Index (jaicore-ml 0.1.6 API)

A B C D E F G H I J K L M N O P Q R S T U V W Y Z
All Classes All Packages

A

A - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
AAttributeValue<D> - Class in ai.libs.jaicore.ml.core.dataset.attribute: An abstract class for attribute values implementing basic functionality to store its value as well as getter and setters.
AAttributeValue(IAttributeType<D>) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue: Constructor creating a new attribute value for a certain type.
AAttributeValue(IAttributeType<D>, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue: Constructor creating a new attribute value for a certain type together with a value.
Abandonable - Interface in ai.libs.jaicore.ml.tsc.distances: Interface for Distance measures that can make use of the Early Abandon technique.
ABatchLearner<T,V,I,D extends IDataset> - Class in ai.libs.jaicore.ml.core.predictivemodel: Abstract extension of IBatchLearner to be able to construct prediction of the given type.
ABatchLearner() - Constructor for class ai.libs.jaicore.ml.core.predictivemodel.ABatchLearner
AbstractAugmentedSpaceSampler - Class in ai.libs.jaicore.ml.rqp
AbstractAugmentedSpaceSampler(Instances, Random) - Constructor for class ai.libs.jaicore.ml.rqp.AbstractAugmentedSpaceSampler
AbstractDyadScaler - Class in ai.libs.jaicore.ml.dyadranking.util: A scaler that can be fit to a certain dataset and then be used to standardize datasets, i.e. transform the data to have a mean of 0 and a standard deviation of 1 according to the data it was fit to.
AbstractDyadScaler() - Constructor for class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler
AbstractSplitBasedClassifierEvaluator<I,O> - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation: Connection between an Evaluator (e.g.
AbstractSplitBasedClassifierEvaluator(IMeasure<I, O>) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.AbstractSplitBasedClassifierEvaluator
AccessibleRandomTree - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Random Tree extension providing leaf node information of the constructed tree.
AccessibleRandomTree() - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree
ActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning: Abstract description of a pool-based active learning strategy for dyad ranking.
ActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker
activelyTrain(int) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker: Actively trains the ranker for a certain number of queries.
activelyTrain(int) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ConfidenceIntervalClusteringBasedActiveDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.RandomPoolBasedActiveDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.UCBPoolBasedActiveDyadRanker
add(double[]) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl
add(double[][]) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Add a time series variable without timestamps to the dataset.
add(double[][], double[][]) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Add a time series variable with timestamps to the dataset.
add(int, TimeSeriesInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
add(int, Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue
add(int, Instance) - Method in class ai.libs.jaicore.ml.SubInstances
add(TimeSeriesInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
add(FeatureDomain) - Method in class ai.libs.jaicore.ml.core.FeatureSpace
add(SimpleInstanceImpl) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl
add(LabeledInstance<String>) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
add(LabeledInstance<String>) - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
add(Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
add(Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue: Adds an element at a random position within the
add(INDArray, INDArray) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset: Add a time series variable to the dataset.
add(Instance) - Method in class ai.libs.jaicore.ml.SubInstances
addAll(int, Collection<? extends TimeSeriesInstance<L>>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
addAll(Collection<? extends TimeSeriesInstance<L>>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
addAll(Collection<? extends Node<N, V>>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
addAllFromJson(JsonNode) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl
addAllFromJson(JsonNode) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
addAllFromJson(File) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl
addAllFromJson(File) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
addAllFromJson(File) - Method in interface ai.libs.jaicore.ml.interfaces.Instances
addAllFromJson(String) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl
addAllFromJson(String) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
addAllFromJson(String) - Method in interface ai.libs.jaicore.ml.interfaces.Instances
addChild(MCTreeNode) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
addChild(MCTreeNode) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
addChild(List<String>, Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
addChild(List<String>, Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
addInstance(ProblemInstance) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group
addLandmarkerCharacterizers(ArrayList<Characterizer>) - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
addLocalFiles(File...) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
addLocalFiles(List<File>) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
addNode(String, Instruction) - Method in class ai.libs.jaicore.ml.cache.InstructionGraph
addNode(String, Instruction, List<Pair<String, Integer>>) - Method in class ai.libs.jaicore.ml.cache.InstructionGraph
addNoProbingCharacterizers(ArrayList<Characterizer>) - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
addOpenMLDatasets(int...) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
ADecomposableDoubleMeasure - Class in ai.libs.jaicore.ml.core.evaluation.measure: A measure that is decomposable by instances and aggregated by averaging.
ADecomposableDoubleMeasure() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableDoubleMeasure
ADecomposableMeasure<I,O> - Class in ai.libs.jaicore.ml.core.evaluation.measure: A measure that is aggregated from e.g. instance-wise computations of the respective measure and which is then aggregated, e.g. taking the mean.
ADecomposableMeasure() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableMeasure
ADecomposableMultilabelMeasure - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
ADecomposableMultilabelMeasure() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.ADecomposableMultilabelMeasure
ADerivateFilter - Class in ai.libs.jaicore.ml.tsc.filter.derivate: Abstract superclass for all derivate filters.
ADerivateFilter() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
ADerivateFilter(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
ADyadRankedNodeQueue<N,V extends java.lang.Comparable<V>> - Class in ai.libs.jaicore.ml.dyadranking.search: A queue whose elements are nodes, sorted by a dyad ranker.
ADyadRankedNodeQueue(Vector) - Constructor for class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue: Constructs a new DyadRankedNodeQueue that ranks the nodes in the queue according to the given context characterization.
ADyadRankedNodeQueue(Vector, IDyadRanker, AbstractDyadScaler) - Constructor for class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue: Constructs a new DyadRankedNodeQueue that ranks the nodes in the queue according to the given context characterization and given dyad ranker.
ADyadRankedNodeQueueConfig<N> - Class in ai.libs.jaicore.ml.dyadranking.search: A configuration for a dyad ranked node queue.
ADyadRankedNodeQueueConfig() - Constructor for class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig: Construct a new dyad ranking node queue configuration.
ADyadRankingInstance - Class in ai.libs.jaicore.ml.dyadranking.dataset
ADyadRankingInstance() - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.ADyadRankingInstance
AFileSamplingAlgorithm - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles: An abstract class for file-based sampling algorithms providing basic functionality of an algorithm.
AFileSamplingAlgorithm(File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm
AFilter - Class in ai.libs.jaicore.ml.tsc.filter
AFilter() - Constructor for class ai.libs.jaicore.ml.tsc.filter.AFilter
aggregate(List<Double>) - Method in class ai.libs.jaicore.ml.intervaltree.aggregation.AggressiveAggregator
aggregate(List<Double>) - Method in interface ai.libs.jaicore.ml.intervaltree.aggregation.IntervalAggregator
aggregate(List<Double>) - Method in class ai.libs.jaicore.ml.intervaltree.aggregation.QuantileAggregator
AggressiveAggregator - Class in ai.libs.jaicore.ml.intervaltree.aggregation: An IntervalAggregator that makes predictions using the minimum of the predictions as the lower bound and the maximum as the upper bound.
AggressiveAggregator() - Constructor for class ai.libs.jaicore.ml.intervaltree.aggregation.AggressiveAggregator
ai.libs.jaicore.ml - package ai.libs.jaicore.ml
ai.libs.jaicore.ml.activelearning - package ai.libs.jaicore.ml.activelearning
ai.libs.jaicore.ml.cache - package ai.libs.jaicore.ml.cache: Package to create ReproducibleInstances which can be stored and recreated if needed.
ai.libs.jaicore.ml.classification.multiclass - package ai.libs.jaicore.ml.classification.multiclass
ai.libs.jaicore.ml.classification.multiclass.reduction - package ai.libs.jaicore.ml.classification.multiclass.reduction
ai.libs.jaicore.ml.classification.multiclass.reduction.reducer - package ai.libs.jaicore.ml.classification.multiclass.reduction.reducer
ai.libs.jaicore.ml.classification.multiclass.reduction.splitters - package ai.libs.jaicore.ml.classification.multiclass.reduction.splitters
ai.libs.jaicore.ml.clustering - package ai.libs.jaicore.ml.clustering
ai.libs.jaicore.ml.core - package ai.libs.jaicore.ml.core
ai.libs.jaicore.ml.core.dataset - package ai.libs.jaicore.ml.core.dataset: This package contains the infrastructure for representing datasets and instances with different types of attributes.
ai.libs.jaicore.ml.core.dataset.attribute - package ai.libs.jaicore.ml.core.dataset.attribute: This package contains data structures for representing attributes of a dataset's instance.
ai.libs.jaicore.ml.core.dataset.attribute.categorical - package ai.libs.jaicore.ml.core.dataset.attribute.categorical: This package contains the implementation of a categorical attribute.
ai.libs.jaicore.ml.core.dataset.attribute.multivalue - package ai.libs.jaicore.ml.core.dataset.attribute.multivalue: This package contains the implementation of a multi-value attribute.
ai.libs.jaicore.ml.core.dataset.attribute.primitive - package ai.libs.jaicore.ml.core.dataset.attribute.primitive: This package contains the implementation of primitive data type attributes.
ai.libs.jaicore.ml.core.dataset.attribute.timeseries - package ai.libs.jaicore.ml.core.dataset.attribute.timeseries: This package contains the implementation of a time series attribute.
ai.libs.jaicore.ml.core.dataset.attribute.transformer - package ai.libs.jaicore.ml.core.dataset.attribute.transformer
ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue - package ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue
ai.libs.jaicore.ml.core.dataset.sampling - package ai.libs.jaicore.ml.core.dataset.sampling: This package contains algorithms for creating samples of a dataset.
ai.libs.jaicore.ml.core.dataset.sampling.infiles - package ai.libs.jaicore.ml.core.dataset.sampling.infiles
ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling - package ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling
ai.libs.jaicore.ml.core.dataset.sampling.inmemory - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory
ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol
ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces
ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling
ai.libs.jaicore.ml.core.dataset.standard - package ai.libs.jaicore.ml.core.dataset.standard: This package contains a straight-forward implementation of a dataset.
ai.libs.jaicore.ml.core.dataset.weka - package ai.libs.jaicore.ml.core.dataset.weka: This package contains classes for weka-specific logics regarding the dataset.
ai.libs.jaicore.ml.core.evaluation.measure - package ai.libs.jaicore.ml.core.evaluation.measure
ai.libs.jaicore.ml.core.evaluation.measure.multilabel - package ai.libs.jaicore.ml.core.evaluation.measure.multilabel
ai.libs.jaicore.ml.core.evaluation.measure.singlelabel - package ai.libs.jaicore.ml.core.evaluation.measure.singlelabel
ai.libs.jaicore.ml.core.exception - package ai.libs.jaicore.ml.core.exception: This package contains Exceptions defined by jaicore-ml.
ai.libs.jaicore.ml.core.optimizing - package ai.libs.jaicore.ml.core.optimizing
ai.libs.jaicore.ml.core.optimizing.graddesc - package ai.libs.jaicore.ml.core.optimizing.graddesc
ai.libs.jaicore.ml.core.predictivemodel - package ai.libs.jaicore.ml.core.predictivemodel: This package contains interfaces related to predictive models and learning algorithms.
ai.libs.jaicore.ml.dyadranking - package ai.libs.jaicore.ml.dyadranking
ai.libs.jaicore.ml.dyadranking.activelearning - package ai.libs.jaicore.ml.dyadranking.activelearning
ai.libs.jaicore.ml.dyadranking.algorithm - package ai.libs.jaicore.ml.dyadranking.algorithm
ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform - package ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform
ai.libs.jaicore.ml.dyadranking.dataset - package ai.libs.jaicore.ml.dyadranking.dataset
ai.libs.jaicore.ml.dyadranking.loss - package ai.libs.jaicore.ml.dyadranking.loss
ai.libs.jaicore.ml.dyadranking.optimizing - package ai.libs.jaicore.ml.dyadranking.optimizing
ai.libs.jaicore.ml.dyadranking.search - package ai.libs.jaicore.ml.dyadranking.search
ai.libs.jaicore.ml.dyadranking.util - package ai.libs.jaicore.ml.dyadranking.util
ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization - package ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization
ai.libs.jaicore.ml.dyadranking.zeroshot.util - package ai.libs.jaicore.ml.dyadranking.zeroshot.util
ai.libs.jaicore.ml.evaluation - package ai.libs.jaicore.ml.evaluation
ai.libs.jaicore.ml.evaluation.evaluators.weka - package ai.libs.jaicore.ml.evaluation.evaluators.weka
ai.libs.jaicore.ml.evaluation.evaluators.weka.events - package ai.libs.jaicore.ml.evaluation.evaluators.weka.events
ai.libs.jaicore.ml.evaluation.evaluators.weka.factory - package ai.libs.jaicore.ml.evaluation.evaluators.weka.factory
ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation - package ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation
ai.libs.jaicore.ml.experiments - package ai.libs.jaicore.ml.experiments
ai.libs.jaicore.ml.interfaces - package ai.libs.jaicore.ml.interfaces
ai.libs.jaicore.ml.intervaltree - package ai.libs.jaicore.ml.intervaltree
ai.libs.jaicore.ml.intervaltree.aggregation - package ai.libs.jaicore.ml.intervaltree.aggregation
ai.libs.jaicore.ml.intervaltree.util - package ai.libs.jaicore.ml.intervaltree.util
ai.libs.jaicore.ml.latex - package ai.libs.jaicore.ml.latex
ai.libs.jaicore.ml.learningcurve.extrapolation - package ai.libs.jaicore.ml.learningcurve.extrapolation
ai.libs.jaicore.ml.learningcurve.extrapolation.client - package ai.libs.jaicore.ml.learningcurve.extrapolation.client
ai.libs.jaicore.ml.learningcurve.extrapolation.ipl - package ai.libs.jaicore.ml.learningcurve.extrapolation.ipl
ai.libs.jaicore.ml.learningcurve.extrapolation.lc - package ai.libs.jaicore.ml.learningcurve.extrapolation.lc
ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet - package ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet
ai.libs.jaicore.ml.metafeatures - package ai.libs.jaicore.ml.metafeatures: Provides means of computing meta features for a data set.
ai.libs.jaicore.ml.openml - package ai.libs.jaicore.ml.openml
ai.libs.jaicore.ml.ranking - package ai.libs.jaicore.ml.ranking
ai.libs.jaicore.ml.ranking.clusterbased - package ai.libs.jaicore.ml.ranking.clusterbased
ai.libs.jaicore.ml.ranking.clusterbased.candidateprovider - package ai.libs.jaicore.ml.ranking.clusterbased.candidateprovider
ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes - package ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes
ai.libs.jaicore.ml.ranking.clusterbased.datamanager - package ai.libs.jaicore.ml.ranking.clusterbased.datamanager
ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac - package ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation - package ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation
ai.libs.jaicore.ml.rqp - package ai.libs.jaicore.ml.rqp
ai.libs.jaicore.ml.scikitwrapper - package ai.libs.jaicore.ml.scikitwrapper
ai.libs.jaicore.ml.tsc - package ai.libs.jaicore.ml.tsc
ai.libs.jaicore.ml.tsc.classifier - package ai.libs.jaicore.ml.tsc.classifier
ai.libs.jaicore.ml.tsc.classifier.ensemble - package ai.libs.jaicore.ml.tsc.classifier.ensemble: A package consisting of ensemble classifiers used in implemented time series classifiers.
ai.libs.jaicore.ml.tsc.classifier.neighbors - package ai.libs.jaicore.ml.tsc.classifier.neighbors
ai.libs.jaicore.ml.tsc.classifier.shapelets - package ai.libs.jaicore.ml.tsc.classifier.shapelets: This package contains implementations for Shapelet based classifier and training algorithms.
ai.libs.jaicore.ml.tsc.classifier.trees - package ai.libs.jaicore.ml.tsc.classifier.trees
ai.libs.jaicore.ml.tsc.complexity - package ai.libs.jaicore.ml.tsc.complexity: This package contains implementations for time series complexity measures.
ai.libs.jaicore.ml.tsc.dataset - package ai.libs.jaicore.ml.tsc.dataset: This package contains implementations related to the time series dataset,
ai.libs.jaicore.ml.tsc.distances - package ai.libs.jaicore.ml.tsc.distances: This package contains implementations for time series distance measures.
ai.libs.jaicore.ml.tsc.exceptions - package ai.libs.jaicore.ml.tsc.exceptions
ai.libs.jaicore.ml.tsc.features - package ai.libs.jaicore.ml.tsc.features
ai.libs.jaicore.ml.tsc.filter - package ai.libs.jaicore.ml.tsc.filter
ai.libs.jaicore.ml.tsc.filter.derivate - package ai.libs.jaicore.ml.tsc.filter.derivate: Package containing filters that calculate derivates of time series.
ai.libs.jaicore.ml.tsc.filter.transform - package ai.libs.jaicore.ml.tsc.filter.transform: Package containing filters that calculate transforms of time series.
ai.libs.jaicore.ml.tsc.quality_measures - package ai.libs.jaicore.ml.tsc.quality_measures
ai.libs.jaicore.ml.tsc.shapelets - package ai.libs.jaicore.ml.tsc.shapelets
ai.libs.jaicore.ml.tsc.shapelets.search - package ai.libs.jaicore.ml.tsc.shapelets.search: This package contains search strategies applied to Shapelet objects.
ai.libs.jaicore.ml.tsc.util - package ai.libs.jaicore.ml.tsc.util: This package contains utility functions for time series classification.
ai.libs.jaicore.ml.weka.dataset.splitter - package ai.libs.jaicore.ml.weka.dataset.splitter
AILabeledAttributeArrayDataset,L> - Interface in ai.libs.jaicore.ml.core.dataset: Common interface of a dataset defining methods to access meta-data and instances contained in the dataset.
AINumericLabeledAttributeArrayDataset,L> - Interface in ai.libs.jaicore.ml.core.dataset
algorithm - Variable in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier: The algorithm object used for the training of the classifier.
ALGORITHMMODES - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
ALGORITHMS - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
ALLPAIRS - ai.libs.jaicore.ml.classification.multiclass.reduction.EMCNodeType
AllPairsTable - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
AllPairsTable(Instances, Instances, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable
ALPHA - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
ALPHA - Static variable in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Predefined alpha parameter used within the calculations.
alphabet() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig: The alphabet consists of doubles representing letters and defines each word.
alphabetSize() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig: The alphabet size determines the number of Bins for the SFA Histograms.
AMCTreeNode<C> - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
AMCTreeNode(List<C>) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.AMCTreeNode
AMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.tsc.shapelets.search: Abstract class for minimum distance search strategies.
AMinimumDistanceSearchStrategy(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.search.AMinimumDistanceSearchStrategy: Constructor.
AMonteCarloCrossValidationBasedEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory: An abstract factory for configuring Monte Carlo cross-validation based evaluators.
AMonteCarloCrossValidationBasedEvaluatorFactory() - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Standard c'tor.
AnalyticalLearningCurve - Interface in ai.libs.jaicore.ml.interfaces: Added some analytical functions to a learning curve.
andersonDarlingTest(double[]) - Method in class ai.libs.jaicore.ml.clustering.GMeans
AOnlineLearner<T,V,I,D extends IDataset> - Class in ai.libs.jaicore.ml.core.predictivemodel: Abstract extension of IOnlineLearner to be able to construct prediction of the given type.
AOnlineLearner() - Constructor for class ai.libs.jaicore.ml.core.predictivemodel.AOnlineLearner
apply(Vector) - Method in class ai.libs.jaicore.ml.core.optimizing.graddesc.BlackBoxGradient
apply(Vector) - Method in interface ai.libs.jaicore.ml.core.optimizing.IGradientDescendableFunction: Applies the function for the point represented by the given vector.
apply(Vector) - Method in interface ai.libs.jaicore.ml.core.optimizing.IGradientFunction: Returns the result of applying the gradient to the point represented by the given vector.
apply(Vector) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood: Algorithm (18) of [1].
apply(Vector) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative
apply(Instances) - Method in class ai.libs.jaicore.ml.rqp.AugSpaceAllPairs
APredictiveModel<T,V,I,D extends IDataset> - Class in ai.libs.jaicore.ml.core.predictivemodel: Abstract extension of IPredictiveModel to be able to construct prediction of the given type.
APredictiveModel() - Constructor for class ai.libs.jaicore.ml.core.predictivemodel.APredictiveModel
AProcessListener - Class in ai.libs.jaicore.ml.scikitwrapper: The process listener may be attached to a process in order to handle its ouputs streams in a controlled way.
AProcessListener() - Constructor for class ai.libs.jaicore.ml.scikitwrapper.AProcessListener
ARandomlyInitializingDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning
ARandomlyInitializingDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
ArbitrarySplitter - Class in ai.libs.jaicore.ml.weka.dataset.splitter: Generates a purely random split of the dataset depending on the seed and on the portions provided.
ArbitrarySplitter() - Constructor for class ai.libs.jaicore.ml.weka.dataset.splitter.ArbitrarySplitter
AREA_ABOVE_ROC - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
AREA_UNDER_ROC - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
areInstancesEqual(Instance, Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
ARFFFILE - ai.libs.jaicore.ml.cache.DataProvider
ArffUtilities - Class in ai.libs.jaicore.ml.core.dataset: Utility class for handling Arff dataset files.
argmax(int[]) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil: Calculates the index of the maximum value in the given array (argmax).
ASamplingAlgorithm - Class in ai.libs.jaicore.ml.core.dataset.sampling: An abstract class for sampling algorithms providing basic functionality of an algorithm.
ASamplingAlgorithm<I,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory: An abstract class for sampling algorithms providing basic functionality of an algorithm.
ASamplingAlgorithm(IAlgorithmConfig, AILabeledAttributeArrayDataset) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.ASamplingAlgorithm
ASamplingAlgorithm(AILabeledAttributeArrayDataset) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.ASamplingAlgorithm
ASamplingAlgorithm(D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm
ASimpleInstancesImpl - Class in ai.libs.jaicore.ml.core
ASimpleInstancesImpl() - Constructor for class ai.libs.jaicore.ml.core.ASimpleInstancesImpl
ASimpleInstancesImpl(int) - Constructor for class ai.libs.jaicore.ml.core.ASimpleInstancesImpl
ASimplifiedTSClassifier<T> - Class in ai.libs.jaicore.ml.tsc.classifier: Simplified batch-learning time series classifier which can be trained and used as a predictor.
ASimplifiedTSClassifier() - Constructor for class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier
ASimplifiedTSCLearningAlgorithm<T,C extends ASimplifiedTSClassifier<T>> - Class in ai.libs.jaicore.ml.tsc.classifier
ASimplifiedTSCLearningAlgorithm(IAlgorithmConfig, C, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm
ASquaredErrorLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel: Measure computing the squared error of two doubles.
ASquaredErrorLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.ASquaredErrorLoss
assessQuality(List<Double>, int[]) - Method in class ai.libs.jaicore.ml.tsc.quality_measures.FStat: Computes a quality score based on the distances of each instance to the shapelet and the corresponding classValues.
assessQuality(List<Double>, int[]) - Method in interface ai.libs.jaicore.ml.tsc.quality_measures.IQualityMeasure: Computes a quality score based on the distances of each instance to the shapelet and the corresponding classValues.
assignDatapoint(String) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner
assignDatapoint(String) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.IStratiFileAssigner: Select the suitable stratum for a datapoint and write it into the corresponding temporary file.
assignToStrati(I) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner
assignToStrati(I) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
assignToStrati(I) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner: Custom logic for assigning datapoints into strati.
ATransformFilter - Class in ai.libs.jaicore.ml.tsc.filter.transform: Abstract superclass for all transform filters.
ATransformFilter() - Constructor for class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
ATSCAlgorithm<L,V,D extends TimeSeriesDataset<L>,C extends TSClassifier<L,V,D>> - Class in ai.libs.jaicore.ml.tsc.classifier: Abstract algorithm class which is able to take TimeSeriesDataset objects and builds TSClassifier instances specified by the generic parameter .
ATSCAlgorithm() - Constructor for class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm
AttributeBasedStratiAmountSelectorAndAssigner,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling: This class is responsible for computing the amount of strati in attribute-based stratified sampling and assigning elements to the strati.
AttributeBasedStratiAmountSelectorAndAssigner() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner: SCALE-54: Explicitly allow to not provide an attribute list
AttributeBasedStratiAmountSelectorAndAssigner(List<Integer>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner
AttributeBasedStratiAmountSelectorAndAssigner(List<Integer>, DiscretizationHelper.DiscretizationStrategy, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner
AttributeBasedStratiAmountSelectorAndAssigner(List<Integer>, Map<Integer, AttributeDiscretizationPolicy>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner
AttributeDiscretizationPolicy - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling
AttributeDiscretizationPolicy(List<Interval>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
AugSpaceAllPairs - Class in ai.libs.jaicore.ml.rqp
AugSpaceAllPairs() - Constructor for class ai.libs.jaicore.ml.rqp.AugSpaceAllPairs
augSpaceSample() - Method in class ai.libs.jaicore.ml.rqp.ChooseKAugSpaceSampler
augSpaceSample() - Method in class ai.libs.jaicore.ml.rqp.ExactIntervalAugSpaceSampler
augSpaceSample() - Method in interface ai.libs.jaicore.ml.rqp.IAugmentedSpaceSampler: Generates a point in the augmented space from the AugmentedSpaceSampler's precise dataset.
augSpaceSample() - Method in class ai.libs.jaicore.ml.rqp.KNNAugSpaceSampler
AUTO_MEKA_GGP_FITNESS - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
AUTO_MEKA_GGP_FITNESS_LOSS - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
AutoMekaGGPFitness - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
AutoMekaGGPFitness() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMekaGGPFitness
AutoMEKAGGPFitnessMeasure - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel: Fitness function for a linear combination of 4 well-known multi-label metrics: ExactMatch, Hamming, Rank and F1MacroAverageL.
AutoMEKAGGPFitnessMeasure() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMEKAGGPFitnessMeasure
AutoMEKAGGPFitnessMeasureLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel: Measure combining exact match, hamming loss, f1macroavgL and rankloss.
AutoMEKAGGPFitnessMeasureLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMEKAGGPFitnessMeasureLoss
AVG_COST - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
AWeightedTrigometricDistance - Class in ai.libs.jaicore.ml.tsc.distances
AWeightedTrigometricDistance(double) - Constructor for class ai.libs.jaicore.ml.tsc.distances.AWeightedTrigometricDistance

B

B - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
backwardDifferenceDerivate(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Calclualtes f'(n) = f(n-1) - f(n)
BackwardDifferenceDerivate - Class in ai.libs.jaicore.ml.tsc.filter.derivate: Filter that calculate the Backward Difference derivate.
BackwardDifferenceDerivate() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.BackwardDifferenceDerivate
BackwardDifferenceDerivate(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.BackwardDifferenceDerivate
backwardDifferenceDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Calclualtes f'(n) = f(n-1) - f(n)
bestScore - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: The best score.
BETA - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
BilinFunction - Class in ai.libs.jaicore.ml.dyadranking.optimizing: Wraps the NLL optimizing problem into the QNMinimizer optimizer.
BilinFunction(Map<IDyadRankingInstance, Map<Dyad, Vector>>, DyadRankingDataset, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.optimizing.BilinFunction: Creates a NLL optimizing problem for the kronecker product as the bilinear feature transform.
BiliniearFeatureTransform - Class in ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform: Implementation of the feature transformation method using the Kroenecker Product.
BiliniearFeatureTransform() - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.BiliniearFeatureTransform
BlackBoxGradient - Class in ai.libs.jaicore.ml.core.optimizing.graddesc: Difference quotient based gradient estimation.
BlackBoxGradient(IGradientDescendableFunction, double) - Constructor for class ai.libs.jaicore.ml.core.optimizing.graddesc.BlackBoxGradient: Sets up a gradient-estimator for the given function.
BooleanAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.primitive: The boolean attribute type.
BooleanAttributeType() - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType
BooleanAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.primitive: Numeric attribute value as it can be part of an instance.
BooleanAttributeValue(BooleanAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeValue: Standard c'tor.
BooleanAttributeValue(BooleanAttributeType, Boolean) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeValue: C'tor setting the value of this attribute as well.
BOSSClassifier - Class in ai.libs.jaicore.ml.tsc.classifier
BOSSClassifier(int, int, double[], int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
BOSSClassifier(BOSSLearningAlgorithm.IBossAlgorithmConfig) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
BOSSEnsembleClassifier - Class in ai.libs.jaicore.ml.tsc.classifier
BOSSEnsembleClassifier(Map<Integer, Integer>, double[], boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier
BOSSEnsembleClassifier(Map<Integer, Integer>, int, double[], boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier
BOSSLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier
BOSSLearningAlgorithm(BOSSLearningAlgorithm.IBossAlgorithmConfig, BOSSClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm
BOSSLearningAlgorithm.IBossAlgorithmConfig - Interface in ai.libs.jaicore.ml.tsc.classifier
buildAttributeValue(boolean) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType
buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType
buildAttributeValue(Object) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeType: Casts the value to the respective type and returns an attribute value with the creating attribute type as the referenced type.
buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType
buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType
buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType
buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType
buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType
buildAttributeValue(String) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeType: Builds an attribute value object from a string description.
buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType
buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType
buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType
buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.Ensemble
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.MajorityConfidenceVote: Builds the ensemble by assessing the classifier weights using a cross validation of each classifier of the ensemble and then training the classifiers using the complete data.
buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree
buildGroup(List<ProblemInstance>) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.IGroupBuilder
buildGroup(List<ProblemInstance<Instance>>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACGroupBuilder
buildRanker() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISAC
buildRanker() - Method in interface ai.libs.jaicore.ml.ranking.Ranker
buildWekaClassifierFromSimplifiedTS(Classifier, TimeSeriesDataset) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Trains a given Weka classifier using the simplified time series data set timeSeriesDataset.
buildWekaClassifierFromTS(Classifier, TimeSeriesDataset<L>) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Trains a given Weka classifier using the time series data set timeSeriesDataset.

C

C - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
cacheClassifier(String, EMCNodeType, Instances, Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ClassifierCache
cacheRetrievals - Static variable in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMEKAGGPFitnessMeasureLoss
calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.F1MacroAverageL
calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.HammingLoss
calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.InstanceWiseF1
calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.RankLoss
calculateAvgMeasure(List, List) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableDoubleMeasure
calculateAvgMeasure(List, List) - Method in interface ai.libs.jaicore.ml.core.evaluation.measure.IMeasure: Computes the measure for lists of input actual and the expected outcome expected and aggregates the measured values with the mean, as this is the most frequently used aggregate function.
calculateAvgMeasure(List<Double>, List<Double>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss
calculateAvgMeasure(List<Double>, List<Double>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.RootMeanSquaredErrorLoss
calculateD(double[][][], int, int, double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Function to calculate the distance between the j-th segment of the given time series instance and the k-th shapelet stored in the shapelet tensor S.
calculateDeltaEntropy(double[], int[], double, List<Integer>, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Function calculating the delta entropy for a given thresholdCandidate and parentEntropy.
calculateEntrance(double, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Calculates the entrance gain specified by Deng et. al. in the paper's chapter 4.1.
calculateFeature(TimeSeriesFeature.FeatureType, double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature: Function calculating the feature specified by the feature type fType for a given instance vector of the interval [t1, t2].
calculateFinalInstanceBoundaries(D, Classifier) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.LocalCaseControlSampling
calculateFinalInstanceBoundaries(D, Classifier) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.OSMAC
calculateInstanceBoundaries(HashMap<Object, Integer>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling
calculateMargin(double[], double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Function calculating the margin between the given thresholdCandidate and the nearest feature value from the given dataValues.
calculateMeasure(double[][], int[][]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMekaGGPFitness
calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMEKAGGPFitnessMeasureLoss
calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.ExactMatchLoss
calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.HammingLoss
calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.InstanceWiseF1
calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.JaccardScore
calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.RankLoss
calculateMeasure(I, I) - Method in interface ai.libs.jaicore.ml.core.evaluation.measure.IMeasure: Computes the measure for a measured input actual and the expected outcome expected.
calculateMeasure(I, I) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.LossScoreTransformer
calculateMeasure(Double, Double) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.ASquaredErrorLoss
calculateMeasure(Double, Double) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MeanSquaredErrorLoss
calculateMeasure(Double, Double) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss
calculateMeasure(Double, Double) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.ZeroOneLoss
calculateMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.F1MacroAverageL
calculateMeasure(List, List) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableMeasure
calculateMeasure(List, List) - Method in interface ai.libs.jaicore.ml.core.evaluation.measure.IMeasure: Computes the measure for a lists of input actual and the expected outcome expected.
calculateMeasure(List, List, IAggregateFunction<O>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableMeasure
calculateMeasure(List, List, IAggregateFunction<O>) - Method in interface ai.libs.jaicore.ml.core.evaluation.measure.IMeasure: Computes the measure for lists of input actual and the expected outcome expected and aggregates the measured values with the given aggregation.
calculateMeasure(List<Double>, List<Double>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss
calculateMeasure(List<Double>, List<Double>, IAggregateFunction<Double>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss
calculateMHat(double[][][], int, int, double[], int, int, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Function to calculate the soft-minimum function which is a differentiable approximation of the minimum distance matrix given in the paper in section 3.1.4.
calculateNearestNeigbors(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Determine the k nearest neighbors for a test instance.
calculatePrediction(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Calculates predicition on a single test instance.
calculateWeights(int) - Method in class ai.libs.jaicore.ml.tsc.distances.WeightedDynamicTimeWarping: Calculates the weight vector via the Modified logistic weight function (see paper 4.2).
calculateWindowLengthPredictions(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Calculates predicitions for a test instance using 1NN with Shotgun Distance and different window lengths.
calculateWindowLengthPredictions(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Calculates predicitions for a test dataset using 1NN with Shotgun Distance and different window lengths.
call() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm
call() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborLearningAlgorithm
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Main function to train a LearnShapeletsClassifier.
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Training procedure for ShapeletTransformTSClassifier using the training algorithm described in the paper.
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm: Training procedure for a LearnPatternSimilarityClassifier.
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Training procedure construction a Time Series Bag-of-Features (TSBF) classifier using the given input data.
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm: Training procedure construction a time series tree using the given input data.
call() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Training procedure construction a time series tree using the given input data.
cancel() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
cancel() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator
cancel() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm
cancel() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
CaseControlLikeSampling,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol
CaseControlLikeSampling(D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling
CaseControlSampling,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol: Case control sampling.
CaseControlSampling(Random, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlSampling: Constructor
CaseControlSamplingFactory,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
CaseControlSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.CaseControlSamplingFactory
CategoricalAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.categorical: The categorical attribute type describes the domain a value of a respective categorical attribute value stems from.
CategoricalAttributeType(List<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType: Constructor setting the domain of the categorical attribute values.
CategoricalAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.categorical: Categorical attribute value as it can be part of an instance.
CategoricalAttributeValue(ICategoricalAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeValue: Standard c'tor.
CategoricalAttributeValue(ICategoricalAttributeType, String) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeValue: C'tor setting the value of this attribute as well.
CategoricalFeatureDomain - Class in ai.libs.jaicore.ml.core: Description of a categorical feature domain.
CategoricalFeatureDomain(double[]) - Constructor for class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
CategoricalFeatureDomain(CategoricalFeatureDomain) - Constructor for class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
center - Variable in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans
characterize(Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue: Provide a characterization of the given node to be used by the dyad ranker.
characterize(Instances) - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
characterizerNames - Variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: The names of the characterizers used
characterizers - Variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: The list of characterizers used in the computation of meta features
CheckedJaicoreMLException - Exception in ai.libs.jaicore.ml.core.exception: The CheckedJaicoreMLException serves as a base class for all checked Exceptions defined as part of jaicore-ml.
CheckedJaicoreMLException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.CheckedJaicoreMLException: Creates a new CheckedJaicoreMLException with the given parameters.
CheckedJaicoreMLException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.CheckedJaicoreMLException: Creates a new CheckedJaicoreMLException with the given parameters.
checkWhetherPredictionIsPossible(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier
ChooseKAugSpaceSampler - Class in ai.libs.jaicore.ml.rqp: Samples interval-valued data from a dataset of precise points by sampling k precise points (with replacement) and generating a point in the interval-valued augmented space by only considering those k points, i.e. choosing respective minima and maxima for each attribute from the chosen precise points.
ChooseKAugSpaceSampler(Instances, Random, int) - Constructor for class ai.libs.jaicore.ml.rqp.ChooseKAugSpaceSampler
CLASSIFICATION - ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper.ProblemType
ClassifierCache - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
ClassifierCache() - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.ClassifierCache
ClassifierEvaluatorConstructionFailedException - Exception in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory
ClassifierEvaluatorConstructionFailedException(Exception) - Constructor for exception ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ClassifierEvaluatorConstructionFailedException
ClassifierMetricGetter - Class in ai.libs.jaicore.ml.core.evaluation.measure: Class for getting metrics by their name for single- and multilabel classifiers.
ClassifierRankingForGroup - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
ClassifierWeightedSampling,D extends IOrderedDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol: The idea behind this Sampling method is to weight instances depended on the way a pilot estimator p classified them.
ClassifierWeightedSampling(Random, Instances, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.ClassifierWeightedSampling
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.Ensemble
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AMCTreeNode
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier
classifyInstance(Instance) - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.ITreeClassifier
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.MajorityConfidenceVote
classifyInstances(Instances) - Method in interface ai.libs.jaicore.ml.evaluation.IInstancesClassifier
classifyInstances(Instances) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
classMapper - Variable in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier: Class mapper object used to encode and decode predicted values if String values are used as classes.
ClassMapper - Class in ai.libs.jaicore.ml.tsc.util: Class mapper used for predictions of String objects which are internally predicted by time series classifiers as ints.
ClassMapper(List<String>) - Constructor for class ai.libs.jaicore.ml.tsc.util.ClassMapper: Constructor using a list of String value to realize the mapping
ClassStratiFileAssigner - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling
ClassStratiFileAssigner() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner: Constructor without a given target attribute.
ClassStratiFileAssigner(int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner: Constructor with a given target attribute.
cleanUp() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm: Implement custom clean up behaviour.
cleanUp() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.ReservoirSampling
cleanUp() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.StratifiedFileSampling
cleanUp() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.SystematicFileSampling
clear() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
clear() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
clearCache() - Static method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
clone() - Method in class ai.libs.jaicore.ml.cache.Instruction
clone() - Method in class ai.libs.jaicore.ml.cache.LoadDataSetInstructionForARFFFile
clone() - Method in class ai.libs.jaicore.ml.cache.LoadDatasetInstructionForOpenML
clone() - Method in class ai.libs.jaicore.ml.cache.StratifiedSplitSubsetInstruction
clone() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier
cloneClassifier(Classifier) - Static method in class ai.libs.jaicore.ml.WekaUtil
cluster() - Method in class ai.libs.jaicore.ml.clustering.GMeans
Cluster - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
clusterDeprecated() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans
clusterResults - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
clusters - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
ClusterSampling,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory
ClusterSampling(long, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
ClusterSampling(long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
clusterShapelets() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig: Indicator whether clustering of shapelets should be used.
clusterShapelets(List<Shapelet>, int, long) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Clusters the given shapelets into noClusters clusters (cf. algorithm 6 of the original paper).
ClusterStratiAssigner> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling
ClusterStratiAssigner() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
collectLeafCounts(int[], Instance, AccessibleRandomTree) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm: Function collecting the leaf counts for the given instance as predicted by regTree.
compactString() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
compactString() - Method in class ai.libs.jaicore.ml.core.FeatureDomain
compactString() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
complexity(double[]) - Method in interface ai.libs.jaicore.ml.tsc.complexity.ITimeSeriesComplexity
complexity(double[]) - Method in class ai.libs.jaicore.ml.tsc.complexity.SquaredBackwardDifferenceComplexity
complexity(double[]) - Method in class ai.libs.jaicore.ml.tsc.complexity.StretchingComplexity
ComplexityInvariantDistance - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Complexity Invariant Distance (CID) measure as published in "A Complexity-Invariant Distance Measure for Time Series" by Gustavo E.A.P.A.
computationTimes - Variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: The time it took to compute the meta features for each characterizer by name
computeAverageLoss(DyadRankingLossFunction, DyadRankingDataset, IDyadRanker) - Static method in class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingLossUtil
computeAverageLoss(DyadRankingLossFunction, DyadRankingDataset, IDyadRanker, Random) - Static method in class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingLossUtil: Computes the average loss over several dyad orderings.
computeAverageLoss(DyadRankingLossFunction, DyadRankingDataset, DyadRankingDataset) - Static method in class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingLossUtil: Computes the average loss over several dyad orderings.
computeDistance(double[], double[]) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.L1DistanceMetric
computeDistance(A, B) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.IDistanceMetric
computeLoss(INDArray) - Static method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetLoss: Computes the NLL for PL networks according to equation (27) in [1].
computeLossGradient(INDArray, int) - Static method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetLoss: Computes the gradient of the NLL for PL networks w.r.t. the k-th dyad according to equation (28) in [1].
computeMarginalStandardDeviationForSubsetOfFeatures(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree: Computes the variance contribution of a subset of features.
computeMarginalVarianceContributionForFeatureSubset(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
computeMarginalVarianceContributionForFeatureSubsetNotNormalized(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
computeMarginalVarianceContributionForSubsetOfFeatures(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree: Computes the variance contribution of a subset of features.
computeMarginalVarianceContributionForSubsetOfFeaturesNotNormalized(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree: Computes the variance contribution of a subset of features without normalizing.
computeTotalVarianceOfSubset(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree: Computes the total variance of marginal predictions for a given set of features.
ConfidenceIntervalClusteringBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning: A prototypical active dyad ranker based on clustering of pseudo confidence intervals.
ConfidenceIntervalClusteringBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, int, Clusterer) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.ConfidenceIntervalClusteringBasedActiveDyadRanker
ConfigurationException - Exception in ai.libs.jaicore.ml.core.exception: The ConfigurationException indicates an error during a configuration process.
ConfigurationException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.ConfigurationException: Creates a new ConfigurationException with the given parameters.
ConfigurationException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.ConfigurationException: Creates a new ConfigurationException with the given parameters.
ConfigurationLearningCurveExtrapolationEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka: Predicts the accuracy of a classifier with certain configurations on a point of its learning curve, given some anchorpoint and its configurations using the LCNet of pybnn Note: This code was copied from LearningCurveExtrapolationEvaluator and slightly reworked
ConfigurationLearningCurveExtrapolationEvaluator(int[], ISamplingAlgorithmFactory<WekaInstance<Object>, WekaInstances<Object>, ASamplingAlgorithm<WekaInstance<Object>, WekaInstances<Object>>>, WekaInstances<Object>, double, long, String, double[]) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.ConfigurationLearningCurveExtrapolationEvaluator
ConfigurationLearningCurveExtrapolator,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.learningcurve.extrapolation: This class is a subclass of LearningCurveExtrapolator which deals with the slightly different setup that is required by the LCNet of pybnn
ConfigurationLearningCurveExtrapolator(Classifier, D, double, int[], ISamplingAlgorithmFactory<I, D, ASamplingAlgorithm<I, D>>, long, String, double[]) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ConfigurationLearningCurveExtrapolator
configureBestFirst(BestFirst<GraphSearchWithSubpathEvaluationsInput<T, String, Double>, T, String, Double>) - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueueConfig
ConstantClassifier - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
ConstantClassifier() - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier
containedClasses - Variable in class ai.libs.jaicore.ml.classification.multiclass.reduction.AMCTreeNode
contains(Object) - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
contains(Object) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
contains(Object) - Method in class ai.libs.jaicore.ml.core.FeatureDomain: Checks if the domain contains an item.
contains(Object) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
contains(Object) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
containsAll(Collection<?>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
containsAll(Collection<?>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
containsInstance(double) - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
containsInstance(double) - Method in class ai.libs.jaicore.ml.core.FeatureDomain: Checks whether a given weka instance is contained in the feature domain
containsInstance(double) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
containsInstance(Instance) - Method in class ai.libs.jaicore.ml.core.FeatureSpace
ContainsNonNumericAttributesException - Exception in ai.libs.jaicore.ml.core.dataset
ContainsNonNumericAttributesException(String) - Constructor for exception ai.libs.jaicore.ml.core.dataset.ContainsNonNumericAttributesException
ContainsNonNumericAttributesException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.dataset.ContainsNonNumericAttributesException
containsPartialInstance(List<Integer>, List<Double>) - Method in class ai.libs.jaicore.ml.core.FeatureSpace
CORRECT - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
CORRELATION_COEFFICIENT - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
CosineTransform - Class in ai.libs.jaicore.ml.tsc.filter.transform: Calculates the cosine transform of a time series.
CosineTransform() - Constructor for class ai.libs.jaicore.ml.tsc.filter.transform.CosineTransform
countClassOccurrences(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling: Count occurrences of every class.
countDatasetEntries(File, boolean) - Static method in class ai.libs.jaicore.ml.core.dataset.ArffUtilities: Counts the amount of datapoint entries in an ARFF file.
countFileLines(File) - Static method in class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader: Counts the lines of the given File object in a very efficient way (thanks to https://stackoverflow.com/a/453067).
CP_ASC - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
CP_DS - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
CP_IBK - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
CP_NB - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
CPUS - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
createDatasetForMatrix(double[][]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Function creating a TimeSeriesDataset object given one or multiple valueMatrices.
createDatasetForMatrix(int[], double[][]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Function creating a TimeSeriesDataset object given the targets and one or multiple valueMatrices.
createDataSetIndex(int, int) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper: Creates a list of data sets by id in a file with caps for the maximum of features and instances.
createDefaultDiscretizationPolicies(D, List<Integer>, Map<Integer, Set<Object>>, DiscretizationHelper.DiscretizationStrategy, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper: This method creates a default discretization policy for each numeric attribute in the attributes that have to be considered for stratum assignment.
createEmpty() - Method in interface ai.libs.jaicore.ml.core.dataset.IDataset: Creates an empty copy of the same structure (and same type).
createEmpty() - Method in interface ai.libs.jaicore.ml.core.dataset.ILabeledAttributeArrayDataset: Creates an empty copy with the same attribute types as this IDataset.
createEmpty() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
createEmpty() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
createEmpty() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
createEmpty() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset
createEquidistantTimestamps(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Creates equidistant timestamps for a time series.
createEquidistantTimestamps(INDArray) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Creates equidistant timestamps for a time series.
createImportStatementFromImportFolder(File, boolean) - Static method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper: Makes the given folder a module to be usable as an import for python and creates a string that adds the folder to the python environment and then imports the folder itself as a module.
createNetworkFromDl4jConfigFile(File) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Creates a simple feed-forward MultiLayerNetwork using the json representation of a MultiLayerConfiguration in the file .
CSVFILE - ai.libs.jaicore.ml.cache.DataProvider
currentCluster - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

D

DataProvider - Enum in ai.libs.jaicore.ml.cache
dataset - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
DatasetCapacityReachedException - Exception in ai.libs.jaicore.ml.core.exception: Exception that indicates that the capacity of a TimeSeriesDataset is reached.
DatasetCapacityReachedException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.DatasetCapacityReachedException: Creates a new DatasetCapacityReachedException with the given parameters.
DatasetCapacityReachedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.DatasetCapacityReachedException: Creates a new DatasetCapacityReachedException with the given parameters.
DatasetCharacterizerInitializationFailedException - Exception in ai.libs.jaicore.ml.metafeatures: An exception that signifies something went wrong during the initialization of a dataset characterizer
DatasetCharacterizerInitializationFailedException() - Constructor for exception ai.libs.jaicore.ml.metafeatures.DatasetCharacterizerInitializationFailedException: Create an exception with a default message.
DatasetCharacterizerInitializationFailedException(String) - Constructor for exception ai.libs.jaicore.ml.metafeatures.DatasetCharacterizerInitializationFailedException: Create an exception with the given message.
DatasetCharacterizerInitializationFailedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.metafeatures.DatasetCharacterizerInitializationFailedException: Create an exception with the given cause and additional message
DatasetCharacterizerInitializationFailedException(Throwable) - Constructor for exception ai.libs.jaicore.ml.metafeatures.DatasetCharacterizerInitializationFailedException: Create an exception with the given cause.
DatasetCreationException - Exception in ai.libs.jaicore.ml.core.dataset
DatasetCreationException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.dataset.DatasetCreationException
DatasetCreationException(Throwable) - Constructor for exception ai.libs.jaicore.ml.core.dataset.DatasetCreationException
DatasetFileSorter - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles: Sorts a Dataset file with a Mergesort.
DatasetFileSorter(File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.DatasetFileSorter
DatasetFileSorter(File, TempFileHandler) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.DatasetFileSorter
datasetFolder - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
DATASETS - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
datasetToWekaInstances(IOrderedLabeledAttributeArrayDataset<?, ?>) - Static method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstancesUtil
DataSourceCreationFailedException(Exception) - Constructor for exception ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator.DataSourceCreationFailedException
decide(TreeNode<TimeSeriesTreeClassifier.TimeSeriesTreeNodeDecisionFunction>, double[]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier: Function performing the decision on a treeNode given the instance based on the locally stored splitting criterion.
DEFAULT_CHARSET - Static variable in class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader: Default charset used when extracting from files.
defaultClassifierString() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest
defaultClassifierString() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
DefaultProcessListener - Class in ai.libs.jaicore.ml.scikitwrapper: The DefaultProcessListener might be used to forward any type of outputs of a process to a logger.
DefaultProcessListener(boolean) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener: Constructor to initialize the DefaultProcessListener.
delete() - Method in class ai.libs.jaicore.ml.SubInstances
deleteAttributeAt(int) - Method in class ai.libs.jaicore.ml.SubInstances
deleteNet() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod
deleteNet(String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient
DELTA - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter: Calculates the derivate of a time series.
derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.BackwardDifferenceDerivate
derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ForwardDifferenceDerivate
derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.GulloDerivate
derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.KeoghDerivate
DerivateDistance - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Derivate Distance (DD) measure as published in "Using derivatives in time series classification" by Tomasz Gorecki and Maciej Luczak (2013).
DerivateDistance(double, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateDistance: Constructor that uses the same distance measures for the function and derivate values that uses the BackwardDifferenceDerivate as derivation.
DerivateDistance(double, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateDistance: Constructor with individual distance measures for the function and derivate values that uses the BackwardDifferenceDerivate as derivation.
DerivateDistance(double, ADerivateFilter, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateDistance: Constructor that uses the same distance measures for the function and derivate values.
DerivateDistance(double, ADerivateFilter, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateDistance: Constructor with individual distance measures for the function and derivate values.
DerivateTransformDistance - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Derivate Transform Distance (TD) measure as published in "Non-isometric transforms in time series classification using DTW" by Tomasz Gorecki and Maciej Luczak (2014).
DerivateTransformDistance(double, double, double, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Constructor that uses the same distance measures for function, derivate and transform values.
DerivateTransformDistance(double, double, double, ITimeSeriesDistance, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Constructor with individual distance measure for function, derivate and transform values that uses the BackwardDifferencetransform as derivate and the CosineTransform as transformation.
DerivateTransformDistance(double, double, double, ADerivateFilter, ATransformFilter, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Constructor that uses the same distance measures for function, derivate and transform values.
DerivateTransformDistance(double, double, double, ADerivateFilter, ATransformFilter, ITimeSeriesDistance, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Constructor with individual distance measure for function, derivate and transform values.
derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter: Calcuates the derivates of a time series.
derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.BackwardDifferenceDerivate
derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ForwardDifferenceDerivate
derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.GulloDerivate
derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.KeoghDerivate
derivativeAt(double[]) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.BilinFunction
deserialize(InputStream) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset
DFT - Class in ai.libs.jaicore.ml.tsc.filter
DFT() - Constructor for class ai.libs.jaicore.ml.tsc.filter.DFT
difference(double[], double[]) - Method in class ai.libs.jaicore.ml.clustering.GMeans
DIRECT - ai.libs.jaicore.ml.classification.multiclass.reduction.EMCNodeType
disableRekursiv() - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
DiscretizationHelper<D extends AILabeledAttributeArrayDataset<?,?>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling: This helper class provides methods that are required in order to discretize numeric attributes.
DiscretizationHelper() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper
DiscretizationHelper.DiscretizationStrategy - Enum in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling
discretize(double, AttributeDiscretizationPolicy) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper: Discretizes the particular provided value.
discretizeAttributeValues(Map<Integer, AttributeDiscretizationPolicy>, Map<Integer, Set<Object>>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper: Discretizes the given attribute values with respect to the provided policies
discretizeProbs(int, double[][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Function discretizing probabilities into bins.
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.ComplexityInvariantDistance
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateDistance
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.DynamicTimeWarping
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.EuclideanDistance
distance(double[], double[]) - Method in interface ai.libs.jaicore.ml.tsc.distances.ITimeSeriesDistance: Calculates the distance between two time series.
distance(double[], double[]) - Method in interface ai.libs.jaicore.ml.tsc.distances.ITimeSeriesDistanceWithTimestamps
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.ManhattanDistance
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.MoveSplitMerge
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.ShotgunDistance
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.TransformDistance
distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.WeightedDynamicTimeWarping
distance(double[], double[], double[], double[]) - Method in interface ai.libs.jaicore.ml.tsc.distances.ITimeSeriesDistanceWithTimestamps: Calculates the distance between two time series.
distance(double[], double[], double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.TimeWarpEditDistance
distance(double, double) - Method in interface ai.libs.jaicore.ml.tsc.distances.IScalarDistance: Calculates the distance between two scalars.
distanceMeassure - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
distanceMeasure - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
distanceWithWindow(double[], double[], int) - Method in class ai.libs.jaicore.ml.tsc.distances.DynamicTimeWarping
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.Ensemble
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.MajorityConfidenceVote: Function calculating the distribution for a instance by predicting the distributions for each classifier and multiplying the result by the classifier weights.
distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree
distributionForInstance(Instance, double[]) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
distributionForInstance(Instance, double[]) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
doAlgorithmStep() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
doInactiveStep() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm
domainDimension() - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.BilinFunction
Dyad - Class in ai.libs.jaicore.ml.dyadranking: Represents a dyad consisting of an instance and an alternative, represented by feature vectors.
Dyad(Vector, Vector) - Constructor for class ai.libs.jaicore.ml.dyadranking.Dyad: Construct a new dyad consisting of the given instance and alternative.
DyadDatasetPoolProvider - Class in ai.libs.jaicore.ml.dyadranking.activelearning: A pool provider which is created out of a DyadRankingDataset.
DyadDatasetPoolProvider(DyadRankingDataset) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
DyadMinMaxScaler - Class in ai.libs.jaicore.ml.dyadranking.util: A scaler that can be fit to a certain dataset and then be used to normalize dyad datasets, i.e. transform the data such that the values of each feature lie between 0 and 1.
DyadMinMaxScaler() - Constructor for class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler
DyadRankingDataset - Class in ai.libs.jaicore.ml.dyadranking.dataset: A dataset representation for dyad ranking.
DyadRankingDataset() - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset: Creates an empty dyad ranking dataset.
DyadRankingDataset(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset: Creates an empty dyad ranking dataset with the given initial capacity.
DyadRankingDataset(Collection<IDyadRankingInstance>) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset: Creates a dyad ranking dataset containing all elements in the given Collection in the order specified by the collections iterator.
DyadRankingDataset(List<IDyadRankingInstance>) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset
DyadRankingFeatureTransformNegativeLogLikelihood - Class in ai.libs.jaicore.ml.dyadranking.optimizing: Implements the negative log-likelihood function for the feature transformation Placket-Luce dyad ranker.
DyadRankingFeatureTransformNegativeLogLikelihood() - Constructor for class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood
DyadRankingFeatureTransformNegativeLogLikelihoodDerivative - Class in ai.libs.jaicore.ml.dyadranking.optimizing: Represents the derivate of the negative log likelihood function in the context of feature transformation Placket-Luce dyad ranking [1].
DyadRankingFeatureTransformNegativeLogLikelihoodDerivative() - Constructor for class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative
DyadRankingInstance - Class in ai.libs.jaicore.ml.dyadranking.dataset: A general implementation of a dyad ranking instance that contains an immutable list of dyad to represent the ordering of dyads.
DyadRankingInstance(List<Dyad>) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance: Construct a new dyad ranking instance that saves the given ordering of dyads immutably.
DyadRankingLossFunction - Interface in ai.libs.jaicore.ml.dyadranking.loss: Loss function for evaluating dyad rankers.
DyadRankingLossUtil - Class in ai.libs.jaicore.ml.dyadranking.loss: Class that contains utility methods for handling dyad ranking losses.
DyadRankingMLLossFunctionWrapper - Class in ai.libs.jaicore.ml.dyadranking.loss: A wrapper for dyad ranking loss that enables already implemented multi label classification loss functions to be used in this context.
DyadRankingMLLossFunctionWrapper(ADecomposableDoubleMeasure<double[]>) - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingMLLossFunctionWrapper: Constructs a new loss function wrapper that uses the given measure to compute the loss between a correct and predicted dyad ranking.
DyadStandardScaler - Class in ai.libs.jaicore.ml.dyadranking.util: A scaler that can be fit to a certain dataset and then be used to standardize datasets, i.e. transform the data to have a mean of 0 and a standard deviation of 1 according to the data it was fit to.
DyadStandardScaler() - Constructor for class ai.libs.jaicore.ml.dyadranking.util.DyadStandardScaler
DyadUnitIntervalScaler - Class in ai.libs.jaicore.ml.dyadranking.util: A scaler that can be fit to a certain dataset and then be used to normalize datasets, i.e. transform the data to have a length of 1.
DyadUnitIntervalScaler() - Constructor for class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler
DynamicTimeWarping - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Dynamic Time Warping (DTW) measure as published in "Using Dynamic Time Warping to FindPatterns in Time Series" Donald J.
DynamicTimeWarping() - Constructor for class ai.libs.jaicore.ml.tsc.distances.DynamicTimeWarping: Creates an instance with absolute distance as point distance.
DynamicTimeWarping(IScalarDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DynamicTimeWarping: Creates an instance with a given scalar distance measure.

E

E - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
EarlyAbandonMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.tsc.shapelets.search: Class implementing a search strategy used for finding the minimum distance of a Shapelet object to a time series.
EarlyAbandonMinimumDistanceSearchStrategy(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.search.EarlyAbandonMinimumDistanceSearchStrategy: Standard constructor.
element() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
EMCNodeType - Enum in ai.libs.jaicore.ml.classification.multiclass.reduction
EMulticlassMeasure - Enum in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel: Enum summarizing all multiclass measures.
EMultiClassPerformanceMeasure - Enum in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel
EMultilabelPerformanceMeasure - Enum in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
enableRekursiv() - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
Ensemble - Class in ai.libs.jaicore.ml.classification.multiclass
Ensemble() - Constructor for class ai.libs.jaicore.ml.classification.multiclass.Ensemble
EnsembleProvider - Class in ai.libs.jaicore.ml.tsc.classifier.ensemble: Class statically providing preconfigured ensembles as commonly used in TSC implementations.
ENTROPY_APLHA - Static variable in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Alpha parameter used to weight the importance of the feature's margins to the threshold candidates.
EPSILON - Static variable in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil
EQUAL_LENGTH - ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper.DiscretizationStrategy
EQUAL_SIZE - ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper.DiscretizationStrategy
equalLengthPolicy(List<Double>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper: Creates an equal length policy for the given values with respect to the given number of categories.
equals(Object) - Method in class ai.libs.jaicore.ml.cache.InstructionGraph
equals(Object) - Method in class ai.libs.jaicore.ml.core.ASimpleInstancesImpl
equals(Object) - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
equals(Object) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
equals(Object) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
equals(Object) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
equals(Object) - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset
equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance
equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.Dyad
equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue
equals(Object) - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
equals(Object) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
equals(Object) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration
equals(Object) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet
equals(Object) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.RankingForGroup
equals(Object) - Method in class ai.libs.jaicore.ml.SubInstances
equals(Object) - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet
equalSizePolicy(List<Double>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper: Creates an equal size policy for the given values with respect to the given number of categories.
ERROR_RATE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
ERRORRATE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMultiClassPerformanceMeasure
estimateK() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: Parameter indicator whether estimation of K (number of learned shapelets) should be derived from the number of total segments.
estimateK() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: Parameter indicator whether estimation of K (number of learned shapelets) should be derived from the number of total segments.
estimateShapeletLengthBorders() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig: Indicator whether the min max estimation should be performed.
ETA - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
EuclideanDistance - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Euclidean distance for time series.
EuclideanDistance() - Constructor for class ai.libs.jaicore.ml.tsc.distances.EuclideanDistance
evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ConfigurationLearningCurveExtrapolationEvaluator
evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ExtrapolatedSaturationPointEvaluator
evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator
evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator: Computes the (estimated) measure of the classifier on the full dataset
evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator
evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator
evaluate(Classifier, DescriptiveStatistics) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
evaluate(Classifier, DescriptiveStatistics) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator
evaluateModifiedISACLeaveOneOut(Instances) - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
evaluateSplit(Classifier, Instances, Instances) - Method in interface ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.ISplitBasedClassifierEvaluator: Evaluate a hypothesis h being trained on a set of trainingData for some validationData.
evaluateSplit(Classifier, Instances, Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.SimpleMLCSplitBasedClassifierEvaluator
evaluateSplit(Classifier, Instances, Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.SimpleSLCSplitBasedClassifierEvaluator
evaluateSupervised(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.TimeoutableEvaluator
EvaluationException - Exception in ai.libs.jaicore.ml.core.exception: The EvaluationException indicates that an error occurred during a evaluation process.
EvaluationException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.EvaluationException: Creates a new EvaluationException with the given parameters.
EvaluationException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.EvaluationException: Creates a new EvaluationException with the given parameters.
EXACT_MATCH_ACCURARY - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
EXACT_MATCH_LOSS - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
ExactIntervalAugSpaceSampler - Class in ai.libs.jaicore.ml.rqp: Samples interval-valued data from a dataset of precise points.
ExactIntervalAugSpaceSampler(Instances, Random) - Constructor for class ai.libs.jaicore.ml.rqp.ExactIntervalAugSpaceSampler
ExactMatchAccuracy - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel: Computes the exact match of the predicted multi label vector and the expected.
ExactMatchAccuracy() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.ExactMatchAccuracy
ExactMatchLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
ExactMatchLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.ExactMatchLoss
ExhaustiveMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.tsc.shapelets.search: Class implementing a search strategy used for finding the minimum distance of a Shapelet object to a time series.
ExhaustiveMinimumDistanceSearchStrategy(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.search.ExhaustiveMinimumDistanceSearchStrategy: Standard constructor.
EXP_4 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
ExtendedM5Forest - Class in ai.libs.jaicore.ml.intervaltree
ExtendedM5Forest() - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest
ExtendedM5Forest(int) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest
ExtendedM5Forest(IntervalAggregator, IntervalAggregator) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest
ExtendedM5Tree - Class in ai.libs.jaicore.ml.intervaltree
ExtendedM5Tree() - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Tree
ExtendedM5Tree(IntervalAggregator) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Tree
ExtendedRandomForest - Class in ai.libs.jaicore.ml.intervaltree
ExtendedRandomForest() - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
ExtendedRandomForest(int) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
ExtendedRandomForest(FeatureSpace) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
ExtendedRandomForest(IntervalAggregator, IntervalAggregator) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
ExtendedRandomForest(IntervalAggregator, IntervalAggregator, FeatureSpace) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
ExtendedRandomTree - Class in ai.libs.jaicore.ml.intervaltree: Extension of a classic RandomTree to predict intervals.
ExtendedRandomTree() - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
ExtendedRandomTree(FeatureSpace) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
ExtendedRandomTree(IntervalAggregator) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
extractArffHeader(File) - Static method in class ai.libs.jaicore.ml.core.dataset.ArffUtilities: Extract the header of an ARFF file as a string.
ExtrapolatedSaturationPointEvaluator,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka: For the classifier a learning curve will be extrapolated with a given set of anchorpoints.
ExtrapolatedSaturationPointEvaluator(int[], ISamplingAlgorithmFactory<I, D, ? extends ASamplingAlgorithm<I, D>>, D, double, LearningCurveExtrapolationMethod, long, D) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.ExtrapolatedSaturationPointEvaluator: Create a classifier evaluator with an accuracy measurement at the extrapolated learning curves saturation point.
ExtrapolatedSaturationPointEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory
ExtrapolatedSaturationPointEvaluatorFactory(int[], ISamplingAlgorithmFactory<WekaInstance<Object>, WekaInstances<Object>, ? extends ASamplingAlgorithm<WekaInstance<Object>, WekaInstances<Object>>>, double, LearningCurveExtrapolationMethod) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ExtrapolatedSaturationPointEvaluatorFactory
extrapolateLearningCurve() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator: Measure the learner accuracy at the given anchorpoints and extrapolate a learning curve based the results.
extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawExtrapolationMethod
extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationExtrapolationMethod
extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod
extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in interface ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolationMethod
extrapolationMethod - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
ExtrapolationRequest - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.client: This class describes the request that is sent to an Extrapolation Service.
ExtrapolationRequest() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
ExtrapolationServiceClient<C> - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.client: This class describes the client that is responsible for the communication with an Extrapolation Service.
ExtrapolationServiceClient(String, Class<C>) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationServiceClient

F

F_MEASURE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
F1_MACRO_AVG_D - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
F1_MACRO_AVG_D_LOSS - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
F1_MACRO_AVG_L - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
F1_MACRO_AVG_L_LOSS - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
F1MacroAverageL - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
F1MacroAverageL() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.F1MacroAverageL
F1MacroAverageLLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel: Compute the inverted F1 measure macro averaged by label.
F1MacroAverageLLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.F1MacroAverageLLoss
factor - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Factor used to determine whether or not to include a window length into the overall predicition.
FALSE_NEGATIVE_RATE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
FALSE_POSITIVE_RATE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
FeatureDomain - Class in ai.libs.jaicore.ml.core: Abstract description of a feature domain.
FeatureDomain() - Constructor for class ai.libs.jaicore.ml.core.FeatureDomain
FeatureSpace - Class in ai.libs.jaicore.ml.core
FeatureSpace() - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace
FeatureSpace(FeatureDomain[]) - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace
FeatureSpace(FeatureSpace) - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace
FeatureSpace(List<FeatureDomain>) - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace: copy constructor
FeatureSpace(Instances) - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace
FeatureTransformPLDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform: A feature transformation Placket-Luce dyad ranker.
FeatureTransformPLDyadRanker() - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker: Constructs a new feature transform Placket-Luce dyad ranker with bilinear feature transformation.
FeatureTransformPLDyadRanker(IDyadFeatureTransform) - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker: Constructs a new feature transform Placket-Luce dyad ranker with the given feature transformation method.
findDistances(Shapelet, double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Function finding the minimum single squared Euclidean distance for each instance among all of its subsequences compared to the shapelet s.
findMinimumDistance(Shapelet, double[]) - Method in class ai.libs.jaicore.ml.tsc.shapelets.search.AMinimumDistanceSearchStrategy: Function returning the minimum distance among all subsequences of the given timeSeries to the shapelet's data.
findMinimumDistance(Shapelet, double[]) - Method in class ai.libs.jaicore.ml.tsc.shapelets.search.EarlyAbandonMinimumDistanceSearchStrategy: Optimized function returning the minimum distance among all subsequences of the given timeSeries to the shapelet's data.
findMinimumDistance(Shapelet, double[]) - Method in class ai.libs.jaicore.ml.tsc.shapelets.search.ExhaustiveMinimumDistanceSearchStrategy: Function returning the minimum distance among all subsequences of the given timeSeries to the shapelet's data.
findNearestInstanceIndex(int[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier: Performs a simple nearest neighbor search on the stored trainLeafNodes for the given leafNodeCounts using Manhattan distance.
firstInstance() - Method in class ai.libs.jaicore.ml.SubInstances
fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
fit(double[]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter: The function only fits a single instance of the dataset
fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
fit(double[][]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter
fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
fit(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Fits the standard scaler to the dataset.
fit(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler
fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
fit(TimeSeriesDataset) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter: the function computes the needed information for the transform function.
fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
fitTransform(double[]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter: the function fit and transforms a single instance
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
fitTransform(double[][]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
fitTransform(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Fits the standard scaler to the dataset and transforms the entire dataset according to the mean and standard deviation of the dataset.
fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.AFilter
fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
fitTransform(TimeSeriesDataset) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter: a utility function to avoid the added effort of calling the fit and transform function separate
fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
FixedSplitClassifierEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka
FixedSplitClassifierEvaluator(Instances, Instances) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator
FoldBasedSubsetInstruction - Class in ai.libs.jaicore.ml.cache: Instruction to track a fold-based subset computation for a ReproducibleInstances object.
FoldBasedSubsetInstruction(String) - Constructor for class ai.libs.jaicore.ml.cache.FoldBasedSubsetInstruction: Constructor to create a split Instruction that can be converted into json.
formate(Instances) - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.HellFormater
formHistogramsAndRelativeFreqs(int[][], int[], int, int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Function calculating the histograms as described in the paper's section 2.2 ("Codebook and Learning").
forwardDifferenceDerivate(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: f'(n) = f(n+1) - f(n)
ForwardDifferenceDerivate - Class in ai.libs.jaicore.ml.tsc.filter.derivate: Filter that calculate the Forward Difference derivate.
ForwardDifferenceDerivate() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.ForwardDifferenceDerivate
ForwardDifferenceDerivate(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.ForwardDifferenceDerivate
forwardDifferenceDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: f'(n) = f(n+1) - f(n)
fromARFF(File) - Static method in class ai.libs.jaicore.ml.cache.ReproducibleInstances
fromHistory(InstructionGraph, Pair<String, Integer>) - Static method in class ai.libs.jaicore.ml.cache.ReproducibleInstances: Creates a new ReproducibleInstances object.
fromJAICoreInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
fromJAICoreInstance(LabeledInstance<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
fromJAICoreInstances(WekaCompatibleInstancesImpl) - Static method in class ai.libs.jaicore.ml.WekaUtil
fromJAICoreInstances(Instances<Instance>) - Static method in class ai.libs.jaicore.ml.WekaUtil
fromJAICoreInstances(LabeledInstances<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
fromJson(String) - Static method in class ai.libs.jaicore.ml.cache.InstructionGraph
fromOpenML(int, String) - Static method in class ai.libs.jaicore.ml.cache.ReproducibleInstances: Creates a new ReproducibleInstances object.
fromOrderedDyadList(List<Dyad>) - Static method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset
FStat - Class in ai.libs.jaicore.ml.tsc.quality_measures: F-Stat quality measure performing a analysis of variance according to chapter 3.2 of the original paper.
FStat() - Constructor for class ai.libs.jaicore.ml.tsc.quality_measures.FStat

G

gamma() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: Gamma value used for momentum during gradient descent.
gamma() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: Gamma value used for momentum during gradient descent.
generateAugPoint(List<Instance>) - Static method in class ai.libs.jaicore.ml.rqp.AbstractAugmentedSpaceSampler
generateCandidates(double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Function generation shapelet candidates for a given instance vector data, the length l and the candidate index which is used to identify the source of the shapelet's data.
generateFeatures(double[][], int[][], int[][][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Function generating the features for the internal probability measurement model based on the given subseries and their corresponding intervals.
generateHistogramInstances(int[][][], int[][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Generates a matrix consisting of the histogram values for each instance out of the given histograms and the relative frequencies of classes for each instance.
generateSegmentsAndDifferencesForTree(int[], int[], int, int, Random) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm: Method generating the segment start indices and the segment difference locations randomly using random.
generateSplittingInfo(double...) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation: Generate a string representation that represents only the split info part of the split string.
generateSplittingString(double...) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation: Generate a String that represents a split of a data set into portions from the given portions sizes (must add up to <1).
generateSubsequencesAndIntervals(int, int, int, int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Method randomly determining the subsequences and their intervals to be used for feature generation of the instances.
generateSubseriesFeatureInstance(double[], int[], int[], int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm: Function generating subseries feature instances based on the given segments and segmentsDifference matrices.
generateSubseriesFeaturesInstances(List<Attribute>, int, int[], int[], double[][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm: Function generating a dataset storing the features being generated as described in the original paper.
generateThresholdCandidates(Pair<List<Integer>, List<Integer>>, int, double[][][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Function generating threshold candidates for each feature type.
get(int) - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema
get(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
get(int) - Method in class ai.libs.jaicore.ml.SubInstances
getA() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration
getA() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
getA() - Method in class ai.libs.jaicore.ml.tsc.distances.AWeightedTrigometricDistance: Getter for the a parameter.
getA() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Getter for the a parameter.
getAbsoluteDistance() - Static method in class ai.libs.jaicore.ml.tsc.util.ScalarDistanceUtil
getActual() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent
getActual() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAvgComputationEvent
getActual() - Method in class ai.libs.jaicore.ml.evaluation.MeasureListComputationEvent
getActual() - Method in class ai.libs.jaicore.ml.evaluation.MeasureSingleComputationEvent
getAdmissibleSearcherEvaluatorCombinationsForAttributeSelection() - Static method in class ai.libs.jaicore.ml.WekaUtil: Determines all attribute selection variants (search/evaluator combinations with default parametrization)
getAggregator() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent
getAlgorithm() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
getAlgorithm() - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier: Getter for the model's training algorithm object.
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.CaseControlSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory
getAlgorithm(int, D, Random) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces.ISamplingAlgorithmFactory: After the necessary config is done, this method returns a fully configured instance of a sampling algorithm.
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SimpleRandomSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory
getAlgorithmMode() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
getAlgorithmModes() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
getAlgorithms() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
getAllAttributeValues() - Method in interface ai.libs.jaicore.ml.core.dataset.IAttributeArrayInstance
getAllAttributeValues() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance
getAllAttributeValues() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance
getAllAttributeValues() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
getAllAttributeValues() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
getAllClassifier() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector
getAllCreatedStrati() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner
getAllCreatedStrati() - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.IStratiFileAssigner: Get the used strati temporary files and the amount of datapoints inside of it.
getAlpha() - Method in class ai.libs.jaicore.ml.tsc.distances.AWeightedTrigometricDistance: Getter for the alpha value.
getAlternative() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad: Get the alternative.
getAnchorPoints() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
getApiKey() - Method in class ai.libs.jaicore.ml.cache.LoadDatasetInstructionForOpenML
getArbitrarySplit(Instances, Random, double...) - Static method in class ai.libs.jaicore.ml.WekaUtil
getAsDoubleVector() - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance: Turns the instance into a double vector.
getAsDoubleVector() - Method in interface ai.libs.jaicore.ml.core.dataset.INumericArrayInstance: Turns the instance into a double vector.
getAsDoubleVector() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance
getAsDoubleVector() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
getAsDoubleVector() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
getAssumedMemoryOverheadPerProcess() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getAtributesofTrainingsdata() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector
getAttributes() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getAttributes(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
getAttributes(Instances, boolean) - Static method in class ai.libs.jaicore.ml.WekaUtil
getAttributeTypeList() - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema
getAttributeTypes() - Method in interface ai.libs.jaicore.ml.core.dataset.AILabeledAttributeArrayDataset: Returns the list of attribute types.
getAttributeTypes() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
getAttributeTypes() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getAttributeTypes() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
getAttributeValue(int) - Method in interface ai.libs.jaicore.ml.core.dataset.INumericArrayInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
getAttributeValue(int, Class<T>) - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance: Getter for the value of an attribute for the given position.
getAttributeValueAtPosition(int, Class<T>) - Method in interface ai.libs.jaicore.ml.core.dataset.IAttributeArrayInstance: Getter for the value of an attribute for the given position.
getAttributeValueAtPosition(int, Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance
getAttributeValueAtPosition(int, Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance
getAttributeValueAtPosition(int, Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
getAttributeValueAtPosition(int, Class<T>) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
getAverageSeparability(Collection<String>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable
getB() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration
getB() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
getB() - Method in class ai.libs.jaicore.ml.tsc.distances.AWeightedTrigometricDistance: Getter for the a parameter.
getB() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Getter for the b parameter.
getBasicEvaluator() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.AbstractSplitBasedClassifierEvaluator
getBasicLearners() - Static method in class ai.libs.jaicore.ml.WekaUtil
getBestSoFar() - Method in interface ai.libs.jaicore.ml.tsc.distances.Abandonable: Getter for the best-so-far value.
getBestSplitIndex(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Function returning feature type used for the split based on given the deltaEntropy star values.
getBinaryClassifiers() - Static method in class ai.libs.jaicore.ml.WekaUtil
getBridge() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
getBridge() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator
getC() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration
getC() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
getC() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm
getC() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Getter for the c parameter.
getCachedClassifier(String, EMCNodeType, Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ClassifierCache
getCachedTrainingData(String, EMCNodeType, Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ClassifierCache
getCandidate(ProblemInstance) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.candidateprovider.IRankedSolutionCandidateProvider
getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.Ensemble
getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier
getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier
getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer
getCapabilities() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
getCaption() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
getCenter() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACkMeans
getCentersModifiable() - Method in class ai.libs.jaicore.ml.clustering.GMeans
getCertainty(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
getCertainty(I) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.ICertaintyProvider: Returns the certainty for a given IInstance.
getCharacterizerGroups() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Gets the mapping of a Characterizer to the meta features it computes.
getCharacterizerNames() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Gets the names of the used Characterizers.
getCharacterizerNamesMappings() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Gets names for the used Characterizers.
getCharacterizers() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Gets the list of characterizers used in the computation of meta features.
getChildDataIndices(double[][][], int, int, int, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Function returning the data indices assigned to the left and the right child of a binary tree based on the splitting criterion given by the feature type fType, the intervals index t1t2 in the transformed data set transformedData and the threshold.
getChildren() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
getChildren() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
getChosenInstance() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling
getClassAttIndexPerTree() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getClassesActuallyContainedInDataset(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassesAsArray(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassesAsList(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassesDeclaredInDataset(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassesInDataset(TimeSeriesDataset) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Returns a list storing the unique Integer class values in the given dataset.
getClassifier() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
getClassifier() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
getClassifier() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent
getClassifier() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm
getClassifierCache() - Static method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
getClassifierDescriptor(Classifier) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassMapper() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier: Getter for the property classMapper.
getClassName(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassNames(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassNameToIDMap(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassSplitAssignments(List<Instances>) - Static method in class ai.libs.jaicore.ml.WekaUtil
getClassValues() - Method in class ai.libs.jaicore.ml.tsc.util.ClassMapper: Getter for the classValues.
getClusterResults() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
getCollectedClassifierandPerformance() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector
getComplement() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm: Gets the data point contained in the original data that are not part of the
getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm
getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm
getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm
getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm
getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm
getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm
getConfigForAnchorPoints(int[], double[]) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationServiceClient
getConfiguration() - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel: Returns the IPredictiveModelConfiguration of this model.
getConfiguration() - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker
getConfiguration() - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
getConfiguration() - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier: {@inheritDoc ABatchLearner#getConfiguration()}
getContainedClasses() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AMCTreeNode
getContainedClasses() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD: Get the classes contained in the leaves of this node.
getConvergenceValue() - Method in interface ai.libs.jaicore.ml.interfaces.AnalyticalLearningCurve: Calculates or looks-up the value the learning curve converges to.
getConvergenceValue() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
getConvergenceValue() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve
getCpus() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
getCurrentPoints() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans
getCurveValue(double) - Method in interface ai.libs.jaicore.ml.interfaces.LearningCurve: Calculates or looks-up the curves value at a given point.
getCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
getCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve
getCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.PointWiseLearningCurve
getData() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the dataset which is used for splitting.
getData() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Getter for Shapelet.data.
getDataForUnit(Pair<String, Integer>) - Method in class ai.libs.jaicore.ml.cache.InstructionGraph: Recursively computes the data for a node in the graph
getDataset() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
getDataset() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
getDataset() - Method in class ai.libs.jaicore.ml.tsc.TSLearningProblem
getDatasetFolder() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
getDatasets() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
getDatasets() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
getDataSetsFromIndex() - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper
getDatasetsInFolder(File) - Static method in class ai.libs.jaicore.ml.WekaUtil
getDatasetSplitter() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the dataset splitter.
getDataSourceById(int) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper
getDeclaredClasses() - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
getDefaultWindowSize() - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
getDepthOfFirstCommonParent(List<Integer>) - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.ITreeClassifier
getDepthOfFirstCommonParent(List<Integer>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
getDepthOfFirstCommonParent(List<String>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
getDerivativeCurveValue(double) - Method in interface ai.libs.jaicore.ml.interfaces.AnalyticalLearningCurve: Calculates or looks-up the value of the derivative of the learning point at a given point.
getDerivativeCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
getDerivativeCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve
getDeterminedQuality() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Getter for Shapelet.determinedQuality.
getDimensionality() - Method in class ai.libs.jaicore.ml.core.FeatureSpace
getDistanceMeasure() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Getter for the distance measure.
getDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType
getDomain() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.categorical.ICategoricalAttributeType
getDomain() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.multivalue.IMultiValueAttributeType
getDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType
getDyadAtPosition(int) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance
getDyadAtPosition(int) - Method in interface ai.libs.jaicore.ml.dyadranking.dataset.IDyadRankingInstance: Get the dyad at the specified position in the ordering contained in this instance.
getDyadAtPosition(int) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
getDyadRanker() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue: Get the dyad ranker used to rank the nodes.
getDyadsByAlternative(Vector) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
getDyadsByAlternative(Vector) - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider: Returns the set of all Dyads with the given Vector of alternative features.
getDyadsByInstance(Vector) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
getDyadsByInstance(Vector) - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider: Returns the set of all Dyads with the given Vector of instance features.
getDyadStats() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
getEmptyDatasetForJAICoreInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
getEmptySetOfInstancesWithRefactoredClass(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
getEmptySetOfInstancesWithRefactoredClass(Instances, List<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
getEntry(Interval[], T) - Static method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper
getEpoch() - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
getEvaluator(EMultilabelPerformanceMeasure) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MultiClassMeasureBuilder
getEvaluator(EMultiClassPerformanceMeasure) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MultiClassMeasureBuilder
getExpected() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent
getExpected() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAvgComputationEvent
getExpected() - Method in class ai.libs.jaicore.ml.evaluation.MeasureListComputationEvent
getExpected() - Method in class ai.libs.jaicore.ml.evaluation.MeasureSingleComputationEvent
getExtrapolationMethod() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
getExtrapolator() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolatedEvent
getFeatureDomain(int) - Method in class ai.libs.jaicore.ml.core.FeatureSpace
getFeatureDomains() - Method in class ai.libs.jaicore.ml.core.FeatureSpace
getFeatureEvaluators() - Static method in class ai.libs.jaicore.ml.WekaUtil
getFeatures(double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature: Function calculating all features occurring in TimeSeriesFeature.FeatureType at once using an online calculation approach for mean, standard deviation and the slope.
getFeatureSpace() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
getFeatureSpace() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
getFinalClf() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
getFrequency(SimpleInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
getFrequency(TimeSeriesInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getFrequency(WekaInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
getFrequency(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset
getFrequency(I) - Method in interface ai.libs.jaicore.ml.core.dataset.IDataset
getFunctions() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction
getGmeansCluster() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans
getGoalTester() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator
getGraph() - Method in class ai.libs.jaicore.ml.cache.InstructionNode
getHeaderInformation() - Method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper.IntervalAndHeader
getHeight() - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.ITreeClassifier
getHeight() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
getHeight() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
getHeight() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
getHeight() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
getHighestQualityShapeletInList(List<Shapelet>) - Static method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Returns the shapelet with the highest quality in the given list shapelets.
getIClassifierEvaluator(Instances, long) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ExtrapolatedSaturationPointEvaluatorFactory
getIClassifierEvaluator(Instances, long) - Method in interface ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.IClassifierEvaluatorFactory
getIClassifierEvaluator(Instances, long) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.LearningCurveExtrapolationEvaluatorFactory
getIClassifierEvaluator(Instances, long) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory
getIClassifierEvaluator(Instances, long) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory
getId() - Method in class ai.libs.jaicore.ml.cache.LoadDataSetInstruction
getId() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group
getIdentifier() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.GroupIdentifier
getIdentifierForGroup() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.RankingForGroup
getIDs() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
getImportString(Collection<String>) - Static method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
getIndicesOfContainedInstances(Instances, Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil: Compute indices of instances of the original data set that are contained in the given subset.
getIndicesOfSubset(Instances, Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
getInforamtionforRanking(List<ProblemInstance>) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.datamanager.ITableGeneratorandCompleter
getInput() - Method in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm: Getter for the data set input used during algorithm calls.
getInputList() - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.InputOptListener
getInputs() - Method in class ai.libs.jaicore.ml.cache.InstructionNode
getInstance() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad: Get the instance.
getInstance() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance
getInstanceFeatures() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
getInstanceFeatures() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
getInstanceFeatures() - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider: Returns a Collection that contains all instance features contained in the pool.
getInstanceIndex() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Getter for Shapelet.instanceIndex.
getInstances() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group
getInstancesById(int) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper: Downloads the data set with the given id and returns the Instances file for it.
getInstancesOfClass(Instances, String) - Static method in class ai.libs.jaicore.ml.WekaUtil
getInstancesOfClass(Instances, Collection<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
getInstancesPerClass(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
getInstruction() - Method in class ai.libs.jaicore.ml.cache.InstructionNode
getInstructions() - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances
getIntermediateCenter() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans
getIntermediatePoints() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans
getInterval(double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Function extracting the interval [start, end (exclusive)] out of the given timeSeries vector.
getIntervals() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
getIntervals() - Method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper.IntervalAndHeader
getIntervals() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
getIntValOfClassName(Instance, String) - Static method in class ai.libs.jaicore.ml.WekaUtil
getIteration() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
getK() - Method in class ai.libs.jaicore.ml.rqp.ChooseKAugSpaceSampler
getK() - Method in class ai.libs.jaicore.ml.rqp.KNNAugSpaceSampler
getK() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Getter for the k value, @see #k.
getKendallforML() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getL() - Method in class ai.libs.jaicore.ml.dyadranking.loss.NDCGLoss
getLabel() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
getLabel() - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstance
getLabel() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
getLastNode() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree
getLatexCode() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
getLearner() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier
getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier
getLength() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType: Get the length of this time series attribute type.
getLength() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Getter for Shapelet.length.
getLengthOfTopRankingToConsider() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker
getLengthPerTree() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getLoggerName() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator
getLoggerName() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
getLoggerName() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator
getLoggerName() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
getLogProbabilityOfTopKRanking(IDyadRankingInstance, int) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Returns the log of the probablity of the top k of a given IDyadRankingInstance under the Plackett Luce model parametrized by the latent skill values predicted by the PLNet.
getLogProbabilityOfTopRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Returns the the log of the probablity of the top ranking for a given IDyadRankingInstance under the Plackett Luce model parametrized by the latent skill values predicted by the PLNet.
getLogProbabilityRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Computes the logarithmic probability for a particular ranking according to the log Placket-Luce model.
getLoopPoints() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans
getMax() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
getMaximumKeyByValue(Map<T, Integer>) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Returns the key with the maximum integer value.
getMeasures() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
getMemoryInMB() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
getMemoryLimitinMB() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getMessage(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.TimeoutableEvaluator
getMetaFeatureComputationTimes() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Gets the time in milliseconds it took to compute each group of meta features (Computed by a Characterizer).
getMetaLearners() - Static method in class ai.libs.jaicore.ml.WekaUtil
getMin() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
getMinDistanceSearchStrategy() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Getter for ShapeletTransformLearningAlgorithm.minDistanceSearchStrategy.
getMinDistanceSearchStrategy() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Getter for ShapeletTransformTSClassifier.minDistanceSearchStrategy.
getMinibatchSize() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
getMode(int[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Returns the mode of the given array.
getModelPath() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
getMTree() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree
getMultiLabelMetrics() - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter
getMultipliedSeparability(Collection<String>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable
getNaivebais() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getNaivebaismulti() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getName() - Method in class ai.libs.jaicore.ml.cache.InstructionNode
getName() - Method in class ai.libs.jaicore.ml.core.FeatureDomain: Getter for name attribute.
getName() - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation: Get the name of the implementing multilabel cross validation technique.
getName() - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation
getNativeMultiClassClassifiers() - Static method in class ai.libs.jaicore.ml.WekaUtil
getNewClassAttribute(Attribute) - Static method in class ai.libs.jaicore.ml.WekaUtil
getNewClassAttribute(Attribute, List<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
getNodeByName(String) - Method in class ai.libs.jaicore.ml.cache.InstructionGraph
getNodeThatComputesInput(int) - Method in class ai.libs.jaicore.ml.cache.InstructionNode
getNodeType() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
getNosLeafNodes() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree
getNumberOfAllowedCPUs() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getNumberOfAttributes() - Method in interface ai.libs.jaicore.ml.core.dataset.AILabeledAttributeArrayDataset: Getter for the number of attributes (excluding target attribute).
getNumberOfAttributes() - Method in interface ai.libs.jaicore.ml.core.dataset.IAttributeArrayInstance: Getter for the number of attributes for the instance.
getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance
getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance
getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
getNumberOfCandidatesInSelectionPhase() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getNumberOfClasses(TimeSeriesDataset) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Counts the number of unique classes occurring in the given dataset.
getNumberOfClassifier() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector
getNumberOfColumns() - Method in class ai.libs.jaicore.ml.core.ASimpleInstancesImpl
getNumberOfColumns() - Method in class ai.libs.jaicore.ml.core.SimpleInstanceImpl
getNumberOfColumns() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
getNumberOfColumns() - Method in interface ai.libs.jaicore.ml.interfaces.Instance
getNumberOfColumns() - Method in interface ai.libs.jaicore.ml.interfaces.Instances
getNumberOfInstances() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getNumberOfInstances() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Returns the number of instances contained in the dataset.
getNumberOfInstancesFromClass(Instances, String) - Static method in class ai.libs.jaicore.ml.WekaUtil
getNumberOfInstancesFromClass(Instances, Collection<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
getNumberOfInstancesPerClass(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
getNumberOfIterationsInSelectionPhase() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getNumberOfRows() - Method in class ai.libs.jaicore.ml.core.ASimpleInstancesImpl
getNumberOfRows() - Method in interface ai.libs.jaicore.ml.interfaces.Instances
getNumberOfRuns() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getNumberOfSegments(int, int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Returns the number of segments which are available for a instance with Q attributes for a given scale r and a minimum shape length minShapeLength.
getNumberOfVariables() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getNumberOfVariables() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Returns the number of variables, i.e. the number of value matrices contained in the dataset.
getNumberQueries() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider: Returns the number of queries the pool provider has answered so far.
getNumberRandomQueriesAtStart() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
getNumBins() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
getNumClasses() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
getNumCPUs() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner
getNumCPUs() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
getNumCPUs() - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAmountSelector
getNumCPUs() - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner
getNumCPUs() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
getNumInstancesUsedForTraining() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent
getNumInstancesUsedForValidation() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent
getNumMajorColumns() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
getNumMCIterations() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the number of iterations, i.e. the number of splits considered.
getNumSamples() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
getObservedScore() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent
getOccurringLabels() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
getOccurringLabels() - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstances
getOffset() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction
getOptionsOfWekaAlgorithm(Object) - Static method in class ai.libs.jaicore.ml.WekaUtil
getOut() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent
getOut() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAvgComputationEvent
getOut() - Method in class ai.libs.jaicore.ml.evaluation.MeasureListComputationEvent
getOut() - Method in class ai.libs.jaicore.ml.evaluation.MeasureSingleComputationEvent
getOutputInstances(List<IDataset>) - Method in class ai.libs.jaicore.ml.cache.Instruction: Provides the instances induced by this instruction node
getOutputInstances(List<IDataset>) - Method in class ai.libs.jaicore.ml.cache.LoadDataSetInstructionForARFFFile
getOutputInstances(List<IDataset>) - Method in class ai.libs.jaicore.ml.cache.LoadDatasetInstructionForOpenML
getOutputInstances(List<IDataset>) - Method in class ai.libs.jaicore.ml.cache.StratifiedSplitSubsetInstruction
getOutputList() - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.InputOptListener
getOutputUnit() - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances
getPairWithLeastCertainty(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Returns the pair of Dyads for which the model is least certain.
getParameters() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet
getParameterSets() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration
getParams() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.ParametricFunction
getPerformanceMeasure() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
getPlatz1ml() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getPlatz1my() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getPlatz1overall() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getPlNet() - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
getPoint() - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance
getPoint() - Method in interface ai.libs.jaicore.ml.core.dataset.INumericArrayInstance
getPoints() - Method in class ai.libs.jaicore.ml.clustering.GMeans
getPointToInstance() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans
getPool() - Method in interface ai.libs.jaicore.ml.activelearning.IActiveLearningPoolProvider: Returns the pool of unlabeled instances.
getPool() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
getPoolProvider() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker
getPoolSize() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
getPoolSize() - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider
getPortionOfDataForPhase2() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getPortionOfFirstFold() - Method in class ai.libs.jaicore.ml.cache.SplitInstruction
getPossibleClassValues(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
getPreciseInsts() - Method in class ai.libs.jaicore.ml.rqp.AbstractAugmentedSpaceSampler
getPreComputedFeatureTransforms(DyadRankingDataset) - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.IDyadFeatureTransform: Precomputed the feature transforms for the dataset, this can speed up the runtime as the feature transform will be reduced to O(1) at the cost of O(n).
getPrettyMaximaString() - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Returns a String the maxima of all features this scaler has been fit to.
getPrettyMeansString() - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Returns a String for the means of all features this scaler has been fit to.
getPrettyMinimaString() - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Returns a String for the minima of all features this scaler has been fit to.
getPrettySTDString() - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Prints the standard devations of all features this scaler has been fit to.
getProbabilityBoundaries() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling
getProbabilityOfTopKRanking(IDyadRankingInstance, int) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
getProbabilityOfTopRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Returns the probablity of the top ranking for a given IDyadRankingInstance under the Plackett Luce model parametrized by the latent skill values predicted by the PLNet.
getProbabilityRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Returns the probablity of a given IDyadRankingInstance under the Plackett Luce model parametrized by the latent skill values predicted by the PLNet.
getProblemInstances() - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.datamanager.IInstanceCollector
getProblemInstances() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector
getProvider() - Method in class ai.libs.jaicore.ml.cache.LoadDataSetInstruction
getQualityMeasure() - Method in class ai.libs.jaicore.ml.tsc.TSLearningProblem
getQueriedRankings() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
getQueriedRankings() - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider
getRandom() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
getRandomForest() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getRandomForestplatz1() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getRangeSize() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
getRangeSize() - Method in class ai.libs.jaicore.ml.core.FeatureDomain: Computes the size of the domain.
getRangeSize() - Method in class ai.libs.jaicore.ml.core.FeatureSpace
getRangeSize() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
getRangeSizeOfAllButSubset(Set<Integer>) - Method in class ai.libs.jaicore.ml.core.FeatureSpace
getRangeSizeOfFeatureSubspace(Set<Integer>) - Method in class ai.libs.jaicore.ml.core.FeatureSpace
getRanker() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker
getRanker() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig: Set the ranker used to rank the OPEN list.
getRanking(I) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.GroupBasedRanker
getRanking(P) - Method in interface ai.libs.jaicore.ml.ranking.Ranker
getRanking(Instance) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISAC
getRankings() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISAC
getRatioOfOldInstancesForMinibatch() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker
getRawLastClassificationResults() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
getRefactoredInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
getRefactoredInstance(Instance, List<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
getRefactoredInstances(Instances, Map<String, String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
getRelativeNumberOfInstancesFromClass(Instances, String) - Static method in class ai.libs.jaicore.ml.WekaUtil
getRelativeNumberOfInstancesFromClass(Instances, Collection<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
getReplacedAttributeList(List<Attribute>, Attribute) - Static method in class ai.libs.jaicore.ml.WekaUtil
getRng() - Method in class ai.libs.jaicore.ml.rqp.AbstractAugmentedSpaceSampler
getRootGenerator() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator
getRootNode() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier: Getter for the root node.
getS() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier
getSaturationPoint(double) - Method in interface ai.libs.jaicore.ml.interfaces.AnalyticalLearningCurve: Calculated or search a saturation point with a tolerance of epsilon.
getSaturationPoint(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
getSaturationPoint(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve
getScaler() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
getScaler() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig: Get the scaler used to scale the dataset.
getSchema() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
getSearchers() - Static method in class ai.libs.jaicore.ml.WekaUtil
getSeed() - Method in class ai.libs.jaicore.ml.cache.StratifiedSplitSubsetInstruction
getSeed() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the random seed.
getSeed() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator
getSeed() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
getSeeds() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
getSegments() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getSegmentsDifference() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getSeparability(String, String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable
getShapelets() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Getter for ShapeletTransformTSClassifier.shapelets.
getSingleLabelMetrics() - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter
getSize() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
getSkillForDyad(Dyad) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Returns the latent skill value predicted by the PLNet for a given Dyad.
getSolutionLogDir() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getSortedDataset() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling
getSplitBasedEvaluator() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the evaluator that is used for evaluating each split.
getSplitEvaluationTime() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent
getSplitSeparator() - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation: Get the separator used to separate single portions of a split in a given splitInfo.
getSplitSeparator() - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation
getSplitTechniqueAndDetailsSeparator() - Static method in class ai.libs.jaicore.ml.weka.dataset.splitter.MultilabelDatasetSplitter: Obtain the token used to separate a split technique and the details about the split.
getSplitter(int) - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.ISplitterFactory
getSquaredDistance() - Static method in class ai.libs.jaicore.ml.tsc.util.ScalarDistanceUtil
getStartIndex() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Getter for Shapelet.startIndex.
getStatsX() - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler
getStatsY() - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler
getStepdifference() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getStepdifferenceML() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getStrati() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling
getStratifiedSplit(ReproducibleInstances, long, double) - Static method in class ai.libs.jaicore.ml.WekaUtil: Creates a StratifiedSplit for a given ReproducibleInstances Object.
getStratifiedSplit(ReproducibleInstances, Random, double) - Static method in class ai.libs.jaicore.ml.WekaUtil: Creates a stratified split for a given ReproducibleInstances Object.
getStratifiedSplit(Instances, long, double) - Static method in class ai.libs.jaicore.ml.WekaUtil
getStratifiedSplit(Instances, Random, double) - Static method in class ai.libs.jaicore.ml.WekaUtil
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType
getStringDescriptionOfDomain() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeType
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType
getSubsequences() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
getSubseriesClf() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
getSuccessorGenerator() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator
getTargets() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getTargets() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Getter for the target values.
getTargetType() - Method in interface ai.libs.jaicore.ml.core.dataset.AILabeledAttributeArrayDataset: Returns the attribute type of the target attribute.
getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema
getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
getTargetType() - Method in class ai.libs.jaicore.ml.core.predictivemodel.APredictiveModel: Getter method for the given targetType.
getTargetValue() - Method in interface ai.libs.jaicore.ml.core.dataset.ILabeledInstance: Getter for the value of the target attribute.
getTargetValue() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance
getTargetValue() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance
getTargetValue() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
getTargetValue() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance
getTargetValue() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
getTargetValue(Class<T>) - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance: Getter for the value of the target attribute.
getTestSplit(Instances, int, int, String) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation: Gets a test split from the given data based on the seed.
getTestSplit(Instances, int, int, String) - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation
getTestSplit(Instances, String, String, String) - Static method in class ai.libs.jaicore.ml.weka.dataset.splitter.MultilabelDatasetSplitter: Split the Instances object according to the given splitDescription.
getTimeout() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
getTimeout(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.TimeoutableEvaluator
getTimeoutForSolutionEvaluation() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the timeout for evaluating a solution.
getTimeoutInSeconds() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
getTimeoutPerCandidate() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getTimeouts() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
getTimeoutTotal() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getTimes() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getTimestampMatrices() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Getter for TimeSeriesDataset.timestampMatrices.
getTimestamps(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getTimestamps(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Getter for the timestamp matrix at a specific index.
getTimestampsOrNull(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getTimestampsOrNull(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Getter for the timestamp matrix at a specific index.
getTmpDir() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getTop3ml() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
gettop3mymethod() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getTop3overall() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getTotalVariance() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
getTrain() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator
getTrainFoldSize() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the size of the train fold.
getTrainingAndTestDataForFold(int, int, double[][], int[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Functions creating two TimeSeriesDataset objects representing the training and test split for the given fold of a cross validation with numFolds many folds.
getTrainingPortion() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator
getTrainingPortion() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getTrainingTimes() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
getTrainLeafNodes() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getTrainSplit(Instances, int, int, String) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation: Gets a train split from the given data based on the seed.
getTrainSplit(Instances, int, int, String) - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation
getTrainSplit(Instances, String, String, String) - Static method in class ai.libs.jaicore.ml.weka.dataset.splitter.MultilabelDatasetSplitter: Split the Instances object according to the given splitDescription.
getTrainTargets() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getTransformedVectorLength(int, int) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.BiliniearFeatureTransform
getTransformedVectorLength(int, int) - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.IDyadFeatureTransform: Get the length of the vector returned by the transform method.
getTrees() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
getTrees() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier: Getter for the time series trees.
getType() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue
getType() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeValue
getUnivirateHistograms() - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
getUntocedoverall() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getUntochedmy() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getUntouchedml() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator
getUpperBoundOnSeparability(Collection<String>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable
getValidate() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator
getValidationAlgorithm() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue
getValue() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeValue
getValueMatrices() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Getter for TimeSeriesDataset.valueMatrices.
getValueOfMetricForSingleLabelClassifier(Evaluation, String, int) - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter: Extracts the metric with the given name from the Evaluation object that is the result of evaluating a classifier.
getValueOfMultilabelClassifier(Result, String) - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter: Extracts the metric with the given name from the result of evaluating a multilabel classifier (Calls the corresponding method).
getValues() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
getValues(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getValues(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Getter for the value matrix at a specific index.
getValuesOrNull(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
getValuesOrNull(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Getter for the value matrix at a specific index.
getVoteType() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Getter for the vote type.
getW() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier
getW0() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier
getWeights() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction
getWeights() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet
getxValues() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
getyValues() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
getyValues() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
GlobalCharacterizer - Class in ai.libs.jaicore.ml.metafeatures: Characterizer that applies a number of Characterizers to a data set.
GlobalCharacterizer() - Constructor for class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Initializes a new characterizer.
GMeans<C extends org.apache.commons.math3.ml.clustering.Clusterable> - Class in ai.libs.jaicore.ml.clustering: Implementation of Gmeans based on Helen Beierlings implementation of GMeans(https://github.com/helebeen/AILibs/blob/master/JAICore/jaicore-modifiedISAC/src/main/java/jaicore/modifiedISAC/ModifiedISACgMeans.java).
For more Information see: "Hamerly, G., and Elkan, C. 2003.
GMeans(Collection<C>) - Constructor for class ai.libs.jaicore.ml.clustering.GMeans: Initializes a basic cluster for the given Point using Mannhatten distance and seed=1
GMeans(Collection<C>, DistanceMeasure, long) - Constructor for class ai.libs.jaicore.ml.clustering.GMeans: Initializes a cluster for the given Point using a given distance meassure and a seed.
GmeansSampling,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory: Implementation of a sampling method using gmeans-clustering.
GmeansSampling(long, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.GmeansSampling
GmeansSampling(long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.GmeansSampling
GmeansSamplingFactory,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
GmeansSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory
GMeansStratiAmountSelectorAndAssigner> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling: Combined strati amount selector and strati assigner via g-means.
GMeansStratiAmountSelectorAndAssigner(int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner: Constructor for GMeansStratiAmountSelectorAndAssigner with Manhattan distanceMeasure as a default.
GMeansStratiAmountSelectorAndAssigner(DistanceMeasure, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner: Constructor for GMeansStratiAmountSelectorAndAssigner with custom distanceMeasure.
GRAD_DESC_GRADIENT_THRESHOLD - Static variable in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig: Specifies a threshold for the gradient (i.e. if the gradient is below this value no update will be done; if all gradients are below this value, the algorithm will terminate)
GRAD_DESC_LEARNING_RATE - Static variable in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig: The learning rate in the update step (i.e. how much of the gradient should be added to the parameter)
GRAD_DESC_MAX_ITERATIONS - Static variable in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig: Specifies the maximum of gradient update steps.
GradientDescentOptimizer - Class in ai.libs.jaicore.ml.core.optimizing.graddesc: An optimizer based on the gradient descent method [1].
GradientDescentOptimizer() - Constructor for class ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizer
GradientDescentOptimizer(GradientDescentOptimizerConfig) - Constructor for class ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizer
GradientDescentOptimizerConfig - Interface in ai.libs.jaicore.ml.core.optimizing.graddesc
gradientThreshold() - Method in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig
Group<C,I> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes: Group.java - Stores a group with it center as ID and the associated instances
Group(List<ProblemInstance>, GroupIdentifier<C>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group
GroupBasedRanker<C,I,S> - Class in ai.libs.jaicore.ml.ranking.clusterbased
GroupBasedRanker() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.GroupBasedRanker
GroupIdentifier<C> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes
GroupIdentifier(C) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.GroupIdentifier
gulloDerivate(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Calculates the derivative of a timeseries as described first by Gullo et. al (2009).
GulloDerivate - Class in ai.libs.jaicore.ml.tsc.filter.derivate: Calculates the derivative of a timeseries as described first by Gullo et. al (2009).
GulloDerivate() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.GulloDerivate
GulloDerivate(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.GulloDerivate
gulloDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: f'(n) = \frac{f(i+1)-f(i-1)}{2}

H

HAMMING_ACCURACY - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
HAMMING_LOSS - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
HammingAccuracy - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel: Measure for computing how similar two double vectors are according to hamming distance.
HammingAccuracy() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.HammingAccuracy: Standard c'tor.
HammingLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
HammingLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.HammingLoss
handleError(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AProcessListener: Handle the output of the error output stream.
handleError(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener
handleInput(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AProcessListener: Handle the output of the standard output stream.
handleInput(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener
hashCode() - Method in class ai.libs.jaicore.ml.cache.InstructionGraph
hashCode() - Method in class ai.libs.jaicore.ml.core.ASimpleInstancesImpl
hashCode() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
hashCode() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
hashCode() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
hashCode() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
hashCode() - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset
hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance
hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad
hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue
hashCode() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
hashCode() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
hashCode() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration
hashCode() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet
hashCode() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.RankingForGroup
hashCode() - Method in class ai.libs.jaicore.ml.SubInstances
hashCode() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet
hasNext() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm
hasNext() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
hasOnlyNumericAttributes(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
HellFormater - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
HighProbClassifier - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.reducer
HighProbClassifier(Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier
HilbertTransform - Class in ai.libs.jaicore.ml.tsc.filter.transform: Calculates the Hilbert transform of a time series.
HilbertTransform() - Constructor for class ai.libs.jaicore.ml.tsc.filter.transform.HilbertTransform
HILL_3 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
HistogramBuilder - Class in ai.libs.jaicore.ml.tsc
HistogramBuilder() - Constructor for class ai.libs.jaicore.ml.tsc.HistogramBuilder
histogramForInstance(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.HistogramBuilder

I

IActiveLearningPoolProvider - Interface in ai.libs.jaicore.ml.activelearning: Provides a sample pool for pool-based active learning.
IAttributeArrayInstance - Interface in ai.libs.jaicore.ml.core.dataset: Interface of an instance that consists of attributes.
IAttributeType<D> - Interface in ai.libs.jaicore.ml.core.dataset.attribute: Wrapper interface for attribute types.
IAttributeValue<D> - Interface in ai.libs.jaicore.ml.core.dataset.attribute: A general interface for attribute values.
IAugmentedSpaceSampler - Interface in ai.libs.jaicore.ml.rqp: Interface representing a class that samples interval-valued data from a set of precise data points.
IAugSpaceSamplingFunction - Interface in ai.libs.jaicore.ml.rqp
IBatchLearner<T,I,D extends IDataset> - Interface in ai.libs.jaicore.ml.core.predictivemodel: The IBatchLearner models a learning algorithm which works in a batch fashion, i.e. takes a whole AILabeledAttributeArrayDataset as training input.
ICategoricalAttributeType - Interface in ai.libs.jaicore.ml.core.dataset.attribute.categorical: Interface for categorical attribute types.
ICertaintyProvider<T,I,D extends IDataset> - Interface in ai.libs.jaicore.ml.core.predictivemodel: The ICertaintyProvider models an IPredictiveModel that provides uncertainty information for queries in form of IInstances.
IClassifierEvaluator - Interface in ai.libs.jaicore.ml.evaluation.evaluators.weka
IClassifierEvaluatorFactory - Interface in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory
IDataset - Interface in ai.libs.jaicore.ml.core.dataset
IDatasetSplitter - Interface in ai.libs.jaicore.ml.weka.dataset.splitter
IDistanceMetric<D,A,B> - Interface in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
ids - Variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: The names of all the meta features that are computed by this characterizer
IDyadFeatureTransform - Interface in ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform: Feature transformation interface for the FeatureTransformPLDyadRanker.
IDyadRanker - Interface in ai.libs.jaicore.ml.dyadranking.algorithm: An abstract representation of a dyad ranker.
IDyadRankingFeatureTransformPLGradientDescendableFunction - Interface in ai.libs.jaicore.ml.dyadranking.optimizing: An interface for a differentiable function in the context of feature transformation Placket-Luce dyad ranking.
IDyadRankingFeatureTransformPLGradientFunction - Interface in ai.libs.jaicore.ml.dyadranking.optimizing: Represents a differentiable function in the context of dyad ranking based on feature transformation Placket-Luce models.
IDyadRankingInstance - Interface in ai.libs.jaicore.ml.dyadranking.dataset: Represents an instance for a DyadRankingDataset.
IDyadRankingPoolProvider - Interface in ai.libs.jaicore.ml.dyadranking.activelearning: Interface for an active learning pool provider in the context of dyad ranking.
IFilter - Interface in ai.libs.jaicore.ml.tsc.filter
IGradientBasedOptimizer - Interface in ai.libs.jaicore.ml.core.optimizing: Interface for an optimizer that is based on a gradient descent and gets a differentiable function and the derivation of said function to solve an optimization problem.
IGradientDescendableFunction - Interface in ai.libs.jaicore.ml.core.optimizing: This interface represents a function that is differentiable and thus can be used by gradient descent algorithms.
IGradientFunction - Interface in ai.libs.jaicore.ml.core.optimizing: Represents the gradient of a function that is differentiable.
IGroupBuilder<C,I> - Interface in ai.libs.jaicore.ml.ranking.clusterbased: IGroupBuilder discribes the act of building groups out of probleminstances
IGroupSolutionRankingSelect<C,S,I,P> - Interface in ai.libs.jaicore.ml.ranking.clusterbased
IInstance - Interface in ai.libs.jaicore.ml.core.dataset: Interface of an instance which consists of attributes and a target value.
IInstanceCollector - Interface in ai.libs.jaicore.ml.ranking.clusterbased.datamanager
IInstancesClassifier - Interface in ai.libs.jaicore.ml.evaluation
ILabeledAttributeArrayDataset<L> - Interface in ai.libs.jaicore.ml.core.dataset
ILabeledAttributeArrayInstance<L> - Interface in ai.libs.jaicore.ml.core.dataset: Type intersection for IAttributeArrayInstance and ILabeledInstance
ILabeledInstance<T> - Interface in ai.libs.jaicore.ml.core.dataset: Interface of an instance that has a target value.
ILearnShapeletsLearningAlgorithmConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.shapelets
ILOG_2 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
IMeasure<I,O> - Interface in ai.libs.jaicore.ml.core.evaluation.measure: The interface of a measure which compute a value of O from expected and actual values of I.
IModifiableInstance - Interface in ai.libs.jaicore.ml.core.dataset
IMultiClassClassificationExperimentConfig - Interface in ai.libs.jaicore.ml.experiments
IMultilabelCrossValidation - Interface in ai.libs.jaicore.ml.weka.dataset.splitter: Represents an algorithm that realizes a split of a given multilabel instances in folds, given a seed, custom information about the split represented as a string, and the fold that is left out for testing.
IMultilabelMeasure - Interface in ai.libs.jaicore.ml.core.evaluation.measure.multilabel: Interface for measures dealing with multilabel data.
IMultiValueAttributeType - Interface in ai.libs.jaicore.ml.core.dataset.attribute.multivalue: Interface for categorical attribute types.
INCORRECT - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
indArrayToWekaInstances(INDArray) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Converts an INDArray matrix (number of instances x number of attributes) to Weka instances without any class attribute.
indexOf(Object) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
init(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner: Initializes the algorithm for stratum assignment.
init(D, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner
init(D, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner
init(D, int) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner: Initialize custom assigner if necessary.
init(D, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.KMeansStratiAssigner
initialize(DyadRankingDataset, Map<IDyadRankingInstance, Map<Dyad, Vector>>) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood
initialize(DyadRankingDataset, Map<IDyadRankingInstance, Map<Dyad, Vector>>) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative
initialize(DyadRankingDataset, Map<IDyadRankingInstance, Map<Dyad, Vector>>) - Method in interface ai.libs.jaicore.ml.dyadranking.optimizing.IDyadRankingFeatureTransformPLGradientDescendableFunction: Initializes the function with the given dataset.
initialize(DyadRankingDataset, Map<IDyadRankingInstance, Map<Dyad, Vector>>) - Method in interface ai.libs.jaicore.ml.dyadranking.optimizing.IDyadRankingFeatureTransformPLGradientFunction: Initialize the function with the given data set and feature transformation method.
initializeCharacterizerNames() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Initializes GlobalCharacterizer.characterizerNames.
initializeCharacterizers() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Adds the required characterizers to GlobalCharacterizer.characterizers.
initializeCharacterizers() - Method in class ai.libs.jaicore.ml.metafeatures.LandmarkerCharacterizer
initializeCharacterizers() - Method in class ai.libs.jaicore.ml.metafeatures.NoProbingCharacterizer
initializeKMeans() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans
initializeKMeans() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACkMeans
initializeMetaFeatureIds() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer: Initializes GlobalCharacterizer.ids.
initializeRegressionTree(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm: Initializes a new instance of RandomRegressionTree.
initializeS(double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Initializes the tensor S storing the shapelets for each scale.
initializeWeights(double[][][], double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Randomly initializes the weights around zero.
input - Variable in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm: The TimeSeriesDataset object used for maintaining the model.
InputOptimizerLoss - Interface in ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization
InputOptListener - Class in ai.libs.jaicore.ml.dyadranking.zeroshot.util
InputOptListener(int[]) - Constructor for class ai.libs.jaicore.ml.dyadranking.zeroshot.util.InputOptListener
instance(int) - Method in class ai.libs.jaicore.ml.SubInstances
Instance - Interface in ai.libs.jaicore.ml.interfaces
Instances - Interface in ai.libs.jaicore.ml.interfaces
InstanceSchema<L> - Class in ai.libs.jaicore.ml.core.dataset
InstanceSchema(List<IAttributeType<?>>, IAttributeType<L>) - Constructor for class ai.libs.jaicore.ml.core.dataset.InstanceSchema
instancesToJsonString(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
InstanceWiseF1 - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel: Instance-wise F1 measure for multi-label classifiers.
InstanceWiseF1() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.InstanceWiseF1
InstanceWiseF1AsLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel: The F1 Macro Averaged by the number of instances measure.
InstanceWiseF1AsLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.InstanceWiseF1AsLoss
Instruction - Class in ai.libs.jaicore.ml.cache: Instruction class that can be converted into json.
Instruction() - Constructor for class ai.libs.jaicore.ml.cache.Instruction
InstructionFailedException - Exception in ai.libs.jaicore.ml.cache
InstructionFailedException(Exception) - Constructor for exception ai.libs.jaicore.ml.cache.InstructionFailedException
InstructionGraph - Class in ai.libs.jaicore.ml.cache
InstructionGraph() - Constructor for class ai.libs.jaicore.ml.cache.InstructionGraph
InstructionGraph(InstructionGraph) - Constructor for class ai.libs.jaicore.ml.cache.InstructionGraph
InstructionNode - Class in ai.libs.jaicore.ml.cache
InstructionNode() - Constructor for class ai.libs.jaicore.ml.cache.InstructionNode
InstructionNode(InstructionGraph, String, Instruction) - Constructor for class ai.libs.jaicore.ml.cache.InstructionNode
InstructionNode(InstructionGraph, String, Instruction, List<Pair<String, Integer>>) - Constructor for class ai.libs.jaicore.ml.cache.InstructionNode
IntervalAggregator - Interface in ai.libs.jaicore.ml.intervaltree.aggregation: An IntervalAggeregator can aggregate from a list of intervals, more precisely given a list of predictions in the leaf node, it can predict a range.
IntervalAndHeader(Interval[], Instances) - Constructor for class ai.libs.jaicore.ml.intervaltree.util.RQPHelper.IntervalAndHeader
intManhattanDistance(int[], int[]) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil: Simple Manhattan distance (sum of the absolute differences between the vectors' elements) implementation for arrays of Integer.
INumericArrayInstance - Interface in ai.libs.jaicore.ml.core.dataset
INumericLabeledAttributeArrayInstance<L> - Interface in ai.libs.jaicore.ml.core.dataset: Type intersection interface for numeric instances on one hand and labeled instances on the other hand.
INumericLabeledIAttributeDataset<L> - Interface in ai.libs.jaicore.ml.core.dataset
InvalidAnchorPointsException - Exception in ai.libs.jaicore.ml.learningcurve.extrapolation: Exception that is thrown, when the anchorpoints generated for learning curve extrapolation are not suitable.
InvalidAnchorPointsException() - Constructor for exception ai.libs.jaicore.ml.learningcurve.extrapolation.InvalidAnchorPointsException
InversePowerLawConfiguration - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.ipl: This class encapsulates the three parameters that are required in order to create a Inverse Power Law function.
InversePowerLawConfiguration() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration
InversePowerLawExtrapolationMethod - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.ipl: This class describes a method for learning curve extrapolation which generates an Inverse Power Law function.
InversePowerLawExtrapolationMethod() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawExtrapolationMethod
InversePowerLawExtrapolationMethod(String, String) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawExtrapolationMethod
InversePowerLawLearningCurve - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.ipl: Representation of a learning curve with the Inverse Power Law function, which has three parameters named a, b and c.
InversePowerLawLearningCurve(double, double, double) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
InversePowerLawLearningCurve(InversePowerLawConfiguration) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
IOnlineLearner<T,I,D extends IDataset> - Interface in ai.libs.jaicore.ml.core.predictivemodel: The IOnlineLearner models a learning algorithm which works in an online fashion, i.e. takes either a single IInstance or a Set thereof as training input.
IOrderedDataset - Interface in ai.libs.jaicore.ml.core.dataset
IOrderedLabeledAttributeArrayDataset,L> - Interface in ai.libs.jaicore.ml.core.dataset: Extends the IDataset by including the List interface.
IOrderedLabeledDataset,L> - Interface in ai.libs.jaicore.ml.core.dataset
IPipelineEvaluationConf - Interface in ai.libs.jaicore.ml.experiments
IPLDyadRanker - Interface in ai.libs.jaicore.ml.dyadranking.algorithm: An abstract representation for a dyad ranker using Placket Luce models.
IPLNetDyadRankerConfiguration - Interface in ai.libs.jaicore.ml.dyadranking.algorithm
IPredictiveModel<T,I,D extends IDataset> - Interface in ai.libs.jaicore.ml.core.predictivemodel: The IPredictiveModel corresponds to a model which can be used to make predictions based on given IInstancees.
IPredictiveModelConfiguration - Interface in ai.libs.jaicore.ml.core.predictivemodel: The IPredictiveModelConfiguration models a configuration of an IPredictiveModel.
IPrimitiveAttributeType<D> - Interface in ai.libs.jaicore.ml.core.dataset.attribute.primitive: Interface for categorical attribute types.
IProcessListener - Interface in ai.libs.jaicore.ml.scikitwrapper
IQualityMeasure - Interface in ai.libs.jaicore.ml.tsc.quality_measures: Interface for a quality measure assessing distances of instances to a shapelet given the corresponding class values.
IRankedSolutionCandidateProvider<I,S> - Interface in ai.libs.jaicore.ml.ranking.clusterbased.candidateprovider
IRerunnableSamplingAlgorithmFactory<I,D extends IDataset,A extends ASamplingAlgorithm<I,D>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces: Extension of the ISamplingAlgorithmFactory for sampling algorithms that can re-use informations from a previous run of the Sampling algorithm.
ISamplingAlgorithm<D extends IDataset<?>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory: Interface for sampling algorithms.
ISamplingAlgorithm - Interface in ai.libs.jaicore.ml.core.dataset.sampling: Interface for sampling algorithms.
ISamplingAlgorithmFactory<I,D extends IDataset,A extends ASamplingAlgorithm<I,D>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces: Interface for a factory, which creates a sampling algorithm.
isCacheLookup() - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances: If true signifies that performance on this data should be looked up in cache
isCacheStorage() - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances: If true signifies that performance evaluation should be stored.
IScalarDistance - Interface in ai.libs.jaicore.ml.tsc.distances: Functional interface for the distance of two scalars.
isCompletelyConfigured() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
isCompletelyConfigured() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
isCompletelyConfigured() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
isCompletelyConfigured() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
isDebug() - Static method in class ai.libs.jaicore.ml.WekaUtil
ISelectiveSamplingStrategy - Interface in ai.libs.jaicore.ml.activelearning: A strategy for selective sampling.
isEmpty() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
isEmpty() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
isEmpty() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance
isEmpty() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: States whether the dataset is empty, i.e. contains no value matrices, or not.
ISingleAttributeTransformer - Interface in ai.libs.jaicore.ml.core.dataset.attribute.transformer
isInteger() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
isMultivariate() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
isMultivariate() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: States whether the dataset is a univariate dataset, i.e. contains more than one value matrix, or not.
ISplitBasedClassifierEvaluator<O> - Interface in ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation: Interface for the evaluator measure bridge yielding the measured value as an instance of O.
ISplitter - Interface in ai.libs.jaicore.ml.classification.multiclass.reduction.splitters
ISplitterFactory<T extends ISplitter> - Interface in ai.libs.jaicore.ml.classification.multiclass.reduction.splitters
isSameLength(double[], double[]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks whether multiple arrays have the same length.
isSameLength(INDArray, INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks whether multiple arrays have the same length.
isSameLengthOrException(double[], double[]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks whether multiple arrays have the same length.
isSameLengthOrException(INDArray, INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks whether multiple arrays have the same length.
isTest() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: States whether the dataset is a test dataset, i.e. contains no valid targets after initialization, or not.
isTimeSeries(int, double[]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks, whether given array are valid time series with a given length.
isTimeSeries(int, INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks, whether given INDArrays are valid time series with a given length.
isTimeSeries(INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks, whether given INDArray are valid time series.
isTimeSeriesOrException(int, double[]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks, whether given INDArrays are valid time series with a given length.
isTimeSeriesOrException(int, INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks, whether given INDArrays are valid time series with a given length.
isTimeSeriesOrException(INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Checks, whether given INDArrays are valid time series.
isTrain() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: States whether the dataset is a training dataset, i.e. contains valid targets after initialization, or not.
isTrained() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier
IStratiAmountSelector<D extends IDataset<?>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling: Functional interface to write custom logic for selecting the amount of strati for a dataset.
IStratiAssigner<I,D extends IDataset> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling: Interface to write custom Assigner for datapoints to strati.
IStratiFileAssigner - Interface in ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling: Interface to implement custom Stratum assignment behavior.
isUnivariate() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
isUnivariate() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: States whether the dataset is a univariate dataset, i.e. contains exactly one value matrix, or not.
isUseInstanceReordering() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm
isValidPreprocessorCombination(String, String) - Static method in class ai.libs.jaicore.ml.WekaUtil
isValidValue(D) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeType: Validates whether a value conforms to this type.
isValidValue(Boolean) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType
isValidValue(Double) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType
isValidValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType
isValidValue(Collection<String>) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType
isValidValue(INDArray) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType: Validates whether a INDArray conforms to this time series.
ITableGeneratorandCompleter<I,S,P> - Interface in ai.libs.jaicore.ml.ranking.clusterbased.datamanager
iterator() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
iterator() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
iterator() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance
iterator() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
iterator() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
iterator() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm
iterator() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
ITimeSeriesComplexity - Interface in ai.libs.jaicore.ml.tsc.complexity: Interface that describes the complexity measure of a time series.
ITimeSeriesDistance - Interface in ai.libs.jaicore.ml.tsc.distances: Interface that describes a distance measure of two time series.
ITimeSeriesDistanceWithTimestamps - Interface in ai.libs.jaicore.ml.tsc.distances: Interface that describes a distance measure of two time series that takes the timestamps into account.
ITreeClassifier - Interface in ai.libs.jaicore.ml.classification.multiclass.reduction

J

JACCARD_LOSS - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
JACCARD_SCORE - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
JaccardLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
JaccardLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.JaccardLoss
JaccardScore - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
JaccardScore() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.JaccardScore
JANOSCHEK - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
jsonStringToInstances(String) - Static method in class ai.libs.jaicore.ml.WekaUtil

K

k - Variable in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans
K_ACTIVATION_FUNCTION - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: The activation function for the hidden layers.
K_ALPHABET - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_ALPHABET_SIZE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_CLUSTERSHAPELETS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig
K_EARLY_STOPPING_INTERVAL - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: How often (in epochs) the validation error should be checked for early stopping.
K_EARLY_STOPPING_PATIENCE - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: For how many epochs early stopping should wait until training is stopped if no improvement in the validation error is observed.
K_EARLY_STOPPING_RETRAIN - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: Whether to retrain on the full training data after early stopping, using the same number of epochs the model was trained for before early stopping occured.
K_EARLY_STOPPING_TRAIN_RATIO - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: The ratio of data used for training in early stopping. 1 - this ratio is used for testing.
K_ESTIMATEK - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_ESTIMATEK - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_ESTIMATESHAPELETLENGTHBORDERS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig
K_FEATURECACHING - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig
K_FEATURECACHING - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig
K_GAMMA - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_GAMMA - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_LEARNINGRATE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_LEARNINGRATE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_MAX_EPOCHS - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: The maximum number of epochs to be used during training, i.e. how many times the training algorithm should iterate through the entire training data set.
K_MAXDEPTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig
K_MAXDEPTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig
K_MAXDEPTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig
K_MAXITER - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_MAXITER - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_MEANCORRECTED - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_MEANNORMALIZATION - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
K_MIN_INTERVAL_LENGTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig
K_MINI_BATCH_SIZE - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: The size of mini batches used during training.
K_NUM_SHAPELETS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_NUMBINS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig
K_NUMCLUSTERS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig
K_NUMFOLDS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig
K_NUMFOLDS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig
K_NUMSEGMENTS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig
K_NUMSHAPELETS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_NUMSHAPELETS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig
K_NUMTREES - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig
K_NUMTREES - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig
K_PLNET_HIDDEN_NODES - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: List of integers describing the architecture of the hidden layers.
K_PLNET_LEARNINGRATE - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: The learning rate for the gradient updater.
K_PLNET_SEED - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration: The random seed to use.
K_REGULARIZATION - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_REGULARIZATION - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_SCALER - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_SCALER - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_SHAPELETLENGTH_MAX - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig
K_SHAPELETLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_SHAPELETLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_SHAPELETLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig
K_SHAPELETLENGTH_RELMIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig
K_SHAPELETLENGTH_RELMIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig
K_USE_ZNORMALIZATION - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig
K_USEHIVECOTEENSEMBLE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig
K_WINDOW_SIZE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_WINDOWLENGTH_MAX - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
K_WINDOWLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
K_WORDLENGTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_ZPROP - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig
KAPPA - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
KAPPA - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
KB_INFORMATION - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
KB_MEA_INFORMATION - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
KB_RELATIVE_INFORMATION - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
KendallsTauDyadRankingLoss - Class in ai.libs.jaicore.ml.dyadranking.loss: Computes the rank correlation measure known as Kendall's tau coefficient, i.e.
KendallsTauDyadRankingLoss() - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.KendallsTauDyadRankingLoss
KendallsTauOfTopK - Class in ai.libs.jaicore.ml.dyadranking.loss: Calculates the kendalls-tau loss only for the top k dyads.
KendallsTauOfTopK(int, double) - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.KendallsTauOfTopK
keoghDerivate(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Calculates the derivative of a timeseries as described first by Keogh and Pazzani (2001).
KeoghDerivate - Class in ai.libs.jaicore.ml.tsc.filter.derivate: Calculates the derivative of a timeseries as described first by Keogh and Pazzani (2001).
KeoghDerivate() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.KeoghDerivate
KeoghDerivate(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.KeoghDerivate
keoghDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Calculates the derivateive of a timeseries as described first by Keogh and Pazzani (2001).
Kmeans<A,D> - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
Kmeans(List<A>, IDistanceMetric<D, A, A>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans
kmeanscluster(int) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans
kmeanscluster(int) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACkMeans
KmeansSampling,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory: Implementation of a sampling method using kmeans-clustering.
KmeansSampling(long, int, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling: Implementation of a sampling method using kmeans-clustering.
KmeansSampling(long, int, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling: Implementation of a sampling method using kmeans-clustering.
KmeansSampling(long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling: Implementation of a sampling method using kmeans-clustering.
KmeansSamplingFactory,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
KmeansSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory
KMeansStratiAssigner> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling: Cluster the data set with k-means into k Clusters, where each cluster stands for one stratum.
KMeansStratiAssigner(DistanceMeasure, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.KMeansStratiAssigner: Constructor for KMeansStratiAssigner.
KNNAugSpaceSampler - Class in ai.libs.jaicore.ml.rqp: Samples interval-valued data from a dataset of precise points.
KNNAugSpaceSampler(Instances, Random, int, NearestNeighbourSearch) - Constructor for class ai.libs.jaicore.ml.rqp.KNNAugSpaceSampler

L

L1DistanceMetric - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
L1DistanceMetric() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.L1DistanceMetric
LabeledInstance<L> - Interface in ai.libs.jaicore.ml.interfaces
LabeledInstances<L> - Interface in ai.libs.jaicore.ml.interfaces
LandmarkerCharacterizer - Class in ai.libs.jaicore.ml.metafeatures: A Characterizer that applies several characterizers to a data set, but does not use any probing.
LandmarkerCharacterizer() - Constructor for class ai.libs.jaicore.ml.metafeatures.LandmarkerCharacterizer: Constructs a new LandmarkerCharacterizer.
lastIndexOf(Object) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
LatexDatasetTableGenerator - Class in ai.libs.jaicore.ml.latex
LatexDatasetTableGenerator() - Constructor for class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
LatexDatasetTableGenerator.DataSourceCreationFailedException - Exception in ai.libs.jaicore.ml.latex
LCNetClient - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet
LCNetClient() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient
LCNetExtrapolationMethod - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet: This class represents a learning curve extrapolation using the LCNet from pybnn.
LCNetExtrapolationMethod(String) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod
learner - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
LearningCurve - Interface in ai.libs.jaicore.ml.interfaces: Interface for the result of an learning curve extrapolation.
LearningCurveExtrapolatedEvent - Class in ai.libs.jaicore.ml.learningcurve.extrapolation
LearningCurveExtrapolatedEvent(LearningCurveExtrapolator) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolatedEvent
LearningCurveExtrapolationEvaluator,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka: Evaluates a classifier by predicting its learning curve with a few anchorpoints.
LearningCurveExtrapolationEvaluator(int[], ISamplingAlgorithmFactory<I, D, ? extends ASamplingAlgorithm<I, D>>, D, double, LearningCurveExtrapolationMethod, long) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator: Create a classifier evaluator with learning curve extrapolation.
LearningCurveExtrapolationEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory
LearningCurveExtrapolationEvaluatorFactory(int[], ISamplingAlgorithmFactory<WekaInstance<Object>, WekaInstances<Object>, ? extends ASamplingAlgorithm<WekaInstance<Object>, WekaInstances<Object>>>, double, LearningCurveExtrapolationMethod) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.LearningCurveExtrapolationEvaluatorFactory
LearningCurveExtrapolationMethod - Interface in ai.libs.jaicore.ml.learningcurve.extrapolation: Functional interface for extrapolating a learning curve from anchorpoints.
LearningCurveExtrapolator,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.learningcurve.extrapolation: Abstract class for implementing a learning curve extrapolation method with some anchor points.
LearningCurveExtrapolator(LearningCurveExtrapolationMethod, Classifier, D, double, int[], ISamplingAlgorithmFactory<I, D, ? extends ASamplingAlgorithm<I, D>>, long) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator: Create a learning curve extrapolator with a subsampling factory.
learningRate() - Method in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig
learningRate() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: The learning rate used within the SGD.
learningRate() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: The learning rate used within the SGD.
LearnPatternSimilarityClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Class representing the Learn Pattern Similarity classifier as described in Baydogan, Mustafa & Runger, George. (2015).
LearnPatternSimilarityClassifier(int, int, int, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier: Standard constructor.
LearnPatternSimilarityLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Algorithm training a LearnPatternSimilarityClassifier as described in Baydogan, Mustafa & Runger, George. (2015).
LearnPatternSimilarityLearningAlgorithm(LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig, LearnPatternSimilarityClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm: Standard constructor.
LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.trees
LearnShapeletsClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.shapelets: LearnShapeletsClassifier published in "J.
LearnShapeletsClassifier(int, double, double, int, double, int, double, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Constructor of the LearnShapeletsClassifier.
LearnShapeletsClassifier(int, double, double, int, double, int, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Constructor of the LearnShapeletsClassifier.
LearnShapeletsLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.shapelets: Generalized Shapelets Learning implementation for LearnShapeletsClassifier published in "J.
LearnShapeletsLearningAlgorithm(LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig, LearnShapeletsClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Constructor of the algorithm to train a LearnShapeletsClassifier.
LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.shapelets
length() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance
length() - Method in interface ai.libs.jaicore.ml.dyadranking.dataset.IDyadRankingInstance: Get the number of dyads in the ranking.
length() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
LinearCombinationConstants - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc: This class contains required constant names for the linear combination learning curve.
LinearCombinationExtrapolationMethod - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc: This class describes a method for learning curve extrapolation which generates a linear combination of suitable functions.
LinearCombinationExtrapolationMethod() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationExtrapolationMethod
LinearCombinationExtrapolationMethod(String, String) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationExtrapolationMethod
LinearCombinationFunction - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc: This is a basic class that describes a function which is a weighted combination of individual functions.
LinearCombinationFunction(List<UnivariateFunction>, List<Double>) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction
LinearCombinationLearningCurve - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc: The LinearCombinationLearningCurve consists of the actual linear combination function that describes the learning curve, as well as the derivative of this function.
LinearCombinationLearningCurve(LinearCombinationLearningCurveConfiguration, int) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve
LinearCombinationLearningCurveConfiguration - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc: A configuration for a linear combination learning curve consists of parameterizations for at least one linear combination function.
LinearCombinationLearningCurveConfiguration() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration
LinearCombinationParameterSet - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc: This class encapsulates all parameters that are required in order to create a weighted linear combination of parameterized functions.
LinearCombinationParameterSet() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet
listenTo(Process) - Method in class ai.libs.jaicore.ml.scikitwrapper.AProcessListener
listenTo(Process) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IProcessListener: Lets the process listener listen to the output and error stream of the given process.
listIterator() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
listIterator(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
loadArff(File) - Static method in class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader: Loads a univariate time series dataset from the given arff file.
loadArffs(File...) - Static method in class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader: Loads a multivariate time series dataset from multiple arff files (each for one series).
LoadDataSetInstruction - Class in ai.libs.jaicore.ml.cache: Instruction for dataset loading, provider and id are used to identify the data set.
LoadDataSetInstruction(DataProvider, String) - Constructor for class ai.libs.jaicore.ml.cache.LoadDataSetInstruction: Constructor to create an instruction for loading a dataset that can be converted to json.
LoadDataSetInstructionForARFFFile - Class in ai.libs.jaicore.ml.cache
LoadDataSetInstructionForARFFFile(File) - Constructor for class ai.libs.jaicore.ml.cache.LoadDataSetInstructionForARFFFile
LoadDataSetInstructionForARFFFile(File, int) - Constructor for class ai.libs.jaicore.ml.cache.LoadDataSetInstructionForARFFFile
LoadDatasetInstructionForOpenML - Class in ai.libs.jaicore.ml.cache
LoadDatasetInstructionForOpenML(String, int) - Constructor for class ai.libs.jaicore.ml.cache.LoadDatasetInstructionForOpenML
loadModelFromFile(String) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Restore a trained model from a given file path.
LocalCaseControlSampling,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol
LocalCaseControlSampling(Random, int, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.LocalCaseControlSampling
LocalCaseControlSamplingFactory,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
LocalCaseControlSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory
LOG_LOG_LINEAR - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
LOG_POWER - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
loss(IDyadRankingInstance, IDyadRankingInstance) - Method in interface ai.libs.jaicore.ml.dyadranking.loss.DyadRankingLossFunction: Computes the loss between the actual dyad ordering and predicted dyad ordering, represented by dyad ranking instances.
loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingMLLossFunctionWrapper
loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.KendallsTauDyadRankingLoss
loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.KendallsTauOfTopK
loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.NDCGLoss
loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.TopKOfPredicted
loss(INDArray) - Method in interface ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.InputOptimizerLoss
loss(INDArray) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.NegIdentityInpOptLoss
lossGradient(INDArray) - Method in interface ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.InputOptimizerLoss
lossGradient(INDArray) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.NegIdentityInpOptLoss
LossScoreTransformer - Class in ai.libs.jaicore.ml.core.evaluation.measure: This transformer transforms a decomposable double measure from a scoring function to a loss or vice versa.
LossScoreTransformer(ADecomposableDoubleMeasure) - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.LossScoreTransformer: Constructor for setting the measure to be transformed from loss to score or vice versa.

M

MAJORITY - ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier.VoteType: Majority vote with @see NearestNeighborClassifier#voteMajority.
MajorityConfidenceVote - Class in ai.libs.jaicore.ml.tsc.classifier.ensemble: Vote implementation for majority confidence.
MajorityConfidenceVote(int, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.ensemble.MajorityConfidenceVote: Constructor for a majority confidence vote ensemble classifier.
ManhattanDistance - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Manhattan distance for time series.
ManhattanDistance() - Constructor for class ai.libs.jaicore.ml.tsc.distances.ManhattanDistance
map(int) - Method in class ai.libs.jaicore.ml.tsc.util.ClassMapper: Maps an integer value to a string based on the position index in the classValues.
map(String) - Method in class ai.libs.jaicore.ml.tsc.util.ClassMapper: Maps a String value to an integer value based on the value's position in the classValues.
mapJ48InputsToWekaOptions(double, double) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil
mapMLPInputsToWekaOptions(double, double, double) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil
mapRFInputsToWekaOptions(double, double, double, double, double) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil
mapSMORBFInputsToWekaOptions(double, double) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil
mapWEKAToTree(Instance) - Static method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper: Maps the WEKA query to a tree-friendly query while also preserving the header information of the query, this is important for M5 trees.
MathUtil - Class in ai.libs.jaicore.ml.tsc.util: Utility class consisting of mathematical utility functions.
matrixToWekaInstances(double[][]) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Converts a double[][] matrix (number of instances x number of attributes) to Weka instances without any class attribute.
maxDepth() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig: Maximum depth of the trained trees.
maxDepth() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig: Maximum depth of the trained trees.
maxDepth() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig
maxIterations() - Method in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig
maxIterations() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: The maximum iterations used for the SGD.
maxIterations() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: The maximum iterations used for the SGD.
maxShapeletLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig: The maximum length of shapelets to be considered.
MCCVSplitEvaluationEvent - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.events
MCCVSplitEvaluationEvent(Classifier, int, int, int, double) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent
MCTreeMergeNode - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
MCTreeMergeNode(String, Collection<String>, Classifier, Collection<String>, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeMergeNode
MCTreeMergeNode(Classifier, List<Collection<String>>, List<Classifier>) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeMergeNode
MCTreeNode - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
MCTreeNode(List<Integer>) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
MCTreeNode(List<Integer>, EMCNodeType, String) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
MCTreeNode(List<Integer>, EMCNodeType, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
MCTreeNodeLeaf - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
MCTreeNodeLeaf(int) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
MCTreeNodeReD - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
MCTreeNodeReD() - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
MCTreeNodeReD(MCTreeNodeReD) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
MCTreeNodeReD(String, Collection<String>, String, Collection<String>, String) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
MCTreeNodeReD(String, Collection<String>, Classifier, Collection<String>, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
MCTreeNodeReD(Classifier, Collection<String>, Classifier, Collection<String>, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
MCTreeNodeReD(Classifier, List<Collection<String>>, List<Classifier>) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
MCTreeNodeReDLeaf - Class in ai.libs.jaicore.ml.classification.multiclass.reduction
MCTreeNodeReDLeaf(String) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
mean(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil
mean(double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil: Function calculating the mean of the interval [t1, t2 (inclusive)] of the given vector.
MEAN - ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature.FeatureType
MEAN_ABSOLUTE_ERROR - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
MEAN_SQUARED_ERROR - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMultiClassPerformanceMeasure
meanCorrected() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig: If mean corrected is set to true than the first DFT coefficient is dropped to normalize the mean.
meanNormalization() - Method in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
MeanSquaredErrorLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel
MeanSquaredErrorLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MeanSquaredErrorLoss
MeasureAggregatedComputationEvent<INPUT,OUTPUT> - Class in ai.libs.jaicore.ml.evaluation
MeasureAggregatedComputationEvent(List<INPUT>, List<INPUT>, IAggregateFunction<OUTPUT>, OUTPUT) - Constructor for class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent
MeasureAvgComputationEvent<INPUT,OUTPUT> - Class in ai.libs.jaicore.ml.evaluation
MeasureAvgComputationEvent(List<INPUT>, List<INPUT>, OUTPUT) - Constructor for class ai.libs.jaicore.ml.evaluation.MeasureAvgComputationEvent
MeasureListComputationEvent<INPUT,OUTPUT> - Class in ai.libs.jaicore.ml.evaluation
MeasureListComputationEvent(List<INPUT>, List<INPUT>, List<OUTPUT>) - Constructor for class ai.libs.jaicore.ml.evaluation.MeasureListComputationEvent
measureOOBProbabilitiesUsingCV(double[][], int[], int, int, int, RandomForest) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Function measuring the out-of-bag (OOB) probabilities using a cross validation with numFolds many folds.
MEASURES - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
MeasureSingleComputationEvent<INPUT,OUTPUT> - Class in ai.libs.jaicore.ml.evaluation
MeasureSingleComputationEvent(INPUT, INPUT, OUTPUT) - Constructor for class ai.libs.jaicore.ml.evaluation.MeasureSingleComputationEvent
MEM_MAX - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
MEM_OPP - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
merge(int, List<Map.Entry<Shapelet, Double>>, List<Map.Entry<Shapelet, Double>>) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Function merging shapelet lists based on their quality scores.
merge(Collection<Instances>) - Method in class ai.libs.jaicore.ml.core.WekaInstancesFeatureUnion
merge(Instances, Instances) - Method in class ai.libs.jaicore.ml.core.WekaInstancesFeatureUnion
MERGE - ai.libs.jaicore.ml.classification.multiclass.reduction.EMCNodeType
mergeClassesOfInstances(Instances, Collection<String>, Collection<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil
mergeClassesOfInstances(Instances, List<Set<String>>) - Static method in class ai.libs.jaicore.ml.WekaUtil
mergeCluster(Map<double[], List<C>>) - Method in class ai.libs.jaicore.ml.clustering.GMeans
metric - Variable in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans
MinHashingTransformer - Class in ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue: Converts the sets of multi-value features to short signatures.
MinHashingTransformer(int[][]) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MinHashingTransformer: Constructor where the user gives predefined permutations.
MinHashingTransformer(int, int, long) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MinHashingTransformer: Constructor where suitable permutations are created randomly.
minIntervalLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig: The minimal interval length used for the interval generation.
minShapeLengthPercentage() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: The minimum shape length percentage used to calculate the minimum shape length.
minShapeLengthPercentage() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: The minimum shape length percentage used to calculate the minimum shape length.
minShapeletLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: The minimum shapelet of the shapelets to be learned.
minShapeletLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: The minimum shapelet of the shapelets to be learned.
minShapeletLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig: The minimum length of shapelets to be considered.
MLExperiment - Class in ai.libs.jaicore.ml.experiments
MLExperiment(String, String, String, int, int, int, int, String) - Constructor for class ai.libs.jaicore.ml.experiments.MLExperiment
MMF - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
model - Variable in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm: The model which is maintained during algorithm calls
ModelBuildFailedException - Exception in ai.libs.jaicore.ml.core
ModelBuildFailedException(String) - Constructor for exception ai.libs.jaicore.ml.core.ModelBuildFailedException
ModelBuildFailedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.ModelBuildFailedException
ModifiedISAC - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
ModifiedISAC() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISAC
ModifiedISACEvaluator - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation
ModifiedISACgMeans - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
ModifiedISACgMeans(List<double[]>, List<ProblemInstance<Instance>>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans: inilizes toClusterPoints with the points that are to Cluster and are normalized metafeatures
ModifiedISACGroupBuilder - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
ModifiedISACGroupBuilder() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACGroupBuilder
ModifiedISACInstanceCollector - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
ModifiedISACInstanceCollector() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector: This constructor is used if the default file should be used.
ModifiedISACInstanceCollector(Instances, int, int) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector
ModifiedISACkMeans - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
ModifiedISACkMeans(List<double[]>, IDistanceMetric<Double, double[], double[]>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACkMeans
MonteCarloCrossValidationEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka: A classifier evaluator that can perform a (monte-carlo)cross-validation on the given dataset.
MonteCarloCrossValidationEvaluator(ISplitBasedClassifierEvaluator<Double>, int, Instances, double, long) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
MonteCarloCrossValidationEvaluator(ISplitBasedClassifierEvaluator<Double>, IDatasetSplitter, int, Instances, double, long) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
MonteCarloCrossValidationEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory: Factory for configuring standard Monte Carlo cross-validation evaluators.
MonteCarloCrossValidationEvaluatorFactory() - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory: Standard C'tor.
mostFrequentLabelFromWindowLengthPredicitions(Map<Integer, Integer>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Returns the most frequent predicition given a Map of (window length, prediciton) pairs.
mostFrequentLabelsFromWindowLengthPredicitions(Map<Integer, List<Integer>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Returns for each instance the most frequent predicitions as contained in a Map of (window length, list of prediciton for each instance) pairs.
MoveSplitMerge - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Move-Split-Merge (MSM) measure as published in "The Move-Split-Merge Metric for Time Series" by Alexandra Stefan, Vassilis Athitsos and Gautam Das (2013).
MoveSplitMerge(double) - Constructor for class ai.libs.jaicore.ml.tsc.distances.MoveSplitMerge: Constructor.
MULTI_LABEL_METRICS - Static variable in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter: Available metrics for multilabelclassifiers
MulticlassClassStratifiedSplitter - Class in ai.libs.jaicore.ml.weka.dataset.splitter: Makes use of the WekaUtil to split the data into a class-oriented stratified split preserving the class distribution.
MulticlassClassStratifiedSplitter() - Constructor for class ai.libs.jaicore.ml.weka.dataset.splitter.MulticlassClassStratifiedSplitter
MultiClassMeasureBuilder - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel
MultiClassMeasureBuilder() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MultiClassMeasureBuilder
MultilabelDatasetSplitter - Class in ai.libs.jaicore.ml.weka.dataset.splitter: This class provides methods to obtain train and test splits for a given data set and split technique.
MultiValueAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.multivalue: The multi-value attribute type describes the domain a value of a respective multi-value attribute value stems from.
MultiValueAttributeType(Set<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType: Constructor setting the domain of the multi-value attribute values.
MultiValueAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.multivalue: Multi-value attribute value as it can be part of an instance.
MultiValueAttributeValue(IMultiValueAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeValue: Standard c'tor.
MultiValueAttributeValue(IMultiValueAttributeType, Collection<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeValue: C'tor setting the value of this attribute as well.
MultiValueBinaryzationTransformer - Class in ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue: Transforms a multi-valued feature into a 0/1 Vector, where each dimension represents one of the values, i.e. 1 in one dimension => the feature contains this value, 0 in one dimension => the feature does not contain this value.
MultiValueBinaryzationTransformer() - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MultiValueBinaryzationTransformer

N

NDCGLoss - Class in ai.libs.jaicore.ml.dyadranking.loss: The Normalized Discounted Cumulative Gain for ranking.
NDCGLoss(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.NDCGLoss
nearestNeighborClassifier - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: The nearest neighbor classifier used for prediction.
NearestNeighborClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.neighbors: K-Nearest-Neighbor classifier for time series.
NearestNeighborClassifier(int, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Creates a k nearest neighbor classifier using majority vote.
NearestNeighborClassifier(int, ITimeSeriesDistance, NearestNeighborClassifier.VoteType) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Creates a k nearest neighbor classifier.
NearestNeighborClassifier(ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Creates a 1 nearest neighbor classifier using majority vote.
NearestNeighborClassifier.VoteType - Enum in ai.libs.jaicore.ml.tsc.classifier.neighbors: Votes types that describe how to aggregate the prediciton for a test instance on its nearest neighbors found.
nearestNeighborComparator - Static variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Singleton comparator instance for the nearest neighbor priority queues, used for the nearest neighbor calculation.
NearestNeighborLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.neighbors: Training algorithm for the nearest neighbors classifier.
NearestNeighborLearningAlgorithm(IAlgorithmConfig, NearestNeighborClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborLearningAlgorithm
needsBinarization(Instances, boolean) - Static method in class ai.libs.jaicore.ml.WekaUtil: Returns true if there is at least one nominal attribute in the given dataset that has more than 2 values.
NegIdentityInpOptLoss - Class in ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization: Loss function for PLNet input optimization that maximizes the output of a PLNet.
NegIdentityInpOptLoss() - Constructor for class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.NegIdentityInpOptLoss
next() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm
next() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
nextQueryInstance() - Method in interface ai.libs.jaicore.ml.activelearning.ISelectiveSamplingStrategy: Chooses the IInstance to query next.
nextQueryInstance() - Method in class ai.libs.jaicore.ml.activelearning.PoolBasedUncertaintySamplingStrategy
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.ReservoirSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.StratifiedFileSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.SystematicFileSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.ClassifierWeightedSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.GmeansSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SimpleRandomSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling
nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborLearningAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
NoneFittedFilterExeception - Exception in ai.libs.jaicore.ml.tsc.exceptions
NoneFittedFilterExeception(String) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.NoneFittedFilterExeception
NoneFittedFilterExeception(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.NoneFittedFilterExeception
NoProbingCharacterizer - Class in ai.libs.jaicore.ml.metafeatures: A Characterizer that applies several characterizers to a data set, but does not use any probing.
NoProbingCharacterizer() - Constructor for class ai.libs.jaicore.ml.metafeatures.NoProbingCharacterizer: Constructs a new NoProbingCharacterizer.
normalize(double[]) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Normalizer
normalizeByStandardDeviation(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil
normalizeINDArray(INDArray, boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Normalizes an INDArray vector object.
Normalizer - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac
Normalizer(List<ProblemInstance<Instance>>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Normalizer
NUM_FEATURE_TYPES - Static variable in class ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature: Number of features used within the time series tree.
NUM_THRESH_CANDIDATES - Static variable in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Number of threshold candidates created in each tree recursion step.
numBins() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig: Number of bins used for the CPEs.
numClusters() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig: Number of shapelet clusters when shapelet clustering is used.
numColumns - Variable in class ai.libs.jaicore.ml.core.ASimpleInstancesImpl
NumericAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.primitive: The numeric attribute type.
NumericAttributeType() - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType
NumericAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.primitive: Numeric attribute value as it can be part of an instance.
NumericAttributeValue(NumericAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeValue: Standard c'tor.
NumericAttributeValue(NumericAttributeType, Double) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeValue: C'tor setting the value of this attribute as well.
NumericFeatureDomain - Class in ai.libs.jaicore.ml.core: Description of a numeric feature domain.
NumericFeatureDomain(boolean, double, double) - Constructor for class ai.libs.jaicore.ml.core.NumericFeatureDomain
NumericFeatureDomain(NumericFeatureDomain) - Constructor for class ai.libs.jaicore.ml.core.NumericFeatureDomain
numFolds() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig: Number of folds used within the MajorityConfidenceVote scheme for the ensembles.
numFolds() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig: Number of folds used for the OOB probability estimation in the training phase.
numInstances() - Method in class ai.libs.jaicore.ml.SubInstances
numSegments() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig: Number of segments used for feature generation for each tree.
numShapelets() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: Parameter which determines how many of the most-informative shapelets should be used.
numShapelets() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: Parameter which determines how many of the most-informative shapelets should be used.
numShapelets() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig: Number of shapelets extracted in the shapelet search
numTrees() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig: Number of trees to be trained.
numTrees() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig: Number of trees to be trained.

O

offer(Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
OneHotEncodingTransformer - Class in ai.libs.jaicore.ml.core.dataset.attribute.transformer
OneHotEncodingTransformer() - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.transformer.OneHotEncodingTransformer
ONEVSREST - ai.libs.jaicore.ml.classification.multiclass.reduction.EMCNodeType
OPENML - ai.libs.jaicore.ml.cache.DataProvider
OpenMLHelper - Class in ai.libs.jaicore.ml.openml
optimize(IGradientDescendableFunction, IGradientFunction, Vector) - Method in class ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizer
optimize(IGradientDescendableFunction, IGradientFunction, Vector) - Method in interface ai.libs.jaicore.ml.core.optimizing.IGradientBasedOptimizer: Optimize the given function based on its derivation.
optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, double, int, INDArray) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer: Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.
optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, double, int, Pair<Integer, Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer: Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.
optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, int, INDArray) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer: Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.
optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, int, Pair<Integer, Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer: Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.
OSMAC,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol
OSMAC(Random, int, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.OSMAC
OSMACSamplingFactory,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
OSMACSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory
outputFileWriter - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm

P

ParametricFunction - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc: This is a basic class that describes a function that can be parameterized with a set of parameters.
ParametricFunction() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.ParametricFunction
ParametricFunction(Map<String, Double>) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.ParametricFunction
PCT_CORRECT - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
PCT_INCORRECT - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
peek() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
performSGD(double[][][], double[][][], double[], double[], double[][][], double[][][], double[][], int[][], long, int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Method performing the stochastic gradient descent to learn the weights and shapelets.
PHASE2 - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
PilotEstimateSampling,D extends IDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol
PilotEstimateSampling(D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling
plNetActivationFunction() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
PLNetDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.algorithm: A dyad ranker based on a Plackett-Luce network.
PLNetDyadRanker() - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Constructs a new PLNetDyadRanker using the default IPLNetDyadRankerConfiguration.
PLNetDyadRanker(IPLNetDyadRankerConfiguration) - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Constructs a new PLNetDyadRanker using the given IPLNetDyadRankerConfiguration.
plNetEarlyStoppingInterval() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
plNetEarlyStoppingPatience() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
plNetEarlyStoppingRetrain() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
plNetEarlyStoppingTrainRatio() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
plNetHiddenNodes() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
PLNetInputOptimizer - Class in ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization: Optimizes a given loss function (InputOptimizerLoss) with respect to the input of a PLNet using gradient descent.
PLNetInputOptimizer() - Constructor for class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer
plNetLearningRate() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
PLNetLoss - Class in ai.libs.jaicore.ml.dyadranking.algorithm: Implements the negative log likelihood (NLL) loss function for PL networks as described in [1]
plNetMaxEpochs() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
plNetMiniBatchSize() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
plNetSeed() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration
points - Variable in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans
PointWiseLearningCurve - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet: This class represents a learning curve that gets returned by the LCNet from pybnn
PointWiseLearningCurve(int, double[], String) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.PointWiseLearningCurve
poll() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
PoolBasedUncertaintySamplingStrategy<T,I extends ILabeledInstance,D extends IDataset> - Class in ai.libs.jaicore.ml.activelearning: A simple pool-based uncertainty sampling strategy, which assesses certainty for all instances in the pool and picks the instance with least certainty for the next query.
PoolBasedUncertaintySamplingStrategy(ICertaintyProvider<T, I, D>, IActiveLearningPoolProvider) - Constructor for class ai.libs.jaicore.ml.activelearning.PoolBasedUncertaintySamplingStrategy
poolProvider - Variable in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker
POW_3 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
POW_4 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
ppa(double[], int) - Static method in class ai.libs.jaicore.ml.tsc.PPA
PPA - Class in ai.libs.jaicore.ml.tsc
PRECISION - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
PRECISION - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMultiClassPerformanceMeasure
PrecisionAsLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel
PrecisionAsLoss(int) - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier: Performs a prediction based on the given univariate double[] instance representation and returns the result.
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Predicts on univariate instance.
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Predicts on univariate instance.
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Performs a prediction based on the given univariate double[] instance representation and returns the result.
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Performs a prediction based on the given univariate double[] instance representation and returns the result.
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier: Predicts the class by generated segment and segment difference features based on segments and segmentsDifference.
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier: Method predicting the class of the given univInstance.
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier: Predicts the class of the given instance by taking the majority vote of all trees.
predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier: Predicts the class of the given univariate instance by iterating through the tree starting from the root node to a leaf node to induce a class prediction.
predict(int, double[], String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient
predict(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker
predict(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
predict(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker
predict(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier: Performs predictions based on the given instances in the given dataset.
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Predicts on a dataset.
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Predicts on a dataset.
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Performs predictions based on the given instances in the given dataset.
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Performs predictions based on the given instances in the given dataset.
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier: Performs predictions based on the given instances in the given dataset.
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier: Performs predictions based on the given instances in the given dataset.
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier: Performs predictions based on the given instances in the given dataset.
predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier: Performs predictions based on the given instances in the given dataset.
predict(D) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel: Performs multiple predictions based on the IInstances contained in the given AILabeledAttributeArrayDatasets and returns the result.
predict(D) - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier: {@inheritDoc ABatchLearner#predict(jaicore.ml.core.dataset.IDataset)}
predict(I) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel: Performs a prediction based on the given IInstance and returns the result.
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier: Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier: Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier: Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier: Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier: Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
predictInterval(RQPHelper.IntervalAndHeader) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest
predictInterval(RQPHelper.IntervalAndHeader) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedM5Tree
predictInterval(RQPHelper.IntervalAndHeader) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
predictInterval(RQPHelper.IntervalAndHeader) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
predictInterval(RQPHelper.IntervalAndHeader) - Method in interface ai.libs.jaicore.ml.intervaltree.RangeQueryPredictor
predictInterval(Instance) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest
predictInterval(Instance) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
predictInterval(Instance) - Method in interface ai.libs.jaicore.ml.intervaltree.RangeQueryPredictor
PredictionException - Exception in ai.libs.jaicore.ml.core.exception: The PredictionException indicates that an error occurred during a prediction process.
PredictionException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.PredictionException: Creates a new PredictionException with the given parameters.
PredictionException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.PredictionException: Creates a new PredictionException with the given parameters.
PredictionFailedException - Exception in ai.libs.jaicore.ml.intervaltree
PredictionFailedException(String) - Constructor for exception ai.libs.jaicore.ml.intervaltree.PredictionFailedException
PredictionFailedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.intervaltree.PredictionFailedException
PredictionFailedException(Throwable) - Constructor for exception ai.libs.jaicore.ml.intervaltree.PredictionFailedException
PREFIX - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
PREFIX_MEM - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
PREFIX_SELECTION - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
prepareForest(Instances) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest: Needs to be called before predicting marginal variance contributions!
preprocess() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree: Sets up the tree for fANOVA
PREPROCESSING_PREFIX - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
preSampleSize - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling
printDoubleRepresentation() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
printNestedWekaClassifier(Classifier) - Static method in class ai.libs.jaicore.ml.WekaUtil
printObservations() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
printSizeOfFeatureSpaceAndPartitioning() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
printSplitPoints() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
printVariances() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest
ProbabilisticMonteCarloCrossValidationEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka: A classifier evaluator that can perform a (monte-carlo)cross-validation on the given dataset.
ProbabilisticMonteCarloCrossValidationEvaluator(ISplitBasedClassifierEvaluator<Double>, IDatasetSplitter, int, double, Instances, double, long) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator
ProbabilisticMonteCarloCrossValidationEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory: Factory for configuring probabilistic Monte Carlo cross-validation evaluators.
ProbabilisticMonteCarloCrossValidationEvaluatorFactory() - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory: Standard c'tor.
probabilityBoundaries - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling
ProblemInstance - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes
ProblemInstance() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance
ProblemInstance(I) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance
PrototypicalPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning: A prototypical active dyad ranker based on the idea of uncertainty sampling.
PrototypicalPoolBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, double, int, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker
provideCAWPEEnsembleModel(int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.EnsembleProvider: Initializes the CAWPE ensemble model consisting of five classifiers (SMO, KNN, J48, Logistic and MLP) using a majority voting strategy.
provideHIVECOTEEnsembleModel(int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.EnsembleProvider: Initializes the HIVE COTE ensemble consisting of 7 classifiers using a majority voting strategy as described in J.

Q

QuantileAggregator - Class in ai.libs.jaicore.ml.intervaltree.aggregation: A IntervalAggregator that works based on quantiles.
QuantileAggregator(double) - Constructor for class ai.libs.jaicore.ml.intervaltree.aggregation.QuantileAggregator
query(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
query(I) - Method in interface ai.libs.jaicore.ml.activelearning.IActiveLearningPoolProvider: Labels the given instance.

R

rand - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling
random - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
RandomlyRankedNodeQueue<N,V extends java.lang.Comparable<V>> - Class in ai.libs.jaicore.ml.dyadranking.search: A node queue for the best first search that inserts new nodes at a random position in the list.
RandomlyRankedNodeQueue(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue
RandomlyRankedNodeQueueConfig<T> - Class in ai.libs.jaicore.ml.dyadranking.search: Configuration for a RandomlyRankedNodeQueue
RandomlyRankedNodeQueueConfig(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueueConfig: Construct a new config with the given seed.
randomlySampleNoReplacement(List<Integer>, int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Function sampling a given list randomly without replacement using the given seed.
RandomMultilabelCrossValidation - Class in ai.libs.jaicore.ml.weka.dataset.splitter: Class executing pseudo-random splits to enable multilabelcrossvalidation.
RandomMultilabelCrossValidation() - Constructor for class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation
RandomPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning: A random active dyad ranker.
RandomPoolBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.RandomPoolBasedActiveDyadRanker
randomSeed - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
RandomSplitter - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.splitters
RandomSplitter(Random) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RandomSplitter
RangeQueryPredictor - Interface in ai.libs.jaicore.ml.intervaltree
RANK_LOSS - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
RANK_SCORE - ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure
ranker - Variable in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker
ranker - Variable in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig: the ranker used to rank dyads consisting of pipeline metafeatures and dataset metafeatures
Ranker<S,P> - Interface in ai.libs.jaicore.ml.ranking
Ranking<S> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes
Ranking(Collection<S>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Ranking
RankingForGroup<C,S> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes: RankingForGroup.java - saves a solution ranking for a group identified by thier group
RankingForGroup(GroupIdentifier<C>, List<S>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.RankingForGroup
RankLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
RankLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.RankLoss
RankScore - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel
RankScore() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.RankScore
realizeSplit(Instances, Collection<Integer>[]) - Static method in class ai.libs.jaicore.ml.WekaUtil
realizeSplit(Instances, List<List<Integer>>) - Static method in class ai.libs.jaicore.ml.WekaUtil
realizeSplitAsCopiedInstances(Instances, Collection<Integer>[]) - Static method in class ai.libs.jaicore.ml.WekaUtil
realizeSplitAsCopiedInstances(Instances, List<List<Integer>>) - Static method in class ai.libs.jaicore.ml.WekaUtil
realizeSplitAsSubInstances(Instances, Collection<Integer>[]) - Static method in class ai.libs.jaicore.ml.WekaUtil
receiveEvent(IEvent) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
reduceWithInstruction(String, Instruction, int) - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances: Creates a reduced version of the dataset by using an instruction with one input and one output
ReductionGraphGenerator - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.reducer
ReductionGraphGenerator(Random, Instances) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator
ReductionOptimizer - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.reducer
ReductionOptimizer(long) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer
registerListener(Object) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator: Register observers for learning curve predictions (including estimates of the time)
registerListener(Object) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
registerListener(Object) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm
registerListener(Object) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm
registerListener(Object) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
REGRESSION - ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper.ProblemType
regularization() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: The regularization used wihtin the SGD.
regularization() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: The regularization used wihtin the SGD.
rekursivDFT(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
rekursivDFT(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
RELATIVE_ABSOLUTE_ERROR - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
remove() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
remove(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset: Removes the time series variable at a given index.
remove(int) - Method in class ai.libs.jaicore.ml.SubInstances
remove(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Removes the time series variable at a given index.
remove(Object) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
remove(Object) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
removeAll(Collection<?>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
removeAll(Collection<?>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
removeAttributeValue(int) - Method in interface ai.libs.jaicore.ml.core.dataset.IModifiableInstance: Removes an attribute value for the given position.
removeClassAttribute(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
removeClassAttribute(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
removeNodeAtPosition(int) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
removeSelfSimilar(List<Map.Entry<Shapelet, Double>>) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Function removing self-similar shapelets from a list storing shapelet and their quality entries.
replace(int, double[][], double[][]) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Replaces the time series variable at a given index with a new one.
replace(int, INDArray, INDArray) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset: Replaces the time series variable at a given index with a new one.
reportOptimizationStep(INDArray, double) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.InputOptListener
ReproducibleInstances - Class in ai.libs.jaicore.ml.cache: New Instances class to track splits and data origin.
ReproducibleInstances(ReproducibleInstances) - Constructor for class ai.libs.jaicore.ml.cache.ReproducibleInstances
ReservoirSampling - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles: Implementation of the Reservoir Sampling algorithm(comparable to a Simple Random Sampling for streamed data).
ReservoirSampling(Random, File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.ReservoirSampling
retainAll(Collection<?>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
retainAll(Collection<?>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
ROOT_MEAN_SQUARED_ERROR - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
ROOT_MEAN_SQUARED_ERROR - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMultiClassPerformanceMeasure
ROOT_RELATIVE_SQUARED_ERROR - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
RootMeanSquaredErrorLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel: The root mean squared loss function.
RootMeanSquaredErrorLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.RootMeanSquaredErrorLoss
RPNDSplitter - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.splitters
RPNDSplitter(Random, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RPNDSplitter
RQPHelper - Class in ai.libs.jaicore.ml.intervaltree.util
RQPHelper.IntervalAndHeader - Class in ai.libs.jaicore.ml.intervaltree.util
RUNS - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

S

sample - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm
SampleElementAddedEvent - Class in ai.libs.jaicore.ml.core.dataset.sampling
SampleElementAddedEvent(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.SampleElementAddedEvent
sampleIntervals(int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Function sampling intervals based on the length of the time series m and the given seed.
sampleSize - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm
sampleSize - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm
samplingAlgorithm - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
samplingAlgorithmFactory - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
saveModelToFile(String) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Save a trained model at a given file path.
SAX - Class in ai.libs.jaicore.ml.tsc.filter
SAX(double[], int) - Constructor for class ai.libs.jaicore.ml.tsc.filter.SAX
ScalarDistanceUtil - Class in ai.libs.jaicore.ml.tsc.util: ScalarDistanceUtil
scaler - Variable in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue: for scaling the dyads
scaler - Variable in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig: for scaling the dyads
scaleR() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig: The number of scales used for the shapelet lengths.
scaleR() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig: The number of scales used for the shapelet lengths.
ScikitLearnWrapper - Class in ai.libs.jaicore.ml.scikitwrapper: Wraps a Scikit-Learn Python process by utilizing a template to start a classifier in Scikit with the given classifier.
ScikitLearnWrapper(String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper: Starts a new wrapper and creates its underlying script with the given parameters.
ScikitLearnWrapper(String, String, boolean) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper: Starts a new wrapper and creates its underlying script with the given parameters.
ScikitLearnWrapper(String, String, File) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
ScikitLearnWrapper.ProblemType - Enum in ai.libs.jaicore.ml.scikitwrapper
seed - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
SEEDS - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
selectGroupsolutionRanking(Group<C, I>, Table<I, S, P>) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.IGroupSolutionRankingSelect
SELECTION_CANDIDATES - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
SELECTION_ITERATIONS - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
selectStratiAmount(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner
selectStratiAmount(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner
selectStratiAmount(D) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAmountSelector: Select a suitable amount of strati for a Dataset.
serialize(OutputStream) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset
set(int, TimeSeriesInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
set(int, Instance) - Method in class ai.libs.jaicore.ml.SubInstances
setA(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration
setA(double) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Sets the a parameter.
setAlgorithm(ATSCAlgorithm<L, V, D, ? extends IBatchLearner<V, TimeSeriesInstance<L>, D>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier: Sets the training algorithm for the classifier.
setAlpha(double) - Method in class ai.libs.jaicore.ml.tsc.distances.AWeightedTrigometricDistance: Sets the alpha value and adjusts the measurement parameters a = cos(alpha) and b = sin(alpha) accordingly.
setApiKey(String) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper
setArffHeader(String) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner
setArffHeader(String) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.IStratiFileAssigner: Set the header of the original ARFF input file.
setAttributes(List<Attribute>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
setB(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration
setB(double) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Sets the b parameter.
setBaseClassifier(Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
setBaseClassifier(Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
setBasicEvaluator(IMeasure<I, O>) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.AbstractSplitBasedClassifierEvaluator
setBasselCorrected(boolean) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
setBestSoFar(double) - Method in interface ai.libs.jaicore.ml.tsc.distances.Abandonable: Setter for the best-so-far value.
setC(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration
setC(double) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance: Sets the c parameter.
setC(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Setter for LearnShapeletsClassifier.c
setC(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm
setCacheLookup(boolean) - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances: If true signifies that performance on this data should be looked up in cache
setCacheStorage(boolean) - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances: If set to true, signifies that performance evaluation should be stored.
setCaption(String) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
setChosenInstance(I) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling
setClassAttIndexPerTree(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
setClassifier(Classifier) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Setter for ShapeletTransformTSClassifier.classifier.
setClassMapper(ClassMapper) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier: Setter for the property classMapper.
setClassValues(List<String>) - Method in class ai.libs.jaicore.ml.tsc.util.ClassMapper: Setter for the classValues.
setClusterResults(List<CentroidCluster>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
setClusterSeed(long) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory: Set the seed the clustering will use for initialization.
setClusterSeed(long) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory: Set the seed the clustering will use for initialization.
setComparator(Comparator<String>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.DatasetFileSorter
setConfiguration(IPredictiveModelConfiguration) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel: Sets the IPredictiveModelConfiguration of this model to the given one.
setConfiguration(IPredictiveModelConfiguration) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker
setConfiguration(IPredictiveModelConfiguration) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
setConfiguration(IPredictiveModelConfiguration) - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier: {@inheritDoc ABatchLearner#setConfiguration(IPredictiveModelConfiguration)}
setConfigurations(double[]) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod
setDatapointComparator(Comparator) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory: Set a custom comparator that will be used to sort the datapoints before sampling.
setDebug(boolean) - Static method in class ai.libs.jaicore.ml.WekaUtil
setDefaultWindowSize(int) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
setDeterminedQuality(double) - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Setter for Shapelet.determinedQuality.
setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling
setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory: Set the distance measure for the clustering.
setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory: Set the distance measure for the clustering.
setDyadRanker(IDyadRanker) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue: Set which dyad ranker shall be used to rank the nodes.
setEpsilon(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ExtrapolatedSaturationPointEvaluator
setEstimateK(boolean) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Enables / disabled the parameter estimation of K within the training algorithm.
setFeatureCaching(boolean) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier
setFeatureSpace(FeatureSpace) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree
setFinalClf(RandomForest) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
setFullDatasetSize(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ConfigurationLearningCurveExtrapolationEvaluator
setFullDatasetSize(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator
setFunctions(List<UnivariateFunction>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction
setGroupIdentifier(GroupIdentifier<C>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group
setHistogramUnivirate(List<Map<Integer, Integer>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
setInput(D) - Method in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm: Setter for the data set input used during algorithm calls.
setInstance(I) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance
setInstances(List<ProblemInstance>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group
setIntervals(int[][][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
setIntervals(List<Interval>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
setK(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory: Set how many clusters shall be created.
setK(int) - Method in class ai.libs.jaicore.ml.rqp.ChooseKAugSpaceSampler
setK(int) - Method in class ai.libs.jaicore.ml.rqp.KNNAugSpaceSampler
setL(int) - Method in class ai.libs.jaicore.ml.dyadranking.loss.NDCGLoss
setLabel(String) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
setLabel(String) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
setLabel(L) - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstance
setLength(int) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType
setLengthOfTopRankingToConsider(int) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker
setLengthPerTree(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
setListener(InputOptListener) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer: Set an InputOptListener to record the intermediate steps of the optimization procedure.
setLoggerName(String) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator
setLoggerName(String) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator
setLoggerName(String) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator
setLoggerName(String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
setMax(double) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
setMaxDepth(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier
setMeanCorrected(boolean) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
setMin(double) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
setMinDistanceSearchStrategy(AMinimumDistanceSearchStrategy) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Setter for ShapeletTransformLearningAlgorithm.minDistanceSearchStrategy.
setMinShapeLength(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Setter for LearnShapeletsClassifier#minShapeLength
setModel(T) - Method in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm: Setter for the model to be maintained.
setModelPath(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
setName(String) - Method in class ai.libs.jaicore.ml.core.FeatureDomain: Setter for name attribute.
setNearestNeighborClassifier(NearestNeighborClassifier) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Sets the nearest neighbor classifier, ShotgunEnsembleClassifier.nearestNeighborClassifier.
setNodeType(EMCNodeType) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
setNumberOfClassifier(int) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector
setNumberOfDesieredDFTCoefficients(int) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
setNumberOfDisieredCoefficients(int) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
setNumberOfTrees(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier
setNumBins(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
setNumClasses(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
setNumCPUs(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner
setNumCPUs(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
setNumCPUs(int) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAmountSelector: Sets the number of CPU cores that can be used for parallel computation
setNumCPUs(int) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner: Sets the number of CPU cores that can be used for parallel computation
setNumCPUs(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
setNumMajorColumns(int) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator
setNumSamples(Integer) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
setOffset(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction
setOutputFileName(String) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm
setOutputUnitWithoutRecomputation(Pair<String, Integer>) - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances
setParameters(Map<String, Map<String, Double>>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet
setParameterSets(List<LinearCombinationParameterSet>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration
setParams(Map<String, Double>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.ParametricFunction
setPoints(List<double[]>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACGroupBuilder
setPoolProvider(IDyadRankingPoolProvider) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker
setPreSampleSize(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory: Set the size of the sample the pilot estimator will be trained with.
setPreSampleSize(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory: Set the size of the sample the pilot estimator will be trained with.
setPreviousRun(A) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces.IRerunnableSamplingAlgorithmFactory: Set the previous run of the sampling algorithm, if one occurred, can be set here to get data from it.
setPreviousRun(CaseControlSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.CaseControlSamplingFactory
setPreviousRun(LocalCaseControlSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory
setPreviousRun(OSMAC<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory
setPreviousRun(GmeansSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory
setPreviousRun(KmeansSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory
setPreviousRun(StratifiedSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory
setPreviousRun(SystematicSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory
setProbabilityBoundaries(List<Pair<I, Double>>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling
setProblemType(ScikitLearnWrapper.ProblemType) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
setRanker(IDyadRanker) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig: Set the ranker used to rank the OPEN list.
setRanker(PLNetDyadRanker) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker
setRatioOfOldInstancesForMinibatch(double) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker
setRemoveDyadsWhenQueried(boolean) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider
setRemoveDyadsWhenQueried(boolean) - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider
setS(double[][][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Setter for LearnShapeletsClassifier.s
setSampleSize(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm
setSampleSize(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm
setScaler(AbstractDyadScaler) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
setScaler(AbstractDyadScaler) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig: Set the scaler used to scale the dataset.
setSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator
setSeed(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier
setSegments(int[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
setSegmentsDifference(int[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
setShapelets(List<Shapelet>) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Setter for ShapeletTransformTSClassifier.shapelets.
setSortedDataset(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling
setStrati(IDataset[]) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling
setSubsequences(int[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
setSubseriesClf(RandomForest) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier
setTargets(int...) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
setTargets(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Sets the targets.
setTargets(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Sets the targets.
setTargets(int[]) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Setter for TimeSeriesDataset.targets.
setTargetType(T) - Method in class ai.libs.jaicore.ml.core.predictivemodel.APredictiveModel: Setter method for the given targetType.
setTempFileHandler(TempFileHandler) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner
setTempFileHandler(TempFileHandler) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.IStratiFileAssigner: Set the temporary file handler, which will be used to manage the temporary files for the strati.
setTimeout(long, TimeUnit) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
setTimeout(TimeOut) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm
setTimestampMatrices(List<double[][]>) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Setter for TimeSeriesDataset.timestampMatrices.
setTimestamps(double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Sets the timestamps.
setTrainingData(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier
setTrainingPortion(float) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator
setTrainLeafNodes(int[][][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
setTrainTargets(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
setTrees(AccessibleRandomTree[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier
setTrees(TimeSeriesTreeClassifier[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier: Setter for the time series trees.
setupnormalize() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Normalizer
setUseInstanceReordering(boolean) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm
setValue(D) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue
setValue(D) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeValue
setValueMatrices(List<double[][]>) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Setter for TimeSeriesDataset.valueMatrices.
setValues(double[]) - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
setValues(double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Sets the value matrix.
setValues(double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Sets the value matrix.
setW(double[][][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Setter for LearnShapeletsClassifier.w
setW0(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier: Setter for LearnShapeletsClassifier.w0
setWeights(List<Double>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction
setWeights(Map<String, Double>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet
setWindowLength(int) - Method in class ai.libs.jaicore.ml.tsc.distances.ShotgunDistance: Sets the window length.
setWindows(ArrayList<Pair<Integer, Integer>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Sets the windows and also retreives and sets the @see #bestScore from these windows.
setxValues(List<Integer>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
setyValues(List<Double>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
SFA - Class in ai.libs.jaicore.ml.tsc.filter
SFA(double[], int) - Constructor for class ai.libs.jaicore.ml.tsc.filter.SFA
Shapelet - Class in ai.libs.jaicore.ml.tsc.shapelets: Implementation of a shapelet, i. e. a specific subsequence of a time series representing a characteristic shape.
Shapelet(double[], int, int, int) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Constructs a shapelet specified by the given parameters.
Shapelet(double[], int, int, int, double) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Constructs a shapelet specified by the given parameters.
shapeletTransform(double[], List<Shapelet>, AMinimumDistanceSearchStrategy) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Function transforming the given instance into the new feature space spanned by the shapelets.
shapeletTransform(TimeSeriesDataset, List<Shapelet>, TimeOut, long, AMinimumDistanceSearchStrategy) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Performs a shapelet transform on a complete dataSet.
ShapeletTransformLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.shapelets: Algorithm training a ShapeletTransform classifier as described in Jason Lines, Luke M.
ShapeletTransformLearningAlgorithm(ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig, ShapeletTransformTSClassifier, TimeSeriesDataset, IQualityMeasure) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm: Constructs a training algorithm for the ShapeletTransformTSClassifier classifier specified by the given parameters.
ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.shapelets
ShapeletTransformTSClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.shapelets: Class for a ShapeletTransform classifier as described in Jason Lines, Luke M.
ShapeletTransformTSClassifier(int, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction and the FStat quality measure.
ShapeletTransformTSClassifier(int, int, IQualityMeasure, int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.
ShapeletTransformTSClassifier(int, int, IQualityMeasure, int, boolean, int, int, boolean, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.
ShapeletTransformTSClassifier(int, IQualityMeasure, int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier: Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.
shotgunDistance - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: The Shotgun Distance used by the ShotgunEnsembleClassifier.nearestNeighborClassifier.
ShotgunDistance - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of Shotgun Distance measure as published in "Towards Time Series Classfication without Human Preprocessing" by Patrick SchÃ¤fer (2014).
ShotgunDistance(int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.distances.ShotgunDistance: Constructor for the Shotgun Distance.
ShotgunEnsembleClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.neighbors: Implementation of Shotgun Ensemble Classifier as published in "Towards Time Series Classfication without Human Preprocessing" by Patrick SchÃ¤fer (2014).
ShotgunEnsembleClassifier(int, int, boolean, double) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Creates a Shotgun Ensemble classifier.
ShotgunEnsembleLearnerAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.neighbors: Implementation of Shotgun Ensemble Algorihm as published in "Towards Time Series Classfication without Human Preprocessing" by Patrick SchÃ¤fer (2014).
ShotgunEnsembleLearnerAlgorithm(ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig, ShotgunEnsembleClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm
ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.neighbors
shuffleAccordingToAlternatingClassScheme(List<Integer>, int[], Random) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Shuffles the data in a class alternating scheme.
shuffleTimeSeriesDataset(TimeSeriesDataset, int) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Shuffles the given TimeSeriesDataset object using the given seed.
sigmoid(double) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil: Function to calculate the sigmoid for the given value z.
SimpleDataset<L> - Class in ai.libs.jaicore.ml.core.dataset.standard
SimpleDataset(InstanceSchema<L>) - Constructor for class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
SimpleInstance<L> - Class in ai.libs.jaicore.ml.core.dataset.standard
SimpleInstance(List<IAttributeValue<?>>, L) - Constructor for class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance
SimpleInstanceImpl - Class in ai.libs.jaicore.ml.core
SimpleInstanceImpl() - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl
SimpleInstanceImpl(double[]) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl
SimpleInstanceImpl(int) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl
SimpleInstanceImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl
SimpleInstanceImpl(String) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl
SimpleInstanceImpl(Collection<Double>) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl
SimpleInstancesImpl - Class in ai.libs.jaicore.ml.core
SimpleInstancesImpl() - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl
SimpleInstancesImpl(int) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl
SimpleInstancesImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl
SimpleInstancesImpl(File) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl
SimpleInstancesImpl(String) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl
SimpleLabeledInstanceImpl - Class in ai.libs.jaicore.ml.core
SimpleLabeledInstanceImpl() - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
SimpleLabeledInstanceImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
SimpleLabeledInstanceImpl(String) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
SimpleLabeledInstancesImpl - Class in ai.libs.jaicore.ml.core
SimpleLabeledInstancesImpl() - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
SimpleLabeledInstancesImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
SimpleLabeledInstancesImpl(File) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
SimpleLabeledInstancesImpl(String) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
SimpleMLCSplitBasedClassifierEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation
SimpleMLCSplitBasedClassifierEvaluator(IMeasure<double[], Double>) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.SimpleMLCSplitBasedClassifierEvaluator
SimpleRandomSampling<I,D extends IOrderedDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory
SimpleRandomSampling(Random, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SimpleRandomSampling
SimpleRandomSamplingFactory<I,D extends IOrderedDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
SimpleRandomSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SimpleRandomSamplingFactory
SimpleSLCSplitBasedClassifierEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation: Basic implementation of the AbstractSplitBasedClassifierEvaluator.
SimpleSLCSplitBasedClassifierEvaluator(IMeasure<Double, Double>) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.SimpleSLCSplitBasedClassifierEvaluator
simplifiedTimeSeriesDatasetToWekaInstances(TimeSeriesDataset) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Converts a given simplified TimeSeriesDataset object to a Weka Instances object.
simplifiedTimeSeriesDatasetToWekaInstances(TimeSeriesDataset, List<String>) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Converts a given simplified TimeSeriesDataset object to a Weka Instances object.
SimplifiedTimeSeriesLoader - Class in ai.libs.jaicore.ml.tsc.util: Time series loader class which provides functionality to read datasets from files storing into simplified, more efficient time series datasets.
simplifiedTSInstanceToWekaInstance(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Maps an univariate simplified time series instance to a Weka instance.
SineTransform - Class in ai.libs.jaicore.ml.tsc.filter.transform: Calculates the sine transform of a time series.
SineTransform() - Constructor for class ai.libs.jaicore.ml.tsc.filter.transform.SineTransform
SINGLE_LABEL_METRICS - Static variable in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter: Available metric for singlelabelclassifiers
SingleRandomSplitClassifierEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka
SingleRandomSplitClassifierEvaluator(Instances) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator
singleSquaredEuclideanDistance(double[], double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil: Computes the single squared Euclidean distance between two vectors.
singleVariance(double, double, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree: Computes the variance for a single set
size() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
size() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
size() - Method in class ai.libs.jaicore.ml.SubInstances
skipWithReaderToDatapoints(BufferedReader) - Static method in class ai.libs.jaicore.ml.core.dataset.ArffUtilities: Skips with a given reader all comment lines and the header lines of an ARFF file until the first datapoint is reached.
SlidingWindowBuilder - Class in ai.libs.jaicore.ml.tsc.filter
SlidingWindowBuilder() - Constructor for class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
slope(double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil: Function calculating the slope of the interval [t1, t2 (inclusive)] of the given vector.
SLOPE - ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature.FeatureType
SOLUTIONLOGDIR - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
sort(String) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.DatasetFileSorter
sortByLengthAsc(List<Shapelet>) - Static method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet: Function sorting a list of shapelets in place by the length (ascending).
sortIndexes(double[], boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Sorts the indices of the given vector based on the the vector's values (argsort).
SparseDyadRankingInstance - Class in ai.libs.jaicore.ml.dyadranking.dataset: A dyad ranking instance implementation that assumes the same instance for all dyads contained in its ordering.
SparseDyadRankingInstance(Vector, List<Vector>) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance: Construct a new sparse dyad ranking instance containing the given instance vector and ordering of alternatives.
specialFitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder: This is an extra fit method because it does not return a double[] array even though it gets a double [] as input as it would be defined in the .
split(Collection<String>, Collection<String>, Collection<String>, Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RPNDSplitter
split(Instances) - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.ISplitter
split(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RandomSplitter
split(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RPNDSplitter
split(Instances, long, double) - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.ArbitrarySplitter
split(Instances, long, double) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IDatasetSplitter
split(Instances, long, double) - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.MulticlassClassStratifiedSplitter
SplitFailedException - Exception in ai.libs.jaicore.ml.weka.dataset.splitter
SplitFailedException(Exception) - Constructor for exception ai.libs.jaicore.ml.weka.dataset.splitter.SplitFailedException
SplitInstruction - Class in ai.libs.jaicore.ml.cache: Instruction to track a split for a ReproducibleInstances object.
SplitInstruction(String, double) - Constructor for class ai.libs.jaicore.ml.cache.SplitInstruction
splitToJsonArray(Collection<Integer>[]) - Static method in class ai.libs.jaicore.ml.WekaUtil
SquaredBackwardDifferenceComplexity - Class in ai.libs.jaicore.ml.tsc.complexity: Complexity metric as described in "A Complexity-Invariant Distance Measure for Time Series". $$ c = sum_{i=1}^n-1 \sqrt{ (T_i - T_{i+1})^2 }$$ where $T_i$ are the values of the time series.
SquaredBackwardDifferenceComplexity() - Constructor for class ai.libs.jaicore.ml.tsc.complexity.SquaredBackwardDifferenceComplexity
standardDeviation(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Calculates the (population) standard deviation of the values of a times series.
statsX - Variable in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler
statsY - Variable in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler
stddev(double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil: Function calculating the standard deviation of the interval [t1, t2 (inclusive)] of the given vector.
STDDEV - ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature.FeatureType
StratifiedFileSampling - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling
StratifiedFileSampling(Random, IStratiFileAssigner, File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.StratifiedFileSampling: Constructor for a Stratified File Sampler.
StratifiedSampling<I,D extends IOrderedDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling: Implementation of Stratified Sampling: Divide dataset into strati and sample from each of these.
StratifiedSampling(IStratiAmountSelector<D>, IStratiAssigner<I, D>, Random, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling: Constructor for Stratified Sampling.
StratifiedSamplingFactory<I,D extends IOrderedDataset> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
StratifiedSamplingFactory(IStratiAmountSelector<D>, IStratiAssigner<I, D>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory
StratifiedSplitSubsetInstruction - Class in ai.libs.jaicore.ml.cache: Computes a two-fold split
StratifiedSplitSubsetInstruction(long, double) - Constructor for class ai.libs.jaicore.ml.cache.StratifiedSplitSubsetInstruction
stratify(int) - Method in class ai.libs.jaicore.ml.SubInstances
stratStep(int) - Method in class ai.libs.jaicore.ml.SubInstances
StretchingComplexity - Class in ai.libs.jaicore.ml.tsc.complexity: Stretching Complexity that calulates the length of a time series when stretched to a straight line. $$ c = sum_{i=1}^n-1 \sqrt{ (t_2 - t_1)^2 + (T_{i+1} - T_i)^2 }$$ where $t_i$ are the timestamps (here $t_i = i$) an $T_i$ are the values of the time series.
StretchingComplexity() - Constructor for class ai.libs.jaicore.ml.tsc.complexity.StretchingComplexity
SubInstances - Class in ai.libs.jaicore.ml
SubInstances(Instances, int[]) - Constructor for class ai.libs.jaicore.ml.SubInstances
subList(int, int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
substituteInterval(Interval[], Interval, int) - Static method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper
sum(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil: Sums the values of the given array.
sum(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil
swap(int, int) - Method in class ai.libs.jaicore.ml.SubInstances
SystematicFileSampling - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles: File-level implementation of Systematic Sampling: Sort datapoints and pick every k-th datapoint for the sample.
SystematicFileSampling(Random, File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.SystematicFileSampling: Simple constructor that uses the default datapoint comparator.
SystematicFileSampling(Random, Comparator<String>, File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.SystematicFileSampling: Constructor for a custom datapoint comparator.
SystematicSampling> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory: Implementation of Systematic Sampling: Sort datapoints and pick every k-th datapoint for the sample.
SystematicSampling(Random, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling: Simple constructor that uses the default datapoint comparator.
SystematicSampling(Random, Comparator, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling: Constructor for a custom datapoint comparator.
SystematicSamplingFactory> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories
SystematicSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory

T

Table<I,S,P> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes: Table.java - This class is used to store probleminstance and their according solutions and performances for that solution.
Table() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Table
targets - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Target values for the instances.
targets - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Target values for the instances.
test - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
TIMEOUT_CANDIDATE - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
TIMEOUT_TOTAL - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
TimeoutableEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka
TimeoutableEvaluator(IObjectEvaluator<Classifier, Double>, int) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.TimeoutableEvaluator: C'tor create a timeoutable evaluator out of any other IObjectEvaluator.
TIMEOUTS_IN_SECONDS - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig
TimeSeriesAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.timeseries: Describes a time series type as an 1-NDArray with a fixed length.
TimeSeriesAttributeType(int) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType
TimeSeriesAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.timeseries: Represents a time series attribute value, as it can be part of a jaicore.ml.core.dataset.IInstance
TimeSeriesAttributeValue(TimeSeriesAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeValue
TimeSeriesAttributeValue(TimeSeriesAttributeType, INDArray) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeValue
TimeSeriesBagOfFeaturesClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Implementation of the Time Series Bag-of-Features (TSBF) classifier as described in Baydogan, Mustafa & Runger, George & Tuv, Eugene. (2013).
TimeSeriesBagOfFeaturesClassifier(int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier: Standard constructor using the default parameters (numBins = 10, numFolds = 10, zProp = 0.1, minIntervalLength = 5) for the TSBF classifier.
TimeSeriesBagOfFeaturesClassifier(int, int, int, double, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier: Constructor specifying parameters (cf.
TimeSeriesBagOfFeaturesClassifier(int, int, int, double, int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier: Constructor specifying parameters (cf.
TimeSeriesBagOfFeaturesLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Algorithm to train a Time Series Bag-of-Features (TSBF) classifier as described in Baydogan, Mustafa & Runger, George & Tuv, Eugene. (2013).
TimeSeriesBagOfFeaturesLearningAlgorithm(TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig, TimeSeriesBagOfFeaturesClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Constructor for a TSBF training algorithm.
TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.trees
TimeSeriesBatchLoader - Class in ai.libs.jaicore.ml.tsc.util: BatchLoader
TimeSeriesBatchLoader(TimeSeriesDataset, int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.util.TimeSeriesBatchLoader
TimeSeriesDataset<L> - Class in ai.libs.jaicore.ml.core.dataset: Time Series Dataset.
TimeSeriesDataset - Class in ai.libs.jaicore.ml.tsc.dataset: Dataset for time series.
TimeSeriesDataset(List<double[][]>) - Constructor for class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Creates a time series dataset without timestamps for testing.
TimeSeriesDataset(List<double[][]>, int[]) - Constructor for class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Creates a time series dataset withot timestamps for training.
TimeSeriesDataset(List<double[][]>, List<double[][]>) - Constructor for class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Creates a time series dataset with timestamps for testing.
TimeSeriesDataset(List<double[][]>, List<double[][]>, int[]) - Constructor for class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset: Creates a time series dataset with timestamps for training.
TimeSeriesDataset(List<INDArray>, List<INDArray>, INDArray, IAttributeType<L>) - Constructor for class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset: Creates a TimeSeries dataset.
timeSeriesDatasetToWekaInstances(TimeSeriesDataset<L>) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Converts a given TimeSeriesDataset object to a Weka Instances object.
TimeSeriesFeature - Class in ai.libs.jaicore.ml.tsc.features: Class calculating features (e. g. mean, stddev or slope) on given subsequences of time series.
TimeSeriesFeature() - Constructor for class ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature
TimeSeriesFeature.FeatureType - Enum in ai.libs.jaicore.ml.tsc.features: Feature types used within the time series tree.
TimeSeriesForestClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Time series forest classifier as described in Deng, Houtao et al.
TimeSeriesForestClassifier() - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier: Constructing an untrained ensemble of time series trees.
TimeSeriesForestClassifier(TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier: Constructing an untrained ensemble of time series trees.
TimeSeriesForestLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Algorithm to train a time series forest classifier as described in Deng, Houtao et al.
TimeSeriesForestLearningAlgorithm(TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig, TimeSeriesForestClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm: Constructor for a time series forest training algorithm.
TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.trees
TimeSeriesInstance<L> - Class in ai.libs.jaicore.ml.core.dataset: TimeSeriesInstance
TimeSeriesInstance(IAttributeValue<?>[], L) - Constructor for class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance: Constructor.
TimeSeriesInstance(List<IAttributeValue<?>>, L) - Constructor for class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance
TimeSeriesLengthException - Exception in ai.libs.jaicore.ml.tsc.exceptions: Exception class encapsultes faulty behaviour with length of time series.
TimeSeriesLengthException(String) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.TimeSeriesLengthException
TimeSeriesLengthException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.TimeSeriesLengthException
TimeSeriesLoadingException - Exception in ai.libs.jaicore.ml.tsc.exceptions: Exception thrown when a time series dataset could not be extracted from an external data source (e. g. a file).
TimeSeriesLoadingException(String) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.TimeSeriesLoadingException: Standard constructor.
TimeSeriesLoadingException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.TimeSeriesLoadingException: Constructor using a nested Throwable exception.
TimeSeriesTreeClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Time series tree as described in Deng, Houtao et al.
TimeSeriesTreeClassifier(TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier: Constructs an empty time series tree.
TimeSeriesTreeLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.trees: Algorithm to build a time series tree as described in Deng, Houtao et al.
TimeSeriesTreeLearningAlgorithm(TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig, TimeSeriesTreeClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Constructor.
TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.trees
TimeSeriesUtil - Class in ai.libs.jaicore.ml.tsc.util: Utility class for time series operations.
timestamps - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Timestamp matrix containing the timestamps of the instances.
TimeWarpEditDistance - Class in ai.libs.jaicore.ml.tsc.distances: Time Warp Edit Distance as published in "Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching" by Pierre-Francois Marteau (2009).
TimeWarpEditDistance(double, double) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TimeWarpEditDistance: Constructor.
TimeWarpEditDistance(double, double, IScalarDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TimeWarpEditDistance: Constructor.
TMPDIR - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
toArray() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
toArray() - Method in class ai.libs.jaicore.ml.core.FeatureSpace
toArray() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
toArray(T[]) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset
toArray(T[]) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue
toJAICoreInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
toJAICoreInstances(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
toJAICoreLabeledInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil
toJAICoreLabeledInstances(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil
toJson() - Method in class ai.libs.jaicore.ml.core.SimpleInstanceImpl
toJson() - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl
toJson() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
toJson() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl
toJson() - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
toJson() - Method in interface ai.libs.jaicore.ml.interfaces.Instance
toJson() - Method in interface ai.libs.jaicore.ml.interfaces.Instances
toMatrix() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.ADyadRankingInstance
toMatrix() - Method in interface ai.libs.jaicore.ml.dyadranking.dataset.IDyadRankingInstance: Converts a dyad ranking to a INDArray matrix where each row corresponds to a dyad.
toND4j() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset: Converts this data set to a list of ND4j INDArrays.
TopKOfPredicted - Class in ai.libs.jaicore.ml.dyadranking.loss: Calculates if the top-k dyads of the predicted ranking match the top-k dyads of the actual ranking.
TopKOfPredicted(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.TopKOfPredicted: Specifies the amount of top rankings to consider.
toString() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
toString() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
toString() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
toString() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
toString() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain
toString() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType
toString() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType
toString() - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema
toString() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
toString() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset
toString() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance
toString() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
toString() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain
toString() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl
toString() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance
toString() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance
toString() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad
toString() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment
toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest
toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration
toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve
toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration
toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet
toString() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer
toString() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper
toString() - Method in class ai.libs.jaicore.ml.SubInstances: Returns the dataset as a string in ARFF format.
toString() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet
toString(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Enables printing of time series.
toStringWithOffset() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
toStringWithOffset() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
toStringWithOffset(String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode
toStringWithOffset(String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf
toStringWithOffset(String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD
toStringWithOffset(String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf
toVector() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad: Converts a dyad to a INDArray row vector consisting of a concatenation of the instance and alternative features.
train - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator
train(int[], double[], int, double[][], String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient
train(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker
train(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
train(DyadRankingDataset, int, double) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
train(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier: Trains the simplified time series classifier model using the given TimeSeriesDataset.
train(D) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IBatchLearner: Trains this IBatchLearner using the given AILabeledAttributeArrayDataset.
train(D) - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier: {@inheritDoc ABatchLearner#train(jaicore.ml.core.dataset.IDataset)}
train(List<INDArray>) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
train(List<INDArray>, int, double) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
trained - Variable in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier: Variable indicating whether the classifier has been trained.
TRAINING - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
TrainingException - Exception in ai.libs.jaicore.ml.core.exception: The TrainingException indicates that an error occurred during a training process.
TrainingException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.TrainingException: Creates a new TrainingException with the given parameters.
TrainingException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.TrainingException: Creates a new TrainingException with the given parameters.
trainNet(int[], double[], int, double[][]) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
transform(double[]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter: This function transforms only a single instance.
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.CosineTransform
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.HilbertTransform
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.SineTransform
transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
transform(double[][]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter
transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
transform(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms the entire dataset according to the mean and standard deviation of the data the scaler has been fit to.
transform(Dyad) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.BiliniearFeatureTransform
transform(Dyad) - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.IDyadFeatureTransform: Transform the instance of the given dyad (models the skill).
transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter
transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT
transform(TimeSeriesDataset) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter: represents a function working on a dataset by transforming the dataset itself.
transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX
transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA
transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder
transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter
transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer
transformAlternatives(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the alternatives of each dyad according to the mean and standard deviation of the data the scaler has been fit to.
transformAlternatives(DyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the alternatives of each dyad in a DyadRankingDataset according to the mean and standard deviation of the data the scaler has been fit to.
transformAlternatives(DyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler
transformAlternatives(IDyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the alternatives of each dyad in an IDyadRankingInstance according to the mean and standard deviation of the data the scaler has been fit to.
transformAlternatives(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the alternatives of each dyad according to the mean and standard deviation of the data the scaler has been fit to.
transformAlternatives(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler
transformAlternatives(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadStandardScaler
transformAlternatives(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler
transformAttribute(IAttributeValue<?>) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.transformer.ISingleAttributeTransformer
transformAttribute(IAttributeValue<?>) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MinHashingTransformer
transformAttribute(IAttributeValue<?>) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MultiValueBinaryzationTransformer
transformAttribute(IAttributeValue<?>) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.transformer.OneHotEncodingTransformer
TransformDistance - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Transform Distance (TD) measure as published in "Non-isometric transforms in time series classification using DTW" by Tomasz Gorecki and Maciej Luczak (2014).
TransformDistance(double, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TransformDistance: Constructor that uses the same distance measures for the function and transform values that uses the CosineTransform as transformation.
TransformDistance(double, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TransformDistance: Constructor with individual distance measures for the function and transform values that uses the CosineTransform as transformation.
TransformDistance(double, ATransformFilter, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TransformDistance: Constructor that uses the same distance measures for the function and transform values.
TransformDistance(double, ATransformFilter, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TransformDistance: Constructor with individual distance measures for the function and transform values.
transformInstaceVector(Vector, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms an instance feature vector.
transformInstaceVector(Vector, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler
transformInstaceVector(Vector, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadStandardScaler
transformInstaceVector(Vector, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler
transformInstances(double[][], Pair<List<Integer>, List<Integer>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Method transforming the given dataset using the interval pairs specified in T1T2 by calculating each TimeSeriesFeature.FeatureType for every instance and interval pair.
transformInstances(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the instances of each dyad according to the mean and standard of the data the scaler has been fit to.
transformInstances(DyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the instances of each dyad in a DyadRankingDataset according to the mean and standard deviation of the data the scaler has been fit to.
transformInstances(DyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler
transformInstances(DyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the instances of each dyad in a DyadRankingInstance according to the mean and standard deviation of the data the scaler has been fit to.
transformInstances(SparseDyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the instances of each dyad in a SparseDyadRankingInstance according to the mean and standard deviation of the data the scaler has been fit to.
transformInstances(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler: Transforms only the instances of each dyad according to the mean and standard deviation of the data the scaler has been fit to.
transformInstances(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler
transformInstances(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadStandardScaler
transformInstances(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler
transformWEKAAttributeToAttributeType(Attribute) - Static method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstancesUtil
tree - Variable in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree: Internal tree object providing access to leaf node information.
tree(double[][], int[], double, TreeNode<TimeSeriesTreeClassifier.TimeSeriesTreeNodeDecisionFunction>, int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Tree generation (cf.
TSClassifier<L,V,D extends TimeSeriesDataset<L>> - Class in ai.libs.jaicore.ml.tsc.classifier: Time series classifier which can be trained and used as a predictor.
TSClassifier(ATSCAlgorithm<L, V, D, ? extends TSClassifier<L, V, D>>) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.TSClassifier: Constructor for a time series classifier.
tsInstanceToWekaInstance(TimeSeriesInstance<?>) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Maps a time series instance to a Weka instance.
TSLearningProblem - Class in ai.libs.jaicore.ml.tsc
TSLearningProblem(IQualityMeasure, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.TSLearningProblem

U

UCBPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning: A prototypical active dyad ranker based on the UCB decision rule.
UCBPoolBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.UCBPoolBasedActiveDyadRanker
UncheckedJaicoreMLException - Exception in ai.libs.jaicore.ml.core.exception: The UncheckedJaicoreMLException serves as a base class for all unchecked Exceptions defined as part of jaicore-ml.
UncheckedJaicoreMLException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.UncheckedJaicoreMLException: Creates a new UncheckedJaicoreMLException with the given parameters.
UncheckedJaicoreMLException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.UncheckedJaicoreMLException: Creates a new UncheckedJaicoreMLException with the given parameters.
unscaleParameters(INDArray, DyadMinMaxScaler, int) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil
untransform(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler
untransformAlternative(Dyad) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Undoes the transformation on the alternative of a single dyad.
untransformAlternative(Dyad, int) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Undoes the transformation on the alternative of a single dyad.
untransformAlternatives(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Undoes the transformation of the alternatives of each dyad.
untransformAlternatives(DyadRankingDataset, int) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Undoes the transformation of the alternatives of each dyad.
untransformInstance(Dyad) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Undoes the transformation of the instance of a single dyad.
untransformInstance(Dyad, int) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Undoes the transformation of the instance of a single dyad.
untransformInstances(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Undoes the transformation of the instances of each dyad.
untransformInstances(DyadRankingDataset, int) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler: Undoes the transformation of the instances of each dyad.
update(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker: Updates this PLNetDyadRanker based on the given IInstance, which needs to be an IDyadRankingInstance.
update(I) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IOnlineLearner: Updates this IOnlineLearner based on the given IInstance.
update(Set<IDyadRankingInstance>) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker
update(Set) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IOnlineLearner: Updates this IOnlineLearner based on the given Set of IInstances.
updateBestScore(Double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator
updateRanker(Set<IDyadRankingInstance>) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker
USE_BIAS_CORRECTION - Static variable in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm: Indicator whether Bessel's correction should be used when normalizing arrays.
USE_BIAS_CORRECTION - Static variable in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm: Indicator whether Bessel's correction should in feature generation.
USE_BIAS_CORRECTION - Static variable in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm: Indicator that the bias (Bessel's) correction should be used for the calculation of the standard deviation.
useBiasCorrection - Variable in class ai.libs.jaicore.ml.tsc.shapelets.search.AMinimumDistanceSearchStrategy: Indicator whether Bessel's correction should be used within any distance calculation;
useFeatureCaching() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig: Indicator whether feature caching should be used.
useFeatureCaching() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig: Indicator whether feature caching should be used.
useFilterOnSingleInstance(Instance, Filter) - Static method in class ai.libs.jaicore.ml.WekaUtil
useHIVECOTEEnsemble() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig: Indicator whether the HIVE COTE ensemble should be used.

V

VALIDATION - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf
value(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction
valueAt(double[]) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.BilinFunction
valueOf(String) - Static method in enum ai.libs.jaicore.ml.cache.DataProvider: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.classification.multiclass.reduction.EMCNodeType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper.DiscretizationStrategy: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMultiClassPerformanceMeasure: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper.ProblemType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier.VoteType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature.FeatureType: Returns the enum constant of this type with the specified name.
values - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Value matrix containing the time series instances.
values - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Value matrix containing the time series instances.
values() - Static method in enum ai.libs.jaicore.ml.cache.DataProvider: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.libs.jaicore.ml.classification.multiclass.reduction.EMCNodeType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper.DiscretizationStrategy: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMultiClassPerformanceMeasure: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper.ProblemType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier.VoteType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature.FeatureType: Returns an array containing the constants of this enum type, in the order they are declared.
VAPOR_PRESSURE - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
variance(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Calculates the (population) variance of the values of a times series.
verbose - Variable in class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener: Flag whether standard outputs are forwarded to the logger.
vote(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Performs a vote on the nearest neighbors found.
voteMajority(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Performs a majority vote on the set nearest neighbors found.
voteWeightedProportionalToDistance(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Performs a vote with weights proportional to the distance on the set nearest neighbors found.
voteWeightedStepwise(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier: Performs a vote with stepwise weights 1, 2, .., k on the set nearest neighbors found.

W

WaitForSamplingStepEvent - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory
WaitForSamplingStepEvent(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.WaitForSamplingStepEvent
WEIBULL - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants
WEIGHTED_AREA_UNDER_ROC - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
WEIGHTED_F_MEASURE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
WEIGHTED_FALSE_NEGATIVE_RATE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
WEIGHTED_FALSE_POSITIVE_RATE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
WEIGHTED_PRECISION - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
WEIGHTED_PROPORTIONAL_TO_DISTANCE - ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier.VoteType: Weighted proportional to distance vote with @see NearestNeighborClassifier#voteWeightedProportionalToDistance.
WEIGHTED_RECALL - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
WEIGHTED_STEPWISE - ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier.VoteType: Weighted stepwise vote with @see NearestNeighborClassifier#voteWeightedStepwise.
WEIGHTED_TRUE_NEGATIVE_RATE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
WEIGHTED_TRUE_POSITIVE_RATE - ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure
WeightedDynamicTimeWarping - Class in ai.libs.jaicore.ml.tsc.distances: Implementation of the Dynamic Time Warping (DTW) measure as published in "Weighted dynamic time warping for time series classification" by Young-Seon Jeong, Myong K.
WeightedDynamicTimeWarping(double, double, IScalarDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.WeightedDynamicTimeWarping: Constructor.
WekaCompatibleInstancesImpl - Class in ai.libs.jaicore.ml.core
WekaCompatibleInstancesImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
WekaCompatibleInstancesImpl(File) - Constructor for class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
WekaCompatibleInstancesImpl(String) - Constructor for class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
WekaCompatibleInstancesImpl(List<String>) - Constructor for class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl
WekaInstance<L> - Class in ai.libs.jaicore.ml.core.dataset.weka
WekaInstance(Instance) - Constructor for class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance
WekaInstances<L> - Class in ai.libs.jaicore.ml.core.dataset.weka
WekaInstances(Instances) - Constructor for class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances
WekaInstancesFeatureUnion - Class in ai.libs.jaicore.ml.core
WekaInstancesFeatureUnion() - Constructor for class ai.libs.jaicore.ml.core.WekaInstancesFeatureUnion
wekaInstancesToDataset(Instances) - Static method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstancesUtil
wekaInstancesToINDArray(Instances, boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil: Converts Weka instances to an INDArray matrix.
WekaInstancesUtil - Class in ai.libs.jaicore.ml.core.dataset.weka
WekaUtil - Class in ai.libs.jaicore.ml.tsc.util: WekaUtil
WekaUtil - Class in ai.libs.jaicore.ml
windows - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier: Holds pairs of (number of correct predictions, window length) obtained in training phase.
windowSize() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig: The size of the sliding window that is used over each instance and splits it into multiple smaller instances.
windowSizeMax() - Method in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
windowSizeMin() - Method in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
withBoundaries - Variable in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter: Flag that states wheter the filter should add a padding to the derivate assure that is has the same length as the origin time series or not.
withData(Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the dataset which is split into train and test data.
withData(Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory
withData(Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory
withDatasetSplitter(IDatasetSplitter) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the evaluator to use the given dataset splitter.
withDatasetSplitter(IDatasetSplitter) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory
withDatasetSplitter(IDatasetSplitter) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory
withNumMCIterations(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the number of monte carlo cross-validation iterations.
withNumMCIterations(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory
withNumMCIterations(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory
withSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the evaluator to use the given random seed.
withSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory
withSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory
withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<Double>) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the evaluator to use the given classifier evaluator.
withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<Double>) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory
withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<Double>) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory
withTimeoutForSolutionEvaluation(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures a timeout for evaluating a solution.
withTimeoutForSolutionEvaluation(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory
withTimeoutForSolutionEvaluation(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory
withTrainFoldSize(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the portion of the training data relative to the entire dataset size.
withTrainFoldSize(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory
withTrainFoldSize(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory
wordLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig: The word length determines the number of used DFT-coefficients.

Y

Y - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants

Z

ZeroOneLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel
ZeroOneLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.ZeroOneLoss
ZeroShotUtil - Class in ai.libs.jaicore.ml.dyadranking.zeroshot.util: A collection of utility methods used to map the results of a input optimization of PLNetInputOptimizer back to Weka options for the respective classifiers.
zNormalization() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig: Indicator whether the z transformation should be used for the instances at training and prediction time.
zNormalize(double[], boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil: Z-normalizes a given dataVector.
zProportion() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig: Proportion of the total time series length to be used for the subseries generation.
zTransform(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil
ZTransformer - Class in ai.libs.jaicore.ml.tsc.filter
ZTransformer() - Constructor for class ai.libs.jaicore.ml.tsc.filter.ZTransformer

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