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A

accumulatedSumVectors(double[], double[]) - Static method in class eu.amidst.core.utils.Utils
 
activateTransitionMethod() - Method in class eu.amidst.core.conceptdrift.NaiveBayesVirtualConceptDriftDetector
 
activeParametersNodes() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
add(E) - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Adds a new DataInstance.
add(KMeans.Point) - Method in class eu.amidst.flinklink.examples.misc.KMeans.Point
 
add(E) - Method in class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Adds a new DataInstance.
addAll(Collection<E>) - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Adds a list of DataInstances.
addDynamicEvidence(DynamicAssignment) - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Sets the evidence for this InferenceAlgorithmForDBN.
addDynamicEvidence(DynamicAssignment) - Method in class eu.amidst.dynamic.inference.DynamicVMP
Sets the evidence for this InferenceAlgorithmForDBN.
addDynamicEvidence(DynamicAssignment) - Method in class eu.amidst.dynamic.inference.FactoredFrontierForDBN
Sets the evidence for this InferenceAlgorithmForDBN.
addDynamicEvidence(DynamicAssignment) - Method in interface eu.amidst.dynamic.inference.InferenceAlgorithmForDBN
Sets the evidence for this InferenceAlgorithmForDBN.
addDynamicEvidence(DynamicAssignment) - Static method in class eu.amidst.dynamic.inference.InferenceEngineForDBN
Sets the evidence for this InferenceEngineForDBN.
addDynamicEvidence(DynamicAssignment) - Method in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Sets the evidence for this InferenceAlgorithmForDBN.
addMissingVariable(Variable) - Method in class eu.amidst.core.variables.MissingAssignment
Adds a variable with missing values to this MissingAssignment.
addParent(Variable) - Method in interface eu.amidst.core.models.ParentSet
Adds a given Variable as a new parent of the main variable.
addRandomColumnForExternalShuffle(String[]) - Static method in class eu.amidst.lda.utils.Main
 
addVector(E, Vector) - Method in class eu.amidst.core.utils.KeyCompoundVector
Adds a vector to this KeyCompoundVector.
AmidstClassifier - Class in moa.classifiers.bayes
This class extends the AbstractClassifier and defines the AMIDST Classifier that could be run using the MOA’s graphical user interface.
AmidstClassifier() - Constructor for class moa.classifiers.bayes.AmidstClassifier
 
AmidstClassifier - Class in weka.classifiers.bayes
This class extends the AbstractClassifier and defines the AMIDST Classifier that could be run using the MOA’s graphical user interface.
AmidstClassifier() - Constructor for class weka.classifiers.bayes.AmidstClassifier
 
AmidstClustering - Class in weka.clusterers
Created by ana@cs.aau.dk on 21/03/16.
AmidstClustering() - Constructor for class weka.clusterers.AmidstClustering
 
AmidstClusteringAlgorithm - Class in moa.clusterers
This class extends the AbstractClusterer and defines the AMIDST Clustering algorithm that could be run using the MOA’s graphical user interface.
AmidstClusteringAlgorithm() - Constructor for class moa.clusterers.AmidstClusteringAlgorithm
 
amidstDBN - Variable in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Represents the Dynamic Bayesian network model in AMIDST format.
AmidstOptionsHandler - Interface in eu.amidst.core.utils
This interface handles the different possible options or parameters.
AmidstOptionsHandlerStatic - Class in eu.amidst.core.utils
This class is just meant to serve as a temporary example to fill in options in STATIC CLASSES, that should "overwrites" the methods below, it will be removed in the future.
AmidstOptionsHandlerStatic() - Constructor for class eu.amidst.core.utils.AmidstOptionsHandlerStatic
 
AmidstRegressor - Class in moa.classifiers.bayes
This class extends the AbstractClassifier and defines the AMIDST Regressor that could be run using the MOA’s graphical user interface.
AmidstRegressor() - Constructor for class moa.classifiers.bayes.AmidstRegressor
 
AmidstRegressor - Class in weka.classifiers.bayes
This class extends the AbstractClassifier and defines the AMIDST Classifier that could be run using the MOA’s graphical user interface.
AmidstRegressor() - Constructor for class weka.classifiers.bayes.AmidstRegressor
 
AODE - Class in eu.amidst.latentvariablemodels.staticmodels.classifiers
The AODE class implements the interface Classifier and defines a (G)AODE Classifier.
AODE(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.classifiers.AODE
Constructor of a classifier which is initialized with the default arguments: the last variable in attributes is the class variable and importance sampling is the inference algorithm for making the predictions.
apply(Function<Double, Double>) - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
apply(A, B) - Method in interface eu.amidst.flinklink.core.utils.Function2
 
applyTransition() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Apply the transition method defined by the method setTransitionMethod.
areParentsCompatible(List<Variable>) - Method in class eu.amidst.core.variables.DistributionType
Tests whether the given parents are compatible or not.
ARFFDataFolderReader - Class in eu.amidst.core.datastream.filereaders.arffFileReader
This class implements the interface DataFileReader for reading the "folder-ARFF" format.
ARFFDataFolderReader() - Constructor for class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataFolderReader
 
ARFFDataReader - Class in eu.amidst.core.datastream.filereaders.arffFileReader
This class implements the interface DataFileReader and defines an ARFF (Weka Attribute-Relation File Format) data reader.
ARFFDataReader() - Constructor for class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataReader
 
ARFFDataWriter - Class in eu.amidst.core.datastream.filereaders.arffFileReader
This class implements the interface DataFileWriter and defines an ARFF (Weka Attribute-Relation File Format) data writer.
ARFFDataWriter() - Constructor for class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataWriter
 
ARFFtoSparkFormat(String, String, String, SQLContext, JavaSparkContext) - Static method in class eu.amidst.sparklink.core.util.FileConverter
 
ArrayVector - Class in eu.amidst.core.utils
This class implements the interfaces MomentParameters, NaturalParameters, and SufficientStatistics.
ArrayVector(int) - Constructor for class eu.amidst.core.utils.ArrayVector
Creates a new array vector given an int size.
ArrayVector(double[]) - Constructor for class eu.amidst.core.utils.ArrayVector
Creates a new array vector given an array of double.
ArrayVectorParameter(int) - Constructor for class eu.amidst.core.exponentialfamily.EF_Normal.ArrayVectorParameter
Creates a new array vector given an int size.
ArrayVectorParameter(double[]) - Constructor for class eu.amidst.core.exponentialfamily.EF_Normal.ArrayVectorParameter
Creates a new array vector given an array of double.
ArrayVectorParameter(int) - Constructor for class eu.amidst.core.exponentialfamily.EF_NormalParameter.ArrayVectorParameter
Creates a new array vector given an int size.
ArrayVectorParameter(double[]) - Constructor for class eu.amidst.core.exponentialfamily.EF_NormalParameter.ArrayVectorParameter
Creates a new array vector given an array of double.
assignment - Variable in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Represents an Assignment object.
Assignment - Interface in eu.amidst.core.variables
This interface defines a collection of assignments to variables.
Attribute - Class in eu.amidst.core.datastream
If we consider a data sets as a data matrix, an Attribute class would represent a column of the matrix.
Attribute(int, String, StateSpaceType) - Constructor for class eu.amidst.core.datastream.Attribute
Creates a new Attribute.
Attributes - Class in eu.amidst.core.datastream
This class acts as a container of the Attribute objects of a data set.
Attributes(List<Attribute>) - Constructor for class eu.amidst.core.datastream.Attributes
Creates a new Attributes from a given List of attribute objects.
ATTRIBUTES - Static variable in class eu.amidst.flinklink.core.utils.ConversionToBatches
 
ATTRIBUTES_NAME - Static variable in class eu.amidst.flinklink.core.io.DataFlinkLoader
 
attributeToARFFString(Attribute) - Static method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataWriter
 
attributeToARFFStringWithIndex(Attribute, boolean) - Static method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataWriter
 
atts - Variable in class eu.amidst.latentvariablemodels.staticmodels.Model
 
AutoRegressiveHMM - Class in eu.amidst.latentvariablemodels.dynamicmodels
This class implements an Auto-regressive Hidden Markov Model.
AutoRegressiveHMM(Attributes) - Constructor for class eu.amidst.latentvariablemodels.dynamicmodels.AutoRegressiveHMM
 
auxiliarBuilder(List<Variable>) - Static method in class eu.amidst.core.variables.Variables
Auxiliar builder.
average() - Method in class eu.amidst.cim2015.examples.ParallelKMeans.Averager
 
Averager(int) - Constructor for class eu.amidst.cim2015.examples.ParallelKMeans.Averager
 
Averager(DataInstance) - Constructor for class eu.amidst.cim2015.examples.ParallelKMeans.Averager
 

B

BaseDistribution_MultinomialParents<E extends Distribution> - Class in eu.amidst.core.distribution
This class extends the abstract class ConditionalDistribution.
BaseDistribution_MultinomialParents(List<Variable>, List<E>) - Constructor for class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Creates a new BaseDistribution_MultinomialParents given the lists of multinomial parents and distributions.
BaseDistribution_MultinomialParents(Variable, List<Variable>) - Constructor for class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Creates a new BaseDistribution_MultinomialParents given a variable and the list of its parents.
Batch<T> - Class in eu.amidst.flinklink.core.utils
Created by andresmasegosa on 21/1/16.
Batch(double, List<T>) - Constructor for class eu.amidst.flinklink.core.utils.Batch
 
BATCH_SIZE - Static variable in class eu.amidst.flinklink.core.utils.ConversionToBatches
 
BatchesSpliterator<T extends DataInstance> - Class in eu.amidst.core.datastream
The BatchesSpliterator class implements a Spliterator for iterating over data batches of a DataStream.
BatchesSpliterator(DataStream<T>, long, int) - Constructor for class eu.amidst.core.datastream.BatchesSpliterator
Creates a new BatchesSpliterator.
BatchesSpliterator(DataStream<T>, int) - Constructor for class eu.amidst.core.datastream.BatchesSpliterator
Creates a new BatchesSpliterator.
BatchOutput(CompoundVector, double) - Constructor for class eu.amidst.core.learning.parametric.bayesian.SVB.BatchOutput
 
batchSize - Variable in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
batchSize - Variable in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
batchSize - Variable in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
batchSize - Variable in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
batchSize - Variable in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
BATCHSIZE - Static variable in class eu.amidst.flinklink.examples.reviewMeeting2015.GenerateData
 
batchSize_ - Variable in class moa.classifiers.bayes.AmidstClassifier
Represents the batch size.
batchSize_ - Variable in class moa.classifiers.bayes.AmidstRegressor
Represents the batch size.
batchSizeOption - Variable in class moa.classifiers.bayes.AmidstClassifier
Creates a new object of the class IntOption.
batchSizeOption - Variable in class moa.classifiers.bayes.AmidstRegressor
Creates a new object of the class IntOption.
BatchSpliteratorByID<T extends DataInstance> - Class in eu.amidst.lda.core
The DataSequenceSpliterator class implements a Spliterator of DataOnMemory.
BatchSpliteratorByID(DataStream<T>, long, int) - Constructor for class eu.amidst.lda.core.BatchSpliteratorByID
Creates a new DataSequenceSpliterator.
BatchSpliteratorByID(DataStream<T>, int) - Constructor for class eu.amidst.lda.core.BatchSpliteratorByID
Creates a new DataSequenceSpliterator.
BayesianLearningAlgorithm - Interface in eu.amidst.dynamic.learning.parametric.bayesian
This interface defines the Dynamic Bayesian learning algorithm for DynamicBayesianNetwork models.
BayesianLinearRegression - Class in eu.amidst.latentvariablemodels.staticmodels
This class implements a Bayesian Linear Regression Model.
BayesianLinearRegression(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.BayesianLinearRegression
Constructor from a list of attributes.
BayesianNetwork - Class in eu.amidst.core.models
The BayesianNetwork class represents a Bayesian network model.
BayesianNetwork(DAG) - Constructor for class eu.amidst.core.models.BayesianNetwork
Creates a new BayesianNetwork from a dag.
BayesianNetwork(DAG, List<ConditionalDistribution>) - Constructor for class eu.amidst.core.models.BayesianNetwork
Creates a new BayesianNetwork from a dag and a list of distributions.
BayesianNetworkGenerator - Class in eu.amidst.core.utils
This class defines a random BayesianNetwork generator.
BayesianNetworkGenerator() - Constructor for class eu.amidst.core.utils.BayesianNetworkGenerator
 
BayesianNetworkIOExample - Class in eu.amidst.core.examples.io
In this example we show how to load and save Bayesian networks models for a binary file with ".bn" extension.
BayesianNetworkIOExample() - Constructor for class eu.amidst.core.examples.io.BayesianNetworkIOExample
 
BayesianNetworkLoader - Class in eu.amidst.core.io
This class allows to load a BayesianNetwork model from a file.
BayesianNetworkLoader() - Constructor for class eu.amidst.core.io.BayesianNetworkLoader
 
BayesianNetworkSampler - Class in eu.amidst.core.utils
This class implements the interface AmidstOptionsHandler.
BayesianNetworkSampler(BayesianNetwork) - Constructor for class eu.amidst.core.utils.BayesianNetworkSampler
Creates a new BayesianNetworkSampler given an input BayesianNetwork object.
BayesianNetworkSampler - Class in eu.amidst.flinklink.core.utils
It defines a sampler of data from a BayesianNetwork.
BayesianNetworkSampler(BayesianNetwork) - Constructor for class eu.amidst.flinklink.core.utils.BayesianNetworkSampler
Creates a new BayesianNetworkSampler given an input BayesianNetwork object.
BayesianNetworkSampler - Class in eu.amidst.sparklink.core.util
Created by jarias on 22/06/16.
BayesianNetworkSampler(BayesianNetwork) - Constructor for class eu.amidst.sparklink.core.util.BayesianNetworkSampler
Creates a new BayesianNetworkSampler given an input BayesianNetwork object.
BayesianNetworkSamplerExample - Class in eu.amidst.core.examples.utils
This example shows how to use the BayesianNetworkSampler class to randomly generate a data sample for a given Bayesian network.
BayesianNetworkSamplerExample() - Constructor for class eu.amidst.core.examples.utils.BayesianNetworkSamplerExample
 
BayesianNetworkWriter - Class in eu.amidst.core.io
This class allows to save a BayesianNetwork model in a file.
BayesianNetworkWriter() - Constructor for class eu.amidst.core.io.BayesianNetworkWriter
 
BayesianNetworkWriterToHugin - Class in eu.amidst.huginlink.io
This class is a writer to create Hugin Bayesian network files from AMIDST Bayesian networks.
BayesianNetworkWriterToHugin() - Constructor for class eu.amidst.huginlink.io.BayesianNetworkWriterToHugin
 
BayesianParameterLearningAlgorithm - Interface in eu.amidst.core.learning.parametric.bayesian
This interface extends ParameterLearningAlgorithm and defines the Bayesian parameter learning algorithm.
BayesianParameterLearningAlgorithm - Interface in eu.amidst.flinklink.core.learning.parametric
This interface extends ParameterLearningAlgorithm and defines the Bayesian parameter learning algorithm.
BCC - Class in eu.amidst.bnaic2015.examples
This class constains the example code given at the demo session in BNAIC2015 about the AMIDST Toolbox.
BCC() - Constructor for class eu.amidst.bnaic2015.examples.BCC
 
block() - Method in class eu.amidst.core.variables.Variables
Defines the list of Variables as an unmodifiable list.
block() - Method in class eu.amidst.dynamic.variables.DynamicVariables
Blocks this DynamicVariables.
blockParents() - Method in interface eu.amidst.core.models.ParentSet
Defines the set of parent as unmodifiable.
BN_NAME - Static variable in interface eu.amidst.flinklink.core.learning.parametric.ParameterLearningAlgorithm
 
BN_NAME - Static variable in interface eu.amidst.sparklink.core.learning.ParameterLearningAlgorithm
 
BNConverterExample - Class in eu.amidst.huginlink.examples.converters
This example shows how to use the class BNConverterToAMIDST and BNConverterToHugin to convert a Bayesian network models from Hugin to AMIDST and vice versa, respectively.
BNConverterExample() - Constructor for class eu.amidst.huginlink.examples.converters.BNConverterExample
 
BNConverterToAMIDST - Class in eu.amidst.huginlink.converters
The BNConverterToAMIDST class converts a Bayesian network model from Hugin to AMIDST format.
BNConverterToAMIDST(Domain) - Constructor for class eu.amidst.huginlink.converters.BNConverterToAMIDST
Class constructor.
BNConverterToHugin - Class in eu.amidst.huginlink.converters
The BNConverterToHugin class converts a Bayesian network model from AMIDST to Hugin.
BNConverterToHugin() - Constructor for class eu.amidst.huginlink.converters.BNConverterToHugin
Class constructor.
BNLoaderFromHugin - Class in eu.amidst.huginlink.io
This class is a loader to create AMIDST Bayesian networks from Hugin Bayesian network files.
BNLoaderFromHugin() - Constructor for class eu.amidst.huginlink.io.BNLoaderFromHugin
 
BNLoaderWriterExample - Class in eu.amidst.huginlink.examples.io
This example shows how to use the BNLoaderFromHugin and BayesianNetworkWriterToHugin classes to load and write Bayesian networks in Hugin format, respectively.
BNLoaderWriterExample() - Constructor for class eu.amidst.huginlink.examples.io.BNLoaderWriterExample
 
buildClassifier(Instances) - Method in class weka.classifiers.bayes.AmidstClassifier
 
buildClassifier(Instances) - Method in class weka.classifiers.bayes.AmidstRegressor
 
buildClusterer(Instances) - Method in class weka.clusterers.AmidstClustering
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.AutoRegressiveHMM
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicLatentClassificationModel
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.FactorialHMM
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.HiddenMarkovModel
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.InputOutputHMM
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.KalmanFilter
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.SwitchingKalmanFilter
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.BayesianLinearRegression
Builds the DAG of the model.
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.AODE
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.GaussianDiscriminantAnalysis
Builds the DAG over the set of variables given with the naive Bayes structure
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.HODE
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.LatentClassificationModel
Builds the DAG over the set of variables given with the naive Bayes structure
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.NaiveBayesClassifier
Builds the DAG over the set of variables given with the naive Bayes structure
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.TAN
In this class this method does nothing: the DAG is built in the hugin classes
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
Builds the DAG over the set of variables given with the structure of the model
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.CustomGaussianMixture
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.FactorAnalysis
Builds the DAG
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.GaussianMixture
Builds the DAG over the set of variables given with the structure of the model
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.MixtureOfFactorAnalysers
Builds the graph
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
buildDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.MultivariateGaussianDistribution
Builds the DAG over the set of variables given with the structure of the model
buildDAG() - Method in class eu.amidst.tutorial.usingAmidst.practice.CustomGaussianMixture
 
buildDAG() - Method in class eu.amidst.tutorial.usingAmidst.practice.CustomKalmanFilter
 
buildPlateuStructure() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 

C

cajaMarDefaulterPredictor() - Static method in class eu.amidst.dynamic.examples.models.CajaMarModels
In this example, we create the proposed dynamic model for making predictions about the defaulting behaviour of a client.
CajaMarLearnMapInferenceAssignment(Attributes, List<Variable>) - Constructor for class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.CajaMarLearnMapInferenceAssignment
 
CajaMarModels - Class in eu.amidst.dynamic.examples.models
This class contains examples about how we can create CajaMar's dynamic models using the AMIDST Toolbox.
cascadingSample(ExecutionEnvironment, DataFlink<DynamicDataInstance>) - Method in class eu.amidst.flinklink.core.utils.DBNSampler
 
cascadingSampleConceptDrift(ExecutionEnvironment, DataFlink<DynamicDataInstance>, boolean) - Method in class eu.amidst.flinklink.core.utils.DBNSampler
 
Centroid() - Constructor for class eu.amidst.flinklink.examples.misc.KMeans.Centroid
 
Centroid(int, double, double) - Constructor for class eu.amidst.flinklink.examples.misc.KMeans.Centroid
 
Centroid(int, KMeans.Point) - Constructor for class eu.amidst.flinklink.examples.misc.KMeans.Centroid
 
CentroidAccumulator() - Constructor for class eu.amidst.flinklink.examples.misc.KMeans.CentroidAccumulator
 
CentroidAverager() - Constructor for class eu.amidst.flinklink.examples.misc.KMeans.CentroidAverager
 
CENTROIDS - Static variable in class eu.amidst.flinklink.examples.misc.KMeansData
 
characteristics() - Method in class eu.amidst.core.datastream.BatchesSpliterator
characteristics() - Method in class eu.amidst.core.utils.FixedBatchParallelSpliteratorWrapper
characteristics() - Method in class eu.amidst.dynamic.datastream.DataSequenceSpliterator
characteristics() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataInstanceSpliterator
characteristics() - Method in class eu.amidst.lda.core.BatchSpliteratorByID
Classifier<T extends Classifier> - Class in eu.amidst.latentvariablemodels.staticmodels.classifiers
The Classifier abstract class is defined for Bayesian classification models.
Classifier(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.classifiers.Classifier
Constructor of a classifier which is initialized with the default arguments: the last variable in attributes is the class variable and importance sampling is the inference algorithm for making the predictions.
classifyInstance(Instance) - Method in class weka.classifiers.bayes.AmidstRegressor
 
classNameID() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
classNameID() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
classNameID() - Static method in class eu.amidst.core.models.BayesianNetwork
 
classNameID() - Method in interface eu.amidst.core.utils.AmidstOptionsHandler
Returns the name of this class.
classNameID() - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Returns the name of this class.
classNameID() - Method in class eu.amidst.core.utils.BayesianNetworkSampler
Returns the name of this class.
classNameID() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
Returns the name of this class.
classNameID() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
classNameID() - Static method in class eu.amidst.flinklink.examples.misc.GenerateRandom
 
classNameID() - Method in class eu.amidst.huginlink.learning.ParallelPC
Returns the name of this class.
classNameID() - Method in class eu.amidst.huginlink.learning.ParallelTAN
Returns the name of this class.
classVar - Variable in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicClassifier
class variable
classVar - Variable in class eu.amidst.latentvariablemodels.staticmodels.classifiers.Classifier
class variable
clear() - Method in class eu.amidst.flinklink.examples.misc.KMeans.Point
 
close() - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Closes this DataStream.
close() - Method in interface eu.amidst.core.datastream.DataStream
Closes this DataStream.
close() - Method in interface eu.amidst.core.datastream.filereaders.DataFileReader
Closes this DataFileReader.
close() - Method in class eu.amidst.core.datastream.filereaders.DataOnMemoryFromFile
Closes this DataStream.
close() - Method in class eu.amidst.core.datastream.filereaders.DataStreamFromFile
Closes this DataStream.
close() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataOnMemoryFromFile
Closes this DataStream.
close() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataStreamFromFile
Closes this DataStream.
close() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ParallelVBMap
 
close() - Method in class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments
 
close() - Method in class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Closes this DataStream.
collectDataStream() - Method in interface eu.amidst.sparklink.core.data.DataSpark
 
combine(ParallelKMeans.Averager) - Method in class eu.amidst.cim2015.examples.ParallelKMeans.Averager
 
combine(Message<E>) - Method in class eu.amidst.core.inference.messagepassing.Message
Combines the given message.
combine(Potential) - Method in interface eu.amidst.core.potential.Potential
Combines this Potential with an input given potential.
combineNonStateless(Message<E>, Message<E>) - Static method in class eu.amidst.core.inference.messagepassing.Message
Combines two given Messages.
combineStateless(Message<E>, Message<E>) - Static method in class eu.amidst.core.inference.messagepassing.Message
Combines two given Messages.
compareBatchSizes() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
compareNumberOfCores() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
compareNumberOfCores() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
CompoundVector(int) - Constructor for class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
CompoundVector - Class in eu.amidst.core.utils
This class implements the interfaces MomentParameters, NaturalParameters, and SufficientStatistics.
CompoundVector(int, int) - Constructor for class eu.amidst.core.utils.CompoundVector
Creates a new CompoundVector for a given size and number of vectors.
CompoundVector(List<Vector>) - Constructor for class eu.amidst.core.utils.CompoundVector
Creates a new CompoundVector from a given list of vectors.
computeCountSufficientStatistics(EF_BayesianNetwork, DataInstance) - Static method in class eu.amidst.core.learning.parametric.ParallelMLMissingData
 
computeELBO(Node) - Method in class eu.amidst.core.inference.messagepassing.VMP
Computes the evidence lower bound (ELBO) for a given Node.
computeELBO(DataFlink<DataInstance>, SVB) - Static method in class eu.amidst.flinklink.core.learning.parametric.StochasticVI
 
computeELBO(DataFlink<DataInstance>, SVB, Function2<DataFlink<DataInstance>, Integer, DataSet<DataOnMemory<DataInstance>>>) - Static method in class eu.amidst.flinklink.core.learning.parametric.StochasticVI
 
computeLocalKLDirichlet(EF_Dirichlet, EF_Dirichlet) - Static method in class eu.amidst.lda.core.MultiDriftLDAv1
 
computeLocalKLDirichletBinary(EF_Dirichlet, EF_Dirichlet) - Static method in class eu.amidst.lda.core.MultiDriftLDAv1
 
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_Dirichlet
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_InverseGamma
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
computeLogBaseMeasure(double) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Computes the logarithm of the base measure function for a given value.
computeLogBaseMeasure(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Computes the logarithm of the base measure function for a given assignment.
computeLogBaseMeasure(DynamicDataInstance) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Computes the logarithm of the base measure function for a given DynamicDataInstance.
computeLogBaseMeasure(DynamicDataInstance) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Computes the logarithm of the base measure function for a given DynamicDataInstance.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_Dirichlet
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_InverseGamma
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
computeLogNormalizer() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Computes the log-normalizer function of this EF_Distribution.
computeLogNormalizer() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Computes the log-normalizer function of this EF_Distribution.
computeLogProbability(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_ConditionalDistribution
 
computeLogProbability(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
 
computeLogProbabilityOf(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Computes the log probability for a given assignment.
computeLogProbabilityOf(double) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Returns the log probability of a given value according to this EF_UnivariateDistribution.
computeLogProbabilityOf(double) - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Returns the log probability of a given value according to this EF_UnivariateDistribution.
computeLogProbabilityOf(double) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Returns the log probability of a given value according to this EF_UnivariateDistribution.
computeLogProbabilityOf(double) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Returns the log probability of a given value according to this EF_UnivariateDistribution.
computeLogProbabilityOf(double) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the log probability of a given value according to this EF_UnivariateDistribution.
computeLogProbabilityOf(DynamicDataInstance) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
 
computeLogProbabilityOf(DynamicDataInstance) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Computes the log probability for a given DynamicDataInstance.
computeLogProbabilityOfEvidence() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the log probability of the evidence.
computeLogProbabilityOfEvidence() - Method in class eu.amidst.core.inference.messagepassing.VMP
Returns the log probability of the evidence.
computeMergedClassVarModels() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Computes Dynamic MAP for the even static model.
computeMonthlyAverage() - Static method in class eu.amidst.bnaic2015.examples.BCC
This method contains an example about how to compute the monthly average value of one variable.
computeNaturalParameterVectorPrior() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Computes the natural parameter priors.
computePosterior(DataOnMemory<DataInstance>) - Method in class eu.amidst.core.conceptdrift.SVBFading
 
computePosterior(DataOnMemory<DataInstance>, List<Variable>) - Method in class eu.amidst.core.conceptdrift.SVBFading
Compute the posterior over a given set of latent variables for a given set of data instances
computePosterior(DataOnMemory<DataInstance>) - Method in interface eu.amidst.core.learning.parametric.bayesian.BayesianParameterLearningAlgorithm
Compute the posterior over all the latent variables for a given set of data instances
computePosterior(DataOnMemory<DataInstance>, List<Variable>) - Method in interface eu.amidst.core.learning.parametric.bayesian.BayesianParameterLearningAlgorithm
Compute the posterior over a given set of latent variables for a given set of data instances
computePosterior(DataOnMemory<DataInstance>) - Method in class eu.amidst.core.learning.parametric.bayesian.ParallelSVB
 
computePosterior(DataOnMemory<DataInstance>, List<Variable>) - Method in class eu.amidst.core.learning.parametric.bayesian.ParallelSVB
Compute the posterior over a given set of latent variables for a given set of data instances
computePosterior(DataOnMemory<DataInstance>) - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
computePosterior(DataOnMemory<DataInstance>, List<Variable>) - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
computePosterior(DataOnMemory<DataInstance>) - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Compute the posterior over all the latent variables for a given set of data instances
computePosterior(DataOnMemory<DataInstance>, List<Variable>) - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Compute the posterior over a given set of latent variables for a given set of data instances
computePosterior(List<Variable>) - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
computePosterior(List<Variable>) - Method in interface eu.amidst.flinklink.core.learning.dynamic.ParameterLearningAlgorithm
 
computePosterior(List<Variable>) - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
computePosterior() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
computePosterior(DataFlink<DataInstance>, List<Variable>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
computePosterior(DataFlink<DataInstance>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
computePosterior(DataFlink<DataInstance>, List<Variable>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
computePosterior(DataFlink<DataInstance>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
computePosterior(DataFlink<DataInstance>, List<Variable>) - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
computePosterior(DataFlink<DataInstance>) - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
computePosteriorAssignment(DataOnMemory<DataInstance>, List<Variable>) - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
 
computePosteriorAssignment(List<Variable>) - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
computePosteriorAssignment(DataFlink<DataInstance>, List<Variable>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
computePosteriorAssignment(DataFlink<DataInstance>, List<Variable>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
computePosteriorAssignment(DataFlink<DataInstance>, List<Variable>) - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
computeProbabilityOf(DynamicDataInstance) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Computes the probability for a given DynamicDataInstance.
ConceptDriftDetector - Class in eu.amidst.flinklink.examples.reviewMeeting2015
Created by ana@cs.aau.dk on 18/01/16.
ConceptDriftDetector() - Constructor for class eu.amidst.flinklink.examples.reviewMeeting2015.ConceptDriftDetector
 
ConceptDriftDetector - Class in eu.amidst.latentvariablemodels.staticmodels
 
ConceptDriftDetector(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
Constructor of classifier from a list of attributes (e.g.
ConceptDriftDetector.DriftDetector - Enum in eu.amidst.latentvariablemodels.staticmodels
Represents the drift detection mode.
ConditionalDistribution - Class in eu.amidst.core.distribution
This class extends the abstract class Distribution.
ConditionalDistribution() - Constructor for class eu.amidst.core.distribution.ConditionalDistribution
 
ConditionalLinearGaussian - Class in eu.amidst.core.distribution
This class extends the abstract class ConditionalDistribution.
ConditionalLinearGaussian(Variable, List<Variable>) - Constructor for class eu.amidst.core.distribution.ConditionalLinearGaussian
Creates a new ConditionalLinearGaussian distribution for a given variable and the list of its parents.
connectDBN - Static variable in class eu.amidst.flinklink.examples.reviewMeeting2015.GenerateData
 
constructOptions() - Static method in class eu.amidst.huginlink.examples.demos.ParallelTANDemo
Constructs and provides the Options.
containCycles() - Method in class eu.amidst.core.models.DAG
Tests if this DAG contains cycles.
containCycles() - Method in class eu.amidst.dynamic.models.DynamicDAG
Tests if this DynamicDAG contains cycles.
contains(Variable) - Method in interface eu.amidst.core.models.ParentSet
Tests if a given variable pertains to this parent set.
containsParentsThisDistributionType(List<Variable>, DistributionTypeEnum) - Static method in class eu.amidst.core.variables.DistributionType
Tests whether the list of parents contains this DistributionType.
ConvergenceELBO(double, long) - Constructor for class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ConvergenceELBO
 
ConvergenceELBO(double, long) - Constructor for class eu.amidst.flinklink.core.learning.parametric.dVMP.ConvergenceELBO
 
ConvergenceELBO(double, long) - Constructor for class eu.amidst.flinklink.core.learning.parametric.dVMPv1.ConvergenceELBO
 
ConvergenceELBO(double, long) - Constructor for class eu.amidst.flinklink.core.learning.parametric.ParallelVB.ConvergenceELBO
 
ConvergenceELBObyTime(double, long) - Constructor for class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ConvergenceELBObyTime
 
ConvergenceELBObyTime(double, long) - Constructor for class eu.amidst.flinklink.core.learning.parametric.dVMP.ConvergenceELBObyTime
 
ConvergenceELBObyTime(double, long, int) - Constructor for class eu.amidst.flinklink.core.learning.parametric.dVMPv1.ConvergenceELBObyTime
 
ConvergenceELBObyTime(double, long) - Constructor for class eu.amidst.flinklink.core.learning.parametric.ParallelVB.ConvergenceELBObyTime
 
ConversionToBatches - Class in eu.amidst.flinklink.core.utils
Created by andresmasegosa on 15/9/15.
ConversionToBatches() - Constructor for class eu.amidst.flinklink.core.utils.ConversionToBatches
 
convertAttributes(InstancesHeader) - Static method in class eu.amidst.moalink.converterFromMoaToAmidst.Converter
Creates a set of Attributes from a given InstancesHeader object.
convertAttributes(Enumeration<Attribute>, Attribute) - Static method in class eu.amidst.moalink.converterFromMoaToAmidst.Converter
Creates a set of Attributes, including the class, from a given Enumeration of Attributes and a Attribute.
convertAttributes(Enumeration<Attribute>) - Static method in class eu.amidst.moalink.converterFromMoaToAmidst.Converter
Creates a set of Attributes from a given Enumeration of Attributes.
convertAttributes(Instances) - Static method in class eu.amidst.wekalink.converterFromWekaToAmidst.Converter
Creates a set of Attributes from a given Instances object.
convertAttributes(Enumeration<Attribute>, Attribute) - Static method in class eu.amidst.wekalink.converterFromWekaToAmidst.Converter
Creates a set of Attributes, including the class, from a given Enumeration of Attributes and a Attribute.
convertAttributes(Enumeration<Attribute>) - Static method in class eu.amidst.wekalink.converterFromWekaToAmidst.Converter
Creates a set of Attributes from a given Enumeration of Attributes.
convertDBNtoBN(DynamicBayesianNetwork, int) - Static method in class eu.amidst.dynamic.utils.DynamicToStaticBNConverter
Converts a given DynamicBayesianNetwork to a static BayesianNetwork
Converter - Class in eu.amidst.moalink.converterFromMoaToAmidst
This class converts attributes from MOA to AMIDST format.
Converter() - Constructor for class eu.amidst.moalink.converterFromMoaToAmidst.Converter
 
Converter - Class in eu.amidst.wekalink.converterFromWekaToAmidst
This class converts attributes from MOA to AMIDST format.
Converter() - Constructor for class eu.amidst.wekalink.converterFromWekaToAmidst.Converter
 
convertFilesFromFolder(String) - Static method in class eu.amidst.huginlink.converters.FileConverterFromHuginToAmidst
Converts a set the Hugin model files (dynamic and static) into AMIDST format.
convertToAmidst(Domain) - Static method in class eu.amidst.huginlink.converters.BNConverterToAMIDST
Converts a Bayesian network from Hugin to AMIDST format.
convertToAmidst(Class) - Static method in class eu.amidst.huginlink.converters.DBNConverterToAmidst
Converts a Dynamic Bayesian network from Hugin to AMIDST format.
convertToDynamic(DataFlink<DataInstance>) - Static method in class eu.amidst.flinklink.core.data.DataFlinkConverter
 
convertToHugin(BayesianNetwork) - Static method in class eu.amidst.huginlink.converters.BNConverterToHugin
Converts a Bayesian network from AMIDST to Hugin format.
convertToHugin(DynamicBayesianNetwork) - Static method in class eu.amidst.huginlink.converters.DBNConverterToHugin
Converts a Dynamic Bayesian network from AMIDST to Hugin format.
convertToStatic(DataFlink<DynamicDataInstance>) - Static method in class eu.amidst.flinklink.core.data.DataFlinkConverter
 
copy(EF_Normal.ArrayVectorParameter) - Method in class eu.amidst.core.exponentialfamily.EF_Normal.ArrayVectorParameter
Copies the input source vector to this ArrayVector.
copy(Vector) - Method in class eu.amidst.core.exponentialfamily.EF_Normal.ArrayVectorParameter
Copies the input source Vector to this Vector.
copy(Vector) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
copy(EF_Normal_NormalParents.CompoundVector) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
copy(EF_NormalParameter.ArrayVectorParameter) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter.ArrayVectorParameter
Copies the input source vector to this ArrayVector.
copy(Vector) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter.ArrayVectorParameter
Copies the input source Vector to this Vector.
copy(ParallelMLMissingData.PartialSufficientSatistics) - Method in class eu.amidst.core.learning.parametric.ParallelMLMissingData.PartialSufficientSatistics
 
copy(ArrayVector) - Method in class eu.amidst.core.utils.ArrayVector
Copies the input source vector to this ArrayVector.
copy(Vector) - Method in class eu.amidst.core.utils.ArrayVector
Copies the input source Vector to this Vector.
copy(Vector) - Method in class eu.amidst.core.utils.CompoundVector
Copies the input source Vector to this Vector.
copy(CompoundVector) - Method in class eu.amidst.core.utils.CompoundVector
Copies the input source CompoundVector object to this CompoundVector.
copy(Vector) - Method in class eu.amidst.core.utils.KeyCompoundVector
Copies the input source Vector to this Vector.
copy(KeyCompoundVector) - Method in class eu.amidst.core.utils.KeyCompoundVector
Copies the input source KeyCompoundVector object to this KeyCompoundVector.
copy(Vector) - Method in class eu.amidst.core.utils.SparseVector
 
copy(Vector) - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
copy(Vector) - Method in interface eu.amidst.core.utils.Vector
Copies the input source Vector to this Vector.
copy(Vector) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
copy(EF_DynamicBayesianNetwork.DynamiceBNCompoundVector) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
CountAppender() - Constructor for class eu.amidst.flinklink.examples.misc.KMeans.CountAppender
 
COUNTER_NAME - Static variable in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood
 
COUNTER_NAME - Static variable in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood2
 
createAttributeFromLine(int, String) - Static method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataReader
Creates an Attribute from a given index and line.
createBN(int) - Static method in class eu.amidst.flinklink.examples.reviewMeeting2015.GenerateData
 
CreateCajamarDataContinuous - Class in eu.amidst.tutorial.usingAmidst.examples
Created by rcabanas on 20/05/16.
CreateCajamarDataContinuous() - Constructor for class eu.amidst.tutorial.usingAmidst.examples.CreateCajamarDataContinuous
 
CreateDataSet - Class in eu.amidst.tutorial.usingAmidst.examples
Created by rcabanas on 20/05/16.
CreateDataSet() - Constructor for class eu.amidst.tutorial.usingAmidst.examples.CreateDataSet
 
createDataSets(List<String>, List<String>, int, int, int) - Static method in class eu.amidst.flinklink.examples.reviewMeeting2015.GenerateData
 
createDataSetsDBN(List<String>, List<String>, int, int, int) - Static method in class eu.amidst.flinklink.examples.reviewMeeting2015.GenerateData
 
createDBN1(int) - Static method in class eu.amidst.flinklink.examples.reviewMeeting2015.GenerateData
 
createDynamicNaiveBayes(Attributes) - Static method in class eu.amidst.flinklink.examples.reviewMeeting2015.DynamicNaiveBayes
 
createDynamicNaiveBayesWithHidden(Attributes) - Static method in class eu.amidst.flinklink.examples.reviewMeeting2015.DynamicNaiveBayes
 
createEmptyZeroedVector() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Returns an empty zeroed parameter vector (i.e., a vector filled with zeros).
createEmtpyCompoundVector() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Creates an empty compound parameter vector.
createInitPartialSufficientStatistics(EF_BayesianNetwork) - Static method in class eu.amidst.core.learning.parametric.ParallelMLMissingData.PartialSufficientSatistics
 
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Dirichlet
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_InverseGamma
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createInitSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
createInitSufficientStatistics() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
 
createInitSufficientStatistics() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Creates the initial sufficient statistics vector (i.e., a vector with the initial counts).
createZeroMomentParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Creates a zero moment parameter vector (i.e., a vector filled with zeros).
createZeroMomentParameters() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Creates a zero moment parameter vector (i.e., a vector filled with zeros).
createZeroNaturalParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Creates a zero natural parameter vector (i.e., a vector filled with zeros).
createZeroNaturalParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Creates a zero natural parameter vector (i.e., a vector filled with zeros).
createZeroNaturalParameters() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Creates a zero natural parameter vector (i.e., a vector filled with zeros).
createZeroNaturalParameters() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Creates a zero natural parameter vector (i.e., a vector filled with zeros).
createZeroPartialSufficientStatistics(EF_BayesianNetwork) - Static method in class eu.amidst.core.learning.parametric.ParallelMLMissingData.PartialSufficientSatistics
 
createZeroSufficientStatistics() - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Creates a zero sufficient statistics vector (i.e., a vector filled with zeros).
createZeroSufficientStatistics() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Creates a zero sufficient statistics vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_Dirichlet
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_InverseGamma
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
createZeroVector() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Creates a zero vector (i.e., a vector filled with zeros).
createZeroVector() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Creates a zero vector (i.e., a vector filled with zeros).
CreatingBayesianNetworks - Class in eu.amidst.core.examples.models
In this example, we take a data set, create a BN and we compute the log-likelihood of all the samples of this data set.
CreatingBayesianNetworks() - Constructor for class eu.amidst.core.examples.models.CreatingBayesianNetworks
 
CreatingBayesianNetworksWithLatentVariables - Class in eu.amidst.core.examples.models
In this example, we simply show how to create a BN model with latent variables.
CreatingBayesianNetworksWithLatentVariables() - Constructor for class eu.amidst.core.examples.models.CreatingBayesianNetworksWithLatentVariables
 
CreatingDBNs - Class in eu.amidst.dynamic.examples.models
This example creates a dynamic BN from a dynamic data stream, with randomly generated probability distributions, then saves it to a file.
CreatingDBNs() - Constructor for class eu.amidst.dynamic.examples.models.CreatingDBNs
 
CreatingDBNsWithLatentVariables - Class in eu.amidst.dynamic.examples.models
In this example, we show how to create a DBN model with latent variables.
CreatingDBNsWithLatentVariables() - Constructor for class eu.amidst.dynamic.examples.models.CreatingDBNsWithLatentVariables
 
current() - Method in class eu.amidst.core.utils.LocalRandomGenerator
Returns a new random number generator using the next pseudorandom integer.
CustomGaussianMixture - Class in eu.amidst.latentvariablemodels.staticmodels
Created by rcabanas on 23/05/16.
CustomGaussianMixture(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.CustomGaussianMixture
 
CustomGaussianMixture - Class in eu.amidst.tutorial.usingAmidst.practice
Created by rcabanas on 23/05/16.
CustomGaussianMixture(Attributes) - Constructor for class eu.amidst.tutorial.usingAmidst.practice.CustomGaussianMixture
 
CustomKalmanFilter - Class in eu.amidst.tutorial.usingAmidst.practice
Created by rcabanas on 23/05/16.
CustomKalmanFilter(Attributes) - Constructor for class eu.amidst.tutorial.usingAmidst.practice.CustomKalmanFilter
 

D

dag - Variable in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.core.learning.parametric.bayesian.SVB
Represents a directed acyclic graph DAG.
dag - Variable in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Represents the directed acyclic graph DAG.
DAG - Class in eu.amidst.core.models
The DAG class represents the Directed Acyclic Graph of a BayesianNetwork.
DAG(Variables) - Constructor for class eu.amidst.core.models.DAG
Creates a new DAG from a set of variables.
dag - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Represents the directed acyclic graph DynamicDAG.
dag - Variable in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.flinklink.core.learning.parametric.dVMP
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood2
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
Represents the directed acyclic graph DAG.
dag - Variable in class eu.amidst.latentvariablemodels.staticmodels.Model
 
dag - Variable in class eu.amidst.sparklink.core.learning.ParallelMaximumLikelihood
Represents the directed acyclic graph DAG.
DAGGenerator - Class in eu.amidst.core.utils
This class contains several utility methods for generating specific kind of DAGs.
DAGGenerator() - Constructor for class eu.amidst.core.utils.DAGGenerator
 
Daimler_LE_acceleration() - Static method in class eu.amidst.dynamic.examples.models.DaimlerModels
In this example we show how to create an OOBN fragment for the LE hypothesis with a hidden node for acceleration (as in Figure 4.14 of D2.1).
DaimlerModels - Class in eu.amidst.dynamic.examples.models
This class contains examples about how we can create Daimler's dynamic models using the AMIDST Toolbox.
DataFileReader - Interface in eu.amidst.core.datastream.filereaders
This interface defines a data file reader.
DataFileWriter - Interface in eu.amidst.core.datastream.filereaders
This interface defines a data file writer.
DataFlink<T extends DataInstance> - Interface in eu.amidst.flinklink.core.data
Created by andresmasegosa on 8/9/15.
dataFlink - Variable in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
Represents the DataFlink used for learning the parameters.
DataFlinkConverter - Class in eu.amidst.flinklink.core.data
Created by andresmasegosa on 22/09/15.
DataFlinkConverter() - Constructor for class eu.amidst.flinklink.core.data.DataFlinkConverter
 
DataFlinkLoader - Class in eu.amidst.flinklink.core.io
Created by andresmasegosa on 1/9/15.
DataFlinkLoader() - Constructor for class eu.amidst.flinklink.core.io.DataFlinkLoader
 
DataFlinkWriter - Class in eu.amidst.flinklink.core.io
Created by andresmasegosa on 23/9/15.
DataFlinkWriter() - Constructor for class eu.amidst.flinklink.core.io.DataFlinkWriter
 
DataInstance - Interface in eu.amidst.core.datastream
The DataInstance interface represents a data sample.
dataInstanceCount - Variable in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Represents the data instance count.
dataInstanceCount - Variable in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Represents the data instance count.
dataInstanceCount - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Represents the data instance count.
dataInstanceCount - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Represents the data instance count.
DataInstanceFromAssignment(long, Assignment, Attributes, List<Variable>) - Constructor for class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.DataInstanceFromAssignment
 
DataInstanceFromAssignment(Assignment, Attributes, List<Variable>) - Constructor for class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments.DataInstanceFromAssignment
 
DataInstanceFromDataRow - Class in eu.amidst.core.datastream.filereaders
This class implements the DataInstance interface and handles the operations related to the data instances.
DataInstanceFromDataRow(DataRow) - Constructor for class eu.amidst.core.datastream.filereaders.DataInstanceFromDataRow
Creates a new DataInstanceImpl from a given DataRow object.
dataInstanceToARFFString(DataInstance) - Static method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataWriter
Converts a DataInstance object to an ARFF format String.
dataInstanceToARFFString(Attributes, DataInstance) - Static method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataWriter
Converts a DataInstance object to an ARFF format String.
dataInstanceToARFFString(Attribute, DataInstance, String) - Static method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataWriter
 
DataOnMemory<E extends DataInstance> - Interface in eu.amidst.core.datastream
The DataOnMemory interface is a specialization of the DataStream interface that keeps all the data on main memory.
DataOnMemoryFromFile - Class in eu.amidst.core.datastream.filereaders
This class implements the interface DataOnMemory and produces DataOnMemory objects from a given file.
DataOnMemoryFromFile(DataFileReader) - Constructor for class eu.amidst.core.datastream.filereaders.DataOnMemoryFromFile
Creates a new DataOnMemoryFromFile from a given DataFileReader object.
DataOnMemoryListContainer<E extends DataInstance> - Class in eu.amidst.core.datastream
The DataOnMemoryListContainer class implements the DataOnMemory interface.
DataOnMemoryListContainer(Attributes) - Constructor for class eu.amidst.core.datastream.DataOnMemoryListContainer
Creates a new DataOnMemoryListContainer initialized with the Attributes object of the data set.
DataOnMemoryListContainer(Attributes, List<E>) - Constructor for class eu.amidst.core.datastream.DataOnMemoryListContainer
Creates a new DataOnMemoryListContainer initialized with the Attributes object of the data set.
DataOnMemoryListContainerSerializable<E extends DataInstance> - Class in eu.amidst.sparklink.core.data
The DataOnMemoryListContainer class implements the DataOnMemory interface.
DataOnMemoryListContainerSerializable(Attributes) - Constructor for class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Creates a new DataOnMemoryListContainer initialized with the Attributes object of the data set.
DataOnMemoryListContainerSerializable(Attributes, List<E>) - Constructor for class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Creates a new DataOnMemoryListContainer initialized with the Attributes object of the data set.
DataPosterior - Class in eu.amidst.core.learning.parametric.bayesian.utils
This class stores the posterior probabilities over a set of latent variables for an item with a given id.
DataPosterior(long, List<UnivariateDistribution>) - Constructor for class eu.amidst.core.learning.parametric.bayesian.utils.DataPosterior
Creates a new object using a given id_ and a list of univariate posteriors.
DataPosteriorAssignment - Class in eu.amidst.core.learning.parametric.bayesian.utils
This class stores the posterior probabilities over a set of latent variables and a joint assignment for another ones.
DataPosteriorAssignment(DataPosterior, Assignment) - Constructor for class eu.amidst.core.learning.parametric.bayesian.utils.DataPosteriorAssignment
 
DataPosteriorInstance(DataPosteriorAssignment, DynamicDataInstance) - Constructor for class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.DataPosteriorInstance
 
DataRow - Interface in eu.amidst.core.datastream.filereaders
This interface defines a row of the data matrix.
DataRowMissing - Class in eu.amidst.core.datastream.filereaders
This class implements the DataRow interface.
DataRowMissing() - Constructor for class eu.amidst.core.datastream.filereaders.DataRowMissing
 
DataRowSpark - Class in eu.amidst.sparklink.core.data
Created by jarias on 21/06/16.
DataRowSpark(double[], Attributes) - Constructor for class eu.amidst.sparklink.core.data.DataRowSpark
 
DataRowWeka - Class in eu.amidst.core.datastream.filereaders.arffFileReader
This class implements the interface DataRow and defines a Weka data row.
DataRowWeka(Attributes, String) - Constructor for class eu.amidst.core.datastream.filereaders.arffFileReader.DataRowWeka
Creates a new DataRowWeka from a given line and list of attributes.
DataRowWeka - Class in eu.amidst.moalink.converterFromMoaToAmidst
This class implements the interface DataRow and defines a row of the data in WEKA format.
DataRowWeka(Instance, Attributes) - Constructor for class eu.amidst.moalink.converterFromMoaToAmidst.DataRowWeka
Creates a new DataRowWeka from a given instance and a set of attributes.
DataRowWeka - Class in eu.amidst.wekalink.converterFromWekaToAmidst
This class implements the interface DataRow and defines a row of the data in WEKA format.
DataRowWeka(Instance, Attributes) - Constructor for class eu.amidst.wekalink.converterFromWekaToAmidst.DataRowWeka
Creates a new DataRowWeka from a given instance and a set of attributes.
DataSequence - Interface in eu.amidst.dynamic.datastream
The DataSequence interface represents a data sequence.
DataSequenceSpliterator - Class in eu.amidst.dynamic.datastream
The DataSequenceSpliterator class implements a Spliterator of DataSequence.
DataSequenceSpliterator(DataStream<DynamicDataInstance>, long) - Constructor for class eu.amidst.dynamic.datastream.DataSequenceSpliterator
Creates a new DataSequenceSpliterator.
DataSequenceSpliterator(DataStream<DynamicDataInstance>) - Constructor for class eu.amidst.dynamic.datastream.DataSequenceSpliterator
Creates a new DataSequenceSpliterator.
DataSequenceStream - Class in eu.amidst.dynamic.datastream
The DataSequenceStream class defines a Stream of DataSequence.
DataSequenceStream() - Constructor for class eu.amidst.dynamic.datastream.DataSequenceStream
 
DataSetGenerator - Class in eu.amidst.core.utils
This class aims at generate randomly a data set with some given features as number of samples, number of continuous and number of discrete variables.
DataSetGenerator() - Constructor for class eu.amidst.core.utils.DataSetGenerator
 
DataSetGenerator - Class in eu.amidst.dynamic.utils
This class aims at generate randomly a data set of dynamic data instances with some given features as number of samples, number of continuous and number of discrete variables.
DataSetGenerator() - Constructor for class eu.amidst.dynamic.utils.DataSetGenerator
 
DataSetGenerator - Class in eu.amidst.flinklink.core.utils
Created by rcabanas on 10/06/16.
DataSetGenerator() - Constructor for class eu.amidst.flinklink.core.utils.DataSetGenerator
 
DataSetGenerator - Class in eu.amidst.sparklink.core.util
Created by rcabanas on 30/09/16.
DataSetGenerator() - Constructor for class eu.amidst.sparklink.core.util.DataSetGenerator
 
DataSetSerializer - Class in eu.amidst.flinklink.core.io
Created by andresmasegosa on 16/9/15.
DataSetSerializer() - Constructor for class eu.amidst.flinklink.core.io.DataSetSerializer
 
dataSetSize - Variable in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
DataSpark - Interface in eu.amidst.sparklink.core.data
Created by jarias on 22/06/16.
dataSpark - Variable in class eu.amidst.sparklink.core.learning.ParallelMaximumLikelihood
Represents the DataSpark used for learning the parameters.
DataSparkFromDataFrame - Class in eu.amidst.sparklink.core.data
Created by jarias on 20/06/16.
DataSparkFromDataFrame(DataFrame) - Constructor for class eu.amidst.sparklink.core.data.DataSparkFromDataFrame
 
DataSparkFromDataStream - Class in eu.amidst.sparklink.core.data
Created by rcabanas on 30/09/16.
DataSparkFromDataStream(DataStream<DataInstance>, JavaSparkContext) - Constructor for class eu.amidst.sparklink.core.data.DataSparkFromDataStream
 
DataSparkFromRDD - Class in eu.amidst.sparklink.core.data
Created by jarias on 22/06/16.
DataSparkFromRDD(JavaRDD<DataInstance>, Attributes) - Constructor for class eu.amidst.sparklink.core.data.DataSparkFromRDD
 
DataSparkLoader - Class in eu.amidst.sparklink.core.io
Created by jarias and rcabanas on 22/06/16.
DataSparkLoader() - Constructor for class eu.amidst.sparklink.core.io.DataSparkLoader
 
DataSparkWriter - Class in eu.amidst.sparklink.core.io
Created by rcabanas on 23/9/15.
DataSparkWriter() - Constructor for class eu.amidst.sparklink.core.io.DataSparkWriter
 
DataStream<E extends DataInstance> - Interface in eu.amidst.core.datastream
The DataStream class is an interface for dealing with data streams.
dataStream - Variable in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
Represents the DataStream used for learning the parameters.
dataStream - Variable in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Represents the DataStream used for learning the parameters.
dataStream - Variable in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Represents the DataStream used for learning the parameters.
dataStream - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Represents the DataStream used for learning the parameters.
dataStream - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Represents the DataStream used for learning the parameters.
DataStreamFromFile - Class in eu.amidst.core.datastream.filereaders
This class implements the DataStream interface and produces DataStream objects from a given file.
DataStreamFromFile(DataFileReader) - Constructor for class eu.amidst.core.datastream.filereaders.DataStreamFromFile
Creates a new DataStreamFromFile from a given DataFileReader object.
DataStreamFromStreamOfAssignments - Class in eu.amidst.flinklink.core.utils
Created by andresmasegosa on 24/9/15.
DataStreamFromStreamOfAssignments(Variables, Stream<Assignment>) - Constructor for class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments
 
DataStreamFromStreamOfAssignments.DataInstanceFromAssignment - Class in eu.amidst.flinklink.core.utils
 
DataStreamIOExample - Class in eu.amidst.core.examples.io
In this example we show how to load and save data sets from ".arff" files (http://www.cs.waikato.ac.nz/ml/weka/arff.html) Created by andresmasegosa on 18/6/15.
DataStreamIOExample() - Constructor for class eu.amidst.core.examples.io.DataStreamIOExample
 
DataStreamLoader - Class in eu.amidst.core.io
This class allows to load a DataStream from disk.
DataStreamLoader() - Constructor for class eu.amidst.core.io.DataStreamLoader
 
DataStreamLoaderExample - Class in eu.amidst.flinklink.examples.io
Created by rcabanas on 10/06/16.
DataStreamLoaderExample() - Constructor for class eu.amidst.flinklink.examples.io.DataStreamLoaderExample
 
DataStreamLoaderExample - Class in eu.amidst.sparklink.examples.io
Created by rcabanas on 10/06/16.
DataStreamLoaderExample() - Constructor for class eu.amidst.sparklink.examples.io.DataStreamLoaderExample
 
DataStreamOperations - Class in eu.amidst.core.examples.datastream
Created by rcabanas on 24/10/16.
DataStreamOperations() - Constructor for class eu.amidst.core.examples.datastream.DataStreamOperations
 
DataStreamsExample - Class in eu.amidst.core.examples.datastream
An example showing how to use the main features of a DataStream object.
DataStreamsExample() - Constructor for class eu.amidst.core.examples.datastream.DataStreamsExample
 
DataStreamsExample - Class in eu.amidst.dynamic.examples.datastream
An example showing how to load an use a DataStream object.
DataStreamsExample() - Constructor for class eu.amidst.dynamic.examples.datastream.DataStreamsExample
 
DataStreamWriter - Class in eu.amidst.core.io
This class allows to save a DataStream in a file.
DataStreamWriter() - Constructor for class eu.amidst.core.io.DataStreamWriter
 
DataStreamWriterExample - Class in eu.amidst.flinklink.examples.io
Created by rcabanas on 09/06/16.
DataStreamWriterExample() - Constructor for class eu.amidst.flinklink.examples.io.DataStreamWriterExample
 
DataStreamWriterExample - Class in eu.amidst.sparklink.examples.io
Created by rcabanas on 30/09/16.
DataStreamWriterExample() - Constructor for class eu.amidst.sparklink.examples.io.DataStreamWriterExample
 
DBNConverterToAmidst - Class in eu.amidst.huginlink.converters
The DBNConverterToAmidst class converts a Dynamic Bayesian network model from Hugin to AMIDST.
DBNConverterToAmidst(Class) - Constructor for class eu.amidst.huginlink.converters.DBNConverterToAmidst
Class constructor.
DBNConverterToHugin - Class in eu.amidst.huginlink.converters
The DBNConverterToHugin class converts a Dynamic Bayesian network model from AMIDST to Hugin.
DBNConverterToHugin() - Constructor for class eu.amidst.huginlink.converters.DBNConverterToHugin
Class constructor.
DBNLoaderFromHugin - Class in eu.amidst.huginlink.io
This class is a loader of dynamic Bayesian networks in AMIDST format from Hugin files.
DBNLoaderFromHugin() - Constructor for class eu.amidst.huginlink.io.DBNLoaderFromHugin
 
DBNSampler - Class in eu.amidst.flinklink.core.utils
Created by andresmasegosa on 24/9/15.
DBNSampler(DynamicBayesianNetwork) - Constructor for class eu.amidst.flinklink.core.utils.DBNSampler
 
deactivateTransitionMethod() - Method in class eu.amidst.core.conceptdrift.NaiveBayesVirtualConceptDriftDetector
 
debug - Variable in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Represents if the class is in debug mode
debug - Variable in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Represents if the class is in debug mode
debug - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Represents if the class is in debug mode
debug - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Represents if the class is in debug mode
decimalFormat - Static variable in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataWriter
 
deepCopy(Variable) - Method in class eu.amidst.core.distribution.DeltaDistribution
 
deepCopy(Variable) - Method in class eu.amidst.core.distribution.Multinomial
 
deepCopy(Variable) - Method in class eu.amidst.core.distribution.Normal
 
deepCopy(Variable) - Method in class eu.amidst.core.distribution.Uniform
 
deepCopy(Variable) - Method in class eu.amidst.core.distribution.UnivariateDistribution
Returns a deep copy of this UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_Dirichlet
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_InverseGamma
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Creates a deep copy of this this EF_UnivariateDistribution.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Creates a deep copy of this this EF_UnivariateDistribution.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
deepCopy(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns a deep copy of this EF_UnivariateDistribution and changes the current main variable to the one given as input parameter.
deepCopy() - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Creates a deep copy of this this EF_UnivariateDistribution.
deepCopy(T) - Static method in class eu.amidst.core.utils.Serialization
Performs a deep copy of a given object by serialization.
DELTA - Static variable in class eu.amidst.core.exponentialfamily.EF_InverseGamma
 
DeltaDistribution - Class in eu.amidst.core.distribution
This class extends the abstract class UnivariateDistribution and defines the Delta distribution.
DeltaDistribution(Variable, double) - Constructor for class eu.amidst.core.distribution.DeltaDistribution
Creates a new DeltaDistribution for a given variable and delta value.
demo() - Static method in class eu.amidst.huginlink.examples.demos.InferenceDemo
The demo for the Cajamar data set.
demoLive() - Static method in class eu.amidst.huginlink.examples.demos.ParallelTANDemo
 
demoLuxembourg() - Static method in class eu.amidst.huginlink.examples.demos.ParallelTANDemo
 
demoOnServer() - Static method in class eu.amidst.huginlink.examples.demos.ParallelTANDemo
 
demoPigs() - Static method in class eu.amidst.huginlink.examples.demos.ParallelTANDemo
 
desactiveParametersNodes() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
deserializeDataSet(String) - Static method in class eu.amidst.flinklink.core.io.DataSetSerializer
 
deserializeObject(byte[]) - Static method in class eu.amidst.core.utils.Serialization
Deserilizes an object from an array of bytes.
DirichletParameterType - Class in eu.amidst.core.variables.distributionTypes
This class extends the abstract class DistributionType and defines the Dirichlet parameter type.
DirichletParameterType(Variable) - Constructor for class eu.amidst.core.variables.distributionTypes.DirichletParameterType
Creates a new DirichletParameterType for the given variable.
DistributedVI - Class in eu.amidst.flinklink.core.learning.parametric
This class implements the ParameterLearningAlgorithm interface, and defines the parallel Maximum Likelihood algorithm.
DistributedVI() - Constructor for class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
DistributedVI.ConvergenceELBO - Class in eu.amidst.flinklink.core.learning.parametric
 
DistributedVI.ConvergenceELBObyTime - Class in eu.amidst.flinklink.core.learning.parametric
 
DistributedVI.ParallelVBMap - Class in eu.amidst.flinklink.core.learning.parametric
 
DistributedVI.ParallelVBMapInference - Class in eu.amidst.flinklink.core.learning.parametric
 
DistributedVI.ParallelVBMapInferenceAssignment - Class in eu.amidst.flinklink.core.learning.parametric
 
DistributedVI.ParallelVBReduce - Class in eu.amidst.flinklink.core.learning.parametric
 
DistributedVIExample - Class in eu.amidst.flinklink.examples.learning
Created by rcabanas on 14/06/16.
DistributedVIExample() - Constructor for class eu.amidst.flinklink.examples.learning.DistributedVIExample
 
Distribution - Class in eu.amidst.core.distribution
This class defines and handles Distribution.
Distribution() - Constructor for class eu.amidst.core.distribution.Distribution
 
distributionForInstance(Instance) - Method in class weka.classifiers.bayes.AmidstClassifier
Calculates the class membership probabilities for the given test instance.
distributionForInstance(Instance) - Method in class weka.clusterers.AmidstClustering
 
DistributionType - Class in eu.amidst.core.variables
This class handles the different distribution types supported in AMIDST.
DistributionType(Variable) - Constructor for class eu.amidst.core.variables.DistributionType
Creates a new DistributionType for the given variable.
DistributionTypeEnum - Enum in eu.amidst.core.variables
This class defines the different distribution types supported in AMIDST toolbox.
div(long) - Method in class eu.amidst.flinklink.examples.misc.KMeans.Point
 
divideBy(double) - Method in class eu.amidst.core.exponentialfamily.EF_Normal.ArrayVectorParameter
Updates the values of this Vector via dividing its initial values by an input double value.
divideBy(double) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
divideBy(double) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter.ArrayVectorParameter
Updates the values of this Vector via dividing its initial values by an input double value.
divideBy(double) - Method in class eu.amidst.core.utils.ArrayVector
Updates the values of this Vector via dividing its initial values by an input double value.
divideBy(double) - Method in class eu.amidst.core.utils.CompoundVector
Updates the values of this Vector via dividing its initial values by an input double value.
divideBy(double) - Method in class eu.amidst.core.utils.KeyCompoundVector
Updates the values of this Vector via dividing its initial values by an input double value.
divideBy(double) - Method in class eu.amidst.core.utils.SparseVector
 
divideBy(double) - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
divideBy(double) - Method in interface eu.amidst.core.utils.Vector
Updates the values of this Vector via dividing its initial values by an input double value.
divideBy(double) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
doesItReadThisFile(String) - Method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataFolderReader
Tests if this DataFileReader could read the given filename.
doesItReadThisFile(String) - Method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataReader
Tests if this DataFileReader could read the given filename.
doesItReadThisFile(String) - Method in interface eu.amidst.core.datastream.filereaders.DataFileReader
Tests if this DataFileReader could read the given filename.
domainObject - Variable in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Represnts the expanded dynamic model over which the inference is performed.
dotProduct(Vector) - Method in class eu.amidst.core.exponentialfamily.EF_Normal.ArrayVectorParameter
Returns the dot product of this Vector and an input vector, defined as the sumNonStateless of the pairwise products of the values of the two vectors.
dotProduct(Vector) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
dotProduct(EF_Normal_NormalParents.CompoundVector) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
dotProduct(Vector) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter.ArrayVectorParameter
Returns the dot product of this Vector and an input vector, defined as the sumNonStateless of the pairwise products of the values of the two vectors.
dotProduct(Vector) - Method in class eu.amidst.core.utils.ArrayVector
Returns the dot product of this Vector and an input vector, defined as the sumNonStateless of the pairwise products of the values of the two vectors.
dotProduct(SparseVectorDefaultValue) - Method in class eu.amidst.core.utils.ArrayVector
 
dotProduct(ArrayVector) - Method in class eu.amidst.core.utils.ArrayVector
 
dotProduct(Vector) - Method in class eu.amidst.core.utils.CompoundVector
Returns the dot product of this Vector and an input vector, defined as the sumNonStateless of the pairwise products of the values of the two vectors.
dotProduct(CompoundVector) - Method in class eu.amidst.core.utils.CompoundVector
Returns the dot product of this CompoundVector and an input CompoundVector, defined as the sumNonStateless of the pairwise products of the values of the two CompoundVectors.
dotProduct(Vector) - Method in class eu.amidst.core.utils.KeyCompoundVector
Returns the dot product of this Vector and an input vector, defined as the sumNonStateless of the pairwise products of the values of the two vectors.
dotProduct(KeyCompoundVector) - Method in class eu.amidst.core.utils.KeyCompoundVector
Returns the dot product of this KeyCompoundVector and an input KeyCompoundVector, defined as the sumNonStateless of the pairwise products of the values of the two KeyCompoundVectors.
dotProduct(Vector) - Method in class eu.amidst.core.utils.SparseVector
 
dotProduct(Vector) - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
dotProduct(Vector) - Method in interface eu.amidst.core.utils.Vector
Returns the dot product of this Vector and an input vector, defined as the sumNonStateless of the pairwise products of the values of the two vectors.
dotProduct(Vector) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
dotProduct(EF_DynamicBayesianNetwork.DynamiceBNCompoundVector) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
DriftSVB - Class in eu.amidst.core.learning.parametric.bayesian
Created by andresmasegosa on 14/4/16.
DriftSVB() - Constructor for class eu.amidst.core.learning.parametric.bayesian.DriftSVB
 
dVMP - Class in eu.amidst.flinklink.core.learning.parametric
This class implements the ParameterLearningAlgorithm interface, and defines the parallel Maximum Likelihood algorithm.
dVMP() - Constructor for class eu.amidst.flinklink.core.learning.parametric.dVMP
 
dVMP.ConvergenceELBO - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMP.ConvergenceELBObyTime - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMP.ParallelVBMap - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMP.ParallelVBMapInference - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMP.ParallelVBMapInferenceAssignment - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMP.ParallelVBReduce - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMP_LDA - Class in eu.amidst.lda.flink
Created by andresmasegosa on 12/5/16.
dVMP_LDA() - Constructor for class eu.amidst.lda.flink.dVMP_LDA
 
dVMPExample - Class in eu.amidst.flinklink.examples.learning
Created by rcabanas on 14/06/16.
dVMPExample() - Constructor for class eu.amidst.flinklink.examples.learning.dVMPExample
 
dVMPv1 - Class in eu.amidst.flinklink.core.learning.parametric
This class implements the ParameterLearningAlgorithm interface, and defines the parallel Maximum Likelihood algorithm.
dVMPv1() - Constructor for class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
dVMPv1.ConvergenceELBO - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMPv1.ConvergenceELBObyTime - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMPv1.ParallelVBMap - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMPv1.ParallelVBMapInference - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMPv1.ParallelVBMapInferenceAssignment - Class in eu.amidst.flinklink.core.learning.parametric
 
dVMPv1.ParallelVBReduce - Class in eu.amidst.flinklink.core.learning.parametric
 
DynamicAssignment - Interface in eu.amidst.dynamic.variables
This interface extends the interface Assignment and defines a collection of assignments to dynamic variables.
DynamicBayesianNetwork - Class in eu.amidst.dynamic.models
The DynamicBayesianNetwork class represents a Bayesian network model.
DynamicBayesianNetwork(DynamicDAG) - Constructor for class eu.amidst.dynamic.models.DynamicBayesianNetwork
Creates a new DynamicBayesianNetwork from a DynamicDAG object.
DynamicBayesianNetwork(DynamicDAG, List<ConditionalDistribution>, List<ConditionalDistribution>) - Constructor for class eu.amidst.dynamic.models.DynamicBayesianNetwork
Creates a new DynamicBayesianNetwork from a DynamicDAG and a list of distributions for both at Time 0 and T.
DynamicBayesianNetworkGenerator - Class in eu.amidst.dynamic.utils
This class defines a random DynamicBayesianNetwork generator.
DynamicBayesianNetworkGenerator() - Constructor for class eu.amidst.dynamic.utils.DynamicBayesianNetworkGenerator
 
DynamicBayesianNetworkLoader - Class in eu.amidst.dynamic.io
This class allows to load a DynamicBayesianNetwork model from a file.
DynamicBayesianNetworkLoader() - Constructor for class eu.amidst.dynamic.io.DynamicBayesianNetworkLoader
 
DynamicBayesianNetworkSampler - Class in eu.amidst.dynamic.utils
This class implements the interface AmidstOptionsHandler.
DynamicBayesianNetworkSampler(DynamicBayesianNetwork) - Constructor for class eu.amidst.dynamic.utils.DynamicBayesianNetworkSampler
Creates a new DynamicBayesianNetworkSampler given an input DynamicBayesianNetwork object.
DynamicBayesianNetworkSamplerExample - Class in eu.amidst.dynamic.examples.utils
This example shows how to use the DynamicBayesianNetworkSampler class to randomly generate a data sample for a given Dynamic Bayesian network.
DynamicBayesianNetworkSamplerExample() - Constructor for class eu.amidst.dynamic.examples.utils.DynamicBayesianNetworkSamplerExample
 
DynamicBayesianNetworkWriter - Class in eu.amidst.dynamic.io
This class allows to save a DynamicBayesianNetwork model in a file.
DynamicBayesianNetworkWriter() - Constructor for class eu.amidst.dynamic.io.DynamicBayesianNetworkWriter
 
DynamicBayesianNetworkWriterToHugin - Class in eu.amidst.huginlink.io
This class is a writer of dynamic Bayesian networks in AMIDST format to Hugin files.
DynamicBayesianNetworkWriterToHugin() - Constructor for class eu.amidst.huginlink.io.DynamicBayesianNetworkWriterToHugin
 
DynamicClassifier<T extends DynamicClassifier> - Class in eu.amidst.latentvariablemodels.dynamicmodels.classifiers
The DynamicClassifier abstract class is defined for dynamic Bayesian classification models.
DynamicClassifier(Attributes) - Constructor for class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicClassifier
 
DynamicDAG - Class in eu.amidst.dynamic.models
The DynamicDAG class defines the graphical structure a DynamicBayesianNetwork.
DynamicDAG(DynamicVariables) - Constructor for class eu.amidst.dynamic.models.DynamicDAG
Creates a new DynamicDAG from a set of given dynamic variables.
dynamicDAG - Variable in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
DynamicDataInstance - Interface in eu.amidst.dynamic.datastream
The DynamicDataInstance interface represents a dynamic data sample.
DynamicDataInstanceSpliterator - Class in eu.amidst.dynamic.datastream.filereaders
The DynamicDataInstanceSpliterator class defines a Spliterator over DynamicDataInstance elements.
DynamicDataInstanceSpliterator(DataFileReader) - Constructor for class eu.amidst.dynamic.datastream.filereaders.DynamicDataInstanceSpliterator
Creates a new DynamicDataInstanceSpliterator given a valid DataFileReader object.
DynamicDataOnMemoryFromFile - Class in eu.amidst.dynamic.datastream.filereaders
The DynamicDataStreamFromFile class implements the DataOnMemory interface and loads a dynamic data on memory from a given file.
DynamicDataOnMemoryFromFile(DataFileReader) - Constructor for class eu.amidst.dynamic.datastream.filereaders.DynamicDataOnMemoryFromFile
Creates a new DynamicDataOnMemoryFromFile.
DynamicDataSets - Class in eu.amidst.flinklink.examples.misc
Created by Hanen on 08/10/15.
DynamicDataSets() - Constructor for class eu.amidst.flinklink.examples.misc.DynamicDataSets
 
DynamicDataStreamFromFile - Class in eu.amidst.dynamic.datastream.filereaders
The DynamicDataStreamFromFile class implements the DataStream interface and loads a dynamic data stream from a given file.
DynamicDataStreamFromFile(DataFileReader) - Constructor for class eu.amidst.dynamic.datastream.filereaders.DynamicDataStreamFromFile
Creates a new DynamicDataStreamFromFile object.
DynamicDataStreamLoader - Class in eu.amidst.dynamic.io
This class allows to load a Dynamic Data Stream from disk.
DynamicDataStreamLoader() - Constructor for class eu.amidst.dynamic.io.DynamicDataStreamLoader
 
DynamiceBNCompoundVector(int) - Constructor for class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
DynamiceBNCompoundVector(Vector, Vector) - Constructor for class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
DynamicHugin_FactoredFrontier - Class in eu.amidst.dynamic.examples.inference
This example shows how to use the Factored Frontier algorithm with the Hugin inference engine as described in Deliverable 3.4 (Section 6).
DynamicHugin_FactoredFrontier() - Constructor for class eu.amidst.dynamic.examples.inference.DynamicHugin_FactoredFrontier
 
DynamicIS_FactoredFrontier - Class in eu.amidst.dynamic.examples.inference
This example shows how to use the Factored Frontier algorithm with Importance Sampling described in Deliverable 3.4 (Section 6).
DynamicIS_FactoredFrontier() - Constructor for class eu.amidst.dynamic.examples.inference.DynamicIS_FactoredFrontier
 
DynamicIS_Scalability - Class in eu.amidst.dynamic.examples.inference
This example shows the scalability with respect to the number of cores of the Factored Frontier algorithm with the Importance Sampling inference engine, as described in Deliverable 3.4 (Section 6).
DynamicIS_Scalability() - Constructor for class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
DynamicLatentClassificationModel - Class in eu.amidst.latentvariablemodels.dynamicmodels.classifiers
This class implements the dynamic latent classification models.
DynamicLatentClassificationModel(Attributes) - Constructor for class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicLatentClassificationModel
 
DynamicMAPInference - Class in eu.amidst.dynamic.examples.inference
This example shows how to use the Dynamic MAP Inference algorithm described in Deliverable 3.4 (Section 6).
DynamicMAPInference() - Constructor for class eu.amidst.dynamic.examples.inference.DynamicMAPInference
 
DynamicMAPInference - Class in eu.amidst.dynamic.inference
This class implements the MAP Inference algorithm for DynamicBayesianNetwork models.
DynamicMAPInference() - Constructor for class eu.amidst.dynamic.inference.DynamicMAPInference
 
DynamicMAPInference.SearchAlgorithm - Enum in eu.amidst.dynamic.inference
Represents the search algorithm to be used.
DynamicModel<T extends DynamicModel> - Class in eu.amidst.latentvariablemodels.dynamicmodels
The DynamicModel abstract class is defined as a superclass to all dynamic standard models (not used for classification, if so, extends DynamicClassifier) Created by andresmasegosa, ana@cs.aau.dk on 04/03/16.
DynamicModel(Attributes) - Constructor for class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
DynamicModelInference - Class in eu.amidst.tutorial.usingAmidst.examples
Created by rcabanas on 23/05/16.
DynamicModelInference() - Constructor for class eu.amidst.tutorial.usingAmidst.examples.DynamicModelInference
 
DynamicModelLearning - Class in eu.amidst.tutorial.usingAmidst.examples
Created by rcabanas on 23/05/16.
DynamicModelLearning() - Constructor for class eu.amidst.tutorial.usingAmidst.examples.DynamicModelLearning
 
DynamicNaiveBayes - Class in eu.amidst.flinklink.examples.reviewMeeting2015
Created by ana@cs.aau.dk on 20/01/16.
DynamicNaiveBayes() - Constructor for class eu.amidst.flinklink.examples.reviewMeeting2015.DynamicNaiveBayes
 
DynamicNaiveBayesClassifier - Class in eu.amidst.dynamic.learning.parametric
This class defines a Dynamic Naive Bayes Classifier model.
DynamicNaiveBayesClassifier() - Constructor for class eu.amidst.dynamic.learning.parametric.DynamicNaiveBayesClassifier
 
DynamicNaiveBayesClassifierDemo - Class in eu.amidst.dynamic.examples.models
Created by andresmasegosa on 15/01/15.
DynamicNaiveBayesClassifierDemo() - Constructor for class eu.amidst.dynamic.examples.models.DynamicNaiveBayesClassifierDemo
 
DynamicParallelVB - Class in eu.amidst.flinklink.core.learning.dynamic
Created by andresmasegosa on 21/09/15.
DynamicParallelVB() - Constructor for class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
DynamicParallelVB.CajaMarLearnMapInferenceAssignment - Class in eu.amidst.flinklink.core.learning.dynamic
 
DynamicParallelVB.DataInstanceFromAssignment - Class in eu.amidst.flinklink.core.learning.dynamic
 
DynamicParallelVB.DataPosteriorInstance - Class in eu.amidst.flinklink.core.learning.dynamic
 
DynamicParallelVB.ParallelVBMap - Class in eu.amidst.flinklink.core.learning.dynamic
 
DynamicParallelVB.ParallelVBTranslate - Class in eu.amidst.flinklink.core.learning.dynamic
 
DynamicParallelVMPExtended - Class in eu.amidst.flinklink.examples.misc
 
DynamicParallelVMPExtended() - Constructor for class eu.amidst.flinklink.examples.misc.DynamicParallelVMPExtended
 
DynamicToStaticBNConverter - Class in eu.amidst.dynamic.utils
This class converts a DynamicBayesianNetwork to a static BayesianNetwork.
DynamicToStaticBNConverter() - Constructor for class eu.amidst.dynamic.utils.DynamicToStaticBNConverter
 
DynamicVariables - Class in eu.amidst.dynamic.variables
The DynamicVariables class defines and handles the operations related to the set of variables of a DynamicBayesianNetwork model.
DynamicVariables() - Constructor for class eu.amidst.dynamic.variables.DynamicVariables
Creates a new DynamicVariables object.
DynamicVariables(Attributes) - Constructor for class eu.amidst.dynamic.variables.DynamicVariables
Creates a new DynamicVariables object given a set of Attributes.
DynamicVariables(Attributes, Map<Attribute, DistributionTypeEnum>) - Constructor for class eu.amidst.dynamic.variables.DynamicVariables
Creates a new DynamicVariables object given a list of Attributes and their corresponding distribution types.
DynamicVariablesExample - Class in eu.amidst.dynamic.examples.variables
This example show the basic functionalities related to dynamic variables.
DynamicVariablesExample() - Constructor for class eu.amidst.dynamic.examples.variables.DynamicVariablesExample
 
DynamicVMP - Class in eu.amidst.dynamic.inference
This class implements the interfaces InferenceAlgorithmForDBN.
DynamicVMP() - Constructor for class eu.amidst.dynamic.inference.DynamicVMP
Creates a new DynamicVMP object.
DynamicVMP_FactoredFrontier - Class in eu.amidst.dynamic.examples.inference
This example shows how to use the Factored Frontier algorithm with Variational Message Passing described in Deliverable 3.4 (Section 6).
DynamicVMP_FactoredFrontier() - Constructor for class eu.amidst.dynamic.examples.inference.DynamicVMP_FactoredFrontier
 

E

EF_BaseDistribution_MultinomialParents<E extends EF_ConditionalDistribution> - Class in eu.amidst.core.exponentialfamily
This class defines conditional distributions with a set of multinomial parents.
EF_BaseDistribution_MultinomialParents(List<Variable>, List<E>) - Constructor for class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Creates a new EF_BaseDistribution_MultinomialParents object from a list of multinomial parents and base distributions.
EF_BaseDistribution_MultinomialParents(List<Variable>, List<E>, boolean) - Constructor for class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Creates a new EF_BaseDistribution_MultinomialParents object from a list of multinomial parents and base distributions.
EF_BayesianNetwork - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_Distribution and defines a BayesianNetwork as a conjugate exponential family (EF) model, consisting of EF distributions in canonical form.
EF_BayesianNetwork() - Constructor for class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Creates a new EF_BayesianNetwork object.
EF_BayesianNetwork(BayesianNetwork) - Constructor for class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Creates a new EF_BayesianNetwork object from a BayesianNetwork object.
EF_BayesianNetwork(DAG) - Constructor for class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Creates a new EF_BayesianNetwork object from a DAG object.
EF_BayesianNetwork(List<ParentSet>) - Constructor for class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Creates a new EF_BayesianNetwork object from a list of ParentSet objects.
EF_ConditionalDistribution - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_Distribution and defines the Conditional Distribution in Exponential Family (EF) form.
EF_ConditionalDistribution() - Constructor for class eu.amidst.core.exponentialfamily.EF_ConditionalDistribution
 
EF_Dirichlet - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines a Dirichlet distribution in Exponential Family (EF) canonical form.
EF_Dirichlet(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_Dirichlet
Creates a new uniform EF_Dirichlet distribution for a given Variable object.
EF_Dirichlet(Variable, double) - Constructor for class eu.amidst.core.exponentialfamily.EF_Dirichlet
Creates a new uniform EF_Dirichlet distribution for a given Variable object with a given scale.
EF_Distribution - Class in eu.amidst.core.exponentialfamily
This class defines an Exponential Family (EF) distribution in canonical form.
EF_Distribution() - Constructor for class eu.amidst.core.exponentialfamily.EF_Distribution
 
EF_DynamicBayesianNetwork - Class in eu.amidst.dynamic.exponentialfamily
This class extends the abstract class EF_DynamicDistribution and defines a DynamicBayesianNetwork as a conjugate exponential family (EF) model, consisting of EF distributions in canonical form.
EF_DynamicBayesianNetwork(DynamicDAG) - Constructor for class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Creates a new EF_BayesianNetwork object given a DynamicDAG object.
EF_DynamicBayesianNetwork(DynamicBayesianNetwork) - Constructor for class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Creates a new EF_BayesianNetwork object given a DynamicBayesianNetwork object.
EF_DynamicBayesianNetwork.DynamiceBNCompoundVector - Class in eu.amidst.dynamic.exponentialfamily
The class CompoundVector implements the interfaces SufficientStatistics, MomentParameters, and NaturalParameters, and it handles some utility methods of compound parameter vector for EF_DynamicBayesianNetwork.
EF_DynamicDistribution - Class in eu.amidst.dynamic.exponentialfamily
This class defines a Exponential Family (EF) distribution in canonical form for Dynamic Bayesian networks.
EF_DynamicDistribution() - Constructor for class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
 
ef_extendedBN - Variable in class eu.amidst.core.learning.parametric.bayesian.SVB
Represents a EF_LearningBayesianNetwork object.
EF_Gamma - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines a Gamma distribution in exponential family canonical form.
EF_Gamma(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_Gamma
Creates a new EF_Gamma distribution, i.e., Gamma(1,1), for a given Variable object.
EF_InverseGamma - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines an inverse Gamma distribution in exponential family canonical form.
EF_InverseGamma(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_InverseGamma
Creates a new EF_InverseGamma distribution for a given Variable object.
EF_JointNormalGamma - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines a Gamma distribution in exponential family canonical form.
EF_JointNormalGamma(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Creates a new EF_Gamma distribution, i.e., Gamma(1,1), for a given Variable object.
EF_LearningBayesianNetwork - Class in eu.amidst.core.exponentialfamily
This class the abstract class EF_Distribution and defines a "Bayesian extended" model for a given Bayesian network.
EF_LearningBayesianNetwork(DAG) - Constructor for class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Creates a new EF_LearningBayesianNetwork object from a given DAG object.
EF_LearningBayesianNetwork(List<EF_ConditionalDistribution>) - Constructor for class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Creates a new EF_LearningBayesianNetwork object from a given list EF_ConditionalDistribution objects.
EF_LearningBayesianNetwork(List<EF_ConditionalDistribution>, List<Variable>) - Constructor for class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Creates a new EF_LearningBayesianNetwork object from a given list EF_ConditionalDistribution objects.
ef_learningmodel - Variable in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Represents the EF_LearningBayesianNetwork model.
ef_model - Variable in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Represents the EF_BayesianNetwork model.
EF_Multinomial - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines a Multinomial distribution in exponential family canonical form.
EF_Multinomial(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_Multinomial
Creates a new EF_Multinomial distribution for a given Variable object.
EF_Multinomial_Dirichlet - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_ConditionalDistribution and defines a conditional Multinomial Dirichlet distribution.
EF_Multinomial_Dirichlet(Variable, Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Creates a new EF_Multinomial_Dirichlet distribution for given Multinomial and Dirichlet variables.
EF_Normal - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines a Normal distribution in exponential family canonical form.
EF_Normal(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_Normal
Creates a new EF_Normal distribution for a given variable.
EF_Normal.ArrayVectorParameter - Class in eu.amidst.core.exponentialfamily
This class implements the interfaces MomentParameters, NaturalParameters, and SufficientStatistics.
EF_Normal_Normal_Gamma - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_ConditionalDistribution and defines, in exponential family canonical form, a conditional Normal distribution given Normal parents (or CLG distribution) and given, as a parents too, a set of of Normal and Gamma parameter variables.
EF_Normal_Normal_Gamma(Variable, List<Variable>, Variable, List<Variable>, Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Creates a new EF_Normal_Normal_Gamma distribution.
EF_Normal_NormalParents - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_ConditionalDistribution and defines a Conditional Linear Gaussian (CLG) distribution in exponential family canonical form.
EF_Normal_NormalParents(Variable, List<Variable>) - Constructor for class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Creates a new EF_Normal_NormalParents distribution.
EF_Normal_NormalParents.CompoundVector - Class in eu.amidst.core.exponentialfamily
 
EF_NormalGivenIndependentNormalGamma - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_ConditionalDistribution and defines a conditional Normal distribution given a Normal and Gamma parameter variables in exponential family canonical form.
EF_NormalGivenIndependentNormalGamma(Variable, Variable, Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Creates a new EF_NormalGamma distribution.
EF_NormalGivenJointNormalGamma - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_ConditionalDistribution and defines a conditional Normal distribution given a Normal and Gamma parameter variables in exponential family canonical form.
EF_NormalGivenJointNormalGamma(Variable, Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Creates a new EF_NormalGamma distribution.
EF_NormalParameter - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines a Normal distribution in exponential family canonical form.
EF_NormalParameter(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_NormalParameter
Creates a new EF_Normal distribution for a given variable.
EF_NormalParameter.ArrayVectorParameter - Class in eu.amidst.core.exponentialfamily
This class implements the interfaces MomentParameters, NaturalParameters, and SufficientStatistics.
EF_SparseDirichlet - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines a Dirichlet distribution in Exponential Family (EF) canonical form.
EF_SparseDirichlet(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Creates a new uniform EF_Dirichlet distribution for a given Variable object.
EF_SparseDirichlet(Variable, double) - Constructor for class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Creates a new uniform EF_Dirichlet distribution for a given Variable object with a given scale.
EF_SparseMultinomial - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_UnivariateDistribution and defines a Multinomial distribution in exponential family canonical form.
EF_SparseMultinomial(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Creates a new EF_Multinomial distribution for a given Variable object.
EF_SparseMultinomial_Dirichlet - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_ConditionalDistribution and defines a conditional Multinomial Dirichlet distribution.
EF_SparseMultinomial_Dirichlet(Variable, Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Creates a new EF_Multinomial_Dirichlet distribution for given Multinomial and Dirichlet variables.
EF_SparseMultinomial_SparseDirichlet - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_ConditionalDistribution and defines a conditional Multinomial Dirichlet distribution.
EF_SparseMultinomial_SparseDirichlet(Variable, Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Creates a new EF_Multinomial_Dirichlet distribution for given Multinomial and Dirichlet variables.
EF_TruncatedExponential - Class in eu.amidst.core.exponentialfamily
Created by andresmasegosa on 14/4/16.
EF_TruncatedExponential(Variable) - Constructor for class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
Creates a new EF_TruncatedExponential distribution, for a given Variable object.
EF_TruncatedExponential(Variable, double) - Constructor for class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
Creates a new EF_TruncatedExponential distribution, for a given Variable object.
EF_UnivariateDistribution - Class in eu.amidst.core.exponentialfamily
This class extends the abstract class EF_ConditionalDistribution and defines the univariate distribution in exponential family form.
EF_UnivariateDistribution() - Constructor for class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
 
efBayesianNetwork - Variable in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Represents a EF_BayesianNetwork object
efBayesianNetwork - Variable in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Represents a EF_BayesianNetwork object
efBayesianNetwork - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Represents a EF_DynamicBayesianNetwork object
efBayesianNetwork - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Represents a EF_DynamicBayesianNetwork object
efBayesianNetwork - Variable in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood
Represents a EF_BayesianNetwork object
efBayesianNetwork - Variable in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood2
Represents a EF_BayesianNetwork object
efBayesianNetwork - Variable in class eu.amidst.sparklink.core.learning.ParallelMaximumLikelihood
Represents a EF_BayesianNetwork object
EFBN_NAME - Static variable in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood
 
EFBN_NAME - Static variable in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood2
 
entrySet() - Method in class eu.amidst.core.variables.HashMapAssignment
Returns a Set view of the mappings contained in this HashMapAssignment.
equal_efBN(EF_BayesianNetwork, double) - Method in class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Tests whether a given EF_BayesianNetwork model is equal to this EF_BayesianNetwork model.
equalBNs(BayesianNetwork, double) - Method in class eu.amidst.core.models.BayesianNetwork
Tests if two Bayesian networks are equals.
equalDBNs(DynamicBayesianNetwork, double) - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Tests if two Dynamic Bayesian networks are equals.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Tests if a given distribution is equal to this Distribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Tests if a given distribution is equal to this Distribution.
equalDist(ConditionalLinearGaussian, double) - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.DeltaDistribution
Tests if a given distribution is equal to this Distribution.
equalDist(DeltaDistribution) - Method in class eu.amidst.core.distribution.DeltaDistribution
Tests if a given delta distribution is equal to this DeltaDistribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.Distribution
Tests if a given distribution is equal to this Distribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.GaussianMixture
Tests if a given distribution is equal to this Distribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.IndicatorDistribution
Tests if a given distribution is equal to this Distribution.
equalDist(IndicatorDistribution, double) - Method in class eu.amidst.core.distribution.IndicatorDistribution
Tests if a given indicator distribution is equal to this IndicatorDistribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.Multinomial
Tests if a given distribution is equal to this Distribution.
equalDist(Multinomial, double) - Method in class eu.amidst.core.distribution.Multinomial
Tests if a given Multinomial distribution is equal to this Multinomial distribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Tests if a given distribution is equal to this Distribution.
equalDist(Multinomial_LogisticParents, double) - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
 
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Tests if a given distribution is equal to this Distribution.
equalDist(Multinomial_MultinomialParents, double) - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Tests if a given Multinomial_MultinomialParents distribution is equal to this Multinomial_MultinomialParents distribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.Normal
Tests if a given distribution is equal to this Distribution.
equalDist(Normal, double) - Method in class eu.amidst.core.distribution.Normal
Tests if a given Normal distribution is equal to this Normal distribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Tests if a given distribution is equal to this Distribution.
equalDist(Normal_MultinomialNormalParents, double) - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
 
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Tests if a given distribution is equal to this Distribution.
equalDist(Normal_MultinomialParents, double) - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Tests if a given Normal_MultinomialParents distribution is equal to this Normal_MultinomialParents distribution.
equalDist(Distribution, double) - Method in class eu.amidst.core.distribution.Uniform
Tests if a given distribution is equal to this Distribution.
equalDist(Uniform, double) - Method in class eu.amidst.core.distribution.Uniform
Tests if a given Uniform distribution is equal to this Uniform distribution.
equals(Object) - Method in class eu.amidst.core.datastream.Attribute
Tests whether two attributes are equal or not.
equals(Object) - Method in class eu.amidst.core.models.DAG
equals(Object) - Method in interface eu.amidst.core.models.ParentSet
equals(Object) - Method in interface eu.amidst.core.variables.Variable
equals(Object) - Method in class eu.amidst.dynamic.models.DynamicDAG
equalsVector(Vector, double) - Method in class eu.amidst.core.utils.SparseVector
 
equalsVector(Vector, double) - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
equalsVector(Vector, double) - Method in interface eu.amidst.core.utils.Vector
Tests if this Vector is equal to an input vector given a threshold.
errorMessage - Variable in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
errorMessage - Variable in class eu.amidst.latentvariablemodels.staticmodels.Model
 
estimateProbabilityOfPartialAssignment(Assignment) - Method in class eu.amidst.core.inference.MAPInference
 
estimateSize() - Method in class eu.amidst.core.datastream.BatchesSpliterator
estimateSize() - Method in class eu.amidst.core.utils.FixedBatchParallelSpliteratorWrapper
estimateSize() - Method in class eu.amidst.dynamic.datastream.DataSequenceSpliterator
estimateSize() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataInstanceSpliterator
estimateSize() - Method in class eu.amidst.lda.core.BatchSpliteratorByID
eu.amidst - package eu.amidst
 
eu.amidst.bnaic2015.examples - package eu.amidst.bnaic2015.examples
 
eu.amidst.cim2015.examples - package eu.amidst.cim2015.examples
 
eu.amidst.core.conceptdrift - package eu.amidst.core.conceptdrift
 
eu.amidst.core.conceptdrift.utils - package eu.amidst.core.conceptdrift.utils
 
eu.amidst.core.datastream - package eu.amidst.core.datastream
 
eu.amidst.core.datastream.filereaders - package eu.amidst.core.datastream.filereaders
 
eu.amidst.core.datastream.filereaders.arffFileReader - package eu.amidst.core.datastream.filereaders.arffFileReader
 
eu.amidst.core.distribution - package eu.amidst.core.distribution
 
eu.amidst.core.examples.classifiers - package eu.amidst.core.examples.classifiers
 
eu.amidst.core.examples.conceptdrift - package eu.amidst.core.examples.conceptdrift
 
eu.amidst.core.examples.datastream - package eu.amidst.core.examples.datastream
 
eu.amidst.core.examples.huginlink - package eu.amidst.core.examples.huginlink
 
eu.amidst.core.examples.inference - package eu.amidst.core.examples.inference
 
eu.amidst.core.examples.io - package eu.amidst.core.examples.io
 
eu.amidst.core.examples.learning - package eu.amidst.core.examples.learning
 
eu.amidst.core.examples.models - package eu.amidst.core.examples.models
 
eu.amidst.core.examples.utils - package eu.amidst.core.examples.utils
 
eu.amidst.core.examples.variables - package eu.amidst.core.examples.variables
 
eu.amidst.core.exponentialfamily - package eu.amidst.core.exponentialfamily
 
eu.amidst.core.inference - package eu.amidst.core.inference
 
eu.amidst.core.inference.messagepassing - package eu.amidst.core.inference.messagepassing
 
eu.amidst.core.io - package eu.amidst.core.io
 
eu.amidst.core.learning.parametric - package eu.amidst.core.learning.parametric
 
eu.amidst.core.learning.parametric.bayesian - package eu.amidst.core.learning.parametric.bayesian
 
eu.amidst.core.learning.parametric.bayesian.utils - package eu.amidst.core.learning.parametric.bayesian.utils
 
eu.amidst.core.learning.structural - package eu.amidst.core.learning.structural
 
eu.amidst.core.models - package eu.amidst.core.models
 
eu.amidst.core.potential - package eu.amidst.core.potential
 
eu.amidst.core.utils - package eu.amidst.core.utils
 
eu.amidst.core.variables - package eu.amidst.core.variables
 
eu.amidst.core.variables.distributionTypes - package eu.amidst.core.variables.distributionTypes
 
eu.amidst.core.variables.stateSpaceTypes - package eu.amidst.core.variables.stateSpaceTypes
 
eu.amidst.dynamic.datastream - package eu.amidst.dynamic.datastream
 
eu.amidst.dynamic.datastream.filereaders - package eu.amidst.dynamic.datastream.filereaders
 
eu.amidst.dynamic.examples - package eu.amidst.dynamic.examples
 
eu.amidst.dynamic.examples.datastream - package eu.amidst.dynamic.examples.datastream
 
eu.amidst.dynamic.examples.inference - package eu.amidst.dynamic.examples.inference
 
eu.amidst.dynamic.examples.learning - package eu.amidst.dynamic.examples.learning
 
eu.amidst.dynamic.examples.models - package eu.amidst.dynamic.examples.models
 
eu.amidst.dynamic.examples.utils - package eu.amidst.dynamic.examples.utils
 
eu.amidst.dynamic.examples.variables - package eu.amidst.dynamic.examples.variables
 
eu.amidst.dynamic.exponentialfamily - package eu.amidst.dynamic.exponentialfamily
 
eu.amidst.dynamic.inference - package eu.amidst.dynamic.inference
 
eu.amidst.dynamic.io - package eu.amidst.dynamic.io
 
eu.amidst.dynamic.learning.parametric - package eu.amidst.dynamic.learning.parametric
 
eu.amidst.dynamic.learning.parametric.bayesian - package eu.amidst.dynamic.learning.parametric.bayesian
 
eu.amidst.dynamic.learning.structural - package eu.amidst.dynamic.learning.structural
 
eu.amidst.dynamic.models - package eu.amidst.dynamic.models
 
eu.amidst.dynamic.utils - package eu.amidst.dynamic.utils
 
eu.amidst.dynamic.variables - package eu.amidst.dynamic.variables
 
eu.amidst.flinklink.core.conceptdrift - package eu.amidst.flinklink.core.conceptdrift
 
eu.amidst.flinklink.core.data - package eu.amidst.flinklink.core.data
 
eu.amidst.flinklink.core.io - package eu.amidst.flinklink.core.io
 
eu.amidst.flinklink.core.learning.dynamic - package eu.amidst.flinklink.core.learning.dynamic
 
eu.amidst.flinklink.core.learning.parametric - package eu.amidst.flinklink.core.learning.parametric
 
eu.amidst.flinklink.core.learning.parametric.utils - package eu.amidst.flinklink.core.learning.parametric.utils
 
eu.amidst.flinklink.core.utils - package eu.amidst.flinklink.core.utils
 
eu.amidst.flinklink.examples.extensions - package eu.amidst.flinklink.examples.extensions
 
eu.amidst.flinklink.examples.io - package eu.amidst.flinklink.examples.io
 
eu.amidst.flinklink.examples.learning - package eu.amidst.flinklink.examples.learning
 
eu.amidst.flinklink.examples.misc - package eu.amidst.flinklink.examples.misc
 
eu.amidst.flinklink.examples.reviewMeeting2015 - package eu.amidst.flinklink.examples.reviewMeeting2015
 
eu.amidst.huginlink.converters - package eu.amidst.huginlink.converters
 
eu.amidst.huginlink.examples.converters - package eu.amidst.huginlink.examples.converters
 
eu.amidst.huginlink.examples.demos - package eu.amidst.huginlink.examples.demos
 
eu.amidst.huginlink.examples.inference - package eu.amidst.huginlink.examples.inference
 
eu.amidst.huginlink.examples.io - package eu.amidst.huginlink.examples.io
 
eu.amidst.huginlink.examples.learning - package eu.amidst.huginlink.examples.learning
 
eu.amidst.huginlink.inference - package eu.amidst.huginlink.inference
 
eu.amidst.huginlink.io - package eu.amidst.huginlink.io
 
eu.amidst.huginlink.learning - package eu.amidst.huginlink.learning
 
eu.amidst.jmlr2015.examples - package eu.amidst.jmlr2015.examples
 
eu.amidst.latentvariablemodels.dynamicmodels - package eu.amidst.latentvariablemodels.dynamicmodels
 
eu.amidst.latentvariablemodels.dynamicmodels.classifiers - package eu.amidst.latentvariablemodels.dynamicmodels.classifiers
 
eu.amidst.latentvariablemodels.staticmodels - package eu.amidst.latentvariablemodels.staticmodels
 
eu.amidst.latentvariablemodels.staticmodels.classifiers - package eu.amidst.latentvariablemodels.staticmodels.classifiers
 
eu.amidst.latentvariablemodels.staticmodels.exceptions - package eu.amidst.latentvariablemodels.staticmodels.exceptions
 
eu.amidst.lda.core - package eu.amidst.lda.core
 
eu.amidst.lda.flink - package eu.amidst.lda.flink
 
eu.amidst.lda.utils - package eu.amidst.lda.utils
 
eu.amidst.moalink.converterFromMoaToAmidst - package eu.amidst.moalink.converterFromMoaToAmidst
 
eu.amidst.moduleall - package eu.amidst.moduleall
 
eu.amidst.multicore - package eu.amidst.multicore
 
eu.amidst.sparklink.core.data - package eu.amidst.sparklink.core.data
 
eu.amidst.sparklink.core.io - package eu.amidst.sparklink.core.io
 
eu.amidst.sparklink.core.learning - package eu.amidst.sparklink.core.learning
 
eu.amidst.sparklink.core.util - package eu.amidst.sparklink.core.util
 
eu.amidst.sparklink.examples.io - package eu.amidst.sparklink.examples.io
 
eu.amidst.sparklink.examples.learning - package eu.amidst.sparklink.examples.learning
 
eu.amidst.sparklink.examples.util - package eu.amidst.sparklink.examples.util
 
eu.amidst.tutorial.usingAmidst - package eu.amidst.tutorial.usingAmidst
 
eu.amidst.tutorial.usingAmidst.examples - package eu.amidst.tutorial.usingAmidst.examples
 
eu.amidst.tutorial.usingAmidst.practice - package eu.amidst.tutorial.usingAmidst.practice
 
eu.amidst.wekalink.converterFromWekaToAmidst - package eu.amidst.wekalink.converterFromWekaToAmidst
 
euclideanDistance(KMeans.Point) - Method in class eu.amidst.flinklink.examples.misc.KMeans.Point
 
EXPECTED_MEAN - Static variable in class eu.amidst.core.exponentialfamily.EF_Normal
 
EXPECTED_MEAN - Static variable in class eu.amidst.core.exponentialfamily.EF_NormalParameter
 
EXPECTED_SQUARE - Static variable in class eu.amidst.core.exponentialfamily.EF_Normal
 
EXPECTED_SQUARE - Static variable in class eu.amidst.core.exponentialfamily.EF_NormalParameter
 
ExperimentsParallelkMeans - Class in eu.amidst.cim2015.examples
Created by ana@cs.aau.dk on 01/07/15.
ExperimentsParallelkMeans() - Constructor for class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
ExperimentsParallelML - Class in eu.amidst.cim2015.examples
Created by ana@cs.aau.dk on 30/06/15.
ExperimentsParallelML() - Constructor for class eu.amidst.cim2015.examples.ExperimentsParallelML
 
ExperimentsParallelML - Class in eu.amidst.flinklink.examples.misc
Created by ana@cs.aau.dk on 04/11/15.
ExperimentsParallelML() - Constructor for class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
ExperimentsParallelSVB - Class in eu.amidst.jmlr2015.examples
This class presents the experiment included in the submitted manuscript to the JMLR Machine Learning Open Source Software.
ExperimentsParallelSVB() - Constructor for class eu.amidst.jmlr2015.examples.ExperimentsParallelSVB
 

F

FactorAnalysis - Class in eu.amidst.latentvariablemodels.staticmodels
This class implements Factor Analysis.
FactorAnalysis(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.FactorAnalysis
Constructor from a list of attributes.
FactoredFrontierForDBN - Class in eu.amidst.dynamic.inference
This class implements the interfaces InferenceAlgorithmForDBN.
FactoredFrontierForDBN(InferenceAlgorithm) - Constructor for class eu.amidst.dynamic.inference.FactoredFrontierForDBN
Creates a new FactoredFrontierForDBN object.
FactorialHMM - Class in eu.amidst.latentvariablemodels.dynamicmodels
This class implements a Factorial Hidden Markov Model.
FactorialHMM(Attributes) - Constructor for class eu.amidst.latentvariablemodels.dynamicmodels.FactorialHMM
 
Fading - Class in eu.amidst.core.conceptdrift.utils
Created by andresmasegosa on 13/4/15.
Fading(double) - Constructor for class eu.amidst.core.conceptdrift.utils.Fading
 
FadingLearner - Interface in eu.amidst.core.conceptdrift
This interface defines the Fading Learner.
FileConverter - Class in eu.amidst.sparklink.core.util
Created by rcabanas on 30/09/16.
FileConverter() - Constructor for class eu.amidst.sparklink.core.util.FileConverter
 
FileConverterFromHuginToAmidst - Class in eu.amidst.huginlink.converters
The FileConverterFromHuginToAmidst class converts a set of Hugin networks (static and dynamic) into AMIDST networks.
FileConverterFromHuginToAmidst() - Constructor for class eu.amidst.huginlink.converters.FileConverterFromHuginToAmidst
 
filter(Predicate<? super E>) - Method in interface eu.amidst.core.datastream.DataStream
Returns a data stream consisting of the elements of this stream that match the given predicate.
filter(FilterFunction<T>) - Method in interface eu.amidst.flinklink.core.data.DataFlink
 
filterAssignment(Assignment, List<Variable>, List<Variable>) - Static method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
filterList(List<Integer>, MyIntegerUtils.NbrPredicate) - Static method in class eu.amidst.cim2015.examples.MyIntegerUtils
 
FiniteStateSpace - Class in eu.amidst.core.variables.stateSpaceTypes
This class extends the abstract class StateSpaceType and implements the interface Iterable<String>.
FiniteStateSpace(int) - Constructor for class eu.amidst.core.variables.stateSpaceTypes.FiniteStateSpace
Creates a new FiniteStateSpace given the number of states.
FiniteStateSpace(List<String>) - Constructor for class eu.amidst.core.variables.stateSpaceTypes.FiniteStateSpace
Creates a new FiniteStateSpace given a list of state space names.
FixedBatchParallelSpliteratorWrapper<T> - Class in eu.amidst.core.utils
This class implements the Spliterator interface for providing streams which can be transversed using a batches of a fixed size.
FixedBatchParallelSpliteratorWrapper(Spliterator<T>, long, int) - Constructor for class eu.amidst.core.utils.FixedBatchParallelSpliteratorWrapper
Creates a new FixedBatchParallelSpliteratorWrapper object.
FixedBatchParallelSpliteratorWrapper(Spliterator<T>, int) - Constructor for class eu.amidst.core.utils.FixedBatchParallelSpliteratorWrapper
Creates a new FixedBatchParallelSpliteratorWrapper object.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_Dirichlet
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_InverseGamma
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
fixNumericalInstability() - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Fixes the numerical instability problems when invoked in this EF_UnivariateDistribution.
flatMap(Batch<DataPosteriorAssignment>, Collector<DataPosteriorAssignment>) - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.CajaMarLearnMapInferenceAssignment
 
flatMap(DataOnMemory<DataInstance>, Collector<DataPosterior>) - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ParallelVBMapInference
 
flatMap(DataOnMemory<DataInstance>, Collector<DataPosteriorAssignment>) - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ParallelVBMapInferenceAssignment
 
flatMap(DataOnMemory<DataInstance>, Collector<DataPosterior>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP.ParallelVBMapInference
 
flatMap(DataOnMemory<DataInstance>, Collector<DataPosteriorAssignment>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP.ParallelVBMapInferenceAssignment
 
flatMap(DataOnMemory<DataInstance>, Collector<DataPosterior>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1.ParallelVBMapInference
 
flatMap(DataOnMemory<DataInstance>, Collector<DataPosteriorAssignment>) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1.ParallelVBMapInferenceAssignment
 
flatMap(DataOnMemory<DataInstance>, Collector<DataPosterior>) - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB.ParallelVBMapInference
 
flatMap(DataOnMemory<DataInstance>, Collector<DataPosteriorAssignment>) - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB.ParallelVBMapInferenceAssignment
 
flatMap(String, Collector<Tuple2<String, Integer>>) - Method in class eu.amidst.flinklink.examples.misc.StreamWordCountExample.Splitter
 
flatMap(String, Collector<Tuple2<String, Integer>>) - Method in class eu.amidst.flinklink.examples.misc.WordCountExample.LineSplitter
 
forEachRemaining(Consumer<? super T>) - Method in class eu.amidst.core.utils.FixedBatchParallelSpliteratorWrapper
forEachRemaining(Consumer<? super DynamicDataInstance>) - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataInstanceSpliterator
FromBaseDistributionToConditionalDistribution(BaseDistribution_MultinomialParents) - Static method in enum eu.amidst.core.variables.DistributionTypeEnum
Function2<A,B,R> - Interface in eu.amidst.flinklink.core.utils
Created by andresmasegosa on 12/5/16.

G

GAMMA - Static variable in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
 
GammaParameterType - Class in eu.amidst.core.variables.distributionTypes
This class extends the abstract class DistributionType and defines the Gamma parameter type.
GammaParameterType(Variable) - Constructor for class eu.amidst.core.variables.distributionTypes.GammaParameterType
Creates a new GammaParameterType for the given variable.
GaussianDiscriminantAnalysis - Class in eu.amidst.latentvariablemodels.staticmodels.classifiers
Created by andresmasegosa and rcabanas on 4/3/16.
GaussianDiscriminantAnalysis(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.classifiers.GaussianDiscriminantAnalysis
Constructor of classifier from a list of attributes.
GaussianHiddenTransitionMethod - Class in eu.amidst.core.conceptdrift.utils
Created by andresmasegosa on 13/4/15.
GaussianHiddenTransitionMethod(List<Variable>, double, double) - Constructor for class eu.amidst.core.conceptdrift.utils.GaussianHiddenTransitionMethod
 
GaussianMixture - Class in eu.amidst.core.distribution
This class extends the abstract class UnivariateDistribution and defines the Gaussian Mixture distribution.
GaussianMixture(Variable) - Constructor for class eu.amidst.core.distribution.GaussianMixture
Creates a new GaussianMixture distribution for a given variable.
GaussianMixture(List<Normal>, double[]) - Constructor for class eu.amidst.core.distribution.GaussianMixture
Creates a new GaussianMixture distribution given a list of Normal distributions and a set of coefficients.
GaussianMixture(double[]) - Constructor for class eu.amidst.core.distribution.GaussianMixture
Creates a new GaussianMixture distribution given a set of parameters.
GaussianMixture - Class in eu.amidst.latentvariablemodels.staticmodels
This class implements a (Multivariate) Gaussian Mixture Model See Murphy, K.
GaussianMixture(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.GaussianMixture
Constructor of classifier from a list of attributes (e.g.
generate(int, int, int, int) - Static method in class eu.amidst.core.utils.DataSetGenerator
Generate a DataStream with the given number of samples and attributes (discrete and continuous).
generate(int, int, int, int) - Static method in class eu.amidst.dynamic.utils.DataSetGenerator
Generate a DataStream with the given number of samples and attributes (discrete and continuous).
generate(int, int, int[], int) - Static method in class eu.amidst.dynamic.utils.DataSetGenerator
 
generate(ExecutionEnvironment, int, int, int, int) - Static method in class eu.amidst.flinklink.core.utils.DataSetGenerator
Generate a DataFlink with the given number of samples and attributes (discrete and continuous).
generate(JavaSparkContext, int, int, int, int) - Static method in class eu.amidst.sparklink.core.util.DataSetGenerator
Generate a DataSpark with the given number of samples and attributes (discrete and continuous).
generateBayesianNetwork() - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Generates a BayesianNetwork randomly.
generateBNtoFile(int, int, int, int, int, String) - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Generates a BayesianNetwork then saves it in a file.
GenerateData - Class in eu.amidst.flinklink.examples.misc
Created by Hanen on 17/11/15.
GenerateData() - Constructor for class eu.amidst.flinklink.examples.misc.GenerateData
 
GenerateData - Class in eu.amidst.flinklink.examples.reviewMeeting2015
Created by ana@cs.aau.dk on 19/01/16.
GenerateData() - Constructor for class eu.amidst.flinklink.examples.reviewMeeting2015.GenerateData
 
generateDynamicBayesianNetwork(int[]) - Static method in class eu.amidst.dynamic.utils.DynamicBayesianNetworkGenerator
Generates a DynamicBayesianNetwork randomly where each multinomial variable Xi contains DynamicBayesianNetworkGenerator.numberOfStates+n[i] states.
generateDynamicBayesianNetwork() - Static method in class eu.amidst.dynamic.utils.DynamicBayesianNetworkGenerator
Generates a DynamicBayesianNetwork randomly.
generateDynamicDataset(String[]) - Method in class eu.amidst.flinklink.examples.misc.DynamicDataSets
 
generateDynamicFAN(Random, int, boolean) - Static method in class eu.amidst.dynamic.utils.DynamicBayesianNetworkGenerator
Generates a Dynamic FAN model with randomly initialized distributions.
generateDynamicNaiveBayes(Random, int, boolean) - Static method in class eu.amidst.dynamic.utils.DynamicBayesianNetworkGenerator
Generates a Dynamic Naive Bayes model with randomly initialized distributions.
generateDynamicNaiveBayesDAG(int, boolean) - Static method in class eu.amidst.dynamic.utils.DynamicBayesianNetworkGenerator
Generates a Dynamic Naive Bayes DAG with a Multinomial class variable.
generateDynamicTAN(Random, int, boolean) - Static method in class eu.amidst.dynamic.utils.DynamicBayesianNetworkGenerator
Generates a Dynamic TAN model with randomly initialized distributions.
generateNaiveBayes(int) - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Generates a NaiveBayesClassifier.
generateNaiveBayesWithGlobalHiddenVar(int, String) - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Generates a NaiveBayesClassifier with a global Gaussian hidden variable.
GenerateRandom - Class in eu.amidst.flinklink.examples.misc
Created by Hanen on 09/12/15.
GenerateRandom() - Constructor for class eu.amidst.flinklink.examples.misc.GenerateRandom
 
generateTreeDAG(Variables) - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Generates a tree DAG given a set of variables.
generateTreeDAG(DynamicVariables) - Static method in class eu.amidst.dynamic.utils.DynamicBayesianNetworkGenerator
Generates a tree DAG given a set of variables.
get(int) - Method in class eu.amidst.core.exponentialfamily.EF_Normal.ArrayVectorParameter
Returns the value of the element in a given position i.
get(int) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
get(int) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter.ArrayVectorParameter
Returns the value of the element in a given position i.
get(int) - Method in class eu.amidst.core.utils.ArrayVector
Returns the value of the element in a given position i.
get(int) - Method in class eu.amidst.core.utils.CompoundVector
Returns the value of the element in a given position i.
get(int) - Method in class eu.amidst.core.utils.KeyCompoundVector
Returns the value of the element in a given position i.
get(int) - Method in class eu.amidst.core.utils.SparseVector
Returns the value of the element in a given position i.
get(int) - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
Returns the value of the element in a given position i.
get(int) - Method in interface eu.amidst.core.utils.Vector
Returns the value of the element in a given position i.
get(int) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
getAssignment() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the Assignment associated with this Node.
getAssignment() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.DataPosteriorAssignment
 
getAttribute() - Method in interface eu.amidst.core.variables.Variable
Returns the Attribute associated with this Variable.
getAttribute() - Method in class eu.amidst.core.variables.VariableBuilder
Returns the Attribute associated with this Variable.
getAttributeByName(String) - Method in class eu.amidst.core.datastream.Attributes
Returns the Attribute corresponding to the given name.
getAttributes() - Method in interface eu.amidst.core.datastream.DataInstance
Returns the set of attributes that have assigned values stored in this DataInstance.
getAttributes() - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Returns an Attributes object containing the attributes of this DataStream.
getAttributes() - Method in interface eu.amidst.core.datastream.DataStream
Returns an Attributes object containing the attributes of this DataStream.
getAttributes() - Method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataFolderReader
Returns the set of Attributes in this DataFileReader.
getAttributes() - Method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataReader
Returns the set of Attributes in this DataFileReader.
getAttributes() - Method in class eu.amidst.core.datastream.filereaders.arffFileReader.DataRowWeka
Returns the set of Attributes that have observed values in this DataRow.
getAttributes() - Method in interface eu.amidst.core.datastream.filereaders.DataFileReader
Returns the set of Attributes in this DataFileReader.
getAttributes() - Method in class eu.amidst.core.datastream.filereaders.DataInstanceFromDataRow
Returns the set of attributes that have assigned values stored in this DataInstance.
getAttributes() - Method in class eu.amidst.core.datastream.filereaders.DataOnMemoryFromFile
Returns an Attributes object containing the attributes of this DataStream.
getAttributes() - Method in interface eu.amidst.core.datastream.filereaders.DataRow
Returns the set of Attributes that have observed values in this DataRow.
getAttributes() - Method in class eu.amidst.core.datastream.filereaders.DataRowMissing
No implementation is provided for this method because all the attributes have no assigned values.
getAttributes() - Method in class eu.amidst.core.datastream.filereaders.DataStreamFromFile
Returns an Attributes object containing the attributes of this DataStream.
getAttributes() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataOnMemoryFromFile
Returns an Attributes object containing the attributes of this DataStream.
getAttributes() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataStreamFromFile
Returns an Attributes object containing the attributes of this DataStream.
getAttributes() - Method in interface eu.amidst.flinklink.core.data.DataFlink
 
getAttributes() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.DataInstanceFromAssignment
 
getAttributes() - Method in class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments.DataInstanceFromAssignment
 
getAttributes() - Method in class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments
 
getAttributes() - Method in class eu.amidst.moalink.converterFromMoaToAmidst.DataRowWeka
Returns the set of Attributes that have observed values in this DataRow.
getAttributes() - Method in class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Returns an Attributes object containing the attributes of this DataStream.
getAttributes() - Method in class eu.amidst.sparklink.core.data.DataRowSpark
 
getAttributes() - Method in interface eu.amidst.sparklink.core.data.DataSpark
 
getAttributes() - Method in class eu.amidst.sparklink.core.data.DataSparkFromDataFrame
 
getAttributes() - Method in class eu.amidst.sparklink.core.data.DataSparkFromDataStream
 
getAttributes() - Method in class eu.amidst.sparklink.core.data.DataSparkFromRDD
 
getAttributes() - Method in class eu.amidst.wekalink.converterFromWekaToAmidst.DataRowWeka
Returns the set of Attributes that have observed values in this DataRow.
getAverageNumOfIterations() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the average number of iterations.
getBaseConditionalDistribution(int) - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns a base conditional distribution given its position.
getBaseDistribution(Assignment) - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns a base distribution given a parent assignment.
getBaseDistribution(int) - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns a base distribution given its position.
getBaseDistributions() - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the list of base distributions.
getBaseEFConditionalDistribution(int) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns the base EF conditional distribution for a given indexed configuration of the multinomial parents.
getBaseEFDistribution(int) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns a base EF distribution for a given indexed configuration of the multinomial parents.
getBaseEFUnivariateDistribution(int) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Gets the base EF univariate distribution for a given indexed configuration of the multinomial parents.
getBaseUnivariateDistribution(int) - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns a base univariate distribution given its position.
getBatchedDataSet(int) - Method in interface eu.amidst.flinklink.core.data.DataFlink
 
getBatchedDataSet(int, Function2<DataFlink<T>, Integer, DataSet<DataOnMemory<T>>>) - Method in interface eu.amidst.flinklink.core.data.DataFlink
 
getBatchedDataSet(int) - Method in interface eu.amidst.sparklink.core.data.DataSpark
 
getBatchID() - Method in interface eu.amidst.core.datastream.DataOnMemory
Returns an ID for the data set.
getBatchID() - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Returns an ID for the data set.
getBatchID() - Method in class eu.amidst.flinklink.core.utils.Batch
 
getBatchID() - Method in class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Returns an ID for the data set.
getBatchSize() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getBatchSize() - Static method in class eu.amidst.cim2015.examples.ParallelKMeans
 
getBatchSize() - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
getBatchSize() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getBatchSize() - Method in class eu.amidst.huginlink.learning.ParallelPC
Returns the batch size to be used during the learning process.
getBatchSize() - Method in class eu.amidst.huginlink.learning.ParallelTAN
Returns the batch size to be used during the learning process.
getBatchSize() - Method in class eu.amidst.sparklink.core.learning.ParallelMaximumLikelihood
 
getBatchSize() - Method in interface eu.amidst.sparklink.core.learning.ParameterLearningAlgorithm
Returns the batch size.
getBatchSize_() - Method in class moa.classifiers.bayes.AmidstClassifier
Returns the batch size.
getBatchSize_() - Method in class moa.classifiers.bayes.AmidstRegressor
Returns the batch size.
getBayesianNetworkTime0() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Returns the EF_BayesianNetwork at Time 0 of this EF_DynamicBayesianNetwork.
getBayesianNetworkTimeT() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Returns the EF_BayesianNetwork at Time T of this EF_DynamicBayesianNetwork.
getBestSequencesForEachSubmodel() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
 
getBooleanOption(String) - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getBooleanOption(String) - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getBooleanOption(String) - Method in interface eu.amidst.core.utils.AmidstOptionsHandler
Returns the boolean value of an option given its name.
getBooleanOption(String) - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getCapabilities() - Method in class weka.classifiers.bayes.AmidstClassifier
Returns default capabilities of the classifier.
getCapabilities() - Method in class weka.classifiers.bayes.AmidstRegressor
Returns default capabilities of the classifier.
getCausalOrderTime0(DynamicDAG) - Static method in class eu.amidst.dynamic.utils.Utils
 
getCausalOrderTimeT(DynamicDAG) - Static method in class eu.amidst.dynamic.utils.Utils
 
getChildren() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the list of children nodes of this Node.
getClassIndex() - Method in class eu.amidst.core.conceptdrift.NaiveBayesVirtualConceptDriftDetector
Gets the index of the class variable of the model
getClassIndex() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetector
Gets the index of the class variable of the model
getClassIndex() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetectorDBN
Gets the index of the class variable of the model
getClassIndex() - Method in class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
 
getClassVar() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicClassifier
Method to obtain the class variable
getClassVar() - Method in class eu.amidst.latentvariablemodels.staticmodels.BayesianLinearRegression
Method to obtain the class variable
getClassVar() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.Classifier
Method to obtain the class variable
getClassVariable() - Method in class eu.amidst.core.conceptdrift.NaiveBayesVirtualConceptDriftDetector
Returns the class variable of the classifier
getClassVariable() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetector
Returns the class variable of the classifier
getClassVariable() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetectorDBN
Returns the class variable of the classifier
getClassVariable(InstancesHeader, Attributes) - Static method in class eu.amidst.moalink.converterFromMoaToAmidst.Converter
Returns the class variable.
getClassVariable(Instances, Attributes) - Static method in class eu.amidst.wekalink.converterFromWekaToAmidst.Converter
Returns the class variable.
getClassVarID() - Method in class eu.amidst.dynamic.learning.parametric.DynamicNaiveBayesClassifier
Returns the ID of the class variable.
getClusterID() - Method in class eu.amidst.cim2015.examples.ParallelKMeans.Pair
 
getClusteringResult() - Method in class moa.clusterers.AmidstClusteringAlgorithm
getCoeffForParent(Variable) - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the coefficient of a given parent variable.
getCoeffParents() - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the set of coefficients of the parent variables.
getCoeffParents(int) - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Returns the set of coefficients for a given state.
getComparator() - Method in class eu.amidst.core.datastream.BatchesSpliterator
getComparator() - Method in class eu.amidst.core.utils.FixedBatchParallelSpliteratorWrapper
getComparator() - Method in class eu.amidst.dynamic.datastream.DataSequenceSpliterator
getComparator() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataInstanceSpliterator
getComparator() - Method in class eu.amidst.lda.core.BatchSpliteratorByID
getCompoundVector() - Method in class eu.amidst.core.learning.parametric.ParallelMLMissingData.PartialSufficientSatistics
 
getConditionalDistribution() - Method in class eu.amidst.core.distribution.IndicatorDistribution
Returns the ConditionalDistribution of this IndicatorDistribution.
getConditionalDistribution(Variable) - Method in class eu.amidst.core.models.BayesianNetwork
Returns the conditional probability distribution of a variable.
getConditionalDistributions() - Method in class eu.amidst.core.models.BayesianNetwork
Returns the list of the conditional probability distributions of this BayesianNetwork.
getConditionalDistributionsTime0() - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the list of the conditional probability distributions at Time 0 of this DynamicBayesianNetwork.
getConditionalDistributionsTimeT() - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the list of the conditional probability distributions at Time T of this DynamicBayesianNetwork.
getConditionalDistributionTime0(Variable) - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the conditional probability distribution at Time 0 of a given Variable.
getConditionalDistributionTimeT(Variable) - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the conditional probability distribution at Time T of a given Variable.
getConditionalDistributionType(Variable, BayesianNetwork) - Static method in class eu.amidst.core.utils.Utils
Returns the conditional distribution type for a given Variable in a given BayesianNetwork.
getConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.ConditionalDistribution
Returns the conditional probability of an Assignment given this ConditionalDistribution.
getConditioningVariables() - Method in class eu.amidst.core.distribution.ConditionalDistribution
Returns the set of conditioning variables, i.e., the list of parents in this ConditionalDistribution.
getConditioningVariables() - Method in class eu.amidst.core.distribution.UnivariateDistribution
Returns the set of conditioning variables, i.e., the list of parents in this ConditionalDistribution.
getConditioningVariables() - Method in class eu.amidst.core.exponentialfamily.EF_ConditionalDistribution
Returns the list of conditioning parent variables.
getConditioningVariables() - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the list of conditioning parent variables.
getContHiddenList() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicLatentClassificationModel
method for getting the list of continuous hidden variables
getContHiddenList() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.LatentClassificationModel
method for getting the list of continuous hidden variables
getcovbaseMatrix() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getDAG() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the associated DAG defining the PGM structure
getDAG() - Method in class eu.amidst.core.models.BayesianNetwork
Returns the directed acyclic graph of this BayesianNetwork.
getDAG() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
getDagLDA() - Method in class eu.amidst.lda.core.PlateauLDA
 
getDagLDA() - Method in class eu.amidst.lda.flink.PlateauLDAFlink
 
getDataFrame(SQLContext) - Method in interface eu.amidst.sparklink.core.data.DataSpark
 
getDataFrame(SQLContext) - Method in class eu.amidst.sparklink.core.data.DataSparkFromDataFrame
 
getDataFrame(SQLContext) - Method in class eu.amidst.sparklink.core.data.DataSparkFromDataStream
 
getDataFrame(SQLContext) - Method in class eu.amidst.sparklink.core.data.DataSparkFromRDD
 
getDataInstance() - Method in class eu.amidst.cim2015.examples.ParallelKMeans.Pair
 
getDataInstance(int) - Method in interface eu.amidst.core.datastream.DataOnMemory
Returns the data instance in the i-th position.
getDataInstance(int) - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Returns the data instance in the i-th position.
getDataInstance(int) - Method in class eu.amidst.core.datastream.filereaders.DataOnMemoryFromFile
Returns the data instance in the i-th position.
getDataInstance(int) - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataOnMemoryFromFile
Returns the data instance in the i-th position.
getDataInstance() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.DataPosteriorInstance
 
getDataInstance(int) - Method in class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Returns the data instance in the i-th position.
getDataPosterior() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.DataPosteriorInstance
 
getDataPosteriorDataSet() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
getDataSet() - Method in interface eu.amidst.flinklink.core.data.DataFlink
 
getDataSet() - Method in interface eu.amidst.sparklink.core.data.DataSpark
 
getDataSet() - Method in class eu.amidst.sparklink.core.data.DataSparkFromDataFrame
 
getDataSet() - Method in class eu.amidst.sparklink.core.data.DataSparkFromDataStream
 
getDataSet() - Method in class eu.amidst.sparklink.core.data.DataSparkFromRDD
 
getDefaultCentroidDataSet(ExecutionEnvironment) - Static method in class eu.amidst.flinklink.examples.misc.KMeansData
 
getDefaultPointDataSet(ExecutionEnvironment) - Static method in class eu.amidst.flinklink.examples.misc.KMeansData
 
getDefaultValue() - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
getDelta() - Method in class eu.amidst.core.learning.parametric.bayesian.DriftSVB
 
getDelta() - Method in class eu.amidst.core.learning.parametric.bayesian.MultiDriftSVB
 
getDelta() - Method in class eu.amidst.lda.core.MultiDriftLDAv1
 
getDelta() - Method in class eu.amidst.lda.core.MultiDriftLDAv2
 
getDeltaValue() - Method in class eu.amidst.core.distribution.DeltaDistribution
Returns the delta value of this DeltaDistribution.
getDeltaValue() - Method in class eu.amidst.core.variables.distributionTypes.IndicatorType
 
getDirichletVariable() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Returns the Dirichlet conditioning variable of this EF_Multinomial_Dirichlet distribution.
getDirichletVariable() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Returns the Dirichlet conditioning variable of this EF_Multinomial_Dirichlet distribution.
getDirichletVariable() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Returns the Dirichlet conditioning variable of this EF_Multinomial_Dirichlet distribution.
getDistribution() - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the list of the ConditionalLinearGaussian distributions.
getDistribution(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Returns the EF Conditional Distribution associated to a given variable.
getDistribution(Variable) - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Returns the EF_ConditionalDistribution object associated with a given variable.
getDistributionList() - Method in class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Returns the list of EF_Distribution objects of this EF_BayesianNetwork.
getDistributionList() - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Returns the list of EF_ConditionalDistribution objects.
getDistributionType() - Method in interface eu.amidst.core.variables.Variable
Returns the distribution type of this Variable.
getDistributionType() - Method in class eu.amidst.core.variables.VariableBuilder
Returns the distribution type of this variable.
getDistributionTypeEnum() - Method in interface eu.amidst.core.variables.Variable
Returns the distribution type of this Variable.
getDoubleOption(String) - Method in interface eu.amidst.core.utils.AmidstOptionsHandler
Returns the double value of an option given its name.
getDynamicBNModel() - Method in class eu.amidst.dynamic.learning.parametric.DynamicNaiveBayesClassifier
Returns this DynamicNaiveBayesClassifier considered as a dynamic Bayesian network model.
getDynamicDAG() - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the dynamic directed acyclic graph of this DynamicBayesianNetwork.
getDynamicDAG() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
getDynamicVariables() - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the set of dynamic variables in this DynamicBayesianNetwork.
getDynamicVariables() - Method in class eu.amidst.dynamic.models.DynamicDAG
Returns the set of dynamic variables in this DynamicDAG.
getED(double[], double[]) - Static method in class eu.amidst.cim2015.examples.ParallelKMeans.Pair
 
getEFLearningBN() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Returns the EF_LearningBayesianNetwork of this PlateuStructure.
getEFLearningBNTime0() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the EF_LearningBayesianNetwork at time 0 of this DynamicPlateauStructure.
getEFLearningBNTimeT() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the EF_LearningBayesianNetwork at time T of this DynamicPlateauStructure.
getEFModel() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the EF_BayesianNetwork model.
getEFParameterPosterior(Variable) - Method in class eu.amidst.core.conceptdrift.utils.PlateuHiddenVariableConceptDrift
 
getEFParameterPosterior(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Returns the exponential family parameter posterior for a given Variable object.
getEFParameterPosteriorTime0(Variable) - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the exponential family variable posterior at time 0 for a given Variable.
getEFParameterPosteriorTimeT(Variable) - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the exponential family variable posterior at time T for a given Variable.
getEFPosterior(Variable) - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the exponential family posterior of a given Variable.
getEFVariablePosterior(Variable, int) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Returns the exponential family variable posterior for a given Variable object and a slice value.
getElbo() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB.BatchOutput
 
getELBO() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ConvergenceELBO
 
getELBO() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ConvergenceELBObyTime
 
getELBO() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP.ConvergenceELBO
 
getELBO() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP.ConvergenceELBObyTime
 
getELBO() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1.ConvergenceELBO
 
getELBO() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1.ConvergenceELBObyTime
 
getELBO() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB.ConvergenceELBO
 
getELBO() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB.ConvergenceELBObyTime
 
getElements() - Method in class eu.amidst.flinklink.core.utils.Batch
 
getErrorMessage() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
getErrorMessage() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
getEstimate() - Method in class eu.amidst.core.inference.MAPInference
Returns the log probability of the computed estimate.
getEstimate() - Method in class eu.amidst.core.inference.MPEInference
 
getEstimate() - Method in interface eu.amidst.core.inference.PointEstimator
Returns the log probability of the computed estimate.
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_ConditionalDistribution
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_ConditionalDistribution
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the expected value of the log-normalizer for a given parent according to the given moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedLogNormalizer(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the expected value of the log-normalizer according to the given moment parameters of the parent variables.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_ConditionalDistribution
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalFromParents(Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the expected natural parameter vector given the parent moment parameters.
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_ConditionalDistribution
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedNaturalToParent(Variable, Map<Variable, MomentParameters>) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the expected natural parameter vector for a given parent variable given the moment parameters of the rest of parent variables (i.e., Co-Parents).
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Dirichlet
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_InverseGamma
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
getExpectedParameters() - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the vector of expected parameters of this EF_UnivariateDistribution.
getExpectedValue(Variable, Function<Double, Double>) - Method in class eu.amidst.core.inference.ImportanceSampling
Returns the expected value.
getExpectedValue(Variable, Function<Double, Double>) - Method in class eu.amidst.core.inference.ImportanceSamplingRobust
Returns the expected value.
getExpectedValue(Variable, Function<Double, Double>) - Method in interface eu.amidst.core.inference.InferenceAlgorithm
Returns the expected value.
getExpectedValue(Variable, BayesianNetwork, Function<Double, Double>) - Static method in class eu.amidst.core.inference.InferenceEngine
Returns the expected value given an input Variable, BayesianNetwork, and a Function.
getFading() - Method in class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
 
getFadingFactor() - Method in class eu.amidst.core.conceptdrift.MaximumLikelihoodFading
Returns the fading factor.
getFadingFactor() - Method in class eu.amidst.core.conceptdrift.SVBFading
 
getFadingFactor() - Method in class eu.amidst.core.conceptdrift.utils.Fading
 
getFileExtension() - Method in class eu.amidst.core.datastream.filereaders.arffFileReader.ARFFDataWriter
Returns the filename extension.
getFileExtension() - Method in interface eu.amidst.core.datastream.filereaders.DataFileWriter
Returns the filename extension.
getFilteredPosterior(Variable) - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns the filtered posterior distribution of a given Variable object.
getFilteredPosterior(Variable) - Method in class eu.amidst.dynamic.inference.DynamicVMP
Returns the filtered posterior distribution of a given Variable object.
getFilteredPosterior(Variable) - Method in class eu.amidst.dynamic.inference.FactoredFrontierForDBN
Returns the filtered posterior distribution of a given Variable object.
getFilteredPosterior(Variable) - Method in interface eu.amidst.dynamic.inference.InferenceAlgorithmForDBN
Returns the filtered posterior distribution of a given Variable object.
getFilteredPosterior(Variable) - Static method in class eu.amidst.dynamic.inference.InferenceEngineForDBN
Returns the filtered posterior distribution of a given Variable object.
getFilteredPosterior(Variable) - Method in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Returns the filtered posterior distribution of a given Variable object.
getFullListOfAttributes() - Method in class eu.amidst.core.datastream.Attributes
Returns the full list of all the Attributes, including the special ones (i.e.
getGammaParameterVariable() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Returns the Gamma parameter variable of the variance value.
getGlobalDAG() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetector
 
getGroupedPosteriorDistributions() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
 
getHiddenDynamicNaiveBayesStructure(Attributes, boolean) - Static method in class eu.amidst.flinklink.examples.misc.DynamicParallelVMPExtended
Creates a DAG object with a naive Bayes structure from a given DataStream.
getHiddenMultinomial() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicLatentClassificationModel
method for getting the hidden multinomial variable
getHiddenMultinomial() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.LatentClassificationModel
method for getting the hidden multinomial variable
getHiddenNaiveBayesStructure(Attributes, String, int) - Static method in class eu.amidst.core.utils.DAGGenerator
This method creates a DAG object with a naive Bayes structure for the attributes of the passed data stream.
getHiddenNaiveBayesStructure(Attributes) - Static method in class eu.amidst.flinklink.examples.misc.ParallelVMPExtended
Creates a DAG object with a naive Bayes structure from a given DataStream.
getHiddenNaiveBayesStructure(DataFlink<DataInstance>) - Static method in class eu.amidst.flinklink.examples.misc.SetBNwithHidden
 
getHiddenNaiveBayesStructure(DataStream<DataInstance>) - Static method in class eu.amidst.jmlr2015.examples.ExperimentsParallelSVB
Creates a DAG object with a naive Bayes structure from a given DataStream.
getHiddenVar() - Method in class eu.amidst.latentvariablemodels.staticmodels.GaussianMixture
Method to obtain the hidden (latent) variable
getHiddenVars() - Method in class eu.amidst.core.conceptdrift.NaiveBayesVirtualConceptDriftDetector
Returns the list of global hidden variables
getHiddenVars() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetector
Returns the list of global hidden variables
getHiddenVars() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetectorDBN
Returns the list of global hidden variables
getHuginBN() - Method in class eu.amidst.huginlink.inference.HuginInference
 
getId() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.DataPosterior
Returns the id of the item
getIndex() - Method in class eu.amidst.core.datastream.Attribute
Returns the index of this Attribute.
getIndexFromDataInstance(List<Variable>, DataInstance) - Static method in class eu.amidst.core.utils.MultinomialIndex
Returns the index of an assignment given a set of multinomial variables and a DataInstance.
getIndexFromVariableAssignment(List<Variable>, Assignment) - Static method in class eu.amidst.core.utils.MultinomialIndex
Returns the index of an assignment given a set of multinomial variables and a corresponding assignment.
getIndexFromVariableAssignment(List<Variable>, List<Double>) - Static method in class eu.amidst.core.utils.MultinomialIndex
Returns the index of an assignment given the set of multinomial variables and their corresponding values.
getIndexFromVariableAssignment(List<Variable>, double[]) - Static method in class eu.amidst.core.utils.MultinomialIndex
Returns the index of an assignment given a set of multinomial variables and their corresponding values.
getIndexOfState(String) - Method in class eu.amidst.core.variables.stateSpaceTypes.FiniteStateSpace
Returns the index of the state space given its name.
getIndicatorTime0() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
getIndicatorTimeT() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
getIndicatorVar() - Method in class eu.amidst.core.distribution.IndicatorDistribution
Returns the indicator variable of this IndicatorDistribution.
getInferenceAlgoPredict() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicClassifier
 
getInferenceAlgoPredict() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.Classifier
Method to obtain the inference algorithm used for making the predictions.
getIntercept() - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the intercept of this ConditionalLinearGaussian distribution.
getIntercept(int) - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Returns the intercept for a given state.
getInterfaceVariable() - Method in interface eu.amidst.core.variables.Variable
Returns the interface variable.
getInterfaceVariable(Variable) - Method in class eu.amidst.dynamic.variables.DynamicVariables
Returns the interface variable of a corresponding given variable.
getInterfaceVariableByName(String) - Method in class eu.amidst.dynamic.variables.DynamicVariables
Returns an interface variable given its name.
getIntOption(String) - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getIntOption(String) - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getIntOption(String) - Method in interface eu.amidst.core.utils.AmidstOptionsHandler
Returns the int value of an option given its name.
getIntOption(String) - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Returns the int value of an option given its name.
getIntOption(String) - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getIntOption(String) - Static method in class eu.amidst.flinklink.examples.misc.GenerateRandom
 
getK() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getK() - Static method in class eu.amidst.cim2015.examples.ParallelKMeans
 
getLambdaMomentParameter() - Method in class eu.amidst.core.learning.parametric.bayesian.DriftSVB
 
getLambdaMomentParameters() - Method in class eu.amidst.core.learning.parametric.bayesian.MultiDriftSVB
 
getLambdaMomentParameters() - Method in class eu.amidst.lda.core.MultiDriftLDAv1
 
getLambdaMomentParameters() - Method in class eu.amidst.lda.core.MultiDriftLDAv2
 
getLambdaNaturalParameter() - Method in class eu.amidst.core.learning.parametric.bayesian.DriftSVB
 
getLambdaNaturalParameters() - Method in class eu.amidst.core.learning.parametric.bayesian.MultiDriftSVB
 
getLambdaNaturalParameters() - Method in class eu.amidst.lda.core.MultiDriftLDAv1
 
getLambdaNaturalParameters() - Method in class eu.amidst.lda.core.MultiDriftLDAv2
 
getLastFilteredPosteriorInTheSequence(DataSequence, Variable) - Static method in class eu.amidst.dynamic.inference.InferenceEngineForDBN
Returns the last filtered posterior distribution of a given Variable object in an input DataSequence.
getLastPredictivePosteriorInTheSequence(DataSequence, Variable, int) - Static method in class eu.amidst.dynamic.inference.InferenceEngineForDBN
Returns the last predictive posterior distribution of a given Variable object in an input DataSequence.
getLearningAlgorithm() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
getLearningAlgorithmFlink() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
getLearntBayesianNetwork() - Method in class eu.amidst.core.conceptdrift.NaiveBayesVirtualConceptDriftDetector
Returns the Bayesian network learnt with the concept drift adaptation method.
getLearntBayesianNetwork() - Method in class eu.amidst.core.conceptdrift.SVBFading
 
getLearntBayesianNetwork() - Method in class eu.amidst.core.learning.parametric.bayesian.DriftSVB
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.core.learning.parametric.bayesian.MultiDriftSVB
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.core.learning.parametric.bayesian.ParallelSVB
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in interface eu.amidst.core.learning.parametric.ParameterLearningAlgorithm
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood2
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in interface eu.amidst.flinklink.core.learning.parametric.ParameterLearningAlgorithm
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.flinklink.core.learning.parametric.StochasticVI
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.lda.core.MultiDriftLDAv1
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.lda.core.MultiDriftLDAv2
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in class eu.amidst.sparklink.core.learning.ParallelMaximumLikelihood
Returns the learnt BayesianNetwork model.
getLearntBayesianNetwork() - Method in interface eu.amidst.sparklink.core.learning.ParameterLearningAlgorithm
Returns the learnt BayesianNetwork model.
getLearntDBN() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.SVB
Returns the learnt DynamicBayesianNetwork.
getLearntDBN() - Method in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Returns the learnt DynamicBayesianNetwork.
getLearntDBN() - Method in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Returns the learnt DynamicBayesianNetwork.
getLearntDBN() - Method in interface eu.amidst.dynamic.learning.parametric.ParameterLearningAlgorithm
Returns the learnt DynamicBayesianNetwork.
getLearntDynamicBayesianNetwork() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetector
Returns the Dynamic Bayesian network learnt with the concept drift adaptation method.
getLearntDynamicBayesianNetwork() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetectorDBN
Returns the Dynamic Bayesian network learnt with the concept drift adaptation method.
getLearntDynamicBayesianNetwork() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
getLearntDynamicBayesianNetwork() - Method in interface eu.amidst.flinklink.core.learning.dynamic.ParameterLearningAlgorithm
 
getList() - Method in interface eu.amidst.core.datastream.DataOnMemory
Returns a list with all the DataInstance objects in the data set.
getList() - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Returns a list with all the DataInstance objects in the data set.
getList() - Method in class eu.amidst.core.datastream.filereaders.DataOnMemoryFromFile
Returns a list with all the DataInstance objects in the data set.
getList() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataOnMemoryFromFile
Returns a list with all the DataInstance objects in the data set.
getList() - Method in class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Returns a list with all the DataInstance objects in the data set.
getListOfDynamicVariables() - Method in class eu.amidst.dynamic.variables.DynamicVariables
Returns the list of Dynamic Variables.
getListOfInterfaceVariables() - Method in class eu.amidst.dynamic.variables.DynamicVariables
Returns the list of Interface Variables.
getListOfNonParameterVariables() - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Returns the list of non parameter variables included in this EF_LearningBayesianNetwork model.
getListOfNonSpecialAttributes() - Method in class eu.amidst.core.datastream.Attributes
Returns the list of this Attributes, except the TIME_ID and SEQUENCE_ID ones.
getListOfParamaterVariables() - Method in class eu.amidst.core.exponentialfamily.ParameterVariables
Returns a list including all the parameter variables.
getListOfParametersVariables() - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Returns the list of parameter variables included in this EF_LearningBayesianNetwork model.
getListOfVariables() - Method in class eu.amidst.core.variables.Variables
Returns the list of Variables.
getListOptions(String) - Static method in class eu.amidst.core.utils.OptionParser
Returns the list of options of a given a class name ID.
getListOptionsRecursively(String) - Static method in class eu.amidst.core.utils.OptionParser
Returns recursively the list of options of a given a class name ID.
getLocalSampler() - Method in class eu.amidst.flinklink.core.utils.BayesianNetworkSampler
 
getLocalSampler() - Method in class eu.amidst.sparklink.core.util.BayesianNetworkSampler
 
getLocalThreshold() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the log conditional probability of an Assignment given this ConditionalDistribution.
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.ConditionalDistribution
Returns the log conditional probability of an Assignment given this ConditionalDistribution.
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the log conditional probability of an Assignment given this ConditionalDistribution.
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.IndicatorDistribution
Returns the log conditional probability of an Assignment given this ConditionalDistribution.
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Returns the log conditional probability of an Assignment given this ConditionalDistribution.
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the log conditional probability of an Assignment given this ConditionalDistribution.
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the logarithm of the evaluated density function in a point after restricting the distribution to a given parent Assignment.
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns the logarithm of the evaluated density function in a point after conditioning the distribution to a given parent Assignment.
getLogConditionalProbability(Assignment) - Method in class eu.amidst.core.distribution.UnivariateDistribution
Returns the log conditional probability of an Assignment given this ConditionalDistribution.
getLogMarginalProbability() - Method in class eu.amidst.core.conceptdrift.SVBFading
 
getLogMarginalProbability() - Method in class eu.amidst.core.learning.parametric.bayesian.ParallelSVB
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Returns the log marginal probability.
getLogMarginalProbability() - Method in interface eu.amidst.core.learning.parametric.ParameterLearningAlgorithm
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.SVB
Returns the log of the marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Returns the log of the marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Returns the log of the marginal probability.
getLogMarginalProbability() - Method in interface eu.amidst.dynamic.learning.parametric.ParameterLearningAlgorithm
Returns the log of the marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood2
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
Returns the log marginal probability.
getLogMarginalProbability() - Method in interface eu.amidst.flinklink.core.learning.parametric.ParameterLearningAlgorithm
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.flinklink.core.learning.parametric.StochasticVI
Returns the log marginal probability.
getLogMarginalProbability() - Method in class eu.amidst.sparklink.core.learning.ParallelMaximumLikelihood
Returns the log marginal probability.
getLogMarginalProbability() - Method in interface eu.amidst.sparklink.core.learning.ParameterLearningAlgorithm
Returns the log marginal probability.
getLogProbability(Assignment) - Method in class eu.amidst.core.distribution.ConditionalDistribution
Returns the log probability of an Assignment object according to this Distribution.
getLogProbability(double) - Method in class eu.amidst.core.distribution.DeltaDistribution
Returns the log probability for a given input value.
getLogProbability(Assignment) - Method in class eu.amidst.core.distribution.Distribution
Returns the log probability of an Assignment object according to this Distribution.
getLogProbability(double) - Method in class eu.amidst.core.distribution.GaussianMixture
Returns the log probability for a given input value.
getLogProbability(double) - Method in class eu.amidst.core.distribution.Multinomial
Returns the logarithm of the probability for a given variable state.
getLogProbability(double) - Method in class eu.amidst.core.distribution.Normal
Returns the log probability for a given input value.
getLogProbability(double) - Method in class eu.amidst.core.distribution.Uniform
Returns the log probability for a given input value.
getLogProbability(double) - Method in class eu.amidst.core.distribution.UnivariateDistribution
Returns the log probability for a given input value.
getLogProbability(Assignment) - Method in class eu.amidst.core.distribution.UnivariateDistribution
Returns the log probability of an Assignment object according to this Distribution.
getLogProbabilityOfEstimate() - Method in class eu.amidst.core.inference.MAPInference
Returns the log probability of the computed estimate.
getLogProbabilityOfEstimate() - Method in class eu.amidst.core.inference.MPEInference
 
getLogProbabilityOfEstimate() - Method in interface eu.amidst.core.inference.PointEstimator
Returns the log probability of the computed estimate.
getLogProbabilityOfEvidence() - Method in class eu.amidst.core.inference.ImportanceSampling
Returns the log probability of the evidence.
getLogProbabilityOfEvidence() - Method in class eu.amidst.core.inference.ImportanceSamplingRobust
Returns the log probability of the evidence.
getLogProbabilityOfEvidence() - Method in interface eu.amidst.core.inference.InferenceAlgorithm
Returns the log probability of the evidence.
getLogProbabilityOfEvidence() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the log probability of the evidence.
getLogProbabilityOfEvidence() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Returns the log probability of the evidence.
getLogProbabilityOfEvidence() - Method in class eu.amidst.huginlink.inference.HuginInference
Returns the log probability of the evidence.
getLogProbabilityOfEvidence() - Method in class eu.amidst.lda.core.PlateauLDA
Returns the log probability of the evidence.
getLogProbabilityOfEvidence() - Method in class eu.amidst.lda.flink.PlateauLDAFlink
Returns the log probability of the evidence.
getLogProbabilityOfEvidenceTime0() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the log probability of the evidence at time T.
getLogProbabilityOfEvidenceTimeT() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the log probability of the evidence at time T.
getLogProbabiltyOf(Assignment) - Method in class eu.amidst.core.models.BayesianNetwork
Returns the log probability of a valid assignment.
getLogProbabiltyOfFullAssignmentTime0(Assignment) - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the log probability of a valid assignment at Time 0.
getLogProbabiltyOfFullAssignmentTimeT(Assignment) - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the log probability of a valid assignment at Time T.
getLowerInterval() - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
getMainVar() - Method in interface eu.amidst.core.models.ParentSet
Returns the main variable.
getMainVariable() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the main Variable.
getMAPestimate() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns the MAP sequence found as an Assignment of variables
getMAPestimateLogProbability() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
 
getMAPestimateProbability() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
 
getMAPsequence() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns the MAP sequence found as array of integers
getMatrixByPosition(int) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getMaxGlobaIter() - Method in class eu.amidst.flinklink.core.learning.parametric.utils.VMPParameterv1
 
getMaximumLocalIterations() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
getMaxInterval() - Method in class eu.amidst.core.variables.stateSpaceTypes.RealStateSpace
Returns the maximum value of the interval.
getMaxIter() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the maximum number of iterations of this MessagePassingAlgorithm.
getMean() - Method in class eu.amidst.core.distribution.Normal
Returns the mean of this Normal distribution.
getMean() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
 
getMean() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
 
getMeanParameterVariable() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Returns the Normal parameter variable of the mean value.
getMergedClassVarModels() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns the list of models with merged class variable.
getMinInterval() - Method in class eu.amidst.core.variables.stateSpaceTypes.RealStateSpace
Returns the minimum value of the interval.
getModel() - Static method in class eu.amidst.dynamic.inference.InferenceEngineForDBN
Returns the original model of this InferenceEngineForDBN.
getModel() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
getModel() - Method in class eu.amidst.latentvariablemodels.staticmodels.LDA
 
getModel() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
getModelDescription(StringBuilder, int) - Method in class moa.classifiers.bayes.AmidstClassifier
getModelDescription(StringBuilder, int) - Method in class moa.classifiers.bayes.AmidstRegressor
getModelDescription(StringBuilder, int) - Method in class moa.clusterers.AmidstClusteringAlgorithm
getModelMeasurementsImpl() - Method in class moa.classifiers.bayes.AmidstClassifier
getModelMeasurementsImpl() - Method in class moa.classifiers.bayes.AmidstRegressor
getModelMeasurementsImpl() - Method in class moa.clusterers.AmidstClusteringAlgorithm
getMomentParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Returns the vector of moment parameters of this EF_Distribution.
getMomentParameters() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Returns the vector of moment parameters of this EF_DynamicDistribution.
getMomentParents() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the MomentParameters of the exponential family distributions of the parents of this Node.
getMultinomial(Assignment) - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Returns a Multinomial distribution given an Assignment for the set of parents.
getMultinomial(int) - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the Multinomial distribution for a given position.
getMultinomial(Assignment) - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the Multinomial distribution for a given parents assignment.
getMultinomialDistributions() - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the list of Multinomial distributions.
getMultinomialParents() - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the list of the multinomial parents.
getMultinomialParents() - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the list of the Multinomial parents.
getMultinomialParents() - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns the list of multinomial parents.
getNaiveBayesStructure(DataStream<DataInstance>, int) - Static method in class eu.amidst.core.examples.learning.MaximimumLikelihoodByBatchExample
This method returns a DAG object with naive Bayes structure for the attributes of the passed data stream.
getNaiveBayesStructure(Attributes, String) - Static method in class eu.amidst.core.utils.DAGGenerator
Returns the graphical structure for this NaiveBayesClassifier.
getNaiveBayesStructure(Attributes, int) - Static method in class eu.amidst.dynamic.examples.learning.MLforDBNfromDataset
This method returns a DynamicDAG object with naive Bayes structure for the given attributes.
getName() - Method in class eu.amidst.core.datastream.Attribute
Returns the name of this Attribute.
getName() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the name of this Node.
getName() - Method in class eu.amidst.core.models.BayesianNetwork
Returns the name of the BN
getName() - Method in class eu.amidst.core.models.DAG
Returns the name of the DAG
getName() - Method in interface eu.amidst.core.variables.Variable
Returns the name of this Variable.
getName() - Method in class eu.amidst.core.variables.VariableBuilder
Returns the name of this Variable.
getName() - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the name of the dynamic BN.
getName() - Method in class eu.amidst.dynamic.models.DynamicDAG
Returns the name of this DynamicDAG.
getName() - Method in interface eu.amidst.flinklink.core.data.DataFlink
 
getNameTarget() - Method in class eu.amidst.huginlink.learning.ParallelTAN
Gets the name of the class variable in the TAN model.
getNaturalParameterPosterior() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the natural parameter posterior.
getNaturalParameterPrior() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the natural parameter priors.
getNaturalParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Returns the vector of natural parameters of this EF_Distribution.
getNaturalParameters() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Returns the vector of natural parameters of this EF_Distribution.
getNaturalParameters() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Returns the vector of natural parameters of this EF_DynamicDistribution.
getnBatches() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
getnBatches() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
getNDefaultValues() - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
getNode() - Method in class eu.amidst.core.inference.messagepassing.Message
Returns the Node object of this Message.
getNodeOfNonReplicatedVar(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
getNodeOfVar(Variable, int) - Method in class eu.amidst.core.conceptdrift.utils.PlateuHiddenVariableConceptDrift
 
getNodeOfVar(Variable) - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the Node associated with a given Variable.
getNodeOfVar(Variable, int) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Returns the Node for a given variable and slice.
getNodeOfVarTime0(Variable) - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the Node corresponding to a given Variable at time 0.
getNodeOfVarTimeT(Variable, int) - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the Node corresponding to a given Variable at time T.
getNodes() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the list of nodes.
getnOfGaussianHiddenVars_() - Method in class moa.classifiers.bayes.AmidstClassifier
Returns the number of Gaussian hidden variables in this AmidstClassifier.
getnOfGaussianHiddenVars_() - Method in class moa.classifiers.bayes.AmidstRegressor
Returns the number of Gaussian hidden variables in this AmidstRegressor.
getnOfGaussianHiddenVars_() - Method in class weka.classifiers.bayes.AmidstClassifier
 
getnOfGaussianHiddenVars_() - Method in class weka.classifiers.bayes.AmidstRegressor
 
getnOfStatesMultHiddenVar_() - Method in class moa.classifiers.bayes.AmidstClassifier
Returns the number of states of the Multinomial hidden variables in this AmidstClassifier.
getnOfStatesMultHiddenVar_() - Method in class moa.classifiers.bayes.AmidstRegressor
Returns the number of states of the Multinomial hidden variables in this AmidstRegressor.
getnOfStatesMultHiddenVar_() - Method in class weka.classifiers.bayes.AmidstClassifier
 
getNonMultinomialParents() - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the list of the multinomial parents.
getNonReplicatedVariables() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Returns the list of non replicated Variables
getNonReplictedNodes() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
getNonZeroEntries() - Method in class eu.amidst.core.utils.SparseVector
 
getNonZeroEntries() - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
getNormal(Assignment) - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the univariate Normal distribution after conditioning this distribution to a given parent assignment.
getNormal(int) - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns a Normal distribution given an input position in the array of distributions.
getNormal(Assignment) - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns a Normal distribution given a multinomial parent assignment.
getNormal_NormalParentsDistribution(Assignment) - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns a ConditionalLinearGaussian distribution given an assignment over a set of Multinomial parents.
getNormal_NormalParentsDistribution(int) - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns a ConditionalLinearGaussian distribution given an input position in the array of distributions.
getNormalDistributions() - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns the list of Normal distributions.
getNormalGammaParameterVariable() - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Returns the NormalGamma parameter variable of the mean value.
getNormalParents() - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the list of the Normal parents.
getNtopics() - Method in class eu.amidst.latentvariablemodels.staticmodels.LDA
 
getNumberOfAttributes() - Method in class eu.amidst.core.datastream.Attributes
Returns the number of this Attributes.
getNumberOfBaseDistributions() - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the total number of base distributions.
getNumberOfBaseVectors() - Method in class eu.amidst.core.utils.CompoundVector
Returns the number of base vectors
getNumberOfBatches() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the number of batches.
getNumberOfContinuousVars() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
getNumberOfDataInstances() - Method in interface eu.amidst.core.datastream.DataOnMemory
Returns the number of data instances in the data set.
getNumberOfDataInstances() - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Returns the number of data instances in the data set.
getNumberOfDataInstances() - Method in class eu.amidst.core.datastream.filereaders.DataOnMemoryFromFile
Returns the number of data instances in the data set.
getNumberOfDataInstances() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataOnMemoryFromFile
Returns the number of data instances in the data set.
getNumberOfDataInstances() - Method in class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Returns the number of data instances in the data set.
getNumberOfDiscreteHiddenVars() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
getNumberOfDiscreteVars() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
getNumberOfDynamicVars() - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the total number of variables in this DynamicBayesianNetwork.
getNumberOfEpochs() - Method in class eu.amidst.flinklink.core.conceptdrift.IdentifiableIDAModel
 
getNumberOfEpochs() - Method in interface eu.amidst.flinklink.core.learning.parametric.utils.IdenitifableModelling
 
getNumberOfEpochs() - Method in class eu.amidst.flinklink.core.learning.parametric.utils.ParameterIdentifiableModel
 
getNumberOfGlobalVars() - Method in class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
 
getNumberOfIterations() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the number of iterations of this MessagePassingAlgorithm.
getNumberOfLatentVariables() - Method in class eu.amidst.latentvariablemodels.staticmodels.FactorAnalysis
Sets the number of latent (i.e.
getNumberOfLatentVariables() - Method in class eu.amidst.latentvariablemodels.staticmodels.MixtureOfFactorAnalysers
Obtains the number of continuous latent variables
getNumberOfLinks() - Method in class eu.amidst.core.models.DAG
Returns the total number of links in this DAG.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.DeltaDistribution
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.Distribution
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.GaussianMixture
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.IndicatorDistribution
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.Multinomial
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.Normal
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns the total number of parameters in this Distribution.
getNumberOfParameters() - Method in class eu.amidst.core.distribution.Uniform
Returns the total number of parameters in this Distribution.
getNumberOfParentAssignments() - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the number of parent assignments.
getNumberOfParentAssignments() - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the number of parent assignments.
getNumberOfParentAssignments() - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns the number of parent assignments.
getNumberOfParents() - Method in interface eu.amidst.core.models.ParentSet
Returns the number of parents.
getNumberOfPossibleAssignments(List<Variable>) - Static method in class eu.amidst.core.utils.MultinomialIndex
Returns the number of possible assignments for a list of variables.
getNumberOfReplications() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Returns the number of replications of this PlateuStructure.
getNumberOfSamples() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
getNumberOfStates() - Method in class eu.amidst.core.datastream.Attribute
Returns the number of states of this attribute, in case it has a finite state space.
getNumberOfStates() - Method in class eu.amidst.core.variables.stateSpaceTypes.FiniteStateSpace
Returns the number of states.
getNumberOfStates() - Method in class eu.amidst.core.variables.stateSpaceTypes.SparseFiniteStateSpace
Returns the number of states.
getNumberOfStates() - Method in interface eu.amidst.core.variables.Variable
Returns the number of states of this Variable, in case it has a finite state space.
getNumberOfStates() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
getNumberOfStatesLatentDiscreteVar() - Method in class eu.amidst.latentvariablemodels.staticmodels.MixtureOfFactorAnalysers
Obtains the number of states in the discrete latent variable
getNumberOfVars() - Method in class eu.amidst.core.exponentialfamily.ParameterVariables
Returns the number of parameter variables.
getNumberOfVars() - Method in class eu.amidst.core.models.BayesianNetwork
Returns the total number of variables in this BayesianNetwork.
getNumberOfVars() - Method in class eu.amidst.core.variables.Variables
Returns the number of all variables.
getNumberOfVars() - Method in class eu.amidst.dynamic.models.DynamicBayesianNetwork
Returns the total number of variables in this DynamicBayesianNetwork.
getNumberOfVars() - Method in class eu.amidst.dynamic.variables.DynamicVariables
Returns the total number of variables.
getNumClusters() - Method in class moa.clusterers.AmidstClusteringAlgorithm
Returns the number of clusters in this AmidstClusteringAlgorithm.
getNumContinuousHidden() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicLatentClassificationModel
 
getNumContinuousHidden() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.LatentClassificationModel
Method to obtain the number of continuous hidden variables
getNumCores() - Method in class eu.amidst.huginlink.learning.ParallelPC
Returns the number of cores to be used during the learning process.
getNumCores() - Method in class eu.amidst.huginlink.learning.ParallelTAN
Returns the number of cores to be used during the learning process.
getNumDiscVars() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getNumDiscVars() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getNumDiscVars() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getNumDiscVars() - Static method in class eu.amidst.flinklink.examples.misc.GenerateRandom
 
getNumGaussVars() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getNumGaussVars() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getNumGaussVars() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getNumGaussVars() - Static method in class eu.amidst.flinklink.examples.misc.GenerateRandom
 
getNumHidden() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.FactorialHMM
 
getNumHidden() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.KalmanFilter
 
getNumHiddenGaussVars() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getNumHiddenGaussVars() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getNumOfSequences() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
getNumSamplesOnMemory() - Method in class eu.amidst.huginlink.learning.ParallelPC
Returns the number of samples on memory.
getNumSamplesOnMemory() - Method in class eu.amidst.huginlink.learning.ParallelTAN
Returns the number of samples on memory.
getNumStates() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getNumStates() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getNumStates() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getNumStates() - Static method in class eu.amidst.flinklink.examples.misc.GenerateRandom
 
getNumStates() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.AutoRegressiveHMM
 
getNumStates() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.InputOutputHMM
 
getNumStates() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.SwitchingKalmanFilter
 
getNumStates() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.HODE
 
getNumStatesHidden() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicLatentClassificationModel
 
getNumStatesHidden() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.LatentClassificationModel
method for getting number of states of the hidden multinomial variable
getNumStatesHiddenDiscVars() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getNumStatesHiddenDiscVars() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getNumStatesHiddenVar() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.HiddenMarkovModel
 
getNumStatesHiddenVar() - Method in class eu.amidst.latentvariablemodels.staticmodels.GaussianMixture
Method to obtain the number of states of the hidden (latent) variable
getOption(String) - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getOption(String) - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getOption(String) - Method in interface eu.amidst.core.utils.AmidstOptionsHandler
Returns the String value of an option given its name.
getOption(String) - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Returns the String value of an option given its name.
getOption(String) - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getOption(String) - Static method in class eu.amidst.flinklink.examples.misc.GenerateRandom
 
getOptions() - Method in class weka.classifiers.bayes.AmidstClassifier
Gets the current settings of the classifier.
getOptions() - Method in class weka.classifiers.bayes.AmidstRegressor
Gets the current settings of the classifier.
getOriginalModel() - Method in class eu.amidst.core.inference.ImportanceSampling
Returns the original model of this InferenceAlgorithm.
getOriginalModel() - Method in class eu.amidst.core.inference.ImportanceSamplingRobust
Returns the original model of this InferenceAlgorithm.
getOriginalModel() - Method in interface eu.amidst.core.inference.InferenceAlgorithm
Returns the original model of this InferenceAlgorithm.
getOriginalModel() - Method in class eu.amidst.core.inference.MAPInference
Returns the original model of this PointEstimator.
getOriginalModel() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the original model of this InferenceAlgorithm.
getOriginalModel() - Method in class eu.amidst.core.inference.MPEInference
Returns the original model of this PointEstimator.
getOriginalModel() - Method in interface eu.amidst.core.inference.PointEstimator
Returns the original model of this PointEstimator.
getOriginalModel() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns the original model of this InferenceAlgorithmForDBN.
getOriginalModel() - Method in class eu.amidst.dynamic.inference.DynamicVMP
Returns the original model of this InferenceAlgorithmForDBN.
getOriginalModel() - Method in class eu.amidst.dynamic.inference.FactoredFrontierForDBN
Returns the original model of this InferenceAlgorithmForDBN.
getOriginalModel() - Method in interface eu.amidst.dynamic.inference.InferenceAlgorithmForDBN
Returns the original model of this InferenceAlgorithmForDBN.
getOriginalModel() - Method in class eu.amidst.huginlink.inference.HuginInference
Returns the original model of this InferenceAlgorithm.
getOriginalModel() - Method in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Returns the original model of this InferenceAlgorithmForDBN.
getParameterPosterior(Variable) - Method in interface eu.amidst.core.learning.parametric.bayesian.BayesianParameterLearningAlgorithm
Returns the parameter posterior.
getParameterPosterior(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
getParameterPosterior(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the parameter posterior.
getParameterPosterior(Variable) - Method in interface eu.amidst.flinklink.core.learning.parametric.BayesianParameterLearningAlgorithm
Returns the parameter posterior.
getParameterPosterior(Variable) - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
getParameterPosterior(Variable) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
Returns the parameter posterior.
getParameterPosterior(Variable) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
getParameterPosterior(Variable) - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
getParameterPosterior(Variable) - Method in class eu.amidst.flinklink.core.learning.parametric.StochasticVI
 
getParameterPosteriorTime0(Variable) - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
getParameterPosteriorTimeT(Variable) - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
getParameters() - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.DeltaDistribution
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.Distribution
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.GaussianMixture
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.IndicatorDistribution
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.Multinomial
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.Normal
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.distribution.Uniform
Returns the parameters of this Distribution.
getParameters() - Method in class eu.amidst.core.models.BayesianNetwork
Returns the parameter values of this BayesianNetwork.
getParametersVariables() - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Returns the set of parameter variables included in this EF_LearningBayesianNetwork model.
getParents() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the list of parent nodes of this Node.
getParents() - Method in interface eu.amidst.core.models.ParentSet
Returns a list of parents.
getParentSet(Variable) - Method in class eu.amidst.core.models.DAG
Returns the parent set of a given variable.
getParentSets() - Method in class eu.amidst.core.models.DAG
Returns the list of all parent sets in this DAG.
getParentSetsTime0() - Method in class eu.amidst.dynamic.models.DynamicDAG
Returns a list of parents at time 0.
getParentSetsTimeT() - Method in class eu.amidst.dynamic.models.DynamicDAG
Returns a list of parents at time T.
getParentSetTime0(Variable) - Method in class eu.amidst.dynamic.models.DynamicDAG
Returns the parent set of a given variable at time 0.
getParentSetTimeT(Variable) - Method in class eu.amidst.dynamic.models.DynamicDAG
Returns the parent set of a given variable at time T.
getPathToFile() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getPathToFile() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getPDist() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the exponential family conditional distribution of this Node.
getPlateauMomentParameterPosterior() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
getPlateauNaturalParameterPosterior() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
getPlateauNaturalParameterPrior() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
getPlateauStructure() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.SVB
Returns the dynamic plateu structure of this DynamicSVB.
getPlateuStructure() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the plateu structure of this SVB.
getPlateuStructure() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
getPlateuStructure() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
getPlateuStructure() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
getPosterior(Variable) - Method in class eu.amidst.core.inference.ImportanceSampling
Returns the posterior of a given Variable.
getPosterior(Variable) - Method in class eu.amidst.core.inference.ImportanceSamplingRobust
Returns the posterior of a given Variable.
getPosterior(Variable) - Method in interface eu.amidst.core.inference.InferenceAlgorithm
Returns the posterior of a given Variable.
getPosterior(int) - Method in interface eu.amidst.core.inference.InferenceAlgorithm
Returns the posterior of a given Variable.
getPosterior(Variable, BayesianNetwork, Assignment) - Static method in class eu.amidst.core.inference.InferenceEngine
Returns the posterior distribution for a given input Variable, BayesianNetwork, and a Assignment.
getPosterior(Variable, BayesianNetwork) - Static method in class eu.amidst.core.inference.InferenceEngine
Returns the posterior distribution for a given input Variable and a BayesianNetwork.
getPosterior(Variable) - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the posterior of a given Variable.
getPosterior(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.DataPosterior
Returns the posterior of a given variable.
getPosterior() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.DataPosteriorAssignment
 
getPosterior(Variable) - Method in class eu.amidst.huginlink.inference.HuginInference
Returns the posterior of a given Variable.
getPosteriorDistribution(String) - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
getPosteriorlDistribution(String) - Method in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
getPosteriorSampleSize() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
getPosteriorSampleSize() - Method in class eu.amidst.lda.core.PlateauLDA
 
getPrecision() - Method in class eu.amidst.core.exponentialfamily.EF_Normal
 
getPrecision() - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
 
getPredictivePosterior(Variable, int) - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns the predictive posterior distribution of a given Variable object for nTimesAhead.
getPredictivePosterior(Variable, int) - Method in class eu.amidst.dynamic.inference.DynamicVMP
Returns the predictive posterior distribution of a given Variable object for nTimesAhead.
getPredictivePosterior(Variable, int) - Method in class eu.amidst.dynamic.inference.FactoredFrontierForDBN
Returns the predictive posterior distribution of a given Variable object for nTimesAhead.
getPredictivePosterior(Variable, int) - Method in interface eu.amidst.dynamic.inference.InferenceAlgorithmForDBN
Returns the predictive posterior distribution of a given Variable object for nTimesAhead.
getPredictivePosterior(Variable, int) - Static method in class eu.amidst.dynamic.inference.InferenceEngineForDBN
Returns the predictive posterior distribution of a given Variable object for nTimesAhead.
getPredictivePosterior(Variable, int) - Method in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Returns the predictive posterior distribution of a given Variable object for nTimesAhead.
getProbabilities() - Method in class eu.amidst.core.distribution.Multinomial
Returns the set of probabilities for the different states of the variable.
getProbability(double) - Method in class eu.amidst.core.distribution.Normal
Returns the probability for a given input value.
getProbability(double) - Method in class eu.amidst.core.distribution.UnivariateDistribution
Returns the probability for a given input value.
getProbabilityOfState(int) - Method in class eu.amidst.core.distribution.Multinomial
Returns the probability value of a given position in the array of probabilities.
getProbabilityOfState(String) - Method in class eu.amidst.core.distribution.Multinomial
Returns the probability value of a given multinomial state.
getQDist() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the exponential family univariate distribution of this Node.
getQMomentParameters() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the MomentParameters of the univariate exponential family distribution of this Node.
getRandom() - Method in class eu.amidst.core.inference.messagepassing.VMP
Gets the random number generator.
getReplicatedMAPVariables() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns replicated MAP variables.
getReplicatedNodes() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
getReplicatedVariables() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
getSamples() - Method in class eu.amidst.core.inference.ImportanceSampling
Returns a Stream containing the drawn samples after running the inference.
getSampleSize() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
getSampleSize() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
getSampleSize() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
getSampleSize() - Static method in class eu.amidst.flinklink.examples.misc.GenerateRandom
 
getSamplingModel() - Method in class eu.amidst.core.inference.ImportanceSampling
 
getSamplingModel() - Method in class eu.amidst.core.inference.ImportanceSamplingRobust
 
getSamplingModel() - Method in class eu.amidst.core.inference.messagepassing.VMP
Returns the sampling model used by the inference algorithm.
getSamplingModel() - Method in interface eu.amidst.core.inference.Sampler
Returns the sampling model used by the inference algorithm.
getSd() - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the standard deviation of the variable.
getSd() - Method in class eu.amidst.core.distribution.Normal
Returns the standard deviation of this Normal distribution.
getSeed() - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
getSeed() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the seed.
getSeed() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
getSeed() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.SVB
Returns the seed value.
getSeed() - Method in class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
 
getSeed() - Method in class weka.classifiers.bayes.AmidstClassifier
 
getSeed() - Method in class weka.classifiers.bayes.AmidstRegressor
 
getSeq_id() - Method in class eu.amidst.core.datastream.Attributes
Returns the attribute sequence_id.
getSequenceID() - Method in interface eu.amidst.dynamic.datastream.DataSequence
Returns the sequence ID of this DataSequence.
getSequenceID() - Method in interface eu.amidst.dynamic.datastream.DynamicDataInstance
Returns the sequence ID of this DynamicDataInstance.
getSequenceID() - Method in interface eu.amidst.dynamic.variables.DynamicAssignment
Returns the SequenceID of this DynamicAssignment.
getSequenceID() - Method in class eu.amidst.dynamic.variables.HashMapDynamicAssignment
Returns the SequenceID of this DynamicAssignment.
getSequenceLength() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
getStatesName(int) - Method in class eu.amidst.core.variables.stateSpaceTypes.FiniteStateSpace
Returns the name of the state space given its index.
getStatesNames() - Method in class eu.amidst.core.variables.stateSpaceTypes.FiniteStateSpace
Returns the list of the state names.
getStateSpaceType() - Method in class eu.amidst.core.datastream.Attribute
Returns a StateSpaceType object describing this Attribute.
getStateSpaceType() - Method in interface eu.amidst.core.variables.Variable
Returns the state space type of this Variable.
getStateSpaceType() - Method in class eu.amidst.core.variables.VariableBuilder
Returns the state space type StateSpaceType of this variable.
getStateSpaceTypeEnum() - Method in class eu.amidst.core.variables.StateSpaceType
Returns the state space type.
getStateSpaceTypeEnum() - Method in interface eu.amidst.core.variables.Variable
Returns the state space type of this Variable.
getStreamOfFilteredPosteriors(DataSequence, Variable) - Static method in class eu.amidst.dynamic.inference.InferenceEngineForDBN
Returns the filtered posterior distributions of a given Variable object and an input DataSequence.
getStreamOfPredictivePosteriors(DataSequence, Variable, int) - Static method in class eu.amidst.dynamic.inference.InferenceEngineForDBN
Returns the predictive posterior distributions of a given Variable object and an input DataSequence.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_BayesianNetwork
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_Dirichlet
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Distribution
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Gamma
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_InverseGamma
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_JointNormalGamma
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_LearningBayesianNetwork
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Multinomial_Dirichlet
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_Normal_Gamma
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenIndependentNormalGamma
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_NormalGivenJointNormalGamma
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_SparseDirichlet
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_Dirichlet
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_SparseMultinomial_SparseDirichlet
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
getSufficientStatistics(double) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the vector of sufficient statistics for a given value.
getSufficientStatistics(Assignment) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Returns the vector of sufficient statistics for a given Assignment object.
getSufficientStatistics() - Method in class eu.amidst.core.inference.messagepassing.Node
Returns the SufficientStatistics for this Node.
getSufficientStatistics(DynamicDataInstance) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork
Returns the vector of sufficient statistics for a given DynamicDataInstance object.
getSufficientStatistics(DynamicDataInstance) - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Returns the vector of sufficient statistics for a given DynamicDataInstance object.
getSvb() - Method in class eu.amidst.core.conceptdrift.NaiveBayesVirtualConceptDriftDetector
Retuns the SVB learningn engine
getSVB() - Method in class eu.amidst.core.conceptdrift.SVBFading
 
getSVB() - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
getSvb() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetector
Retuns the SVB learningn engine
getSvb() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetectorDBN
Retuns the SVB learningn engine
getSVB() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
getSVB() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
getSVB() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
getSVB() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
getSVBEngine() - Method in class eu.amidst.core.learning.parametric.bayesian.ParallelSVB
Returns the SVB engine.
getSVI() - Method in class eu.amidst.flinklink.core.learning.parametric.StochasticVI
 
getTheta_Beta() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getTheta_beta0() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getTheta_beta0Beta() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getTheta_beta0BetaRV() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getTheta_BetaBeta() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getTheta_BetaBetaRM() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getTheta_BetaRV() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getTheta_Minus1() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
getThreshold() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Returns the threshold of this MessagePassingAlgorithm.
getTime_id() - Method in class eu.amidst.core.datastream.Attributes
Returns the attribute time_id.
getTimeID() - Method in interface eu.amidst.dynamic.datastream.DynamicDataInstance
Returns the time ID of this DynamicDataInstance.
getTimeID() - Method in interface eu.amidst.dynamic.variables.DynamicAssignment
Returns the TimeID of this DynamicAssignment.
getTimeID() - Method in class eu.amidst.dynamic.variables.HashMapDynamicAssignment
Returns the TimeID of this DynamicAssignment.
getTimeIDOfLastEvidence() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns the time of the last evidence of this InferenceAlgorithmForDBN.
getTimeIDOfLastEvidence() - Method in class eu.amidst.dynamic.inference.DynamicVMP
Returns the time of the last evidence of this InferenceAlgorithmForDBN.
getTimeIDOfLastEvidence() - Method in class eu.amidst.dynamic.inference.FactoredFrontierForDBN
Returns the time of the last evidence of this InferenceAlgorithmForDBN.
getTimeIDOfLastEvidence() - Method in interface eu.amidst.dynamic.inference.InferenceAlgorithmForDBN
Returns the time of the last evidence of this InferenceAlgorithmForDBN.
getTimeIDOfLastEvidence() - Method in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Returns the time of the last evidence of this InferenceAlgorithmForDBN.
getTimeIDOfPosterior() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns the time ID of the posterior of this InferenceAlgorithmForDBN.
getTimeIDOfPosterior() - Method in class eu.amidst.dynamic.inference.DynamicVMP
Returns the time ID of the posterior of this InferenceAlgorithmForDBN.
getTimeIDOfPosterior() - Method in class eu.amidst.dynamic.inference.FactoredFrontierForDBN
Returns the time ID of the posterior of this InferenceAlgorithmForDBN.
getTimeIDOfPosterior() - Method in interface eu.amidst.dynamic.inference.InferenceAlgorithmForDBN
Returns the time ID of the posterior of this InferenceAlgorithmForDBN.
getTimeIDOfPosterior() - Method in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Returns the time ID of the posterior of this InferenceAlgorithmForDBN.
getTopologicalOrder(DAG) - Static method in class eu.amidst.core.utils.Utils
Returns the topological order of Variables of a given DAG.
getTransitionMethod() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the transition method of this SVB.
getTransitionMethod() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
getTransitionVariance() - Method in class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
 
getUnfoldedEvidence() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
 
getUnfoldedStaticModel() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
Returns a single BayesianNetwork object, corresponding to the unfolded dynamic network over the specified number of time steps.
getUngroupedPosteriorDistributions() - Method in class eu.amidst.dynamic.inference.DynamicMAPInference
 
getUnit() - Method in class eu.amidst.core.variables.StateSpaceType
Returns the unit of this StateSpaceType.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.ConditionalDistribution
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.IndicatorDistribution
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUnivariateDistribution(Assignment) - Method in class eu.amidst.core.distribution.UnivariateDistribution
Returns the univariate distribution of an Assignment given this ConditionalDistribution.
getUpperInterval() - Method in class eu.amidst.core.exponentialfamily.EF_TruncatedExponential
 
getValue(Variable) - Method in interface eu.amidst.core.datastream.DataInstance
Returns the value assigned to a given variable.
getValue(Attribute) - Method in interface eu.amidst.core.datastream.DataInstance
Returns the value of a given Attribute stored in this DataInstance.
getValue(Attribute) - Method in class eu.amidst.core.datastream.filereaders.arffFileReader.DataRowWeka
Returns the value assigned to a given Attribute in this DataRow.
getValue(Attribute) - Method in class eu.amidst.core.datastream.filereaders.DataInstanceFromDataRow
Returns the value of a given Attribute stored in this DataInstance.
getValue(Attribute) - Method in interface eu.amidst.core.datastream.filereaders.DataRow
Returns the value assigned to a given Attribute in this DataRow.
getValue(Attribute) - Method in class eu.amidst.core.datastream.filereaders.DataRowMissing
Returns a Double.NaN value indicating that the observation is missing.
getValue(Variable) - Method in interface eu.amidst.core.variables.Assignment
Returns the value assigned to a given variable.
getValue(Variable) - Method in class eu.amidst.core.variables.HashMapAssignment
Returns the value assigned to a given variable.
getValue(Variable) - Method in class eu.amidst.core.variables.MissingAssignment
Returns the value assigned to a given variable.
getValue(Attribute, boolean) - Method in interface eu.amidst.dynamic.datastream.DynamicDataInstance
Returns the value of a given Attribute in this DynamicDataInstance.
getValue(Attribute) - Method in interface eu.amidst.dynamic.datastream.DynamicDataInstance
Returns the value of a given Attribute stored in this DataInstance.
getValue(Variable) - Method in interface eu.amidst.dynamic.datastream.DynamicDataInstance
Returns the value assigned to a given variable.
getValue(Variable) - Method in class eu.amidst.dynamic.variables.HashMapDynamicAssignment
Returns the value assigned to a given variable.
getValue(Variable) - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.DataInstanceFromAssignment
 
getValue(Attribute) - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.DataInstanceFromAssignment
 
getValue(Variable) - Method in class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments.DataInstanceFromAssignment
 
getValue(Attribute) - Method in class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments.DataInstanceFromAssignment
 
getValue(Attribute) - Method in class eu.amidst.moalink.converterFromMoaToAmidst.DataRowWeka
Returns the value assigned to a given Attribute in this DataRow.
getValue(Attribute) - Method in class eu.amidst.sparklink.core.data.DataRowSpark
 
getValue(Attribute) - Method in class eu.amidst.wekalink.converterFromWekaToAmidst.DataRowWeka
Returns the value assigned to a given Attribute in this DataRow.
getValues() - Method in class eu.amidst.core.utils.SparseVectorDefaultValue
 
getVariable() - Method in class eu.amidst.core.distribution.Distribution
Returns the variable of this Distribution.
getVariable() - Method in class eu.amidst.core.distribution.GaussianMixture
Returns the variable of this Distribution.
getVariable() - Method in class eu.amidst.core.exponentialfamily.EF_ConditionalDistribution
Returns the main variable of this EF_ConditionalDistribution.
getVariable() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicDistribution
Returns the variable of the distribution.
getVariableArrayAssignmentFromIndex(List<Variable>, int) - Static method in class eu.amidst.core.utils.MultinomialIndex
Returns an assignment given a list of variables and an input index.
getVariableAssignmentFromIndex(List<Variable>, int) - Static method in class eu.amidst.core.utils.MultinomialIndex
Returns an assignment given a list of variables and an input index.
getVariableBuilder() - Method in interface eu.amidst.core.variables.Variable
Returns the associated, properly created, VariableBuilder object.
getVariableById(int) - Method in class eu.amidst.core.exponentialfamily.ParameterVariables
Returns the parameter variable given its index.
getVariableById(int) - Method in class eu.amidst.core.variables.Variables
Returns a Variable given its ID.
getVariableById(int) - Method in class eu.amidst.dynamic.variables.DynamicVariables
Returns a variable given its ID.
getVariableByName(String) - Method in class eu.amidst.core.exponentialfamily.ParameterVariables
Returns the parameter variable given its name.
getVariableByName(String) - Method in class eu.amidst.core.variables.Variables
Returns a Variable given its name.
getVariableByName(String) - Method in class eu.amidst.dynamic.variables.DynamicVariables
Returns a variable given its name.
getVariableFromInterface(Variable) - Method in class eu.amidst.dynamic.variables.DynamicVariables
Returns the variable of a coressponding interface variable.
getVariables() - Method in interface eu.amidst.core.datastream.DataInstance
Returns the set of variables included in the assignment.
getVariables() - Method in class eu.amidst.core.models.BayesianNetwork
Returns the set of variables in this BayesianNetwork.
getVariables() - Method in class eu.amidst.core.models.DAG
Returns the set of Variables in this DAG.
getVariables() - Method in interface eu.amidst.core.potential.Potential
Returns the list of variables in this Potential.
getVariables() - Method in interface eu.amidst.core.variables.Assignment
Returns the set of variables included in the assignment.
getVariables() - Method in class eu.amidst.core.variables.HashMapAssignment
Returns the set of variables included in the assignment.
getVariables() - Method in class eu.amidst.core.variables.MissingAssignment
Returns the set of variables included in the assignment.
getVariables() - Method in class eu.amidst.dynamic.variables.HashMapDynamicAssignment
Returns the set of variables included in the assignment.
getVariables() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB.DataInstanceFromAssignment
 
getVariables() - Method in class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments.DataInstanceFromAssignment
 
getVariablesForListOfAttributes(List<Attribute>) - Method in class eu.amidst.core.variables.Variables
 
getVariablesForListOfAttributes(List<Attribute>) - Method in class eu.amidst.dynamic.variables.DynamicVariables
 
getVariance() - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the variance of the variable.
getVariance() - Method in class eu.amidst.core.distribution.Normal
Returns the variance of this Normal distribution.
getVarID() - Method in interface eu.amidst.core.variables.Variable
Returns the ID of this Variable.
getVector() - Method in class eu.amidst.core.inference.messagepassing.Message
Returns the vector of this Message.
getVector() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB.BatchOutput
 
getVectorByPosition(int) - Method in class eu.amidst.core.utils.CompoundVector
Returns a vector located at a given position in this CompoundVector.
getVectorByPosition(E) - Method in class eu.amidst.core.utils.KeyCompoundVector
Returns a vector located at a given position in this KeyCompoundVector.
getVectors() - Method in class eu.amidst.core.utils.CompoundVector
Returns a list of Vectors.
getVectorTime0() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
getVectorTimeT() - Method in class eu.amidst.dynamic.exponentialfamily.EF_DynamicBayesianNetwork.DynamiceBNCompoundVector
 
getVMP() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Returns the VMP object of this PlateuStructure.
getVMPTime0() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the VMP object at time 0 of this DynamicPlateauStructure.
getVMPTimeT() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.PlateauStructure
Returns the VMP object at time T of this DynamicPlateauStructure.
getVotesForInstance(Instance) - Method in class moa.classifiers.bayes.AmidstClassifier
getVotesForInstance(Instance) - Method in class moa.classifiers.bayes.AmidstRegressor
getVotesForInstance(Instance) - Method in class moa.clusterers.AmidstClusteringAlgorithm
getWindowSize() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
getWindowsSize() - Method in class eu.amidst.core.conceptdrift.SVBFading
 
getWindowsSize() - Method in class eu.amidst.core.learning.parametric.bayesian.ParallelSVB
Sets the windows size.
getWindowsSize() - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
getWindowsSize() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Returns the window size.
getWindowsSize() - Method in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Sets the windows size.
getWindowsSize() - Method in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Sets the windows size.
getWindowsSize() - Method in interface eu.amidst.core.learning.parametric.ParameterLearningAlgorithm
Returns the window size.
getWindowsSize() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.SVB
Returns the window size.
getWindowsSize() - Method in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Sets the windows size.
getWindowsSize() - Method in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Sets the windows size.
getWindowsSize() - Method in interface eu.amidst.dynamic.learning.parametric.ParameterLearningAlgorithm
Returns the window size.
getXYbaseMatrix() - Method in class eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.CompoundVector
 
globalELBO - Variable in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
globalELBO - Variable in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
globalELBO - Variable in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
globalELBO - Variable in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
globalThreshold - Variable in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
globalThreshold - Variable in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
globalThreshold - Variable in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
globalThreshold - Variable in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
globalThreshold - Variable in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
GlobalvsLocalUpdate - Interface in eu.amidst.flinklink.core.learning.parametric.utils
Created by andresmasegosa on 12/5/16.

H

hashCode() - Method in class eu.amidst.core.datastream.Attribute
Returns the hashCode of this Attribute.
hashCode() - Method in interface eu.amidst.core.variables.Variable
HashMapAssignment - Class in eu.amidst.core.variables
This class implements the interface Assignment and handles the assignments using a HashMap.
HashMapAssignment() - Constructor for class eu.amidst.core.variables.HashMapAssignment
Creates a new HashMapAssignment.
HashMapAssignment(int) - Constructor for class eu.amidst.core.variables.HashMapAssignment
Creates a new HashMapAssignment given the number of variables.
HashMapAssignment(Assignment) - Constructor for class eu.amidst.core.variables.HashMapAssignment
Creates a new HashMapAssignment given an Assignment object.
HashMapDynamicAssignment - Class in eu.amidst.dynamic.variables
This class implements the interface DynamicAssignment and handles the dynamic assignments using a HashMap.
HashMapDynamicAssignment(int) - Constructor for class eu.amidst.dynamic.variables.HashMapDynamicAssignment
Creates a new HashMapDynamicAssignment given the number of variables.
HiddenMarkovModel - Class in eu.amidst.latentvariablemodels.dynamicmodels
This class implements a Hidden Markov Model (HMM).
HiddenMarkovModel(Attributes) - Constructor for class eu.amidst.latentvariablemodels.dynamicmodels.HiddenMarkovModel
 
HODE - Class in eu.amidst.latentvariablemodels.staticmodels.classifiers
This class implements the HODE classification model (extended NB with a multinomial hidden as superparent).
HODE(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.classifiers.HODE
Constructor of a classifier which is initialized with the default arguments: the last variable in attributes is the class variable and importance sampling is the inference algorithm for making the predictions.
HuginConversionExample - Class in eu.amidst.core.examples.huginlink
Created by rcabanas on 24/06/16.
HuginConversionExample() - Constructor for class eu.amidst.core.examples.huginlink.HuginConversionExample
 
huginDBN - Variable in class eu.amidst.huginlink.inference.HuginInferenceForDBN
Represents the Dynamic Bayesian network model in Hugin format.
HuginInference - Class in eu.amidst.huginlink.inference
This class provides an interface to perform Bayesian network inference using the Hugin inference engine.
HuginInference() - Constructor for class eu.amidst.huginlink.inference.HuginInference
 
HuginInferenceExample - Class in eu.amidst.core.examples.huginlink
Created by rcabanas on 24/06/16.
HuginInferenceExample() - Constructor for class eu.amidst.core.examples.huginlink.HuginInferenceExample
 
HuginInferenceExample - Class in eu.amidst.huginlink.examples.inference
This example shows how to perform inference in AMIDST using the Hugin inference engine.
HuginInferenceExample() - Constructor for class eu.amidst.huginlink.examples.inference.HuginInferenceExample
 
HuginInferenceForDBN - Class in eu.amidst.huginlink.inference
This class provides an interface to perform inference over Dynamic Bayesian networks using the Hugin inference engine.
HuginInferenceForDBN() - Constructor for class eu.amidst.huginlink.inference.HuginInferenceForDBN
Class constructor.
HuginIOExample - Class in eu.amidst.core.examples.huginlink
Created by rcabanas on 24/06/16.
HuginIOExample() - Constructor for class eu.amidst.core.examples.huginlink.HuginIOExample
 

I

id - Variable in class eu.amidst.flinklink.examples.misc.KMeans.Centroid
 
IDAConceptDriftDetector - Class in eu.amidst.flinklink.core.conceptdrift
This class contains the functionality for using the concept drift apporoach based on probabilitic graphical models detailed in the following paper, Borchani et al.
IDAConceptDriftDetector() - Constructor for class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetector
 
IDAConceptDriftDetector.DriftDetector - Enum in eu.amidst.flinklink.core.conceptdrift
Represents the drift detection mode.
IDAConceptDriftDetectorDBN - Class in eu.amidst.flinklink.core.conceptdrift
This class contains the functionality for using the concept drift apporoach based on probabilitic graphical models detailed in the following paper, Borchani et al.
IDAConceptDriftDetectorDBN() - Constructor for class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetectorDBN
 
IDAConceptDriftDetectorDBN.DriftDetector - Enum in eu.amidst.flinklink.core.conceptdrift
Represents the drift detection mode.
IdenitifableModelling - Interface in eu.amidst.flinklink.core.learning.parametric.utils
Created by andresmasegosa on 18/1/16.
IdentifiableIDAModel - Class in eu.amidst.flinklink.core.conceptdrift
Created by andresmasegosa on 21/1/16.
IdentifiableIDAModel() - Constructor for class eu.amidst.flinklink.core.conceptdrift.IdentifiableIDAModel
 
ImportanceSampling - Class in eu.amidst.core.inference
This class implements the interface InferenceAlgorithm and defines the Importance Sampling algorithm.
ImportanceSampling() - Constructor for class eu.amidst.core.inference.ImportanceSampling
 
ImportanceSamplingExample - Class in eu.amidst.core.examples.inference
This example we show how to perform inference on a general Bayesian network using an importance sampling algorithm detailed in Fung, R., and Chang, K.
ImportanceSamplingExample() - Constructor for class eu.amidst.core.examples.inference.ImportanceSamplingExample
 
ImportanceSamplingRobust - Class in eu.amidst.core.inference
This class implements the interface InferenceAlgorithm and defines the Importance Sampling algorithm.
ImportanceSamplingRobust() - Constructor for class eu.amidst.core.inference.ImportanceSamplingRobust
 
INDEX_MEAN - Static variable in class eu.amidst.core.exponentialfamily.EF_Normal
 
INDEX_MEAN - Static variable in class eu.amidst.core.exponentialfamily.EF_NormalParameter
 
INDEX_PRECISION - Static variable in class eu.amidst.core.exponentialfamily.EF_Normal
 
INDEX_PRECISION - Static variable in class eu.amidst.core.exponentialfamily.EF_NormalParameter
 
IndicatorDistribution - Class in eu.amidst.core.distribution
This class extends the abstract class ConditionalDistribution and defines the Indicator distribution.
IndicatorDistribution(Variable, ConditionalDistribution) - Constructor for class eu.amidst.core.distribution.IndicatorDistribution
Creates a new IndicatorDistribution for a given indicator variable and a conditional distribution.
IndicatorType - Class in eu.amidst.core.variables.distributionTypes
This class extends the abstract class DistributionType and defines the Indicator type.
IndicatorType(Variable) - Constructor for class eu.amidst.core.variables.distributionTypes.IndicatorType
Creates a new IndicatorType for the given variable.
inferenceAlgoPredict - Variable in class eu.amidst.latentvariablemodels.staticmodels.classifiers.Classifier
Represents the inference algorithm.
InferenceAlgorithm - Interface in eu.amidst.core.inference
This interface handles and defines the algorithm used to run inference in BayesianNetwork models.
InferenceAlgorithmForDBN - Interface in eu.amidst.dynamic.inference
This interface handles and defines the algorithm used to run inference in DynamicBayesianNetwork models.
InferenceDemo - Class in eu.amidst.huginlink.examples.demos
This class is a demo for making inference in a Dynamic Bayesian network model learnt from the Cajamar data set using the Hugin inference engine.
InferenceDemo() - Constructor for class eu.amidst.huginlink.examples.demos.InferenceDemo
 
InferenceEngine - Class in eu.amidst.core.inference
This class defines the Inference Engine for Bayesian Network models.
InferenceEngine() - Constructor for class eu.amidst.core.inference.InferenceEngine
 
InferenceEngineExample - Class in eu.amidst.core.examples.inference
This example show how to perform inference in a Bayesian network model using the InferenceEngine static class.
InferenceEngineExample() - Constructor for class eu.amidst.core.examples.inference.InferenceEngineExample
 
InferenceEngineForDBN - Class in eu.amidst.dynamic.inference
This class defines the Inference Engine for Dynamic Bayesian Network models.
InferenceEngineForDBN() - Constructor for class eu.amidst.dynamic.inference.InferenceEngineForDBN
 
init() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.VMPLocalUpdates
 
initialized - Variable in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
initialized - Variable in class eu.amidst.latentvariablemodels.staticmodels.Model
 
initialNonReplicatedVariablesList - Variable in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Represents the initial list of non-replicated variables
initLearning() - Method in class eu.amidst.core.conceptdrift.NaiveBayesVirtualConceptDriftDetector
Initialises the class for concept drift detection.
initLearning() - Method in class eu.amidst.core.conceptdrift.SVBFading
 
initLearning() - Method in class eu.amidst.core.learning.parametric.bayesian.DriftSVB
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.core.learning.parametric.bayesian.MultiDriftSVB
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.core.learning.parametric.bayesian.ParallelSVB
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.core.learning.parametric.bayesian.StochasticVI
 
initLearning() - Method in class eu.amidst.core.learning.parametric.bayesian.SVB
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Initializes the parameter learning process.
initLearning() - Method in interface eu.amidst.core.learning.parametric.ParameterLearningAlgorithm
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.dynamic.learning.parametric.bayesian.SVB
 
initLearning() - Method in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Initializes the parameter learning process.
initLearning() - Method in interface eu.amidst.dynamic.learning.parametric.ParameterLearningAlgorithm
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetector
Initialises the class for concept drift detection.
initLearning() - Method in class eu.amidst.flinklink.core.conceptdrift.IDAConceptDriftDetectorDBN
Initialises the class for concept drift detection.
initLearning() - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
initLearning() - Method in interface eu.amidst.flinklink.core.learning.dynamic.ParameterLearningAlgorithm
 
initLearning() - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
initLearning() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
initLearning() - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
initLearning() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood
 
initLearning() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelMaximumLikelihood2
 
initLearning() - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
initLearning() - Method in interface eu.amidst.flinklink.core.learning.parametric.ParameterLearningAlgorithm
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.flinklink.core.learning.parametric.StochasticVI
 
initLearning() - Method in class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
 
initLearning() - Method in class eu.amidst.latentvariablemodels.staticmodels.LDA
 
initLearning() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
initLearning() - Method in class eu.amidst.lda.core.MultiDriftLDAv1
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.lda.core.MultiDriftLDAv2
Initializes the parameter learning process.
initLearning() - Method in class eu.amidst.sparklink.core.learning.ParallelMaximumLikelihood
 
initLearning() - Method in interface eu.amidst.sparklink.core.learning.ParameterLearningAlgorithm
Initializes the parameter learning process.
initLearningFlink() - Method in class eu.amidst.latentvariablemodels.staticmodels.ConceptDriftDetector
 
initLearningFlink() - Method in class eu.amidst.latentvariablemodels.staticmodels.LDA
 
initLearningFlink() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
initModel(EF_LearningBayesianNetwork, PlateuStructure) - Method in class eu.amidst.core.conceptdrift.utils.GaussianHiddenTransitionMethod
 
initModel(EF_LearningBayesianNetwork, PlateuStructure) - Method in interface eu.amidst.core.learning.parametric.bayesian.utils.TransitionMethod
Initializes the model.
initTransientDataStructure() - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
Initializes the interal transient data structures.
InputOutputHMM - Class in eu.amidst.latentvariablemodels.dynamicmodels
This class implements an Input-Output Hidden Markov Model.
InputOutputHMM(Attributes, List<Attribute>, List<Attribute>) - Constructor for class eu.amidst.latentvariablemodels.dynamicmodels.InputOutputHMM
 
INTERFACE_SUFFIX - Static variable in class eu.amidst.dynamic.variables.DynamicVariables
Represents a suffix used for the name of interface variable.
invDigamma(double) - Static method in class eu.amidst.core.utils.Utils
Returns the Inverse digamma of a given double value.
InverseGammaParameterType - Class in eu.amidst.core.variables.distributionTypes
This class extends the abstract class DistributionType and defines the Inverse Gamma parameter type.
InverseGammaParameterType(Variable) - Constructor for class eu.amidst.core.variables.distributionTypes.InverseGammaParameterType
Creates a new InverseGammaParameterType for the given variable.
INVX - Static variable in class eu.amidst.core.exponentialfamily.EF_Gamma
 
INVX - Static variable in class eu.amidst.core.exponentialfamily.EF_InverseGamma
 
isActivateMiddleLayer() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
isActive() - Method in class eu.amidst.core.inference.messagepassing.Node
Tests whether this Node is active or not.
isActiveAtEpoch(Variable, int) - Method in class eu.amidst.flinklink.core.conceptdrift.IdentifiableIDAModel
 
isActiveAtEpoch(Variable, int) - Method in interface eu.amidst.flinklink.core.learning.parametric.utils.IdenitifableModelling
 
isActiveAtEpoch(Variable, int) - Method in class eu.amidst.flinklink.core.learning.parametric.utils.ParameterIdentifiableModel
 
isArffFolder(String) - Static method in class eu.amidst.flinklink.core.io.DataFlinkLoader
Determines if the path given as argument correspond to an ARFF distributed dataset
isBaseConditionalDistribution() - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Tests if this distribution is a base conditional distribution or not.
isBaseConditionalDistribution() - Method in class eu.amidst.core.exponentialfamily.EF_BaseDistribution_MultinomialParents
Returns whether the base distributions are conditional or not.
isConnectChildrenTemporally() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
isConnectChildrenTemporally() - Method in class eu.amidst.dynamic.learning.parametric.DynamicNaiveBayesClassifier
Returns whether the children are connected temporally or not.
isConverged(int, DoubleValue) - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ConvergenceELBO
 
isConverged(int, DoubleValue) - Method in class eu.amidst.flinklink.core.learning.parametric.DistributedVI.ConvergenceELBObyTime
 
isConverged(int, DoubleValue) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP.ConvergenceELBO
 
isConverged(int, DoubleValue) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMP.ConvergenceELBObyTime
 
isConverged(int, DoubleValue) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1.ConvergenceELBO
 
isConverged(int, DoubleValue) - Method in class eu.amidst.flinklink.core.learning.parametric.dVMPv1.ConvergenceELBObyTime
 
isConverged(int, DoubleValue) - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB.ConvergenceELBO
 
isConverged(int, DoubleValue) - Method in class eu.amidst.flinklink.core.learning.parametric.ParallelVB.ConvergenceELBObyTime
 
isCoreComparison() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
isDiagonal() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.AutoRegressiveHMM
 
isDiagonal() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.FactorialHMM
 
isDiagonal() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.HiddenMarkovModel
 
isDiagonal() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.InputOutputHMM
 
isDiagonal() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.KalmanFilter
 
isDiagonal() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.SwitchingKalmanFilter
 
isDiagonal() - Method in class eu.amidst.latentvariablemodels.staticmodels.BayesianLinearRegression
Method to obtain the value of the diagonal flag.
isDiagonal() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.GaussianDiscriminantAnalysis
Method to obtain the value of the diagonal flag.
isDiagonal() - Method in class eu.amidst.latentvariablemodels.staticmodels.GaussianMixture
Method to obtain the value of the diagonal flag.
isDirichletParameter() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a Dirichlet parameter distribution.
isDone() - Method in class eu.amidst.core.inference.messagepassing.Message
Tests is this Message is done.
isDone() - Method in class eu.amidst.core.inference.messagepassing.Node
Test whether this Node is done.
isDynamicVariable() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable is dynamic or not.
isEven(Integer) - Static method in class eu.amidst.cim2015.examples.MyIntegerUtils
 
isGammaParameter() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a gamma parameter distribution.
isGlobalUpdate() - Method in interface eu.amidst.flinklink.core.learning.parametric.utils.GlobalvsLocalUpdate
 
isGlobalUpdate() - Method in class eu.amidst.lda.core.PlateauLDA
 
isGlobalUpdate() - Method in class eu.amidst.lda.flink.PlateauLDAFlink
 
isIndicator() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a "indicator" distribution.
isInterfaceVariable() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable is an interface variable or not.
isInverseGammaParameter() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows an inverse gamma parameter distribution.
isMissingValue(double) - Static method in class eu.amidst.core.utils.Utils
Tests if a given value is missing or not.
isMultinomial() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a multinomial distribution.
isMultinomialLogistic() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a multinomial logistic distribution.
isNonReplicatedVar(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
isNormal() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a normal distribution.
isNormalGammaParameter() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a gamma parameter distribution.
isNormalize() - Method in class eu.amidst.flinklink.core.io.DataFlinkLoader
 
isNormalParameter() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a normal parameter distribution.
isObservable() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable is observable or not.
isObservable() - Method in class eu.amidst.core.variables.VariableBuilder
Tests whether this Variable is observable or not.
isObserved() - Method in class eu.amidst.core.inference.messagepassing.Node
Tests whether this Node is observed or not.
isObserved(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.DataPosteriorAssignment
 
isOdd(Integer) - Static method in class eu.amidst.cim2015.examples.MyIntegerUtils
 
isOutput() - Method in class eu.amidst.core.inference.messagepassing.MessagePassingAlgorithm
Gets whether output for this MessagePassingAlgorithm.
isParallel() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
isParallel() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
isParallelActivated() - Method in class eu.amidst.core.inference.messagepassing.Node
Tests whether the parallel mode is activated for this Node or not.
isParallelMode() - Method in class eu.amidst.dynamic.learning.parametric.DynamicNaiveBayesClassifier
Returns whether the parallel mode is supported or not.
isParallelMode_() - Method in class moa.classifiers.bayes.AmidstClassifier
Tests whether the learning of this AmidstClassifier is performed in parallel.
isParallelMode_() - Method in class moa.classifiers.bayes.AmidstRegressor
Tests whether the learning of this AmidstRegressor is performed in parallel.
isParallelMode_() - Method in class moa.clusterers.AmidstClusteringAlgorithm
Tests whether the learning of this AmidstClusteringAlgorithm is performed in parallel.
isParameterVariable() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable is a parameter variable or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.DistributionType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.DirichletParameterType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.GammaParameterType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.IndicatorType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.InverseGammaParameterType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.MultinomialLogisticType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.MultinomialType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.NormalGammaParameterType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.NormalParameterType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.NormalType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.SparseDirichletParameterType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.SparseMultinomialType
Test whether the given parent is compatible or not.
isParentCompatible(Variable) - Method in class eu.amidst.core.variables.distributionTypes.TruncatedExponentialType
Test whether the given parent is compatible or not.
isPresent(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.DataPosterior
Returns whethers a given variable is present in the object.
isRandomizable() - Method in class moa.classifiers.bayes.AmidstClassifier
isRandomizable() - Method in class moa.classifiers.bayes.AmidstRegressor
isRandomizable() - Method in class moa.clusterers.AmidstClusteringAlgorithm
isReplicatedVar(Variable) - Method in class eu.amidst.core.learning.parametric.bayesian.utils.PlateuStructure
 
isRestartable() - Method in class eu.amidst.core.datastream.DataOnMemoryListContainer
Returns whether this DataOnMemoryListContainer can restart.
isRestartable() - Method in interface eu.amidst.core.datastream.DataStream
Returns whether this DataStream can restart.
isRestartable() - Method in class eu.amidst.core.datastream.filereaders.DataOnMemoryFromFile
Returns whether this DataStream can restart.
isRestartable() - Method in class eu.amidst.core.datastream.filereaders.DataStreamFromFile
Returns whether this DataStream can restart.
isRestartable() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataOnMemoryFromFile
Returns whether this DataStream can restart.
isRestartable() - Method in class eu.amidst.dynamic.datastream.filereaders.DynamicDataStreamFromFile
Returns whether this DataStream can restart.
isRestartable() - Method in class eu.amidst.flinklink.core.utils.DataStreamFromStreamOfAssignments
 
isRestartable() - Method in class eu.amidst.sparklink.core.data.DataOnMemoryListContainerSerializable
Returns whether this DataOnMemoryListContainer can restart.
isSampleData() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
isSampleData() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
isSampleData() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
isSeqId() - Method in class eu.amidst.core.datastream.Attribute
Indicates whether the attribute is a seq_id
isSparseDirichletParameter() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a Sparse Dirichlet parameter distribution.
isSparseMultinomial() - Method in interface eu.amidst.core.variables.Variable
Tests whether this Variable follows a sparse multinomial distribution.
isSpecialAttribute() - Method in class eu.amidst.core.datastream.Attribute
Indicates whether the attribute is a special one (e.g.
isTimeId() - Method in class eu.amidst.core.datastream.Attribute
Indicates whether the attribute is a time_id
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.AutoRegressiveHMM
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.classifiers.DynamicLatentClassificationModel
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.DynamicModel
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.FactorialHMM
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.HiddenMarkovModel
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.InputOutputHMM
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.KalmanFilter
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.dynamicmodels.SwitchingKalmanFilter
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.BayesianLinearRegression
tests if the attributes passed as an argument in the constructor are suitable
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.AODE
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.GaussianDiscriminantAnalysis
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.HODE
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.LatentClassificationModel
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.NaiveBayesClassifier
tests if the attributes passed as an argument in the constructor are suitable for this classifier
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.classifiers.TAN
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.FactorAnalysis
tests if the attributes passed as an argument in the constructor are suitable
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.GaussianMixture
tests if the attributes passed as an argument in the constructor are suitable for this classifier
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.MixtureOfFactorAnalysers
tests if the attributes passed as an argument in the constructor are suitable
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.Model
 
isValidConfiguration() - Method in class eu.amidst.latentvariablemodels.staticmodels.MultivariateGaussianDistribution
tests if the attributes passed as an argument in the constructor are suitable for this classifier
iterableOverBatches(int) - Method in interface eu.amidst.core.datastream.DataStream
Returns an iterator over DataOnMemory objects.
iterableOverDocuments(DataStream<T>, int) - Static method in class eu.amidst.lda.core.BatchSpliteratorByID
 
iterator() - Method in class eu.amidst.core.datastream.Attributes
Returns an iterator over this Attributes.
iterator() - Method in interface eu.amidst.core.datastream.DataStream
Returns an Iterator object that iterates over all the data instances of this DataStream.
iterator() - Method in interface eu.amidst.core.datastream.filereaders.DataFileReader
Returns an Iterator over the stream of DataRow objects.
iterator() - Method in class eu.amidst.core.exponentialfamily.ParameterVariables
iterator() - Method in interface eu.amidst.core.models.ParentSet
iterator() - Method in class eu.amidst.core.variables.stateSpaceTypes.FiniteStateSpace
Returns an iterator over elements of type String, i.e.
iterator() - Method in class eu.amidst.core.variables.Variables
Returns an iterator over elements of type Variable, i.e.
iterator() - Method in class eu.amidst.dynamic.variables.DynamicVariables

J

joinData(DataSet<DynamicDataInstance>) - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
joinData2(DataSet<DynamicDataInstance>) - Method in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 

K

KalmanFilter - Class in eu.amidst.latentvariablemodels.dynamicmodels
This class implements a Kalman Filter (KF) or State Space Model (SSM).
KalmanFilter(Attributes) - Constructor for class eu.amidst.latentvariablemodels.dynamicmodels.KalmanFilter
 
keepClassLabel() - Method in class moa.clusterers.AmidstClusteringAlgorithm
KeyCompoundVector<E> - Class in eu.amidst.core.utils
This class implements the interfaces MomentParameters, NaturalParameters, and SufficientStatistics.
KeyCompoundVector() - Constructor for class eu.amidst.core.utils.KeyCompoundVector
Creates a new KeyCompoundVector.
kl(NaturalParameters, double) - Method in class eu.amidst.core.exponentialfamily.EF_Normal
Compute the kullback-leibler distance between two distributions of the same kind in exponential family form, KL(P,Q), where P is the object invoking the method and Q is provided by argument.
kl(NaturalParameters, double) - Method in class eu.amidst.core.exponentialfamily.EF_NormalParameter
Compute the kullback-leibler distance between two distributions of the same kind in exponential family form, KL(P,Q), where P is the object invoking the method and Q is provided by argument.
kl(NaturalParameters, double) - Method in class eu.amidst.core.exponentialfamily.EF_UnivariateDistribution
Compute the kullback-leibler distance between two distributions of the same kind in exponential family form, KL(P,Q), where P is the object invoking the method and Q is provided by argument.
KMeans - Class in eu.amidst.flinklink.examples.misc
This example implements a basic K-Means clustering algorithm.
KMeans() - Constructor for class eu.amidst.flinklink.examples.misc.KMeans
 
KMeans.Centroid - Class in eu.amidst.flinklink.examples.misc
A simple two-dimensional centroid, basically a point with an ID.
KMeans.CentroidAccumulator - Class in eu.amidst.flinklink.examples.misc
Sums and counts point coordinates.
KMeans.CentroidAverager - Class in eu.amidst.flinklink.examples.misc
Computes new centroid from coordinate sumNonStateless and count of points.
KMeans.CountAppender - Class in eu.amidst.flinklink.examples.misc
Appends a count variable to the tuple.
KMeans.Point - Class in eu.amidst.flinklink.examples.misc
A simple two-dimensional point.
KMeans.SelectNearestCenter - Class in eu.amidst.flinklink.examples.misc
Determines the closest cluster center for a data point.
KMeans.TupleCentroidConverter - Class in eu.amidst.flinklink.examples.misc
Converts a Tuple3 into a Centroid.
KMeans.TuplePointConverter - Class in eu.amidst.flinklink.examples.misc
Converts a Tuple2 into a Point.
KMeansData - Class in eu.amidst.flinklink.examples.misc
Provides the default data sets used for the K-Means example program.
KMeansData() - Constructor for class eu.amidst.flinklink.examples.misc.KMeansData
 

L

label() - Method in class eu.amidst.core.distribution.BaseDistribution_MultinomialParents
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.ConditionalLinearGaussian
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.DeltaDistribution
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.Distribution
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.GaussianMixture
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.IndicatorDistribution
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.Multinomial
 
label() - Method in class eu.amidst.core.distribution.Multinomial_LogisticParents
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.Multinomial_MultinomialParents
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.Normal
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.Normal_MultinomialNormalParents
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.Normal_MultinomialParents
Returns the name of this Distribution.
label() - Method in class eu.amidst.core.distribution.Uniform
Returns the name of this Distribution.
laplace - Variable in class eu.amidst.core.learning.parametric.ParallelMaximumLikelihood
Represents whether Laplace correction (i.e.
laplace - Variable in class eu.amidst.core.learning.parametric.ParallelMLMissingData
Represents whether Laplace correction (i.e.
laplace - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMaximumLikelihood
Represents whether Laplace correction (i.e.
laplace - Variable in class eu.amidst.dynamic.learning.parametric.ParallelMLMissingData
Represents whether Laplace correction (i.e.
LATENT_VARS - Static variable in class eu.amidst.flinklink.core.learning.parametric.DistributedVI
 
LATENT_VARS - Static variable in class eu.amidst.flinklink.core.learning.parametric.dVMP
 
LATENT_VARS - Static variable in class eu.amidst.flinklink.core.learning.parametric.dVMPv1
 
LATENT_VARS - Static variable in class eu.amidst.flinklink.core.learning.parametric.ParallelVB
 
LatentClassificationModel - Class in eu.amidst.latentvariablemodels.staticmodels.classifiers
This class implements the latent classification models.
LatentClassificationModel(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.classifiers.LatentClassificationModel
Constructor of classifier from a list of attributes.
latentInterfaceVariablesNames - Variable in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
LatentModelsFlink - Class in eu.amidst.flinklink.examples.extensions
Created by rcabanas on 14/06/16.
LatentModelsFlink() - Constructor for class eu.amidst.flinklink.examples.extensions.LatentModelsFlink
 
latentVariablesNames - Variable in class eu.amidst.flinklink.core.learning.dynamic.DynamicParallelVB
 
LDA - Class in eu.amidst.latentvariablemodels.staticmodels
The Model abstract class is defined as a superclass to all static standard models (not used for classification, if so, extends Classifier) Created by andresmasegosa on 4/3/16.
LDA(Attributes) - Constructor for class eu.amidst.latentvariablemodels.staticmodels.LDA
 
LDAModelLearning - Class in eu.amidst.tutorial.usingAmidst.examples
Created by rcabanas on 23/05/16.
LDAModelLearning() - Constructor for class eu.amidst.tutorial.usingAmidst.examples.LDAModelLearning
 
LDAModelLearningFlink - Class in eu.amidst.tutorial.usingAmidst.examples
Created by rcabanas on 23/05/16.
LDAModelLearningFlink() - Constructor for class eu.amidst.tutorial.usingAmidst.examples.LDAModelLearningFlink
 
learn(DataStream<DynamicDataInstance>) - Method in class eu.amidst.dynamic.learning.parametric.DynamicNaiveBayesClassifier
Learns this DynamicNaiveBayesClassifier from a given data stream.
learn(DataStream<DataInstance>) - Method in class eu.amidst.huginlink.learning.ParallelPC
Learns the parameters of a TAN structure using the ParallelMaximumLikelihood.
learn(DataStream<DataInstance>, int) - Method in class eu.amidst.huginlink.learning.ParallelPC
Learns the parameters of a TAN structure using the ParallelMaximumLikelihood.
learn(DataStream<DataInstance>) - Method in class eu.amidst.huginlink.learning.ParallelTAN
Learns the parameters of a TAN structure using the ParallelMaximumLikelihood.
learn(DataStream<DataInstance>, int) - Method in class eu.amidst.huginlink.learning.ParallelTAN
Learns the parameters of a TAN structure using the ParallelMaximumLikelihood.
learnDAG(DataStream) - Method in class eu.amidst.huginlink.learning.ParallelPC
Learns a TAN structure from data using the Chow-Liu algorithm included in the Hugin API.
learnDAG(DataStream) - Method in class eu.amidst.huginlink.learning.ParallelTAN
Learns a TAN structure from data using the Chow-Liu algorithm included in the Hugin API.
learnIDAConceptDriftDetector(int) - Static method in class eu.amidst.flinklink.examples.reviewMeeting2015.ConceptDriftDetector
 
learningAlgorithm - Variable in class eu.amidst.latentvariablemodels.staticmodels.Model
 
learningAlgorithmFlink - Variable in class eu.amidst.latentvariablemodels.staticmodels.Model
 
learnKMeans(int, DataStream<DataInstance>) - Static method in class eu.amidst.cim2015.examples.ParallelKMeans
 
learnModel(ParallelSVB) - Static method in class eu.amidst.bnaic2015.examples.BCC
This method constains the code needed to learn the model and produce the output.
LineSplitter() - Constructor for class eu.amidst.flinklink.examples.misc.WordCountExample.LineSplitter
 
listOptions() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelkMeans
 
listOptions() - Static method in class eu.amidst.cim2015.examples.ExperimentsParallelML
 
listOptions() - Static method in class eu.amidst.core.models.BayesianNetwork
Returns this class name.
listOptions() - Method in interface eu.amidst.core.utils.AmidstOptionsHandler
Returns the list of options.
listOptions() - Static method in class eu.amidst.core.utils.BayesianNetworkGenerator
Returns the list of options used by this BayesianNetworkGenerator.
listOptions() - Method in class eu.amidst.core.utils.BayesianNetworkSampler
Returns the list of options.
listOptions() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
listOptions() - Static method in class eu.amidst.flinklink.examples.misc.ExperimentsParallelML
 
listOptions() - Static method in class eu.amidst.flinklink.examples.misc.GenerateRandom
 
listOptions() - Method in class eu.amidst.huginlink.learning.ParallelPC
Returns the list of options.
listOptions() - Method in class eu.amidst.huginlink.learning.ParallelTAN
Returns the list of options.
listOptions() - Method in class weka.classifiers.bayes.AmidstClassifier
Returns an enumeration describing the available options.
listOptions() - Method in class weka.classifiers.bayes.AmidstRegressor
Returns an enumeration describing the available options.
listOptionsRecursively() - Static method in class eu.amidst.core.models.BayesianNetwork
 
listOptionsRecursively() - Method in interface eu.amidst.core.utils.AmidstOptionsHandler
Returns the list of options recursively.
listOptionsRecursively(Class) - Static method in interface eu.amidst.core.utils.AmidstOptionsHandler
Returns recursively the list of options of a given a Class instance.
listOptionsRecursively() - Method in class eu.amidst.core.utils.BayesianNetworkSampler
Returns the list of options recursively.
listOptionsRecursively() - Method in class eu.amidst.dynamic.examples.inference.DynamicIS_Scalability
 
listOptionsRecursively() - Method in class eu.amidst.huginlink.learning.ParallelPC
Returns the list of options recursively.
listOptionsRecursively() - Method in class eu.amidst.huginlink.learning.ParallelTAN
Returns the list of options recursively.
loadCommandLineOptions(String) - Static method in class eu.amidst.core.utils.OptionParser
Loads the command line options.
loadDataFromFile(ExecutionEnvironment, String, boolean) - Static method in class eu.amidst.flinklink.core.io.DataFlinkLoader
 
loadDataFromFolder(ExecutionEnvironment, String, boolean) - Static method in class eu.amidst.flinklink.core.io.DataFlinkLoader
 
loadDataOnMemoryFromFile(String) - Static method in class eu.amidst.core.io.DataStreamLoader
Loads a DataOnMemory from a file.
loadDefaultOptions(String) - Static method in class eu.amidst.core.utils.OptionParser
Loads the set of options by default.
loadDynamicDataFromFile(ExecutionEnvironment, String, boolean) - Static method in class eu.amidst.flinklink.core.io.DataFlinkLoader
 
loadDynamicDataFromFolder(ExecutionEn