Index (jaicore-ml 0.2.5 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.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
AAreaUnderCurvePerformanceMeasure - Class in ai.libs.jaicore.ml.classification.loss.dataset
AAreaUnderCurvePerformanceMeasure() - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.AAreaUnderCurvePerformanceMeasure
AAreaUnderCurvePerformanceMeasure(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.AAreaUnderCurvePerformanceMeasure
AAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
AAttribute(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.AAttribute
AbsoluteError - Class in ai.libs.jaicore.ml.regression.loss.dataset
AbsoluteError - Class in ai.libs.jaicore.ml.regression.loss.instance
AbsoluteError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.AbsoluteError
AbsoluteError() - Constructor for class ai.libs.jaicore.ml.regression.loss.instance.AbsoluteError
AbstractDyadScaler - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.util.AbstractDyadScaler
acceptanceThresholds - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
ACollectionOfObjectsAttribute<O> - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
ACollectionOfObjectsAttribute(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.ACollectionOfObjectsAttribute
ActiveDyadRanker - Class in ai.libs.jaicore.ml.ranking.dyad.learner.activelearning: Abstract description of a pool-based active learning strategy for dyad ranking.
ActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker
activelyTrain(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker: Actively trains the ranker for a certain number of queries.
activelyTrain(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.PrototypicalPoolBasedActiveDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.RandomPoolBasedActiveDyadRanker
activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.UCBPoolBasedActiveDyadRanker
ADataset - Class in ai.libs.jaicore.ml.core.dataset
ADataset(ILabeledInstanceSchema) - Constructor for class ai.libs.jaicore.ml.core.dataset.ADataset
add(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Add a time series variable without timestamps to the dataset.
add(double[][], double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Add a time series variable with timestamps to the dataset.
add(int) - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
add(int, int) - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
add(int, BackPointerPath<N, A, V>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.RandomlyRankedNodeQueue
add(int, E) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
add(BackPointerPath<N, A, V>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.RandomlyRankedNodeQueue: Adds an element at a random position within the
add(E) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
add(String, INDArray, INDArray) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset: Add a time series variable to the dataset.
add(IEvaluatedPath<N, ?, V>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
addAll(int, Collection<? extends E>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
addAll(Collection<? extends E>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
addAll(Collection<? extends IEvaluatedPath<N, ?, V>>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
addAttribute(int, IAttribute) - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
addAttribute(IAttribute) - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
addIndices(Collection<Integer>) - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
addIndices(Collection<Integer>, int) - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
addInstance(ProblemInstance) - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Group
addInstruction(IReconstructionInstruction) - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
addInstruction(IReconstructionInstruction) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.ReproducibleSplit
addInstruction(IReconstructionInstruction) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
additionalParameters - Variable in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
addLocalFiles(File...) - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
addLocalFiles(List<File>) - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
addOpenMLDatasets(int...) - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
addPair(E, A) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
addSplit(List<D>) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.DatasetSplitSet
addValue(int, double) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeries: Adds a timestep-value pair to the this time series.
ADyadRankedNodeQueue<N,V extends java.lang.Comparable<V>> - Class in ai.libs.jaicore.ml.ranking.dyad.learner.search: A queue whose elements are nodes, sorted by a dyad ranker.
ADyadRankedNodeQueue(IVector) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue: Constructs a new DyadRankedNodeQueue that ranks the nodes in the queue according to the given context characterization.
ADyadRankedNodeQueue(IVector, IDyadRanker, AbstractDyadScaler) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.search: A configuration for a dyad ranked node queue.
ADyadRankedNodeQueueConfig() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueueConfig: Construct a new dyad ranking node queue configuration.
ADyadRankingInstance - Class in ai.libs.jaicore.ml.ranking.dyad.dataset
ADyadRankingInstance() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
AFileSamplingAlgorithm - Class in ai.libs.jaicore.ml.core.filter.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.filter.sampling.infiles.AFileSamplingAlgorithm
AFilter - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter
AFilter() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.AFilter
AGeneralDatasetBackedDataset<E extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance> - Class in ai.libs.jaicore.ml.ranking.dyad.dataset
AGeneralDatasetBackedDataset() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
AGeneralDatasetBackedDataset(Dataset) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
AGenericObjectAttribute<O> - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
AGenericObjectAttribute(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.AGenericObjectAttribute
AggregatingPredictionPerformanceMeasure<E,A> - Class in ai.libs.jaicore.ml.core.evaluation
AggregatingPredictionPerformanceMeasure(IRealsAggregateFunction, IDeterministicPredictionPerformanceMeasure<E, A>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.AggregatingPredictionPerformanceMeasure
ai.libs.jaicore.ml.classification.loss - package ai.libs.jaicore.ml.classification.loss
ai.libs.jaicore.ml.classification.loss.dataset - package ai.libs.jaicore.ml.classification.loss.dataset
ai.libs.jaicore.ml.classification.loss.instance - package ai.libs.jaicore.ml.classification.loss.instance
ai.libs.jaicore.ml.classification.multilabel - package ai.libs.jaicore.ml.classification.multilabel
ai.libs.jaicore.ml.classification.multilabel.evaluation.loss - package ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive - package ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive
ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic - package ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic
ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa - package ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa
ai.libs.jaicore.ml.classification.singlelabel - package ai.libs.jaicore.ml.classification.singlelabel
ai.libs.jaicore.ml.classification.singlelabel.learner - package ai.libs.jaicore.ml.classification.singlelabel.learner
ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset
ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute
ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception
ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter
ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner
ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors
ai.libs.jaicore.ml.classification.singlelabel.timeseries.model - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.model
ai.libs.jaicore.ml.classification.singlelabel.timeseries.quality - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.quality
ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets
ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.search - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.search: This package contains search strategies applied to Shapelet objects.
ai.libs.jaicore.ml.classification.singlelabel.timeseries.util - package ai.libs.jaicore.ml.classification.singlelabel.timeseries.util: This package contains utility functions for time series classification.
ai.libs.jaicore.ml.clustering.learner - package ai.libs.jaicore.ml.clustering.learner
ai.libs.jaicore.ml.core - package ai.libs.jaicore.ml.core
ai.libs.jaicore.ml.core.dataset - package ai.libs.jaicore.ml.core.dataset
ai.libs.jaicore.ml.core.dataset.clusterable - package ai.libs.jaicore.ml.core.dataset.clusterable
ai.libs.jaicore.ml.core.dataset.schema - package ai.libs.jaicore.ml.core.dataset.schema
ai.libs.jaicore.ml.core.dataset.schema.attribute - package ai.libs.jaicore.ml.core.dataset.schema.attribute: This package contains various types of attributes and their respective values that can be used within a dataset.
ai.libs.jaicore.ml.core.dataset.serialization - package ai.libs.jaicore.ml.core.dataset.serialization
ai.libs.jaicore.ml.core.dataset.serialization.arff - package ai.libs.jaicore.ml.core.dataset.serialization.arff
ai.libs.jaicore.ml.core.dataset.splitter - package ai.libs.jaicore.ml.core.dataset.splitter
ai.libs.jaicore.ml.core.dataset.util - package ai.libs.jaicore.ml.core.dataset.util
ai.libs.jaicore.ml.core.evaluation - package ai.libs.jaicore.ml.core.evaluation
ai.libs.jaicore.ml.core.evaluation.evaluator - package ai.libs.jaicore.ml.core.evaluation.evaluator
ai.libs.jaicore.ml.core.evaluation.evaluator.events - package ai.libs.jaicore.ml.core.evaluation.evaluator.events
ai.libs.jaicore.ml.core.evaluation.evaluator.factory - package ai.libs.jaicore.ml.core.evaluation.evaluator.factory
ai.libs.jaicore.ml.core.evaluation.experiment - package ai.libs.jaicore.ml.core.evaluation.experiment
ai.libs.jaicore.ml.core.evaluation.splitsetgenerator - package ai.libs.jaicore.ml.core.evaluation.splitsetgenerator
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.filter - package ai.libs.jaicore.ml.core.filter
ai.libs.jaicore.ml.core.filter.sampling - package ai.libs.jaicore.ml.core.filter.sampling: This package contains algorithms for creating samples of a dataset.
ai.libs.jaicore.ml.core.filter.sampling.infiles - package ai.libs.jaicore.ml.core.filter.sampling.infiles
ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling - package ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling
ai.libs.jaicore.ml.core.filter.sampling.inmemory - package ai.libs.jaicore.ml.core.filter.sampling.inmemory
ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol - package ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol
ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories - package ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.interfaces - package ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.interfaces
ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling - package ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling
ai.libs.jaicore.ml.core.learner - package ai.libs.jaicore.ml.core.learner
ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation - package ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation
ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client - package ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client
ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl - package ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl
ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc - package ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc
ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet - package ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet
ai.libs.jaicore.ml.hpo.ggp - package ai.libs.jaicore.ml.hpo.ggp
ai.libs.jaicore.ml.hpo.multifidelity - package ai.libs.jaicore.ml.hpo.multifidelity
ai.libs.jaicore.ml.hpo.multifidelity.hyperband - package ai.libs.jaicore.ml.hpo.multifidelity.hyperband
ai.libs.jaicore.ml.pdm.dataset - package ai.libs.jaicore.ml.pdm.dataset
ai.libs.jaicore.ml.ranking - package ai.libs.jaicore.ml.ranking
ai.libs.jaicore.ml.ranking.dyad - package ai.libs.jaicore.ml.ranking.dyad: Dyad Ranking package.
ai.libs.jaicore.ml.ranking.dyad.dataset - package ai.libs.jaicore.ml.ranking.dyad.dataset
ai.libs.jaicore.ml.ranking.dyad.learner - package ai.libs.jaicore.ml.ranking.dyad.learner
ai.libs.jaicore.ml.ranking.dyad.learner.activelearning - package ai.libs.jaicore.ml.ranking.dyad.learner.activelearning
ai.libs.jaicore.ml.ranking.dyad.learner.algorithm - package ai.libs.jaicore.ml.ranking.dyad.learner.algorithm
ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform - package ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform
ai.libs.jaicore.ml.ranking.dyad.learner.optimizing - package ai.libs.jaicore.ml.ranking.dyad.learner.optimizing
ai.libs.jaicore.ml.ranking.dyad.learner.search - package ai.libs.jaicore.ml.ranking.dyad.learner.search
ai.libs.jaicore.ml.ranking.dyad.learner.util - package ai.libs.jaicore.ml.ranking.dyad.learner.util
ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization - package ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization
ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.util - package ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.util
ai.libs.jaicore.ml.ranking.filter - package ai.libs.jaicore.ml.ranking.filter
ai.libs.jaicore.ml.ranking.label.learner - package ai.libs.jaicore.ml.ranking.label.learner
ai.libs.jaicore.ml.ranking.label.learner.clusterbased - package ai.libs.jaicore.ml.ranking.label.learner.clusterbased
ai.libs.jaicore.ml.ranking.label.learner.clusterbased.candidateprovider - package ai.libs.jaicore.ml.ranking.label.learner.clusterbased.candidateprovider
ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes - package ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes
ai.libs.jaicore.ml.ranking.label.learner.clusterbased.datamanager - package ai.libs.jaicore.ml.ranking.label.learner.clusterbased.datamanager
ai.libs.jaicore.ml.ranking.loss - package ai.libs.jaicore.ml.ranking.loss
ai.libs.jaicore.ml.regression.learner - package ai.libs.jaicore.ml.regression.learner
ai.libs.jaicore.ml.regression.loss - package ai.libs.jaicore.ml.regression.loss
ai.libs.jaicore.ml.regression.loss.dataset - package ai.libs.jaicore.ml.regression.loss.dataset
ai.libs.jaicore.ml.regression.loss.instance - package ai.libs.jaicore.ml.regression.loss.instance
ai.libs.jaicore.ml.regression.singlelabel - package ai.libs.jaicore.ml.regression.singlelabel
ai.libs.jaicore.ml.scikitwrapper - package ai.libs.jaicore.ml.scikitwrapper
ai.libs.jaicore.ml.scikitwrapper.simple - package ai.libs.jaicore.ml.scikitwrapper.simple
AInstance - Class in ai.libs.jaicore.ml.core.dataset
AInstance() - Constructor for class ai.libs.jaicore.ml.core.dataset.AInstance
AInstance(Object) - Constructor for class ai.libs.jaicore.ml.core.dataset.AInstance
AInstanceMeasure<E,A> - Class in ai.libs.jaicore.ml.classification.loss.instance: Abstract class for instance-based measures.
AInstanceMeasure() - Constructor for class ai.libs.jaicore.ml.classification.loss.instance.AInstanceMeasure
ALabeledDataset - Class in ai.libs.jaicore.ml.core.dataset
ALabeledDataset(ILabeledInstanceSchema) - Constructor for class ai.libs.jaicore.ml.core.dataset.ALabeledDataset
algorithm - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATimeSeriesClassificationModel: The algorithm object used for the training of the classifier.
ALGORITHMMODES - Static variable in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
ALGORITHMS - Static variable in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
ALPHA - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
alphabet() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm.IBossAlgorithmConfig: The alphabet consists of doubles representing letters and defines each word.
alphabetSize() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm.IBossAlgorithmConfig: The alphabet size determines the number of Bins for the SFA Histograms.
AMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.search: Abstract class for minimum distance search strategies.
AMinimumDistanceSearchStrategy(boolean) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.search.AMinimumDistanceSearchStrategy: Constructor.
AMonteCarloCrossValidationBasedEvaluatorFactory<F extends AMonteCarloCrossValidationBasedEvaluatorFactory<F>> - Class in ai.libs.jaicore.ml.core.evaluation.evaluator.factory: An abstract factory for configuring Monte Carlo cross-validation based evaluators.
AMonteCarloCrossValidationBasedEvaluatorFactory() - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Standard c'tor.
AMultiLabelClassificationMeasure - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
AMultiLabelClassificationMeasure() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
AMultiLabelClassificationMeasure(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
andersonDarlingTest(double[]) - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
APilotEstimateSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol
APilotEstimateSampling(D, ISamplingAlgorithmFactory<D, ?>, int, IClassifier) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.APilotEstimateSampling
APilotEstimateSampling(D, IClassifier) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.APilotEstimateSampling
apply(IVector) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood: Algorithm (18) of [1].
apply(IVector) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative
APredictionPerformanceMeasure<E,P> - Class in ai.libs.jaicore.ml.classification.loss.dataset
APredictionPerformanceMeasure() - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure
ARandomlyInitializingDyadRanker - Class in ai.libs.jaicore.ml.ranking.dyad.learner.activelearning
ARandomlyInitializingDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, int) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
ARankingAttribute<O> - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
ARankingAttribute(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.ARankingAttribute
ARankingPredictionPerformanceMeasure - Class in ai.libs.jaicore.ml.ranking.loss
ARankingPredictionPerformanceMeasure() - Constructor for class ai.libs.jaicore.ml.ranking.loss.ARankingPredictionPerformanceMeasure
AreaUnderPrecisionRecallCurve - Class in ai.libs.jaicore.ml.classification.loss.dataset
AreaUnderPrecisionRecallCurve(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.AreaUnderPrecisionRecallCurve
AreaUnderROCCurve - Class in ai.libs.jaicore.ml.classification.loss.dataset
AreaUnderROCCurve(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.AreaUnderROCCurve
ARegressionMeasure - Class in ai.libs.jaicore.ml.regression.loss.dataset
ARegressionMeasure() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.ARegressionMeasure
ArffDatasetAdapter - Class in ai.libs.jaicore.ml.core.dataset.serialization: Handles dataset files in the arff format {@link https://waikato.github.io/weka-wiki/formats_and_processing/arff/}
ArffDatasetAdapter() - Constructor for class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
ArffDatasetAdapter(boolean) - Constructor for class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
ArffDatasetAdapter(boolean, IDatasetDescriptor) - Constructor for class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
ArffUtilities - Class in ai.libs.jaicore.ml.core.filter.sampling.infiles: Utility class for handling Arff dataset files.
argmax(int[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.MathUtil: Calculates the index of the maximum value in the given array (argmax).
ARRAY_STRING_SPLITTER - Static variable in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
ASampleAlgorithmFactory<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>,A extends ASamplingAlgorithm<D>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
ASampleAlgorithmFactory() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
ASamplingAlgorithm<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory: An abstract class for sampling algorithms providing basic functionality of an algorithm.
ASamplingAlgorithm(D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
ASamplingAlgorithm(D, Class) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
AScikitLearnWrapper - Class in ai.libs.jaicore.ml.scikitwrapper
AScikitLearnWrapper(EScikitLearnProblemType, String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
ASimpleScikitLearnWrapper - Class in ai.libs.jaicore.ml.scikitwrapper.simple
ASimpleScikitLearnWrapper(String, String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
ASimpleScikitLearnWrapper(String, String, String, IPythonConfig) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
ASimplifiedTSClassifier<T> - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner: Simplified batch-learning time series classifier which can be trained and used as a predictor.
ASimplifiedTSClassifier() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier
ASimplifiedTSCLearningAlgorithm<T,C extends ASimplifiedTSClassifier<T>> - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner
ASimplifiedTSCLearningAlgorithm(IOwnerBasedAlgorithmConfig, C, TimeSeriesDataset2) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSCLearningAlgorithm
ASingleLabelClassificationPerformanceMeasure - Class in ai.libs.jaicore.ml.classification.loss.dataset
ASingleLabelClassificationPerformanceMeasure() - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.ASingleLabelClassificationPerformanceMeasure
ASingleLabelClassifier - Class in ai.libs.jaicore.ml.classification.singlelabel.learner
ASingleLabelClassifier() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.learner.ASingleLabelClassifier
ASingleLabelClassifier(Map<String, Object>) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.learner.ASingleLabelClassifier
assertNonEmptyCollection(Collection<?>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
assertOnlyDyadsInCollection(Collection<?>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
assessQuality(List<Double>, int[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.quality.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.classification.singlelabel.timeseries.quality.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.filter.sampling.infiles.stratified.sampling.ClassStratiFileAssigner
assignDatapoint(String) - Method in interface ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.IStratiFileAssigner: Select the suitable stratum for a datapoint and write it into the corresponding temporary file.
ASupervisedLearner,P extends org.api4.java.ai.ml.core.evaluation.IPrediction,B extends org.api4.java.ai.ml.core.evaluation.IPredictionBatch> - Class in ai.libs.jaicore.ml.core.learner
ASupervisedLearner() - Constructor for class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
ASupervisedLearner(Map<String, Object>) - Constructor for class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
ASYMMETRIC_LOSS - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
ASYMMETRIC_LOSS2 - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
AsymmetricLoss - Class in ai.libs.jaicore.ml.regression.loss.dataset
AsymmetricLoss() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.AsymmetricLoss
AsymmetricLoss(double, double) - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.AsymmetricLoss
AsymmetricLoss2 - Class in ai.libs.jaicore.ml.regression.loss.dataset
AsymmetricLoss2() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.AsymmetricLoss2
AsymmetricLoss2(double, double) - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.AsymmetricLoss2
AThresholdBasedMultiLabelClassificationMeasure - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
AThresholdBasedMultiLabelClassificationMeasure() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
AThresholdBasedMultiLabelClassificationMeasure(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
ATimeseriesAttribute<O> - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute
ATimeseriesAttribute(String, int) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.ATimeseriesAttribute
ATimeSeriesClassificationModel<L,D extends TimeSeriesDataset> - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner: Time series classifier which can be trained and used as a predictor.
ATimeSeriesClassificationModel(ATSCAlgorithm<L, D, ? extends ATimeSeriesClassificationModel<L, D>>) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATimeSeriesClassificationModel: Constructor for a time series classifier.
ATSCAlgorithm<Y,D extends TimeSeriesDataset,C extends ATimeSeriesClassificationModel<Y,D>> - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner: Abstract algorithm class which is able to take TimeSeriesDataset objects and builds ATimeSeriesClassificationModel instances specified by the generic parameter .
ATSCAlgorithm() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATSCAlgorithm
attribute - Variable in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttributeValue
ATTRIBUTE - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffItem
AttributeBasedStratifier - Class in ai.libs.jaicore.ml.core.filter.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.
AttributeBasedStratifier() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
AttributeBasedStratifier(List<Integer>) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
AttributeBasedStratifier(List<Integer>, DiscretizationHelper.DiscretizationStrategy, int) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
AttributeBasedStratifier(List<Integer>, Map<Integer, AttributeDiscretizationPolicy>) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
AttributeDiscretizationPolicy - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling
AttributeDiscretizationPolicy(List<Interval>) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
AUnboundedRegressionMeasure - Class in ai.libs.jaicore.ml.regression.loss.dataset
AUnboundedRegressionMeasure() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.AUnboundedRegressionMeasure
AutoMEKAGGPFitnessMeasureLoss - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss: Measure combining exact match, hamming loss, f1macroavgL and rankloss.
AutoMEKAGGPFitnessMeasureLoss() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AutoMEKAGGPFitnessMeasureLoss
AutoMEKAGGPFitnessMeasureLoss(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AutoMEKAGGPFitnessMeasureLoss
AveragedInstanceLoss - Class in ai.libs.jaicore.ml.classification.loss.dataset
AveragedInstanceLoss(IDeterministicInstancePredictionPerformanceMeasure<ISingleLabelClassification, Integer>) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.AveragedInstanceLoss
averageInstanceWiseLoss(List<? extends E>, List<? extends P>, IDeterministicInstancePredictionPerformanceMeasure<P, E>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure
averageInstanceWiseScore(List<? extends E>, List<? extends P>, IDeterministicInstancePredictionPerformanceMeasure<P, E>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure
AveragingPredictionPerformanceMeasure<E,A> - Class in ai.libs.jaicore.ml.core.evaluation
AveragingPredictionPerformanceMeasure(IDeterministicPredictionPerformanceMeasure<E, A>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.AveragingPredictionPerformanceMeasure

B

B - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
backwardDifferenceDerivate(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Calclualtes f'(n) = f(n-1) - f(n)
backwardDifferenceDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Calclualtes f'(n) = f(n-1) - f(n)
bestScore - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: The best score.
BETA - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
BilinFunction - Class in ai.libs.jaicore.ml.ranking.dyad.learner.optimizing: Wraps the NLL optimizing problem into the QNMinimizer optimizer.
BilinFunction(Map<IDyadRankingInstance, Map<IDyad, IVector>>, IDyadRankingDataset, int) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.BilinFunction: Creates a NLL optimizing problem for the kronecker product as the bilinear feature transform.
BiliniearFeatureTransform - Class in ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform: Implementation of the feature transformation method using the Kroenecker Product.
BiliniearFeatureTransform() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform.BiliniearFeatureTransform
BOSSClassifier - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner
BOSSClassifier(int, int, double[], int, boolean) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSClassifier
BOSSClassifier(BOSSLearningAlgorithm.IBossAlgorithmConfig) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSClassifier
BOSSEnsembleClassifier - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner
BOSSEnsembleClassifier(Map<Integer, Integer>, double[], boolean) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSEnsembleClassifier
BOSSEnsembleClassifier(Map<Integer, Integer>, int, double[], boolean) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSEnsembleClassifier
BOSSLearningAlgorithm - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner
BOSSLearningAlgorithm(BOSSLearningAlgorithm.IBossAlgorithmConfig, BOSSClassifier, TimeSeriesDataset2) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm
BOSSLearningAlgorithm.IBossAlgorithmConfig - Interface in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner
build() - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
buildGroup(List<ProblemInstance>) - Method in interface ai.libs.jaicore.ml.ranking.label.learner.clusterbased.IGroupBuilder

C

C - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
CachingMonteCarloCrossValidationSplitSetGenerator<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> - Class in ai.libs.jaicore.ml.core.evaluation.splitsetgenerator
CachingMonteCarloCrossValidationSplitSetGenerator(IRandomDatasetSplitter<D>, int, Random) - Constructor for class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.CachingMonteCarloCrossValidationSplitSetGenerator
calculateAcceptanceThresholdsWithTrainedPilot(D, IClassifier) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.APilotEstimateSampling
calculateAcceptanceThresholdsWithTrainedPilot(D, IClassifier) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.ClassifierWeightedSampling
calculateAcceptanceThresholdsWithTrainedPilot(D, IClassifier) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.OSMAC
calculateAcceptanceThresholdsWithTrainedPilot(ILabeledDataset<?>, IClassifier) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.LocalCaseControlSampling
calculateFeature(TimeSeriesFeature.FeatureType, double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesFeature: Function calculating the feature specified by the feature type fType for a given instance vector of the interval [t1, t2].
calculateNearestNeigbors(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Determine the k nearest neighbors for a test instance.
calculatePrediction(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Calculates predicition on a single test instance.
calculateWindowLengthPredictions(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Calculates predicitions for a test instance using 1NN with Shotgun Distance and different window lengths.
calculateWindowLengthPredictions(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm
call() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborLearningAlgorithm
call() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm
call() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.AFileSamplingAlgorithm
call() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
cancel() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSCLearningAlgorithm
cancel() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.DatasetFileSorter
cancel() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.SystematicFileSampling
CaseControlLikeSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol
CaseControlLikeSampling(D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
CFGConverter - Class in ai.libs.jaicore.ml.hpo.ggp
CFGConverter(Collection<? extends IComponent>, String) - Constructor for class ai.libs.jaicore.ml.hpo.ggp.CFGConverter
characterize(IEvaluatedPath<N, ?, V>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue: Provide a characterization of the given node to be used by the dyad ranker.
checkConsistency(List<? extends E>, List<? extends P>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure
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.
CheckedJaicoreMLException(Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.CheckedJaicoreMLException: Creates a new CheckedJaicoreMLException with the given parameters.
checkRequirements() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
checkRequirementsTestMode() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
checkRequirementsTrainMode() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
checkRequirementsTrainTestMode() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
checkWhetherPredictionIsPossible(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier
ChoquisticRelevanceLoss - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive
ChoquisticRelevanceLoss(double, IMassFunction) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.ChoquisticRelevanceLoss
ChoquisticRelevanceLoss(IMassFunction) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.ChoquisticRelevanceLoss
CLASSIFICATION - ai.libs.jaicore.ml.core.EScikitLearnProblemType
ClassifierWeightedSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol: The idea behind this Sampling method is to weight instances depended on the way a pilot estimator p classified them.
ClassifierWeightedSampling(IClassifier, Random, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.ClassifierWeightedSampling
classMapper - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.util.ClassMapper: Constructor using a list of String value to realize the mapping
ClassStratiFileAssigner - Class in ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling
ClassStratiFileAssigner() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.ClassStratiFileAssigner: Constructor without a given target attribute.
ClassStratiFileAssigner(int) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.ClassStratiFileAssigner: Constructor with a given target attribute.
cleanUp() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.AFileSamplingAlgorithm: Implement custom clean up behaviour.
cleanUp() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.ReservoirSampling
cleanUp() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.StratifiedFileSampling
cleanUp() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.SystematicFileSampling
clear() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
clear() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
cluster() - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
ClusterableDataset - Class in ai.libs.jaicore.ml.core.dataset.clusterable
ClusterableDataset(ILabeledInstanceSchema) - Constructor for class ai.libs.jaicore.ml.core.dataset.clusterable.ClusterableDataset
ClusterableDataset(ILabeledDataset<ILabeledInstance>) - Constructor for class ai.libs.jaicore.ml.core.dataset.clusterable.ClusterableDataset
clusterResults - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
ClusterSampling> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory
ClusterSampling(long, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
ClusterSampling(long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
ClusterStratiAssigner - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling
ClusterStratiAssigner() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
compareTo(Hyperband.MultiFidelityScore) - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.MultiFidelityScore
computeAcceptanceThresholds() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.APilotEstimateSampling
computeAcceptanceThresholds() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
computeAttributeValues(IDataset<?>) - Method in class ai.libs.jaicore.ml.core.dataset.schema.DatasetPropertyComputer
computeAttributeValues(IDataset<?>, List<Integer>, int) - Method in class ai.libs.jaicore.ml.core.dataset.schema.DatasetPropertyComputer: This method computes for each desired attribute the set of occurring values.
computeLoss(INDArray) - Static method in class ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.algorithm.PLNetLoss: Computes the gradient of the NLL for PL networks w.r.t. the k-th dyad according to equation (28) in [1].
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.core.evaluation.evaluator: 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<ILabeledDataset<?>, ASamplingAlgorithm<ILabeledDataset<?>>>, ILabeledDataset<?>, double, long, String, double[]) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.ConfigurationLearningCurveExtrapolationEvaluator
ConfigurationLearningCurveExtrapolator - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation: This class is a subclass of LearningCurveExtrapolator which deals with the slightly different setup that is required by the LCNet of pybnn
ConfigurationLearningCurveExtrapolator(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>, ILabeledDataset<?>, double, int[], ISamplingAlgorithmFactory<ILabeledDataset<?>, ASamplingAlgorithm<ILabeledDataset<?>>>, long, String, double[]) - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ConfigurationLearningCurveExtrapolator
configurationUID - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
configureBestFirst(BestFirst<GraphSearchWithSubpathEvaluationsInput<T, String, Double>, T, String, Double>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.RandomlyRankedNodeQueueConfig
ConfusionMatrix - Class in ai.libs.jaicore.ml.classification.loss: Given two equal-length lists/vectors of values, this class computes a confusion matrix
ConfusionMatrix(List<?>, List<?>) - Constructor for class ai.libs.jaicore.ml.classification.loss.ConfusionMatrix: Constructor computing the confusion matrix based on the given equal-length lists expected and actual.
ConstantRegressor - Class in ai.libs.jaicore.ml.regression.learner
ConstantRegressor() - Constructor for class ai.libs.jaicore.ml.regression.learner.ConstantRegressor
ConstantSplitSetGenerator> - Class in ai.libs.jaicore.ml.core.evaluation.splitsetgenerator
ConstantSplitSetGenerator(IDatasetSplitSet<D>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.ConstantSplitSetGenerator
constructCommandLineParametersForFitAndPredictMode(File, File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
constructCommandLineParametersForFitAndPredictMode(File, File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnMultiTargetRegressionWrapper
constructCommandLineParametersForFitAndPredictMode(File, File, File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
constructCommandLineParametersForFitMode(File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
constructCommandLineParametersForFitMode(File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnMultiTargetRegressionWrapper
constructCommandLineParametersForFitMode(File, File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
constructCommandLineParametersForPredictMode(File, File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
constructCommandLineParametersForPredictMode(File, File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnMultiTargetRegressionWrapper
constructorCall - Variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
contains(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
contains(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
contains(IInstance) - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
containsAll(Collection<?>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
containsAll(Collection<?>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
convertInstanceSchemaIntoLabeledInstanceSchema(IInstanceSchema, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
convertToClassificationDataset(ILabeledDataset<?>) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
convertToRegressionDataset(ILabeledDataset<?>) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
countClassOccurrences(D) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling: Count occurrences of every class.
countDatasetEntries(File, boolean) - Static method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.ArffUtilities: Counts the amount of datapoint entries in an ARFF file.
countFileLines(File) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.SimplifiedTimeSeriesLoader: Counts the lines of the given File object in a very efficient way (thanks to https://stackoverflow.com/a/453067).
create(Class<T>, int, long) - Static method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
createCopy() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
createCopy() - Method in class ai.libs.jaicore.ml.core.dataset.clusterable.ClusterableDataset
createCopy() - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
createCopy() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
createDataset(KVStore, List<IAttribute>) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
createDatasetForMatrix(double[][]...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Function creating a TimeSeriesDataset2 object given one or multiple valueMatrices.
createDatasetForMatrix(int[], double[][]...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Function creating a TimeSeriesDataset2 object given the targets and one or multiple valueMatrices.
createDefaultDiscretizationPolicies(IDataset<?>, List<Integer>, Map<Integer, Set<Object>>, DiscretizationHelper.DiscretizationStrategy, int) - Method in class ai.libs.jaicore.ml.core.filter.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.
createEmptyCopy() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
createEmptyCopy() - Method in class ai.libs.jaicore.ml.core.dataset.clusterable.ClusterableDataset
createEmptyCopy() - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
createEmptyCopy() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
createEquidistantTimestamps(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Creates equidistant timestamps for a time series.
createEquidistantTimestamps(INDArray) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Creates equidistant timestamps for a time series.
createNetworkFromDl4jConfigFile(File) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker: Creates a simple feed-forward MultiLayerNetwork using the json representation of a MultiLayerConfiguration in the file .
createSplit(D, long, double...) - Static method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
createSplit(D, long, Logger, double...) - Static method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter: This static method exists to enable reproducibility.
createStrati(IDataset<?>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
createStrati(IDataset<?>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.GMeansStratifier
createStrati(IDataset<?>) - Method in interface ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.IStratifier: Prepares the stratification technique but does not assign instances to strati.
createStrati(IDataset<?>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.KMeansStratifier
CrossEntropyLoss - Class in ai.libs.jaicore.ml.classification.loss.instance
CrossEntropyLoss() - Constructor for class ai.libs.jaicore.ml.classification.loss.instance.CrossEntropyLoss
CrossEntropyLoss(double) - Constructor for class ai.libs.jaicore.ml.classification.loss.instance.CrossEntropyLoss
CSVDatasetAdapter - Class in ai.libs.jaicore.ml.core.dataset.serialization
CSVDatasetAdapter() - Constructor for class ai.libs.jaicore.ml.core.dataset.serialization.CSVDatasetAdapter
currentCluster - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
currentSizeOfTarget() - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver

D

data - Variable in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
data - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
DATA - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffItem
dataset - Variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
Dataset - Class in ai.libs.jaicore.ml.core.dataset
Dataset(ILabeledInstanceSchema) - Constructor for class ai.libs.jaicore.ml.core.dataset.Dataset
Dataset(ILabeledInstanceSchema, List<ILabeledInstance>) - Constructor for class ai.libs.jaicore.ml.core.dataset.Dataset
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.
DatasetDeriver<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.dataset
DatasetDeriver(D) - Constructor for class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
DatasetDeriver(D, Class) - Constructor for class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
DatasetFileSorter - Class in ai.libs.jaicore.ml.core.filter.sampling.infiles: Sorts a Dataset file with a Mergesort.
DatasetFileSorter(File) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.infiles.DatasetFileSorter
DatasetFileSorter(File, TempFileHandler) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.infiles.DatasetFileSorter
DATASETFOLDER - Static variable in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
DatasetPropertyComputer - Class in ai.libs.jaicore.ml.core.dataset.schema
DatasetPropertyComputer() - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.DatasetPropertyComputer
DATASETS - Static variable in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
DatasetSplitSet<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.dataset.splitter
DatasetSplitSet() - Constructor for class ai.libs.jaicore.ml.core.dataset.splitter.DatasetSplitSet
DatasetSplitSet(List<List<D>>) - Constructor for class ai.libs.jaicore.ml.core.dataset.splitter.DatasetSplitSet
DatasetSplitSet(IDatasetSplitSet<D>) - Constructor for class ai.libs.jaicore.ml.core.dataset.splitter.DatasetSplitSet
DatasetUtil - Class in ai.libs.jaicore.ml.core.dataset
DataSourceCreationFailedException(Exception) - Constructor for exception ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator.DataSourceCreationFailedException
decodeValue(double) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AGenericObjectAttribute
decodeValue(double) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
decodeValue(double) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
DEF_EPSILON - Static variable in class ai.libs.jaicore.ml.classification.loss.instance.CrossEntropyLoss
DEF_NULL_ELEMENT - Static variable in class ai.libs.jaicore.ml.core.dataset.SparseInstance
DEF_TEMP_FOLDER - Static variable in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
DEFAULT_CHARSET - Static variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.SimplifiedTimeSeriesLoader: Default charset used when extracting from files.
deleteNet() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetExtrapolationMethod
deleteNet(String) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetClient
DELTA - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
DenseDyadRankingInstance - Class in ai.libs.jaicore.ml.ranking.dyad.dataset
DenseDyadRankingInstance(List<IDyad>) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
DenseDyadRankingInstance(Set<IDyad>) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
DenseInstance - Class in ai.libs.jaicore.ml.core.dataset
DenseInstance() - Constructor for class ai.libs.jaicore.ml.core.dataset.DenseInstance
DenseInstance(Object[], Object) - Constructor for class ai.libs.jaicore.ml.core.dataset.DenseInstance
DenseInstance(List<Object>, Object) - Constructor for class ai.libs.jaicore.ml.core.dataset.DenseInstance
derivativeAt(double[]) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.BilinFunction
deserialize(InputStream) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttribute
deserializeAttributeValue(String) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttribute: Given format:: "t1:v1 t2:v2 ... tn:vn"
deserializeDataset() - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
deserializeDataset(int) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
deserializeDataset(int, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
deserializeDataset(IDatasetDescriptor) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
deserializeDataset(IDatasetDescriptor) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.CSVDatasetAdapter
deserializeDataset(IDatasetDescriptor) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
deserializeDataset(IFileDatasetDescriptor, int) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
deserializeDataset(IFileDatasetDescriptor, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
DFT - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter
DFT() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
difference(double[], double[]) - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
disableRekursiv() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
DiscretizationHelper - Class in ai.libs.jaicore.ml.core.filter.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.filter.sampling.inmemory.stratified.sampling.DiscretizationHelper
DiscretizationHelper.DiscretizationStrategy - Enum in ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling
discretize(double, AttributeDiscretizationPolicy) - Method in class ai.libs.jaicore.ml.core.filter.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.filter.sampling.inmemory.stratified.sampling.DiscretizationHelper: Discretizes the given attribute values with respect to the provided policies
distanceMeassure - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
distanceMeasure - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
doAlgorithmStep() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
doInactiveStep() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
doLabelsFitToProblemType(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
doLabelsFitToProblemType(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnClassificationWrapper
doLabelsFitToProblemType(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnMultiTargetRegressionWrapper
doLabelsFitToProblemType(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnRegressionWrapper
doLabelsFitToProblemType(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
domainDimension() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.BilinFunction
Dyad - Class in ai.libs.jaicore.ml.ranking.dyad.learner: Represents a dyad consisting of an instance and an alternative, represented by feature vectors.
Dyad(IVector, IVector) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.Dyad: Construct a new dyad consisting of the given instance and alternative.
DyadDatasetPoolProvider - Class in ai.libs.jaicore.ml.ranking.dyad.learner.activelearning: A pool provider which is created out of a DyadRankingDataset.
DyadDatasetPoolProvider(IDyadRankingDataset) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider
DyadMinMaxScaler - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.util.DyadMinMaxScaler
DyadRankingAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
DyadRankingAttribute(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttribute
DyadRankingAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
DyadRankingAttributeValue(IRankingAttribute<IDyad>, IRanking<IDyad>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttributeValue
DyadRankingDataset - Class in ai.libs.jaicore.ml.ranking.dyad.dataset: A dataset representation for dyad ranking.
DyadRankingDataset() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
DyadRankingDataset(LabeledInstanceSchema) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
DyadRankingDataset(String) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
DyadRankingDataset(String, Collection<IDyadRankingInstance>) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
DyadRankingDataset(Collection<IDyadRankingInstance>) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
DyadRankingFeatureTransformNegativeLogLikelihood - Class in ai.libs.jaicore.ml.ranking.dyad.learner.optimizing: Implements the negative log-likelihood function for the feature transformation Placket-Luce dyad ranker.
DyadRankingFeatureTransformNegativeLogLikelihood() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood
DyadRankingFeatureTransformNegativeLogLikelihoodDerivative - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative
DyadStandardScaler - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.util.DyadStandardScaler
DyadUnitIntervalScaler - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.util.DyadUnitIntervalScaler

E

E - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
EAggregatedClassifierMetric - Enum in ai.libs.jaicore.ml.classification.loss.dataset
EArffAttributeType - Enum in ai.libs.jaicore.ml.core.dataset.serialization.arff: Enum of supported attribute types
EArffItem - Enum in ai.libs.jaicore.ml.core.dataset.serialization.arff: enum of possible arff declarations
EarlyAbandonMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.shapelets.search.EarlyAbandonMinimumDistanceSearchStrategy: Standard constructor.
EClassificationPerformanceMeasure - Enum in ai.libs.jaicore.ml.classification.loss.dataset
element() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
EMPTY_STRING - Static variable in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
enableRekursiv() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
encodeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AGenericObjectAttribute
encodeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
encodeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
EQUAL_LENGTH - ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.DiscretizationHelper.DiscretizationStrategy
EQUAL_SIZE - ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.DiscretizationHelper.DiscretizationStrategy
equalLengthPolicy(List<Double>, int) - Method in class ai.libs.jaicore.ml.core.filter.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.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
equals(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.ATimeseriesAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
equals(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.ADataset
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.ALabeledDataset
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.MapInstance
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AGenericObjectAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttributeValue
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.ThreeDimensionalAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.LabeledInstanceSchema
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.ReproducibleSplit
equals(Object) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
equals(Object) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
equals(Object) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurveConfiguration
equals(Object) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationParameterSet
equals(Object) - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.MultiFidelityScore
equals(Object) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeries
equals(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
equals(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
equals(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
equals(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.Dyad
equals(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.RandomlyRankedNodeQueue
equals(Object) - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.RankingForGroup
equalSizePolicy(List<Double>, int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.DiscretizationHelper: Creates an equal size policy for the given values with respect to the given number of categories.
ERegressionPerformanceMeasure - Enum in ai.libs.jaicore.ml.regression.loss
ErrorRate - Class in ai.libs.jaicore.ml.classification.loss.dataset
ErrorRate() - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.ErrorRate
ERRORRATE - ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
ERulPerformanceMeasure - Enum in ai.libs.jaicore.ml.regression.loss
EScikitLearnProblemType - Enum in ai.libs.jaicore.ml.core
ETA - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
evaluate(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.ConfigurationLearningCurveExtrapolationEvaluator
evaluate(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.ExtrapolatedSaturationPointEvaluator
evaluate(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearningCurveExtrapolationEvaluator: Computes the (estimated) measure of the classifier on the full dataset
evaluate(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PreTrainedPredictionBasedClassifierEvaluator
evaluate(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.TrainPredictionBasedClassifierEvaluator
evaluate(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>, ISupervisedLearnerEvaluator<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>) - Static method in class ai.libs.jaicore.ml.core.evaluation.MLEvaluationUtil
evaluate(T) - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.IMultiFidelityObjectEvaluator
evaluate(T, double) - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.IMultiFidelityObjectEvaluator: Evaluate the object t with the specified budget.
evaluateMaximal(T) - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.IMultiFidelityObjectEvaluator: Evaluate the object t with maximal resources.
evaluateMinimal(T) - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.IMultiFidelityObjectEvaluator: Evaluate the object t with minimal resources.
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.
ExactMatch - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
ExactMatch() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.ExactMatch
ExactMatch(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.ExactMatch
execute(ISupervisedLearner<I, D>, D) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.SupervisedLearnerExecutor
execute(ISupervisedLearner<I, D>, D, D) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.SupervisedLearnerExecutor
executePipeline(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
ExhaustiveMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.shapelets.search.ExhaustiveMinimumDistanceSearchStrategy: Standard constructor.
EXP_4 - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
EXPANSION_LOGARITHM - Static variable in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
EXPANSION_PRODUCTS - Static variable in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
EXPANSION_SQUARES - Static variable in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
extractArffHeader(File) - Static method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.ArffUtilities: Extract the header of an ARFF file as a string.
ExtrapolatedSaturationPointEvaluator - Class in ai.libs.jaicore.ml.core.evaluation.evaluator: For the classifier a learning curve will be extrapolated with a given set of anchorpoints.
ExtrapolatedSaturationPointEvaluator(int[], ISamplingAlgorithmFactory<ILabeledDataset<?>, ? extends ASamplingAlgorithm<ILabeledDataset<?>>>, ILabeledDataset<?>, double, LearningCurveExtrapolationMethod, long, ILabeledDataset<?>, IDeterministicPredictionPerformanceMeasure<?, ?>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.ExtrapolatedSaturationPointEvaluator: Create a classifier evaluator with an accuracy measurement at the extrapolated learning curves saturation point.
ExtrapolatedSaturationPointEvaluatorFactory - Class in ai.libs.jaicore.ml.core.evaluation.evaluator.factory
ExtrapolatedSaturationPointEvaluatorFactory(int[], ISamplingAlgorithmFactory<ILabeledDataset<?>, ? extends ASamplingAlgorithm<ILabeledDataset<?>>>, double, LearningCurveExtrapolationMethod) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ExtrapolatedSaturationPointEvaluatorFactory
extrapolateLearningCurve() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawExtrapolationMethod
extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationExtrapolationMethod
extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetExtrapolationMethod
extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in interface ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolationMethod
extrapolationMethod - Variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
ExtrapolationRequest - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client: This class describes the request that is sent to an Extrapolation Service.
ExtrapolationRequest() - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
ExtrapolationServiceClient<C> - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationServiceClient

F

F1_WITH_1_POSITIVE - ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
F1MacroAverageL - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
F1MacroAverageL() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.F1MacroAverageL
F1MacroAverageL(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.F1MacroAverageL
F1Measure - Class in ai.libs.jaicore.ml.classification.loss.dataset
F1Measure(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.F1Measure
F1MicroAverage - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
F1MicroAverage() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.F1MicroAverage
F1MicroAverage(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.F1MicroAverage
factor - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Factor used to determine whether or not to include a window length into the overall predicition.
FALSE_NEGATIVES_WITH_1_POSITIVE - ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
FALSE_POSITIVES_WITH_1_POSITIVE - ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
FalseNegatives - Class in ai.libs.jaicore.ml.classification.loss.dataset
FalseNegatives(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.FalseNegatives
FalsePositives - Class in ai.libs.jaicore.ml.classification.loss.dataset
FalsePositives(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.FalsePositives
FeatureTransformPLDyadRanker - Class in ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform: A feature transformation Plackett-Luce dyad ranker.
FeatureTransformPLDyadRanker() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.algorithm.featuretransform.FeatureTransformPLDyadRanker: Constructs a new feature transform Placket-Luce dyad ranker with the given feature transformation method.
FileDatasetDescriptor - Class in ai.libs.jaicore.ml.core.dataset
FileDatasetDescriptor - Class in ai.libs.jaicore.ml.core.dataset.serialization
FileDatasetDescriptor(File) - Constructor for class ai.libs.jaicore.ml.core.dataset.FileDatasetDescriptor
FileDatasetDescriptor(File) - Constructor for class ai.libs.jaicore.ml.core.dataset.serialization.FileDatasetDescriptor
FilterBasedDatasetSplitter<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.filter
FilterBasedDatasetSplitter(ISamplingAlgorithmFactory<D, ?>) - Constructor for class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
FilterBasedDatasetSplitter(ISamplingAlgorithmFactory<D, ?>, double, Random) - Constructor for class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
findMinimumDistance(Shapelet, double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.shapelets.search.ExhaustiveMinimumDistanceSearchStrategy: Function returning the minimum distance among all subsequences of the given timeSeries to the shapelet's data.
fit(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
fit(double[]) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter: The function only fits a single instance of the dataset
fit(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
fit(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
fit(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
fit(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
fit(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
fit(double[][]) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter
fit(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
fit(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
fit(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
fit(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
fit(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
fit(TimeSeriesDataset2) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter: the function computes the needed information for the transform function.
fit(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
fit(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
fit(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
fit(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
fit(DyadRankingDataset, int, double) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
fit(D) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATimeSeriesClassificationModel: {@inheritDoc ABatchLearner#train(jaicore.ml.core.dataset.IDataset)}
fit(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
fit(String) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
fit(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
fit(List<INDArray>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
fit(List<INDArray>, int, double) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
fit(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.learner.MajorityClassifier
fit(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.regression.learner.ConstantRegressor
fit(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
fit(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
fit(IDyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform.FeatureTransformPLDyadRanker
fit(IDyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
fit(IDyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler: Fits the standard scaler to the dataset.
fit(IDyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadUnitIntervalScaler
fitAndPredict(D, D) - Method in class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
fitAndPredict(D, I) - Method in class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
fitAndPredict(D, I[]) - Method in class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
fitAndPredict(File, String, File, String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
fitAndPredict(ILabeledDataset<? extends ILabeledInstance>, ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
fitDataFile - Variable in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
fitOutputFile - Variable in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
fitTransform(double[]) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter: the function fit and transforms a single instance
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
fitTransform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
fitTransform(double[][]) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
fitTransform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.AFilter
fitTransform(TimeSeriesDataset2) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter: a utility function to avoid the added effort of calling the fit and transform function separate
fitTransform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
fitTransform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
fitTransform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
fitTransform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
fitTransform(IDyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler: Fits the standard scaler to the dataset and transforms the entire dataset according to the mean and standard deviation of the dataset.
FixedDataSplitSetGenerator<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.evaluation.splitsetgenerator: This is an IDatasetSplitSetGenerator that produces splits for one initially fixed dataset.
FixedDataSplitSetGenerator(D, IDatasetSplitSetGenerator<D>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.FixedDataSplitSetGenerator
FixedSplitClassifierEvaluator - Class in ai.libs.jaicore.ml.core.evaluation.evaluator
FixedSplitClassifierEvaluator(ILabeledDataset<? extends ILabeledInstance>, ILabeledDataset<? extends ILabeledInstance>, IDeterministicPredictionPerformanceMeasure<?, ?>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.FixedSplitClassifierEvaluator
FMeasure - Class in ai.libs.jaicore.ml.classification.loss.dataset
FMeasure(double, int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.FMeasure
formGenericMultidimensionalArray(String[]) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
formGenericMultidimensionalArray(String[]) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.ThreeDimensionalAttribute
formGenericMultidimensionalArray(String[]) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute: parses String string to MutidimensionalAttributeValue
forwardDifferenceDerivate(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: f'(n) = f(n+1) - f(n)
forwardDifferenceDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: f'(n) = f(n+1) - f(n)
FStat - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.quality: 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.classification.singlelabel.timeseries.quality.FStat

G

get(int) - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassificationPredictionBatch
get(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
get(int) - Static method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
get(int) - Method in class ai.libs.jaicore.ml.core.evaluation.PredictionBatch
get(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
getA() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawConfiguration
getA() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
getAcceptanceThresholds() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
getAlgorithm() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATimeSeriesClassificationModel: Getter for the model's training algorithm object.
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.GmeansSamplingFactory
getAlgorithm(int, D, Random) - Method in interface ai.libs.jaicore.ml.core.filter.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.filter.sampling.inmemory.factories.KmeansSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.LabelBasedStratifiedSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.SimpleRandomSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.StratifiedSamplingFactory
getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.SystematicSamplingFactory
getAlgorithm(int, ILabeledDataset<?>, Random) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.LocalCaseControlSamplingFactory
getAlgorithm(int, ILabeledDataset<?>, Random) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.OSMACSamplingFactory
getAlgorithm(D) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
getAlgorithm(D) - Method in interface ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.interfaces.ISamplingAlgorithmFactory: After the necessary config is done, this method returns a fully configured instance of a sampling algorithm.
getAlgorithmModes() - Method in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
getAlgorithms() - Method in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
getAllCreatedStrati() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.ClassStratiFileAssigner
getAllCreatedStrati() - Method in interface ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.IStratiFileAssigner: Get the used strati temporary files and the amount of datapoints inside of it.
getAlternative() - Method in interface ai.libs.jaicore.ml.ranking.dyad.IVectorDyad
getAlternative() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.Dyad: Get the alternative.
getAnchorPoints() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
getAreaUnderCurve(List<Pair<Double, Double>>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AAreaUnderCurvePerformanceMeasure: Computes the area under the curve coordinates, assuming a linear interpolation between the coordinates.
getArffFileOfOpenMLID(int) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
getAsAttributeValue(double) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AGenericObjectAttribute
getAsAttributeValue(double) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
getAsAttributeValue(double) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttribute
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.ThreeDimensionalAttribute: This method takes object type double[][][] or MultidimensionalAttributeValue and returns a MultidimensionalAttributeValue that holds the same values.
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute: This method takes a parameter of type double[][] or MultidimensionalAttributeValue and returns a MultidimensionalAttributeValue that holds the same values
getAsAttributeValue(Object) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttribute
getAttribute() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttributeValue
getAttribute() - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttributeValue
getAttribute(int) - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
getAttributeList() - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
getAttributeMap() - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
getAttributes() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
getAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
getAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.MapInstance
getAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
getAttributes() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
getAttributeValue(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
getAttributeWithName(IFileDatasetDescriptor, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
getB() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawConfiguration
getB() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
getBaseMeasure() - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EAggregatedClassifierMetric
getBaseMeasure() - Method in class ai.libs.jaicore.ml.core.evaluation.AggregatingPredictionPerformanceMeasure
getBaseMeasure() - Method in class ai.libs.jaicore.ml.core.evaluation.SingleEvaluationAggregatedMeasure
getC() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawConfiguration
getC() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
getCacheSplitSets() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
getCandidate(ProblemInstance) - Method in interface ai.libs.jaicore.ml.ranking.label.learner.clusterbased.candidateprovider.IRankedSolutionCandidateProvider
getCaption() - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
getCastedView(Class<E1>, Class<A1>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getCentersModifiable() - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
getCertainty(ILabeledInstance, Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
getCertainty(IDyadRankingInstance, IRanking<IDyad>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
getChosenIndices() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SimpleRandomSampling
getClassConfidence() - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
getClassConfidence() - Method in class ai.libs.jaicore.ml.core.evaluation.Prediction
getClassConfidence() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Ranking
getClassDistribution() - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
getClassDistribution() - Method in class ai.libs.jaicore.ml.core.evaluation.Prediction
getClassDistribution() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Ranking
getClassesInDataset(TimeSeriesDataset2) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Returns a list storing the unique Integer class values in the given dataset.
getClassifier() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSCLearningAlgorithm
getClassifier() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.MCCVSplitEvaluationEvent
getClassMapper() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier: Getter for the property classMapper.
getClassValues() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.ClassMapper: Getter for the classValues.
getClusterResults() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
getClusters() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
getCommandBuilder() - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getCommandBuilder() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
getCommandBuilder(ScikitLearnWrapperCommandBuilder) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getComplement(D, D) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SampleComplementComputer: Gets the data point contained in the original data that are not part of the
getComplementOfLastSample() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm: Gets the data point contained in the original data that are not part of the
getComponentInstance() - Method in class ai.libs.jaicore.ml.hpo.ggp.GrammarBasedGeneticProgramming.GGPSolutionCandidate
getComponentInstance() - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.HyperbandSolutionCandidate
getCompositionEvaluator() - Method in class ai.libs.jaicore.ml.hpo.multifidelity.MultiFidelitySoftwareConfigurationProblem
getConfig() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm
getConfig() - Method in class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
getConfig() - Method in class ai.libs.jaicore.ml.hpo.ggp.GrammarBasedGeneticProgramming
getConfig() - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband
getConfigForAnchorPoints(int[], double[]) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationServiceClient
getConfusionMatrix() - Method in class ai.libs.jaicore.ml.classification.loss.ConfusionMatrix: Returns an integer matrix with counts of the confusions.
getConstructionInstruction() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
getConstructionPlan() - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
getConstructionPlan() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.ReproducibleSplit
getConstructionPlan() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
getContext() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
getContext() - Method in interface ai.libs.jaicore.ml.ranking.dyad.IVectorDyad
getContext() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.Dyad: Get the instance.
getConvergenceValue() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
getConvergenceValue() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurve
getCopy() - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
getCopy() - Method in class ai.libs.jaicore.ml.core.dataset.schema.LabeledInstanceSchema
getCrashScore() - Method in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig
getCrossoverRate() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig
getCurveValue(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
getCurveValue(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurve
getCurveValue(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.PointWiseLearningCurve
getData() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet: Getter for Shapelet.data.
getData() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the dataset which is used for splitting.
getData() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ExtrapolatedSaturationPointEvaluatorFactory
getData() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.LearningCurveExtrapolationEvaluatorFactory
getDataName(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getDataName(ILabeledDataset<? extends ILabeledInstance>) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
getDataName(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
getDataName(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
getDataset() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TSLearningProblem
getDataset() - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
getDataset() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.ConstantSplitSetGenerator
getDataset() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.FixedDataSplitSetGenerator
getDataset() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
getDatasetDescription() - Method in class ai.libs.jaicore.ml.core.dataset.FileDatasetDescriptor
getDatasetDescription() - Method in class ai.libs.jaicore.ml.core.dataset.serialization.FileDatasetDescriptor
getDatasetDescription() - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetDescriptor
getDatasetFolder() - Method in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
getDatasetFromMapCollection(Collection<Map<String, Object>>, String) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getDatasetFromMapCollection(Collection<Map<String, Object>>, String, List<String>) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getDatasets() - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
getDatasets() - Method in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
getDatasetSplitter() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the dataset splitter.
getDatasetSplitter() - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ISplitBasedSupervisedLearnerEvaluatorFactory
getDefaultWindowSize() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
getDeleteFileOnExit() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
getDerivativeCurveValue(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
getDerivativeCurveValue(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurve
getDeterminedQuality() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet: Getter for Shapelet.determinedQuality.
getDistanceMeasure() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Getter for the distance measure.
getDoublePrediction() - Method in class ai.libs.jaicore.ml.regression.singlelabel.SingleTargetRegressionPrediction
getDyadRanker() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue: Get the dyad ranker used to rank the nodes.
getDyads() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
getDyadsByAlternative(IVector) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider
getDyadsByAlternative(IVector) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.IDyadRankingPoolProvider: Returns the set of all Dyads with the given IVector of alternative features.
getDyadsByInstance(IVector) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider
getDyadsByInstance(IVector) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.IDyadRankingPoolProvider: Returns the set of all Dyads with the given IVector of instance features.
getDyadStats() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
getEarlyStopping() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig: Early stopping terminates the evolutionary process early if there were no changes for a certain amount of time.
getElitismSize() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig
getEpoch() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
getEta() - Method in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig: The parameter eta defines that after each round eta^-1 many solutions of the current population are preserved for the next stage of a race.
getException() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
getExpansionOfDataset(ILabeledDataset<?>, int...) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getExpansionOfDataset(ILabeledDataset<?>, Pair<List<IAttribute>, Map<IAttribute, Function<ILabeledInstance, Double>>>) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getExpansionOfInstance(ILabeledInstance, Pair<List<IAttribute>, Map<IAttribute, Function<ILabeledInstance, Double>>>) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getExtrapolationMethod() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
getExtrapolator() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolatedEvent
getFailedEvaluationScore() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig: If the evaluation of an individual fails, we will need to nevertheless assign it a score.
getFeatureMatrix() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getFeatureMatrix() - Method in class ai.libs.jaicore.ml.core.dataset.ADataset
getFeatureMatrix() - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
getFeatureMatrix() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
getFeatures(double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesFeature: Function calculating all features occurring in TimeSeriesFeature.FeatureType at once using an online calculation approach for mean, standard deviation and the slope.
getFirstReport() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationFailedEvent
getFoldOfSplit(D, long, int, double...) - Static method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
getFoldOfSplit(D, ISamplingAlgorithmFactory<D, ?>, long, int, List<Double>) - Static method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
getFolds(int) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.DatasetSplitSet
getFunctions() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationFunction
getGmeansCluster() - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
getGroundTruth(int) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getGroundTruthAsArray() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getGroundTruthAsList() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getGroundTruthAsList(Class<T>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getHighestQualityShapeletInList(List<Shapelet>) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet: Returns the shapelet with the highest quality in the given list shapelets.
getId() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Group
getIdentifier() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.GroupIdentifier
getIdentifierForGroup() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.RankingForGroup
getIndexOfObject(Object) - Method in class ai.libs.jaicore.ml.classification.loss.ConfusionMatrix: Returns the index of an object in the confusion matrix.
getIndicesOfNewInstancesInOriginalDataset() - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
getIndicesOfNewInstancesInOriginalDataset(Collection<Integer>) - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
getIndicesOfNewInstancesInOriginalDataset(List<Integer>) - Method in class ai.libs.jaicore.ml.core.dataset.DatasetDeriver
getInforamtionforRanking(List<ProblemInstance>) - Method in interface ai.libs.jaicore.ml.ranking.label.learner.clusterbased.datamanager.ITableGeneratorandCompleter
getInput() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATSCAlgorithm: Getter for the data set input used during algorithm calls.
getInputList() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.util.InputOptListener
getInstance() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.ProblemInstance
getInstanceFeatures() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
getInstanceFeatures() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider
getInstanceFeatures() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.IDyadRankingPoolProvider: Returns a Collection that contains all instance features contained in the pool.
getInstanceFromMap(ILabeledInstanceSchema, Map<String, Object>, String) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getInstanceFromMap(ILabeledInstanceSchema, Map<String, Object>, String, Map<IAttribute, Function<ILabeledInstance, Double>>) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getInstanceIndex() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet: Getter for Shapelet.instanceIndex.
getInstances() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Group
getInstancesAsString() - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
getInstanceSchema() - Method in class ai.libs.jaicore.ml.core.dataset.ADataset
getInstanceSchema() - Method in class ai.libs.jaicore.ml.core.dataset.ALabeledDataset
getInstanceSchema() - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
getInstanceSchema() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
getInstanceSchemaForQuery(String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
getInstanceSchemaForQuery(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
getInstanceSchemaForQuery(String, String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
getInstanceSchemaForQuery(String, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
getInstanceSchemaFromResultList(List<IKVStore>) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
getInstanceSchemaFromResultList(List<IKVStore>, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
getInstanceSchemaOfTable(String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
getInstanceSchemaOfTable(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
getInstanceSchemaOfTable(String, String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
getInstanceSchemaOfTable(String, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
getInternalDataset() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
getInterval(double[], int, int) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Function extracting the interval [start, end (exclusive)] out of the given timeSeries vector.
getIntervals() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
getIntPrediction() - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
getIrrelevantLabels(double) - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
getIteration() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
getIterations() - Method in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig: The number of iterations can either be defined by 'auto' to be calculated as proposed in the paper or by defining a custom positive integer.
getK() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Getter for the k value, @see #k.
getL() - Method in class ai.libs.jaicore.ml.ranking.loss.NDCGLoss
getLabel() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
getLabel() - Method in class ai.libs.jaicore.ml.core.dataset.AInstance
getLabel() - Method in class ai.libs.jaicore.ml.core.dataset.MapInstance
getLabel() - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
getLabel() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
getLabel() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
getLabelA() - Method in class ai.libs.jaicore.ml.ranking.filter.PairWisePreferenceToBinaryClassificationFilter
getLabelAttribute() - Method in class ai.libs.jaicore.ml.core.dataset.schema.LabeledInstanceSchema
getLabelB() - Method in class ai.libs.jaicore.ml.ranking.filter.PairWisePreferenceToBinaryClassificationFilter
getLabelCountDifference(ILabeledDataset<?>, ILabeledDataset<?>) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getLabelCounts(ILabeledDataset<?>) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getLabelOfCategory(Number) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
getLabels() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
getLabels() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
getLabelStratifiedTrainTestSplit(D, long, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getLabelStratifiedTrainTestSplit(D, long, double, String) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getLabelStratifiedTrainTestSplit(ILabeledDataset<?>, Random, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getLabelVector() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getLabelVector() - Method in class ai.libs.jaicore.ml.core.dataset.ADataset
getLabelVector() - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
getLabelVector() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
getLabelWithHighestProbability() - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
getLabelWithHighestProbability() - Method in class ai.libs.jaicore.ml.core.evaluation.Prediction
getLabelWithHighestProbability() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Ranking
getLastRatedPopulation() - Method in class ai.libs.jaicore.ml.hpo.ggp.GrammarBasedGeneticProgramming
getLastReport() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationFailedEvent
getLatexCode() - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
getLearner() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationCompletedEvent
getLearner() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationFailedEvent
getLearner() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
getLearnerEvaluator() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ExtrapolatedSaturationPointEvaluatorFactory
getLearnerEvaluator() - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ISupervisedLearnerEvaluatorFactory
getLearnerEvaluator() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.LearningCurveExtrapolationEvaluatorFactory
getLearnerEvaluator() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.MonteCarloCrossValidationEvaluatorFactory
getLearningAlgorithm(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier
getLearningAlgorithm(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSClassifier
getLearningAlgorithm(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSEnsembleClassifier
getLearningAlgorithm(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier
getLearningAlgorithm(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier
getLength() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.ATimeseriesAttribute: Get the length of this time series attribute type.
getLength() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet: Getter for Shapelet.length.
getLength() - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeries
getLengthOfTopRankingToConsider() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.PrototypicalPoolBasedActiveDyadRanker
getLogger() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
getLoggerName() - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
getLoggerName() - Method in class ai.libs.jaicore.ml.core.dataset.schema.DatasetPropertyComputer
getLoggerName() - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
getLoggerName() - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
getLoggerName() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
getLoggerName() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearningCurveExtrapolationEvaluator
getLoggerName() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.SupervisedLearnerExecutor
getLoggerName() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.TrainPredictionBasedClassifierEvaluator
getLoggerName() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.FixedDataSplitSetGenerator
getLoggerName() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.MonteCarloCrossValidationSplitSetGenerator
getLoggerName() - Method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
getLoggerName() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
getLoggerName() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.APilotEstimateSampling
getLoggerName() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
getLoggerName() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
getLoggerName() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.DiscretizationHelper
getLoggerName() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.StratifiedSampling
getLoggerName() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
getLoggerName() - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getLoggerName() - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
getLogProbabilityOfTopKRanking(IDyadRankingInstance, int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.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.ranking.dyad.learner.algorithm.PLNetDyadRanker: Computes the logarithmic probability for a particular ranking according to the log Placket-Luce model.
getLossForTrainedClassifier(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>, ILabeledDataset<? extends ILabeledInstance>, IDeterministicPredictionPerformanceMeasure<Integer, ISingleLabelClassification>) - Static method in class ai.libs.jaicore.ml.core.evaluation.MLEvaluationUtil
getMaxBudget() - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.IMultiFidelityObjectEvaluator
getMaxDepth() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig
getMaximumKeyByValue(Map<T, Integer>) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Returns the key with the maximum integer value.
getMaxIterationsInnerLoop() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.GmeansSamplingFactory
getMeasure() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
getMeasures() - Method in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
getMetric() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
getMetric() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.TrainPredictionBasedClassifierEvaluator
getMinBudget() - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.IMultiFidelityObjectEvaluator
getMinibatchSize() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
getMode(int[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Returns the mode of the given array.
getModelDumpsDirectory() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
getModelDumpsDirectoryName() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
getModelFile() - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getModelFile() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
getModelFile() - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
getModelFileName(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getModelPath() - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getModelPath() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
getModelPath() - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
getMutationRate() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig
getName() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AAttribute
getName() - Method in enum ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType
getName() - Method in enum ai.libs.jaicore.ml.core.EScikitLearnProblemType
getNameOfCategory(int) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
getNumAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
getNumberOfCategories() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
getNumberOfClasses(TimeSeriesDataset2) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Counts the number of unique classes occurring in the given dataset.
getNumberOfFoldsForSplit(int) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.DatasetSplitSet
getNumberOfFoldsPerSplit() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.DatasetSplitSet
getNumberOfFoldsPerSplit() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
getNumberOfFoldsPerSplit() - Method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
getNumberOfInstances() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getNumberOfInstances() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Returns the number of instances contained in the dataset.
getNumberOfRankedElements() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
getNumberOfRankedElements() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
getNumberOfSplits() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.DatasetSplitSet
getNumberOfVariables() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getNumberOfVariables() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Returns the number of variables, i.e. the number of value matrices contained in the dataset.
getNumberQueries() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider: Returns the number of queries the pool provider has answered so far.
getNumberRandomQueriesAtStart() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
getNumCPUs() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
getNumCPUs() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
getNumFoldsPerSplit() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
getNumFoldsPerSplit() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.ConstantSplitSetGenerator
getNumFoldsPerSplit() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.FixedDataSplitSetGenerator
getNumFoldsPerSplit() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.MonteCarloCrossValidationSplitSetGenerator
getNumGenerations() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig: The maximum number of generations to conduct.
getNumInstancesUsedForTraining() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.MCCVSplitEvaluationEvent
getNumInstancesUsedForValidation() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.MCCVSplitEvaluationEvent
getNumMajorColumns() - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
getNumMCIterations() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the number of iterations, i.e. the number of splits considered.
getNumPredictions() - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassificationPredictionBatch
getNumPredictions() - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassificationPredictionBatch
getNumPredictions() - Method in class ai.libs.jaicore.ml.core.evaluation.PredictionBatch
getNumPredictions() - Method in class ai.libs.jaicore.ml.regression.singlelabel.SingleTargetRegressionPredictionBatch
getNumReports() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationFailedEvent
getNumSamples() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
getNumSplitsPerSet() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
getNumSplitsPerSet() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.ConstantSplitSetGenerator
getNumSplitsPerSet() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.FixedDataSplitSetGenerator
getNumSplitsPerSet() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.MonteCarloCrossValidationSplitSetGenerator
getObjectIndex() - Method in class ai.libs.jaicore.ml.classification.loss.ConfusionMatrix: Gets the order of all the occurring elements which also defines the index of an element.
getObservedScore() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.MCCVSplitEvaluationEvent
getOffset() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationFunction
getOpenMLId() - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetDescriptor
getOutputFile(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getOutputFile(String) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
getOutputFile(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
getOutputFile(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
getOutputList() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.util.InputOptListener
getPairOfNewAttributesAndExpansionMap(ILabeledDataset<?>, int...) - Static method in class ai.libs.jaicore.ml.core.dataset.DatasetUtil
getPairWithLeastCertainty(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker: Returns the pair of Dyads for which the model is least certain.
getParameters() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationParameterSet
getParameterSets() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurveConfiguration
getParams() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.ParametricFunction
getPickleFileExtension() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
getPilot() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.LocalCaseControlSamplingFactory
getPilot() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.OSMACSamplingFactory
getPilotEstimator() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.APilotEstimateSampling
getPlNet() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
getPoint() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
getPoint() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.model.INDArrayTimeseries
getPoint() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.model.NDArrayTimeseries
getPoint() - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
getPoint() - Method in class ai.libs.jaicore.ml.core.dataset.MapInstance
getPoint() - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
getPoint() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
getPoints() - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
getPointValue(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
getPointValue(int) - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
getPointValue(int) - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
getPool() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider
getPoolProvider() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker
getPoolSize() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider
getPoolSize() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.IDyadRankingPoolProvider
getPopulationSize() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig
getPositiveClass() - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AAreaUnderCurvePerformanceMeasure
getPreComputedFeatureTransforms(IDyadRankingDataset) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.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).
getPrediction() - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
getPrediction() - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
getPrediction() - Method in class ai.libs.jaicore.ml.core.evaluation.Prediction
getPrediction() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Ranking
getPrediction() - Method in class ai.libs.jaicore.ml.regression.singlelabel.SingleTargetRegressionPrediction
getPrediction(double) - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
getPrediction(double[]) - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
getPrediction(int) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getPredictionDiffList() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
getPredictionList(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AAreaUnderCurvePerformanceMeasure
getPredictionMatrix() - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassificationPredictionBatch
getPredictions() - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassificationPredictionBatch
getPredictions() - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassificationPredictionBatch
getPredictions() - Method in class ai.libs.jaicore.ml.core.evaluation.PredictionBatch
getPredictions() - Method in class ai.libs.jaicore.ml.ranking.RankingPredictionBatch
getPredictions() - Method in class ai.libs.jaicore.ml.regression.singlelabel.SingleTargetRegressionPredictionBatch
getPredictionsAsArray() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getPredictionsAsList() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getPredictionsAsList(Class<T>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
getPrettyMaximaString() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Returns a String the maxima of all features this scaler has been fit to.
getPrettyMeansString() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.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.ranking.dyad.learner.util.AbstractDyadScaler: Prints the standard devations of all features this scaler has been fit to.
getPrintFitnessStats() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig
getProbabilityOfLabel(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
getProbabilityOfLabel(Object) - Method in class ai.libs.jaicore.ml.core.evaluation.Prediction
getProbabilityOfLabel(Object) - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Ranking
getProbabilityOfTopKRanking(IDyadRankingInstance, int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
getProbabilityOfTopRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.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.label.learner.clusterbased.datamanager.IInstanceCollector
getPythonFileExtension() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
getPythonOptionalModules() - Method in enum ai.libs.jaicore.ml.core.EScikitLearnProblemType
getPythonRequiredModules() - Method in enum ai.libs.jaicore.ml.core.EScikitLearnProblemType
getQualityMeasure() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TSLearningProblem
getQueriedRankings() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider
getQueriedRankings() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.IDyadRankingPoolProvider
getRandom() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
getRandom() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
getRandomRestart() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig: In order to increase diversity, the population (except for elite individuals) is substituted by randomly generated individuals to perform a random restart (seeded with elite individuals only).
getRanker() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker
getRanker() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueueConfig: Set the ranker used to rank the OPEN list.
getRanking(I) - Method in interface ai.libs.jaicore.ml.ranking.label.learner.clusterbased.IGroupBasedRanker
getRatioOfOldInstancesForMinibatch() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.PrototypicalPoolBasedActiveDyadRanker
getRawPredictionResults(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getRealizationOfSplitSpecification(ILabeledDataset<? extends ILabeledInstance>, Collection<? extends Collection<Integer>>) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getRelationName() - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
getRelevantLabels(double) - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
getRepeats() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.MonteCarloCrossValidationEvaluator
getReport() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationCompletedEvent
getReportList() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationFailedEvent
getResultFileExtension() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
getSampleSize() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
getSampleSize() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
getSaturationPoint(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
getSaturationPoint(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurve
getScaler() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
getScaler() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueueConfig: Get the scaler used to scale the dataset.
getScikitLearnCommandLineFlag() - Method in enum ai.libs.jaicore.ml.core.EScikitLearnProblemType
getScore() - Method in class ai.libs.jaicore.ml.hpo.ggp.GrammarBasedGeneticProgramming.GGPSolutionCandidate
getScore() - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.HyperbandSolutionCandidate
getSeed() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
getSeed() - Method in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig
getSeeds() - Method in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
getSelf() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
getSelf() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.MonteCarloCrossValidationEvaluatorFactory
getSimpleTrainTestSplit(ILabeledDataset<?>, long, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getSimpleTrainTestSplit(ILabeledDataset<?>, Random, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getSkillForDyad(IDyad) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker: Returns the latent skill value predicted by the PLNet for a given Dyad.
getSKLearnScriptFile() - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
getSKLearnScriptFile() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
getSKLearnScriptFile() - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
getSortedDataset() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SystematicSampling
getSplit(D, ISamplingAlgorithmFactory<D, ?>, long, double...) - Static method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
getSplit(D, ISamplingAlgorithmFactory<D, ?>, long, List<Double>) - Static method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
getSplit(D, ISamplingAlgorithmFactory<D, ?>, long, Logger, double...) - Static method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
getSplitEvaluationTime() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.MCCVSplitEvaluationEvent
getSplitGenerator() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.TrainPredictionBasedClassifierEvaluator
getStartIndex() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet: Getter for Shapelet.startIndex.
getStatsX() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler
getStatsY() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler
getStratum(IInstance) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
getStratum(IInstance) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
getStratum(IInstance) - Method in interface ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.IStratifier: Determines to which stratum this instance belongs
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.ThreeDimensionalAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute
getStringDescriptionOfDomain() - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttribute
getTargets() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getTargets() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Getter for the target values.
getTargetsAsINDArray() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getTempFolder() - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
getTestEndTime() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
getTestFoldOfLabelStratifiedTrainTestSplit(ILabeledDataset<?>, long, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getTestFoldOfLabelStratifiedTrainTestSplit(ILabeledDataset<?>, Random, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getTestFoldOfSimpleTrainTestSplit(ILabeledDataset<?>, long, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getTestFoldOfSimpleTrainTestSplit(ILabeledDataset<?>, Random, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getTestSet() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
getTestStartTime() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
getThreshold() - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
getThreshold() - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
getThresholdedPrediction() - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
getThresholdedPredictionAsSet(IMultiLabelClassification) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
getThresholdedPredictionMatrix(double) - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassificationPredictionBatch
getThresholdedPredictionMatrix(double[]) - Method in class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassificationPredictionBatch
getTimeoutForSolutionEvaluation() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the timeout for evaluating a solution.
getTimeouts() - Method in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
getTimestamp() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.MCCVSplitEvaluationEvent
getTimestamp() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationCompletedEvent
getTimestamp() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationFailedEvent
getTimestamp() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolatedEvent
getTimestampMatrices() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Getter for TimeSeriesDataset2.timestampMatrices.
getTimestamps(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getTimestamps(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Getter for the timestamp matrix at a specific index.
getTimestampsOrNull(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getTimestampsOrNull(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Getter for the timestamp matrix at a specific index.
getTournamentSize() - Method in interface ai.libs.jaicore.ml.hpo.ggp.IGrammarBasedGeneticProgrammingConfig
getTrainEndTime() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
getTrainFoldOfLabelStratifiedTrainTestSplit(ILabeledDataset<?>, long, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getTrainFoldOfLabelStratifiedTrainTestSplit(ILabeledDataset<?>, Random, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getTrainFoldOfSimpleTrainTestSplit(ILabeledDataset<?>, long, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getTrainFoldOfSimpleTrainTestSplit(ILabeledDataset<?>, Random, double) - Static method in class ai.libs.jaicore.ml.core.filter.SplitterUtil
getTrainFoldSize() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Getter for the size of the train fold.
getTrainingAndTestDataForFold(int, int, double[][], int[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Functions creating two TimeSeriesDataset2 objects representing the training and test split for the given fold of a cross validation with numFolds many folds.
getTrainingTimes() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
getTrainSet() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
getTrainStartTime() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
getTransformedVectorLength(int, int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform.BiliniearFeatureTransform
getTransformedVectorLength(int, int) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform.IDyadFeatureTransform: Get the length of the vector returned by the transform method.
getUnivirateHistograms() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSClassifier
getValue() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttributeValue
getValue() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.model.NDArrayTimeseries
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttributeValue
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttributeValue
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttributeValue
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttributeValue
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttributeValue
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttributeValue
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttributeValue
getValue() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttributeValue
getValue() - Method in enum ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffItem
getValue() - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttributeValue
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttribute
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AGenericObjectAttribute
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttribute
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttribute
getValueAsTypeInstance(Object) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttribute
getValueMatrices() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Getter for TimeSeriesDataset2.valueMatrices.
getValueOrNull(int) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeries: Returns the value of the given timestep if one exists, null otherwise.
getValues() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
getValues(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getValues(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Getter for the value matrix at a specific index.
getValuesOrNull(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
getValuesOrNull(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Getter for the value matrix at a specific index.
getVoteType() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Getter for the vote type.
getWeights() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationFunction
getWeights() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationParameterSet
getWindowedTimeSeries(int, int) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeries: Returns a part of this time series starting at the given fromTimestep and ending at the given toTimestep excluding.
getXsize() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute
getXValue(int, int, int, int) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AAreaUnderCurvePerformanceMeasure
getXValue(int, int, int, int) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AreaUnderPrecisionRecallCurve
getXValue(int, int, int, int) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AreaUnderROCCurve
getxValues() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
getYsize() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute
getYValue(int, int, int, int) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AAreaUnderCurvePerformanceMeasure
getYValue(int, int, int, int) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AreaUnderPrecisionRecallCurve
getYValue(int, int, int, int) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AreaUnderROCCurve
getyValues() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
getyValues() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
GMeans<C extends org.apache.commons.math3.ml.clustering.Clusterable> - Class in ai.libs.jaicore.ml.clustering.learner: 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.learner.GMeans: Initializes a basic cluster for the given Point using Mannhatten distance and seed=1
GMeans(Collection<C>, DistanceMeasure, int, long) - Constructor for class ai.libs.jaicore.ml.clustering.learner.GMeans: Initializes a cluster for the given Point using a given distance meassure and a seed.
GmeansSampling> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory: Implementation of a sampling method using gmeans-clustering.
GmeansSampling(int, long, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.GmeansSampling
GmeansSampling(int, long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.GmeansSampling
GmeansSampling(long, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.GmeansSampling
GmeansSampling(long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.GmeansSampling
GmeansSamplingFactory> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
GmeansSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.GmeansSamplingFactory
GMeansStratifier - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling: Combined strati amount selector and strati assigner via g-means.
GMeansStratifier(int) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.GMeansStratifier: Constructor for GMeansStratiAmountSelectorAndAssigner with Manhattan distanceMeasure as a default.
GMeansStratifier(DistanceMeasure, int) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.GMeansStratifier: Constructor for GMeansStratiAmountSelectorAndAssigner with custom distanceMeasure.
GrammarBasedGeneticProgramming - Class in ai.libs.jaicore.ml.hpo.ggp: Grammar-based genetic programming is an evolutionary algorithm capable of evolving individuals in the form of trees, where the trees are derived from a context-free grammar (CFG).
GrammarBasedGeneticProgramming(IOwnerBasedAlgorithmConfig, SoftwareConfigurationProblem<Double>, long) - Constructor for class ai.libs.jaicore.ml.hpo.ggp.GrammarBasedGeneticProgramming
GrammarBasedGeneticProgramming(SoftwareConfigurationProblem<Double>, long) - Constructor for class ai.libs.jaicore.ml.hpo.ggp.GrammarBasedGeneticProgramming
GrammarBasedGeneticProgramming.GGPSolutionCandidate - Class in ai.libs.jaicore.ml.hpo.ggp
grammarStringToComponentInstance(String) - Method in class ai.libs.jaicore.ml.hpo.ggp.CFGConverter
Group<C,I> - Class in ai.libs.jaicore.ml.ranking.label.learner.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.label.learner.clusterbased.customdatatypes.Group
GroupIdentifier<C> - Class in ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes
GroupIdentifier(C) - Constructor for class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.GroupIdentifier
gulloDerivate(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Calculates the derivative of a timeseries as described first by Gullo et. al (2009).
gulloDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: f'(n) = \frac{f(i+1)-f(i-1)}{2}

H

Hamming - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
Hamming() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.Hamming
Hamming(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.Hamming
HammingMassFunction - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic
HammingMassFunction() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic.HammingMassFunction
handleOutput(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
handleOutput(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnClassificationWrapper
handleOutput(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnMultiTargetRegressionWrapper
handleOutput(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnRegressionWrapper
handleOutput(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
handleOutput(File, File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
hashCode() - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
hashCode() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.ATimeseriesAttribute
hashCode() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
hashCode() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.ADataset
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.ALabeledDataset
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.MapInstance
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AAttribute
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.AGenericObjectAttribute
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttributeValue
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.ThreeDimensionalAttribute
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.schema.LabeledInstanceSchema
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.ReproducibleSplit
hashCode() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
hashCode() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
hashCode() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurveConfiguration
hashCode() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationParameterSet
hashCode() - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.MultiFidelityScore
hashCode() - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeries
hashCode() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
hashCode() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
hashCode() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
hashCode() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.Dyad
hashCode() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.RandomlyRankedNodeQueue
hashCode() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.RankingForGroup
hasNext() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSCLearningAlgorithm
HILL_3 - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
HistogramBuilder - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.util
HistogramBuilder() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.HistogramBuilder
histogramForInstance(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.HistogramBuilder
Hyperband - Class in ai.libs.jaicore.ml.hpo.multifidelity.hyperband: HyperBand is a simple but effective hyper-parameter optimization technique, heavily relying on a technique called successive halving.
Hyperband(IHyperbandConfig, MultiFidelitySoftwareConfigurationProblem<Double>) - Constructor for class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband
Hyperband.HyperbandSolutionCandidate - Class in ai.libs.jaicore.ml.hpo.multifidelity.hyperband
Hyperband.MultiFidelityScore - Class in ai.libs.jaicore.ml.hpo.multifidelity.hyperband
HyperbandSolutionCandidate(ComponentInstance, double, double) - Constructor for class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.HyperbandSolutionCandidate

I

IClusterableInstance - Interface in ai.libs.jaicore.ml.core.filter.sampling
IConfigurableLabelRanker - Interface in ai.libs.jaicore.ml.ranking.label.learner
IDatasetFilter - Interface in ai.libs.jaicore.ml.core.filter
IDyadFeatureTransform - Interface in ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform: Feature transformation interface for the FeatureTransformPLDyadRanker.
IDyadRanker - Interface in ai.libs.jaicore.ml.ranking.dyad.learner.algorithm: An abstract representation of a dyad ranker.
IDyadRankingFeatureTransformPLGradientDescendableFunction - Interface in ai.libs.jaicore.ml.ranking.dyad.learner.optimizing: An interface for a differentiable function in the context of feature transformation Placket-Luce dyad ranking.
IDyadRankingFeatureTransformPLGradientFunction - Interface in ai.libs.jaicore.ml.ranking.dyad.learner.optimizing: Represents a differentiable function in the context of dyad ranking based on feature transformation Placket-Luce models.
IDyadRankingPoolProvider - Interface in ai.libs.jaicore.ml.ranking.dyad.learner.activelearning: Interface for an active learning pool provider in the context of dyad ranking.
IFilter - Interface in ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter
IGeneralPredictionAndGroundTruthTable - Interface in ai.libs.jaicore.ml.core.evaluation
IGrammarBasedGeneticProgrammingConfig - Interface in ai.libs.jaicore.ml.hpo.ggp
IGroupBasedRanker<O,I extends org.api4.java.ai.ml.ranking.dataset.IRankingInstance<O>,D extends org.api4.java.ai.ml.ranking.dataset.IRankingDataset<O,I>,Z> - Interface in ai.libs.jaicore.ml.ranking.label.learner.clusterbased
IGroupBuilder<C,I> - Interface in ai.libs.jaicore.ml.ranking.label.learner.clusterbased: IGroupBuilder discribes the act of building groups out of probleminstances
IGroupSolutionRankingSelect<C,S,I,P> - Interface in ai.libs.jaicore.ml.ranking.label.learner.clusterbased
IHyperbandConfig - Interface in ai.libs.jaicore.ml.hpo.multifidelity.hyperband
IInstanceCollector - Interface in ai.libs.jaicore.ml.ranking.label.learner.clusterbased.datamanager
ILOG_2 - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
IMassFunction - Interface in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic
imports - Variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
IMultiClassClassificationExperimentConfig - Interface in ai.libs.jaicore.ml.core.evaluation.experiment
IMultiFidelityObjectEvaluator<T,V extends java.lang.Comparable<V>> - Interface in ai.libs.jaicore.ml.core.evaluation.evaluator: A multi-fidelity object evaluator allows for specifying a certain amount of an evaluation resource.
InconsistentDataFormatException - Exception in ai.libs.jaicore.ml.core.exception
InconsistentDataFormatException() - Constructor for exception ai.libs.jaicore.ml.core.exception.InconsistentDataFormatException
InconsistentDataFormatException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.InconsistentDataFormatException
InconsistentDataFormatException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.InconsistentDataFormatException
InconsistentDataFormatException(Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.InconsistentDataFormatException
INDArrayDyadRankingInstance - Interface in ai.libs.jaicore.ml.ranking.dyad.dataset
INDArrayTimeseries - Interface in ai.libs.jaicore.ml.classification.singlelabel.timeseries.model
indexOf(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
initialize(IDyadRankingDataset, Map<IDyadRankingInstance, Map<IDyad, IVector>>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood
initialize(IDyadRankingDataset, Map<IDyadRankingInstance, Map<IDyad, IVector>>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative
initialize(IDyadRankingDataset, Map<IDyadRankingInstance, Map<IDyad, IVector>>) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.IDyadRankingFeatureTransformPLGradientDescendableFunction: Initializes the function with the given dataset.
initialize(IDyadRankingDataset, Map<IDyadRankingInstance, Map<IDyad, IVector>>) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.IDyadRankingFeatureTransformPLGradientFunction: Initialize the function with the given data set and feature transformation method.
INNTER_ARRAY_SPLITTER - Static variable in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
input - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATSCAlgorithm: The TimeSeriesDataset object used for maintaining the model.
InputOptimizerLoss - Interface in ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization
InputOptListener - Class in ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.util
InputOptListener(int[]) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.util.InputOptListener
InstanceSchema - Class in ai.libs.jaicore.ml.core.dataset.schema
InstanceSchema() - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
InstanceSchema(String, Collection<IAttribute>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
InstanceWiseF1 - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss: Instance-wise F1 measure for multi-label classifiers.
InstanceWiseF1() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.InstanceWiseF1
InstanceWiseF1(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.InstanceWiseF1
IntBasedCategoricalAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
IntBasedCategoricalAttribute(String, List<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
IntBasedCategoricalAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
IntBasedCategoricalAttributeValue(ICategoricalAttribute, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttributeValue
IntBasedCategoricalAttributeValue(ICategoricalAttributeValue) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttributeValue
INTEGER - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType
intManhattanDistance(int[], int[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.MathUtil: Simple Manhattan distance (sum of the absolute differences between the vectors' elements) implementation for arrays of Integer.
InvalidAnchorPointsException - Exception in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation: Exception that is thrown, when the anchorpoints generated for learning curve extrapolation are not suitable.
InvalidAnchorPointsException() - Constructor for exception ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.InvalidAnchorPointsException
InversePowerLawConfiguration - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawConfiguration
InversePowerLawExtrapolationMethod - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawExtrapolationMethod
InversePowerLawExtrapolationMethod(String, String) - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawExtrapolationMethod
InversePowerLawLearningCurve - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
InversePowerLawLearningCurve(InversePowerLawConfiguration) - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
IOWAValueFunction - Interface in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa
IPLDyadRanker - Interface in ai.libs.jaicore.ml.ranking.dyad.learner.algorithm: An abstract representation for a dyad ranker using Placket Luce models.
IPLNetDyadRankerConfiguration - Interface in ai.libs.jaicore.ml.ranking.dyad.learner.algorithm
IPredictedClassPerformanceMeasure - Interface in ai.libs.jaicore.ml.classification.loss.dataset
IQualityMeasure - Interface in ai.libs.jaicore.ml.classification.singlelabel.timeseries.quality: 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.label.learner.clusterbased.candidateprovider
IRerunnableSamplingAlgorithmFactory<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>,A extends ASamplingAlgorithm<D>> - Interface in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.interfaces: Extension of the ISamplingAlgorithmFactory for sampling algorithms that can re-use informations from a previous run of the Sampling algorithm.
ISamplingAlgorithmFactory<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>,A extends ASamplingAlgorithm<D>> - Interface in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.interfaces: Interface for a factory, which creates a sampling algorithm.
isBinary() - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
IScikitLearnWrapper - Interface in ai.libs.jaicore.ml.scikitwrapper: Handles the execution of a scikit-learn pipeline in python and makes the according predictions available.
IScikitLearnWrapperConfig - Interface in ai.libs.jaicore.ml.scikitwrapper
isEmpty() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
isEmpty() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: States whether the dataset is empty, i.e. contains no value matrices, or not.
isEmpty() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
isEmpty() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
isEmpty() - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.ProblemInstance
isLabelPresent() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
isLabelPresent() - Method in class ai.libs.jaicore.ml.core.dataset.AInstance
isMultivariate() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
isMultivariate() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: States whether the dataset is a univariate dataset, i.e. contains more than one value matrix, or not.
ISplitBasedSupervisedLearnerEvaluatorFactory,F> - Interface in ai.libs.jaicore.ml.core.evaluation.evaluator.factory
ISQLDatasetMapper - Interface in ai.libs.jaicore.ml.core.dataset.serialization: This interface is meant to offer the ability to serialize and unserialize datasets from and to database tables.
isSameLength(double[], double[]...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Checks whether multiple arrays have the same length.
isSameLength(INDArray, INDArray...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Checks whether multiple arrays have the same length.
isSameLengthOrException(double[], double[]...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Checks whether multiple arrays have the same length.
isSameLengthOrException(INDArray, INDArray...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Checks whether multiple arrays have the same length.
isTest() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: 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.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.util.TimeSeriesUtil: Checks, whether given INDArrays are valid time series with a given length.
isTimeSeries(INDArray...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Checks, whether given INDArray are valid time series.
isTimeSeriesOrException(int, double[]...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.util.TimeSeriesUtil: Checks, whether given INDArrays are valid time series with a given length.
isTimeSeriesOrException(INDArray...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Checks, whether given INDArrays are valid time series.
isTrain() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: 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.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier
IStratifier - Interface in ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling
IStratiFileAssigner - Interface in ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling: Interface to implement custom Stratum assignment behavior.
isUnivariate() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
isUnivariate() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: States whether the dataset is a univariate dataset, i.e. contains exactly one value matrix, or not.
ISupervisedLearnerEvaluatorFactory> - Interface in ai.libs.jaicore.ml.core.evaluation.evaluator.factory
isValidValue(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttribute: Validates whether a INDArray conforms to this time series.
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.ThreeDimensionalAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute
isValidValue(Object) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttribute
ITableGeneratorandCompleter<I,S,P> - Interface in ai.libs.jaicore.ml.ranking.label.learner.clusterbased.datamanager
iterator() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.ITimeSeriesDataset
iterator() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
iterator() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
iterator() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSCLearningAlgorithm
iterator() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
iterator() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
iterator() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
iterator() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
ITimeSeriesDataset - Interface in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset
ITimeSeriesInstance - Interface in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset
IVectorDyad - Interface in ai.libs.jaicore.ml.ranking.dyad

J

JaccardScore - Class in ai.libs.jaicore.ml.classification.loss.instance
JaccardScore - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
JaccardScore() - Constructor for class ai.libs.jaicore.ml.classification.loss.instance.JaccardScore
JaccardScore() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.JaccardScore
JaccardScore(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.JaccardScore
JANOSCHEK - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants

K

K_ACTIVATION_FUNCTION - Static variable in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration: The activation function for the hidden layers.
K_ALPHABET - Static variable in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_ALPHABET_SIZE - Static variable in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_CLASS_INDEX - Static variable in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
K_CRASH_SCORE - Static variable in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig
K_EARLY_STOPPING_INTERVAL - Static variable in interface ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.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.ranking.dyad.learner.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.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration: The ratio of data used for training in early stopping. 1 - this ratio is used for testing.
K_ETA - Static variable in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig
K_ITERATIONS - Static variable in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig
K_MAX_EPOCHS - Static variable in interface ai.libs.jaicore.ml.ranking.dyad.learner.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_MEANCORRECTED - Static variable in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_MEANNORMALIZATION - Static variable in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
K_MINI_BATCH_SIZE - Static variable in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration: The size of mini batches used during training.
K_PLNET_HIDDEN_NODES - Static variable in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration: List of integers describing the architecture of the hidden layers.
K_PLNET_LEARNINGRATE - Static variable in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration: The learning rate for the gradient updater.
K_PLNET_SEED - Static variable in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration: The random seed to use.
K_RELATION_NAME - Static variable in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
K_SEED - Static variable in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig
K_TEMP_FOLDER - Static variable in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapperConfig
K_WINDOW_SIZE - Static variable in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm.IBossAlgorithmConfig
K_WINDOWLENGTH_MAX - Static variable in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
K_WINDOWLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
K_WORDLENGTH - Static variable in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm.IBossAlgorithmConfig
KAPPA - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
KendallsTauDyadRankingLoss - Class in ai.libs.jaicore.ml.ranking.loss: Computes the rank correlation measure known as Kendall's tau coefficient, i.e.
KendallsTauDyadRankingLoss() - Constructor for class ai.libs.jaicore.ml.ranking.loss.KendallsTauDyadRankingLoss
KendallsTauOfTopK - Class in ai.libs.jaicore.ml.ranking.loss: Calculates the kendalls-tau loss only for the top k dyads.
KendallsTauOfTopK(int, double) - Constructor for class ai.libs.jaicore.ml.ranking.loss.KendallsTauOfTopK
keoghDerivate(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Calculates the derivative of a timeseries as described first by Keogh and Pazzani (2001).
keoghDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Calculates the derivateive of a timeseries as described first by Keogh and Pazzani (2001).
KmeansSampling> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory: Implementation of a sampling method using kmeans-clustering.
KmeansSampling(int, long, int, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.KmeansSampling: Implementation of a sampling method using kmeans-clustering.
KmeansSampling(int, long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.KmeansSampling: Implementation of a sampling method using kmeans-clustering.
KmeansSampling(long, int, int, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.KmeansSampling: Implementation of a sampling method using kmeans-clustering.
KmeansSamplingFactory> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
KmeansSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.KmeansSamplingFactory
KMeansStratifier - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling: Cluster the data set with k-means into k Clusters, where each cluster stands for one stratum.
KMeansStratifier(int, DistanceMeasure, int) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.KMeansStratifier: Constructor for KMeansStratiAssigner.

L

LabelBasedStratifiedSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling
LabelBasedStratifiedSampling(Random, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.LabelBasedStratifiedSampling
LabelBasedStratifiedSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
LabelBasedStratifiedSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.LabelBasedStratifiedSamplingFactory
LabeledInstanceSchema - Class in ai.libs.jaicore.ml.core.dataset.schema
LabeledInstanceSchema() - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.LabeledInstanceSchema
LabeledInstanceSchema(String, List<IAttribute>, IAttribute) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.LabeledInstanceSchema
LabelRankingAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
LabelRankingAttribute(String, Collection<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
LabelRankingAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
LabelRankingAttributeValue(IRankingAttribute<String>, IRanking<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttributeValue
lastIndexOf(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
LatexDatasetTableGenerator - Class in ai.libs.jaicore.ml.core.dataset.util
LatexDatasetTableGenerator() - Constructor for class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
LatexDatasetTableGenerator.DataSourceCreationFailedException - Exception in ai.libs.jaicore.ml.core.dataset.util
LCNetClient - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet
LCNetClient() - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetClient
LCNetExtrapolationMethod - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet: This class represents a learning curve extrapolation using the LCNet from pybnn.
LCNetExtrapolationMethod(String) - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetExtrapolationMethod
learner - Variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
LearnerEvaluatorConstructionFailedException - Exception in ai.libs.jaicore.ml.core.evaluation.evaluator.factory
LearnerEvaluatorConstructionFailedException(Exception) - Constructor for exception ai.libs.jaicore.ml.core.evaluation.evaluator.factory.LearnerEvaluatorConstructionFailedException
LearnerRunReport - Class in ai.libs.jaicore.ml.core.evaluation.evaluator
LearnerRunReport(ILabeledDataset<?>, ILabeledDataset<?>, long, long, long, long, Throwable) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
LearnerRunReport(ILabeledDataset<?>, ILabeledDataset<?>, long, long, long, long, IPredictionAndGroundTruthTable<?, ?>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
LearnerRunReport(ILabeledDataset<?>, ILabeledDataset<?>, long, long, Throwable) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
LearningCurveExtrapolatedEvent - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation
LearningCurveExtrapolatedEvent(LearningCurveExtrapolator) - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolatedEvent
LearningCurveExtrapolationEvaluator - Class in ai.libs.jaicore.ml.core.evaluation.evaluator: Evaluates a classifier by predicting its learning curve with a few anchorpoints.
LearningCurveExtrapolationEvaluator(int[], ISamplingAlgorithmFactory<ILabeledDataset<?>, ? extends ASamplingAlgorithm<ILabeledDataset<?>>>, ILabeledDataset<?>, double, LearningCurveExtrapolationMethod, long) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.LearningCurveExtrapolationEvaluator: Create a classifier evaluator with learning curve extrapolation.
LearningCurveExtrapolationEvaluatorFactory - Class in ai.libs.jaicore.ml.core.evaluation.evaluator.factory
LearningCurveExtrapolationEvaluatorFactory(int[], ISamplingAlgorithmFactory<ILabeledDataset<?>, ? extends ASamplingAlgorithm<ILabeledDataset<?>>>, double, LearningCurveExtrapolationMethod) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.LearningCurveExtrapolationEvaluatorFactory
LearningCurveExtrapolationMethod - Interface in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation: Functional interface for extrapolating a learning curve from anchorpoints.
LearningCurveExtrapolator - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation: Abstract class for implementing a learning curve extrapolation method with some anchor points.
LearningCurveExtrapolator(LearningCurveExtrapolationMethod, ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>, ILabeledDataset<?>, double, int[], ISamplingAlgorithmFactory<ILabeledDataset<?>, ? extends ASamplingAlgorithm<ILabeledDataset<?>>>, long) - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator: Create a learning curve extrapolator with a subsampling factory.
length() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.model.INDArrayTimeseries
length() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.model.NDArrayTimeseries
LINEAR_MEAN_SQUARED_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
LinearCombinationConstants - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc: This class contains required constant names for the linear combination learning curve.
LinearCombinationExtrapolationMethod - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationExtrapolationMethod
LinearCombinationExtrapolationMethod(String, String) - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationExtrapolationMethod
LinearCombinationFunction - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationFunction
LinearCombinationLearningCurve - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurve
LinearCombinationLearningCurveConfiguration - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurveConfiguration
LinearCombinationParameterSet - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationParameterSet
LinearMeanSquaredError - Class in ai.libs.jaicore.ml.regression.loss.dataset
LinearMeanSquaredError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.LinearMeanSquaredError
LinearMeanSquaredError(double) - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.LinearMeanSquaredError
listIterator() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
listIterator(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
listToMatrix(List<? extends int[]>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
listToMatrix(List<? extends int[]>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
listToRelevanceMatrix(List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
listToRelevanceMatrix(List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
listToThresholdedRelevanceMatrix(List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AMultiLabelClassificationMeasure
listToThresholdedRelevanceMatrix(List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
loadArff(File) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.SimplifiedTimeSeriesLoader: Loads a univariate time series dataset from the given arff file.
loadArffs(File...) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.SimplifiedTimeSeriesLoader: Loads a multivariate time series dataset from multiple arff files (each for one series).
loadModelFromFile(String) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker: Restore a trained model from a given file path.
loadTaskFold(int, int) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
LocalCaseControlSampling - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol
LocalCaseControlSampling(Random, int, ILabeledDataset<?>, IClassifier) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.LocalCaseControlSampling
LocalCaseControlSamplingFactory - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
LocalCaseControlSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.LocalCaseControlSamplingFactory
LOG_LOG_LINEAR - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
LOG_POWER - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
logger - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
LogLoss - Class in ai.libs.jaicore.ml.classification.loss.instance
LogLoss() - Constructor for class ai.libs.jaicore.ml.classification.loss.instance.LogLoss
LogLoss(double) - Constructor for class ai.libs.jaicore.ml.classification.loss.instance.LogLoss
loss(double[], ISingleLabelClassification) - Method in class ai.libs.jaicore.ml.classification.loss.instance.CrossEntropyLoss
loss(E, A) - Method in class ai.libs.jaicore.ml.classification.loss.instance.AInstanceMeasure
loss(Double, Double) - Method in class ai.libs.jaicore.ml.regression.loss.instance.AbsoluteError
loss(Double, Double) - Method in class ai.libs.jaicore.ml.regression.loss.instance.SquaredError
loss(Double, Double) - Method in class ai.libs.jaicore.ml.regression.loss.instance.SquaredLogarithmicError
loss(Integer, ISingleLabelClassification) - Method in class ai.libs.jaicore.ml.classification.loss.instance.LogLoss
loss(Object, Object) - Method in class ai.libs.jaicore.ml.classification.loss.instance.ZeroOneLoss
loss(List<? extends E>, List<? extends P>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure: If this performance measure is originally a score function its score is transformed into a loss by multiplying the score with -1.
loss(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AutoMEKAGGPFitnessMeasureLoss
loss(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.ExactMatch
loss(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.Hamming
loss(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.JaccardScore
loss(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.ChoquisticRelevanceLoss
loss(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.OWARelevanceLoss
loss(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.RankLoss
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.AbsoluteError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.AsymmetricLoss
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.AUnboundedRegressionMeasure
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.LinearMeanSquaredError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.MeanAbsoluteError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.MeanAbsolutePercentageError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.MeanPercentageError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredLogarithmicError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredLogarithmicMeanSquaredError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredPercentageError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.QuadraticQuadraticError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.RootMeanSquaredError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.SquaredError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.WeightedAbsoluteError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.WeightedAsymmetricAbsoluteError
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in enum ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
loss(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in enum ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
loss(List<? extends Integer>, List<? extends Integer>) - Method in interface ai.libs.jaicore.ml.classification.loss.dataset.IPredictedClassPerformanceMeasure
loss(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AveragedInstanceLoss
loss(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
loss(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.ErrorRate
loss(List<? extends IRanking<?>>, List<? extends IRanking<?>>) - Method in class ai.libs.jaicore.ml.ranking.loss.ARankingPredictionPerformanceMeasure
loss(List<? extends IRanking<?>>, List<? extends IRanking<?>>) - Method in class ai.libs.jaicore.ml.ranking.loss.KendallsTauOfTopK
loss(List<? extends IRanking<?>>, List<? extends IRanking<?>>) - Method in class ai.libs.jaicore.ml.ranking.loss.NDCGLoss
loss(List<List<? extends E>>, List<List<? extends A>>) - Method in class ai.libs.jaicore.ml.core.evaluation.AggregatingPredictionPerformanceMeasure
loss(List<List<? extends E>>, List<List<? extends A>>) - Method in class ai.libs.jaicore.ml.core.evaluation.SingleEvaluationAggregatedMeasure
loss(List<List<? extends Integer>>, List<List<? extends ISingleLabelClassification>>) - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EAggregatedClassifierMetric
loss(List<IPredictionAndGroundTruthTable<? extends E, ? extends A>>) - Method in class ai.libs.jaicore.ml.core.evaluation.AggregatingPredictionPerformanceMeasure
loss(List<IPredictionAndGroundTruthTable<? extends E, ? extends A>>) - Method in class ai.libs.jaicore.ml.core.evaluation.SingleEvaluationAggregatedMeasure
loss(List<IPredictionAndGroundTruthTable<? extends Integer, ? extends ISingleLabelClassification>>) - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EAggregatedClassifierMetric
loss(IPredictionAndGroundTruthTable<? extends E, ? extends P>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure
loss(IPredictionAndGroundTruthTable<? extends Double, ? extends IRegressionPrediction>) - Method in enum ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
loss(IPredictionAndGroundTruthTable<? extends Double, ? extends IRegressionPrediction>) - Method in enum ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
loss(IPredictionAndGroundTruthTable<? extends Integer, ? extends ISingleLabelClassification>) - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
loss(IRanking<?>, IRanking<?>) - Method in class ai.libs.jaicore.ml.ranking.loss.ARankingPredictionPerformanceMeasure
loss(IRanking<?>, IRanking<?>) - Method in class ai.libs.jaicore.ml.ranking.loss.KendallsTauDyadRankingLoss
loss(IRanking<?>, IRanking<?>) - Method in class ai.libs.jaicore.ml.ranking.loss.KendallsTauOfTopK
loss(IRanking<?>, IRanking<?>) - Method in class ai.libs.jaicore.ml.ranking.loss.NDCGLoss
loss(IRanking<?>, IRanking<?>) - Method in class ai.libs.jaicore.ml.ranking.loss.TopKOfPredicted
loss(INDArray) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization.InputOptimizerLoss
loss(INDArray) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization.NegIdentityInpOptLoss
lossGradient(INDArray) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization.InputOptimizerLoss
lossGradient(INDArray) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization.NegIdentityInpOptLoss

M

MAE - ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
MAJORITY - ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier.VoteType: Majority vote with @see NearestNeighborClassifier#voteMajority.
MajorityClassifier - Class in ai.libs.jaicore.ml.classification.singlelabel.learner
MajorityClassifier() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.learner.MajorityClassifier
map(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.util.ClassMapper: Maps a String value to an integer value based on the value's position in the classValues.
MAPE - ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
MapInstance - Class in ai.libs.jaicore.ml.core.dataset
MapInstance(ILabeledInstanceSchema, IAttribute) - Constructor for class ai.libs.jaicore.ml.core.dataset.MapInstance
MathUtil - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.util: Utility class consisting of mathematical utility functions.
mccv(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>, ILabeledDataset<? extends ILabeledInstance>, int, double, long) - Static method in class ai.libs.jaicore.ml.core.evaluation.MLEvaluationUtil
mccv(ISupervisedLearner<ILabeledInstance, ILabeledDataset<? extends ILabeledInstance>>, ILabeledDataset<? extends ILabeledInstance>, int, double, long, EAggregatedClassifierMetric) - Static method in class ai.libs.jaicore.ml.core.evaluation.MLEvaluationUtil
MCCVSplitEvaluationEvent - Class in ai.libs.jaicore.ml.core.evaluation.evaluator.events
MCCVSplitEvaluationEvent(IClassifier, int, int, int, double) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.events.MCCVSplitEvaluationEvent
mean(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil
mean(double[], int, int) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.MathUtil: Function calculating the mean of the interval [t1, t2 (inclusive)] of the given vector.
MEAN - ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesFeature.FeatureType
MEAN_ABSOLUTE_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
MEAN_ABSOLUTE_PERCENTAGE_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
MEAN_ASYMMETRIC_LOSS2 - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
MEAN_ERRORRATE - ai.libs.jaicore.ml.classification.loss.dataset.EAggregatedClassifierMetric
MEAN_PERCENTAGE_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
MEAN_SQUARED_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
MEAN_SQUARED_LOGARITHMIC_MEAN_SQUARED_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
MEAN_SQUARED_PERCENTAGE_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
MeanAbsoluteError - Class in ai.libs.jaicore.ml.regression.loss.dataset
MeanAbsoluteError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanAbsoluteError
MeanAbsolutePercentageError - Class in ai.libs.jaicore.ml.regression.loss.dataset
MeanAbsolutePercentageError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanAbsolutePercentageError
MeanAsymmetricLoss2 - Class in ai.libs.jaicore.ml.regression.loss.dataset
MeanAsymmetricLoss2() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanAsymmetricLoss2
MeanAsymmetricLoss2(double, double) - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanAsymmetricLoss2
meanCorrected() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
MeanPercentageError - Class in ai.libs.jaicore.ml.regression.loss.dataset
MeanPercentageError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanPercentageError
MeanSquaredError - Class in ai.libs.jaicore.ml.regression.loss.dataset
MeanSquaredError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredError
MeanSquaredLogarithmicError - Class in ai.libs.jaicore.ml.regression.loss.dataset
MeanSquaredLogarithmicError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredLogarithmicError
MeanSquaredLogarithmicMeanSquaredError - Class in ai.libs.jaicore.ml.regression.loss.dataset
MeanSquaredLogarithmicMeanSquaredError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredLogarithmicMeanSquaredError
MeanSquaredPercentageError - Class in ai.libs.jaicore.ml.regression.loss.dataset
MeanSquaredPercentageError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.MeanSquaredPercentageError
MEASURES - Static variable in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
mergeCluster(Map<double[], List<C>>) - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
metric - Variable in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
MLEvaluationUtil - Class in ai.libs.jaicore.ml.core.evaluation
MMF - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
model - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATSCAlgorithm: The model which is maintained during algorithm calls
modelFile - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
modelFile - Variable in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
MoebiusTransformOWAValueFunction - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa
MoebiusTransformOWAValueFunction(int) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa.MoebiusTransformOWAValueFunction
MonteCarloCrossValidationEvaluator - Class in ai.libs.jaicore.ml.core.evaluation.evaluator
MonteCarloCrossValidationEvaluator(boolean, ILabeledDataset<? extends ILabeledInstance>, int, double, Random, IAggregatedPredictionPerformanceMeasure<?, ?>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.MonteCarloCrossValidationEvaluator
MonteCarloCrossValidationEvaluator(boolean, ILabeledDataset<? extends ILabeledInstance>, IRandomDatasetSplitter<ILabeledDataset<? extends ILabeledInstance>>, int, Random, IAggregatedPredictionPerformanceMeasure<?, ?>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.MonteCarloCrossValidationEvaluator
MonteCarloCrossValidationEvaluator(ILabeledDataset<? extends ILabeledInstance>, int, double, Random) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.MonteCarloCrossValidationEvaluator
MonteCarloCrossValidationEvaluatorFactory - Class in ai.libs.jaicore.ml.core.evaluation.evaluator.factory: Factory for configuring standard Monte Carlo cross-validation evaluators.
MonteCarloCrossValidationEvaluatorFactory() - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.MonteCarloCrossValidationEvaluatorFactory
MonteCarloCrossValidationSplitSetGenerator<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> - Class in ai.libs.jaicore.ml.core.evaluation.splitsetgenerator: A DatasetSplitSetGenerator that create k independent splits of the given dataset.
MonteCarloCrossValidationSplitSetGenerator(IRandomDatasetSplitter<D>, int, Random) - Constructor for class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.MonteCarloCrossValidationSplitSetGenerator
mostFrequentLabelFromWindowLengthPredicitions(Map<Integer, Integer>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.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.
MSE - ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
mu(Collection<Double>, int) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic.HammingMassFunction
mu(Collection<Double>, int) - Method in interface ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic.IMassFunction
mu(Collection<Double>, int) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic.SubsetZeroOneMassFunction
MULTIDIMENSIONAL - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType
MultidimensionalAttribute<O> - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute: This is an IAttribute class that holds Multidimensional Double Arrays.
MultidimensionalAttribute(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
MultidimensionalAttributeValue<O> - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
MultidimensionalAttributeValue(MultidimensionalAttribute<O>, O) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttributeValue
MultiFidelityScore(double, double) - Constructor for class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.MultiFidelityScore
MultiFidelitySoftwareConfigurationProblem<V extends java.lang.Comparable<V>> - Class in ai.libs.jaicore.ml.hpo.multifidelity: A multi fidelity software configuration problem is a software configuraiton problem but requiring the composition evaluator to support multi-fidelity, i.e. evaluating a candidate with a specified amount of resources.
MultiFidelitySoftwareConfigurationProblem(MultiFidelitySoftwareConfigurationProblem<V>) - Constructor for class ai.libs.jaicore.ml.hpo.multifidelity.MultiFidelitySoftwareConfigurationProblem
MultiFidelitySoftwareConfigurationProblem(File, String, IMultiFidelityObjectEvaluator<IComponentInstance, V>) - Constructor for class ai.libs.jaicore.ml.hpo.multifidelity.MultiFidelitySoftwareConfigurationProblem
MultiFidelitySoftwareConfigurationProblem(Collection<IComponent>, String, IMultiFidelityObjectEvaluator<IComponentInstance, V>) - Constructor for class ai.libs.jaicore.ml.hpo.multifidelity.MultiFidelitySoftwareConfigurationProblem
MultiLabelAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
MultiLabelAttribute(String, List<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
MultiLabelAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
MultiLabelAttributeValue(MultiLabelAttribute, Collection<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttributeValue
MultiLabelClassification - Class in ai.libs.jaicore.ml.classification.multilabel
MultiLabelClassification(double[]) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
MultiLabelClassification(double[], double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
MultiLabelClassification(double[], double[]) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassification
MultiLabelClassificationPredictionBatch - Class in ai.libs.jaicore.ml.classification.multilabel
MultiLabelClassificationPredictionBatch(List<? extends IMultiLabelClassification>) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassificationPredictionBatch
MultiLabelClassificationPredictionBatch(IMultiLabelClassification[]) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.MultiLabelClassificationPredictionBatch
MySQLDatasetMapper - Class in ai.libs.jaicore.ml.core.dataset.serialization
MySQLDatasetMapper(IDatabaseAdapter) - Constructor for class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper

N

NDArrayTimeseries - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.model
NDArrayTimeseries(INDArray) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.model.NDArrayTimeseries
NDArrayTimeseriesAttribute - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute: Describes a time series type as an 1-NDArray with a fixed length.
NDArrayTimeseriesAttribute(String, int) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttribute
NDArrayTimeseriesAttributeValue - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute
NDArrayTimeseriesAttributeValue(ITimeseriesAttribute<?>, ITimeseries<INDArray>) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttributeValue
NDCGLoss - Class in ai.libs.jaicore.ml.ranking.loss: The Normalized Discounted Cumulative Gain for ranking.
NDCGLoss(int) - Constructor for class ai.libs.jaicore.ml.ranking.loss.NDCGLoss
nearestNeighborClassifier - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: The nearest neighbor classifier used for prediction.
NearestNeighborClassifier - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors: K-Nearest-Neighbor classifier for time series.
NearestNeighborClassifier(int, IDistanceMetric) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Creates a k nearest neighbor classifier using majority vote.
NearestNeighborClassifier(int, IDistanceMetric, NearestNeighborClassifier.VoteType) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Creates a k nearest neighbor classifier.
NearestNeighborClassifier(IDistanceMetric) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Creates a 1 nearest neighbor classifier using majority vote.
NearestNeighborClassifier.VoteType - Enum in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Singleton comparator instance for the nearest neighbor priority queues, used for the nearest neighbor calculation.
NearestNeighborLearningAlgorithm - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors: Training algorithm for the nearest neighbors classifier.
NearestNeighborLearningAlgorithm(IOwnerBasedAlgorithmConfig, NearestNeighborClassifier, TimeSeriesDataset2) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborLearningAlgorithm
NegIdentityInpOptLoss - Class in ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization: Loss function for PLNet input optimization that maximizes the output of a PLNet.
NegIdentityInpOptLoss() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization.NegIdentityInpOptLoss
next() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSCLearningAlgorithm
nextSample() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
nextSplitSet() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.ConstantSplitSetGenerator
nextSplitSet() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.FixedDataSplitSetGenerator
nextSplitSet(D) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
nextSplitSet(D) - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.CachingMonteCarloCrossValidationSplitSetGenerator
nextSplitSet(D) - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.MonteCarloCrossValidationSplitSetGenerator
nextWithException() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSCLearningAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborLearningAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.ReservoirSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.StratifiedFileSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.SystematicFileSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.GmeansSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.KmeansSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SimpleRandomSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.StratifiedSampling
nextWithException() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SystematicSampling
nextWithException() - Method in class ai.libs.jaicore.ml.hpo.ggp.GrammarBasedGeneticProgramming
nextWithException() - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband
NOMINAL - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType
NoneFittedFilterExeception - Exception in ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception
NoneFittedFilterExeception(String) - Constructor for exception ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception.NoneFittedFilterExeception
NoneFittedFilterExeception(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception.NoneFittedFilterExeception
normalizeByStandardDeviation(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil
normalizeINDArray(INDArray, boolean) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Normalizes an INDArray vector object.
NS - Static variable in interface ai.libs.jaicore.ml.hpo.multifidelity.hyperband.IHyperbandConfig
NUM_FEATURE_TYPES - Static variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesFeature: Number of features used within the time series tree.
NUMERIC - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType
NumericAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
NumericAttribute(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
NumericAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
NumericAttributeValue(INumericAttribute, double) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttributeValue
NumericAttributeValue(INumericAttributeValue) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttributeValue
numMCIterations - Variable in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

O

offer(IEvaluatedPath<N, ?, V>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
OPEN_OR_CLOSED_BRACES_REGEX - Static variable in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
OpenMLDatasetDescriptor - Class in ai.libs.jaicore.ml.core.dataset.serialization
OpenMLDatasetDescriptor(int) - Constructor for class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetDescriptor
OpenMLDatasetReader - Class in ai.libs.jaicore.ml.core.dataset.serialization
OpenMLDatasetReader() - Constructor for class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, double, int, INDArray) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.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.ranking.dyad.learner.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.ranking.dyad.learner.zeroshot.inputoptimization.PLNetInputOptimizer: Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.
OSMAC<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<? extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledInstance>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol
OSMAC(Random, D, ISamplingAlgorithmFactory<D, ?>, int, IClassifier) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.OSMAC
OSMAC(Random, D, IClassifier) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.OSMAC
OSMACSamplingFactory - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
OSMACSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.OSMACSamplingFactory
outputFileWriter - Variable in class ai.libs.jaicore.ml.core.filter.sampling.infiles.AFileSamplingAlgorithm
OWARelevanceLoss - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive
OWARelevanceLoss(IOWAValueFunction) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.OWARelevanceLoss

P

PairWisePreferenceToBinaryClassificationFilter - Class in ai.libs.jaicore.ml.ranking.filter
PairWisePreferenceToBinaryClassificationFilter(Object, Object) - Constructor for class ai.libs.jaicore.ml.ranking.filter.PairWisePreferenceToBinaryClassificationFilter
ParametricFunction - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.lc.ParametricFunction
ParametricFunction(Map<String, Double>) - Constructor for class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.ParametricFunction
parseAttribute(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter: parses an attribute definition of an ARff file.
parseInstance(boolean, List<IAttribute>, int, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter: Parses a single instance of an ARff file containing values for each attribute given (attributes parameter).
parseRelation(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter: Extracts meta data about a relation from a string.
peek() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
pipeline - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
plNetActivationFunction() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
PLNetDyadRanker - Class in ai.libs.jaicore.ml.ranking.dyad.learner.algorithm: A dyad ranker based on a Plackett-Luce network.
PLNetDyadRanker() - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker: Constructs a new PLNetDyadRanker using the default IPLNetDyadRankerConfiguration.
PLNetDyadRanker(IPLNetDyadRankerConfiguration) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker: Constructs a new PLNetDyadRanker using the given IPLNetDyadRankerConfiguration.
plNetEarlyStoppingInterval() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
plNetEarlyStoppingPatience() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
plNetEarlyStoppingRetrain() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
plNetEarlyStoppingTrainRatio() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
plNetHiddenNodes() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
PLNetInputOptimizer - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.zeroshot.inputoptimization.PLNetInputOptimizer
plNetLearningRate() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
PLNetLoss - Class in ai.libs.jaicore.ml.ranking.dyad.learner.algorithm: Implements the negative log likelihood (NLL) loss function for PL networks as described in [1]
plNetMaxEpochs() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
plNetMiniBatchSize() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
plNetSeed() - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.IPLNetDyadRankerConfiguration
PointWiseLearningCurve - Class in ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.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.functionprediction.learner.learningcurveextrapolation.lcnet.PointWiseLearningCurve
poll() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
PolynomialOWAValueFunction - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa
PolynomialOWAValueFunction(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa.PolynomialOWAValueFunction
poolProvider - Variable in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker
POW_3 - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
POW_4 - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
Precision - Class in ai.libs.jaicore.ml.classification.loss.dataset
Precision(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.Precision
PRECISION_WITH_1_POSITIVE - ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
predict(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.BOSSClassifier
predict(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSEnsembleClassifier
predict(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Predicts on univariate instance.
predict(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Predicts on univariate instance.
predict(int, double[], String) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetClient
predict(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier: Performs predictions based on the given instances in the given dataset.
predict(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSClassifier
predict(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSEnsembleClassifier
predict(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Predicts on a dataset.
predict(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Predicts on a dataset.
predict(D) - Method in class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
predict(I) - Method in class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
predict(I[]) - Method in class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
predict(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.BOSSClassifier
predict(List<double[]>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSEnsembleClassifier
predict(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
predict(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.SimpleScikitLearnClassifier
predict(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.SimpleScikitLearnRegressor
predict(ILabeledDataset<ILabeledInstance>) - Method in class ai.libs.jaicore.ml.ranking.filter.PairWisePreferenceToBinaryClassificationFilter
predict(ILabeledInstance) - Method in class ai.libs.jaicore.ml.classification.singlelabel.learner.MajorityClassifier
predict(ILabeledInstance) - Method in class ai.libs.jaicore.ml.ranking.filter.PairWisePreferenceToBinaryClassificationFilter
predict(ILabeledInstance) - Method in class ai.libs.jaicore.ml.regression.learner.ConstantRegressor
predict(ILabeledInstance) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
predict(ILabeledInstance) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
predict(ILabeledInstance[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.learner.ASingleLabelClassifier
predict(ILabeledInstance[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.learner.MajorityClassifier
predict(ILabeledInstance[]) - Method in class ai.libs.jaicore.ml.regression.learner.ConstantRegressor
predict(ILabeledInstance[]) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
predict(ILabeledInstance[]) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
predict(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform.FeatureTransformPLDyadRanker
predict(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
predict(IDyadRankingInstance[]) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform.FeatureTransformPLDyadRanker
predict(IDyadRankingInstance[]) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
predictDataFile - Variable in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
Prediction - Class in ai.libs.jaicore.ml.core.evaluation
Prediction(Object) - Constructor for class ai.libs.jaicore.ml.core.evaluation.Prediction
PredictionBatch - Class in ai.libs.jaicore.ml.core.evaluation
PredictionBatch(I[]) - Constructor for class ai.libs.jaicore.ml.core.evaluation.PredictionBatch
PredictionBatch(List<? extends IPrediction>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.PredictionBatch
PredictionDiff<E,A> - Class in ai.libs.jaicore.ml.core.evaluation.evaluator
PredictionDiff() - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
PredictionDiff(List<? extends E>, List<? extends A>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
predictOutputFile - Variable in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
preSampleSize - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.APilotEstimateSampling
PreTrainedPredictionBasedClassifierEvaluator - Class in ai.libs.jaicore.ml.core.evaluation.evaluator: This evaluator can be used to compute the performance of a pre-trained classifier on a given validation dataset
PreTrainedPredictionBasedClassifierEvaluator(ILabeledDataset<?>, IDeterministicPredictionPerformanceMeasure) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.PreTrainedPredictionBasedClassifierEvaluator
problem - Variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
ProblemInstance - Class in ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes
ProblemInstance() - Constructor for class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.ProblemInstance
ProblemInstance(I) - Constructor for class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.ProblemInstance
problemType - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
PrototypicalPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.activelearning.PrototypicalPoolBasedActiveDyadRanker
PYTHON_MINIMUM_REQUIRED_VERSION_MAJ - Static variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
PYTHON_MINIMUM_REQUIRED_VERSION_MAJ - Static variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
PYTHON_MINIMUM_REQUIRED_VERSION_MIN - Static variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
PYTHON_MINIMUM_REQUIRED_VERSION_MIN - Static variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
PYTHON_MINIMUM_REQUIRED_VERSION_REL - Static variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
PYTHON_MINIMUM_REQUIRED_VERSION_REL - Static variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
PYTHON_OPTIONAL_MODULES - Static variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
PYTHON_OPTIONAL_MODULES - Static variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
PYTHON_REQUIRED_MODULES - Static variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
PYTHON_REQUIRED_MODULES - Static variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
pythonC - Variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
pythonConfig - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper

Q

QUADRATIC_QUADRATIC_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
QuadraticQuadraticError - Class in ai.libs.jaicore.ml.regression.loss.dataset
QuadraticQuadraticError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.QuadraticQuadraticError
QuadraticQuadraticError(double) - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.QuadraticQuadraticError
query(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider

R

R2 - Class in ai.libs.jaicore.ml.regression.loss.dataset: The R^2, aka. the coefficient of determination describes the proportion of the variance in the target variable and the predicted values.
R2 - ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
R2() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.R2
rand - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
random - Variable in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
random - Variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
RandomHoldoutSplitter<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.dataset.splitter: This splitter just creates random split without looking at the data.
RandomHoldoutSplitter(double...) - Constructor for class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
RandomHoldoutSplitter(Random, double...) - Constructor for class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
RandomlyRankedNodeQueue<N,A,V extends java.lang.Comparable<V>> - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.search.RandomlyRankedNodeQueue
RandomlyRankedNodeQueueConfig<T> - Class in ai.libs.jaicore.ml.ranking.dyad.learner.search: Configuration for a RandomlyRankedNodeQueue
RandomlyRankedNodeQueueConfig(int) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.search.RandomlyRankedNodeQueueConfig: Construct a new config with the given seed.
RandomPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.ranking.dyad.learner.activelearning: A random active dyad ranker.
RandomPoolBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.RandomPoolBasedActiveDyadRanker
randomSeed - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
ranker - Variable in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker
ranker - Variable in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueueConfig: the ranker used to rank dyads consisting of pipeline metafeatures and dataset metafeatures
Ranking<O> - Class in ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes
Ranking() - Constructor for class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Ranking
Ranking(Collection<O>) - Constructor for class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Ranking
RankingForGroup<C,O> - Class in ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes: RankingForGroup.java - saves a solution ranking for a group identified by thier group
RankingForGroup(GroupIdentifier<C>, List<O>) - Constructor for class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.RankingForGroup
RankingPredictionBatch - Class in ai.libs.jaicore.ml.ranking
RankingPredictionBatch(List<IRanking<?>>) - Constructor for class ai.libs.jaicore.ml.ranking.RankingPredictionBatch
RankingPredictionBatch(IRanking<?>[]) - Constructor for class ai.libs.jaicore.ml.ranking.RankingPredictionBatch
RankLoss - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss
RankLoss() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.RankLoss
RankLoss(double) - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.RankLoss: Create a Ranking Loss measure instance.
readDataset(boolean, File) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
readDataset(boolean, File, int) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter: Parses the ARff dataset from the given file into a ILabeledDataset
readDataset(File) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
readDataset(File) - Static method in class ai.libs.jaicore.ml.core.dataset.serialization.CSVDatasetAdapter
readDatasetFromQuery(String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
readDatasetFromQuery(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
readDatasetFromQuery(String, String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
readDatasetFromQuery(String, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
readDatasetFromTable(String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
readDatasetFromTable(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
readDatasetFromTable(String, String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
readDatasetFromTable(String, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper
REAL - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType
Recall - Class in ai.libs.jaicore.ml.classification.loss.dataset
Recall(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.Recall
RECALL_WITH_1_POSITIVE - ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
REG_EXP_DATA_LINE - Static variable in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
registerListener(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSCLearningAlgorithm
registerListener(Object) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearningCurveExtrapolationEvaluator: Register observers for learning curve predictions (including estimates of the time)
registerListener(Object) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.TrainPredictionBasedClassifierEvaluator
REGRESSION - ai.libs.jaicore.ml.core.EScikitLearnProblemType
rekursivDFT(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
rekursivDFT(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
RELATION - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffItem
remove() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
remove(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Removes the time series variable at a given index.
remove(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
remove(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
remove(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
removeAll(Collection<?>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
removeAll(Collection<?>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
removeAttribute(int) - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
removeColumn(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset: Removes the time series variable at a given index.
removeColumn(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
removeColumn(int) - Method in class ai.libs.jaicore.ml.core.dataset.clusterable.ClusterableDataset
removeColumn(int) - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
removeColumn(int) - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
removeColumn(int) - Method in class ai.libs.jaicore.ml.core.dataset.MapInstance
removeColumn(int) - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
removeColumn(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
removeColumn(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
removeColumn(String) - Method in class ai.libs.jaicore.ml.core.dataset.ADataset
removeColumn(String) - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
removeColumn(String) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
removeColumn(IAttribute) - Method in class ai.libs.jaicore.ml.core.dataset.ADataset
removeColumn(IAttribute) - Method in class ai.libs.jaicore.ml.core.dataset.Dataset
removeColumn(IAttribute) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
removeNodeAtPosition(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
replace(int, double[][], double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Replaces the time series variable at a given index with a new one.
replace(int, INDArray, INDArray) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset: Replaces the time series variable at a given index with a new one.
replaceLabelAttribute(IAttribute) - Method in class ai.libs.jaicore.ml.core.dataset.schema.LabeledInstanceSchema
reportOptimizationStep(INDArray, double) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.util.InputOptListener
ReproducibleSplit<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.dataset.splitter
ReproducibleSplit(ReconstructionInstruction, D, D...) - Constructor for class ai.libs.jaicore.ml.core.dataset.splitter.ReproducibleSplit
ReservoirSampling - Class in ai.libs.jaicore.ml.core.filter.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.filter.sampling.infiles.ReservoirSampling
retainAll(Collection<?>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
retainAll(Collection<?>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
RMSE - ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
RMSLE - ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
ROOT_MEAN_SQUARED_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
RootMeanSquaredError - Class in ai.libs.jaicore.ml.regression.loss.dataset: The root mean squared loss function.
RootMeanSquaredError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.RootMeanSquaredError
RootMeanSquaredLogarithmError - Class in ai.libs.jaicore.ml.regression.loss.dataset
RootMeanSquaredLogarithmError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.RootMeanSquaredLogarithmError

S

sample - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
SampleComplementComputer - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory
SampleComplementComputer() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SampleComplementComputer
SampleElementAddedEvent - Class in ai.libs.jaicore.ml.core.filter.sampling
SampleElementAddedEvent(IAlgorithm<?, ?>) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.SampleElementAddedEvent
sampleSize - Variable in class ai.libs.jaicore.ml.core.filter.sampling.infiles.AFileSamplingAlgorithm
sampleSize - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
samplingAlgorithm - Variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
samplingAlgorithmFactory - Variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
saveModelToFile(String) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker: Save a trained model at a given file path.
SAX - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter
SAX(double[], int) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
scaler - Variable in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue: for scaling the dyads
scaler - Variable in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueueConfig: for scaling the dyads
ScikitLearnClassificationWrapper - Class in ai.libs.jaicore.ml.scikitwrapper
ScikitLearnClassificationWrapper(String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnClassificationWrapper
ScikitLearnMultiTargetRegressionWrapper - Class in ai.libs.jaicore.ml.scikitwrapper
ScikitLearnMultiTargetRegressionWrapper(EScikitLearnProblemType, String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnMultiTargetRegressionWrapper
ScikitLearnMultiTargetRegressionWrapper(String, String, int[]) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnMultiTargetRegressionWrapper
ScikitLearnRegressionWrapper - Class in ai.libs.jaicore.ml.scikitwrapper
ScikitLearnRegressionWrapper(EScikitLearnProblemType, String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnRegressionWrapper
ScikitLearnRegressionWrapper(String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnRegressionWrapper
ScikitLearnTimeSeriesFeatureEngineeringWrapper - Class in ai.libs.jaicore.ml.scikitwrapper
ScikitLearnTimeSeriesFeatureEngineeringWrapper(String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesFeatureEngineeringWrapper
ScikitLearnTimeSeriesRegressionWrapper - Class in ai.libs.jaicore.ml.scikitwrapper
ScikitLearnTimeSeriesRegressionWrapper(String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnTimeSeriesRegressionWrapper
ScikitLearnWrapperCommandBuilder - Class in ai.libs.jaicore.ml.scikitwrapper
ScikitLearnWrapperCommandBuilder(String, File) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
scikitLearnWrapperConfig - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
ScikitLearnWrapperExecutionFailedException - Exception in ai.libs.jaicore.ml.scikitwrapper
ScikitLearnWrapperExecutionFailedException(String) - Constructor for exception ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperExecutionFailedException: Creates a new ScikitLearnWrapperExecutionFailedException with the given parameters.
ScikitLearnWrapperExecutionFailedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperExecutionFailedException: Creates a new ScikitLearnWrapperExecutionFailedException with the given parameters.
score(E, A) - Method in class ai.libs.jaicore.ml.classification.loss.instance.AInstanceMeasure
score(Object, Object) - Method in class ai.libs.jaicore.ml.classification.loss.instance.ZeroOneLoss
score(Collection<Integer>, Collection<Integer>) - Method in class ai.libs.jaicore.ml.classification.loss.instance.JaccardScore
score(List<? extends E>, List<? extends P>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure: If this performance measure is originally a loss function its loss is transformed into a score by multiplying the loss with -1.
score(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.F1MacroAverageL
score(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.F1MicroAverage
score(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.InstanceWiseF1
score(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.JaccardScore
score(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.ChoquisticRelevanceLoss
score(List<? extends int[]>, List<? extends IMultiLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.OWARelevanceLoss
score(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.AsymmetricLoss2
score(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.AUnboundedRegressionMeasure
score(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.MeanAsymmetricLoss2
score(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.R2
score(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in class ai.libs.jaicore.ml.regression.loss.dataset.RootMeanSquaredLogarithmError
score(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in enum ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
score(List<? extends Double>, List<? extends IRegressionPrediction>) - Method in enum ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
score(List<? extends Integer>, List<? extends Integer>) - Method in interface ai.libs.jaicore.ml.classification.loss.dataset.IPredictedClassPerformanceMeasure
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.AAreaUnderCurvePerformanceMeasure
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.FalseNegatives
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.FalsePositives
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.FMeasure
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.Precision
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.Recall
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.TrueNegatives
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.TruePositives
score(List<? extends Integer>, List<? extends ISingleLabelClassification>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.WeightedAUROC
score(List<List<? extends E>>, List<List<? extends A>>) - Method in class ai.libs.jaicore.ml.core.evaluation.AggregatingPredictionPerformanceMeasure
score(List<List<? extends E>>, List<List<? extends A>>) - Method in class ai.libs.jaicore.ml.core.evaluation.SingleEvaluationAggregatedMeasure
score(List<List<? extends Integer>>, List<List<? extends ISingleLabelClassification>>) - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EAggregatedClassifierMetric
score(List<IPredictionAndGroundTruthTable<? extends E, ? extends A>>) - Method in class ai.libs.jaicore.ml.core.evaluation.AggregatingPredictionPerformanceMeasure
score(List<IPredictionAndGroundTruthTable<? extends E, ? extends A>>) - Method in class ai.libs.jaicore.ml.core.evaluation.SingleEvaluationAggregatedMeasure
score(List<IPredictionAndGroundTruthTable<? extends Integer, ? extends ISingleLabelClassification>>) - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EAggregatedClassifierMetric
score(IPredictionAndGroundTruthTable<? extends E, ? extends P>) - Method in class ai.libs.jaicore.ml.classification.loss.dataset.APredictionPerformanceMeasure
score(IPredictionAndGroundTruthTable<? extends Double, ? extends IRegressionPrediction>) - Method in enum ai.libs.jaicore.ml.regression.loss.ERegressionPerformanceMeasure
score(IPredictionAndGroundTruthTable<? extends Double, ? extends IRegressionPrediction>) - Method in enum ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
score(IPredictionAndGroundTruthTable<? extends Integer, ? extends ISingleLabelClassification>) - Method in enum ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
seed - Variable in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
seed - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
SEEDS - Static variable in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
selectGroupsolutionRanking(Group<C, I>, Table<I, S, P>) - Method in interface ai.libs.jaicore.ml.ranking.label.learner.clusterbased.IGroupSolutionRankingSelect
SensorTimeSeries - Class in ai.libs.jaicore.ml.pdm.dataset
SensorTimeSeries() - Constructor for class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeries
SensorTimeSeriesAttribute - Class in ai.libs.jaicore.ml.pdm.dataset
SensorTimeSeriesAttribute(String) - Constructor for class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttribute
SensorTimeSeriesAttributeValue - Class in ai.libs.jaicore.ml.pdm.dataset
SensorTimeSeriesAttributeValue(SensorTimeSeriesAttribute, SensorTimeSeries) - Constructor for class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttributeValue
serialize(OutputStream) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttribute
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttribute
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute: This method takes and parameter of type MultidimensionalAttributeValue or O and serializes it.
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttribute
serializeAttributeValue(Object) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttribute: Returns format: "t1:v1 t2:v2 ... tn:vn"
serializeDataset(File, ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
set(int, E) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
setA(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawConfiguration
setAcceptanceTresholds(List<Pair<ILabeledInstance, Double>>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
setAlgorithm(ATSCAlgorithm<L, D, ? extends ATimeSeriesClassificationModel<L, D>>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATimeSeriesClassificationModel: Sets the training algorithm for the classifier.
setArffHeader(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.ClassStratiFileAssigner
setArffHeader(String) - Method in interface ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.IStratiFileAssigner: Set the header of the original ARFF input file.
setAttributeValue(int, Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
setAttributeValue(int, Object) - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
setAttributeValue(int, Object) - Method in class ai.libs.jaicore.ml.core.dataset.MapInstance
setAttributeValue(int, Object) - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
setAttributeValue(int, Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
setB(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawConfiguration
setBasselCorrected(boolean) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
setC(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawConfiguration
setCaption(String) - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
setClassMapper(ClassMapper) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier: Setter for the property classMapper.
setClassValues(List<String>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.ClassMapper: Setter for the classValues.
setClusterResults(List<CentroidCluster>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
setClusters(List<CentroidCluster<Clusterable>>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
setClusterSeed(long) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.GmeansSamplingFactory: Set the seed the clustering will use for initialization.
setClusterSeed(long) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.KmeansSamplingFactory: Set the seed the clustering will use for initialization.
setComparator(Comparator<String>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.DatasetFileSorter
setConfig(Map<String, Object>) - Method in class ai.libs.jaicore.ml.core.learner.ASupervisedLearner
setConfigurations(double[]) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetExtrapolationMethod
setData(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
setData(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ExtrapolatedSaturationPointEvaluatorFactory
setData(ILabeledDataset<? extends ILabeledInstance>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.LearningCurveExtrapolationEvaluatorFactory
setDatapointComparator(Comparator<IInstance>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.SystematicSamplingFactory: Set a custom comparator that will be used to sort the datapoints before sampling.
setDataset(IDataset<?>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
setDefaultWindowSize(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
setDeterminedQuality(double) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet: Setter for Shapelet.determinedQuality.
setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ClusterSampling
setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.GmeansSamplingFactory: Set the distance measure for the clustering.
setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.KmeansSamplingFactory: Set the distance measure for the clustering.
setDyadRanker(IDyadRanker) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue: Set which dyad ranker shall be used to rank the nodes.
setDyads(Set<IDyad>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
setDyads(Set<IDyad>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
setDyads(Set<IDyad>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
setEpsilon(double) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.ExtrapolatedSaturationPointEvaluator
setFullDatasetSize(int) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.ConfigurationLearningCurveExtrapolationEvaluator
setFullDatasetSize(int) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearningCurveExtrapolationEvaluator
setFunctions(List<UnivariateFunction>) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationFunction
setGroupIdentifier(GroupIdentifier<C>) - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Group
setHistogramUnivirate(List<Map<Integer, Integer>>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSClassifier
setInput(D) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATSCAlgorithm: Setter for the data set input used during algorithm calls.
setInstance(I) - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.ProblemInstance
setInstances(List<ProblemInstance>) - Method in class ai.libs.jaicore.ml.ranking.label.learner.clusterbased.customdatatypes.Group
setInternalDataset(Dataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
setIntervals(List<Interval>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
setK(int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.KmeansSamplingFactory: Set how many clusters shall be created.
setL(int) - Method in class ai.libs.jaicore.ml.ranking.loss.NDCGLoss
setLabel(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
setLabel(Object) - Method in class ai.libs.jaicore.ml.core.dataset.AInstance
setLabel(Object) - Method in class ai.libs.jaicore.ml.core.dataset.MapInstance
setLabel(Object) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
setLabel(String) - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
setLength(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.ATimeseriesAttribute
setLengthOfTopRankingToConsider(int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.PrototypicalPoolBasedActiveDyadRanker
setListener(InputOptListener) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.zeroshot.inputoptimization.PLNetInputOptimizer: Set an InputOptListener to record the intermediate steps of the optimization procedure.
setLoggerName(String) - Method in class ai.libs.jaicore.ml.clustering.learner.GMeans
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.dataset.schema.DatasetPropertyComputer
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.OpenMLDatasetReader
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearningCurveExtrapolationEvaluator
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.SupervisedLearnerExecutor
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.TrainPredictionBasedClassifierEvaluator
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.FixedDataSplitSetGenerator
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.MonteCarloCrossValidationSplitSetGenerator
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.APilotEstimateSampling
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.CaseControlLikeSampling
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.DiscretizationHelper
setLoggerName(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.StratifiedSampling
setLoggerName(String) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
setLoggerName(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
setLoggerName(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
setMaxIterations(int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.KmeansSamplingFactory
setMaxIterationsInnerLoop(int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.GmeansSamplingFactory
setMeanCorrected(boolean) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
setMeasure(IDeterministicPredictionPerformanceMeasure<?, ?>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
setMeasure(IDeterministicPredictionPerformanceMeasure<?, ?>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ExtrapolatedSaturationPointEvaluatorFactory
setModel(T) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ATSCAlgorithm: Setter for the model to be maintained.
setModelPath(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
setModelPath(String) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
setModelPath(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
setNearestNeighborClassifier(NearestNeighborClassifier) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Sets the nearest neighbor classifier, ShotgunEnsembleClassifier.nearestNeighborClassifier.
setNumberOfDesieredDFTCoefficients(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
setNumberOfDisieredCoefficients(int) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
setNumCPUs(int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeBasedStratifier
setNumCPUs(int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.ClusterStratiAssigner
setNumMajorColumns(int) - Method in class ai.libs.jaicore.ml.core.dataset.util.LatexDatasetTableGenerator
setNumSamples(Integer) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
setOffset(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationFunction
SetOfObjectsAttribute<O> - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
SetOfObjectsAttribute(String, Class<O>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
SetOfObjectsAttribute(String, Class<O>, Set<O>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
SetOfObjectsAttributeValue<O> - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
SetOfObjectsAttributeValue(Set<O>, ISetOfObjectsAttribute<O>) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttributeValue
setOutputFileName(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.AFileSamplingAlgorithm
setParameters(Map<String, Map<String, Double>>) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationParameterSet
setParameterSets(List<LinearCombinationParameterSet>) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurveConfiguration
setParams(Map<String, Double>) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.ParametricFunction
setPilot(IClassifier) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.LocalCaseControlSamplingFactory
setPilot(IClassifier) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.OSMACSamplingFactory
setPoolProvider(IDyadRankingPoolProvider) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker
setPreSampleSize(int) - Method in class ai.libs.jaicore.ml.core.filter.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.filter.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.filter.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(LocalCaseControlSampling) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.LocalCaseControlSamplingFactory
setPreviousRun(OSMAC<ILabeledDataset<?>>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.OSMACSamplingFactory
setPreviousRun(GmeansSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.GmeansSamplingFactory
setPreviousRun(KmeansSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.KmeansSamplingFactory
setPreviousRun(StratifiedSampling<D>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.LabelBasedStratifiedSamplingFactory
setPreviousRun(SystematicSampling<D>) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.SystematicSamplingFactory
setPythonConfig(IPythonConfig) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
setPythonConfig(IPythonConfig) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
setPythonConfig(IPythonConfig) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
setPythonTemplate(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
setPythonTemplate(String) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
setPythonTemplate(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
setRandom(Random) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
setRandom(Random) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ExtrapolatedSaturationPointEvaluatorFactory
setRandom(Random) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.LearningCurveExtrapolationEvaluatorFactory
setRandom(Random) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
setRanker(IDyadRanker) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueueConfig: Set the ranker used to rank the OPEN list.
setRanker(PLNetDyadRanker) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ActiveDyadRanker
setRanking(Ranking<IDyad>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
setRanking(Ranking<IDyad>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
setRanking(Ranking<IDyad>) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
setRatioOfOldInstancesForMinibatch(double) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.PrototypicalPoolBasedActiveDyadRanker
setRemoveDyadsWhenQueried(boolean) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.DyadDatasetPoolProvider
setRemoveDyadsWhenQueried(boolean) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.IDyadRankingPoolProvider
setSampleSize(double) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
setSampleSize(int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.AFileSamplingAlgorithm
setSampleSize(int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.ASamplingAlgorithm
setSampleSize(int) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
setScaler(AbstractDyadScaler) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
setScaler(AbstractDyadScaler) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueueConfig: Set the scaler used to scale the dataset.
setScikitLearnWrapperConfig(IScikitLearnWrapperConfig) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
setScikitLearnWrapperConfig(IScikitLearnWrapperConfig) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
setScikitLearnWrapperConfig(IScikitLearnWrapperConfig) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
setSeed(long) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.ASampleAlgorithmFactory
setSeed(long) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
setSeed(long) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
setSeed(long) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
setSortedDataset(D) - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SystematicSampling
setTargetIndices(int...) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
setTargetIndices(int...) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
setTargetIndices(int...) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
setTargets(int[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Setter for TimeSeriesDataset2.targets.
setTargets(int[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Sets the targets.
setTargets(int[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Sets the targets.
setTempFileHandler(TempFileHandler) - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.ClassStratiFileAssigner
setTempFileHandler(TempFileHandler) - Method in interface ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.IStratiFileAssigner: Set the temporary file handler, which will be used to manage the temporary files for the strati.
setTimeout(Timeout) - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
setTimeout(Timeout) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IScikitLearnWrapper
setTimeout(Timeout) - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
setTimestampMatrices(List<double[][]>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Setter for TimeSeriesDataset2.timestampMatrices.
setTimestamps(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Sets the timestamps.
setTrainingData(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSClassifier
setValueMatrices(List<double[][]>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Setter for TimeSeriesDataset2.valueMatrices.
setValues(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Sets the value matrix.
setValues(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Sets the value matrix.
setWeights(List<Double>) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationFunction
setWeights(Map<String, Double>) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationParameterSet
setWindows(ArrayList<Pair<Integer, Integer>>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
setyValues(List<Double>) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
SFA - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter
SFA(double[], int) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
Shapelet - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.shapelets.Shapelet: Constructs a shapelet specified by the given parameters.
Shapelet(double[], int, int, int, double) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet: Constructs a shapelet specified by the given parameters.
shotgunDistance - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: The Shotgun Distance used by the ShotgunEnsembleClassifier.nearestNeighborClassifier.
ShotgunEnsembleClassifier - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Creates a Shotgun Ensemble classifier.
ShotgunEnsembleLearnerAlgorithm - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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, TimeSeriesDataset2) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm
ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig - Interface in ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors
shuffleTimeSeriesDataset(TimeSeriesDataset2, int) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Shuffles the given TimeSeriesDataset2 object using the given seed.
sigmoid(double) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.MathUtil: Function to calculate the sigmoid for the given value z.
SimpleRandomSampling<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory
SimpleRandomSampling(Random, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SimpleRandomSampling
SimpleRandomSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
SimpleRandomSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.SimpleRandomSamplingFactory
SimpleScikitLearnClassifier - Class in ai.libs.jaicore.ml.scikitwrapper.simple
SimpleScikitLearnClassifier(String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.simple.SimpleScikitLearnClassifier
SimpleScikitLearnRegressor - Class in ai.libs.jaicore.ml.scikitwrapper.simple
SimpleScikitLearnRegressor(String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.simple.SimpleScikitLearnRegressor
SimplifiedTimeSeriesLoader - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.util: Time series loader class which provides functionality to read datasets from files storing into simplified, more efficient time series datasets.
SINGLE_SPACE - Static variable in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
SingleEvaluationAggregatedMeasure<E,A> - Class in ai.libs.jaicore.ml.core.evaluation
SingleEvaluationAggregatedMeasure(IDeterministicPredictionPerformanceMeasure<E, A>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.SingleEvaluationAggregatedMeasure
SingleLabelClassification - Class in ai.libs.jaicore.ml.classification.singlelabel
SingleLabelClassification(double[]) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
SingleLabelClassification(int, int) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
SingleLabelClassification(Map<Integer, Double>) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
SingleLabelClassificationPredictionBatch - Class in ai.libs.jaicore.ml.classification.singlelabel
SingleLabelClassificationPredictionBatch(Collection<ISingleLabelClassification>) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassificationPredictionBatch
SingleRandomSplitClassifierEvaluator - Class in ai.libs.jaicore.ml.core.evaluation.evaluator
SingleRandomSplitClassifierEvaluator(ILabeledDataset<?>, double, Random) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.SingleRandomSplitClassifierEvaluator
singleSquaredEuclideanDistance(double[], double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.MathUtil: Computes the single squared Euclidean distance between two vectors.
SingleTargetRegressionPrediction - Class in ai.libs.jaicore.ml.regression.singlelabel
SingleTargetRegressionPrediction(Object) - Constructor for class ai.libs.jaicore.ml.regression.singlelabel.SingleTargetRegressionPrediction
SingleTargetRegressionPredictionBatch - Class in ai.libs.jaicore.ml.regression.singlelabel
SingleTargetRegressionPredictionBatch(Collection<IRegressionPrediction>) - Constructor for class ai.libs.jaicore.ml.regression.singlelabel.SingleTargetRegressionPredictionBatch
size() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.PredictionDiff
size() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
size() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
skipWithReaderToDatapoints(BufferedReader) - Static method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.ArffUtilities: Skips with a given reader all comment lines and the header lines of an ARFF file until the first datapoint is reached.
sklearnClassifierConfig - Variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
SlidingWindowBuilder - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter
SlidingWindowBuilder() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
slope(double[], int, int) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.MathUtil: Function calculating the slope of the interval [t1, t2 (inclusive)] of the given vector.
SLOPE - ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesFeature.FeatureType
sort(String) - Method in class ai.libs.jaicore.ml.core.filter.sampling.infiles.DatasetFileSorter
sortByLengthAsc(List<Shapelet>) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.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.classification.singlelabel.timeseries.util.TimeSeriesUtil: Sorts the indices of the given vector based on the the vector's values (argsort).
SparseDyadRankingInstance - Class in ai.libs.jaicore.ml.ranking.dyad.dataset: A dyad ranking instance implementation that assumes the same instance for all dyads contained in its ordering.
SparseDyadRankingInstance(IVector, List<IVector>) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
SparseDyadRankingInstance(IVector, Set<IVector>) - Constructor for class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
SparseInstance - Class in ai.libs.jaicore.ml.core.dataset
SparseInstance(int, Map<Integer, Object>, Object) - Constructor for class ai.libs.jaicore.ml.core.dataset.SparseInstance
SparseInstance.ENullElement - Enum in ai.libs.jaicore.ml.core.dataset: Determines a default interpretation of values not contained in the map of attributes.
specialFitTransform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.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(D) - Method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
split(D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
split(D, Random, double...) - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
split(D, Random, double...) - Method in class ai.libs.jaicore.ml.core.filter.FilterBasedDatasetSplitter
splitDenseInstanceLine(String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
SplitterUtil - Class in ai.libs.jaicore.ml.core.filter
SquaredError - Class in ai.libs.jaicore.ml.regression.loss.dataset
SquaredError - Class in ai.libs.jaicore.ml.regression.loss.instance: Measure computing the squared error of two doubles.
SquaredError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.SquaredError
SquaredError() - Constructor for class ai.libs.jaicore.ml.regression.loss.instance.SquaredError
SquaredLogarithmicError - Class in ai.libs.jaicore.ml.regression.loss.instance
SquaredLogarithmicError() - Constructor for class ai.libs.jaicore.ml.regression.loss.instance.SquaredLogarithmicError
SquaredLogarithmicError(double) - Constructor for class ai.libs.jaicore.ml.regression.loss.instance.SquaredLogarithmicError
standardDeviation(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Calculates the (population) standard deviation of the values of a times series.
statsX - Variable in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler
statsY - Variable in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler
stddev(double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.MathUtil: Function calculating the standard deviation of the interval [t1, t2 (inclusive)] of the given vector.
STDDEV - ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesFeature.FeatureType
StratifiedFileSampling - Class in ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling
StratifiedFileSampling(Random, IStratiFileAssigner, File) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.infiles.stratified.sampling.StratifiedFileSampling: Constructor for a Stratified File Sampler.
StratifiedSampling<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling: Implementation of Stratified Sampling: Divide dataset into strati and sample from each of these.
StratifiedSampling(IStratifier, Random, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.StratifiedSampling: Constructor for Stratified Sampling.
StratifiedSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.IDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
StratifiedSamplingFactory(IStratifier) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.StratifiedSamplingFactory
STRING - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType
StringAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
StringAttribute(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttribute
StringAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute
StringAttributeValue(IStringAttribute, String) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttributeValue
subList(int, int) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
SubsetZeroOneMassFunction - Class in ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic
SubsetZeroOneMassFunction() - Constructor for class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.choquistic.SubsetZeroOneMassFunction
sum(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.MathUtil: Sums the values of the given array.
sum(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil
SupervisedLearnerExecutor - Class in ai.libs.jaicore.ml.core.evaluation.evaluator
SupervisedLearnerExecutor() - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.SupervisedLearnerExecutor
SystematicFileSampling - Class in ai.libs.jaicore.ml.core.filter.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.filter.sampling.infiles.SystematicFileSampling: Simple constructor that uses the default datapoint comparator.
SystematicFileSampling(Random, Comparator<String>, File) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.infiles.SystematicFileSampling: Constructor for a custom datapoint comparator.
SystematicSampling<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.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.filter.sampling.inmemory.SystematicSampling: Simple constructor that uses the default datapoint comparator.
SystematicSampling(Random, Comparator<IInstance>, D) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.SystematicSampling: Constructor for a custom datapoint comparator.
SystematicSamplingFactory<D extends org.api4.java.ai.ml.core.dataset.supervised.ILabeledDataset<?>> - Class in ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories
SystematicSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.factories.SystematicSamplingFactory

T

Table<I,S,P> - Class in ai.libs.jaicore.ml.ranking.label.learner.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.label.learner.clusterbased.customdatatypes.Table
targetIndices - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
targets - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Target values for the instances.
targets - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Target values for the instances.
test - Variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
ThreeDimensionalAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute: A MultidimensionalAttribute that holds three dimensional double arrays.
ThreeDimensionalAttribute(String, int, int, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.ThreeDimensionalAttribute
ThresholdComputationFailedException - Exception in ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol
ThresholdComputationFailedException(Exception) - Constructor for exception ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.ThresholdComputationFailedException
ThresholdComputationFailedException(String) - Constructor for exception ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.ThresholdComputationFailedException
ThresholdComputationFailedException(String, Exception) - Constructor for exception ai.libs.jaicore.ml.core.filter.sampling.inmemory.casecontrol.ThresholdComputationFailedException
TIME_SERIES_FEATURE_ENGINEERING - ai.libs.jaicore.ml.core.EScikitLearnProblemType
TIME_SERIES_REGRESSION - ai.libs.jaicore.ml.core.EScikitLearnProblemType
timeout - Variable in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
TIMEOUTS_IN_SECONDS - Static variable in interface ai.libs.jaicore.ml.core.evaluation.experiment.IMultiClassClassificationExperimentConfig
TIMESERIES - ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType
TimeSeriesBatchLoader - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.util: BatchLoader
TimeSeriesBatchLoader(TimeSeriesDataset, int, boolean) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesBatchLoader
TimeSeriesDataset - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset: Time Series Dataset.
TimeSeriesDataset(ILabeledInstanceSchema) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset
TimeSeriesDataset(ILabeledInstanceSchema, List<INDArray>, List<INDArray>, List<Object>) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset: Creates a TimeSeries dataset.
TimeSeriesDataset2 - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset: Dataset for time series.
TimeSeriesDataset2(List<double[][]>) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Creates a time series dataset without timestamps for testing.
TimeSeriesDataset2(List<double[][]>, int[]) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Creates a time series dataset withot timestamps for training.
TimeSeriesDataset2(List<double[][]>, List<double[][]>) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Creates a time series dataset with timestamps for testing.
TimeSeriesDataset2(List<double[][]>, List<double[][]>, int[]) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesDataset2: Creates a time series dataset with timestamps for training.
TimeSeriesFeature - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset: Class calculating features (e. g. mean, stddev or slope) on given subsequences of time series.
TimeSeriesFeature() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesFeature
TimeSeriesFeature.FeatureType - Enum in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset: Feature types used within the time series tree.
TimeSeriesInstance - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset: TimeSeriesInstance
TimeSeriesInstance(INDArrayTimeseries[], Object) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance: Constructor.
TimeSeriesInstance(List<INDArrayTimeseries>, Object) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesInstance
TimeSeriesLengthException - Exception in ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception: Exception class encapsultes faulty behaviour with length of time series.
TimeSeriesLengthException(String) - Constructor for exception ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception.TimeSeriesLengthException
TimeSeriesLengthException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception.TimeSeriesLengthException
TimeSeriesLoadingException - Exception in ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception: 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.classification.singlelabel.timeseries.exception.TimeSeriesLoadingException: Standard constructor.
TimeSeriesLoadingException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.classification.singlelabel.timeseries.exception.TimeSeriesLoadingException: Constructor using a nested Throwable exception.
TimeSeriesUtil - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.util: Utility class for time series operations.
timestamps - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Timestamp matrix containing the timestamps of the instances.
toArray() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
toArray() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
toArray(T[]) - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.AGeneralDatasetBackedDataset
toArray(T[]) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.search.ADyadRankedNodeQueue
toCommandArray() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
toDouble(Object) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.attribute.NDArrayTimeseriesAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.DyadRankingAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.IntBasedCategoricalAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.LabelRankingAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultiLabelAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.NumericAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.SetOfObjectsAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.core.dataset.schema.attribute.StringAttribute
toDouble(Object) - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeriesAttribute
toDoubleVector() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.Dyad
toGrammar() - Method in class ai.libs.jaicore.ml.hpo.ggp.CFGConverter
toMatrix() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.ADyadRankingInstance
toMatrix() - Method in interface ai.libs.jaicore.ml.ranking.dyad.dataset.INDArrayDyadRankingInstance: Converts a dyad ranking to a INDArray matrix where each row corresponds to a dyad.
toND4j() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DyadRankingDataset: Converts this data set to a list of ND4j INDArrays.
TopKOfPredicted - Class in ai.libs.jaicore.ml.ranking.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.ranking.loss.TopKOfPredicted: Specifies the amount of top rankings to consider.
toString() - Method in class ai.libs.jaicore.ml.classification.loss.ConfusionMatrix
toString() - Method in class ai.libs.jaicore.ml.classification.singlelabel.SingleLabelClassification
toString() - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.Shapelet
toString() - Method in class ai.libs.jaicore.ml.core.dataset.DenseInstance
toString() - Method in class ai.libs.jaicore.ml.core.dataset.schema.InstanceSchema
toString() - Method in class ai.libs.jaicore.ml.core.dataset.schema.LabeledInstanceSchema
toString() - Method in class ai.libs.jaicore.ml.core.dataset.SparseInstance
toString() - Method in class ai.libs.jaicore.ml.core.dataset.splitter.RandomHoldoutSplitter
toString() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.LearnerRunReport
toString() - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.MonteCarloCrossValidationEvaluator
toString() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.FixedDataSplitSetGenerator
toString() - Method in class ai.libs.jaicore.ml.core.evaluation.splitsetgenerator.MonteCarloCrossValidationSplitSetGenerator
toString() - Method in class ai.libs.jaicore.ml.core.filter.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy
toString() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.client.ExtrapolationRequest
toString() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawConfiguration
toString() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.ipl.InversePowerLawLearningCurve
toString() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationLearningCurveConfiguration
toString() - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationParameterSet
toString() - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.HyperbandSolutionCandidate
toString() - Method in class ai.libs.jaicore.ml.hpo.multifidelity.hyperband.Hyperband.MultiFidelityScore
toString() - Method in class ai.libs.jaicore.ml.pdm.dataset.SensorTimeSeries
toString() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.DenseDyadRankingInstance
toString() - Method in class ai.libs.jaicore.ml.ranking.dyad.dataset.SparseDyadRankingInstance
toString() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.Dyad
toString() - Method in class ai.libs.jaicore.ml.scikitwrapper.AScikitLearnWrapper
toString() - Method in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
toString(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Enables printing of time series.
toVector() - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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.functionprediction.learner.learningcurveextrapolation.LearningCurveExtrapolator
train(int[], double[], int, double[][], String) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetClient
train(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier: Trains the simplified time series classifier model using the given TimeSeriesDataset2.
trained - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.ASimplifiedTSClassifier: Variable indicating whether the classifier has been trained.
trainingData - Variable in class ai.libs.jaicore.ml.scikitwrapper.simple.ASimpleScikitLearnWrapper
trainNet(int[], double[], int, double[][]) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lcnet.LCNetExtrapolationMethod
TrainPredictionBasedClassifierEvaluator - Class in ai.libs.jaicore.ml.core.evaluation.evaluator
TrainPredictionBasedClassifierEvaluator(IFixedDatasetSplitSetGenerator<ILabeledDataset<?>>, IAggregatedPredictionPerformanceMeasure<?, ?>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.TrainPredictionBasedClassifierEvaluator
TrainTestSplitEvaluationCompletedEvent> - Class in ai.libs.jaicore.ml.core.evaluation.evaluator.events
TrainTestSplitEvaluationCompletedEvent(ISupervisedLearner<I, D>, ILearnerRunReport) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationCompletedEvent
TrainTestSplitEvaluationFailedEvent> - Class in ai.libs.jaicore.ml.core.evaluation.evaluator.events
TrainTestSplitEvaluationFailedEvent(ISupervisedLearner<I, D>, List<ILearnerRunReport>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationFailedEvent
TrainTestSplitEvaluationFailedEvent(ISupervisedLearner<I, D>, ILearnerRunReport) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.events.TrainTestSplitEvaluationFailedEvent
transform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
transform(double[]) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter: This function transforms only a single instance.
transform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
transform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
transform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
transform(double[]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
transform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
transform(double[][]) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter
transform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
transform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
transform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
transform(double[][]) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
transform(double, double) - Method in interface ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa.IOWAValueFunction
transform(double, double) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa.MoebiusTransformOWAValueFunction
transform(double, double) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.nonadditive.owa.PolynomialOWAValueFunction
transform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.DFT
transform(TimeSeriesDataset2) - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.IFilter: represents a function working on a dataset by transforming the dataset itself.
transform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SAX
transform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SFA
transform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.SlidingWindowBuilder
transform(TimeSeriesDataset2) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer
transform(IDyad) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform.BiliniearFeatureTransform
transform(IDyad) - Method in interface ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.featuretransform.IDyadFeatureTransform: Transform the instance of the given dyad (models the skill).
transform(IDyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler: Transforms the entire dataset according to the mean and standard deviation of the data the scaler has been fit to.
transformAlternatives(IDyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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(IDyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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(IDyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadUnitIntervalScaler
transformAlternatives(IDyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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(IDyad, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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(IDyad, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler
transformAlternatives(IDyad, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadStandardScaler
transformAlternatives(IDyad, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadUnitIntervalScaler
transformInstaceVector(IVector, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler: Transforms an instance feature vector.
transformInstaceVector(IVector, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler
transformInstaceVector(IVector, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadStandardScaler
transformInstaceVector(IVector, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadUnitIntervalScaler
transformInstances(SparseDyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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(IDyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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(IDyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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(IDyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadUnitIntervalScaler
transformInstances(IDyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.AbstractDyadScaler: Transforms only the instances of each dyad in a DenseDyadRankingInstance according to the mean and standard deviation of the data the scaler has been fit to.
transformInstances(IDyad, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.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(IDyad, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler
transformInstances(IDyad, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadStandardScaler
transformInstances(IDyad, List<Integer>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadUnitIntervalScaler
transposeMatrix(double[][]) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
transposeMatrix(int[][]) - Method in class ai.libs.jaicore.ml.classification.multilabel.evaluation.loss.AThresholdBasedMultiLabelClassificationMeasure
TRUE_NEGATIVES_WITH_1_POSITIVE - ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
TRUE_POSITIVES_WITH_1_POSITIVE - ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure
TrueNegatives - Class in ai.libs.jaicore.ml.classification.loss.dataset
TrueNegatives(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.TrueNegatives
TruePositives - Class in ai.libs.jaicore.ml.classification.loss.dataset
TruePositives(int) - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.TruePositives
TSLearningProblem - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.util
TSLearningProblem(IQualityMeasure, TimeSeriesDataset2) - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TSLearningProblem
TwoDimensionalAttribute - Class in ai.libs.jaicore.ml.core.dataset.schema.attribute: A MultidimensionalAttribute that holds two dimensional double arrays.
TwoDimensionalAttribute(String, int, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.schema.attribute.TwoDimensionalAttribute
TypelessPredictionDiff - Class in ai.libs.jaicore.ml.core.evaluation.evaluator: This is a helper class with which one can create a prediction diff object without caring about the types of ground truths and predictions.
TypelessPredictionDiff() - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.TypelessPredictionDiff
TypelessPredictionDiff(List<?>, List<?>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.evaluator.TypelessPredictionDiff

U

UCBPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.ranking.dyad.learner.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.ranking.dyad.learner.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.
UNKNOWN - ai.libs.jaicore.ml.core.dataset.SparseInstance.ENullElement
untransform(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler
untransformAlternative(IDyad) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Undoes the transformation on the alternative of a single dyad.
untransformAlternative(IDyad, int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Undoes the transformation on the alternative of a single dyad.
untransformAlternatives(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Undoes the transformation of the alternatives of each dyad.
untransformAlternatives(DyadRankingDataset, int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Undoes the transformation of the alternatives of each dyad.
untransformInstance(IDyad) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Undoes the transformation of the instance of a single dyad.
untransformInstance(IDyad, int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Undoes the transformation of the instance of a single dyad.
untransformInstances(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Undoes the transformation of the instances of each dyad.
untransformInstances(DyadRankingDataset, int) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.util.DyadMinMaxScaler: Undoes the transformation of the instances of each dyad.
update(Set<IDyadRankingInstance>) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker
update(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.algorithm.PLNetDyadRanker: Updates this PLNetDyadRanker based on the given IInstance, which needs to be an IDyadRankingInstance.
updateRanker(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.activelearning.ARandomlyInitializingDyadRanker
useBiasCorrection - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.shapelets.search.AMinimumDistanceSearchStrategy: Indicator whether Bessel's correction should be used within any distance calculation;

V

V_MISSING_VALUE - Static variable in class ai.libs.jaicore.ml.core.dataset.serialization.ArffDatasetAdapter
value - Variable in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttributeValue
value(double) - Method in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationFunction
valueAt(double[]) - Method in class ai.libs.jaicore.ml.ranking.dyad.learner.optimizing.BilinFunction
valueOf(String) - Static method in enum ai.libs.jaicore.ml.classification.loss.dataset.EAggregatedClassifierMetric: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.classification.loss.dataset.EClassificationPerformanceMeasure: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.classification.singlelabel.timeseries.dataset.TimeSeriesFeature.FeatureType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier.VoteType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffAttributeType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.dataset.serialization.arff.EArffItem: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.dataset.SparseInstance.ENullElement: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.EScikitLearnProblemType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.filter.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.regression.loss.ERegressionPerformanceMeasure: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure: Returns the enum constant of this type with the specified name.
values - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Value matrix containing the time series instances.
values - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Value matrix containing the time series instances.
values() - Static method in enum ai.libs.jaicore.ml.classification.loss.dataset.EAggregatedClassifierMetric: 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.loss.dataset.EClassificationPerformanceMeasure: 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.singlelabel.timeseries.dataset.TimeSeriesFeature.FeatureType: 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.singlelabel.timeseries.learner.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.core.dataset.serialization.arff.EArffAttributeType: 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.serialization.arff.EArffItem: 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.SparseInstance.ENullElement: 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.EScikitLearnProblemType: 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.filter.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.regression.loss.ERegressionPerformanceMeasure: 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.regression.loss.ERulPerformanceMeasure: 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.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
variance(double[]) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Calculates the (population) variance of the values of a times series.
vote(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Performs a vote on the nearest neighbors found.
voteMajority(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier: Performs a majority vote on the set nearest neighbors found.
voteWeightedProportionalToDistance(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.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.filter.sampling.inmemory
WaitForSamplingStepEvent(IAlgorithm<?, ?>) - Constructor for class ai.libs.jaicore.ml.core.filter.sampling.inmemory.WaitForSamplingStepEvent
WEIBULL - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants
WEIGHTED_ABSOLUTE_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
WEIGHTED_ASYMMETRIC_ABSOLUTE_ERROR - ai.libs.jaicore.ml.regression.loss.ERulPerformanceMeasure
WEIGHTED_PROPORTIONAL_TO_DISTANCE - ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier.VoteType: Weighted proportional to distance vote with @see NearestNeighborClassifier#voteWeightedProportionalToDistance.
WEIGHTED_STEPWISE - ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.NearestNeighborClassifier.VoteType: Weighted stepwise vote with @see NearestNeighborClassifier#voteWeightedStepwise.
WeightedAbsoluteError - Class in ai.libs.jaicore.ml.regression.loss.dataset
WeightedAbsoluteError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.WeightedAbsoluteError
WeightedAbsoluteError(double) - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.WeightedAbsoluteError
WeightedAsymmetricAbsoluteError - Class in ai.libs.jaicore.ml.regression.loss.dataset
WeightedAsymmetricAbsoluteError() - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.WeightedAsymmetricAbsoluteError
WeightedAsymmetricAbsoluteError(double, double) - Constructor for class ai.libs.jaicore.ml.regression.loss.dataset.WeightedAsymmetricAbsoluteError
WeightedAUROC - Class in ai.libs.jaicore.ml.classification.loss.dataset: Computes the AUROC weighted by class sizes, that is, it first computes the size of each class, then computes AUROC in a one-vs-rest fashion and balances the final score proportional to the size of each class.
WeightedAUROC() - Constructor for class ai.libs.jaicore.ml.classification.loss.dataset.WeightedAUROC
WHITESPACE_REGEX - Static variable in class ai.libs.jaicore.ml.core.dataset.schema.attribute.MultidimensionalAttribute
windows - Variable in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleClassifier: Holds pairs of (number of correct predictions, window length) obtained in training phase.
windowSize() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.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.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
windowSizeMin() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig
withAdditionalCommandLineParameters(List<String>) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withCacheSplitSets(boolean) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
withData(ILabeledDataset<?>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the dataset which is split into train and test data.
withDatasetSplitter(IDatasetSplitter<? extends ILabeledDataset<?>>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the evaluator to use the given dataset splitter.
withDatasetSplitter(IDatasetSplitter<? extends ILabeledDataset<?>>) - Method in interface ai.libs.jaicore.ml.core.evaluation.evaluator.factory.ISplitBasedSupervisedLearnerEvaluatorFactory: Sets the dataset spliter to the given dataset splitter.
withFitAndPredictMode() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withFitDataFile(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withFitMode() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withFitOutputFile(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withLogger(Logger) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withMeasure(IDeterministicPredictionPerformanceMeasure<?, ?>) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
withModelFile(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withNumMCIterations(int) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the number of monte carlo cross-validation iterations.
withPredictDataFile(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withPredictMode() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withPredictOutputFile(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withPythonConfig(IPythonConfig) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withRandom(Random) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory
withScriptFile(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withSeed(long) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withTargetIndices(int...) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withTimeout(Timeout) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapperCommandBuilder
withTimeoutForSolutionEvaluation(int) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures a timeout for evaluating a solution.
withTrainFoldSize(double) - Method in class ai.libs.jaicore.ml.core.evaluation.evaluator.factory.AMonteCarloCrossValidationBasedEvaluatorFactory: Configures the portion of the training data relative to the entire dataset size.
wordLength() - Method in interface ai.libs.jaicore.ml.classification.singlelabel.timeseries.learner.BOSSLearningAlgorithm.IBossAlgorithmConfig: The word length determines the number of used DFT-coefficients.
writeDataset(File, ILabeledDataset<? extends ILabeledInstance>) - Static method in class ai.libs.jaicore.ml.core.dataset.serialization.CSVDatasetAdapter
writeDatasetToDatabase(IDataset<?>, String) - Method in interface ai.libs.jaicore.ml.core.dataset.serialization.ISQLDatasetMapper
writeDatasetToDatabase(IDataset<?>, String) - Method in class ai.libs.jaicore.ml.core.dataset.serialization.MySQLDatasetMapper

Y

Y - Static variable in class ai.libs.jaicore.ml.functionprediction.learner.learningcurveextrapolation.lc.LinearCombinationConstants

Z

ZERO - ai.libs.jaicore.ml.core.dataset.SparseInstance.ENullElement
ZeroOneLoss - Class in ai.libs.jaicore.ml.classification.loss.instance
ZeroOneLoss() - Constructor for class ai.libs.jaicore.ml.classification.loss.instance.ZeroOneLoss
zNormalize(double[], boolean) - Static method in class ai.libs.jaicore.ml.classification.singlelabel.timeseries.util.TimeSeriesUtil: Z-normalizes a given dataVector.
ZTransformer - Class in ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter
ZTransformer() - Constructor for class ai.libs.jaicore.ml.classification.singlelabel.timeseries.filter.ZTransformer

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