling.DataSampler

Sample a dataset from the provided model.

generateModel(int, int, int, int, double, double, double, int, int, int, boolean, boolean, boolean[][]) - Static method in class es.upm.fi.cig.multictbnc.sampling.DataSampler

Generates a Multi-CTBNC that can be used to sample data.

generateModifiedModel(MultiCTBNC<CPTNode, CIMNode>, int, boolean, int, int, int, double, String) - Static method in class es.upm.fi.cig.multictbnc.sampling.MainDataStreamSampling.MainDataStreamSamplingFX

Generates a modified Multi-CTBNC to simulate concept drift.

generateRandomCIM(CIMNode, double, double) - Static method in class es.upm.fi.cig.multictbnc.sampling.DataSampler

Generate a uniformly distributed random conditional intensity matrix for a node of a continuous-time Bayesian network.

generateRandomCIMs(CTBN<CIMNode>, double, double) - Static method in class es.upm.fi.cig.multictbnc.sampling.DataSampler

Generate uniformly distributed random conditional intensity matrices for a continuous-time Bayesian network.

generateRandomCPT(CPTNode, boolean) - Static method in class es.upm.fi.cig.multictbnc.sampling.DataSampler

Generate an uniformly distributed random conditional probability table for a Bayesian network node.

generateRandomCPTs(BN<CPTNode>, boolean) - Static method in class es.upm.fi.cig.multictbnc.sampling.DataSampler

Generate uniformly distributed random conditional probability tables for a Bayesian network.

generateTrainAndTest() - Method in class es.upm.fi.cig.multictbnc.performance.HoldOutMethod

Generates a training and a test dataset.

getAdjacencyMatrix() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbingSolution

Returns the adjacency matrix of the structure.

getAdjacencyMatrix() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns the adjacency matrix of the PGM by analysing the parents of each node.

getAdjacencyMatrix() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns the adjacency matrix.

getAlgorithm(String, Double) - Static method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNParameterLearningAlgorithmFactory

Build the specified parameter learning algorithm.

getAlgorithm(String, Double, Double) - Static method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithmFactory

Builds the specified parameter learning algorithm.

getAlgorithmBN(String, Map<String, String>) - Static method in class es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithmFactory

Builds the specified structure learning algorithm for Bayesian networks.

getAlgorithmCTBN(String, Map<String, String>) - Static method in class es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithmFactory

Builds the specified structure learning algorithm for continuous-time Bayesian networks.

getAvailableDatasetReaders() - Static method in class es.upm.fi.cig.multictbnc.data.reader.DatasetReaderFactory

Returns the name of available dataset readers.

getAvailableLearningMethods() - Static method in class es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithmFactory

Returns the name of available optimisation methods.

getAvailableModels() - Static method in class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

Returns a list with the currently available classifiers.

getAvailableStrategies() - Static method in class es.upm.fi.cig.multictbnc.data.reader.DatasetReaderFactory

Returns the name of available strategies for the extraction of sequences.

getBN() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the Bayesian network used to model the class subgraph of the Multi-CTBNC.

getBnClassSubgraph() - Method in class es.upm.fi.cig.multictbnc.models.CTBN

Returns the class subgraph (Bayesian network).

getChildren() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

getChildren() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Returns the children of the node.

getClassVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns a Map object with the class variables' names and values.

getCP(int, int) - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

Returns the conditional probability of a state of the node given the state of the parents.

getCPT() - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

Returns the conditional probability table (CPT) of the node.

getCTBN() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the continuous-time Bayesian network used to model the bridge and feature subgraphs of the Multi-CTBNC.

getDataset() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns the dataset used to learn the PGM.

getDatasetReader(String, String, int) - Static method in class es.upm.fi.cig.multictbnc.data.reader.DatasetReaderFactory

Generates the correct dataset reader for the given dataset path.

getEdgesPGM(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

Retrieves all possible edges between nodes of a PGM.

getEntryLargestValue(Map<k, v>) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns entry with the largest value of a Map.

getEvents() - Method in class es.upm.fi.cig.multictbnc.data.representation.State

Returns the events of the state.

getExecutionTime() - Method in class es.upm.fi.cig.multictbnc.fss.SubsetSelectedFeatures

Retrieves the execution time of the FSS algorithm.

getExperiment(String...) - Static method in class es.upm.fi.cig.multictbnc.experiments.ExperimentFactory

Gets an instance of an experiment based on the provided arguments.

getExperimentConfig() - Method in class es.upm.fi.cig.multictbnc.experiments.AbstractExperiment

Retrieves the queue of experiment configuration parameters.

getFeatures() - Method in class es.upm.fi.cig.multictbnc.fss.SubsetSelectedFeatures

Provides the list of features selected by the FSS algorithm.

getFeatureStreamImplementation(String, String, String, List<String>, DatasetReader, DatasetReader, CTBNParameterLearningAlgorithm, int, int, int, boolean, double, double) - Static method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamExperimentFactory

Creates an instance of a feature stream experiment based on the specified type of processing.

getFilenameResults() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamAsStaticDatasetExperiment

getFilenameResults() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamImplementationExperiment

Provides the filename for storing results.

getFilenameResults() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithFSSExperiment

getFilenameResults() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithFSSWithoutUpdatingExperiment

getFilenameResults() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.featurestreamexperiments.FeatureStreamWithoutFSSExperiment

getFilePath() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the path of the file from which the sequence was extracted.

getHyperparameters() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns the hyperparameters of the model the user sets.

getHyperparameters() - Method in class es.upm.fi.cig.multictbnc.models.submodels.DAG_maxK_MultiCTBNC

getHyperparameters() - Method in class es.upm.fi.cig.multictbnc.models.submodels.Empty_maxK_MultiCTBNC

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNBayesianEstimation

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNMaximumLikelihoodEstimation

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNBayesianEstimation

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNMaximumLikelihoodEstimation

getIdentifier() - Method in interface es.upm.fi.cig.multictbnc.learning.parameters.ParameterLearningAlgorithm

Returns a unique identifier for the parameter learning algorithm.

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.BNHybridStructureLearningAlgorithm

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.CTBNHybridStructureLearningAlgorithm

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbing

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

getIdentifier() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.HillClimbingImplementation

Returns a unique identifier for the hill climbing-based algorithm.

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.RandomRestartHillClimbing

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNBayesianScore

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNLogLikelihood

getIdentifier() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNScoreFunction

Gets an identifier for the score function.

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNBayesianScore

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNConditionalLogLikelihood

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNLogLikelihood

getIdentifier() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNScoreFunction

Gets an identifier for the score function.

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.BNTabuSearch

getIdentifier() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.CTBNTabuSearchIndividual

getIdentifier() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithm

Returns a unique identifier for the structure learning algorithm.

getIdxFeatureVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Retrieves the mapping of feature variable names to their indexes within the sequence.

getIdxFeatureVariables(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Returns the indexes of the feature nodes in a PGM.

getIdxFeatureVariables(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.OnlineMarkovBlanketCTPC

Retrieves the index nodes representing feature variables from a given list of index nodes.

getIdxParentsNode(int, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Returns the indexes of a node's parents.

getIdxState() - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Gets the index of the current state of the node.

getIdxStateParents() - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Gets the index for the current state of the node's parents.

getIdxStateParents(List<String>) - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Gets the index for the current state of the specified parents of the node.

getIndexLargestValue(double[]) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns the index of the largest value in an array.

getIndexNodeByName(String) - Method in class es.upm.fi.cig.multictbnc.nodes.NodeIndexer

Returns the index of a node whose name is provided.

getIndexNodes() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

getIndexNodes() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns the indexes of the nodes.

getIndexNodes() - Method in class es.upm.fi.cig.multictbnc.nodes.NodeIndexer

Returns the indexes of all the nodes.

getIndexOfNode(Node) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

getIndexOfNode(Node) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns the index of the provided node.

getInfoScoreFunction() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

getInfoScoreFunction() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbing

getInfoScoreFunction() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

getInfoScoreFunction() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.HillClimbingImplementation

Returns a Map with the name of the score function that is optimised and the name of the applied penalisation function (if any).

getInitialStructure() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Return the name of the initial structure of the model.

getLabelPowerset() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns a multi-class dataset generated from the multidimensional dataset.

getLastChangedNodes() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftAdaptiveMethod

Retrieves the list of nodes that were last identified as having undergone concept drift.

getLastChangedNodes() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftGloballyAdaptiveMethod

getLastChangedNodes() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftLocallyAdaptiveMethod

getLastExecutionYieldAnyChange() - Method in class es.upm.fi.cig.multictbnc.fss.ConInd

Returns whether the last execution of the feature subset selection algorithm resulted in any changes.

getLastExecutionYieldAnyChange() - Method in interface es.upm.fi.cig.multictbnc.fss.OnlineFeatureSubsetSelection

Returns a boolean indicating whether the last execution of the feature subset selection algorithm resulted in any changes to the selected feature subset.

getLearningAlgsBN() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns learning algorithms for class subgraph (Bayesian network).

getLearningAlgsCTBN() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns learning algorithms for bridge and features subgraphs (continuous time Bayesian network).

getLearntNodes() - Method in class es.upm.fi.cig.multictbnc.models.BN

Returns the nodes with the learnt parameters.

getMaxValue(double...) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns the maximum value between those passed as parameters

getMeanGlobalAccuracy() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Retrieves the mean global accuracy across all processed data batches.

getMeanGlobalBrierScore() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Retrieves the mean global Brier score across all processed data batches.

getMeanMacroAveragedF1Score() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Retrieves the mean macro-averaged F1 score across all processed data batches.

getMeanMeanAccuracy() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Retrieves the average mean accuracy across all processed data batches.

getMeanMicroAveragedF1Score() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Retrieves the mean micro-averaged F1 score across all processed data batches.

getModelIdentifier() - Method in class es.upm.fi.cig.multictbnc.models.BN

getModelIdentifier() - Method in class es.upm.fi.cig.multictbnc.models.CTBN

getModelIdentifier() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

getModelIdentifier() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns a String that identifies the model.

getModelIdentifier() - Method in class es.upm.fi.cig.multictbnc.models.submodels.DAG_maxK_MultiCTBNC

getModelIdentifier() - Method in class es.upm.fi.cig.multictbnc.models.submodels.Empty_digraph_MultiCTBNC

getModelIdentifier() - Method in class es.upm.fi.cig.multictbnc.models.submodels.Empty_maxK_MultiCTBNC

getModelIdentifier() - Method in class es.upm.fi.cig.multictbnc.models.submodels.MultiCTNBC

getModifiedArcs() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbingSolution

Returns the last arc modified.

getMultiCTBNC() - Static method in class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

Generates a Multi-CTBNC including some default nodes and algorithms for the learning of its parameters and structure.

getMultiCTBNC(String, BNLearningAlgorithms, CTBNLearningAlgorithms) - Static method in class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

Builds the specified classifier.

getMultiCTBNC(String, BNLearningAlgorithms, CTBNLearningAlgorithms, Map<String, String>) - Static method in class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

Builds the specified classifier with the provided hyperparameters.

getMultiCTBNC(String, BNLearningAlgorithms, CTBNLearningAlgorithms, Map<String, String>, Class<NodeTypeBN>, Class<NodeTypeCTBN>) - Static method in class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

Builds the specified classifier with the provided hyperparameters.

getMultiCTBNCLearnedWithCTPC() - Static method in class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

Generates a Multi-CTBNC including some default nodes and algorithms for the learning of its parameters and structure.

getMultiCTBNCLearnedWithHybridAlgorithm() - Static method in class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

Generates a Multi-CTBNC including some default nodes and algorithms for the learning of its parameters and structure.

getMultiCTBNCLearnedWithMBCTPC() - Static method in class es.upm.fi.cig.multictbnc.classification.ClassifierFactory

Generates a Multi-CTBNC including some default nodes and algorithms for the learning of its parameters and structure.

getMx() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Returns the sufficient statistic with the number of times the variable leaves every state (i.e., the state changes) while its parents have certain values.

getMxHyperparameter() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Returns the value of the hyperparameter with the number of 'imaginary' transitions that occurred from a certain state before seeing the data.

getMxy() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Returns the sufficient statistic with the number of times the variable transition from a certain state to another while its parents have certain values

getMxyHyperparameter() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Returns the value of the hyperparameter with the number of 'imaginary' transitions that occurred from a certain state to another before seeing the data.

getName() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

getName() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Returns the name of the node.

getNameAllVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the name of all the variables, including the time variable.

getNameAllVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the name of all the variables, including the time variable.

getNameClassVariables() - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

getNameClassVariables() - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Returns the name of the class variables.

getNameClassVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the name of the class variables.

getNameClassVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the names of the class variables.

getNameFeatureVariables() - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

getNameFeatureVariables() - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Returns the name of the feature variables.

getNameFeatureVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the names of the feature variables.

getNameFeatureVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the names of the feature variables.

getNameFeatureVariables() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the names of the feature variables.

getNameLastFeatureReceived() - Method in class es.upm.fi.cig.multictbnc.data.reader.FeatureStreamMultipleCSVReader

Retrieves the name of the last feature variable received from the feature stream.

getNameMethod() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNBayesianEstimation

getNameMethod() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNMaximumLikelihoodEstimation

getNameMethod() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNBayesianEstimation

getNameMethod() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNMaximumLikelihoodEstimation

getNameMethod() - Method in interface es.upm.fi.cig.multictbnc.learning.parameters.ParameterLearningAlgorithm

Gets the name of the method to learn the parameters.

getNamePenalisationFunction() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.AbstractLikelihood

Returns the name of the penalisation function.

getNamePenalisationFunction() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNBayesianScore

getNamePenalisationFunction() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.bn.BNScoreFunction

Gets the name of the penalisation applied (if any) to the score function.

getNamePenalisationFunction() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNBayesianScore

getNamePenalisationFunction() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNScoreFunction

Gets the name of the penalisation applied (if any) to the score function.

getNamesNodesByIndex(List<Integer>) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

getNamesNodesByIndex(List<Integer>) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Return the names of the nodes whose indexes are given.

getNameTimeVariable() - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

getNameTimeVariable() - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Returns the name of the time variable.

getNameTimeVariable() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the name of the time variable.

getNameTimeVariable() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the name of the time variable.

getNameVariables() - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

getNameVariables() - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Returns the names of all the variables of the dataset, including those that are not used.

getNameVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the name of all the variables except the time variable.

getNameVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.State

Returns the names of the variables collected by the State object.

getNameVariables() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns the names of the variables of the PGM.

getNodeByIndex(int) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

getNodeByIndex(int) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Obtains the node (feature or class variable) with a certain index.

getNodeByIndex(int) - Method in class es.upm.fi.cig.multictbnc.nodes.NodeIndexer

Returns the name of a node whose index is provided.

getNodeByName(String) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

getNodeByName(String) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns the node whose variable name is given.

getNodeClass() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns the type of the nodes.

getNodeFactory() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns a NodeFactory for the nodes of the PGM.

getNodeIndexer() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns the node indexer of the model.

getNodeIndexer() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns the node indexer used by the PGM.

getNodes() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

getNodes() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

getNodes() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns all the nodes in the model.

getNodesClassVariables() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the list of nodes for the class variables.

getNodesCTBN() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the nodes of the continuous-time Bayesian network modelling the feature and bridge subgraphs of the Multi-CTBNC.

getNodesCTBNInMarkovBlanketClassVariables() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the nodes of the continuous-time Bayesian network modelling the feature and bridge subgraphs of the Multi-CTBNC, which are in the Markov blaket of at least one class variable.

getNodesFeatureVariables() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the list of nodes for the feature variables.

getNumClassAndFeatureVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the total number of class and feature variables in the sequence.

getNumClassVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the number of class variables.

getNumClassVariables() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the number of nodes for the class variables.

getNumDataPoints() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the number of data points.

getNumEdgesTested() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

Returns the number of edges that have been evaluated so far.

getNumFeatureVariables() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the number of nodes for the feature variables.

getNumNodes() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

getNumNodes() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns the number of nodes.

getNumObservation() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the number of observations in the dataset, i.e., the number of observations that occur in all the sequences.

getNumObservations() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the number of observations that the sequence contains.

getNumParents() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

getNumParents() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Returns the number of parents of the node.

getNumStates() - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Returns the number of possible states of the node.

getNumStatesParents() - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Returns the number of possible states of the parents of the node.

getNumTimesModelUpdated() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Retrieves the number of times the model was updated during the experiment.

getNx() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNSufficientStatistics

Returns the sufficient statistics of the node.

getNxHyperparameter() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNSufficientStatistics

Returns the hyperparameter value of the Dirichlet prior distribution.

getOxy() - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

Return matrix with the probabilities of the variable leaving a certain state for another one while their parents take a certain instantiation

getOxy(int, int, int) - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

Returns the probability of the variable leaving a state for a certain one given the state of its parents

getPageHinkleyValue() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.PageHinkleyTest

Returns the last Page Hinkley value calculated.

getParameterLearningAlg() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

getParameterLearningAlg() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Returns the algorithm that is used to learn the parameters of the PGM.

getParameterLearningAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.BNLearningAlgorithms

Returns the parameter learning algorithm for a BN.

getParameterLearningAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.CTBNLearningAlgorithms

Returns the parameter learning algorithm for a CTBN.

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNBayesianEstimation

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNMaximumLikelihoodEstimation

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNBayesianEstimation

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNMaximumLikelihoodEstimation

getParametersAlgorithm() - Method in interface es.upm.fi.cig.multictbnc.learning.parameters.ParameterLearningAlgorithm

Returns the parameters that are used by the algorithm.

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.BNHybridStructureLearningAlgorithm

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.CTBNHybridStructureLearningAlgorithm

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbing

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

getParametersAlgorithm() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.HillClimbingImplementation

Returns the parameters that are used by the hill climbing implementation.

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.RandomRestartHillClimbing

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.BNTabuSearch

getParametersAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.CTBNTabuSearchIndividual

getParametersAlgorithm() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithm

Returns the parameters that are used by the algorithm.

getParents() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

getParents() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Returns the parents of the node.

getPathsExperiments() - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Retrieves the array of dataset paths for the experiments.

getPenalisationFunction() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.AbstractLikelihood

Returns the name of the penalisation function.

getPossibleStatesVariable(String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the possible states of the specified variable.

getPredictedClasses() - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Returns the prediction.

getPredictionTime() - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Returns the prediction time.

getProbabilities() - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Returns the probabilities of every possible class configuration.

getProbabilityPrediction() - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Returns the probability of the prediction.

getQx() - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

Return matrix with the intensities of the variables leaving a certain state while their parents take a certain instantiation.

getQx(int, int) - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

Returns the intensity of the variable leaving a certain state given the state of its parents

getRandomElements(List<Integer>, int) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Get k random elements from an {\@code Integer} list.

getResults() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftAdaptiveMethod

Returns a String describing the results of the last concept drift detection.

getResults() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftGloballyAdaptiveMethod

getResults() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftLocallyAdaptiveMethod

getScore() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbingSolution

Returns the score of the structure found.

getSequences() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Returns the sequences of the dataset.

getState() - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Gets the state of the node.

getStates() - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Returns a list of the states that the node can take.

getStates(String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Gets all the possible states of a specific variable.

getStatesClassVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Gets the states of the class variables for each of the sequences.

getStatesVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Gets the possible states of all variables.

getStructureConstraints() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns the constraints that the PGM needs to meet.

getStructureConstraintsBN() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the structure constraints for the BN.

getStructureConstraintsBN() - Method in class es.upm.fi.cig.multictbnc.models.submodels.DAG_maxK_MultiCTBNC

getStructureConstraintsBN() - Method in class es.upm.fi.cig.multictbnc.models.submodels.Empty_digraph_MultiCTBNC

getStructureConstraintsBN() - Method in class es.upm.fi.cig.multictbnc.models.submodels.Empty_maxK_MultiCTBNC

getStructureConstraintsBN() - Method in class es.upm.fi.cig.multictbnc.models.submodels.MultiCTNBC

getStructureConstraintsCTBN() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the structure constraints for the CTBN.

getStructureConstraintsCTBN() - Method in class es.upm.fi.cig.multictbnc.models.submodels.DAG_maxK_MultiCTBNC

getStructureConstraintsCTBN() - Method in class es.upm.fi.cig.multictbnc.models.submodels.Empty_maxK_MultiCTBNC

getStructureConstraintsCTBN() - Method in class es.upm.fi.cig.multictbnc.models.submodels.MultiCTNBC

getStructureLearningAlg() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Returns the algorithm used to learn the structure of the PGM.

getStructureLearningAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.BNLearningAlgorithms

Returns the structure learning algorithm for a BN.

getStructureLearningAlgorithm() - Method in class es.upm.fi.cig.multictbnc.learning.CTBNLearningAlgorithms

Returns the structure learning algorithm for a CTBN.

getSubsets(List<type>, int, type) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Retrieves the possible subsets of a certain size from a given set of elements without including a certain element.

getSufficientStatistics() - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

Gets the sufficient statistics of a CIM node.

getSufficientStatistics() - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

Returns sufficient statistics of the node.

getSufficientStatisticsNode(DiscreteStateNode, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNBayesianEstimation

getSufficientStatisticsNode(DiscreteStateNode, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNMaximumLikelihoodEstimation

getSufficientStatisticsNode(DiscreteStateNode, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNParameterLearningAlgorithm

Returns the sufficient statistics of a DiscreteNode for a given Dataset.

getSufficientStatisticsNode(DiscreteStateNode, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNBayesianEstimation

getSufficientStatisticsNode(DiscreteStateNode, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNMaximumLikelihoodEstimation

getSufficientStatisticsNode(DiscreteStateNode, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithm

Returns the sufficient statistics of a DiscreteNode for a given Dataset.

getTimeValue(int) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the value of the time variable in a given observation.

getTimeValues() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the values of the time variable in the sequence.

getTopologicalOrdering() - Method in class es.upm.fi.cig.multictbnc.models.BN

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

getTraining() - Method in class es.upm.fi.cig.multictbnc.performance.HoldOutMethod

Returns the training dataset.

getTx() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Returns the sufficient statistic with the time that the variable stays in every state, while its parents take different values.

getTxHyperparameter() - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Returns the value of the hyperparameter with the 'imaginary' time that was spent in a certain state before seeing the data.

getType() - Method in class es.upm.fi.cig.multictbnc.models.BN

getType() - Method in class es.upm.fi.cig.multictbnc.models.CTBN

getType() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

getType() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Provides the type of PGM.

getType() - Method in class es.upm.fi.cig.multictbnc.models.submodels.DAG_maxK_MultiCTBNC

getType() - Method in class es.upm.fi.cig.multictbnc.models.submodels.Empty_digraph_MultiCTBNC

getType() - Method in class es.upm.fi.cig.multictbnc.models.submodels.Empty_maxK_MultiCTBNC

getType() - Method in class es.upm.fi.cig.multictbnc.models.submodels.MultiCTNBC

getTypeNodeClassVariable() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

NodeFactory Returns the type of the class variable nodes.

getTypeNodeFeature() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Returns the type of the feature nodes.

getUpdatingTime() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftAdaptiveMethod

Returns the time taken for the last update of the model.

getUpdatingTime() - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftLocallyAdaptiveMethod

getValidationMethod(String, DatasetReader, DatasetReader, double, int, boolean, boolean, Long) - Static method in class es.upm.fi.cig.multictbnc.performance.ValidationMethodFactory

Builds the specified validation method.

getValueClassVariable(String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the values of the specified class variable.

getValueFeatureVariable(int, String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the value of a certain feature variable for a given observation.

getValues() - Method in class es.upm.fi.cig.multictbnc.data.representation.State

Returns all the values in the State.

getValuesFeatureVariables(int) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the values of all feature variables for a given observation.

getValueVariable(int, String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Returns the value of a certain variable for a given observation.

getValueVariable(String) - Method in class es.upm.fi.cig.multictbnc.data.representation.State

Returns the value for a specific variable.

getVariablesValues() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Retrieves the array of values for all variables (excluding the time variable) for each observation in the sequence.

globalAccuracy(Prediction[], Dataset) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Computes the global accuracy, which is the ratio between the number of instances that were correctly classified for all the class variables and the total number of instances.

globalBrierScore(Prediction[], Dataset) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

The Brier score measures the performance of probabilistic predictions.

H

hasChildren() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

hasChildren() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Specifies if the node has children.

hasClassVariableAsParent() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

hasClassVariableAsParent() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Specifies if the node has a class variable as parent.

hasClassVariableAsSpouse() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

hasClassVariableAsSpouse() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Specifies if the node has a class variable as spouse.

hashCode() - Method in class es.upm.fi.cig.multictbnc.data.representation.State

hashCode() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

hasParents() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

hasParents() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Specifies if the node has parents.

HillClimbing - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing

Implements common attributes and methods for hill climbing algorithms.

HillClimbing() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

HillClimbingImplementation - Interface in es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation

Defines an interface for different implementations of the hill climbing algorithm.

HillClimbingSolution - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing

Class used to contain the solution given by the hill climbing algorithms.

HillClimbingSolution() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbingSolution

HITONPC - Class in es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC

Implementation of the HITON-PC algorithm.

HITONPC(double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.HITONPC

Constructor that initialises the HITON-PC algorithm by proving the significance level used.

HoldOutMethod - Class in es.upm.fi.cig.multictbnc.performance

Implements hold-out validation method.

HoldOutMethod(DatasetReader, double, boolean, boolean, Long) - Constructor for class es.upm.fi.cig.multictbnc.performance.HoldOutMethod

Constructs a HoldOut by receiving a DatasetReader, the size of the training set and if the data should be shuffled.

I

increaseNumEdgesTested() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

Increases the number of evaluated edges in one.

increaseNumEdgesTested() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.CTBNHillClimbingIndividual

Increases the number of evaluated edges in one.

initialiazeStatesClassVariables() - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Retrieves the states of the class variables and stores them in a Map.

initialiseModel(Dataset) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

initialiseModel(Dataset) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Sets the dataset that will be used to estimate the structure and parameters of the model and creates its nodes.

initialiseService(MultiCTBNC<?, ?>, DatasetReader) - Method in class es.upm.fi.cig.multictbnc.services.TrainingService

Initialises the TrainingService receiving the model to learn and the a DatasetReader to read the training dataset.

initialiseService(MultiCTBNC<?, ?>, DatasetReader, boolean) - Method in class es.upm.fi.cig.multictbnc.services.ClassificationService

Initialises the ClassificationService by receiving the learnt MultiCTBNC model and a DatasetReader to read the dataset to classify.

initialiseService(ValidationMethod, MultiCTBNC<?, ?>) - Method in class es.upm.fi.cig.multictbnc.services.EvaluationService

Initialises the EvaluationService by receiving a ValidationMethod and a MultiCTBNC model.

initialiseStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.AbstractStructureConstraints

initialiseStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.BN.EmptyBN

initialiseStructure(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC.NaiveBayes

initialiseStructure(PGM<? extends Node>) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.constraints.StructureConstraints

The structure of the PGM is initialised.

initialiseSufficientStatistics(DiscreteStateNode) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Initialises the structures to store the sufficient statistics of the node.

initialize() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Initialises the controller.

isArrayEmpty(T[]) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Checks if an array is empty.

isClassVariable() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

isClassVariable() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Specifies if the node is a class variable.

isClassVariable(boolean) - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

isClassVariable(boolean) - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Defines if the node is a class variable.

isDataArriving() - Method in class es.upm.fi.cig.multictbnc.data.reader.DataStreamMultipleCSVReader

Checks if there is more data to be read.

isDataArriving() - Method in class es.upm.fi.cig.multictbnc.data.reader.FeatureStreamMultipleCSVReader

Checks whether there is more feature data arriving in the feature stream.

isDatasetOutdated() - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

isDatasetOutdated() - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Indicates if the dataset is out-of-date.

isDecomposable() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNBayesianScore

isDecomposable() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNConditionalLogLikelihood

isDecomposable() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNLogLikelihood

isDecomposable() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.optimisation.scores.ctbn.CTBNScoreFunction

Determines if the score is decomposable.

isDisconnected() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

isDisconnected() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Specifies if the node is disconnected, i.e., it has neither parents or children.

isInMarkovBlanketClassVariable() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

isInMarkovBlanketClassVariable() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Specifies if the node is in the Markov blanket of at least one class variable.

isScoreImproved(HillClimbingSolution, boolean[][][], int, double) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

Checks if a solution given by the hill climbing algorithm in a certain iteration is better than the current solution.

isScoreImproved(HillClimbingSolution, boolean[][][], int, double) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.tabusearch.BNTabuSearch

isStructureLegal(boolean[][]) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Determines if the structure is legal.

isStructureLegal(boolean[][]) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Checks if a structure is legal for the PGM.

isStructureLegal(boolean[][], NodeIndexer<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.BN.DAG

Checks if the structure (given by an adjacencyMatrix) is legal for a Bayesian network without restrictions.

isStructureLegal(boolean[][], NodeIndexer<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.BN.EmptyBN

isStructureLegal(boolean[][], NodeIndexer<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC.Digraph

isStructureLegal(boolean[][], NodeIndexer<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC.MaxKCTBNC

isStructureLegal(boolean[][], NodeIndexer<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC.NaiveBayes

isStructureLegal(boolean[][], NodeIndexer<? extends Node>) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.constraints.StructureConstraints

Determines if the structure of a PGM is legal.

K

kroneckerDelta(String[], String[]) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Kronecker delta function.

L

learn() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learn() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the structure and parameters of the model.

learn(Dataset) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learn(Dataset) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

learn(Dataset) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the structure and parameters of the model from a given dataset.

learn(Dataset, int) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learn(Dataset, int) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the parameters and parent set of a model's node from a given dataset.

learn(Dataset, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learn(Dataset, List<Integer>) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the parameters and parent set of some nodes of the model from a given dataset.

learn(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

learn(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.MarkovBlanketCTPC

learn(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

learn(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.BNHybridStructureLearningAlgorithm

learn(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.CTBNHybridStructureLearningAlgorithm

learn(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

learn(PGM<? extends Node>) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithm

Learns the structure of a certain PGM.

learn(PGM<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

learn(PGM<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

learn(PGM<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.BNHybridStructureLearningAlgorithm

learn(PGM<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.CTBNHybridStructureLearningAlgorithm

learn(PGM<? extends Node>, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

learn(PGM<? extends Node>, int) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithm

Learn the local structure of a certain node of a PGM.

learn(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

learn(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.HITONPC

learn(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.OnlineMarkovBlanketCTPC

learn(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

learn(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.BNHybridStructureLearningAlgorithm

learn(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.CTBNHybridStructureLearningAlgorithm

learn(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbing

learn(PGM<? extends Node>, List<Integer>) - Method in interface es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithm

Learns the local structure of certain nodes of a PGM.

learn(Node, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNParameterLearningAlgorithm

learn(Node, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithm

learn(Node, Dataset) - Method in interface es.upm.fi.cig.multictbnc.learning.parameters.ParameterLearningAlgorithm

Learns the parameters of a certain node of a PGM.

learn(List<? extends Node>, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNParameterLearningAlgorithm

learn(List<? extends Node>, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithm

learn(List<? extends Node>, Dataset) - Method in interface es.upm.fi.cig.multictbnc.learning.parameters.ParameterLearningAlgorithm

Learns the parameters of a certain PGM.

LearningStreamExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments

This class implements a experiment on streaming data.

LearningStreamExperiment(String...) - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.LearningStreamExperiment

Constructs a LearningStreamExperiment with the given configuration parameters.

learnInitialStructure(PGM<? extends Node>, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC.CTPCHybridAlgorithm

Learns the initial structure of a given PGM.

learnParameters() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learnParameters() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the parameters of the PGM.

learnParameters(Dataset) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learnParameters(Dataset) - Method in class es.upm.fi.cig.multictbnc.models.CTBN

learnParameters(Dataset) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

learnParameters(Dataset) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the parameters of the PGM using the provided dataset.

learnParameters(Dataset, int) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learnParameters(Dataset, int) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

learnParameters(Dataset, int) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the parameters of a certain node of the PGM using the provided dataset.

learnParameters(Integer) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learnParameters(Integer) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the parameters of the node whose index is specified.

learnParameters(List<Integer>) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learnParameters(List<Integer>) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the parameters of the nodes whose indexes are specified.

learnParameters(List<Integer>, ParameterLearningAlgorithm) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

learnParameters(List<Integer>, ParameterLearningAlgorithm) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Learns the parameters of the nodes whose indexes are specified using a provider parameter learning algorithm.

learnParentSetNode(PGM<? extends Node>, int, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Learns the parent set of a node.

learnParentSetNode(PGM<? extends Node>, int, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC.CTPCHybridAlgorithm

learnSkeleton(PGM<? extends Node>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC.PCHybridAlgorithm

Learns the skeleton of a given PGM.

learnSkeleton(PGM<? extends Node>, boolean[][], List<List<Integer>>) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

Finds the skeleton and separation sets of the given PGM.

listsOfNodeContainSameElements(List<typeNode>, List<typeNode>) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Checks if two given lists of Node contain the same elements independently of their orders.

listToArray(List<?>) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Transforms a list into an String array.

logLikelihoodSequence(Sequence, List<NodeTypeCTBN>, State) - Static method in class es.upm.fi.cig.multictbnc.util.ProbabilityUtil

Computes the log-likelihood of a sequence, also known as temporal likelihood (Stella and Amer 2012), given the state of the class variables.

logPriorProbabilityClassVariables(List<NodeTypeBN>, State) - Static method in class es.upm.fi.cig.multictbnc.util.ProbabilityUtil

Computes the logarithm of the prior probability of the class variables taking certain values.

M

macroAveraging(Prediction[], Dataset, Metric) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Computes the value of a given evaluation metric for a multi-dimensional classification problem using macro-averaging (Gil-Begue et al., 2021).

main(String[]) - Static method in class es.upm.fi.cig.multictbnc.experiments.MainExperiment

Entry point of the application.

main(String[]) - Static method in class es.upm.fi.cig.multictbnc.Main

Application entry point.

main(String[]) - Static method in class es.upm.fi.cig.multictbnc.sampling.MainDataStreamSampling

Entry point of the application.

main(String[]) - Static method in class es.upm.fi.cig.multictbnc.sampling.MainFeatureStreamSampling

Entry point of the application.

main(String[]) - Static method in class es.upm.fi.cig.multictbnc.sampling.MainFeatureStreamSampling.MainFeatureStreamSamplingFX

Entry point for the JavaFX application responsible for the generation of feature streams.

main(String[]) - Static method in class es.upm.fi.cig.multictbnc.sampling.MainSampling

Application entry point.

main(Queue<String>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

The main method to execute the data stream experiments.

main(Queue<String>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.FeatureStreamExperiment

The main method to execute the feature stream experiments.

Main - Class in es.upm.fi.cig.multictbnc

JavaFX application to interact with the CTBNLab software.

Main() - Constructor for class es.upm.fi.cig.multictbnc.Main

MainDataStreamSampling - Class in es.upm.fi.cig.multictbnc.sampling

This class serves as the entry point for the data stream sampling application.

MainDataStreamSampling() - Constructor for class es.upm.fi.cig.multictbnc.sampling.MainDataStreamSampling

MainDataStreamSampling.MainDataStreamSamplingFX - Class in es.upm.fi.cig.multictbnc.sampling

This class represents the JavaFX application for data stream sampling.

MainDataStreamSamplingFX() - Constructor for class es.upm.fi.cig.multictbnc.sampling.MainDataStreamSampling.MainDataStreamSamplingFX

MainExperiment - Class in es.upm.fi.cig.multictbnc.experiments

Main class for running experiments.

MainExperiment() - Constructor for class es.upm.fi.cig.multictbnc.experiments.MainExperiment

MainFeatureStreamSampling - Class in es.upm.fi.cig.multictbnc.sampling

This class serves as the entry point for the feature stream sampling application.

MainFeatureStreamSampling() - Constructor for class es.upm.fi.cig.multictbnc.sampling.MainFeatureStreamSampling

MainFeatureStreamSampling.MainFeatureStreamSamplingFX - Class in es.upm.fi.cig.multictbnc.sampling

This class represents the JavaFX application for feature stream sampling.

MainFeatureStreamSamplingFX() - Constructor for class es.upm.fi.cig.multictbnc.sampling.MainFeatureStreamSampling.MainFeatureStreamSamplingFX

MainSampling - Class in es.upm.fi.cig.multictbnc.sampling

Class to sample datasets from Multi-CTBNCs with provided or randomly generated structures.

MainSampling() - Constructor for class es.upm.fi.cig.multictbnc.sampling.MainSampling

marginalLogLikelihoodSequence(double[]) - Static method in class es.upm.fi.cig.multictbnc.util.ProbabilityUtil

Computes the marginal log-likelihood of a sequence given the unnormalised log-a-posteriori probability for each class configuration.

MarkovBlanketCTPC - Class in es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC

Implementation of the MB-CTPC algorithm.

MarkovBlanketCTPC(double, double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.MarkovBlanketCTPC

Initialises the MB-CTPC algorithm by providing the significances to be used.

MaxKCTBNC - Class in es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC

Implements the structure constraints of a Max-k continuous-time Bayesian network classifier, i.e., a CTBNC where the number of parents of the feature nodes is bounded by a positive number (Codecasa and Stella, 2014).

MaxKCTBNC(int) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC.MaxKCTBNC

Receives the maximum number of parents the nodes can have.

meanAccuracy(Prediction[], Dataset) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Computes the mean of the accuracies for each class variable (Bielza et al., 2011).

meanAccuracy(Prediction[], Dataset, Map<String, Double>) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Computes the mean of the accuracies for each class variable (Bielza et al., 2011).

Metric - Interface in es.upm.fi.cig.multictbnc.performance

Interface used to be able to pass evaluation metrics as parameters of other methods.

Metrics - Class in es.upm.fi.cig.multictbnc.performance

Computes different metrics for the evaluation of multi-dimensional classifications.

Metrics() - Constructor for class es.upm.fi.cig.multictbnc.performance.Metrics

MetricsWriter - Class in es.upm.fi.cig.multictbnc.writers.performance

Defines classes that write the results of evaluation metrics on different outputs.

MetricsWriter() - Constructor for class es.upm.fi.cig.multictbnc.writers.performance.MetricsWriter

microAveraging(Prediction[], Dataset, Metric) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Computes the value of a given evaluation metric for a multi-dimensional classification problem using a micro-averaging (Gil-Begue et al., 2021).

ModelComparisonExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments

Represents an experiment for comparing different models' performance on datasets with different settings.

ModelComparisonExperiment(String...) - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.ModelComparisonExperiment

Constructor for the ModelComparisonExperiment class.

modifyEventValue(String, String) - Method in class es.upm.fi.cig.multictbnc.data.representation.State

Modifies the value of a given variable.

MultiCTBNC<NodeTypeBN extends Node,NodeTypeCTBN extends Node> - Class in es.upm.fi.cig.multictbnc.models

Implements the multi-dimensional continuous-time Bayesian network classifier (Multi-CTBNC).

MultiCTBNC(BNLearningAlgorithms, CTBNLearningAlgorithms, Class<NodeTypeBN>, Class<NodeTypeCTBN>) - Constructor for class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Receives learning algorithms for Bayesian networks and continuous-time Bayesian networks to generate a Multi-CTBNC.

MultiCTBNC(BN<NodeTypeBN>, CTBN<NodeTypeCTBN>) - Constructor for class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Receives a Bayesian network and a continuous-time Bayesian network that represent the class subgraph and feature/bridge subgraph of a Multi-CTBNC, respectively.

MultiCTNBC<NodeTypeBN extends Node,NodeTypeCTBN extends Node> - Class in es.upm.fi.cig.multictbnc.models.submodels

Specifies the structure constraints of a multidimensional continuous-time naive Bayes classifier (Multi-CTBNC) where any subgraph has arcs except the bridge subgraph (fully naive multi-dimensional classifier).

MultiCTNBC(BNLearningAlgorithms, CTBNLearningAlgorithms, Class<NodeTypeBN>, Class<NodeTypeCTBN>) - Constructor for class es.upm.fi.cig.multictbnc.models.submodels.MultiCTNBC

Constructs a multidimensional continuous-time naive Bayes classifier given the learning algorithms for BNs and CTBNs.

MultipleCSVReader - Class in es.upm.fi.cig.multictbnc.data.reader

Provides the logic to read separate CSV files.

MultipleCSVReader(String) - Constructor for class es.upm.fi.cig.multictbnc.data.reader.MultipleCSVReader

Constructs a MultipleCSVReader that extracts all the CSV files from the specified folder.

MultipleCSVWriter - Class in es.upm.fi.cig.multictbnc.data.writer

Manages the writing of datasets into CSV files

MultipleCSVWriter() - Constructor for class es.upm.fi.cig.multictbnc.data.writer.MultipleCSVWriter

N

NaiveBayes - Class in es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC

Defines the structure of a continuous-time Naive Bayes classifier.

NaiveBayes() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC.NaiveBayes

NeverSeenStateException - Exception in es.upm.fi.cig.multictbnc.exceptions

Thrown when a state was never seen before by the classifier.

NeverSeenStateException(String) - Constructor for exception es.upm.fi.cig.multictbnc.exceptions.NeverSeenStateException

Constructs a NeverSeenStateException with the specified detail message.

Node - Interface in es.upm.fi.cig.multictbnc.nodes

Interface for a generic node of a PGM.

NodeFactory<NodeType extends Node> - Class in es.upm.fi.cig.multictbnc.nodes

Provides static methods for the creation of nodes.

NodeIndexer<NodeType extends Node> - Class in es.upm.fi.cig.multictbnc.nodes

Links nodes with a unique index.

NodeIndexer(List<NodeType>) - Constructor for class es.upm.fi.cig.multictbnc.nodes.NodeIndexer

Constructs a NodeIndexer.

NotImplementedException - Exception in es.upm.fi.cig.multictbnc.exceptions

Thrown when a requested task is not implemented.

NotImplementedException(String) - Constructor for exception es.upm.fi.cig.multictbnc.exceptions.NotImplementedException

Constructs a NotImplementedException with the specified detail message.

O

OnlineFeatureSubsetSelection - Interface in es.upm.fi.cig.multictbnc.fss

This interface defines the structure for classes that implement online feature subset selection algorithms.

OnlineMarkovBlanketCTPC - Class in es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC

This class extends the MB-CTPC algorithm to an online learning context, allowing for dynamic updates to the learned model based on new data.

OnlineMarkovBlanketCTPC(double, double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.OnlineMarkovBlanketCTPC

Initialises the Online-MB-CTPC algorithm by providing the significances to be used.

onlyPositiveDouble(TextField) - Static method in class es.upm.fi.cig.multictbnc.util.ControllerUtil

Checks that the text field only contains positive decimals.

onlyPositiveInteger(TextField) - Static method in class es.upm.fi.cig.multictbnc.util.ControllerUtil

Checks that the text field only contains positive integers.

onlyZeroOrGreaterInteger(TextField) - Static method in class es.upm.fi.cig.multictbnc.util.ControllerUtil

Checks that the text field only contains integers greater than zero.

orientRemainingUndirectedEdges(boolean[][]) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

Orient the remaining undirected edges of the Bayesian network.

P

PageHinkleyTest - Class in es.upm.fi.cig.multictbnc.conceptdriftdetection

Implements the Page Hinkley Test for concept drift detection.

PageHinkleyTest(double, double, boolean, Integer) - Constructor for class es.upm.fi.cig.multictbnc.conceptdriftdetection.PageHinkleyTest

Initializes the Page Hinkley Test with the specified parameters.

ParameterLearningAlgorithm - Interface in es.upm.fi.cig.multictbnc.learning.parameters

Interface for parameter learning algorithms.

PC - Class in es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC

Implementation of the PC algorithm discrete-state Bayesian networks.

PC(double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.PC

Constructor that initialises the PC algorithm by proving the significance level used.

PCHybridAlgorithm - Class in es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC

Implements the restriction phase (PC algorithm) of the hybrid structure learning algorithm.

PCHybridAlgorithm(double) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.hybrid.PC.PCHybridAlgorithm

Initialises the algorithm by proving a significance level.

penalisationFunctionMap - Variable in class es.upm.fi.cig.multictbnc.conceptdriftdetection.AverageLocalLogLikelihood

A map that associates penalisation function names with their corresponding mathematical functions.

PGM<NodeType extends Node> - Interface in es.upm.fi.cig.multictbnc.models

Defines the methods of a probabilistic graphical model (PGM)

precision(Map<String, Double>) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Computes the precision evaluation metric from a Map containing a confusion matrix.

predict(Dataset, boolean) - Method in interface es.upm.fi.cig.multictbnc.classification.Classifier

Predicts the values of the class variables for each instance of a dataset.

predict(Dataset, boolean) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Performs classification over the sequences of a dataset according to the maximum a posteriori probability, i.e., the classes that obtain the highest posterior probability given each sequence are predicted.

Prediction - Class in es.upm.fi.cig.multictbnc.classification

Contains a multidimensional prediction and its probability.

Prediction() - Constructor for class es.upm.fi.cig.multictbnc.classification.Prediction

ProbabilityUtil - Class in es.upm.fi.cig.multictbnc.util

Utility class with methods related to the estimation of probabilities.

R

RandomRestartHillClimbing - Class in es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing

Implements random-restart hill climbing.

RandomRestartHillClimbing(HillClimbingImplementation, int) - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.RandomRestartHillClimbing

Constructs a RandomRestartHillClimbing by receiving the implementation of the hill climbing algorithm (for a Bayesian network, continuous-time Bayesian network...) and the number of restarts.

readCompleteDataset() - Method in class es.upm.fi.cig.multictbnc.data.reader.FeatureStreamMultipleCSVReader

Reads the complete feature stream to generate a static dataset.

readCSV(String, List<String>) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

Reads a CSV file.

readDataset() - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Returns a dataset.

readDataset() - Method in class es.upm.fi.cig.multictbnc.data.reader.FeatureStreamMultipleCSVReader

Reads a new feature variable from the feature stream and adds it to the current dataset.

readDataset() - Method in class es.upm.fi.cig.multictbnc.data.reader.MultipleCSVReader

readDataset() - Method in class es.upm.fi.cig.multictbnc.data.reader.SingleCSVReader

readDataset(int) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

readDataset(int) - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Creates a dataset using only the specified number of files.

readDataset(int) - Method in class es.upm.fi.cig.multictbnc.data.reader.DataStreamMultipleCSVReader

Reads a specified number of CSV files from the dataset folder and processes them into a dataset.

readDataset(int) - Method in class es.upm.fi.cig.multictbnc.data.reader.MultipleCSVReader

recall(Map<String, Double>) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Computes the recall evaluation metric from a Map containing a confusion matrix.

redundancyAnalysis(CIMNode, CPTNode, CIMNode) - Method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Determines if a feature node is redundant with respect to the class node, given another feature node.

redundancyAnalysis(CIMNode, CPTNode, List<CIMNode>) - Method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Determines if a feature node is redundant with respect to a class node, given a set of other feature nodes.

redundancyAnalysis(CIMNode, CPTNode, List<CIMNode>, int) - Method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Determines if a feature node is redundant with respect to a class node, given sets of features nodes with a determine maximum size.

redundancyAnalysis(List<CIMNode>, CPTNode) - Method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Returns a subset of feature nodes that are non-redundant given the class node.

redundancyAnalysis(List<CIMNode>, CPTNode, CIMNode) - Method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Returns a subset of feature nodes that are non-redundant given a conditioned feature node.

removeAllEdges() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

removeAllEdges() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Remove all edges between the nodes of the PGM.

removeAllEdges() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

removeAllEdges() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Removes the parents and children of the node.

removeAllNodes() - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

removeAllNodes() - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Removes all the nodes from the PGM.

removeChild(Node) - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

removeChild(Node) - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Removes a certain child of the node.

removeChildren() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

removeChildren() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Removes the children of the node.

removeColumnArray(String[][], int) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Remove a column from an array.

removeFeatureVariable(String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Remove the specified feature variable from the dataset.

removeFeatureVariable(String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Removes a feature from the sequence.

removeFeatureVariables(List<String>) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Remove the specified feature variables from the dataset.

removeParent(Node) - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

removeParent(Node) - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

removeParent(Node) - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Removes a certain parent of the node.

removeParents() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

removeParents() - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Removes the parents of the node.

removeZeroVarianceVariables(boolean) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

removeZeroVarianceVariables(boolean) - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Defines if the feature variables with no variance should be removed.

resetNumEdgesTested() - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.implementation.BNHillClimbing

Sets to zero the number of evaluated edges.

retrieveParametersAndSuffStatistics(PGM<? extends Node>, int, Map<String, List<Object>>, List<Integer>, Integer...) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Retrieves the parameter and sufficient statistics of a node.

retrieveSubfolders(String) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Returns the names of the subfolders in a certain folder.

S

sample(double) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Samples a sequence given its duration.

sample(double, double, double) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Samples a sequence given its duration with added noise.

sampleNextState(double) - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

Samples the next state of the node given the current one and that of its parents.

sampleState(double) - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

Samples the state of the node given evidence using forward sampling.

sampleTimeState(double) - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

Samples the time that the node stays in its current state given the state of its parents.

saveGraph(String, String, List<Integer>) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

saveGraph(String, String, List<Integer>) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Saves the PGM graph to a file.

selectCrossValidation() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Cross-validation method was selected.

selectHoldOutValidation() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Hold-out-validation method was selected.

selectTestDataset() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Test dataset method was selected.

Sequence - Class in es.upm.fi.cig.multictbnc.data.representation

Represents a sequence of multivariate data, i.e., a set of data points with multiple variables where the order is relevant.

Sequence(State, List<State>, String, List<Double>) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.Sequence

Constructs a Sequence.

Sequence(String, List<String>, List<String>, double[], String[][], String[], Map<String, Integer>) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.Sequence

Constructs a Sequence.

Sequence(List<String>, String, List<String>, List<String[]>) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.Sequence

Constructs a Sequence.

Sequence(List<String>, String, List<String>, List<String>, List<String[]>) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.Sequence

Constructs a Sequence.

setAdjacencyMatrix(boolean[][]) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbingSolution

Sets the adjacency matrix of the structure.

setArcModified(int, int, int) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbingSolution

Define the last arc that was modified.

setAreResultsSaved(boolean) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Sets the flag indicating whether the results of the experiment should be saved.

setBnLearningAlgs(BNLearningAlgorithms) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Sets the learning algorithms used to define the class subgraph (BN).

setChild(Node) - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

setChild(Node) - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Defines a provided node as a child of this one.

setCPT(double[][]) - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

Establishes the CPT of the node.

setCtbnLearningAlgs(CTBNLearningAlgorithms) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Sets the learning algorithms used to define the bridge and feature subgraphs (CTBN).

setCurrentFeatureVariables(List<String>) - Method in class es.upm.fi.cig.multictbnc.fss.ConInd

Sets the current feature variables for the algorithm.

setCurrentFeatureVariables(List<String>) - Method in interface es.upm.fi.cig.multictbnc.fss.OnlineFeatureSubsetSelection

Sets the current set of feature variables.

setDataset(Dataset) - Method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Sets the dataset to be used in the feature subset selection.

setDataset(Dataset) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Set the dataset used to learn the PGM.

setDatasetAsOutdated(boolean) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

setDatasetAsOutdated(boolean) - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Defines a previously read dataset as out-of-date, so it should be reloaded.

setFilePath(String) - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

Sets the path of the file from which the sequence was extracted.

setFolderDataset() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Opens a dialog to select the folder where the dataset for training and evaluation is located.

setFolderDatasetClassification() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Opens a dialog to select the folder where the dataset on which classification is performed is located.

setFolderTestDataset() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Opens a dialog to select the folder where the dataset for testing is located.

setIgnoredClassVariables(List<String>) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Sets the class variables to ignored.

setInitialStructure(String) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Establishes the approach that will be used to define the initial structure of the Multi-CTBNC.

setNameVariables(List<String>) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Set the name of the variables in the PGM.

setParameterLearningAlgorithm(ParameterLearningAlgorithm) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Establishes the algorithm that will be used to learn the parameters of the PGM.

setParameters(double[][], double[][][]) - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

Sets the parameters of a node.

setParametersExperiment(Queue<String>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.DataStreamExperiment

Sets up the parameters for a data stream experiment using a queue of arguments.

setParametersExperiment(Queue<String>) - Method in class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.FeatureStreamExperiment

Sets up the parameters for a feature stream experiment using a queue of arguments.

setParametersModel(MultiCTBNC<CPTNode, CIMNode>) - Static method in class es.upm.fi.cig.multictbnc.sampling.MainFeatureStreamSampling.MainFeatureStreamSamplingFX

Sets the parameters of the given Multi-CTBNC.

setParent(Node) - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

setParent(Node) - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

setParent(Node) - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Defines a provided node as a parent of this one.

setPredictedClasses(State) - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Sets the predicted classes.

setPredictionTime(double) - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Sets the prediction time.

setProbabilities(Map<State, Double>) - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Sets the probabilities of every possible class configuration.

setProbability(State, double) - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Sets the probability of a class configuration.

setProbabilityPrediction(double) - Method in class es.upm.fi.cig.multictbnc.classification.Prediction

Sets the probability of the prediction.

setScore(double) - Method in class es.upm.fi.cig.multictbnc.learning.structure.optimisation.hillclimbing.HillClimbingSolution

Sets the score of the structure found.

setStage(Stage) - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Establishes the stage used by the application to show dialogs.

setState(int) - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Sets the state index of the node.

setState(String) - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Sets the state of the node and returns its id.

setStateNodeAndParents(DiscreteStateNode, State) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Sets the state of a given node and its parents from a State object.

setStateParents(int) - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

Sets the states of the parents of the node given the index related to their state.

setStatesVariables(Map<String, List<String>>) - Method in class es.upm.fi.cig.multictbnc.data.representation.Dataset

Sets states of all variables.

setStructure(boolean[][]) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

setStructure(boolean[][]) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Modifies the structure of the PGM by changing the parents of the nodes.

setStructure(int, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

setStructure(int, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.models.CTBN

Modifies the structure of the continuous-time Bayesian network by changing the parent set of a specified node and updates its parameters.

setStructure(int, boolean[][]) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Updates the structure of the model only for the specified node.

setStructure(List<Integer>, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

setStructure(List<Integer>, boolean[][]) - Method in class es.upm.fi.cig.multictbnc.models.CTBN

Modifies the structure of the continuous-time Bayesian network by changing the parent set of some specified nodes and updates their parameters.

setStructure(List<Integer>, boolean[][]) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Updates the structure of the model only for the specified node.

setStructureConstraints(StructureConstraints) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Establishes the constraints that the PGM needs to meet.

setStructureLearningAlgorithm(StructureLearningAlgorithm) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

Establishes the algorithm that will be used to learn the structure of the PGM.

setStructureModifiedNodes(boolean[][]) - Method in class es.upm.fi.cig.multictbnc.models.AbstractPGM

setStructureModifiedNodes(boolean[][]) - Method in interface es.upm.fi.cig.multictbnc.models.PGM

Modifies the structure of the PGM by changing the parents and CPDs of those nodes which have different parents between the current adjacency matrix and the new one.

setSufficientStatistics(SufficientStatistics) - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

setSufficientStatistics(SufficientStatistics) - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

Establishes the sufficient statistics of a CPT node.

setSufficientStatistics(SufficientStatistics) - Method in interface es.upm.fi.cig.multictbnc.nodes.Node

Establishes the sufficient statistics of the node.

setSufficientStatistics(Node, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNParameterLearningAlgorithm

setSufficientStatistics(Node, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithm

setSufficientStatistics(Node, Dataset) - Method in interface es.upm.fi.cig.multictbnc.learning.parameters.ParameterLearningAlgorithm

Obtains the sufficient statistics of a BN node.

setSufficientStatistics(List<? extends Node>, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.bn.BNParameterLearningAlgorithm

Obtains for each node the number of times it takes a certain state while its parents take a certain instantiation.

setSufficientStatistics(List<? extends Node>, Dataset) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNParameterLearningAlgorithm

Obtains the sufficient statistics of each node of a CTBN.

setTimeAndClassVariables(String, List<String>) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

setTimeAndClassVariables(String, List<String>) - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Receives the names of the time and class variables of a dataset.

setTimeAndFeatureVariables(String, List<String>) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

setTimeAndFeatureVariables(String, List<String>) - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Receives the names of the time and feature variables of a dataset.

setTimeVariable(String) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

setTimeVariable(String) - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Receives the name of the time variable of a dataset.

setUpLineChart(String, double) - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftAdaptiveMethod

Sets up line charts for visualizing concept drift detection results if the charts are enabled.

setUpLineChart(String, double) - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftGloballyAdaptiveMethod

Sets up line charts for visualizing concept drift detection results if the charts are enabled.

setUpLineChart(String, double) - Method in class es.upm.fi.cig.multictbnc.conceptdriftdetection.ConceptDriftLocallyAdaptiveMethod

Sets up line charts for visualizing the evolution of the average local log-likelihood and Page Hinkley values if the charts are enabled.

setVariables(String, List<String>, List<String>) - Method in class es.upm.fi.cig.multictbnc.data.reader.AbstractCSVReader

setVariables(String, List<String>, List<String>) - Method in interface es.upm.fi.cig.multictbnc.data.reader.DatasetReader

Receives the names of the time variable, feature variables and class variables of a dataset.

setWriter(MetricsWriter) - Method in class es.upm.fi.cig.multictbnc.performance.ValidationMethod

Defines the metrics writer used to save the results of the evaluation.

showNode(Node, boolean) - Static method in class es.upm.fi.cig.multictbnc.util.ControllerUtil

Changes the visibility of a node.

showPredictions(Prediction[], Dataset) - Static method in class es.upm.fi.cig.multictbnc.performance.Metrics

Displays the predictions along with the actual values.

shuffle(List<T>, Long) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Shuffles the elements of a list.

SingleCSVReader - Class in es.upm.fi.cig.multictbnc.data.reader

Reads time series data contained in a single CSV.

SingleCSVReader(String, int) - Constructor for class es.upm.fi.cig.multictbnc.data.reader.SingleCSVReader

Constructs a SingleCSVReader that extracts a CSV file from the specified folder.

SlidingWindow<T> - Class in es.upm.fi.cig.multictbnc.data.representation

Represents a sliding window data structure.

SlidingWindow(int) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.SlidingWindow

Constructs a sliding window with the specified size.

start(Stage) - Method in class es.upm.fi.cig.multictbnc.Main

start(Stage) - Method in class es.upm.fi.cig.multictbnc.sampling.MainDataStreamSampling.MainDataStreamSamplingFX

start(Stage) - Method in class es.upm.fi.cig.multictbnc.sampling.MainFeatureStreamSampling.MainFeatureStreamSamplingFX

State - Class in es.upm.fi.cig.multictbnc.data.representation

Represents the state of certain nodes/variables (events) by keeping their names and values.

State() - Constructor for class es.upm.fi.cig.multictbnc.data.representation.State

Default constructor.

State(State) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.State

Constructor to clone states;

State(String, String) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.State

Creates a State instance with one event.

State(Map<String, String>) - Constructor for class es.upm.fi.cig.multictbnc.data.representation.State

Creates a State instance with some events.

StatisticalBasedFeatureSelection - Class in es.upm.fi.cig.multictbnc.fss

Provides the basis for statistical-based feature subset selection algorithms.

StatisticalBasedFeatureSelection(List<String>, ParameterLearningAlgorithm, int, double, double) - Constructor for class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Initializes the necessary structures for feature subset selection including parameter learning algorithms, significance levels, and a cache for redundancy checks.

stop() - Method in class es.upm.fi.cig.multictbnc.Main

stringToList(String) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Extract a list of Strings from a String representation of a list with the format "element1,element2,element3" (commas are the delimiters).

stringToMap(String) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Extract a map from a String with the format "String:Double,String:Double", where the Strings are the keys and the doubles the values.

StructureConstraints - Interface in es.upm.fi.cig.multictbnc.learning.structure.constraints

Interface used to define classes that specify structure constraints for PGMs.

StructureLearningAlgorithm - Interface in es.upm.fi.cig.multictbnc.learning.structure

Interface used to define algorithms for learning the structure of PGMs.

StructureLearningAlgorithmFactory - Class in es.upm.fi.cig.multictbnc.learning.structure

Builds the specified structure learning algorithms for Bayesian networks and continuous-time Bayesian networks.

StructureLearningAlgorithmFactory() - Constructor for class es.upm.fi.cig.multictbnc.learning.structure.StructureLearningAlgorithmFactory

StructureLearningAlgorithmsComparisonExperiment - Class in es.upm.fi.cig.multictbnc.experiments.implementationsexperiments

Class designed to perform a comparative experiment of structure learning algorithms for Multi-CTBNCs.

StructureLearningAlgorithmsComparisonExperiment(String[]) - Constructor for class es.upm.fi.cig.multictbnc.experiments.implementationsexperiments.StructureLearningAlgorithmsComparisonExperiment

Constructor that initializes the experiment with the provided configuration.

SubsetSelectedFeatures - Class in es.upm.fi.cig.multictbnc.fss

Class that encapsulates the names of the feature variables selected by a FSS algorithm and the execution time of this algorithm to provide that solution.

SubsetSelectedFeatures(List<String>, double) - Constructor for class es.upm.fi.cig.multictbnc.fss.SubsetSelectedFeatures

Constructs an instance of SubsetSelectedFeatureVariables.

succeeded() - Method in class es.upm.fi.cig.multictbnc.tasks.ClassificationTask

succeeded() - Method in class es.upm.fi.cig.multictbnc.tasks.EvaluationTask

succeeded() - Method in class es.upm.fi.cig.multictbnc.tasks.TrainingTask

SufficientStatistics - Interface in es.upm.fi.cig.multictbnc.learning.parameters

Interface for sufficient statistics of discrete nodes.

szudzikFunction(int, int) - Static method in class es.upm.fi.cig.multictbnc.util.Util

Given two non-negative numbers, this method returns a non-negative integer that is uniquely associated with that pair.

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TestDatasetBinaryRelevanceMethod - Class in es.upm.fi.cig.multictbnc.performance

Implements a validation method for evaluating CTBNCs using a test.

TestDatasetBinaryRelevanceMethod(DatasetReader, DatasetReader, boolean) - Constructor for class es.upm.fi.cig.multictbnc.performance.TestDatasetBinaryRelevanceMethod

Constructor that receives the dataset readers and configuration.

TestDatasetMethod - Class in es.upm.fi.cig.multictbnc.performance

This class allows specifying different training and test datasets.

TestDatasetMethod(DatasetReader, boolean) - Constructor for class es.upm.fi.cig.multictbnc.performance.TestDatasetMethod

Constructor that receives a DatasetReader for the test dataset, whether the dataset should be shuffled, a seed for shuffling and whether the probabilities of each class configuration should be estimated during the testing.

TestDatasetMethod(DatasetReader, DatasetReader, boolean) - Constructor for class es.upm.fi.cig.multictbnc.performance.TestDatasetMethod

Constructor that receives two DatasetReader for the training and test datasets, whether the test dataset should be shuffled, a seed for shuffling and whether the probabilities of each class configuration should be estimated during the testing.

testNullStateToStateTransitionHypForGivenSepSet(CIMNode, CIMNode, List<String>, CTBNSufficientStatistics, CTBNSufficientStatistics) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Evaluates null state-to-state transition hypothesis for a given node and parent node given a certain separating set.

testNullStateToStateTransitionHypothesis(CIMNode, List<String>, CTBNSufficientStatistics, CTBNSufficientStatistics) - Static method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Evaluates the null state-to-state transition hypothesis between a feature node and the class node, given a separating set.

testNullTimeToTransitionHypForGivenSepSet(CIMNode, CIMNode, List<String>, double[][], CTBNSufficientStatistics, double[][], CTBNSufficientStatistics) - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraintlearning.PC.CTPC

Evaluate null time to transition hypothesis for a given node and parent given a certain separating set.

testNullTimeToTransitionHypothesis(CIMNode, List<String>, CTBNSufficientStatistics, double[][], CTBNSufficientStatistics, double[][]) - Static method in class es.upm.fi.cig.multictbnc.fss.StatisticalBasedFeatureSelection

Evaluates the null time to transition hypothesis between a feature node and the class node, given a separating set.

TimeSeriesChart - Class in es.upm.fi.cig.multictbnc.gui

A class for creating and managing a time series chart using the JFreeChart library.

TimeSeriesChart(String, String, String, int[], String...) - Constructor for class es.upm.fi.cig.multictbnc.gui.TimeSeriesChart

Constructs a TimeSeriesChart instance.

toString() - Method in class es.upm.fi.cig.multictbnc.data.representation.Sequence

toString() - Method in class es.upm.fi.cig.multictbnc.data.representation.State

toString() - Method in class es.upm.fi.cig.multictbnc.fss.SubsetSelectedFeatures

toString() - Method in class es.upm.fi.cig.multictbnc.models.BN

toString() - Method in class es.upm.fi.cig.multictbnc.models.CTBN

toString() - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

toString() - Method in class es.upm.fi.cig.multictbnc.nodes.AbstractNode

toString() - Method in class es.upm.fi.cig.multictbnc.nodes.CIMNode

toString() - Method in class es.upm.fi.cig.multictbnc.nodes.CPTNode

toString() - Method in class es.upm.fi.cig.multictbnc.nodes.DiscreteStateNode

TrainingService - Class in es.upm.fi.cig.multictbnc.services

Service that creates and manages a TrainingTask.

TrainingService() - Constructor for class es.upm.fi.cig.multictbnc.services.TrainingService

TrainingTask - Class in es.upm.fi.cig.multictbnc.tasks

Task that allows executing the training of a model in a background thread.

TrainingTask(MultiCTBNC<?, ?>, DatasetReader) - Constructor for class es.upm.fi.cig.multictbnc.tasks.TrainingTask

Constructs a TrainingTask that receives an MultiCTBNC model and a datasetReader.

trainModel() - Method in class es.upm.fi.cig.multictbnc.gui.Controller

Trains the selected model.

TxtClassificationWriter - Class in es.upm.fi.cig.multictbnc.writers.classification

Class to write the predictions made on a dataset in a TXT file.

TxtClassificationWriter() - Constructor for class es.upm.fi.cig.multictbnc.writers.classification.TxtClassificationWriter

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uniqueStructure() - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.AbstractStructureConstraints

uniqueStructure() - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.BN.EmptyBN

uniqueStructure() - Method in class es.upm.fi.cig.multictbnc.learning.structure.constraints.CTBNC.NaiveBayes

uniqueStructure() - Method in interface es.upm.fi.cig.multictbnc.learning.structure.constraints.StructureConstraints

Determines if there is only one possible structure.

UnreadDatasetException - Exception in es.upm.fi.cig.multictbnc.exceptions

Thrown when the provided dataset could not be read.

UnreadDatasetException(String) - Constructor for exception es.upm.fi.cig.multictbnc.exceptions.UnreadDatasetException

Constructs a UnreadDatasetException with the specified detail message.

update(double) - Method in class es.upm.fi.cig.multictbnc.gui.TimeSeriesChart

Updates the chart with a new value for the Y axis.

update(double[]) - Method in class es.upm.fi.cig.multictbnc.gui.TimeSeriesChart

Updates the chart providing the Y axis value of several time series.

update(double, double) - Method in class es.upm.fi.cig.multictbnc.gui.XYLineChart

Updates the chart with a new value for the X and Y axis.

update(double, double[]) - Method in class es.upm.fi.cig.multictbnc.gui.XYLineChart

Updates the chart providing the X and Y axis values of several series.

update(List<Node>, Dataset) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Learns the sets of parents and children of some nodes from a provided dataset and update the model with them.

updateBridgeAndFeatureSubgraph(Dataset) - Method in class es.upm.fi.cig.multictbnc.models.MultiCTBNC

Updates the bridge and feature subgraphs of the Multi-CTBNC model with new data from the provided dataset.

updateMx(int, int, double) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Updates the number of occurrences where the node transitions from a certain state to any other state given an instantiation of its parents.

updateMxy(int, int, int, double) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Updates the number of occurrences where the node transitions from a certain state to another given an instantiation of its parents.

updateSufficientStatistics(Sequence, int, int, int, int) - Method in class es.upm.fi.cig.multictbnc.learning.parameters.ctbn.CTBNSufficientStatistics

Update the values of the sufficient statistics.

UserInterfaceUtil - Class in es.upm.fi.cig.multictbnc.util

Utility class with methods related to the user interface.

Util - Class in es.upm.fi.cig.multictbnc.util

Utility class.

V

ValidationMethod - Class in es.upm.fi.cig.multictbnc.performance

Abstract class defining common methods for validation algorithms.

ValidationMethod() - Constructor for class es.upm.fi.cig.multictbnc.performance.ValidationMethod

ValidationMethodFactory - Class in es.upm.fi.cig.multictbnc.performance

Builds validation methods.

ValidationMethodFactory() - Constructor for class es.upm.fi.cig.multictbnc.performance.ValidationMethodFactory

VariableNotFoundException - Exception in es.upm.fi.cig.multictbnc.exceptions

Thrown when an expected variable is not found in a provided dataset.

VariableNotFoundException() - Constructor for exception es.upm.fi.cig.multictbnc.exceptions.VariableNotFoundException

Constructs a VariableNotFoundException with no detail message.

VariableNotFoundException(String) - Constructor for exception es.upm.fi.cig.multictbnc.exceptions.VariableNotFoundException

Constructs a VariableNotFoundException with the specified detail message.

W

write(Dataset, String) - Static method in class es.upm.fi.cig.multictbnc.data.writer.MultipleCSVWriter

Writes the sequences of the provided dataset to multiple CSV files in the specified directory.

write(Dataset, List<String>, String) - Static method in class es.upm.fi.cig.multictbnc.data.writer.MultipleCSVWriter

Writes the sequences of the provided dataset to multiple CSV files in the specified directory, including only the specified feature variables.

write(Sequence, String, String) - Static method in class es.upm.fi.cig.multictbnc.data.writer.MultipleCSVWriter

Writes a sequence to a CSV file in the specified directory.

write(Sequence, List<String>, String, String) - Static method in class es.upm.fi.cig.multictbnc.data.writer.MultipleCSVWriter

Writes a sequence to a CSV file in the specified directory and including only the specified feature variables.

write(List<Map<String, Double>>) - Method in class es.upm.fi.cig.multictbnc.writers.performance.MetricsWriter

Writes the results to an output.

write(Map<String, Double>) - Method in class es.upm.fi.cig.multictbnc.writers.performance.ConsoleExperimentsWriter

write(Map<String, Double>) - Method in class es.upm.fi.cig.multictbnc.writers.performance.ExcelExperimentsWriter

write(Map<String, Double>) - Method in class es.upm.fi.cig.multictbnc.writers.performance.MetricsWriter

Writes the given results.

writePredictions(Prediction[], Dataset, String) - Static method in class es.upm.fi.cig.multictbnc.writers.classification.TxtClassificationWriter

Writes predictions of a dataset in the specified folder.

X

XYLineChart - Class in es.upm.fi.cig.multictbnc.gui

A class for creating and managing an XY line chart using the JFreeChart library.

XYLineChart(String, String, String, int[], String...) - Constructor for class es.upm.fi.cig.multictbnc.gui.XYLineChart

Constructs an XYLineChart instance.

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