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

A

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

AAttributeValue<D> - Class in ai.libs.jaicore.ml.core.dataset.attribute

An abstract class for attribute values implementing basic functionality to store its value as well as getter and setters.

AAttributeValue(IAttributeType<D>) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue

Constructor creating a new attribute value for a certain type.

AAttributeValue(IAttributeType<D>, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue

Constructor creating a new attribute value for a certain type together with a value.

Abandonable - Interface in ai.libs.jaicore.ml.tsc.distances

Interface for Distance measures that can make use of the Early Abandon technique.

ABatchLearner<T,V,I,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.predictivemodel

Abstract extension of IBatchLearner to be able to construct prediction of the given type.

ABatchLearner() - Constructor for class ai.libs.jaicore.ml.core.predictivemodel.ABatchLearner

AbstractAugmentedSpaceSampler - Class in ai.libs.jaicore.ml.rqp

AbstractAugmentedSpaceSampler(Instances, Random) - Constructor for class ai.libs.jaicore.ml.rqp.AbstractAugmentedSpaceSampler

AbstractDyadScaler - Class in ai.libs.jaicore.ml.dyadranking.util

A scaler that can be fit to a certain dataset and then be used to standardize datasets, i.e. transform the data to have a mean of 0 and a standard deviation of 1 according to the data it was fit to.

AbstractDyadScaler() - Constructor for class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

AbstractSplitBasedClassifierEvaluator<I,O> - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation

Connection between an Evaluator (e.g.

AbstractSplitBasedClassifierEvaluator(IMeasure<I, O>) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.AbstractSplitBasedClassifierEvaluator

AccessibleRandomTree - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Random Tree extension providing leaf node information of the constructed tree.

AccessibleRandomTree() - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree

ActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning

Abstract description of a pool-based active learning strategy for dyad ranking.

ActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

activelyTrain(int) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

Actively trains the ranker for a certain number of queries.

activelyTrain(int) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ConfidenceIntervalClusteringBasedActiveDyadRanker

activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker

activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.RandomPoolBasedActiveDyadRanker

activelyTrainWithOneInstance() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.UCBPoolBasedActiveDyadRanker

add(INDArray, INDArray) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

Add a time series variable to the dataset.

add(TimeSeriesInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

add(int, TimeSeriesInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

add(FeatureDomain) - Method in class ai.libs.jaicore.ml.core.FeatureSpace

add(double[]) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl

add(Instance) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl

add(LabeledInstance<String>) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

add(LabeledInstance<String>) - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl

add(Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

add(Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue

Adds an element at a random position within the

add(int, Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue

add(Instance) - Method in class ai.libs.jaicore.ml.SubInstances

add(int, Instance) - Method in class ai.libs.jaicore.ml.SubInstances

add(double[][], double[][]) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Add a time series variable with timestamps to the dataset.

add(double[][]) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Add a time series variable without timestamps to the dataset.

addAll(Collection<? extends TimeSeriesInstance<L>>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

addAll(int, Collection<? extends TimeSeriesInstance<L>>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

addAll(Collection<? extends Node<N, V>>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

addAllFromJson(String) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl

addAllFromJson(JsonNode) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl

addAllFromJson(File) - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl

addAllFromJson(String) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

addAllFromJson(JsonNode) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

addAllFromJson(File) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

addAllFromJson(String) - Method in interface ai.libs.jaicore.ml.interfaces.Instances

addAllFromJson(File) - Method in interface ai.libs.jaicore.ml.interfaces.Instances

addAllFromJson(String) - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstances

addAllFromJson(File) - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstances

addChild(MCTreeNode) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

addChild(MCTreeNode) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

addChild(List<String>, Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

addChild(List<String>, Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

addInstance(ProblemInstance<I>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group

addInstruction(Instruction) - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

Adds a new Instruction to the history of these Instances

addLandmarkerCharacterizers(ArrayList<Characterizer>) - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

addLocalFiles(File...) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

addLocalFiles(List<File>) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

addNoProbingCharacterizers(ArrayList<Characterizer>) - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

addOpenMLDatasets(int...) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

addResultEntry(int, double) - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentDatabase

ADecomposableDoubleMeasure<I> - Class in ai.libs.jaicore.ml.core.evaluation.measure

A measure that is decomposable by instances and aggregated by averaging.

ADecomposableDoubleMeasure() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableDoubleMeasure

ADecomposableMeasure<I,O> - Class in ai.libs.jaicore.ml.core.evaluation.measure

A measure that is aggregated from e.g. instance-wise computations of the respective measure and which is then aggregated, e.g. taking the mean.

ADecomposableMeasure() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableMeasure

ADecomposableMultilabelMeasure - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

ADecomposableMultilabelMeasure() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.ADecomposableMultilabelMeasure

ADerivateFilter - Class in ai.libs.jaicore.ml.tsc.filter.derivate

Abstract superclass for all derivate filters.

ADerivateFilter(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

ADerivateFilter() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

ADyadRankedNodeQueue<N,V extends java.lang.Comparable<V>> - Class in ai.libs.jaicore.ml.dyadranking.search

A queue whose elements are nodes, sorted by a dyad ranker.

ADyadRankedNodeQueue(Vector) - Constructor for class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

Constructs a new DyadRankedNodeQueue that ranks the nodes in the queue according to the given context characterization.

ADyadRankedNodeQueue(Vector, IDyadRanker, AbstractDyadScaler) - Constructor for class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

Constructs a new DyadRankedNodeQueue that ranks the nodes in the queue according to the given context characterization and given dyad ranker.

ADyadRankedNodeQueueConfig<N> - Class in ai.libs.jaicore.ml.dyadranking.search

A configuration for a dyad ranked node queue.

ADyadRankedNodeQueueConfig() - Constructor for class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig

Construct a new dyad ranking node queue configuration.

ADyadRankingInstance - Class in ai.libs.jaicore.ml.dyadranking.dataset

ADyadRankingInstance() - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.ADyadRankingInstance

AFileSamplingAlgorithm - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles

An abstract class for file-based sampling algorithms providing basic functionality of an algorithm.

AFileSamplingAlgorithm(File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm

AFilter - Class in ai.libs.jaicore.ml.tsc.filter

AFilter() - Constructor for class ai.libs.jaicore.ml.tsc.filter.AFilter

aggregate(List<Double>) - Method in class ai.libs.jaicore.ml.intervaltree.aggregation.AggressiveAggregator

aggregate(List<Double>) - Method in interface ai.libs.jaicore.ml.intervaltree.aggregation.IntervalAggregator

aggregate(List<Double>) - Method in class ai.libs.jaicore.ml.intervaltree.aggregation.QuantileAggregator

AggressiveAggregator - Class in ai.libs.jaicore.ml.intervaltree.aggregation

An IntervalAggregator that makes predictions using the minimum of the predictions as the lower bound and the maximum as the upper bound.

AggressiveAggregator() - Constructor for class ai.libs.jaicore.ml.intervaltree.aggregation.AggressiveAggregator

ai.libs.jaicore.ml - package ai.libs.jaicore.ml

ai.libs.jaicore.ml.activelearning - package ai.libs.jaicore.ml.activelearning

ai.libs.jaicore.ml.cache - package ai.libs.jaicore.ml.cache

Package to create ReproducibleInstances which can be stored and recreated if needed.

ai.libs.jaicore.ml.classification.multiclass - package ai.libs.jaicore.ml.classification.multiclass

ai.libs.jaicore.ml.classification.multiclass.reduction - package ai.libs.jaicore.ml.classification.multiclass.reduction

ai.libs.jaicore.ml.classification.multiclass.reduction.reducer - package ai.libs.jaicore.ml.classification.multiclass.reduction.reducer

ai.libs.jaicore.ml.classification.multiclass.reduction.splitters - package ai.libs.jaicore.ml.classification.multiclass.reduction.splitters

ai.libs.jaicore.ml.clustering - package ai.libs.jaicore.ml.clustering

ai.libs.jaicore.ml.core - package ai.libs.jaicore.ml.core

ai.libs.jaicore.ml.core.dataset - package ai.libs.jaicore.ml.core.dataset

This package contains the infrastructure for representing datasets and instances with different types of attributes.

ai.libs.jaicore.ml.core.dataset.attribute - package ai.libs.jaicore.ml.core.dataset.attribute

This package contains data structures for representing attributes of a dataset's instance.

ai.libs.jaicore.ml.core.dataset.attribute.categorical - package ai.libs.jaicore.ml.core.dataset.attribute.categorical

This package contains the implementation of a categorical attribute.

ai.libs.jaicore.ml.core.dataset.attribute.multivalue - package ai.libs.jaicore.ml.core.dataset.attribute.multivalue

This package contains the implementation of a multi-value attribute.

ai.libs.jaicore.ml.core.dataset.attribute.primitive - package ai.libs.jaicore.ml.core.dataset.attribute.primitive

This package contains the implementation of primitive data type attributes.

ai.libs.jaicore.ml.core.dataset.attribute.timeseries - package ai.libs.jaicore.ml.core.dataset.attribute.timeseries

This package contains the implementation of a time series attribute.

ai.libs.jaicore.ml.core.dataset.attribute.transformer - package ai.libs.jaicore.ml.core.dataset.attribute.transformer

ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue - package ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue

ai.libs.jaicore.ml.core.dataset.sampling - package ai.libs.jaicore.ml.core.dataset.sampling

This package contains algorithms for creating samples of a dataset.

ai.libs.jaicore.ml.core.dataset.sampling.infiles - package ai.libs.jaicore.ml.core.dataset.sampling.infiles

ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling - package ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling

ai.libs.jaicore.ml.core.dataset.sampling.inmemory - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory

ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces

ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling - package ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

ai.libs.jaicore.ml.core.dataset.standard - package ai.libs.jaicore.ml.core.dataset.standard

This package contains a straight-forward implementation of a dataset.

ai.libs.jaicore.ml.core.dataset.util - package ai.libs.jaicore.ml.core.dataset.util

ai.libs.jaicore.ml.core.dataset.weka - package ai.libs.jaicore.ml.core.dataset.weka

This package contains classes for weka-specific logics regarding the dataset.

ai.libs.jaicore.ml.core.evaluation.measure - package ai.libs.jaicore.ml.core.evaluation.measure

ai.libs.jaicore.ml.core.evaluation.measure.multilabel - package ai.libs.jaicore.ml.core.evaluation.measure.multilabel

ai.libs.jaicore.ml.core.evaluation.measure.singlelabel - package ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

ai.libs.jaicore.ml.core.exception - package ai.libs.jaicore.ml.core.exception

This package contains Exceptions defined by jaicore-ml.

ai.libs.jaicore.ml.core.optimizing - package ai.libs.jaicore.ml.core.optimizing

ai.libs.jaicore.ml.core.optimizing.graddesc - package ai.libs.jaicore.ml.core.optimizing.graddesc

ai.libs.jaicore.ml.core.predictivemodel - package ai.libs.jaicore.ml.core.predictivemodel

This package contains interfaces related to predictive models and learning algorithms.

ai.libs.jaicore.ml.dyadranking - package ai.libs.jaicore.ml.dyadranking

ai.libs.jaicore.ml.dyadranking.activelearning - package ai.libs.jaicore.ml.dyadranking.activelearning

ai.libs.jaicore.ml.dyadranking.algorithm - package ai.libs.jaicore.ml.dyadranking.algorithm

ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform - package ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform

ai.libs.jaicore.ml.dyadranking.dataset - package ai.libs.jaicore.ml.dyadranking.dataset

ai.libs.jaicore.ml.dyadranking.loss - package ai.libs.jaicore.ml.dyadranking.loss

ai.libs.jaicore.ml.dyadranking.optimizing - package ai.libs.jaicore.ml.dyadranking.optimizing

ai.libs.jaicore.ml.dyadranking.search - package ai.libs.jaicore.ml.dyadranking.search

ai.libs.jaicore.ml.dyadranking.util - package ai.libs.jaicore.ml.dyadranking.util

ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization - package ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization

ai.libs.jaicore.ml.dyadranking.zeroshot.util - package ai.libs.jaicore.ml.dyadranking.zeroshot.util

ai.libs.jaicore.ml.evaluation - package ai.libs.jaicore.ml.evaluation

ai.libs.jaicore.ml.evaluation.evaluators.weka - package ai.libs.jaicore.ml.evaluation.evaluators.weka

ai.libs.jaicore.ml.evaluation.evaluators.weka.events - package ai.libs.jaicore.ml.evaluation.evaluators.weka.events

ai.libs.jaicore.ml.evaluation.evaluators.weka.factory - package ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation - package ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation

ai.libs.jaicore.ml.experiments - package ai.libs.jaicore.ml.experiments

ai.libs.jaicore.ml.interfaces - package ai.libs.jaicore.ml.interfaces

ai.libs.jaicore.ml.intervaltree - package ai.libs.jaicore.ml.intervaltree

ai.libs.jaicore.ml.intervaltree.aggregation - package ai.libs.jaicore.ml.intervaltree.aggregation

ai.libs.jaicore.ml.intervaltree.util - package ai.libs.jaicore.ml.intervaltree.util

ai.libs.jaicore.ml.latex - package ai.libs.jaicore.ml.latex

ai.libs.jaicore.ml.learningcurve.extrapolation - package ai.libs.jaicore.ml.learningcurve.extrapolation

ai.libs.jaicore.ml.learningcurve.extrapolation.client - package ai.libs.jaicore.ml.learningcurve.extrapolation.client

ai.libs.jaicore.ml.learningcurve.extrapolation.ipl - package ai.libs.jaicore.ml.learningcurve.extrapolation.ipl

ai.libs.jaicore.ml.learningcurve.extrapolation.lc - package ai.libs.jaicore.ml.learningcurve.extrapolation.lc

ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet - package ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet

ai.libs.jaicore.ml.metafeatures - package ai.libs.jaicore.ml.metafeatures

Provides means of computing meta features for a data set.

ai.libs.jaicore.ml.openml - package ai.libs.jaicore.ml.openml

ai.libs.jaicore.ml.ranking - package ai.libs.jaicore.ml.ranking

ai.libs.jaicore.ml.ranking.clusterbased - package ai.libs.jaicore.ml.ranking.clusterbased

ai.libs.jaicore.ml.ranking.clusterbased.candidateprovider - package ai.libs.jaicore.ml.ranking.clusterbased.candidateprovider

ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes - package ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes

ai.libs.jaicore.ml.ranking.clusterbased.datamanager - package ai.libs.jaicore.ml.ranking.clusterbased.datamanager

ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac - package ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation - package ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation

ai.libs.jaicore.ml.rqp - package ai.libs.jaicore.ml.rqp

ai.libs.jaicore.ml.scikitwrapper - package ai.libs.jaicore.ml.scikitwrapper

ai.libs.jaicore.ml.tsc - package ai.libs.jaicore.ml.tsc

ai.libs.jaicore.ml.tsc.classifier - package ai.libs.jaicore.ml.tsc.classifier

ai.libs.jaicore.ml.tsc.classifier.ensemble - package ai.libs.jaicore.ml.tsc.classifier.ensemble

A package consisting of ensemble classifiers used in implemented time series classifiers.

ai.libs.jaicore.ml.tsc.classifier.neighbors - package ai.libs.jaicore.ml.tsc.classifier.neighbors

ai.libs.jaicore.ml.tsc.classifier.shapelets - package ai.libs.jaicore.ml.tsc.classifier.shapelets

This package contains implementations for Shapelet based classifier and training algorithms.

ai.libs.jaicore.ml.tsc.classifier.trees - package ai.libs.jaicore.ml.tsc.classifier.trees

ai.libs.jaicore.ml.tsc.complexity - package ai.libs.jaicore.ml.tsc.complexity

This package contains implementations for time series complexity measures.

ai.libs.jaicore.ml.tsc.dataset - package ai.libs.jaicore.ml.tsc.dataset

This package contains implementations related to the time series dataset,

ai.libs.jaicore.ml.tsc.distances - package ai.libs.jaicore.ml.tsc.distances

This package contains implementations for time series distance measures.

ai.libs.jaicore.ml.tsc.exceptions - package ai.libs.jaicore.ml.tsc.exceptions

ai.libs.jaicore.ml.tsc.features - package ai.libs.jaicore.ml.tsc.features

ai.libs.jaicore.ml.tsc.filter - package ai.libs.jaicore.ml.tsc.filter

ai.libs.jaicore.ml.tsc.filter.derivate - package ai.libs.jaicore.ml.tsc.filter.derivate

Package containing filters that calculate derivates of time series.

ai.libs.jaicore.ml.tsc.filter.transform - package ai.libs.jaicore.ml.tsc.filter.transform

Package containing filters that calculate transforms of time series.

ai.libs.jaicore.ml.tsc.quality_measures - package ai.libs.jaicore.ml.tsc.quality_measures

ai.libs.jaicore.ml.tsc.shapelets - package ai.libs.jaicore.ml.tsc.shapelets

ai.libs.jaicore.ml.tsc.shapelets.search - package ai.libs.jaicore.ml.tsc.shapelets.search

This package contains search strategies applied to Shapelet objects.

ai.libs.jaicore.ml.tsc.util - package ai.libs.jaicore.ml.tsc.util

This package contains utility functions for time series classification.

ai.libs.jaicore.ml.weka.dataset.splitter - package ai.libs.jaicore.ml.weka.dataset.splitter

AILabeledAttributeArrayDataset<I extends ILabeledAttributeArrayInstance<L>,L> - Interface in ai.libs.jaicore.ml.core.dataset

Common interface of a dataset defining methods to access meta-data and instances contained in the dataset.

AINumericLabeledAttributeArrayDataset<I extends INumericLabeledAttributeArrayInstance<L>,L> - Interface in ai.libs.jaicore.ml.core.dataset

algorithm - Variable in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier

The algorithm object used for the training of the classifier.

ALGORITHMMODES - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

ALGORITHMS - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

AllPairsTable - Class in ai.libs.jaicore.ml.classification.multiclass.reduction

AllPairsTable(Instances, Instances, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable

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

ALPHA - Static variable in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Predefined alpha parameter used within the calculations.

alphabet() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

The alphabet consists of doubles representing letters and defines each word.

alphabetSize() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

The alphabet size determines the number of Bins for the SFA Histograms.

AMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.tsc.shapelets.search

Abstract class for minimum distance search strategies.

AMinimumDistanceSearchStrategy(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.search.AMinimumDistanceSearchStrategy

Constructor.

AMonteCarloCrossValidationBasedEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

An abstract factory for configuring Monte Carlo cross-validation based evaluators.

AMonteCarloCrossValidationBasedEvaluatorFactory() - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Standard c'tor.

AnalyticalLearningCurve - Interface in ai.libs.jaicore.ml.interfaces

Added some analytical functions to a learning curve.

andersonDarlingTest(double[]) - Method in class ai.libs.jaicore.ml.clustering.GMeans

AOnlineLearner<T,V,I,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.predictivemodel

Abstract extension of IOnlineLearner to be able to construct prediction of the given type.

AOnlineLearner() - Constructor for class ai.libs.jaicore.ml.core.predictivemodel.AOnlineLearner

apply(Vector) - Method in class ai.libs.jaicore.ml.core.optimizing.graddesc.BlackBoxGradient

apply(Vector) - Method in interface ai.libs.jaicore.ml.core.optimizing.IGradientDescendableFunction

Applies the function for the point represented by the given vector.

apply(Vector) - Method in interface ai.libs.jaicore.ml.core.optimizing.IGradientFunction

Returns the result of applying the gradient to the point represented by the given vector.

apply(Vector) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood

Algorithm (18) of [1].

apply(Vector) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative

apply(Instances) - Method in class ai.libs.jaicore.ml.rqp.AugSpaceAllPairs

APredictiveModel<T,V,I,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.predictivemodel

Abstract extension of IPredictiveModel to be able to construct prediction of the given type.

APredictiveModel() - Constructor for class ai.libs.jaicore.ml.core.predictivemodel.APredictiveModel

AProcessListener - Class in ai.libs.jaicore.ml.scikitwrapper

The process listener may be attached to a process in order to handle its ouputs streams in a controlled way.

AProcessListener() - Constructor for class ai.libs.jaicore.ml.scikitwrapper.AProcessListener

ARandomlyInitializingDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning

ARandomlyInitializingDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

ArbitrarySplitter - Class in ai.libs.jaicore.ml.weka.dataset.splitter

Generates a purely random split of the dataset depending on the seed and on the portions provided.

ArbitrarySplitter() - Constructor for class ai.libs.jaicore.ml.weka.dataset.splitter.ArbitrarySplitter

ArffUtilities - Class in ai.libs.jaicore.ml.core.dataset

Utility class for handling Arff dataset files.

argmax(int[]) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil

Calculates the index of the maximum value in the given array (argmax).

ASamplingAlgorithm - Class in ai.libs.jaicore.ml.core.dataset.sampling

An abstract class for sampling algorithms providing basic functionality of an algorithm.

ASamplingAlgorithm(IAlgorithmConfig, AILabeledAttributeArrayDataset) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.ASamplingAlgorithm

ASamplingAlgorithm(AILabeledAttributeArrayDataset) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.ASamplingAlgorithm

ASamplingAlgorithm<D extends IDataset<?>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

An abstract class for sampling algorithms providing basic functionality of an algorithm.

ASamplingAlgorithm(D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

ASimplifiedTSClassifier<T> - Class in ai.libs.jaicore.ml.tsc.classifier

Simplified batch-learning time series classifier which can be trained and used as a predictor.

ASimplifiedTSClassifier() - Constructor for class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

ASimplifiedTSCLearningAlgorithm<T,C extends ASimplifiedTSClassifier<T>> - Class in ai.libs.jaicore.ml.tsc.classifier

ASimplifiedTSCLearningAlgorithm(IAlgorithmConfig, C, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm

ASquaredErrorLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

Measure computing the squared error of two doubles.

ASquaredErrorLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.ASquaredErrorLoss

assessQuality(List<Double>, int[]) - Method in class ai.libs.jaicore.ml.tsc.quality_measures.FStat

Computes a quality score based on the distances of each instance to the shapelet and the corresponding classValues.

assessQuality(List<Double>, int[]) - Method in interface ai.libs.jaicore.ml.tsc.quality_measures.IQualityMeasure

Computes a quality score based on the distances of each instance to the shapelet and the corresponding classValues.

assignDatapoint(String) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner

assignDatapoint(String) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.IStratiFileAssigner

Select the suitable stratum for a datapoint and write it into the corresponding temporary file.

assignToStrati(I) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

assignToStrati(I) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner

assignToStrati(I) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner

Custom logic for assigning datapoints into strati.

associatedRunWithClassifier(int, Classifier) - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentDatabase

This method tells the logger the classifier object that is used for the run.

ATransformFilter - Class in ai.libs.jaicore.ml.tsc.filter.transform

Abstract superclass for all transform filters.

ATransformFilter() - Constructor for class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

ATSCAlgorithm<L,V,D extends TimeSeriesDataset<L>,C extends TSClassifier<L,V,D>> - Class in ai.libs.jaicore.ml.tsc.classifier

Abstract algorithm class which is able to take TimeSeriesDataset objects and builds TSClassifier instances specified by the generic parameter .

ATSCAlgorithm() - Constructor for class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm

AttributeBasedStratiAmountSelectorAndAssigner<I extends INumericLabeledAttributeArrayInstance<?>,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

This class is responsible for computing the amount of strati in attribute-based stratified sampling and assigning elements to the strati.

AttributeBasedStratiAmountSelectorAndAssigner() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

SCALE-54: Explicitly allow to not provide an attribute list

AttributeBasedStratiAmountSelectorAndAssigner(List<Integer>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

AttributeBasedStratiAmountSelectorAndAssigner(List<Integer>, DiscretizationHelper.DiscretizationStrategy, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

AttributeBasedStratiAmountSelectorAndAssigner(List<Integer>, Map<Integer, AttributeDiscretizationPolicy>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

AttributeDiscretizationPolicy - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

AttributeDiscretizationPolicy(List<Interval>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy

AugSpaceAllPairs - Class in ai.libs.jaicore.ml.rqp

AugSpaceAllPairs() - Constructor for class ai.libs.jaicore.ml.rqp.AugSpaceAllPairs

augSpaceSample() - Method in class ai.libs.jaicore.ml.rqp.ChooseKAugSpaceSampler

augSpaceSample() - Method in class ai.libs.jaicore.ml.rqp.ExactIntervalAugSpaceSampler

augSpaceSample() - Method in interface ai.libs.jaicore.ml.rqp.IAugmentedSpaceSampler

Generates a point in the augmented space from the AugmentedSpaceSampler's precise dataset.

augSpaceSample() - Method in class ai.libs.jaicore.ml.rqp.KNNAugSpaceSampler

AutoMekaGGPFitness - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

AutoMekaGGPFitness() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMekaGGPFitness

AutoMEKAGGPFitnessMeasure - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

Fitness function for a linear combination of 4 well-known multi-label metrics: ExactMatch, Hamming, Rank and F1MacroAverageL.

AutoMEKAGGPFitnessMeasure() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMEKAGGPFitnessMeasure

AutoMEKAGGPFitnessMeasureLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

Measure combining exact match, hamming loss, f1macroavgL and rankloss.

AutoMEKAGGPFitnessMeasureLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMEKAGGPFitnessMeasureLoss

B

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

BackwardDifferenceDerivate - Class in ai.libs.jaicore.ml.tsc.filter.derivate

Filter that calculate the Backward Difference derivate.

BackwardDifferenceDerivate() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.BackwardDifferenceDerivate

BackwardDifferenceDerivate(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.BackwardDifferenceDerivate

backwardDifferenceDerivate(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Calclualtes f'(n) = f(n-1) - f(n)

backwardDifferenceDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Calclualtes f'(n) = f(n-1) - f(n)

bestScore - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

The best score.

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

BilinFunction - Class in ai.libs.jaicore.ml.dyadranking.optimizing

Wraps the NLL optimizing problem into the QNMinimizer optimizer.

BilinFunction(Map<IDyadRankingInstance, Map<Dyad, Vector>>, DyadRankingDataset, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.optimizing.BilinFunction

Creates a NLL optimizing problem for the kronecker product as the bilinear feature transform.

BiliniearFeatureTransform - Class in ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform

Implementation of the feature transformation method using the Kroenecker Product.

BiliniearFeatureTransform() - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.BiliniearFeatureTransform

BlackBoxGradient - Class in ai.libs.jaicore.ml.core.optimizing.graddesc

Difference quotient based gradient estimation.

BlackBoxGradient(IGradientDescendableFunction, double) - Constructor for class ai.libs.jaicore.ml.core.optimizing.graddesc.BlackBoxGradient

Sets up a gradient-estimator for the given function.

BooleanAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.primitive

The boolean attribute type.

BooleanAttributeType() - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType

BooleanAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.primitive

Numeric attribute value as it can be part of an instance.

BooleanAttributeValue(BooleanAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeValue

Standard c'tor.

BooleanAttributeValue(BooleanAttributeType, Boolean) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeValue

C'tor setting the value of this attribute as well.

BOSSClassifier - Class in ai.libs.jaicore.ml.tsc.classifier

BOSSClassifier(int, int, double[], int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

BOSSClassifier(BOSSLearningAlgorithm.IBossAlgorithmConfig) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

BOSSEnsembleClassifier - Class in ai.libs.jaicore.ml.tsc.classifier

BOSSEnsembleClassifier(Map<Integer, Integer>, int, double[], boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier

BOSSEnsembleClassifier(Map<Integer, Integer>, double[], boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier

BOSSLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier

BOSSLearningAlgorithm(BOSSLearningAlgorithm.IBossAlgorithmConfig, BOSSClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm

BOSSLearningAlgorithm.IBossAlgorithmConfig - Interface in ai.libs.jaicore.ml.tsc.classifier

buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType

buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType

buildAttributeValue(Object) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeType

Casts the value to the respective type and returns an attribute value with the creating attribute type as the referenced type.

buildAttributeValue(String) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeType

Builds an attribute value object from a string description.

buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType

buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType

buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType

buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType

buildAttributeValue(boolean) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType

buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType

buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType

buildAttributeValue(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType

buildAttributeValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.Ensemble

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.RandomUniformClassifier

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.MajorityConfidenceVote

Builds the ensemble by assessing the classifier weights using a cross validation of each classifier of the ensemble and then training the classifiers using the complete data.

buildClassifier(Instances) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree

buildGroup(List<ProblemInstance<I>>) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.IGroupBuilder

buildGroup(List<ProblemInstance<Instance>>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACGroupBuilder

buildRanker() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISAC

buildRanker() - Method in interface ai.libs.jaicore.ml.ranking.Ranker

buildWekaClassifierFromSimplifiedTS(Classifier, TimeSeriesDataset) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Trains a given Weka classifier using the simplified time series data set timeSeriesDataset.

buildWekaClassifierFromTS(Classifier, TimeSeriesDataset<L>) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Trains a given Weka classifier using the time series data set timeSeriesDataset.

C

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

cacheClassifier(String, EMCNodeType, Instances, Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ClassifierCache

cacheRetrievals - Static variable in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

calculateAvgMeasure(List<I>, List<I>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableDoubleMeasure

calculateAvgMeasure(List<I>, List<I>) - Method in interface ai.libs.jaicore.ml.core.evaluation.measure.IMeasure

Computes the measure for lists of input actual and the expected outcome expected and aggregates the measured values with the mean, as this is the most frequently used aggregate function.

calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMEKAGGPFitnessMeasureLoss

calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.F1MacroAverageL

calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.HammingLoss

calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.InstanceWiseF1

calculateAvgMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.RankLoss

calculateAvgMeasure(List<Double>, List<Double>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss

calculateAvgMeasure(List<Double>, List<Double>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.RootMeanSquaredErrorLoss

calculateD(double[][][], int, int, double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Function to calculate the distance between the j-th segment of the given time series instance and the k-th shapelet stored in the shapelet tensor S.

calculateDeltaEntropy(double[], int[], double, List<Integer>, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Function calculating the delta entropy for a given thresholdCandidate and parentEntropy.

calculateEntrance(double, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Calculates the entrance gain specified by Deng et. al. in the paper's chapter 4.1.

calculateFeature(TimeSeriesFeature.FeatureType, double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature

Function calculating the feature specified by the feature type fType for a given instance vector of the interval [t1, t2].

calculateFinalInstanceBoundaries(D, Classifier) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.LocalCaseControlSampling

calculateFinalInstanceBoundaries(D, Classifier) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.OSMAC

calculateInstanceBoundaries(HashMap<Object, Integer>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

calculateMargin(double[], double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Function calculating the margin between the given thresholdCandidate and the nearest feature value from the given dataValues.

calculateMeasure(List<I>, List<I>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableMeasure

calculateMeasure(List<I>, List<I>, IAggregateFunction<O>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.ADecomposableMeasure

calculateMeasure(I, I) - Method in interface ai.libs.jaicore.ml.core.evaluation.measure.IMeasure

Computes the measure for a measured input actual and the expected outcome expected.

calculateMeasure(List<I>, List<I>) - Method in interface ai.libs.jaicore.ml.core.evaluation.measure.IMeasure

Computes the measure for a lists of input actual and the expected outcome expected.

calculateMeasure(List<I>, List<I>, IAggregateFunction<O>) - Method in interface ai.libs.jaicore.ml.core.evaluation.measure.IMeasure

Computes the measure for lists of input actual and the expected outcome expected and aggregates the measured values with the given aggregation.

calculateMeasure(I, I) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.LossScoreTransformer

calculateMeasure(double[][], int[][]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMekaGGPFitness

calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.AutoMEKAGGPFitnessMeasureLoss

calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.ExactMatchLoss

calculateMeasure(List<double[]>, List<double[]>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.F1MacroAverageL

calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.HammingLoss

calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.InstanceWiseF1

calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.JaccardScore

calculateMeasure(double[], double[]) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.RankLoss

calculateMeasure(Double, Double) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.ASquaredErrorLoss

calculateMeasure(Double, Double) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MeanSquaredErrorLoss

calculateMeasure(Double, Double) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss

calculateMeasure(List<Double>, List<Double>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss

calculateMeasure(List<Double>, List<Double>, IAggregateFunction<Double>) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss

calculateMeasure(Double, Double) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.ZeroOneLoss

calculateMHat(double[][][], int, int, double[], int, int, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Function to calculate the soft-minimum function which is a differentiable approximation of the minimum distance matrix given in the paper in section 3.1.4.

calculateNearestNeigbors(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Determine the k nearest neighbors for a test instance.

calculatePrediction(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Calculates predicition on a single test instance.

calculateWeights(int) - Method in class ai.libs.jaicore.ml.tsc.distances.WeightedDynamicTimeWarping

Calculates the weight vector via the Modified logistic weight function (see paper 4.2).

calculateWindowLengthPredictions(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Calculates predicitions for a test instance using 1NN with Shotgun Distance and different window lengths.

calculateWindowLengthPredictions(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Calculates predicitions for a test dataset using 1NN with Shotgun Distance and different window lengths.

call() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm

call() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborLearningAlgorithm

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Main function to train a LearnShapeletsClassifier.

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Training procedure for ShapeletTransformTSClassifier using the training algorithm described in the paper.

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

Training procedure for a LearnPatternSimilarityClassifier.

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Training procedure construction a Time Series Bag-of-Features (TSBF) classifier using the given input data.

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

Training procedure construction a time series tree using the given input data.

call() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Training procedure construction a time series tree using the given input data.

cancel() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

cancel() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator

cancel() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

cancel() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

CaseControlLikeSampling<I extends ILabeledInstance<?>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

CaseControlLikeSampling(D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

CaseControlSampling<I extends ILabeledInstance<?>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

Case control sampling.

CaseControlSampling(Random, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlSampling

Constructor

CaseControlSamplingFactory<I extends ILabeledInstance<?>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

CaseControlSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.CaseControlSamplingFactory

CategoricalAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.categorical

The categorical attribute type describes the domain a value of a respective categorical attribute value stems from.

CategoricalAttributeType(List<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType

Constructor setting the domain of the categorical attribute values.

CategoricalAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.categorical

Categorical attribute value as it can be part of an instance.

CategoricalAttributeValue(ICategoricalAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeValue

Standard c'tor.

CategoricalAttributeValue(ICategoricalAttributeType, String) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeValue

C'tor setting the value of this attribute as well.

CategoricalFeatureDomain - Class in ai.libs.jaicore.ml.core

Description of a categorical feature domain.

CategoricalFeatureDomain(double[]) - Constructor for class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

CategoricalFeatureDomain(CategoricalFeatureDomain) - Constructor for class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

center - Variable in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans

characterize(Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

Provide a characterization of the given node to be used by the dyad ranker.

characterize(Instances) - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

characterizerNames - Variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

The names of the characterizers used

characterizers - Variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

The list of characterizers used in the computation of meta features

CheckedJaicoreMLException - Exception in ai.libs.jaicore.ml.core.exception

The CheckedJaicoreMLException serves as a base class for all checked Exceptions defined as part of jaicore-ml.

CheckedJaicoreMLException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.CheckedJaicoreMLException

Creates a new CheckedJaicoreMLException with the given parameters.

CheckedJaicoreMLException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.CheckedJaicoreMLException

Creates a new CheckedJaicoreMLException with the given parameters.

ChooseKAugSpaceSampler - Class in ai.libs.jaicore.ml.rqp

Samples interval-valued data from a dataset of precise points by sampling k precise points (with replacement) and generating a point in the interval-valued augmented space by only considering those k points, i.e. choosing respective minima and maxima for each attribute from the chosen precise points.

ChooseKAugSpaceSampler(Instances, Random, int) - Constructor for class ai.libs.jaicore.ml.rqp.ChooseKAugSpaceSampler

ClassifierCache - Class in ai.libs.jaicore.ml.classification.multiclass.reduction

ClassifierCache() - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.ClassifierCache

ClassifierEvaluatorConstructionFailedException - Exception in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

ClassifierEvaluatorConstructionFailedException(Exception) - Constructor for exception ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ClassifierEvaluatorConstructionFailedException

ClassifierMetricGetter - Class in ai.libs.jaicore.ml.core.evaluation.measure

Class for getting metrics by their name for single- and multilabel classifiers.

ClassifierRankingForGroup - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

ClassifierWeightedSampling<I extends ILabeledInstance<?>,D extends IOrderedDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

The idea behind this Sampling method is to weight instances depended on the way a pilot estimator p classified them.

ClassifierWeightedSampling(Random, Instances, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.ClassifierWeightedSampling

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.Ensemble

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier

classifyInstance(Instance) - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.ITreeClassifier

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.RandomUniformClassifier

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

classifyInstance(Instance) - Method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.MajorityConfidenceVote

classifyInstances(Instances) - Method in interface ai.libs.jaicore.ml.evaluation.IInstancesClassifier

classifyInstances(Instances) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

classMapper - Variable in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

Class mapper object used to encode and decode predicted values if String values are used as classes.

ClassMapper - Class in ai.libs.jaicore.ml.tsc.util

Class mapper used for predictions of String objects which are internally predicted by time series classifiers as ints.

ClassMapper(List<String>) - Constructor for class ai.libs.jaicore.ml.tsc.util.ClassMapper

Constructor using a list of String value to realize the mapping

ClassStratiFileAssigner - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling

ClassStratiFileAssigner(int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner

Constructor with a given target attribute.

ClassStratiFileAssigner() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner

Constructor without a given target attribute.

cleanUp() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm

Implement custom clean up behaviour.

cleanUp() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.ReservoirSampling

cleanUp() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.StratifiedFileSampling

cleanUp() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.SystematicFileSampling

clear() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

clear() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

clearCache() - Static method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

clone() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier

clone() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

cloneClassifier(Classifier) - Static method in class ai.libs.jaicore.ml.WekaUtil

cluster() - Method in class ai.libs.jaicore.ml.clustering.GMeans

Cluster - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

clusterDeprecated() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans

clusterResults - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

clusters - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner

ClusterSampling<I extends INumericLabeledAttributeArrayInstance<? extends java.lang.Number>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

ClusterSampling(long, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

ClusterSampling(long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

clusterShapelets(List<Shapelet>, int, long) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Clusters the given shapelets into noClusters clusters (cf. algorithm 6 of the original paper).

clusterShapelets() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

Indicator whether clustering of shapelets should be used.

ClusterStratiAssigner<I extends INumericArrayInstance,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

ClusterStratiAssigner() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner

collectLeafCounts(int[], Instance, AccessibleRandomTree) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

Function collecting the leaf counts for the given instance as predicted by regTree.

command - Variable in class ai.libs.jaicore.ml.cache.Instruction

COMMAND_NAME - Static variable in class ai.libs.jaicore.ml.cache.FoldBasedSubsetInstruction

Constant string to identify this instruction.

COMMAND_NAME - Static variable in class ai.libs.jaicore.ml.cache.LoadDataSetInstruction

Constant String to Identify this Instruction

COMMAND_NAME - Static variable in class ai.libs.jaicore.ml.cache.SplitInstruction

Constant string to identify this instruction.

compactString() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

compactString() - Method in class ai.libs.jaicore.ml.core.FeatureDomain

compactString() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

complexity(double[]) - Method in interface ai.libs.jaicore.ml.tsc.complexity.ITimeSeriesComplexity

complexity(double[]) - Method in class ai.libs.jaicore.ml.tsc.complexity.SquaredBackwardDifferenceComplexity

complexity(double[]) - Method in class ai.libs.jaicore.ml.tsc.complexity.StretchingComplexity

ComplexityInvariantDistance - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Complexity Invariant Distance (CID) measure as published in "A Complexity-Invariant Distance Measure for Time Series" by Gustavo E.A.P.A.

computationTimes - Variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

The time it took to compute the meta features for each characterizer by name

computeAverageLoss(DyadRankingLossFunction, DyadRankingDataset, DyadRankingDataset) - Static method in class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingLossUtil

Computes the average loss over several dyad orderings.

computeAverageLoss(DyadRankingLossFunction, DyadRankingDataset, IDyadRanker, Random) - Static method in class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingLossUtil

Computes the average loss over several dyad orderings.

computeAverageLoss(DyadRankingLossFunction, DyadRankingDataset, IDyadRanker) - Static method in class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingLossUtil

computeDistance(A, B) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.IDistanceMetric

computeDistance(double[], double[]) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.L1DistanceMetric

computeLoss(INDArray) - Static method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetLoss

Computes the NLL for PL networks according to equation (27) in [1].

computeLossGradient(INDArray, int) - Static method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetLoss

Computes the gradient of the NLL for PL networks w.r.t. the k-th dyad according to equation (28) in [1].

ComputeLossInstruction - Class in ai.libs.jaicore.ml.cache

Instruction for loss computation.

ComputeLossInstruction(String, double, long) - Constructor for class ai.libs.jaicore.ml.cache.ComputeLossInstruction

computeMarginalStandardDeviationForSubsetOfFeatures(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

Computes the variance contribution of a subset of features.

computeMarginalVarianceContributionForFeatureSubset(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

computeMarginalVarianceContributionForFeatureSubsetNotNormalized(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

computeMarginalVarianceContributionForSubsetOfFeatures(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

Computes the variance contribution of a subset of features.

computeMarginalVarianceContributionForSubsetOfFeaturesNotNormalized(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

Computes the variance contribution of a subset of features without normalizing.

computeTotalVarianceOfSubset(Set<Integer>) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

Computes the total variance of marginal predictions for a given set of features.

ConfidenceIntervalClusteringBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning

A prototypical active dyad ranker based on clustering of pseudo confidence intervals.

ConfidenceIntervalClusteringBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, int, Clusterer) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.ConfidenceIntervalClusteringBasedActiveDyadRanker

ConfigurationException - Exception in ai.libs.jaicore.ml.core.exception

The ConfigurationException indicates an error during a configuration process.

ConfigurationException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.ConfigurationException

Creates a new ConfigurationException with the given parameters.

ConfigurationException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.ConfigurationException

Creates a new ConfigurationException with the given parameters.

ConfigurationLearningCurveExtrapolationEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

Predicts the accuracy of a classifier with certain configurations on a point of its learning curve, given some anchorpoint and its configurations using the LCNet of pybnn Note: This code was copied from LearningCurveExtrapolationEvaluator and slightly reworked

ConfigurationLearningCurveExtrapolationEvaluator(int[], ISamplingAlgorithmFactory<WekaInstances<Object>, ASamplingAlgorithm<WekaInstances<Object>>>, WekaInstances<Object>, double, long, String, double[]) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.ConfigurationLearningCurveExtrapolationEvaluator

ConfigurationLearningCurveExtrapolator<I extends ILabeledAttributeArrayInstance<?>,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.learningcurve.extrapolation

This class is a subclass of LearningCurveExtrapolator which deals with the slightly different setup that is required by the LCNet of pybnn

ConfigurationLearningCurveExtrapolator(Classifier, D, double, int[], ISamplingAlgorithmFactory<D, ASamplingAlgorithm<D>>, long, String, double[]) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ConfigurationLearningCurveExtrapolator

configureBestFirst(BestFirst<GraphSearchWithSubpathEvaluationsInput<T, String, Double>, T, String, Double>) - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueueConfig

ConstantClassifier - Class in ai.libs.jaicore.ml.classification.multiclass.reduction

ConstantClassifier() - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier

contains(Object) - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

contains(Object) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

contains(Object) - Method in class ai.libs.jaicore.ml.core.FeatureDomain

Checks if the domain contains an item.

contains(Object) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

contains(Object) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

containsAll(Collection<?>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

containsAll(Collection<?>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

containsInstance(double) - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

containsInstance(double) - Method in class ai.libs.jaicore.ml.core.FeatureDomain

Checks whether a given weka instance is contained in the feature domain

containsInstance(Instance) - Method in class ai.libs.jaicore.ml.core.FeatureSpace

containsInstance(double) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

ContainsNonNumericAttributesException - Exception in ai.libs.jaicore.ml.core.dataset

ContainsNonNumericAttributesException(String) - Constructor for exception ai.libs.jaicore.ml.core.dataset.ContainsNonNumericAttributesException

ContainsNonNumericAttributesException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.dataset.ContainsNonNumericAttributesException

containsPartialInstance(List<Integer>, List<Double>) - Method in class ai.libs.jaicore.ml.core.FeatureSpace

CosineTransform - Class in ai.libs.jaicore.ml.tsc.filter.transform

Calculates the cosine transform of a time series.

CosineTransform() - Constructor for class ai.libs.jaicore.ml.tsc.filter.transform.CosineTransform

countClassOccurrences(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

Count occurrences of every class.

countDatasetEntries(File, boolean) - Static method in class ai.libs.jaicore.ml.core.dataset.ArffUtilities

Counts the amount of datapoint entries in an ARFF file.

countFileLines(File) - Static method in class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader

Counts the lines of the given File object in a very efficient way (thanks to https://stackoverflow.com/a/453067).

CP_ASC - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

CP_DS - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

CP_IBK - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

CP_NB - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

CPUS - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

createDatasetForMatrix(int[], double[][]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Function creating a TimeSeriesDataset object given the targets and one or multiple valueMatrices.

createDatasetForMatrix(double[][]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Function creating a TimeSeriesDataset object given one or multiple valueMatrices.

createDataSetIndex(int, int) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper

Creates a list of data sets by id in a file with caps for the maximum of features and instances.

createDefaultDiscretizationPolicies(D, List<Integer>, Map<Integer, Set<Object>>, DiscretizationHelper.DiscretizationStrategy, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper

This method creates a default discretization policy for each numeric attribute in the attributes that have to be considered for stratum assignment.

createEmpty() - Method in interface ai.libs.jaicore.ml.core.dataset.IDataset

Creates an empty copy of the same structure (and same type).

createEmpty() - Method in interface ai.libs.jaicore.ml.core.dataset.ILabeledAttributeArrayDataset

Creates an empty copy with the same attribute types as this IDataset.

createEmpty() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

createEmpty() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

createEmpty() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances

createEmpty() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

createEquidistantTimestamps(INDArray) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Creates equidistant timestamps for a time series.

createEquidistantTimestamps(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Creates equidistant timestamps for a time series.

createImportStatementFromImportFolder(File, boolean) - Static method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

Makes the given folder a module to be usable as an import for python and creates a string that adds the folder to the python environment and then imports the folder itself as a module.

createNetworkFromDl4jConfigFile(File) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Creates a simple feed-forward MultiLayerNetwork using the json representation of a MultiLayerConfiguration in the file .

createRunIfDoesNotExist(MLExperiment) - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentDatabase

currentCluster - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

D

DataProvider - Enum in ai.libs.jaicore.ml.cache

dataset - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

DatasetCapacityReachedException - Exception in ai.libs.jaicore.ml.core.exception

Exception that indicates that the capacity of a TimeSeriesDataset is reached.

DatasetCapacityReachedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.DatasetCapacityReachedException

Creates a new DatasetCapacityReachedException with the given parameters.

DatasetCapacityReachedException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.DatasetCapacityReachedException

Creates a new DatasetCapacityReachedException with the given parameters.

DatasetCharacterizerInitializationFailedException - Exception in ai.libs.jaicore.ml.metafeatures

An exception that signifies something went wrong during the initialization of a dataset characterizer

DatasetCharacterizerInitializationFailedException() - Constructor for exception ai.libs.jaicore.ml.metafeatures.DatasetCharacterizerInitializationFailedException

Create an exception with a default message.

DatasetCharacterizerInitializationFailedException(String) - Constructor for exception ai.libs.jaicore.ml.metafeatures.DatasetCharacterizerInitializationFailedException

Create an exception with the given message.

DatasetCharacterizerInitializationFailedException(Throwable) - Constructor for exception ai.libs.jaicore.ml.metafeatures.DatasetCharacterizerInitializationFailedException

Create an exception with the given cause.

DatasetCharacterizerInitializationFailedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.metafeatures.DatasetCharacterizerInitializationFailedException

Create an exception with the given cause and additional message

DatasetCreationException - Exception in ai.libs.jaicore.ml.core.dataset

DatasetCreationException(Throwable) - Constructor for exception ai.libs.jaicore.ml.core.dataset.DatasetCreationException

DatasetCreationException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.dataset.DatasetCreationException

DatasetFileSorter - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles

Sorts a Dataset file with a Mergesort.

DatasetFileSorter(File, TempFileHandler) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.DatasetFileSorter

DatasetFileSorter(File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.DatasetFileSorter

datasetFolder - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

DATASETS - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

datasetToWekaInstances(IOrderedLabeledAttributeArrayDataset<?, ?>) - Static method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstancesUtil

decide(TreeNode<TimeSeriesTreeClassifier.TimeSeriesTreeNodeDecisionFunction>, double[]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier

Function performing the decision on a treeNode given the instance based on the locally stored splitting criterion.

DEFAULT_CHARSET - Static variable in class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader

Default charset used when extracting from files.

defaultClassifierString() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest

defaultClassifierString() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

DefaultProcessListener - Class in ai.libs.jaicore.ml.scikitwrapper

The DefaultProcessListener might be used to forward any type of outputs of a process to a logger.

DefaultProcessListener(boolean) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener

Constructor to initialize the DefaultProcessListener.

delete() - Method in class ai.libs.jaicore.ml.SubInstances

deleteAttributeAt(int) - Method in class ai.libs.jaicore.ml.SubInstances

deleteNet(String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient

deleteNet() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod

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

derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

Calculates the derivate of a time series.

derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.BackwardDifferenceDerivate

derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ForwardDifferenceDerivate

derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.GulloDerivate

derivate(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.KeoghDerivate

DerivateDistance - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Derivate Distance (DD) measure as published in "Using derivatives in time series classification" by Tomasz Gorecki and Maciej Luczak (2013).

DerivateDistance(double, ADerivateFilter, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

Constructor with individual distance measures for the function and derivate values.

DerivateDistance(double, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

Constructor with individual distance measures for the function and derivate values that uses the BackwardDifferenceDerivate as derivation.

DerivateDistance(double, ADerivateFilter, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

Constructor that uses the same distance measures for the function and derivate values.

DerivateDistance(double, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

Constructor that uses the same distance measures for the function and derivate values that uses the BackwardDifferenceDerivate as derivation.

DerivateTransformDistance - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Derivate Transform Distance (TD) measure as published in "Non-isometric transforms in time series classification using DTW" by Tomasz Gorecki and Maciej Luczak (2014).

DerivateTransformDistance(double, double, double, ADerivateFilter, ATransformFilter, ITimeSeriesDistance, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Constructor with individual distance measure for function, derivate and transform values.

DerivateTransformDistance(double, double, double, ITimeSeriesDistance, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Constructor with individual distance measure for function, derivate and transform values that uses the BackwardDifferencetransform as derivate and the CosineTransform as transformation.

DerivateTransformDistance(double, double, double, ADerivateFilter, ATransformFilter, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Constructor that uses the same distance measures for function, derivate and transform values.

DerivateTransformDistance(double, double, double, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Constructor that uses the same distance measures for function, derivate and transform values.

derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

Calcuates the derivates of a time series.

derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.BackwardDifferenceDerivate

derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ForwardDifferenceDerivate

derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.GulloDerivate

derivateWithBoundaries(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.KeoghDerivate

derivativeAt(double[]) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.BilinFunction

deserialize(InputStream) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

DFT - Class in ai.libs.jaicore.ml.tsc.filter

DFT() - Constructor for class ai.libs.jaicore.ml.tsc.filter.DFT

difference(double[], double[]) - Method in class ai.libs.jaicore.ml.clustering.GMeans

disableRekursiv() - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

DiscretizationHelper<D extends AILabeledAttributeArrayDataset<?,?>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

This helper class provides methods that are required in order to discretize numeric attributes.

DiscretizationHelper() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper

DiscretizationHelper.DiscretizationStrategy - Enum in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

discretize(double, AttributeDiscretizationPolicy) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper

Discretizes the particular provided value.

discretizeAttributeValues(Map<Integer, AttributeDiscretizationPolicy>, Map<Integer, Set<Object>>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper

Discretizes the given attribute values with respect to the provided policies

discretizeProbs(int, double[][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Function discretizing probabilities into bins.

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.ComplexityInvariantDistance

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.DynamicTimeWarping

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.EuclideanDistance

distance(double, double) - Method in interface ai.libs.jaicore.ml.tsc.distances.IScalarDistance

Calculates the distance between two scalars.

distance(double[], double[]) - Method in interface ai.libs.jaicore.ml.tsc.distances.ITimeSeriesDistance

Calculates the distance between two time series.

distance(double[], double[], double[], double[]) - Method in interface ai.libs.jaicore.ml.tsc.distances.ITimeSeriesDistanceWithTimestamps

Calculates the distance between two time series.

distance(double[], double[]) - Method in interface ai.libs.jaicore.ml.tsc.distances.ITimeSeriesDistanceWithTimestamps

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.ManhattanDistance

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.MoveSplitMerge

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.ShotgunDistance

distance(double[], double[], double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.TimeWarpEditDistance

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.TransformDistance

distance(double[], double[]) - Method in class ai.libs.jaicore.ml.tsc.distances.WeightedDynamicTimeWarping

distanceMeassure - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

distanceMeasure - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner

distanceWithWindow(double[], double[], int) - Method in class ai.libs.jaicore.ml.tsc.distances.DynamicTimeWarping

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.Ensemble

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier

distributionForInstance(Instance, double[]) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

distributionForInstance(Instance, double[]) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.RandomUniformClassifier

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.MajorityConfidenceVote

Function calculating the distribution for a instance by predicting the distributions for each classifier and multiplying the result by the classifier weights.

distributionForInstance(Instance) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree

doAlgorithmStep() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

doInactiveStep() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

domainDimension() - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.BilinFunction

doSplit(double) - Method in class ai.libs.jaicore.ml.core.dataset.util.StratifiedSplit

Dyad - Class in ai.libs.jaicore.ml.dyadranking

Represents a dyad consisting of an instance and an alternative, represented by feature vectors.

Dyad(Vector, Vector) - Constructor for class ai.libs.jaicore.ml.dyadranking.Dyad

Construct a new dyad consisting of the given instance and alternative.

DyadDatasetPoolProvider - Class in ai.libs.jaicore.ml.dyadranking.activelearning

A pool provider which is created out of a DyadRankingDataset.

DyadDatasetPoolProvider(DyadRankingDataset) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

DyadMinMaxScaler - Class in ai.libs.jaicore.ml.dyadranking.util

A scaler that can be fit to a certain dataset and then be used to normalize dyad datasets, i.e. transform the data such that the values of each feature lie between 0 and 1.

DyadMinMaxScaler() - Constructor for class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

DyadRankingDataset - Class in ai.libs.jaicore.ml.dyadranking.dataset

A dataset representation for dyad ranking.

DyadRankingDataset() - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

Creates an empty dyad ranking dataset.

DyadRankingDataset(Collection<IDyadRankingInstance>) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

Creates a dyad ranking dataset containing all elements in the given Collection in the order specified by the collections iterator.

DyadRankingDataset(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

Creates an empty dyad ranking dataset with the given initial capacity.

DyadRankingDataset(List<IDyadRankingInstance>) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

DyadRankingFeatureTransformNegativeLogLikelihood - Class in ai.libs.jaicore.ml.dyadranking.optimizing

Implements the negative log-likelihood function for the feature transformation Placket-Luce dyad ranker.

DyadRankingFeatureTransformNegativeLogLikelihood() - Constructor for class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood

DyadRankingFeatureTransformNegativeLogLikelihoodDerivative - Class in ai.libs.jaicore.ml.dyadranking.optimizing

Represents the derivate of the negative log likelihood function in the context of feature transformation Placket-Luce dyad ranking [1].

DyadRankingFeatureTransformNegativeLogLikelihoodDerivative() - Constructor for class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative

DyadRankingInstance - Class in ai.libs.jaicore.ml.dyadranking.dataset

A general implementation of a dyad ranking instance that contains an immutable list of dyad to represent the ordering of dyads.

DyadRankingInstance(List<Dyad>) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance

Construct a new dyad ranking instance that saves the given ordering of dyads immutably.

DyadRankingLossFunction - Interface in ai.libs.jaicore.ml.dyadranking.loss

Loss function for evaluating dyad rankers.

DyadRankingLossUtil - Class in ai.libs.jaicore.ml.dyadranking.loss

Class that contains utility methods for handling dyad ranking losses.

DyadRankingMLLossFunctionWrapper - Class in ai.libs.jaicore.ml.dyadranking.loss

A wrapper for dyad ranking loss that enables already implemented multi label classification loss functions to be used in this context.

DyadRankingMLLossFunctionWrapper(ADecomposableDoubleMeasure<double[]>) - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingMLLossFunctionWrapper

Constructs a new loss function wrapper that uses the given measure to compute the loss between a correct and predicted dyad ranking.

DyadStandardScaler - Class in ai.libs.jaicore.ml.dyadranking.util

A scaler that can be fit to a certain dataset and then be used to standardize datasets, i.e. transform the data to have a mean of 0 and a standard deviation of 1 according to the data it was fit to.

DyadStandardScaler() - Constructor for class ai.libs.jaicore.ml.dyadranking.util.DyadStandardScaler

DyadUnitIntervalScaler - Class in ai.libs.jaicore.ml.dyadranking.util

A scaler that can be fit to a certain dataset and then be used to normalize datasets, i.e. transform the data to have a length of 1.

DyadUnitIntervalScaler() - Constructor for class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler

DynamicTimeWarping - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Dynamic Time Warping (DTW) measure as published in "Using Dynamic Time Warping to FindPatterns in Time Series" Donald J.

DynamicTimeWarping() - Constructor for class ai.libs.jaicore.ml.tsc.distances.DynamicTimeWarping

Creates an instance with absolute distance as point distance.

DynamicTimeWarping(IScalarDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.DynamicTimeWarping

Creates an instance with a given scalar distance measure.

E

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

EarlyAbandonMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.tsc.shapelets.search

Class implementing a search strategy used for finding the minimum distance of a Shapelet object to a time series.

EarlyAbandonMinimumDistanceSearchStrategy(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.search.EarlyAbandonMinimumDistanceSearchStrategy

Standard constructor.

element() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

EMCNodeType - Enum in ai.libs.jaicore.ml.classification.multiclass.reduction

EMulticlassMeasure - Enum in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

Enum summarizing all multiclass measures.

EMultiClassPerformanceMeasure - Enum in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

EMultilabelPerformanceMeasure - Enum in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

enableRekursiv() - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

Ensemble - Class in ai.libs.jaicore.ml.classification.multiclass

Ensemble() - Constructor for class ai.libs.jaicore.ml.classification.multiclass.Ensemble

EnsembleProvider - Class in ai.libs.jaicore.ml.tsc.classifier.ensemble

Class statically providing preconfigured ensembles as commonly used in TSC implementations.

ENTROPY_APLHA - Static variable in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Alpha parameter used to weight the importance of the feature's margins to the threshold candidates.

EPSILON - Static variable in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

equalLengthPolicy(List<Double>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper

Creates an equal length policy for the given values with respect to the given number of categories.

equals(Object) - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue

equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy

equals(Object) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

equals(Object) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

equals(Object) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

equals(Object) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance

equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.Dyad

equals(Object) - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue

equals(Object) - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

equals(Object) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

equals(Object) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration

equals(Object) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet

equals(Object) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.RankingForGroup

equals(Object) - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

equalSizePolicy(List<Double>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper

Creates an equal size policy for the given values with respect to the given number of categories.

estimateK() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

Parameter indicator whether estimation of K (number of learned shapelets) should be derived from the number of total segments.

estimateK() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

Parameter indicator whether estimation of K (number of learned shapelets) should be derived from the number of total segments.

estimateShapeletLengthBorders() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

Indicator whether the min max estimation should be performed.

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

EuclideanDistance - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Euclidean distance for time series.

EuclideanDistance() - Constructor for class ai.libs.jaicore.ml.tsc.distances.EuclideanDistance

evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ConfigurationLearningCurveExtrapolationEvaluator

evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ExtrapolatedSaturationPointEvaluator

evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator

evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator

Computes the (estimated) measure of the classifier on the full dataset

evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

evaluate(Classifier, DescriptiveStatistics) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator

evaluate(Classifier, DescriptiveStatistics) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator

evaluate(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator

evaluateModifiedISACLeaveOneOut(Instances) - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

evaluateSplit(Classifier, Instances, Instances) - Method in interface ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.ISplitBasedClassifierEvaluator

Evaluate a hypothesis h being trained on a set of trainingData for some validationData.

evaluateSplit(Classifier, Instances, Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.SimpleMLCSplitBasedClassifierEvaluator

evaluateSplit(Classifier, Instances, Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.SimpleSLCSplitBasedClassifierEvaluator

evaluateSupervised(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.TimeoutableEvaluator

EvaluationException - Exception in ai.libs.jaicore.ml.core.exception

The EvaluationException indicates that an error occurred during a evaluation process.

EvaluationException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.EvaluationException

Creates a new EvaluationException with the given parameters.

EvaluationException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.EvaluationException

Creates a new EvaluationException with the given parameters.

ExactIntervalAugSpaceSampler - Class in ai.libs.jaicore.ml.rqp

Samples interval-valued data from a dataset of precise points.

ExactIntervalAugSpaceSampler(Instances, Random) - Constructor for class ai.libs.jaicore.ml.rqp.ExactIntervalAugSpaceSampler

ExactMatchAccuracy - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

Computes the exact match of the predicted multi label vector and the expected.

ExactMatchAccuracy() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.ExactMatchAccuracy

ExactMatchLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

ExactMatchLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.ExactMatchLoss

ExhaustiveMinimumDistanceSearchStrategy - Class in ai.libs.jaicore.ml.tsc.shapelets.search

Class implementing a search strategy used for finding the minimum distance of a Shapelet object to a time series.

ExhaustiveMinimumDistanceSearchStrategy(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.search.ExhaustiveMinimumDistanceSearchStrategy

Standard constructor.

EXP_4 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants

ExtendedM5Forest - Class in ai.libs.jaicore.ml.intervaltree

ExtendedM5Forest() - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest

ExtendedM5Forest(IntervalAggregator, IntervalAggregator) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest

ExtendedM5Forest(int) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest

ExtendedM5Tree - Class in ai.libs.jaicore.ml.intervaltree

ExtendedM5Tree() - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Tree

ExtendedM5Tree(IntervalAggregator) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedM5Tree

ExtendedRandomForest - Class in ai.libs.jaicore.ml.intervaltree

ExtendedRandomForest() - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

ExtendedRandomForest(IntervalAggregator, IntervalAggregator) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

ExtendedRandomForest(FeatureSpace) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

ExtendedRandomForest(IntervalAggregator, IntervalAggregator, FeatureSpace) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

ExtendedRandomForest(int) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

ExtendedRandomTree - Class in ai.libs.jaicore.ml.intervaltree

Extension of a classic RandomTree to predict intervals.

ExtendedRandomTree() - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

ExtendedRandomTree(FeatureSpace) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

ExtendedRandomTree(IntervalAggregator) - Constructor for class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

extractArffHeader(File) - Static method in class ai.libs.jaicore.ml.core.dataset.ArffUtilities

Extract the header of an ARFF file as a string.

ExtrapolatedSaturationPointEvaluator<I extends ILabeledAttributeArrayInstance<?>,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

For the classifier a learning curve will be extrapolated with a given set of anchorpoints.

ExtrapolatedSaturationPointEvaluator(int[], ISamplingAlgorithmFactory<D, ? extends ASamplingAlgorithm<D>>, D, double, LearningCurveExtrapolationMethod, long, D) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.ExtrapolatedSaturationPointEvaluator

Create a classifier evaluator with an accuracy measurement at the extrapolated learning curves saturation point.

ExtrapolatedSaturationPointEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

ExtrapolatedSaturationPointEvaluatorFactory(int[], ISamplingAlgorithmFactory<WekaInstances<Object>, ? extends ASamplingAlgorithm<WekaInstances<Object>>>, double, LearningCurveExtrapolationMethod) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ExtrapolatedSaturationPointEvaluatorFactory

extrapolateLearningCurve() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

Measure the learner accuracy at the given anchorpoints and extrapolate a learning curve based the results.

extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawExtrapolationMethod

extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationExtrapolationMethod

extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod

extrapolateLearningCurveFromAnchorPoints(int[], double[], int) - Method in interface ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolationMethod

extrapolationMethod - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

ExtrapolationRequest - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.client

This class describes the request that is sent to an Extrapolation Service.

ExtrapolationRequest() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

ExtrapolationServiceClient<C> - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.client

This class describes the client that is responsible for the communication with an Extrapolation Service.

ExtrapolationServiceClient(String, Class<C>) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationServiceClient

F

F1MacroAverageL - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

F1MacroAverageL() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.F1MacroAverageL

F1MacroAverageLLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

Compute the inverted F1 measure macro averaged by label.

F1MacroAverageLLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.F1MacroAverageLLoss

factor - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Factor used to determine whether or not to include a window length into the overall predicition.

FeatureDomain - Class in ai.libs.jaicore.ml.core

Abstract description of a feature domain.

FeatureDomain() - Constructor for class ai.libs.jaicore.ml.core.FeatureDomain

FeatureSpace - Class in ai.libs.jaicore.ml.core

FeatureSpace() - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace

FeatureSpace(Instances) - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace

FeatureSpace(List<FeatureDomain>) - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace

copy constructor

FeatureSpace(FeatureSpace) - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace

FeatureSpace(FeatureDomain[]) - Constructor for class ai.libs.jaicore.ml.core.FeatureSpace

FeatureTransformPLDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform

A feature transformation Placket-Luce dyad ranker.

FeatureTransformPLDyadRanker() - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker

Constructs a new feature transform Placket-Luce dyad ranker with bilinear feature transformation.

FeatureTransformPLDyadRanker(IDyadFeatureTransform) - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker

Constructs a new feature transform Placket-Luce dyad ranker with the given feature transformation method.

findDistances(Shapelet, double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Function finding the minimum single squared Euclidean distance for each instance among all of its subsequences compared to the shapelet s.

findMinimumDistance(Shapelet, double[]) - Method in class ai.libs.jaicore.ml.tsc.shapelets.search.AMinimumDistanceSearchStrategy

Function returning the minimum distance among all subsequences of the given timeSeries to the shapelet's data.

findMinimumDistance(Shapelet, double[]) - Method in class ai.libs.jaicore.ml.tsc.shapelets.search.EarlyAbandonMinimumDistanceSearchStrategy

Optimized function returning the minimum distance among all subsequences of the given timeSeries to the shapelet's data.

findMinimumDistance(Shapelet, double[]) - Method in class ai.libs.jaicore.ml.tsc.shapelets.search.ExhaustiveMinimumDistanceSearchStrategy

Function returning the minimum distance among all subsequences of the given timeSeries to the shapelet's data.

findNearestInstanceIndex(int[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

Performs a simple nearest neighbor search on the stored trainLeafNodes for the given leafNodeCounts using Manhattan distance.

firstInstance() - Method in class ai.libs.jaicore.ml.SubInstances

fit(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Fits the standard scaler to the dataset.

fit(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler

fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

fit(TimeSeriesDataset) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

the function computes the needed information for the transform function.

fit(double[]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

The function only fits a single instance of the dataset

fit(double[][]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

fit(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

fit(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

fit(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

fitTransform(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Fits the standard scaler to the dataset and transforms the entire dataset according to the mean and standard deviation of the dataset.

fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.AFilter

fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

fitTransform(TimeSeriesDataset) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

a utility function to avoid the added effort of calling the fit and transform function separate

fitTransform(double[]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

the function fit and transforms a single instance

fitTransform(double[][]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

fitTransform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

fitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

fitTransform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

FixedSplitClassifierEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

FixedSplitClassifierEvaluator(Instances, Instances) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator

FoldBasedSubsetInstruction - Class in ai.libs.jaicore.ml.cache

Instruction to track a fold-based subset computation for a ReproducibleInstances object.

FoldBasedSubsetInstruction(String, int...) - Constructor for class ai.libs.jaicore.ml.cache.FoldBasedSubsetInstruction

Constructor to create a split Instruction that can be converted into json.

formate(Instances) - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.HellFormater

formHistogramsAndRelativeFreqs(int[][], int[], int, int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Function calculating the histograms as described in the paper's section 2.2 ("Codebook and Learning").

ForwardDifferenceDerivate - Class in ai.libs.jaicore.ml.tsc.filter.derivate

Filter that calculate the Forward Difference derivate.

ForwardDifferenceDerivate() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.ForwardDifferenceDerivate

ForwardDifferenceDerivate(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.ForwardDifferenceDerivate

forwardDifferenceDerivate(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

f'(n) = f(n+1) - f(n)

forwardDifferenceDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

f'(n) = f(n+1) - f(n)

fromARFF(String) - Static method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

Creates a new ReproducibleInstances object.

fromHistory(List<Instruction>, String) - Static method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

Creates a ReproducibleInstances Object based on the given History.

fromJAICoreInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

fromJAICoreInstance(LabeledInstance<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

fromJAICoreInstances(WekaCompatibleInstancesImpl) - Static method in class ai.libs.jaicore.ml.WekaUtil

fromJAICoreInstances(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

fromJAICoreInstances(LabeledInstances<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

fromOpenML(String, String) - Static method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

Creates a new ReproducibleInstances object.

fromOrderedDyadList(List<Dyad>) - Static method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

FStat - Class in ai.libs.jaicore.ml.tsc.quality_measures

F-Stat quality measure performing a analysis of variance according to chapter 3.2 of the original paper.

FStat() - Constructor for class ai.libs.jaicore.ml.tsc.quality_measures.FStat

G

gamma() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

Gamma value used for momentum during gradient descent.

gamma() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

Gamma value used for momentum during gradient descent.

generateAugPoint(List<Instance>) - Static method in class ai.libs.jaicore.ml.rqp.AbstractAugmentedSpaceSampler

generateCandidates(double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Function generation shapelet candidates for a given instance vector data, the length l and the candidate index which is used to identify the source of the shapelet's data.

generateFeatures(double[][], int[][], int[][][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Function generating the features for the internal probability measurement model based on the given subseries and their corresponding intervals.

generateHistogramInstances(int[][][], int[][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Generates a matrix consisting of the histogram values for each instance out of the given histograms and the relative frequencies of classes for each instance.

generateSegmentsAndDifferencesForTree(int[], int[], int, int, Random) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

Method generating the segment start indices and the segment difference locations randomly using random.

generateSplittingInfo(double...) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation

Generate a string representation that represents only the split info part of the split string.

generateSplittingString(double...) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation

Generate a String that represents a split of a data set into portions from the given portions sizes (must add up to <1).

generateSubsequencesAndIntervals(int, int, int, int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Method randomly determining the subsequences and their intervals to be used for feature generation of the instances.

generateSubseriesFeatureInstance(double[], int[], int[], int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

Function generating subseries feature instances based on the given segments and segmentsDifference matrices.

generateSubseriesFeaturesInstances(List<Attribute>, int, int[], int[], double[][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

Function generating a dataset storing the features being generated as described in the original paper.

generateThresholdCandidates(Pair<List<Integer>, List<Integer>>, int, double[][][]) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Function generating threshold candidates for each feature type.

get(int) - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema

get(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

get(int) - Method in class ai.libs.jaicore.ml.SubInstances

getA() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

getA() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

getA() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

Getter for the a parameter.

getA() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Getter for the a parameter.

getA() - Method in class ai.libs.jaicore.ml.tsc.distances.TransformDistance

Getter for the a parameter.

getAbsoluteDistance() - Static method in class ai.libs.jaicore.ml.tsc.util.ScalarDistanceUtil

getActual() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent

getActual() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAvgComputationEvent

getActual() - Method in class ai.libs.jaicore.ml.evaluation.MeasureListComputationEvent

getActual() - Method in class ai.libs.jaicore.ml.evaluation.MeasureSingleComputationEvent

getAdmissibleSearcherEvaluatorCombinationsForAttributeSelection() - Static method in class ai.libs.jaicore.ml.WekaUtil

Determines all attribute selection variants (search/evaluator combinations with default parametrization)

getAggregator() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent

getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.CaseControlSamplingFactory

getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory

getAlgorithm(int, D, Random) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces.ISamplingAlgorithmFactory

After the necessary config is done, this method returns a fully configured instance of a sampling algorithm.

getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory

getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory

getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SimpleRandomSamplingFactory

getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory

getAlgorithm(int, D, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory

getAlgorithm() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getAlgorithm() - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier

Getter for the model's training algorithm object.

getAlgorithmMode() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getAlgorithmModes() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

getAlgorithms() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

getAllAttributeValues() - Method in interface ai.libs.jaicore.ml.core.dataset.IAttributeArrayInstance

getAllAttributeValues() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getAllAttributeValues() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance

getAllAttributeValues() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

getAllAttributeValues() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

getAllClassifier() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector

getAllCreatedStrati() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner

getAllCreatedStrati() - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.IStratiFileAssigner

Get the used strati temporary files and the amount of datapoints inside of it.

getAlpha() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

Getter for the alpha value.

getAlpha() - Method in class ai.libs.jaicore.ml.tsc.distances.TransformDistance

Getter for the alpha value.

getAlternative() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad

Get the alternative.

getAnchorPoints() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getArbitrarySplit(Instances, Random, double...) - Static method in class ai.libs.jaicore.ml.WekaUtil

getAsDoubleVector() - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance

Turns the instance into a double vector.

getAsDoubleVector() - Method in interface ai.libs.jaicore.ml.core.dataset.INumericArrayInstance

Turns the instance into a double vector.

getAsDoubleVector() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getAsDoubleVector() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

getAsDoubleVector() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

getAssumedMemoryOverheadPerProcess() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getAtributesofTrainingsdata() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector

getAttributes() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getAttributes(Instances, boolean) - Static method in class ai.libs.jaicore.ml.WekaUtil

getAttributes(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

getAttributeTypeList() - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema

getAttributeTypes() - Method in interface ai.libs.jaicore.ml.core.dataset.AILabeledAttributeArrayDataset

Returns the list of attribute types.

getAttributeTypes() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

getAttributeTypes() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getAttributeTypes() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances

getAttributeValue(int, Class<T>) - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance

Getter for the value of an attribute for the given position.

getAttributeValue(int) - Method in interface ai.libs.jaicore.ml.core.dataset.INumericArrayInstance

getAttributeValue(int) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getAttributeValue(int) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

getAttributeValue(int) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

getAttributeValueAtPosition(int, Class<T>) - Method in interface ai.libs.jaicore.ml.core.dataset.IAttributeArrayInstance

Getter for the value of an attribute for the given position.

getAttributeValueAtPosition(int, Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getAttributeValueAtPosition(int, Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance

getAttributeValueAtPosition(int, Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

getAttributeValueAtPosition(int, Class<T>) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

getAverageSeparability(Collection<String>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable

getB() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

getB() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

getB() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

Getter for the a parameter.

getB() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Getter for the b parameter.

getB() - Method in class ai.libs.jaicore.ml.tsc.distances.TransformDistance

Getter for the a parameter.

getBasicEvaluator() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.AbstractSplitBasedClassifierEvaluator

getBasicLearners() - Static method in class ai.libs.jaicore.ml.WekaUtil

getBestSoFar() - Method in interface ai.libs.jaicore.ml.tsc.distances.Abandonable

Getter for the best-so-far value.

getBestSplitIndex(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Function returning feature type used for the split based on given the deltaEntropy star values.

getBinaryClassifiers() - Static method in class ai.libs.jaicore.ml.WekaUtil

getBridge() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

getBridge() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator

getC() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

getC() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

getC() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

getC() - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Getter for the c parameter.

getCachedClassifier(String, EMCNodeType, Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ClassifierCache

getCachedTrainingData(String, EMCNodeType, Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ClassifierCache

getCandidate(ProblemInstance<I>) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.candidateprovider.IRankedSolutionCandidateProvider

getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.Ensemble

getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.ConstantClassifier

getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier

getCapabilities() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer

getCapabilities() - Method in class ai.libs.jaicore.ml.RandomUniformClassifier

getCapabilities() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

getCaption() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

getCenter() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACkMeans

getCentersModifiable() - Method in class ai.libs.jaicore.ml.clustering.GMeans

getCertainty(I) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.ICertaintyProvider

Returns the certainty for a given IInstance.

getCertainty(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

getCharacterizerGroups() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Gets the mapping of a Characterizer to the meta features it computes.

getCharacterizerNames() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Gets the names of the used Characterizers.

getCharacterizerNamesMappings() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Gets names for the used Characterizers.

getCharacterizers() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Gets the list of characterizers used in the computation of meta features.

getChildDataIndices(double[][][], int, int, int, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Function returning the data indices assigned to the left and the right child of a binary tree based on the splitting criterion given by the feature type fType, the intervals index t1t2 in the transformed data set transformedData and the threshold.

getChildren() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

getChildren() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

getChosenInstance() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling

getClassAttIndexPerTree() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getClassesActuallyContainedInDataset(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassesAsArray(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassesAsList(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassesDeclaredInDataset(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassesInDataset(TimeSeriesDataset) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Returns a list storing the unique Integer class values in the given dataset.

getClassifier() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

getClassifier() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

getClassifier() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent

getClassifier() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSCLearningAlgorithm

getClassifierCache() - Static method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

getClassifierDescriptor(Classifier) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassMapper() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

Getter for the property classMapper.

getClassName(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassNames(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassNameToIDMap(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassSplitAssignments(List<Instances>) - Static method in class ai.libs.jaicore.ml.WekaUtil

getClassValues() - Method in class ai.libs.jaicore.ml.tsc.util.ClassMapper

Getter for the classValues.

getClusterResults() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

getCollectedClassifierandPerformance() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector

getCommand() - Method in class ai.libs.jaicore.ml.cache.Instruction

Sets command name that specifies the type of instruction represented by the object.

getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm

getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

getConfig() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

getConfigForAnchorPoints(int[], double[]) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationServiceClient

getConfiguration() - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel

Returns the IPredictiveModelConfiguration of this model.

getConfiguration() - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker

getConfiguration() - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

getConfiguration() - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier

Returns the IPredictiveModelConfiguration of this model.

getContainedClasses() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

getContainedClasses() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

Get the classes contained in the leaves of this node.

getConvergenceValue() - Method in interface ai.libs.jaicore.ml.interfaces.AnalyticalLearningCurve

Calculates or looks-up the value the learning curve converges to.

getConvergenceValue() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

getConvergenceValue() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve

getCpus() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getCurrentPoints() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans

getCurveValue(double) - Method in interface ai.libs.jaicore.ml.interfaces.LearningCurve

Calculates or looks-up the curves value at a given point.

getCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

getCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve

getCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.PointWiseLearningCurve

getData() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Getter for the dataset which is used for splitting.

getData() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Getter for Shapelet.data.

getDataset() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getDataset() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getDataset() - Method in class ai.libs.jaicore.ml.tsc.TSLearningProblem

getDatasetFolder() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

getDatasets() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

getDatasets() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

getDataSetsFromIndex() - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper

getDatasetsInFolder(File) - Static method in class ai.libs.jaicore.ml.WekaUtil

getDatasetSplitter() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Getter for the dataset splitter.

getDataSourceById(int) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper

getDeclaredClasses() - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl

getDefaultWindowSize() - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

getDepthOfFirstCommonParent(List<Integer>) - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.ITreeClassifier

getDepthOfFirstCommonParent(List<Integer>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

getDepthOfFirstCommonParent(List<String>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

getDerivativeCurveValue(double) - Method in interface ai.libs.jaicore.ml.interfaces.AnalyticalLearningCurve

Calculates or looks-up the value of the derivative of the learning point at a given point.

getDerivativeCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

getDerivativeCurveValue(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve

getDeterminedQuality() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Getter for Shapelet.determinedQuality.

getDimensionality() - Method in class ai.libs.jaicore.ml.core.FeatureSpace

getDistanceMeasure() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Getter for the distance measure.

getDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType

getDomain() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.categorical.ICategoricalAttributeType

getDomain() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.multivalue.IMultiValueAttributeType

getDomain() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType

getDyadAtPosition(int) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance

getDyadAtPosition(int) - Method in interface ai.libs.jaicore.ml.dyadranking.dataset.IDyadRankingInstance

Get the dyad at the specified position in the ordering contained in this instance.

getDyadAtPosition(int) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

getDyadRanker() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

Get the dyad ranker used to rank the nodes.

getDyadsByAlternative(Vector) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

getDyadsByAlternative(Vector) - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider

Returns the set of all Dyads with the given Vector of alternative features.

getDyadsByInstance(Vector) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

getDyadsByInstance(Vector) - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider

Returns the set of all Dyads with the given Vector of instance features.

getDyadStats() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

getEmptyDatasetForJAICoreInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

getEmptySetOfInstancesWithRefactoredClass(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

getEmptySetOfInstancesWithRefactoredClass(Instances, List<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

getEntry(Interval[], T) - Static method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper

getEpoch() - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

getEvaluator(EMultiClassPerformanceMeasure) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MultiClassMeasureBuilder

getEvaluator(EMultilabelPerformanceMeasure) - Method in class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MultiClassMeasureBuilder

getExpected() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent

getExpected() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAvgComputationEvent

getExpected() - Method in class ai.libs.jaicore.ml.evaluation.MeasureListComputationEvent

getExpected() - Method in class ai.libs.jaicore.ml.evaluation.MeasureSingleComputationEvent

getExperimentsForWhichARunExists() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentDatabase

getExtrapolationMethod() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getExtrapolator() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolatedEvent

getFeatureDomain(int) - Method in class ai.libs.jaicore.ml.core.FeatureSpace

getFeatureDomains() - Method in class ai.libs.jaicore.ml.core.FeatureSpace

getFeatureEvaluators() - Static method in class ai.libs.jaicore.ml.WekaUtil

getFeatures(double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature

Function calculating all features occurring in TimeSeriesFeature.FeatureType at once using an online calculation approach for mean, standard deviation and the slope.

getFeatureSpace() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

getFeatureSpace() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

getFinalClf() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

getFunctions() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

getGmeansCluster() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans

getGoalTester() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

getHeaderInformation() - Method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper.IntervalAndHeader

getHeight() - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.ITreeClassifier

getHeight() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

getHeight() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

getHeight() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

getHeight() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

getHighestQualityShapeletInList(List<Shapelet>) - Static method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Returns the shapelet with the highest quality in the given list shapelets.

getIClassifierEvaluator(Instances, long) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ExtrapolatedSaturationPointEvaluatorFactory

getIClassifierEvaluator(Instances, long) - Method in interface ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.IClassifierEvaluatorFactory

getIClassifierEvaluator(Instances, long) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.LearningCurveExtrapolationEvaluatorFactory

getIClassifierEvaluator(Instances, long) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

getIClassifierEvaluator(Instances, long) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

getId() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group

getIdentifier() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.GroupIdentifier

getIdentifierForGroup() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.RankingForGroup

getIDs() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

getImportString(Collection<String>) - Static method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

getIndicesOfContainedInstances(Instances, Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

Compute indices of instances of the original data set that are contained in the given subset.

getIndicesOfSubset(Instances, Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

getInforamtionforRanking(List<ProblemInstance<I>>) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.datamanager.ITableGeneratorandCompleter

getInput() - Method in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm

Getter for the data set input used during algorithm calls.

getInputList() - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.InputOptListener

getInputs() - Method in class ai.libs.jaicore.ml.cache.Instruction

Inputs are parameters of the instruction.

getInstance() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad

Get the instance.

getInstance() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance

getInstanceFeatures() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

getInstanceFeatures() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

getInstanceFeatures() - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider

Returns a Collection that contains all instance features contained in the pool.

getInstanceIndex() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Getter for Shapelet.instanceIndex.

getInstances() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group

getInstancesById(int) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper

Downloads the data set with the given id and returns the Instances file for it.

getInstancesOfClass(Instances, Collection<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

getInstancesOfClass(Instances, String) - Static method in class ai.libs.jaicore.ml.WekaUtil

getInstancesPerClass(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

getInstructions() - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

getIntermediateCenter() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans

getIntermediatePoints() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans

getInterval(double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Function extracting the interval [start, end (exclusive)] out of the given timeSeries vector.

getIntervals() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy

getIntervals() - Method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper.IntervalAndHeader

getIntervals() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

getIntValOfClassName(Instance, String) - Static method in class ai.libs.jaicore.ml.WekaUtil

getIteration() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

getK() - Method in class ai.libs.jaicore.ml.rqp.ChooseKAugSpaceSampler

getK() - Method in class ai.libs.jaicore.ml.rqp.KNNAugSpaceSampler

getK() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Getter for the k value, @see #k.

getKendallforML() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getL() - Method in class ai.libs.jaicore.ml.dyadranking.loss.NDCGLoss

getLabel() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

getLabel() - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstance

getLabel() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

getLastNode() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree

getLatexCode() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

getLearner() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

getLearningAlgorithm(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier

getLength() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType

Get the length of this time series attribute type.

getLength() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Getter for Shapelet.length.

getLengthOfTopRankingToConsider() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker

getLengthPerTree() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getLoggerName() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator

getLoggerName() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

getLoggerName() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator

getLoggerName() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getLogProbabilityOfTopKRanking(IDyadRankingInstance, int) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Returns the log of the probablity of the top k of a given IDyadRankingInstance under the Plackett Luce model parametrized by the latent skill values predicted by the PLNet.

getLogProbabilityOfTopRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Returns the the log of the probablity of the top ranking for a given IDyadRankingInstance under the Plackett Luce model parametrized by the latent skill values predicted by the PLNet.

getLogProbabilityRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Computes the logarithmic probability for a particular ranking according to the log Placket-Luce model.

getLoopPoints() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans

getMax() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

getMaximumKeyByValue(Map<T, Integer>) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Returns the key with the maximum integer value.

getMeasures() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

getMemoryInMB() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getMemoryLimitinMB() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getMessage(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.TimeoutableEvaluator

getMetaFeatureComputationTimes() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Gets the time in milliseconds it took to compute each group of meta features (Computed by a Characterizer).

getMetaLearners() - Static method in class ai.libs.jaicore.ml.WekaUtil

getMin() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

getMinDistanceSearchStrategy() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Getter for ShapeletTransformLearningAlgorithm.minDistanceSearchStrategy.

getMinDistanceSearchStrategy() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Getter for ShapeletTransformTSClassifier.minDistanceSearchStrategy.

getMinibatchSize() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

getMode(int[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Returns the mode of the given array.

getModelPath() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

getMTree() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree

getMultiLabelMetrics() - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter

getMultipliedSeparability(Collection<String>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable

getNaivebais() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getNaivebaismulti() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getName() - Method in class ai.libs.jaicore.ml.core.FeatureDomain

Getter for name attribute.

getName() - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation

Get the name of the implementing multilabel cross validation technique.

getName() - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation

getNativeMultiClassClassifiers() - Static method in class ai.libs.jaicore.ml.WekaUtil

getNewClassAttribute(Attribute) - Static method in class ai.libs.jaicore.ml.WekaUtil

getNewClassAttribute(Attribute, List<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

getNodeType() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

getNosLeafNodes() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree

getNumberOfAllowedCPUs() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getNumberOfAttributes() - Method in interface ai.libs.jaicore.ml.core.dataset.AILabeledAttributeArrayDataset

Getter for the number of attributes (excluding target attribute).

getNumberOfAttributes() - Method in interface ai.libs.jaicore.ml.core.dataset.IAttributeArrayInstance

Getter for the number of attributes for the instance.

getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance

getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances

getNumberOfAttributes() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

getNumberOfCandidatesInSelectionPhase() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getNumberOfClasses(TimeSeriesDataset) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Counts the number of unique classes occurring in the given dataset.

getNumberOfClassifier() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector

getNumberOfColumns() - Method in class ai.libs.jaicore.ml.core.SimpleInstanceImpl

getNumberOfColumns() - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl

getNumberOfColumns() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

getNumberOfColumns() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

getNumberOfColumns() - Method in interface ai.libs.jaicore.ml.interfaces.Instance

getNumberOfColumns() - Method in interface ai.libs.jaicore.ml.interfaces.Instances

getNumberOfColumns() - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstances

getNumberOfInstances() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getNumberOfInstances() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Returns the number of instances contained in the dataset.

getNumberOfInstancesFromClass(Instances, String) - Static method in class ai.libs.jaicore.ml.WekaUtil

getNumberOfInstancesFromClass(Instances, Collection<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

getNumberOfInstancesPerClass(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

getNumberOfIterationsInSelectionPhase() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getNumberOfRows() - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl

getNumberOfRows() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

getNumberOfRows() - Method in interface ai.libs.jaicore.ml.interfaces.Instances

getNumberOfRows() - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstances

getNumberOfRuns() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getNumberOfSegments(int, int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Returns the number of segments which are available for a instance with Q attributes for a given scale r and a minimum shape length minShapeLength.

getNumberOfVariables() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getNumberOfVariables() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Returns the number of variables, i.e. the number of value matrices contained in the dataset.

getNumberQueries() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

Returns the number of queries the pool provider has answered so far.

getNumberRandomQueriesAtStart() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

getNumBins() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

getNumClasses() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

getNumCPUs() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

getNumCPUs() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner

getNumCPUs() - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAmountSelector

getNumCPUs() - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner

getNumCPUs() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

getNumInstancesUsedForTraining() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent

getNumInstancesUsedForValidation() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent

getNumMajorColumns() - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

getNumMCIterations() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Getter for the number of iterations, i.e. the number of splits considered.

getNumSamples() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

getObservedScore() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent

getOccurringLabels() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

getOccurringLabels() - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstances

getOffset() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

getOptionsOfWekaAlgorithm(Object) - Static method in class ai.libs.jaicore.ml.WekaUtil

getOut() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent

getOut() - Method in class ai.libs.jaicore.ml.evaluation.MeasureAvgComputationEvent

getOut() - Method in class ai.libs.jaicore.ml.evaluation.MeasureListComputationEvent

getOut() - Method in class ai.libs.jaicore.ml.evaluation.MeasureSingleComputationEvent

getOutputList() - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.InputOptListener

getPairWithLeastCertainty(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Returns the pair of Dyads for which the model is least certain.

getParameters() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet

getParameterSets() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration

getParams() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.ParametricFunction

getPerformanceMeasure() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getPlatz1ml() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getPlatz1my() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getPlatz1overall() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getPlNet() - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

getPoint() - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance

getPoint() - Method in interface ai.libs.jaicore.ml.core.dataset.INumericArrayInstance

getPoints() - Method in class ai.libs.jaicore.ml.clustering.GMeans

getPointToInstance() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans

getPool() - Method in interface ai.libs.jaicore.ml.activelearning.IActiveLearningPoolProvider

Returns the pool of unlabeled instances.

getPool() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

getPoolProvider() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

getPoolSize() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

getPoolSize() - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider

getPortionOfDataForPhase2() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getPossibleClassValues(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

getPreciseInsts() - Method in class ai.libs.jaicore.ml.rqp.AbstractAugmentedSpaceSampler

getPreComputedFeatureTransforms(DyadRankingDataset) - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.IDyadFeatureTransform

Precomputed the feature transforms for the dataset, this can speed up the runtime as the feature transform will be reduced to O(1) at the cost of O(n).

getPrettyMaximaString() - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Returns a String the maxima of all features this scaler has been fit to.

getPrettyMeansString() - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Returns a String for the means of all features this scaler has been fit to.

getPrettyMinimaString() - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Returns a String for the minima of all features this scaler has been fit to.

getPrettySTDString() - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Prints the standard devations of all features this scaler has been fit to.

getProbabilityBoundaries() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

getProbabilityOfTopKRanking(IDyadRankingInstance, int) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

getProbabilityOfTopRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Returns the probablity of the top ranking for a given IDyadRankingInstance under the Plackett Luce model parametrized by the latent skill values predicted by the PLNet.

getProbabilityRanking(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Returns the probablity of a given IDyadRankingInstance under the Plackett Luce model parametrized by the latent skill values predicted by the PLNet.

getProblemInstances() - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.datamanager.IInstanceCollector

getProblemInstances() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector

getQualityMeasure() - Method in class ai.libs.jaicore.ml.tsc.TSLearningProblem

getQueriedRankings() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

getQueriedRankings() - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider

getRandom() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

getRandomForest() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getRandomForestplatz1() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getRangeSize() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

getRangeSize() - Method in class ai.libs.jaicore.ml.core.FeatureDomain

Computes the size of the domain.

getRangeSize() - Method in class ai.libs.jaicore.ml.core.FeatureSpace

getRangeSize() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

getRangeSizeOfAllButSubset(Set<Integer>) - Method in class ai.libs.jaicore.ml.core.FeatureSpace

getRangeSizeOfFeatureSubspace(Set<Integer>) - Method in class ai.libs.jaicore.ml.core.FeatureSpace

getRanker() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

getRanker() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig

Set the ranker used to rank the OPEN list.

getRanking(I) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.GroupBasedRanker

getRanking(Instance) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISAC

getRanking(P) - Method in interface ai.libs.jaicore.ml.ranking.Ranker

getRankings() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISAC

getRatioOfOldInstancesForMinibatch() - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker

getRawLastClassificationResults() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

getRefactoredInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

getRefactoredInstance(Instance, List<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

getRefactoredInstances(Instances, Map<String, String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

getRelativeNumberOfInstancesFromClass(Instances, String) - Static method in class ai.libs.jaicore.ml.WekaUtil

getRelativeNumberOfInstancesFromClass(Instances, Collection<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

getReplacedAttributeList(List<Attribute>, Attribute) - Static method in class ai.libs.jaicore.ml.WekaUtil

getRng() - Method in class ai.libs.jaicore.ml.rqp.AbstractAugmentedSpaceSampler

getRootGenerator() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

getRootNode() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier

Getter for the root node.

getS() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

getSaturationPoint(double) - Method in interface ai.libs.jaicore.ml.interfaces.AnalyticalLearningCurve

Calculated or search a saturation point with a tolerance of epsilon.

getSaturationPoint(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

getSaturationPoint(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve

getScaler() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

getScaler() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig

Get the scaler used to scale the dataset.

getSearchers() - Static method in class ai.libs.jaicore.ml.WekaUtil

getSeed() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Getter for the random seed.

getSeed() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator

getSeed() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getSeeds() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

getSegments() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getSegmentsDifference() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getSeparability(String, String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable

getShapelets() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Getter for ShapeletTransformTSClassifier.shapelets.

getSingleLabelMetrics() - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter

getSize() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

getSkillForDyad(Dyad) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Returns the latent skill value predicted by the PLNet for a given Dyad.

getSolutionLogDir() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getSortedDataset() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling

getSplitBasedEvaluator() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Getter for the evaluator that is used for evaluating each split.

getSplitEvaluationTime() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent

getSplitSeparator() - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation

Get the separator used to separate single portions of a split in a given splitInfo.

getSplitSeparator() - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation

getSplitTechniqueAndDetailsSeparator() - Static method in class ai.libs.jaicore.ml.weka.dataset.splitter.MultilabelDatasetSplitter

Obtain the token used to separate a split technique and the details about the split.

getSplitter(int) - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.ISplitterFactory

getSquaredDistance() - Static method in class ai.libs.jaicore.ml.tsc.util.ScalarDistanceUtil

getStartIndex() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Getter for Shapelet.startIndex.

getStatsX() - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

getStatsY() - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

getStepdifference() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getStepdifferenceML() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getStrati() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling

getStratifiedSplit(Instances, long, double...) - Static method in class ai.libs.jaicore.ml.WekaUtil

getStratifiedSplit(ReproducibleInstances, Random, double...) - Static method in class ai.libs.jaicore.ml.WekaUtil

Creates a stratified split for a given ReproducibleInstances Object.

getStratifiedSplit(ReproducibleInstances, long, double...) - Static method in class ai.libs.jaicore.ml.WekaUtil

Creates a StratifiedSplit for a given ReproducibleInstances Object.

getStratifiedSplitIndices(Instances, Random, double...) - Static method in class ai.libs.jaicore.ml.WekaUtil

getStratifiedSplitIndicesAsList(Instances, Random, double...) - Static method in class ai.libs.jaicore.ml.WekaUtil

getSubsequences() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

getSubseriesClf() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

getSuccessorGenerator() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

getTargets() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getTargets() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Getter for the target values.

getTargetType() - Method in interface ai.libs.jaicore.ml.core.dataset.AILabeledAttributeArrayDataset

Returns the attribute type of the target attribute.

getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema

getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances

getTargetType() - Method in class ai.libs.jaicore.ml.core.predictivemodel.APredictiveModel

Getter method for the given targetType.

getTargetValue(Class<T>) - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance

Getter for the value of the target attribute.

getTargetValue() - Method in interface ai.libs.jaicore.ml.core.dataset.ILabeledInstance

Getter for the value of the target attribute.

getTargetValue() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getTargetValue() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance

getTargetValue() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

getTargetValue() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance

getTargetValue() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

getTestData() - Method in class ai.libs.jaicore.ml.core.dataset.util.StratifiedSplit

getTestSplit(Instances, int, int, String) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation

Gets a test split from the given data based on the seed.

getTestSplit(Instances, String, String, String) - Static method in class ai.libs.jaicore.ml.weka.dataset.splitter.MultilabelDatasetSplitter

Split the Instances object according to the given splitDescription.

getTestSplit(Instances, int, int, String) - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation

getTimeout(Classifier) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.TimeoutableEvaluator

getTimeout() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

getTimeoutForSolutionEvaluation() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Getter for the timeout for evaluating a solution.

getTimeoutInSeconds() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getTimeoutPerCandidate() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getTimeouts() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

getTimeoutTotal() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getTimes() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getTimestampMatrices() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Getter for TimeSeriesDataset.timestampMatrices.

getTimestamps(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getTimestamps(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Getter for the timestamp matrix at a specific index.

getTimestampsOrNull(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getTimestampsOrNull(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Getter for the timestamp matrix at a specific index.

getTmpDir() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getTop3ml() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

gettop3mymethod() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getTop3overall() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getTotalVariance() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

getTrain() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator

getTrainFoldSize() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Getter for the size of the train fold.

getTrainingAndTestDataForFold(int, int, double[][], int[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Functions creating two TimeSeriesDataset objects representing the training and test split for the given fold of a cross validation with numFolds many folds.

getTrainingData() - Method in class ai.libs.jaicore.ml.core.dataset.util.StratifiedSplit

getTrainingPortion() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator

getTrainingPortion() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getTrainingTimes() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getTrainLeafNodes() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getTrainSplit(Instances, int, int, String) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IMultilabelCrossValidation

Gets a train split from the given data based on the seed.

getTrainSplit(Instances, String, String, String) - Static method in class ai.libs.jaicore.ml.weka.dataset.splitter.MultilabelDatasetSplitter

Split the Instances object according to the given splitDescription.

getTrainSplit(Instances, int, int, String) - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation

getTrainTargets() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getTransformedVectorLength(int, int) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.BiliniearFeatureTransform

getTransformedVectorLength(int, int) - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.IDyadFeatureTransform

Get the length of the vector returned by the transform method.

getTrees() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

getTrees() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

Getter for the time series trees.

getType() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue

getType() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeValue

getUnivirateHistograms() - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

getUntocedoverall() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getUntochedmy() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getUntouchedml() - Static method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation.ModifiedISACEvaluator

getUpperBoundOnSeparability(Collection<String>) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable

getValidate() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator

getValidationAlgorithm() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getValue() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue

getValue() - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeValue

getValueMatrices() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Getter for TimeSeriesDataset.valueMatrices.

getValueOfMetricForSingleLabelClassifier(Evaluation, String, int) - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter

Extracts the metric with the given name from the Evaluation object that is the result of evaluating a classifier.

getValueOfMultilabelClassifier(Result, String) - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter

Extracts the metric with the given name from the result of evaluating a multilabel classifier (Calls the corresponding method).

getValues() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

getValues(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getValues(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Getter for the value matrix at a specific index.

getValuesOrNull(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

getValuesOrNull(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Getter for the value matrix at a specific index.

getVoteType() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Getter for the vote type.

getW() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

getW0() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

getWeights() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

getWeights() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet

getxValues() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

getyValues() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

getyValues() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

GlobalCharacterizer - Class in ai.libs.jaicore.ml.metafeatures

Characterizer that applies a number of Characterizers to a data set.

GlobalCharacterizer() - Constructor for class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Initializes a new characterizer.

GMeans<C extends org.apache.commons.math3.ml.clustering.Clusterable> - Class in ai.libs.jaicore.ml.clustering

Implementation of Gmeans based on Helen Beierlings implementation of GMeans(https://github.com/helebeen/AILibs/blob/master/JAICore/jaicore-modifiedISAC/src/main/java/jaicore/modifiedISAC/ModifiedISACgMeans.java).

GMeans(Collection<C>) - Constructor for class ai.libs.jaicore.ml.clustering.GMeans

Initializes a basic cluster for the given Point using Mannhatten distance and seed=1

GMeans(Collection<C>, DistanceMeasure, long) - Constructor for class ai.libs.jaicore.ml.clustering.GMeans

Initializes a cluster for the given Point using a given distance meassure and a seed.

GmeansSampling<I extends INumericLabeledAttributeArrayInstance<? extends java.lang.Number>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

Implementation of a sampling method using gmeans-clustering.

GmeansSampling(long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.GmeansSampling

GmeansSampling(long, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.GmeansSampling

GmeansSamplingFactory<I extends INumericLabeledAttributeArrayInstance<? extends java.lang.Number>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

GmeansSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory

GMeansStratiAmountSelectorAndAssigner<I extends INumericArrayInstance,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

Combined strati amount selector and strati assigner via g-means.

GMeansStratiAmountSelectorAndAssigner(int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner

Constructor for GMeansStratiAmountSelectorAndAssigner with Manhattan distanceMeasure as a default.

GMeansStratiAmountSelectorAndAssigner(DistanceMeasure, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner

Constructor for GMeansStratiAmountSelectorAndAssigner with custom distanceMeasure.

GRAD_DESC_GRADIENT_THRESHOLD - Static variable in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig

Specifies a threshold for the gradient (i.e. if the gradient is below this value no update will be done; if all gradients are below this value, the algorithm will terminate)

GRAD_DESC_LEARNING_RATE - Static variable in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig

The learning rate in the update step (i.e. how much of the gradient should be added to the parameter)

GRAD_DESC_MAX_ITERATIONS - Static variable in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig

Specifies the maximum of gradient update steps.

GradientDescentOptimizer - Class in ai.libs.jaicore.ml.core.optimizing.graddesc

An optimizer based on the gradient descent method [1].

GradientDescentOptimizer(GradientDescentOptimizerConfig) - Constructor for class ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizer

GradientDescentOptimizer() - Constructor for class ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizer

GradientDescentOptimizerConfig - Interface in ai.libs.jaicore.ml.core.optimizing.graddesc

gradientThreshold() - Method in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig

Group<C,I> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes

Group.java - Stores a group with it center as ID and the associated instances

Group(List<ProblemInstance<I>>, GroupIdentifier<C>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group

GroupBasedRanker<C,I,S> - Class in ai.libs.jaicore.ml.ranking.clusterbased

GroupBasedRanker() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.GroupBasedRanker

GroupIdentifier<C> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes

GroupIdentifier(C) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.GroupIdentifier

GulloDerivate - Class in ai.libs.jaicore.ml.tsc.filter.derivate

Calculates the derivative of a timeseries as described first by Gullo et. al (2009).

GulloDerivate() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.GulloDerivate

GulloDerivate(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.GulloDerivate

gulloDerivate(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Calculates the derivative of a timeseries as described first by Gullo et. al (2009).

gulloDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

f'(n) = \frac{f(i+1)-f(i-1)}{2}

H

HammingAccuracy - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

Measure for computing how similar two double vectors are according to hamming distance.

HammingAccuracy() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.HammingAccuracy

Standard c'tor.

HammingLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

HammingLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.HammingLoss

handleError(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AProcessListener

Handle the output of the error output stream.

handleError(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener

handleInput(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.AProcessListener

Handle the output of the standard output stream.

handleInput(String) - Method in class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener

hashCode() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue

hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy

hashCode() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

hashCode() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

hashCode() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

hashCode() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance

hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad

hashCode() - Method in class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue

hashCode() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

hashCode() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

hashCode() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration

hashCode() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet

hashCode() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.RankingForGroup

hashCode() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

hasNext() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

hasNext() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

hasOnlyNumericAttributes(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

HellFormater - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

HighProbClassifier - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.reducer

HighProbClassifier(Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.HighProbClassifier

HilbertTransform - Class in ai.libs.jaicore.ml.tsc.filter.transform

Calculates the Hilbert transform of a time series.

HilbertTransform() - Constructor for class ai.libs.jaicore.ml.tsc.filter.transform.HilbertTransform

HILL_3 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants

HistogramBuilder - Class in ai.libs.jaicore.ml.tsc

HistogramBuilder() - Constructor for class ai.libs.jaicore.ml.tsc.HistogramBuilder

histogramForInstance(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.HistogramBuilder

I

IActiveLearningPoolProvider<I extends ILabeledInstance> - Interface in ai.libs.jaicore.ml.activelearning

Provides a sample pool for pool-based active learning.

IAttributeArrayInstance - Interface in ai.libs.jaicore.ml.core.dataset

Interface of an instance that consists of attributes.

IAttributeType<D> - Interface in ai.libs.jaicore.ml.core.dataset.attribute

Wrapper interface for attribute types.

IAttributeValue<D> - Interface in ai.libs.jaicore.ml.core.dataset.attribute

A general interface for attribute values.

IAugmentedSpaceSampler - Interface in ai.libs.jaicore.ml.rqp

Interface representing a class that samples interval-valued data from a set of precise data points.

IAugSpaceSamplingFunction - Interface in ai.libs.jaicore.ml.rqp

IBatchLearner<T,I,D extends IDataset<I>> - Interface in ai.libs.jaicore.ml.core.predictivemodel

The IBatchLearner models a learning algorithm which works in a batch fashion, i.e. takes a whole AILabeledAttributeArrayDataset as training input.

ICategoricalAttributeType - Interface in ai.libs.jaicore.ml.core.dataset.attribute.categorical

Interface for categorical attribute types.

ICertaintyProvider<T,I,D extends IDataset<I>> - Interface in ai.libs.jaicore.ml.core.predictivemodel

The ICertaintyProvider models an IPredictiveModel that provides uncertainty information for queries in form of IInstances.

IClassifierEvaluator - Interface in ai.libs.jaicore.ml.evaluation.evaluators.weka

IClassifierEvaluatorFactory - Interface in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

IDataset<I> - Interface in ai.libs.jaicore.ml.core.dataset

IDatasetSplitter - Interface in ai.libs.jaicore.ml.weka.dataset.splitter

IDistanceMetric<D,A,B> - Interface in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

ids - Variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

The names of all the meta features that are computed by this characterizer

IDyadFeatureTransform - Interface in ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform

Feature transformation interface for the FeatureTransformPLDyadRanker.

IDyadRanker - Interface in ai.libs.jaicore.ml.dyadranking.algorithm

An abstract representation of a dyad ranker.

IDyadRankingFeatureTransformPLGradientDescendableFunction - Interface in ai.libs.jaicore.ml.dyadranking.optimizing

An interface for a differentiable function in the context of feature transformation Placket-Luce dyad ranking.

IDyadRankingFeatureTransformPLGradientFunction - Interface in ai.libs.jaicore.ml.dyadranking.optimizing

Represents a differentiable function in the context of dyad ranking based on feature transformation Placket-Luce models.

IDyadRankingInstance - Interface in ai.libs.jaicore.ml.dyadranking.dataset

Represents an instance for a DyadRankingDataset.

IDyadRankingPoolProvider - Interface in ai.libs.jaicore.ml.dyadranking.activelearning

Interface for an active learning pool provider in the context of dyad ranking.

IFilter - Interface in ai.libs.jaicore.ml.tsc.filter

IGradientBasedOptimizer - Interface in ai.libs.jaicore.ml.core.optimizing

Interface for an optimizer that is based on a gradient descent and gets a differentiable function and the derivation of said function to solve an optimization problem.

IGradientDescendableFunction - Interface in ai.libs.jaicore.ml.core.optimizing

This interface represents a function that is differentiable and thus can be used by gradient descent algorithms.

IGradientFunction - Interface in ai.libs.jaicore.ml.core.optimizing

Represents the gradient of a function that is differentiable.

IGroupBuilder<C,I> - Interface in ai.libs.jaicore.ml.ranking.clusterbased

IGroupBuilder discribes the act of building groups out of probleminstances

IGroupSolutionRankingSelect<C,S,I,P> - Interface in ai.libs.jaicore.ml.ranking.clusterbased

IInstance - Interface in ai.libs.jaicore.ml.core.dataset

Interface of an instance which consists of attributes and a target value.

IInstanceCollector<I> - Interface in ai.libs.jaicore.ml.ranking.clusterbased.datamanager

IInstancesClassifier - Interface in ai.libs.jaicore.ml.evaluation

ILabeledAttributeArrayDataset<L> - Interface in ai.libs.jaicore.ml.core.dataset

ILabeledAttributeArrayInstance<L> - Interface in ai.libs.jaicore.ml.core.dataset

Type intersection for IAttributeArrayInstance and ILabeledInstance

ILabeledInstance<T> - Interface in ai.libs.jaicore.ml.core.dataset

Interface of an instance that has a target value.

ILearnShapeletsLearningAlgorithmConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.shapelets

ILOG_2 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants

IMeasure<I,O> - Interface in ai.libs.jaicore.ml.core.evaluation.measure

The interface of a measure which compute a value of O from expected and actual values of I.

IModifiableInstance - Interface in ai.libs.jaicore.ml.core.dataset

IMultiClassClassificationExperimentConfig - Interface in ai.libs.jaicore.ml.experiments

IMultiClassClassificationExperimentDatabase - Interface in ai.libs.jaicore.ml.experiments

IMultilabelCrossValidation - Interface in ai.libs.jaicore.ml.weka.dataset.splitter

Represents an algorithm that realizes a split of a given multilabel instances in folds, given a seed, custom information about the split represented as a string, and the fold that is left out for testing.

IMultilabelMeasure - Interface in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

Interface for measures dealing with multilabel data.

IMultiValueAttributeType - Interface in ai.libs.jaicore.ml.core.dataset.attribute.multivalue

Interface for categorical attribute types.

indArrayToWekaInstances(INDArray) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Converts an INDArray matrix (number of instances x number of attributes) to Weka instances without any class attribute.

indexOf(Object) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

init(D, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

init(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

Initializes the algorithm for stratum assignment.

init(D, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner

init(D, int) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner

Initialize custom assigner if necessary.

init(D, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.KMeansStratiAssigner

initialize(DyadRankingDataset, Map<IDyadRankingInstance, Map<Dyad, Vector>>) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihood

initialize(DyadRankingDataset, Map<IDyadRankingInstance, Map<Dyad, Vector>>) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.DyadRankingFeatureTransformNegativeLogLikelihoodDerivative

initialize(DyadRankingDataset, Map<IDyadRankingInstance, Map<Dyad, Vector>>) - Method in interface ai.libs.jaicore.ml.dyadranking.optimizing.IDyadRankingFeatureTransformPLGradientDescendableFunction

Initializes the function with the given dataset.

initialize(DyadRankingDataset, Map<IDyadRankingInstance, Map<Dyad, Vector>>) - Method in interface ai.libs.jaicore.ml.dyadranking.optimizing.IDyadRankingFeatureTransformPLGradientFunction

Initialize the function with the given data set and feature transformation method.

initializeCharacterizerNames() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Initializes GlobalCharacterizer.characterizerNames.

initializeCharacterizers() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Adds the required characterizers to GlobalCharacterizer.characterizers.

initializeCharacterizers() - Method in class ai.libs.jaicore.ml.metafeatures.LandmarkerCharacterizer

initializeCharacterizers() - Method in class ai.libs.jaicore.ml.metafeatures.NoProbingCharacterizer

initializeKMeans() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans

initializeKMeans() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACkMeans

initializeMetaFeatureIds() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

Initializes GlobalCharacterizer.ids.

initializeRegressionTree(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

Initializes a new instance of RandomRegressionTree.

initializeS(double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Initializes the tensor S storing the shapelets for each scale.

initializeWeights(double[][][], double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Randomly initializes the weights around zero.

input - Variable in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm

The TimeSeriesDataset object used for maintaining the model.

InputOptimizerLoss - Interface in ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization

InputOptListener - Class in ai.libs.jaicore.ml.dyadranking.zeroshot.util

InputOptListener(int[]) - Constructor for class ai.libs.jaicore.ml.dyadranking.zeroshot.util.InputOptListener

inputs - Variable in class ai.libs.jaicore.ml.cache.Instruction

Instance - Interface in ai.libs.jaicore.ml.interfaces

instance(int) - Method in class ai.libs.jaicore.ml.SubInstances

Instances - Interface in ai.libs.jaicore.ml.interfaces

InstanceSchema<L> - Class in ai.libs.jaicore.ml.core.dataset

InstanceSchema(List<IAttributeType<?>>, IAttributeType<L>) - Constructor for class ai.libs.jaicore.ml.core.dataset.InstanceSchema

instancesToJsonString(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

InstanceWiseF1 - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

Instance-wise F1 measure for multi-label classifiers.

InstanceWiseF1() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.InstanceWiseF1

InstanceWiseF1AsLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

The F1 Macro Averaged by the number of instances measure.

InstanceWiseF1AsLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.InstanceWiseF1AsLoss

Instruction - Class in ai.libs.jaicore.ml.cache

Instruction class that can be converted into json.

Instruction() - Constructor for class ai.libs.jaicore.ml.cache.Instruction

IntervalAggregator - Interface in ai.libs.jaicore.ml.intervaltree.aggregation

An IntervalAggeregator can aggregate from a list of intervals, more precisely given a list of predictions in the leaf node, it can predict a range.

IntervalAndHeader(Interval[], Instances) - Constructor for class ai.libs.jaicore.ml.intervaltree.util.RQPHelper.IntervalAndHeader

intManhattanDistance(int[], int[]) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil

Simple Manhattan distance (sum of the absolute differences between the vectors' elements) implementation for arrays of Integer.

INumericArrayInstance - Interface in ai.libs.jaicore.ml.core.dataset

INumericLabeledAttributeArrayInstance<L> - Interface in ai.libs.jaicore.ml.core.dataset

Type intersection interface for numeric instances on one hand and labeled instances on the other hand.

INumericLabeledIAttributeDataset<L> - Interface in ai.libs.jaicore.ml.core.dataset

InvalidAnchorPointsException - Exception in ai.libs.jaicore.ml.learningcurve.extrapolation

Exception that is thrown, when the anchorpoints generated for learning curve extrapolation are not suitable.

InvalidAnchorPointsException() - Constructor for exception ai.libs.jaicore.ml.learningcurve.extrapolation.InvalidAnchorPointsException

InversePowerLawConfiguration - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.ipl

This class encapsulates the three parameters that are required in order to create a Inverse Power Law function.

InversePowerLawConfiguration() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

InversePowerLawExtrapolationMethod - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.ipl

This class describes a method for learning curve extrapolation which generates an Inverse Power Law function.

InversePowerLawExtrapolationMethod() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawExtrapolationMethod

InversePowerLawExtrapolationMethod(String, String) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawExtrapolationMethod

InversePowerLawLearningCurve - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.ipl

Representation of a learning curve with the Inverse Power Law function, which has three parameters named a, b and c.

InversePowerLawLearningCurve(double, double, double) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

InversePowerLawLearningCurve(InversePowerLawConfiguration) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

IOnlineLearner<T,I,D extends IDataset<I>> - Interface in ai.libs.jaicore.ml.core.predictivemodel

The IOnlineLearner models a learning algorithm which works in an online fashion, i.e. takes either a single IInstance or a Set thereof as training input.

IOrderedDataset<I> - Interface in ai.libs.jaicore.ml.core.dataset

IOrderedLabeledAttributeArrayDataset<I extends ILabeledAttributeArrayInstance<L>,L> - Interface in ai.libs.jaicore.ml.core.dataset

Extends the IDataset by including the List interface.

IOrderedLabeledDataset<I extends ILabeledInstance<L>,L> - Interface in ai.libs.jaicore.ml.core.dataset

IPipelineEvaluationConf - Interface in ai.libs.jaicore.ml.experiments

IPLDyadRanker - Interface in ai.libs.jaicore.ml.dyadranking.algorithm

An abstract representation for a dyad ranker using Placket Luce models.

IPLNetDyadRankerConfiguration - Interface in ai.libs.jaicore.ml.dyadranking.algorithm

IPredictiveModel<T,I,D extends IDataset<I>> - Interface in ai.libs.jaicore.ml.core.predictivemodel

The IPredictiveModel corresponds to a model which can be used to make predictions based on given IInstancees.

IPredictiveModelConfiguration - Interface in ai.libs.jaicore.ml.core.predictivemodel

The IPredictiveModelConfiguration models a configuration of an IPredictiveModel.

IPrimitiveAttributeType<D> - Interface in ai.libs.jaicore.ml.core.dataset.attribute.primitive

Interface for categorical attribute types.

IProcessListener - Interface in ai.libs.jaicore.ml.scikitwrapper

IQualityMeasure - Interface in ai.libs.jaicore.ml.tsc.quality_measures

Interface for a quality measure assessing distances of instances to a shapelet given the corresponding class values.

IRankedSolutionCandidateProvider<I,S> - Interface in ai.libs.jaicore.ml.ranking.clusterbased.candidateprovider

IRerunnableSamplingAlgorithmFactory<D extends IDataset<?>,A extends ASamplingAlgorithm<D>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces

Extension of the ISamplingAlgorithmFactory for sampling algorithms that can re-use informations from a previous run of the Sampling algorithm.

ISamplingAlgorithm<D extends IDataset<?>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

Interface for sampling algorithms.

ISamplingAlgorithm - Interface in ai.libs.jaicore.ml.core.dataset.sampling

Interface for sampling algorithms.

ISamplingAlgorithmFactory<D extends IDataset<?>,A extends ASamplingAlgorithm<D>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces

Interface for a factory, which creates a sampling algorithm.

isCacheLookup() - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

If true signifies that performance on this data should be looked up in cache

isCacheStorage() - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

If true signifies that performance evaluation should be stored.

IScalarDistance - Interface in ai.libs.jaicore.ml.tsc.distances

Functional interface for the distance of two scalars.

isCompletelyConfigured() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

isCompletelyConfigured() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

isCompletelyConfigured() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

isCompletelyConfigured() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

isDebug() - Static method in class ai.libs.jaicore.ml.WekaUtil

ISelectiveSamplingStrategy<I> - Interface in ai.libs.jaicore.ml.activelearning

A strategy for selective sampling.

isEmpty() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

isEmpty() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

isEmpty() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance

isEmpty() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

States whether the dataset is empty, i.e. contains no value matrices, or not.

ISingleAttributeTransformer - Interface in ai.libs.jaicore.ml.core.dataset.attribute.transformer

isInteger() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

isMultivariate() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

isMultivariate() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

States whether the dataset is a univariate dataset, i.e. contains more than one value matrix, or not.

ISplitBasedClassifierEvaluator<O> - Interface in ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation

Interface for the evaluator measure bridge yielding the measured value as an instance of O.

ISplitter - Interface in ai.libs.jaicore.ml.classification.multiclass.reduction.splitters

ISplitterFactory<T extends ISplitter> - Interface in ai.libs.jaicore.ml.classification.multiclass.reduction.splitters

isSameLength(INDArray, INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks whether multiple arrays have the same length.

isSameLength(double[], double[]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks whether multiple arrays have the same length.

isSameLengthOrException(INDArray, INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks whether multiple arrays have the same length.

isSameLengthOrException(double[], double[]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks whether multiple arrays have the same length.

isSelfContained() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

isTest() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

States whether the dataset is a test dataset, i.e. contains no valid targets after initialization, or not.

isTimeSeries(INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks, whether given INDArray are valid time series.

isTimeSeries(int, INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks, whether given INDArrays are valid time series with a given length.

isTimeSeries(int, double[]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks, whether given array are valid time series with a given length.

isTimeSeriesOrException(INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks, whether given INDArrays are valid time series.

isTimeSeriesOrException(int, INDArray...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks, whether given INDArrays are valid time series with a given length.

isTimeSeriesOrException(int, double[]...) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Checks, whether given INDArrays are valid time series with a given length.

isTrain() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

States whether the dataset is a training dataset, i.e. contains valid targets after initialization, or not.

isTrained() - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

IStratiAmountSelector<D extends IDataset<?>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

Functional interface to write custom logic for selecting the amount of strati for a dataset.

IStratiAssigner<I,D extends IDataset<I>> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

Interface to write custom Assigner for datapoints to strati.

IStratiFileAssigner - Interface in ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling

Interface to implement custom Stratum assignment behavior.

isUnivariate() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

isUnivariate() - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

States whether the dataset is a univariate dataset, i.e. contains exactly one value matrix, or not.

isUseInstanceReordering() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

isValidPreprocessorCombination(String, String) - Static method in class ai.libs.jaicore.ml.WekaUtil

isValidValue(String) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType

isValidValue(D) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeType

Validates whether a value conforms to this type.

isValidValue(Collection<String>) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType

isValidValue(Boolean) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.BooleanAttributeType

isValidValue(Double) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType

isValidValue(INDArray) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType

Validates whether a INDArray conforms to this time series.

ITableGeneratorandCompleter<I,S,P> - Interface in ai.libs.jaicore.ml.ranking.clusterbased.datamanager

iterator() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

iterator() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

iterator() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance

iterator() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

iterator() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

iterator() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

iterator() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

ITimeSeriesComplexity - Interface in ai.libs.jaicore.ml.tsc.complexity

Interface that describes the complexity measure of a time series.

ITimeSeriesDistance - Interface in ai.libs.jaicore.ml.tsc.distances

Interface that describes a distance measure of two time series.

ITimeSeriesDistanceWithTimestamps - Interface in ai.libs.jaicore.ml.tsc.distances

Interface that describes a distance measure of two time series that takes the timestamps into account.

ITreeClassifier - Interface in ai.libs.jaicore.ml.classification.multiclass.reduction

J

JaccardLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

JaccardLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.JaccardLoss

JaccardScore - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

JaccardScore() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.JaccardScore

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

jsonStringToInstances(String) - Static method in class ai.libs.jaicore.ml.WekaUtil

K

k - Variable in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans

K_ACTIVATION_FUNCTION - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

The activation function for the hidden layers.

K_ALPHABET - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

K_ALPHABET_SIZE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

K_CLUSTERSHAPELETS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

K_EARLY_STOPPING_INTERVAL - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

How often (in epochs) the validation error should be checked for early stopping.

K_EARLY_STOPPING_PATIENCE - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

For how many epochs early stopping should wait until training is stopped if no improvement in the validation error is observed.

K_EARLY_STOPPING_RETRAIN - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

Whether to retrain on the full training data after early stopping, using the same number of epochs the model was trained for before early stopping occured.

K_EARLY_STOPPING_TRAIN_RATIO - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

The ratio of data used for training in early stopping. 1 - this ratio is used for testing.

K_ESTIMATEK - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_ESTIMATEK - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_ESTIMATESHAPELETLENGTHBORDERS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

K_FEATURECACHING - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig

K_FEATURECACHING - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig

K_GAMMA - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_GAMMA - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_LEARNINGRATE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_LEARNINGRATE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_MAX_EPOCHS - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

The maximum number of epochs to be used during training, i.e. how many times the training algorithm should iterate through the entire training data set.

K_MAXDEPTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig

K_MAXDEPTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig

K_MAXDEPTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig

K_MAXITER - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_MAXITER - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_MEANCORRECTED - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

K_MEANNORMALIZATION - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig

K_MIN_INTERVAL_LENGTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

K_MINI_BATCH_SIZE - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

The size of mini batches used during training.

K_NUM_SHAPELETS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_NUMBINS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

K_NUMCLUSTERS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

K_NUMFOLDS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

K_NUMFOLDS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

K_NUMSEGMENTS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig

K_NUMSHAPELETS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_NUMSHAPELETS - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

K_NUMTREES - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig

K_NUMTREES - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig

K_PLNET_HIDDEN_NODES - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

List of integers describing the architecture of the hidden layers.

K_PLNET_LEARNINGRATE - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

The learning rate for the gradient updater.

K_PLNET_SEED - Static variable in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

The random seed to use.

K_REGULARIZATION - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_REGULARIZATION - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_SCALER - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_SCALER - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_SHAPELETLENGTH_MAX - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

K_SHAPELETLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_SHAPELETLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_SHAPELETLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

K_SHAPELETLENGTH_RELMIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

K_SHAPELETLENGTH_RELMIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

K_USE_ZNORMALIZATION - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

K_USEHIVECOTEENSEMBLE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

K_WINDOW_SIZE - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

K_WINDOWLENGTH_MAX - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig

K_WINDOWLENGTH_MIN - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig

K_WORDLENGTH - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

K_ZPROP - Static variable in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

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

KendallsTauDyadRankingLoss - Class in ai.libs.jaicore.ml.dyadranking.loss

Computes the rank correlation measure known as Kendall's tau coefficient, i.e.

KendallsTauDyadRankingLoss() - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.KendallsTauDyadRankingLoss

KendallsTauOfTopK - Class in ai.libs.jaicore.ml.dyadranking.loss

Calculates the kendalls-tau loss only for the top k dyads.

KendallsTauOfTopK(int, double) - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.KendallsTauOfTopK

KeoghDerivate - Class in ai.libs.jaicore.ml.tsc.filter.derivate

Calculates the derivative of a timeseries as described first by Keogh and Pazzani (2001).

KeoghDerivate() - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.KeoghDerivate

KeoghDerivate(boolean) - Constructor for class ai.libs.jaicore.ml.tsc.filter.derivate.KeoghDerivate

keoghDerivate(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Calculates the derivative of a timeseries as described first by Keogh and Pazzani (2001).

keoghDerivateWithBoundaries(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Calculates the derivateive of a timeseries as described first by Keogh and Pazzani (2001).

Kmeans<A,D> - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

Kmeans(List<A>, IDistanceMetric<D, A, A>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans

kmeanscluster(int) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans

kmeanscluster(int) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACkMeans

KmeansSampling<I extends INumericLabeledAttributeArrayInstance<? extends java.lang.Number>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

Implementation of a sampling method using kmeans-clustering.

KmeansSampling(long, int, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling

Implementation of a sampling method using kmeans-clustering.

KmeansSampling(long, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling

Implementation of a sampling method using kmeans-clustering.

KmeansSampling(long, int, DistanceMeasure, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling

Implementation of a sampling method using kmeans-clustering.

KmeansSamplingFactory<I extends INumericLabeledAttributeArrayInstance<? extends java.lang.Number>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

KmeansSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

KMeansStratiAssigner<I extends INumericArrayInstance,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

Cluster the data set with k-means into k Clusters, where each cluster stands for one stratum.

KMeansStratiAssigner(DistanceMeasure, int) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.KMeansStratiAssigner

Constructor for KMeansStratiAssigner.

KNNAugSpaceSampler - Class in ai.libs.jaicore.ml.rqp

Samples interval-valued data from a dataset of precise points.

KNNAugSpaceSampler(Instances, Random, int, NearestNeighbourSearch) - Constructor for class ai.libs.jaicore.ml.rqp.KNNAugSpaceSampler

L

L1DistanceMetric - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

L1DistanceMetric() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.L1DistanceMetric

LabeledInstance<L> - Interface in ai.libs.jaicore.ml.interfaces

LabeledInstances<L> - Interface in ai.libs.jaicore.ml.interfaces

LandmarkerCharacterizer - Class in ai.libs.jaicore.ml.metafeatures

A Characterizer that applies several characterizers to a data set, but does not use any probing.

LandmarkerCharacterizer() - Constructor for class ai.libs.jaicore.ml.metafeatures.LandmarkerCharacterizer

Constructs a new LandmarkerCharacterizer.

lastIndexOf(Object) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

LatexDatasetTableGenerator - Class in ai.libs.jaicore.ml.latex

LatexDatasetTableGenerator() - Constructor for class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

LCNetClient - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet

LCNetClient() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient

LCNetExtrapolationMethod - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet

This class represents a learning curve extrapolation using the LCNet from pybnn.

LCNetExtrapolationMethod(String) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod

learner - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

LearningCurve - Interface in ai.libs.jaicore.ml.interfaces

Interface for the result of an learning curve extrapolation.

LearningCurveExtrapolatedEvent - Class in ai.libs.jaicore.ml.learningcurve.extrapolation

LearningCurveExtrapolatedEvent(LearningCurveExtrapolator) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolatedEvent

LearningCurveExtrapolationEvaluator<I extends ILabeledAttributeArrayInstance<?>,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

Evaluates a classifier by predicting its learning curve with a few anchorpoints.

LearningCurveExtrapolationEvaluator(int[], ISamplingAlgorithmFactory<D, ? extends ASamplingAlgorithm<D>>, D, double, LearningCurveExtrapolationMethod, long) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator

Create a classifier evaluator with learning curve extrapolation.

LearningCurveExtrapolationEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

LearningCurveExtrapolationEvaluatorFactory(int[], ISamplingAlgorithmFactory<WekaInstances<Object>, ? extends ASamplingAlgorithm<WekaInstances<Object>>>, double, LearningCurveExtrapolationMethod) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.LearningCurveExtrapolationEvaluatorFactory

LearningCurveExtrapolationMethod - Interface in ai.libs.jaicore.ml.learningcurve.extrapolation

Functional interface for extrapolating a learning curve from anchorpoints.

LearningCurveExtrapolator<I extends ILabeledAttributeArrayInstance<?>,D extends IOrderedLabeledAttributeArrayDataset<I,?>> - Class in ai.libs.jaicore.ml.learningcurve.extrapolation

Abstract class for implementing a learning curve extrapolation method with some anchor points.

LearningCurveExtrapolator(LearningCurveExtrapolationMethod, Classifier, D, double, int[], ISamplingAlgorithmFactory<D, ? extends ASamplingAlgorithm<D>>, long) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

Create a learning curve extrapolator with a subsampling factory.

learningRate() - Method in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig

learningRate() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

The learning rate used within the SGD.

learningRate() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

The learning rate used within the SGD.

LearnPatternSimilarityClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Class representing the Learn Pattern Similarity classifier as described in Baydogan, Mustafa & Runger, George. (2015).

LearnPatternSimilarityClassifier(int, int, int, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

Standard constructor.

LearnPatternSimilarityLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Algorithm training a LearnPatternSimilarityClassifier as described in Baydogan, Mustafa & Runger, George. (2015).

LearnPatternSimilarityLearningAlgorithm(LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig, LearnPatternSimilarityClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

Standard constructor.

LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.trees

LearnShapeletsClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.shapelets

LearnShapeletsClassifier published in "J.

LearnShapeletsClassifier(int, double, double, int, double, int, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Constructor of the LearnShapeletsClassifier.

LearnShapeletsClassifier(int, double, double, int, double, int, double, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Constructor of the LearnShapeletsClassifier.

LearnShapeletsLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.shapelets

Generalized Shapelets Learning implementation for LearnShapeletsClassifier published in "J.

LearnShapeletsLearningAlgorithm(LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig, LearnShapeletsClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Constructor of the algorithm to train a LearnShapeletsClassifier.

LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.shapelets

length() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance

length() - Method in interface ai.libs.jaicore.ml.dyadranking.dataset.IDyadRankingInstance

Get the number of dyads in the ranking.

length() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

LinearCombinationConstants - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc

This class contains required constant names for the linear combination learning curve.

LinearCombinationExtrapolationMethod - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc

This class describes a method for learning curve extrapolation which generates a linear combination of suitable functions.

LinearCombinationExtrapolationMethod() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationExtrapolationMethod

LinearCombinationExtrapolationMethod(String, String) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationExtrapolationMethod

LinearCombinationFunction - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc

This is a basic class that describes a function which is a weighted combination of individual functions.

LinearCombinationFunction(List<UnivariateFunction>, List<Double>) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

LinearCombinationLearningCurve - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc

The LinearCombinationLearningCurve consists of the actual linear combination function that describes the learning curve, as well as the derivative of this function.

LinearCombinationLearningCurve(LinearCombinationLearningCurveConfiguration, int) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurve

LinearCombinationLearningCurveConfiguration - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc

A configuration for a linear combination learning curve consists of parameterizations for at least one linear combination function.

LinearCombinationLearningCurveConfiguration() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration

LinearCombinationParameterSet - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc

This class encapsulates all parameters that are required in order to create a weighted linear combination of parameterized functions.

LinearCombinationParameterSet() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet

listenTo(Process) - Method in class ai.libs.jaicore.ml.scikitwrapper.AProcessListener

listenTo(Process) - Method in interface ai.libs.jaicore.ml.scikitwrapper.IProcessListener

Lets the process listener listen to the output and error stream of the given process.

listIterator() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

listIterator(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

loadArff(File) - Static method in class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader

Loads a univariate time series dataset from the given arff file.

loadArffs(File...) - Static method in class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader

Loads a multivariate time series dataset from multiple arff files (each for one series).

LoadDataSetInstruction - Class in ai.libs.jaicore.ml.cache

Instruction for dataset loading, provider and id are used to identify the data set.

LoadDataSetInstruction(DataProvider, String) - Constructor for class ai.libs.jaicore.ml.cache.LoadDataSetInstruction

Constructor to create an instruction for loading a dataset that can be converted to json.

loadModelFromFile(String) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Restore a trained model from a given file path.

LocalCaseControlSampling<I extends ILabeledAttributeArrayInstance<?>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

LocalCaseControlSampling(Random, int, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.LocalCaseControlSampling

LocalCaseControlSamplingFactory<I extends ILabeledAttributeArrayInstance<?>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

LocalCaseControlSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory

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

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

loss(IDyadRankingInstance, IDyadRankingInstance) - Method in interface ai.libs.jaicore.ml.dyadranking.loss.DyadRankingLossFunction

Computes the loss between the actual dyad ordering and predicted dyad ordering, represented by dyad ranking instances.

loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.DyadRankingMLLossFunctionWrapper

loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.KendallsTauDyadRankingLoss

loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.KendallsTauOfTopK

loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.NDCGLoss

loss(IDyadRankingInstance, IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.loss.TopKOfPredicted

loss(INDArray) - Method in interface ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.InputOptimizerLoss

loss(INDArray) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.NegIdentityInpOptLoss

lossGradient(INDArray) - Method in interface ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.InputOptimizerLoss

lossGradient(INDArray) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.NegIdentityInpOptLoss

LossScoreTransformer<I> - Class in ai.libs.jaicore.ml.core.evaluation.measure

This transformer transforms a decomposable double measure from a scoring function to a loss or vice versa.

LossScoreTransformer(ADecomposableDoubleMeasure<I>) - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.LossScoreTransformer

Constructor for setting the measure to be transformed from loss to score or vice versa.

M

main(String[]) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper

MajorityConfidenceVote - Class in ai.libs.jaicore.ml.tsc.classifier.ensemble

Vote implementation for majority confidence.

MajorityConfidenceVote(int, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.ensemble.MajorityConfidenceVote

Constructor for a majority confidence vote ensemble classifier.

ManhattanDistance - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Manhattan distance for time series.

ManhattanDistance() - Constructor for class ai.libs.jaicore.ml.tsc.distances.ManhattanDistance

map(String) - Method in class ai.libs.jaicore.ml.tsc.util.ClassMapper

Maps a String value to an integer value based on the value's position in the classValues.

map(int) - Method in class ai.libs.jaicore.ml.tsc.util.ClassMapper

Maps an integer value to a string based on the position index in the classValues.

mapJ48InputsToWekaOptions(double, double) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil

mapMLPInputsToWekaOptions(double, double, double) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil

mapRFInputsToWekaOptions(double, double, double, double, double) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil

mapSMORBFInputsToWekaOptions(double, double) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil

mapWEKAToTree(Instance) - Static method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper

Maps the WEKA query to a tree-friendly query while also preserving the header information of the query, this is important for M5 trees.

MathUtil - Class in ai.libs.jaicore.ml.tsc.util

Utility class consisting of mathematical utility functions.

matrixToWekaInstances(double[][]) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Converts a double[][] matrix (number of instances x number of attributes) to Weka instances without any class attribute.

maxDepth() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig

Maximum depth of the trained trees.

maxDepth() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig

Maximum depth of the trained trees.

maxDepth() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig

maxIterations() - Method in interface ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizerConfig

maxIterations() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

The maximum iterations used for the SGD.

maxIterations() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

The maximum iterations used for the SGD.

maxShapeletLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

The maximum length of shapelets to be considered.

MCCVSplitEvaluationEvent - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.events

MCCVSplitEvaluationEvent(Classifier, int, int, int, double) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.events.MCCVSplitEvaluationEvent

MCTreeMergeNode - Class in ai.libs.jaicore.ml.classification.multiclass.reduction

MCTreeMergeNode(String, Collection<String>, Classifier, Collection<String>, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeMergeNode

MCTreeMergeNode(Classifier, List<Collection<String>>, List<Classifier>) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeMergeNode

MCTreeNode - Class in ai.libs.jaicore.ml.classification.multiclass.reduction

MCTreeNode(Classifier, Classifier, String) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

MCTreeNode(List<Integer>) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

MCTreeNode(List<Integer>, EMCNodeType, String) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

MCTreeNode(List<Integer>, EMCNodeType, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

MCTreeNodeLeaf - Class in ai.libs.jaicore.ml.classification.multiclass.reduction

MCTreeNodeLeaf(int) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

MCTreeNodeReD - Class in ai.libs.jaicore.ml.classification.multiclass.reduction

MCTreeNodeReD(String, Collection<String>, String, Collection<String>, String) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

MCTreeNodeReD(Classifier, Collection<String>, Classifier, Collection<String>, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

MCTreeNodeReD(String, Collection<String>, Classifier, Collection<String>, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

MCTreeNodeReD(Classifier, List<Collection<String>>, List<Classifier>) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

MCTreeNodeReD() - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

MCTreeNodeReDLeaf - Class in ai.libs.jaicore.ml.classification.multiclass.reduction

MCTreeNodeReDLeaf(String) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

mean(double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil

Function calculating the mean of the interval [t1, t2 (inclusive)] of the given vector.

mean(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

meanCorrected() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

If mean corrected is set to true than the first DFT coefficient is dropped to normalize the mean.

meanNormalization() - Method in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig

MeanSquaredErrorLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

MeanSquaredErrorLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MeanSquaredErrorLoss

MeasureAggregatedComputationEvent<INPUT,OUTPUT> - Class in ai.libs.jaicore.ml.evaluation

MeasureAggregatedComputationEvent(List<INPUT>, List<INPUT>, IAggregateFunction<OUTPUT>, OUTPUT) - Constructor for class ai.libs.jaicore.ml.evaluation.MeasureAggregatedComputationEvent

MeasureAvgComputationEvent<INPUT,OUTPUT> - Class in ai.libs.jaicore.ml.evaluation

MeasureAvgComputationEvent(List<INPUT>, List<INPUT>, OUTPUT) - Constructor for class ai.libs.jaicore.ml.evaluation.MeasureAvgComputationEvent

MeasureListComputationEvent<INPUT,OUTPUT> - Class in ai.libs.jaicore.ml.evaluation

MeasureListComputationEvent(List<INPUT>, List<INPUT>, List<OUTPUT>) - Constructor for class ai.libs.jaicore.ml.evaluation.MeasureListComputationEvent

measureOOBProbabilitiesUsingCV(double[][], int[], int, int, int, RandomForest) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Function measuring the out-of-bag (OOB) probabilities using a cross validation with numFolds many folds.

MEASURES - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

MeasureSingleComputationEvent<INPUT,OUTPUT> - Class in ai.libs.jaicore.ml.evaluation

MeasureSingleComputationEvent(INPUT, INPUT, OUTPUT) - Constructor for class ai.libs.jaicore.ml.evaluation.MeasureSingleComputationEvent

MEM_MAX - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

MEM_OPP - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

merge(Instances, Instances) - Method in class ai.libs.jaicore.ml.core.WekaInstancesFeatureUnion

merge(Collection<Instances>) - Method in class ai.libs.jaicore.ml.core.WekaInstancesFeatureUnion

merge(int, List<Map.Entry<Shapelet, Double>>, List<Map.Entry<Shapelet, Double>>) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Function merging shapelet lists based on their quality scores.

mergeClassesOfInstances(Instances, Collection<String>, Collection<String>) - Static method in class ai.libs.jaicore.ml.WekaUtil

mergeClassesOfInstances(Instances, List<Set<String>>) - Static method in class ai.libs.jaicore.ml.WekaUtil

mergeCluster(Map<double[], List<C>>) - Method in class ai.libs.jaicore.ml.clustering.GMeans

metric - Variable in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans

MinHashingTransformer - Class in ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue

Converts the sets of multi-value features to short signatures.

MinHashingTransformer(int[][]) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MinHashingTransformer

Constructor where the user gives predefined permutations.

MinHashingTransformer(int, int, long) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MinHashingTransformer

Constructor where suitable permutations are created randomly.

minIntervalLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

The minimal interval length used for the interval generation.

minShapeLengthPercentage() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

The minimum shape length percentage used to calculate the minimum shape length.

minShapeLengthPercentage() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

The minimum shape length percentage used to calculate the minimum shape length.

minShapeletLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

The minimum shapelet of the shapelets to be learned.

minShapeletLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

The minimum shapelet of the shapelets to be learned.

minShapeletLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

The minimum length of shapelets to be considered.

MLExperiment - Class in ai.libs.jaicore.ml.experiments

MLExperiment(String, String, String, int, int, int, int, String) - Constructor for class ai.libs.jaicore.ml.experiments.MLExperiment

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

model - Variable in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm

The model which is maintained during algorithm calls

ModelBuildFailedException - Exception in ai.libs.jaicore.ml.core

ModelBuildFailedException(String) - Constructor for exception ai.libs.jaicore.ml.core.ModelBuildFailedException

ModelBuildFailedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.ModelBuildFailedException

ModifiedISAC - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

ModifiedISAC() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISAC

ModifiedISACEvaluator - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.evalutation

ModifiedISACgMeans - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

ModifiedISACgMeans(List<double[]>, List<ProblemInstance<Instance>>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACgMeans

inilizes toClusterPoints with the points that are to Cluster and are normalized metafeatures

ModifiedISACGroupBuilder - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

ModifiedISACGroupBuilder() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACGroupBuilder

ModifiedISACInstanceCollector - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

ModifiedISACInstanceCollector(Instances, int, int) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector

ModifiedISACInstanceCollector() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector

This constructor is used if the default file should be used.

ModifiedISACkMeans - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

ModifiedISACkMeans(List<double[]>, IDistanceMetric<Double, double[], double[]>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACkMeans

MonteCarloCrossValidationEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

A classifier evaluator that can perform a (monte-carlo)cross-validation on the given dataset.

MonteCarloCrossValidationEvaluator(ISplitBasedClassifierEvaluator<Double>, IDatasetSplitter, int, Instances, double, long) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

MonteCarloCrossValidationEvaluator(ISplitBasedClassifierEvaluator<Double>, int, Instances, double, long) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

MonteCarloCrossValidationEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

Factory for configuring standard Monte Carlo cross-validation evaluators.

MonteCarloCrossValidationEvaluatorFactory() - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

Standard C'tor.

mostFrequentLabelFromWindowLengthPredicitions(Map<Integer, Integer>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Returns the most frequent predicition given a Map of (window length, prediciton) pairs.

mostFrequentLabelsFromWindowLengthPredicitions(Map<Integer, List<Integer>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Returns for each instance the most frequent predicitions as contained in a Map of (window length, list of prediciton for each instance) pairs.

MoveSplitMerge - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Move-Split-Merge (MSM) measure as published in "The Move-Split-Merge Metric for Time Series" by Alexandra Stefan, Vassilis Athitsos and Gautam Das (2013).

MoveSplitMerge(double) - Constructor for class ai.libs.jaicore.ml.tsc.distances.MoveSplitMerge

Constructor.

MULTI_LABEL_METRICS - Static variable in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter

Available metrics for multilabelclassifiers

MulticlassClassStratifiedSplitter - Class in ai.libs.jaicore.ml.weka.dataset.splitter

Makes use of the WekaUtil to split the data into a class-oriented stratified split preserving the class distribution.

MulticlassClassStratifiedSplitter() - Constructor for class ai.libs.jaicore.ml.weka.dataset.splitter.MulticlassClassStratifiedSplitter

MultiClassMeasureBuilder - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

MultiClassMeasureBuilder() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.MultiClassMeasureBuilder

MultilabelDatasetSplitter - Class in ai.libs.jaicore.ml.weka.dataset.splitter

This class provides methods to obtain train and test splits for a given data set and split technique.

MultiValueAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.multivalue

The multi-value attribute type describes the domain a value of a respective multi-value attribute value stems from.

MultiValueAttributeType(Set<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeType

Constructor setting the domain of the multi-value attribute values.

MultiValueAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.multivalue

Multi-value attribute value as it can be part of an instance.

MultiValueAttributeValue(IMultiValueAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeValue

Standard c'tor.

MultiValueAttributeValue(IMultiValueAttributeType, Collection<String>) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.multivalue.MultiValueAttributeValue

C'tor setting the value of this attribute as well.

MultiValueBinaryzationTransformer - Class in ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue

Transforms a multi-valued feature into a 0/1 Vector, where each dimension represents one of the values, i.e. 1 in one dimension => the feature contains this value, 0 in one dimension => the feature does not contain this value.

MultiValueBinaryzationTransformer() - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MultiValueBinaryzationTransformer

N

NDCGLoss - Class in ai.libs.jaicore.ml.dyadranking.loss

The Normalized Discounted Cumulative Gain for ranking.

NDCGLoss(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.NDCGLoss

NearestNeighborClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.neighbors

K-Nearest-Neighbor classifier for time series.

NearestNeighborClassifier(int, ITimeSeriesDistance, NearestNeighborClassifier.VoteType) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Creates a k nearest neighbor classifier.

NearestNeighborClassifier(int, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Creates a k nearest neighbor classifier using majority vote.

NearestNeighborClassifier(ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Creates a 1 nearest neighbor classifier using majority vote.

nearestNeighborClassifier - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

The nearest neighbor classifier used for prediction.

NearestNeighborClassifier.VoteType - Enum in ai.libs.jaicore.ml.tsc.classifier.neighbors

Votes types that describe how to aggregate the prediciton for a test instance on its nearest neighbors found.

nearestNeighborComparator - Static variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Singleton comparator instance for the nearest neighbor priority queues, used for the nearest neighbor calculation.

NearestNeighborLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.neighbors

Training algorithm for the nearest neighbors classifier.

NearestNeighborLearningAlgorithm(IAlgorithmConfig, NearestNeighborClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborLearningAlgorithm

needsBinarization(Instances, boolean) - Static method in class ai.libs.jaicore.ml.WekaUtil

Returns true if there is at least one nominal attribute in the given dataset that has more than 2 values.

NegIdentityInpOptLoss - Class in ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization

Loss function for PLNet input optimization that maximizes the output of a PLNet.

NegIdentityInpOptLoss() - Constructor for class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.NegIdentityInpOptLoss

next() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

next() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

nextQueryInstance() - Method in interface ai.libs.jaicore.ml.activelearning.ISelectiveSamplingStrategy

Chooses the IInstance to query next.

nextQueryInstance() - Method in class ai.libs.jaicore.ml.activelearning.PoolBasedUncertaintySamplingStrategy

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.ReservoirSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.StratifiedFileSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.SystematicFileSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.ClassifierWeightedSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.GmeansSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SimpleRandomSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling

nextWithException() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborLearningAlgorithm

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

nextWithException() - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

NoneFittedFilterExeception - Exception in ai.libs.jaicore.ml.tsc.exceptions

NoneFittedFilterExeception(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.NoneFittedFilterExeception

NoneFittedFilterExeception(String) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.NoneFittedFilterExeception

NoProbingCharacterizer - Class in ai.libs.jaicore.ml.metafeatures

A Characterizer that applies several characterizers to a data set, but does not use any probing.

NoProbingCharacterizer() - Constructor for class ai.libs.jaicore.ml.metafeatures.NoProbingCharacterizer

Constructs a new NoProbingCharacterizer.

normalize(double[]) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Normalizer

normalizeByStandardDeviation(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

normalizeINDArray(INDArray, boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Normalizes an INDArray vector object.

Normalizer - Class in ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac

Normalizer(List<ProblemInstance<Instance>>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Normalizer

NUM_FEATURE_TYPES - Static variable in class ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature

Number of features used within the time series tree.

NUM_THRESH_CANDIDATES - Static variable in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Number of threshold candidates created in each tree recursion step.

numBins() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

Number of bins used for the CPEs.

numClusters() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

Number of shapelet clusters when shapelet clustering is used.

NumericAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.primitive

The numeric attribute type.

NumericAttributeType() - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType

NumericAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.primitive

Numeric attribute value as it can be part of an instance.

NumericAttributeValue(NumericAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeValue

Standard c'tor.

NumericAttributeValue(NumericAttributeType, Double) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeValue

C'tor setting the value of this attribute as well.

NumericFeatureDomain - Class in ai.libs.jaicore.ml.core

Description of a numeric feature domain.

NumericFeatureDomain(boolean, double, double) - Constructor for class ai.libs.jaicore.ml.core.NumericFeatureDomain

NumericFeatureDomain(NumericFeatureDomain) - Constructor for class ai.libs.jaicore.ml.core.NumericFeatureDomain

numFolds() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

Number of folds used within the MajorityConfidenceVote scheme for the ensembles.

numFolds() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

Number of folds used for the OOB probability estimation in the training phase.

numInstances() - Method in class ai.libs.jaicore.ml.SubInstances

numSegments() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig

Number of segments used for feature generation for each tree.

numShapelets() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

Parameter which determines how many of the most-informative shapelets should be used.

numShapelets() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

Parameter which determines how many of the most-informative shapelets should be used.

numShapelets() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

Number of shapelets extracted in the shapelet search

numTrees() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityLearningAlgorithm.IPatternSimilarityConfig

Number of trees to be trained.

numTrees() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig

Number of trees to be trained.

O

offer(Node<N, V>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

OneHotEncodingTransformer - Class in ai.libs.jaicore.ml.core.dataset.attribute.transformer

OneHotEncodingTransformer() - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.transformer.OneHotEncodingTransformer

OpenMLHelper - Class in ai.libs.jaicore.ml.openml

OpenMLHelper() - Constructor for class ai.libs.jaicore.ml.openml.OpenMLHelper

optimize(IGradientDescendableFunction, IGradientFunction, Vector) - Method in class ai.libs.jaicore.ml.core.optimizing.graddesc.GradientDescentOptimizer

optimize(IGradientDescendableFunction, IGradientFunction, Vector) - Method in interface ai.libs.jaicore.ml.core.optimizing.IGradientBasedOptimizer

Optimize the given function based on its derivation.

optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, int, Pair<Integer, Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer

Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.

optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, double, int, Pair<Integer, Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer

Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.

optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, int, INDArray) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer

Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.

optimizeInput(PLNetDyadRanker, INDArray, InputOptimizerLoss, double, double, int, INDArray) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer

Optimizes the given loss function with respect to a given PLNet's inputs using gradient descent.

OSMAC<I extends ILabeledAttributeArrayInstance<?>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

OSMAC(Random, int, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.OSMAC

OSMACSamplingFactory<I extends ILabeledAttributeArrayInstance<?>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

OSMACSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory

outputFileWriter - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm

P

ParametricFunction - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lc

This is a basic class that describes a function that can be parameterized with a set of parameters.

ParametricFunction() - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.ParametricFunction

ParametricFunction(Map<String, Double>) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.ParametricFunction

peek() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

performSGD(double[][][], double[][][], double[], double[], double[][][], double[][][], double[][], int[][], long, int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Method performing the stochastic gradient descent to learn the weights and shapelets.

PHASE2 - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

PilotEstimateSampling<I extends ILabeledAttributeArrayInstance<?>,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

PilotEstimateSampling(D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling

plNetActivationFunction() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

PLNetDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.algorithm

A dyad ranker based on a Plackett-Luce network.

PLNetDyadRanker() - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Constructs a new PLNetDyadRanker using the default IPLNetDyadRankerConfiguration.

PLNetDyadRanker(IPLNetDyadRankerConfiguration) - Constructor for class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Constructs a new PLNetDyadRanker using the given IPLNetDyadRankerConfiguration.

plNetEarlyStoppingInterval() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

plNetEarlyStoppingPatience() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

plNetEarlyStoppingRetrain() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

plNetEarlyStoppingTrainRatio() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

plNetHiddenNodes() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

PLNetInputOptimizer - Class in ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization

Optimizes a given loss function (InputOptimizerLoss) with respect to the input of a PLNet using gradient descent.

PLNetInputOptimizer() - Constructor for class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer

plNetLearningRate() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

PLNetLoss - Class in ai.libs.jaicore.ml.dyadranking.algorithm

Implements the negative log likelihood (NLL) loss function for PL networks as described in [1]

plNetMaxEpochs() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

plNetMiniBatchSize() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

plNetSeed() - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.IPLNetDyadRankerConfiguration

points - Variable in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Kmeans

PointWiseLearningCurve - Class in ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet

This class represents a learning curve that gets returned by the LCNet from pybnn

PointWiseLearningCurve(int, double[], String) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.PointWiseLearningCurve

poll() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

PoolBasedUncertaintySamplingStrategy<T,I extends ILabeledInstance,D extends IDataset<I>> - Class in ai.libs.jaicore.ml.activelearning

A simple pool-based uncertainty sampling strategy, which assesses certainty for all instances in the pool and picks the instance with least certainty for the next query.

PoolBasedUncertaintySamplingStrategy(ICertaintyProvider<T, I, D>, IActiveLearningPoolProvider<I>) - Constructor for class ai.libs.jaicore.ml.activelearning.PoolBasedUncertaintySamplingStrategy

poolProvider - Variable in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

POW_3 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants

POW_4 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants

PPA - Class in ai.libs.jaicore.ml.tsc

ppa(double[], int) - Static method in class ai.libs.jaicore.ml.tsc.PPA

PrecisionAsLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

PrecisionAsLoss(int) - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.PrecisionAsLoss

predict(I) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel

Performs a prediction based on the given IInstance and returns the result.

predict(D) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel

Performs multiple predictions based on the IInstances contained in the given AILabeledAttributeArrayDatasets and returns the result.

predict(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker

predict(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker

predict(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

predict(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

predict(int, double[], String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

Performs a prediction based on the given univariate double[] instance representation and returns the result.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

Performs predictions based on the given instances in the given dataset.

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSEnsembleClassifier

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Predicts on univariate instance.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Predicts on a multivariate instance.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Predicts on a dataset.

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Predicts on univariate instance.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Predicts on a multivariate instance.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Predicts on a dataset.

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Performs a prediction based on the given univariate double[] instance representation and returns the result.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Performs predictions based on the given instances in the given dataset.

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Performs a prediction based on the given univariate double[] instance representation and returns the result.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Performs predictions based on the given instances in the given dataset.

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

Predicts the class by generated segment and segment difference features based on segments and segmentsDifference.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

Performs predictions based on the given instances in the given dataset.

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

Method predicting the class of the given univInstance.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

Performs predictions based on the given instances in the given dataset.

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

Predicts the class of the given instance by taking the majority vote of all trees.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

Performs predictions based on the given instances in the given dataset.

predict(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier

Predicts the class of the given univariate instance by iterating through the tree starting from the root node to a leaf node to induce a class prediction.

predict(List<double[]>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

predict(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier

Performs predictions based on the given instances in the given dataset.

predict(D) - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier

Performs multiple predictions based on the IInstances contained in the given AILabeledAttributeArrayDatasets and returns the result.

predictInterval(Instance) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest

predictInterval(RQPHelper.IntervalAndHeader) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedM5Forest

predictInterval(RQPHelper.IntervalAndHeader) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedM5Tree

predictInterval(Instance) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

predictInterval(RQPHelper.IntervalAndHeader) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

predictInterval(RQPHelper.IntervalAndHeader) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

predictInterval(Instance) - Method in interface ai.libs.jaicore.ml.intervaltree.RangeQueryPredictor

predictInterval(RQPHelper.IntervalAndHeader) - Method in interface ai.libs.jaicore.ml.intervaltree.RangeQueryPredictor

PredictionException - Exception in ai.libs.jaicore.ml.core.exception

The PredictionException indicates that an error occurred during a prediction process.

PredictionException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.PredictionException

Creates a new PredictionException with the given parameters.

PredictionException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.PredictionException

Creates a new PredictionException with the given parameters.

PredictionFailedException - Exception in ai.libs.jaicore.ml.intervaltree

PredictionFailedException(String) - Constructor for exception ai.libs.jaicore.ml.intervaltree.PredictionFailedException

PredictionFailedException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.intervaltree.PredictionFailedException

PredictionFailedException(Throwable) - Constructor for exception ai.libs.jaicore.ml.intervaltree.PredictionFailedException

PREFIX - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

PREFIX_MEM - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

PREFIX_SELECTION - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

prepareForest(Instances) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

Needs to be called before predicting marginal variance contributions!

preprocess() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

Sets up the tree for fANOVA

PREPROCESSING_PREFIX - Static variable in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

preSampleSize - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling

printDoubleRepresentation() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

printNestedWekaClassifier(Classifier) - Static method in class ai.libs.jaicore.ml.WekaUtil

printObservations() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

printSizeOfFeatureSpaceAndPartitioning() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

printSplitPoints() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

printVariances() - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomForest

ProbabilisticMonteCarloCrossValidationEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

A classifier evaluator that can perform a (monte-carlo)cross-validation on the given dataset.

ProbabilisticMonteCarloCrossValidationEvaluator(ISplitBasedClassifierEvaluator<Double>, IDatasetSplitter, int, double, Instances, double, long) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator

ProbabilisticMonteCarloCrossValidationEvaluatorFactory - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

Factory for configuring probabilistic Monte Carlo cross-validation evaluators.

ProbabilisticMonteCarloCrossValidationEvaluatorFactory() - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

Standard c'tor.

probabilityBoundaries - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

ProblemInstance<I> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes

ProblemInstance() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance

ProblemInstance(I) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance

PrototypicalPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning

A prototypical active dyad ranker based on the idea of uncertainty sampling.

PrototypicalPoolBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, double, int, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker

provideCAWPEEnsembleModel(int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.EnsembleProvider

Initializes the CAWPE ensemble model consisting of five classifiers (SMO, KNN, J48, Logistic and MLP) using a majority voting strategy.

provideHIVECOTEEnsembleModel(int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.ensemble.EnsembleProvider

Initializes the HIVE COTE ensemble consisting of 7 classifiers using a majority voting strategy as described in J.

Q

QuantileAggregator - Class in ai.libs.jaicore.ml.intervaltree.aggregation

A IntervalAggregator that works based on quantiles.

QuantileAggregator(double) - Constructor for class ai.libs.jaicore.ml.intervaltree.aggregation.QuantileAggregator

query(I) - Method in interface ai.libs.jaicore.ml.activelearning.IActiveLearningPoolProvider

Labels the given instance.

query(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

R

rand - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

random - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

RandomlyRankedNodeQueue<N,V extends java.lang.Comparable<V>> - Class in ai.libs.jaicore.ml.dyadranking.search

A node queue for the best first search that inserts new nodes at a random position in the list.

RandomlyRankedNodeQueue(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueue

RandomlyRankedNodeQueueConfig<T> - Class in ai.libs.jaicore.ml.dyadranking.search

Configuration for a RandomlyRankedNodeQueue

RandomlyRankedNodeQueueConfig(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.search.RandomlyRankedNodeQueueConfig

Construct a new config with the given seed.

randomlySampleNoReplacement(List<Integer>, int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Function sampling a given list randomly without replacement using the given seed.

RandomMultilabelCrossValidation - Class in ai.libs.jaicore.ml.weka.dataset.splitter

Class executing pseudo-random splits to enable multilabelcrossvalidation.

RandomMultilabelCrossValidation() - Constructor for class ai.libs.jaicore.ml.weka.dataset.splitter.RandomMultilabelCrossValidation

RandomPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning

A random active dyad ranker.

RandomPoolBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.RandomPoolBasedActiveDyadRanker

randomSeed - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner

RandomSplitter - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.splitters

RandomSplitter(Random) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RandomSplitter

RandomUniformClassifier - Class in ai.libs.jaicore.ml

RandomUniformClassifier() - Constructor for class ai.libs.jaicore.ml.RandomUniformClassifier

RangeQueryPredictor - Interface in ai.libs.jaicore.ml.intervaltree

ranker - Variable in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

ranker - Variable in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig

the ranker used to rank dyads consisting of pipeline metafeatures and dataset metafeatures

Ranker<S,P> - Interface in ai.libs.jaicore.ml.ranking

Ranking<S> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes

Ranking(Collection<S>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Ranking

RankingForGroup<C,S> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes

RankingForGroup.java - saves a solution ranking for a group identified by thier group

RankingForGroup(GroupIdentifier<C>, List<S>) - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.RankingForGroup

RankLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

RankLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.RankLoss

RankScore - Class in ai.libs.jaicore.ml.core.evaluation.measure.multilabel

RankScore() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.multilabel.RankScore

realizeSplit(Instances, Collection<Integer>[]) - Static method in class ai.libs.jaicore.ml.WekaUtil

realizeSplit(Instances, List<List<Integer>>) - Static method in class ai.libs.jaicore.ml.WekaUtil

realizeSplitAsCopiedInstances(Instances, List<List<Integer>>) - Static method in class ai.libs.jaicore.ml.WekaUtil

realizeSplitAsCopiedInstances(Instances, Collection<Integer>[]) - Static method in class ai.libs.jaicore.ml.WekaUtil

realizeSplitAsSubInstances(Instances, Collection<Integer>[]) - Static method in class ai.libs.jaicore.ml.WekaUtil

receiveEvent(IEvent) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

ReductionGraphGenerator - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.reducer

ReductionGraphGenerator(Random, Instances) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

ReductionOptimizer - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.reducer

ReductionOptimizer(long) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionOptimizer

registerListener(Object) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator

Register observers for learning curve predictions (including estimates of the time)

registerListener(Object) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

registerListener(Object) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

registerListener(Object) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

registerListener(Object) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

registerListener(Object) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

regularization() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

The regularization used wihtin the SGD.

regularization() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

The regularization used wihtin the SGD.

rekursivDFT(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

rekursivDFT(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

remove(int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

Removes the time series variable at a given index.

remove(Object) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

remove(Object) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

remove() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

remove(int) - Method in class ai.libs.jaicore.ml.SubInstances

remove(int) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Removes the time series variable at a given index.

removeAll(Collection<?>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

removeAll(Collection<?>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

removeAttributeValue(int) - Method in interface ai.libs.jaicore.ml.core.dataset.IModifiableInstance

Removes an attribute value for the given position.

removeClassAttribute(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

removeClassAttribute(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

removeNodeAtPosition(int) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

removeSelfSimilar(List<Map.Entry<Shapelet, Double>>) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Function removing self-similar shapelets from a list storing shapelet and their quality entries.

replace(int, INDArray, INDArray) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

Replaces the time series variable at a given index with a new one.

replace(int, double[][], double[][]) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Replaces the time series variable at a given index with a new one.

reportOptimizationStep(INDArray, double) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.InputOptListener

ReproducibleInstances - Class in ai.libs.jaicore.ml.cache

New Instances class to track splits and data origin.

ReproducibleInstances(ReproducibleInstances) - Constructor for class ai.libs.jaicore.ml.cache.ReproducibleInstances

ReservoirSampling - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles

Implementation of the Reservoir Sampling algorithm(comparable to a Simple Random Sampling for streamed data).

ReservoirSampling(Random, File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.ReservoirSampling

retainAll(Collection<?>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

retainAll(Collection<?>) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

RootMeanSquaredErrorLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

The root mean squared loss function.

RootMeanSquaredErrorLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.RootMeanSquaredErrorLoss

RPNDSplitter - Class in ai.libs.jaicore.ml.classification.multiclass.reduction.splitters

RPNDSplitter(Random, Classifier) - Constructor for class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RPNDSplitter

RQPHelper - Class in ai.libs.jaicore.ml.intervaltree.util

RQPHelper.IntervalAndHeader - Class in ai.libs.jaicore.ml.intervaltree.util

RUNS - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

S

sample - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

SampleElementAddedEvent - Class in ai.libs.jaicore.ml.core.dataset.sampling

SampleElementAddedEvent(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.SampleElementAddedEvent

sampleIntervals(int, int) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Function sampling intervals based on the length of the time series m and the given seed.

sampleSize - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm

sampleSize - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

samplingAlgorithm - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

samplingAlgorithmFactory - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

saveModelToFile(String) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Save a trained model at a given file path.

SAX - Class in ai.libs.jaicore.ml.tsc.filter

SAX(double[], int) - Constructor for class ai.libs.jaicore.ml.tsc.filter.SAX

ScalarDistanceUtil - Class in ai.libs.jaicore.ml.tsc.util

ScalarDistanceUtil

scaler - Variable in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

for scaling the dyads

scaler - Variable in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig

for scaling the dyads

scaleR() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ILearnShapeletsLearningAlgorithmConfig

The number of scales used for the shapelet lengths.

scaleR() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm.ILearnShapeletsLearningAlgorithmConfig

The number of scales used for the shapelet lengths.

ScikitLearnWrapper - Class in ai.libs.jaicore.ml.scikitwrapper

Wraps a Scikit-Learn Python process by utilizing a template to start a classifier in Scikit with the given classifier.

ScikitLearnWrapper(String, String, boolean) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

Starts a new wrapper and creates its underlying script with the given parameters.

ScikitLearnWrapper(String, String) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

Starts a new wrapper and creates its underlying script with the given parameters.

ScikitLearnWrapper(String, String, File) - Constructor for class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

ScikitLearnWrapper.ProblemType - Enum in ai.libs.jaicore.ml.scikitwrapper

seed - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

SEEDS - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

selectGroupsolutionRanking(Group<C, I>, Table<I, S, P>) - Method in interface ai.libs.jaicore.ml.ranking.clusterbased.IGroupSolutionRankingSelect

SELECTION_CANDIDATES - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

SELECTION_ITERATIONS - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

selectStratiAmount(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

selectStratiAmount(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner

selectStratiAmount(D) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAmountSelector

Select a suitable amount of strati for a Dataset.

serialize(OutputStream) - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

set(int, TimeSeriesInstance<L>) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

set(int, Instance) - Method in class ai.libs.jaicore.ml.SubInstances

setA(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

setA(double) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Sets the a parameter.

setAlgorithm(ATSCAlgorithm<L, V, D, ? extends IBatchLearner<V, TimeSeriesInstance<L>, D>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier

Sets the training algorithm for the classifier.

setAlpha(double) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateDistance

Sets the alpha value and adjusts the measurement parameters a = cos(alpha) and b = sin(alpha) accordingly.

setAlpha(double) - Method in class ai.libs.jaicore.ml.tsc.distances.TransformDistance

Sets the alpha value and adjusts the measurement parameters a = cos(alpha) and b = sin(alpha) accordingly.

setApiKey(String) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper

setArffHeader(String) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner

setArffHeader(String) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.IStratiFileAssigner

Set the header of the original ARFF input file.

setAttributes(List<Attribute>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

setB(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

setB(double) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Sets the b parameter.

setBaseClassifier(Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

setBaseClassifier(Classifier) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

setBasicEvaluator(IMeasure<I, O>) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.AbstractSplitBasedClassifierEvaluator

setBasselCorrected(boolean) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

setBestSoFar(double) - Method in interface ai.libs.jaicore.ml.tsc.distances.Abandonable

Setter for the best-so-far value.

setC(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

setC(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Setter for LearnShapeletsClassifier.c

setC(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

setC(double) - Method in class ai.libs.jaicore.ml.tsc.distances.DerivateTransformDistance

Sets the c parameter.

setCacheLookup(boolean) - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

If true signifies that performance on this data should be looked up in cache

setCacheStorage(boolean) - Method in class ai.libs.jaicore.ml.cache.ReproducibleInstances

If set to true, signifies that performance evaluation should be stored.

setCaption(String) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

setChosenInstance(I) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling

setClassAttIndexPerTree(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

setClassifier(Classifier) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Setter for ShapeletTransformTSClassifier.classifier.

setClassMapper(ClassMapper) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

Setter for the property classMapper.

setClassValues(List<String>) - Method in class ai.libs.jaicore.ml.tsc.util.ClassMapper

Setter for the classValues.

setClusterResults(List<CentroidCluster<I>>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

setClusterSeed(long) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory

Set the seed the clustering will use for initialization.

setClusterSeed(long) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

Set the seed the clustering will use for initialization.

setCommand(String) - Method in class ai.libs.jaicore.ml.cache.Instruction

Gets command name that specifies the type of instruction represented by the object.

setComparator(Comparator<String>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.DatasetFileSorter

setConfiguration(IPredictiveModelConfiguration) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel

Sets the IPredictiveModelConfiguration of this model to the given one.

setConfiguration(IPredictiveModelConfiguration) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker

setConfiguration(IPredictiveModelConfiguration) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

setConfiguration(IPredictiveModelConfiguration) - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier

Sets the IPredictiveModelConfiguration of this model to the given one.

setConfigurations(double[]) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod

setDatapointComparator(Comparator<I>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory

Set a custom comparator that will be used to sort the datapoints before sampling.

setDebug(boolean) - Static method in class ai.libs.jaicore.ml.WekaUtil

setDefaultWindowSize(int) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

setDeterminedQuality(double) - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Setter for Shapelet.determinedQuality.

setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory

Set the distance measure for the clustering.

setDistanceMeassure(DistanceMeasure) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

Set the distance measure for the clustering.

setDyadRanker(IDyadRanker) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

Set which dyad ranker shall be used to rank the nodes.

setEpsilon(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ExtrapolatedSaturationPointEvaluator

setEstimateK(boolean) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Enables / disabled the parameter estimation of K within the training algorithm.

setFeatureCaching(boolean) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

setFeatureSpace(FeatureSpace) - Method in class ai.libs.jaicore.ml.intervaltree.ExtendedRandomTree

setFinalClf(RandomForest) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

setFullDatasetSize(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ConfigurationLearningCurveExtrapolationEvaluator

setFullDatasetSize(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator

setFunctions(List<UnivariateFunction>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

setGroupIdentifier(GroupIdentifier<C>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group

setHistogramUnivirate(List<Map<Integer, Integer>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

setInput(D) - Method in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm

Setter for the data set input used during algorithm calls.

setInputs(Map<String, String>) - Method in class ai.libs.jaicore.ml.cache.Instruction

Sets the input parameters that will be used to reproduce the effects done by this instruction.

setInstance(I) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.ProblemInstance

setInstances(List<ProblemInstance<I>>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Group

setIntervals(List<Interval>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy

setIntervals(int[][][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

setK(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

Set how many clusters shall be created.

setK(int) - Method in class ai.libs.jaicore.ml.rqp.ChooseKAugSpaceSampler

setK(int) - Method in class ai.libs.jaicore.ml.rqp.KNNAugSpaceSampler

setL(int) - Method in class ai.libs.jaicore.ml.dyadranking.loss.NDCGLoss

setLabel(String) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

setLabel(L) - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstance

setLabel(String) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

setLength(int) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType

setLengthOfTopRankingToConsider(int) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker

setLengthPerTree(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

setListener(InputOptListener) - Method in class ai.libs.jaicore.ml.dyadranking.zeroshot.inputoptimization.PLNetInputOptimizer

Set an InputOptListener to record the intermediate steps of the optimization procedure.

setLoggerName(String) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.LearningCurveExtrapolationEvaluator

setLoggerName(String) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.MonteCarloCrossValidationEvaluator

setLoggerName(String) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator

setLoggerName(String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

setMax(double) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

setMaxDepth(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

setMeanCorrected(boolean) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

setMin(double) - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

setMinDistanceSearchStrategy(AMinimumDistanceSearchStrategy) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Setter for ShapeletTransformLearningAlgorithm.minDistanceSearchStrategy.

setMinShapeLength(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Setter for LearnShapeletsClassifier#minShapeLength

setModel(T) - Method in class ai.libs.jaicore.ml.tsc.classifier.ATSCAlgorithm

Setter for the model to be maintained.

setModelPath(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

setName(String) - Method in class ai.libs.jaicore.ml.core.FeatureDomain

Setter for name attribute.

setNearestNeighborClassifier(NearestNeighborClassifier) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Sets the nearest neighbor classifier, ShotgunEnsembleClassifier.nearestNeighborClassifier.

setNodeNumbering(boolean) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

setNodeType(EMCNodeType) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

setNumberOfClassifier(int) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACInstanceCollector

setNumberOfDesieredDFTCoefficients(int) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

setNumberOfDisieredCoefficients(int) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

setNumberOfTrees(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

setNumBins(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

setNumClasses(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

setNumCPUs(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

setNumCPUs(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.ClusterStratiAssigner

setNumCPUs(int) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAmountSelector

Sets the number of CPU cores that can be used for parallel computation

setNumCPUs(int) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner

Sets the number of CPU cores that can be used for parallel computation

setNumCPUs(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

setNumMajorColumns(int) - Method in class ai.libs.jaicore.ml.latex.LatexDatasetTableGenerator

setNumSamples(Integer) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

setOffset(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

setOutputFileName(String) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm

setParameters(Map<String, Map<String, Double>>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet

setParameterSets(List<LinearCombinationParameterSet>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration

setParams(Map<String, Double>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.ParametricFunction

setPoints(List<double[]>) - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.ModifiedISACGroupBuilder

setPoolProvider(IDyadRankingPoolProvider) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

setPreSampleSize(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory

Set the size of the sample the pilot estimator will be trained with.

setPreSampleSize(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory

Set the size of the sample the pilot estimator will be trained with.

setPreviousRun(CaseControlSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.CaseControlSamplingFactory

setPreviousRun(GmeansSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory

setPreviousRun(A) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.interfaces.IRerunnableSamplingAlgorithmFactory

Set the previous run of the sampling algorithm, if one occurred, can be set here to get data from it.

setPreviousRun(KmeansSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

setPreviousRun(LocalCaseControlSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory

setPreviousRun(OSMAC<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory

setPreviousRun(StratifiedSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory

setPreviousRun(SystematicSampling<I, D>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory

setProbabilityBoundaries(List<Pair<I, Double>>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

setProblemType(ScikitLearnWrapper.ProblemType) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

setRanker(PLNetDyadRanker) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ActiveDyadRanker

setRanker(IDyadRanker) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig

Set the ranker used to rank the OPEN list.

setRatioOfOldInstancesForMinibatch(double) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.PrototypicalPoolBasedActiveDyadRanker

setRemoveDyadsWhenQueried(boolean) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.DyadDatasetPoolProvider

setRemoveDyadsWhenQueried(boolean) - Method in interface ai.libs.jaicore.ml.dyadranking.activelearning.IDyadRankingPoolProvider

setS(double[][][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Setter for LearnShapeletsClassifier.s

setSampleSize(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.AFileSamplingAlgorithm

setSampleSize(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

setScaler(AbstractDyadScaler) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

setScaler(AbstractDyadScaler) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueueConfig

Set the scaler used to scale the dataset.

setSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator

setSeed(int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

setSegments(int[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

setSegmentsDifference(int[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

setShapelets(List<Shapelet>) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Setter for ShapeletTransformTSClassifier.shapelets.

setSortedDataset(D) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling

setStrati(IDataset[]) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling

setSubsequences(int[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

setSubseriesClf(RandomForest) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

setTargets(int...) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

setTargets(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Sets the targets.

setTargets(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Sets the targets.

setTargets(int[]) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Setter for TimeSeriesDataset.targets.

setTargetType(T) - Method in class ai.libs.jaicore.ml.core.predictivemodel.APredictiveModel

Setter method for the given targetType.

setTempFileHandler(TempFileHandler) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.ClassStratiFileAssigner

setTempFileHandler(TempFileHandler) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.IStratiFileAssigner

Set the temporary file handler, which will be used to manage the temporary files for the strati.

setTimeout(long, TimeUnit) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

setTimeout(TimeOut) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

setTimestampMatrices(List<double[][]>) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Setter for TimeSeriesDataset.timestampMatrices.

setTimestamps(double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Sets the timestamps.

setTrainingData(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.BOSSClassifier

setTrainingPortion(float) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator

setTrainLeafNodes(int[][][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

setTrainTargets(int[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

setTrees(AccessibleRandomTree[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.LearnPatternSimilarityClassifier

setTrees(TimeSeriesTreeClassifier[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

Setter for the time series trees.

setupnormalize() - Method in class ai.libs.jaicore.ml.ranking.clusterbased.modifiedisac.Normalizer

setUseInstanceReordering(boolean) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

setValue(D) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue

setValue(D) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.IAttributeValue

setValueMatrices(List<double[][]>) - Method in class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Setter for TimeSeriesDataset.valueMatrices.

setValues(double[]) - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

setValues(double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Sets the value matrix.

setValues(double[][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Sets the value matrix.

setW(double[][][]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Setter for LearnShapeletsClassifier.w

setW0(double[]) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsClassifier

Setter for LearnShapeletsClassifier.w0

setWeights(List<Double>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

setWeights(Map<String, Double>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet

setWindowLength(int) - Method in class ai.libs.jaicore.ml.tsc.distances.ShotgunDistance

Sets the window length.

setWindows(ArrayList<Pair<Integer, Integer>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Sets the windows and also retreives and sets the @see #bestScore from these windows.

setxValues(List<Integer>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

setyValues(List<Double>) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

SFA - Class in ai.libs.jaicore.ml.tsc.filter

SFA(double[], int) - Constructor for class ai.libs.jaicore.ml.tsc.filter.SFA

Shapelet - Class in ai.libs.jaicore.ml.tsc.shapelets

Implementation of a shapelet, i. e. a specific subsequence of a time series representing a characteristic shape.

Shapelet(double[], int, int, int, double) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Constructs a shapelet specified by the given parameters.

Shapelet(double[], int, int, int) - Constructor for class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Constructs a shapelet specified by the given parameters.

shapeletTransform(TimeSeriesDataset, List<Shapelet>, TimeOut, long, AMinimumDistanceSearchStrategy) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Performs a shapelet transform on a complete dataSet.

shapeletTransform(double[], List<Shapelet>, AMinimumDistanceSearchStrategy) - Static method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Function transforming the given instance into the new feature space spanned by the shapelets.

ShapeletTransformLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.shapelets

Algorithm training a ShapeletTransform classifier as described in Jason Lines, Luke M.

ShapeletTransformLearningAlgorithm(ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig, ShapeletTransformTSClassifier, TimeSeriesDataset, IQualityMeasure) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm

Constructs a training algorithm for the ShapeletTransformTSClassifier classifier specified by the given parameters.

ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.shapelets

ShapeletTransformTSClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.shapelets

Class for a ShapeletTransform classifier as described in Jason Lines, Luke M.

ShapeletTransformTSClassifier(int, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction and the FStat quality measure.

ShapeletTransformTSClassifier(int, IQualityMeasure, int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.

ShapeletTransformTSClassifier(int, int, IQualityMeasure, int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.

ShapeletTransformTSClassifier(int, int, IQualityMeasure, int, boolean, int, int, boolean, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformTSClassifier

Constructs an Shapelet Transform classifier using k shapelets, k/2 clusters of the shapelets after shapelet extraction (if clusterShapelets is true and the quality measure function qm.

shotgunDistance - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

The Shotgun Distance used by the ShotgunEnsembleClassifier.nearestNeighborClassifier.

ShotgunDistance - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of Shotgun Distance measure as published in "Towards Time Series Classfication without Human Preprocessing" by Patrick Schäfer (2014).

ShotgunDistance(int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.distances.ShotgunDistance

Constructor for the Shotgun Distance.

ShotgunEnsembleClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.neighbors

Implementation of Shotgun Ensemble Classifier as published in "Towards Time Series Classfication without Human Preprocessing" by Patrick Schäfer (2014).

ShotgunEnsembleClassifier(int, int, boolean, double) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Creates a Shotgun Ensemble classifier.

ShotgunEnsembleLearnerAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.neighbors

Implementation of Shotgun Ensemble Algorihm as published in "Towards Time Series Classfication without Human Preprocessing" by Patrick Schäfer (2014).

ShotgunEnsembleLearnerAlgorithm(ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig, ShotgunEnsembleClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm

ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.neighbors

shuffleAccordingToAlternatingClassScheme(List<Integer>, int[], Random) - Method in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Shuffles the data in a class alternating scheme.

shuffleTimeSeriesDataset(TimeSeriesDataset, int) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Shuffles the given TimeSeriesDataset object using the given seed.

sigmoid(double) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil

Function to calculate the sigmoid for the given value z.

SimpleDataset<L> - Class in ai.libs.jaicore.ml.core.dataset.standard

SimpleDataset(InstanceSchema<L>) - Constructor for class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

SimpleInstance<L> - Class in ai.libs.jaicore.ml.core.dataset.standard

SimpleInstance(List<IAttributeValue<?>>, L) - Constructor for class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

SimpleInstanceImpl - Class in ai.libs.jaicore.ml.core

SimpleInstanceImpl() - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl

SimpleInstanceImpl(int) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl

SimpleInstanceImpl(Collection<Double>) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl

SimpleInstanceImpl(double[]) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl

SimpleInstanceImpl(String) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl

SimpleInstanceImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstanceImpl

SimpleInstancesImpl - Class in ai.libs.jaicore.ml.core

SimpleInstancesImpl() - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl

SimpleInstancesImpl(int) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl

SimpleInstancesImpl(String) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl

SimpleInstancesImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl

SimpleInstancesImpl(File) - Constructor for class ai.libs.jaicore.ml.core.SimpleInstancesImpl

SimpleLabeledInstanceImpl - Class in ai.libs.jaicore.ml.core

SimpleLabeledInstanceImpl() - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

SimpleLabeledInstanceImpl(String) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

SimpleLabeledInstanceImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

SimpleLabeledInstancesImpl - Class in ai.libs.jaicore.ml.core

SimpleLabeledInstancesImpl() - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

SimpleLabeledInstancesImpl(String) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

SimpleLabeledInstancesImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

SimpleLabeledInstancesImpl(File) - Constructor for class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

SimpleMLCSplitBasedClassifierEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation

SimpleMLCSplitBasedClassifierEvaluator(IMeasure<double[], Double>) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.SimpleMLCSplitBasedClassifierEvaluator

SimpleRandomSampling<I,D extends IOrderedDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

SimpleRandomSampling(Random, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SimpleRandomSampling

SimpleRandomSamplingFactory<I,D extends IOrderedDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

SimpleRandomSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SimpleRandomSamplingFactory

SimpleSLCSplitBasedClassifierEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation

Basic implementation of the AbstractSplitBasedClassifierEvaluator.

SimpleSLCSplitBasedClassifierEvaluator(IMeasure<Double, Double>) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.SimpleSLCSplitBasedClassifierEvaluator

simplifiedTimeSeriesDatasetToWekaInstances(TimeSeriesDataset) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Converts a given simplified TimeSeriesDataset object to a Weka Instances object.

simplifiedTimeSeriesDatasetToWekaInstances(TimeSeriesDataset, List<String>) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Converts a given simplified TimeSeriesDataset object to a Weka Instances object.

SimplifiedTimeSeriesLoader - Class in ai.libs.jaicore.ml.tsc.util

Time series loader class which provides functionality to read datasets from files storing into simplified, more efficient time series datasets.

SimplifiedTimeSeriesLoader() - Constructor for class ai.libs.jaicore.ml.tsc.util.SimplifiedTimeSeriesLoader

simplifiedTSInstanceToWekaInstance(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Maps an univariate simplified time series instance to a Weka instance.

SineTransform - Class in ai.libs.jaicore.ml.tsc.filter.transform

Calculates the sine transform of a time series.

SineTransform() - Constructor for class ai.libs.jaicore.ml.tsc.filter.transform.SineTransform

SINGLE_LABEL_METRICS - Static variable in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter

Available metric for singlelabelclassifiers

SingleRandomSplitClassifierEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

SingleRandomSplitClassifierEvaluator(Instances) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator

singleSquaredEuclideanDistance(double[], double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil

Computes the single squared Euclidean distance between two vectors.

singleVariance(double, double, double) - Static method in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree

Computes the variance for a single set

size() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

size() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

size() - Method in class ai.libs.jaicore.ml.SubInstances

skipWithReaderToDatapoints(BufferedReader) - Static method in class ai.libs.jaicore.ml.core.dataset.ArffUtilities

Skips with a given reader all comment lines and the header lines of an ARFF file until the first datapoint is reached.

SlidingWindowBuilder - Class in ai.libs.jaicore.ml.tsc.filter

SlidingWindowBuilder() - Constructor for class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

slope(double[], int, int) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil

Function calculating the slope of the interval [t1, t2 (inclusive)] of the given vector.

SOLUTIONLOGDIR - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

sort(String) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.infiles.DatasetFileSorter

sortByLengthAsc(List<Shapelet>) - Static method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

Function sorting a list of shapelets in place by the length (ascending).

sortIndexes(double[], boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Sorts the indices of the given vector based on the the vector's values (argsort).

SparseDyadRankingInstance - Class in ai.libs.jaicore.ml.dyadranking.dataset

A dyad ranking instance implementation that assumes the same instance for all dyads contained in its ordering.

SparseDyadRankingInstance(Vector, List<Vector>) - Constructor for class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

Construct a new sparse dyad ranking instance containing the given instance vector and ordering of alternatives.

specialFitTransform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

This is an extra fit method because it does not return a double[] array even though it gets a double [] as input as it would be defined in the .

split(Instances) - Method in interface ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.ISplitter

split(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RandomSplitter

split(Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RPNDSplitter

split(Collection<String>, Collection<String>, Collection<String>, Instances) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.splitters.RPNDSplitter

split(Instances, long, double...) - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.ArbitrarySplitter

split(Instances, long, double...) - Method in interface ai.libs.jaicore.ml.weka.dataset.splitter.IDatasetSplitter

split(Instances, long, double...) - Method in class ai.libs.jaicore.ml.weka.dataset.splitter.MulticlassClassStratifiedSplitter

SplitInstruction - Class in ai.libs.jaicore.ml.cache

Instruction to track a split for a ReproducibleInstances object.

SplitInstruction(String, long, int) - Constructor for class ai.libs.jaicore.ml.cache.SplitInstruction

Constructor to create a split Instruction that can be converted into json.

splitToJsonArray(Collection<Integer>[]) - Static method in class ai.libs.jaicore.ml.WekaUtil

SquaredBackwardDifferenceComplexity - Class in ai.libs.jaicore.ml.tsc.complexity

Complexity metric as described in "A Complexity-Invariant Distance Measure for Time Series". $$ c = sum_{i=1}^n-1 \sqrt{ (T_i - T_{i+1})^2 }$$ where $T_i$ are the values of the time series.

SquaredBackwardDifferenceComplexity() - Constructor for class ai.libs.jaicore.ml.tsc.complexity.SquaredBackwardDifferenceComplexity

standardDeviation(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Calculates the (population) standard deviation of the values of a times series.

statsX - Variable in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

statsY - Variable in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

stddev(double[], int, int, boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil

Function calculating the standard deviation of the interval [t1, t2 (inclusive)] of the given vector.

StratifiedFileSampling - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling

StratifiedFileSampling(Random, IStratiFileAssigner, File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.stratified.sampling.StratifiedFileSampling

Constructor for a Stratified File Sampler.

StratifiedSampling<I,D extends IOrderedDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling

Implementation of Stratified Sampling: Divide dataset into strati and sample from each of these.

StratifiedSampling(IStratiAmountSelector<D>, IStratiAssigner<I, D>, Random, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling

Constructor for Stratified Sampling.

StratifiedSamplingFactory<I,D extends IOrderedDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

StratifiedSamplingFactory(IStratiAmountSelector<D>, IStratiAssigner<I, D>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory

StratifiedSplit<I extends INumericLabeledAttributeArrayInstance<L>,L,D extends IOrderedLabeledAttributeArrayDataset<I,L>> - Class in ai.libs.jaicore.ml.core.dataset.util

StratifiedSplit(D, long) - Constructor for class ai.libs.jaicore.ml.core.dataset.util.StratifiedSplit

stratify(int) - Method in class ai.libs.jaicore.ml.SubInstances

stratStep(int) - Method in class ai.libs.jaicore.ml.SubInstances

StretchingComplexity - Class in ai.libs.jaicore.ml.tsc.complexity

Stretching Complexity that calulates the length of a time series when stretched to a straight line. $$ c = sum_{i=1}^n-1 \sqrt{ (t_2 - t_1)^2 + (T_{i+1} - T_i)^2 }$$ where $t_i$ are the timestamps (here $t_i = i$) an $T_i$ are the values of the time series.

StretchingComplexity() - Constructor for class ai.libs.jaicore.ml.tsc.complexity.StretchingComplexity

SubInstances - Class in ai.libs.jaicore.ml

SubInstances(Instances, int[]) - Constructor for class ai.libs.jaicore.ml.SubInstances

subList(int, int) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

substituteInterval(Interval[], Interval, int) - Static method in class ai.libs.jaicore.ml.intervaltree.util.RQPHelper

sum(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.MathUtil

Sums the values of the given array.

sum(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

swap(int, int) - Method in class ai.libs.jaicore.ml.SubInstances

SystematicFileSampling - Class in ai.libs.jaicore.ml.core.dataset.sampling.infiles

File-level implementation of Systematic Sampling: Sort datapoints and pick every k-th datapoint for the sample.

SystematicFileSampling(Random, File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.SystematicFileSampling

Simple constructor that uses the default datapoint comparator.

SystematicFileSampling(Random, Comparator<String>, File) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.infiles.SystematicFileSampling

Constructor for a custom datapoint comparator.

SystematicSampling<I extends INumericArrayInstance,D extends IOrderedDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

Implementation of Systematic Sampling: Sort datapoints and pick every k-th datapoint for the sample.

SystematicSampling(Random, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling

Simple constructor that uses the default datapoint comparator.

SystematicSampling(Random, Comparator<I>, D) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling

Constructor for a custom datapoint comparator.

SystematicSamplingFactory<I extends INumericArrayInstance,D extends IOrderedDataset<I>> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

SystematicSamplingFactory() - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory

T

Table<I,S,P> - Class in ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes

Table.java - This class is used to store probleminstance and their according solutions and performances for that solution.

Table() - Constructor for class ai.libs.jaicore.ml.ranking.clusterbased.customdatatypes.Table

targets - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Target values for the instances.

targets - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Target values for the instances.

test - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

TIMEOUT_CANDIDATE - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

TIMEOUT_TOTAL - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

TimeoutableEvaluator - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

TimeoutableEvaluator(IObjectEvaluator<Classifier, Double>, int) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.TimeoutableEvaluator

C'tor create a timeoutable evaluator out of any other IObjectEvaluator.

TIMEOUTS_IN_SECONDS - Static variable in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

TimeSeriesAttributeType - Class in ai.libs.jaicore.ml.core.dataset.attribute.timeseries

Describes a time series type as an 1-NDArray with a fixed length.

TimeSeriesAttributeType(int) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeType

TimeSeriesAttributeValue - Class in ai.libs.jaicore.ml.core.dataset.attribute.timeseries

Represents a time series attribute value, as it can be part of a jaicore.ml.core.dataset.IInstance

TimeSeriesAttributeValue(TimeSeriesAttributeType) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeValue

TimeSeriesAttributeValue(TimeSeriesAttributeType, INDArray) - Constructor for class ai.libs.jaicore.ml.core.dataset.attribute.timeseries.TimeSeriesAttributeValue

TimeSeriesBagOfFeaturesClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Implementation of the Time Series Bag-of-Features (TSBF) classifier as described in Baydogan, Mustafa & Runger, George & Tuv, Eugene. (2013).

TimeSeriesBagOfFeaturesClassifier(int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

Standard constructor using the default parameters (numBins = 10, numFolds = 10, zProp = 0.1, minIntervalLength = 5) for the TSBF classifier.

TimeSeriesBagOfFeaturesClassifier(int, int, int, double, int) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

Constructor specifying parameters (cf.

TimeSeriesBagOfFeaturesClassifier(int, int, int, double, int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

Constructor specifying parameters (cf.

TimeSeriesBagOfFeaturesLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Algorithm to train a Time Series Bag-of-Features (TSBF) classifier as described in Baydogan, Mustafa & Runger, George & Tuv, Eugene. (2013).

TimeSeriesBagOfFeaturesLearningAlgorithm(TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig, TimeSeriesBagOfFeaturesClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Constructor for a TSBF training algorithm.

TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.trees

TimeSeriesBatchLoader - Class in ai.libs.jaicore.ml.tsc.util

BatchLoader

TimeSeriesBatchLoader(TimeSeriesDataset, int, boolean) - Constructor for class ai.libs.jaicore.ml.tsc.util.TimeSeriesBatchLoader

TimeSeriesDataset<L> - Class in ai.libs.jaicore.ml.core.dataset

Time Series Dataset.

TimeSeriesDataset(List<INDArray>, List<INDArray>, INDArray, IAttributeType<L>) - Constructor for class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

Creates a TimeSeries dataset.

TimeSeriesDataset - Class in ai.libs.jaicore.ml.tsc.dataset

Dataset for time series.

TimeSeriesDataset(List<double[][]>, List<double[][]>, int[]) - Constructor for class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Creates a time series dataset with timestamps for training.

TimeSeriesDataset(List<double[][]>, List<double[][]>) - Constructor for class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Creates a time series dataset with timestamps for testing.

TimeSeriesDataset(List<double[][]>, int[]) - Constructor for class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Creates a time series dataset withot timestamps for training.

TimeSeriesDataset(List<double[][]>) - Constructor for class ai.libs.jaicore.ml.tsc.dataset.TimeSeriesDataset

Creates a time series dataset without timestamps for testing.

timeSeriesDatasetToWekaInstances(TimeSeriesDataset<L>) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Converts a given TimeSeriesDataset object to a Weka Instances object.

TimeSeriesFeature - Class in ai.libs.jaicore.ml.tsc.features

Class calculating features (e. g. mean, stddev or slope) on given subsequences of time series.

TimeSeriesFeature() - Constructor for class ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature

TimeSeriesFeature.FeatureType - Enum in ai.libs.jaicore.ml.tsc.features

Feature types used within the time series tree.

TimeSeriesForestClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Time series forest classifier as described in Deng, Houtao et al.

TimeSeriesForestClassifier() - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

Constructing an untrained ensemble of time series trees.

TimeSeriesForestClassifier(TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestClassifier

Constructing an untrained ensemble of time series trees.

TimeSeriesForestLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Algorithm to train a time series forest classifier as described in Deng, Houtao et al.

TimeSeriesForestLearningAlgorithm(TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig, TimeSeriesForestClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm

Constructor for a time series forest training algorithm.

TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.trees

TimeSeriesInstance<L> - Class in ai.libs.jaicore.ml.core.dataset

TimeSeriesInstance

TimeSeriesInstance(IAttributeValue<?>[], L) - Constructor for class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance

Constructor.

TimeSeriesInstance(List<IAttributeValue<?>>, L) - Constructor for class ai.libs.jaicore.ml.core.dataset.TimeSeriesInstance

TimeSeriesLengthException - Exception in ai.libs.jaicore.ml.tsc.exceptions

Exception class encapsultes faulty behaviour with length of time series.

TimeSeriesLengthException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.TimeSeriesLengthException

TimeSeriesLengthException(String) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.TimeSeriesLengthException

TimeSeriesLoadingException - Exception in ai.libs.jaicore.ml.tsc.exceptions

Exception thrown when a time series dataset could not be extracted from an external data source (e. g. a file).

TimeSeriesLoadingException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.TimeSeriesLoadingException

Constructor using a nested Throwable exception.

TimeSeriesLoadingException(String) - Constructor for exception ai.libs.jaicore.ml.tsc.exceptions.TimeSeriesLoadingException

Standard constructor.

TimeSeriesTreeClassifier - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Time series tree as described in Deng, Houtao et al.

TimeSeriesTreeClassifier(TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeClassifier

Constructs an empty time series tree.

TimeSeriesTreeLearningAlgorithm - Class in ai.libs.jaicore.ml.tsc.classifier.trees

Algorithm to build a time series tree as described in Deng, Houtao et al.

TimeSeriesTreeLearningAlgorithm(TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig, TimeSeriesTreeClassifier, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Constructor.

TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig - Interface in ai.libs.jaicore.ml.tsc.classifier.trees

TimeSeriesUtil - Class in ai.libs.jaicore.ml.tsc.util

Utility class for time series operations.

TimeSeriesUtil() - Constructor for class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

timestamps - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Timestamp matrix containing the timestamps of the instances.

TimeWarpEditDistance - Class in ai.libs.jaicore.ml.tsc.distances

Time Warp Edit Distance as published in "Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching" by Pierre-Francois Marteau (2009).

TimeWarpEditDistance(double, double, IScalarDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TimeWarpEditDistance

Constructor.

TimeWarpEditDistance(double, double) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TimeWarpEditDistance

Constructor.

TMPDIR - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

toArray() - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

toArray(T[]) - Method in class ai.libs.jaicore.ml.core.dataset.TimeSeriesDataset

toArray() - Method in class ai.libs.jaicore.ml.core.FeatureSpace

toArray() - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

toArray(T[]) - Method in class ai.libs.jaicore.ml.dyadranking.search.ADyadRankedNodeQueue

toJAICoreInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

toJAICoreInstances(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

toJAICoreLabeledInstance(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

toJAICoreLabeledInstances(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

toJson() - Method in class ai.libs.jaicore.ml.core.SimpleInstanceImpl

toJson() - Method in class ai.libs.jaicore.ml.core.SimpleInstancesImpl

toJson() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

toJson() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstancesImpl

toJson() - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl

toJson() - Method in interface ai.libs.jaicore.ml.interfaces.Instance

toJson() - Method in interface ai.libs.jaicore.ml.interfaces.Instances

toJson() - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstances

toMatrix() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.ADyadRankingInstance

toMatrix() - Method in interface ai.libs.jaicore.ml.dyadranking.dataset.IDyadRankingInstance

Converts a dyad ranking to a INDArray matrix where each row corresponds to a dyad.

toND4j() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingDataset

Converts this data set to a list of ND4j INDArrays.

TopKOfPredicted - Class in ai.libs.jaicore.ml.dyadranking.loss

Calculates if the top-k dyads of the predicted ranking match the top-k dyads of the actual ranking.

TopKOfPredicted(int) - Constructor for class ai.libs.jaicore.ml.dyadranking.loss.TopKOfPredicted

Specifies the amount of top rankings to consider.

toString() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

toString() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

toString() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

toString() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

toString() - Method in class ai.libs.jaicore.ml.core.CategoricalFeatureDomain

toString() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.categorical.CategoricalAttributeType

toString() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.primitive.NumericAttributeType

toString() - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema

toString() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy

toString() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

toString() - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

toString() - Method in class ai.libs.jaicore.ml.core.NumericFeatureDomain

toString() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

toString() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.DyadRankingInstance

toString() - Method in class ai.libs.jaicore.ml.dyadranking.dataset.SparseDyadRankingInstance

toString() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad

toString() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.client.ExtrapolationRequest

toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawLearningCurve

toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationLearningCurveConfiguration

toString() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationParameterSet

toString() - Method in class ai.libs.jaicore.ml.metafeatures.GlobalCharacterizer

toString() - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

toString() - Method in class ai.libs.jaicore.ml.SubInstances

Returns the dataset as a string in ARFF format.

toString() - Method in class ai.libs.jaicore.ml.tsc.shapelets.Shapelet

toString(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Enables printing of time series.

toStringWithOffset() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

toStringWithOffset(String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNode

toStringWithOffset(String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeLeaf

toStringWithOffset() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

toStringWithOffset(String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReD

toStringWithOffset(String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.MCTreeNodeReDLeaf

toVector() - Method in class ai.libs.jaicore.ml.dyadranking.Dyad

Converts a dyad to a INDArray row vector consisting of a concatenation of the instance and alternative features.

train(D) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IBatchLearner

Trains this IBatchLearner using the given AILabeledAttributeArrayDataset.

train(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.FeatureTransformPLDyadRanker

train(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

train(List<INDArray>) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

train(DyadRankingDataset, int, double) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

train(List<INDArray>, int, double) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

train(int[], double[], int, double[][], String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient

train - Variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

train(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

Trains the simplified time series classifier model using the given TimeSeriesDataset.

train(D) - Method in class ai.libs.jaicore.ml.tsc.classifier.TSClassifier

Trains this IBatchLearner using the given AILabeledAttributeArrayDataset.

trained - Variable in class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

Variable indicating whether the classifier has been trained.

TRAINING - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

TrainingException - Exception in ai.libs.jaicore.ml.core.exception

The TrainingException indicates that an error occurred during a training process.

TrainingException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.TrainingException

Creates a new TrainingException with the given parameters.

TrainingException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.TrainingException

Creates a new TrainingException with the given parameters.

trainNet(int[], double[], int, double[][]) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod

transform(Dyad) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.BiliniearFeatureTransform

transform(Dyad) - Method in interface ai.libs.jaicore.ml.dyadranking.algorithm.featuretransform.IDyadFeatureTransform

Transform the instance of the given dyad (models the skill).

transform(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms the entire dataset according to the mean and standard deviation of the data the scaler has been fit to.

transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.DFT

transform(TimeSeriesDataset) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

represents a function working on a dataset by transforming the dataset itself.

transform(double[]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

This function transforms only a single instance.

transform(double[][]) - Method in interface ai.libs.jaicore.ml.tsc.filter.IFilter

transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SAX

transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SFA

transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.SlidingWindowBuilder

transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.ATransformFilter

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.CosineTransform

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.HilbertTransform

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.transform.SineTransform

transform(TimeSeriesDataset) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

transform(double[]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

transform(double[][]) - Method in class ai.libs.jaicore.ml.tsc.filter.ZTransformer

transformAlternatives(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the alternatives of each dyad according to the mean and standard deviation of the data the scaler has been fit to.

transformAlternatives(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the alternatives of each dyad according to the mean and standard deviation of the data the scaler has been fit to.

transformAlternatives(IDyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the alternatives of each dyad in an IDyadRankingInstance according to the mean and standard deviation of the data the scaler has been fit to.

transformAlternatives(DyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the alternatives of each dyad in a DyadRankingDataset according to the mean and standard deviation of the data the scaler has been fit to.

transformAlternatives(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

transformAlternatives(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadStandardScaler

transformAlternatives(DyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler

transformAlternatives(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler

transformAttribute(IAttributeValue<?>) - Method in interface ai.libs.jaicore.ml.core.dataset.attribute.transformer.ISingleAttributeTransformer

transformAttribute(IAttributeValue<?>) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MinHashingTransformer

transformAttribute(IAttributeValue<?>) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.transformer.multivalue.MultiValueBinaryzationTransformer

transformAttribute(IAttributeValue<?>) - Method in class ai.libs.jaicore.ml.core.dataset.attribute.transformer.OneHotEncodingTransformer

TransformDistance - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Transform Distance (TD) measure as published in "Non-isometric transforms in time series classification using DTW" by Tomasz Gorecki and Maciej Luczak (2014).

TransformDistance(double, ATransformFilter, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TransformDistance

Constructor with individual distance measures for the function and transform values.

TransformDistance(double, ITimeSeriesDistance, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TransformDistance

Constructor with individual distance measures for the function and transform values that uses the CosineTransform as transformation.

TransformDistance(double, ATransformFilter, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TransformDistance

Constructor that uses the same distance measures for the function and transform values.

TransformDistance(double, ITimeSeriesDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.TransformDistance

Constructor that uses the same distance measures for the function and transform values that uses the CosineTransform as transformation.

transformInstaceVector(Vector, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms an instance feature vector.

transformInstaceVector(Vector, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

transformInstaceVector(Vector, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadStandardScaler

transformInstaceVector(Vector, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler

transformInstances(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the instances of each dyad according to the mean and standard of the data the scaler has been fit to.

transformInstances(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the instances of each dyad according to the mean and standard deviation of the data the scaler has been fit to.

transformInstances(SparseDyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the instances of each dyad in a SparseDyadRankingInstance according to the mean and standard deviation of the data the scaler has been fit to.

transformInstances(DyadRankingInstance, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the instances of each dyad in a DyadRankingInstance according to the mean and standard deviation of the data the scaler has been fit to.

transformInstances(DyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.AbstractDyadScaler

Transforms only the instances of each dyad in a DyadRankingDataset according to the mean and standard deviation of the data the scaler has been fit to.

transformInstances(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

transformInstances(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadStandardScaler

transformInstances(DyadRankingDataset, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler

transformInstances(Dyad, List<Integer>) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadUnitIntervalScaler

transformInstances(double[][], Pair<List<Integer>, List<Integer>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Method transforming the given dataset using the interval pairs specified in T1T2 by calculating each TimeSeriesFeature.FeatureType for every instance and interval pair.

transformWEKAAttributeToAttributeType(Attribute) - Static method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstancesUtil

tree - Variable in class ai.libs.jaicore.ml.tsc.classifier.trees.AccessibleRandomTree

Internal tree object providing access to leaf node information.

tree(double[][], int[], double, TreeNode<TimeSeriesTreeClassifier.TimeSeriesTreeNodeDecisionFunction>, int) - Method in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Tree generation (cf.

TSClassifier<L,V,D extends TimeSeriesDataset<L>> - Class in ai.libs.jaicore.ml.tsc.classifier

Time series classifier which can be trained and used as a predictor.

TSClassifier(ATSCAlgorithm<L, V, D, ? extends TSClassifier<L, V, D>>) - Constructor for class ai.libs.jaicore.ml.tsc.classifier.TSClassifier

Constructor for a time series classifier.

tsInstanceToWekaInstance(TimeSeriesInstance<?>) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Maps a time series instance to a Weka instance.

TSLearningProblem - Class in ai.libs.jaicore.ml.tsc

TSLearningProblem(IQualityMeasure, TimeSeriesDataset) - Constructor for class ai.libs.jaicore.ml.tsc.TSLearningProblem

U

UCBPoolBasedActiveDyadRanker - Class in ai.libs.jaicore.ml.dyadranking.activelearning

A prototypical active dyad ranker based on the UCB decision rule.

UCBPoolBasedActiveDyadRanker(PLNetDyadRanker, IDyadRankingPoolProvider, int, int, int) - Constructor for class ai.libs.jaicore.ml.dyadranking.activelearning.UCBPoolBasedActiveDyadRanker

UncheckedJaicoreMLException - Exception in ai.libs.jaicore.ml.core.exception

The UncheckedJaicoreMLException serves as a base class for all unchecked Exceptions defined as part of jaicore-ml.

UncheckedJaicoreMLException(String, Throwable) - Constructor for exception ai.libs.jaicore.ml.core.exception.UncheckedJaicoreMLException

Creates a new UncheckedJaicoreMLException with the given parameters.

UncheckedJaicoreMLException(String) - Constructor for exception ai.libs.jaicore.ml.core.exception.UncheckedJaicoreMLException

Creates a new UncheckedJaicoreMLException with the given parameters.

unscaleParameters(INDArray, DyadMinMaxScaler, int) - Static method in class ai.libs.jaicore.ml.dyadranking.zeroshot.util.ZeroShotUtil

untransform(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

untransformAlternative(Dyad) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Undoes the transformation on the alternative of a single dyad.

untransformAlternative(Dyad, int) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Undoes the transformation on the alternative of a single dyad.

untransformAlternatives(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Undoes the transformation of the alternatives of each dyad.

untransformAlternatives(DyadRankingDataset, int) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Undoes the transformation of the alternatives of each dyad.

untransformInstance(Dyad) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Undoes the transformation of the instance of a single dyad.

untransformInstance(Dyad, int) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Undoes the transformation of the instance of a single dyad.

untransformInstances(DyadRankingDataset) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Undoes the transformation of the instances of each dyad.

untransformInstances(DyadRankingDataset, int) - Method in class ai.libs.jaicore.ml.dyadranking.util.DyadMinMaxScaler

Undoes the transformation of the instances of each dyad.

update(Set<I>) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IOnlineLearner

Updates this IOnlineLearner based on the given Set of IInstances.

update(I) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IOnlineLearner

Updates this IOnlineLearner based on the given IInstance.

update(IDyadRankingInstance) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

Updates this PLNetDyadRanker based on the given IInstance, which needs to be an IDyadRankingInstance.

update(Set<IDyadRankingInstance>) - Method in class ai.libs.jaicore.ml.dyadranking.algorithm.PLNetDyadRanker

updateBestScore(Double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ProbabilisticMonteCarloCrossValidationEvaluator

updateExperiment(MLExperiment, Map<String, String>) - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentDatabase

updateRanker(Set<IDyadRankingInstance>) - Method in class ai.libs.jaicore.ml.dyadranking.activelearning.ARandomlyInitializingDyadRanker

USE_BIAS_CORRECTION - Static variable in class ai.libs.jaicore.ml.tsc.classifier.shapelets.LearnShapeletsLearningAlgorithm

Indicator whether Bessel's correction should be used when normalizing arrays.

USE_BIAS_CORRECTION - Static variable in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm

Indicator whether Bessel's correction should in feature generation.

USE_BIAS_CORRECTION - Static variable in class ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm

Indicator that the bias (Bessel's) correction should be used for the calculation of the standard deviation.

useBiasCorrection - Variable in class ai.libs.jaicore.ml.tsc.shapelets.search.AMinimumDistanceSearchStrategy

Indicator whether Bessel's correction should be used within any distance calculation;

useFeatureCaching() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesForestLearningAlgorithm.ITimeSeriesForestConfig

Indicator whether feature caching should be used.

useFeatureCaching() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesTreeLearningAlgorithm.ITimeSeriesTreeConfig

Indicator whether feature caching should be used.

useFilterOnSingleInstance(Instance, Filter) - Static method in class ai.libs.jaicore.ml.WekaUtil

useHIVECOTEEnsemble() - Method in interface ai.libs.jaicore.ml.tsc.classifier.shapelets.ShapeletTransformLearningAlgorithm.IShapeletTransformLearningAlgorithmConfig

Indicator whether the HIVE COTE ensemble should be used.

V

VALIDATION - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

value(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

valueAt(double[]) - Method in class ai.libs.jaicore.ml.dyadranking.optimizing.BilinFunction

valueOf(String) - Static method in enum ai.libs.jaicore.ml.cache.DataProvider

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum ai.libs.jaicore.ml.classification.multiclass.reduction.EMCNodeType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper.DiscretizationStrategy

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMultiClassPerformanceMeasure

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper.ProblemType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier.VoteType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature.FeatureType

Returns the enum constant of this type with the specified name.

values() - Static method in enum ai.libs.jaicore.ml.cache.DataProvider

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum ai.libs.jaicore.ml.classification.multiclass.reduction.EMCNodeType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.DiscretizationHelper.DiscretizationStrategy

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.multilabel.EMultilabelPerformanceMeasure

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMulticlassMeasure

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.EMultiClassPerformanceMeasure

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper.ProblemType

Returns an array containing the constants of this enum type, in the order they are declared.

values - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Value matrix containing the time series instances.

values() - Static method in enum ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier.VoteType

Returns an array containing the constants of this enum type, in the order they are declared.

values - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Value matrix containing the time series instances.

values() - Static method in enum ai.libs.jaicore.ml.tsc.features.TimeSeriesFeature.FeatureType

Returns an array containing the constants of this enum type, in the order they are declared.

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

variance(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Calculates the (population) variance of the values of a times series.

verbose - Variable in class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener

Flag whether standard outputs are forwarded to the logger.

vote(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Performs a vote on the nearest neighbors found.

voteMajority(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Performs a majority vote on the set nearest neighbors found.

voteWeightedProportionalToDistance(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Performs a vote with weights proportional to the distance on the set nearest neighbors found.

voteWeightedStepwise(PriorityQueue<Pair<Integer, Double>>) - Method in class ai.libs.jaicore.ml.tsc.classifier.neighbors.NearestNeighborClassifier

Performs a vote with stepwise weights 1, 2, .., k on the set nearest neighbors found.

W

WaitForSamplingStepEvent - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

WaitForSamplingStepEvent(String) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.WaitForSamplingStepEvent

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

WeightedDynamicTimeWarping - Class in ai.libs.jaicore.ml.tsc.distances

Implementation of the Dynamic Time Warping (DTW) measure as published in "Weighted dynamic time warping for time series classification" by Young-Seon Jeong, Myong K.

WeightedDynamicTimeWarping(double, double, IScalarDistance) - Constructor for class ai.libs.jaicore.ml.tsc.distances.WeightedDynamicTimeWarping

Constructor.

WekaCompatibleInstancesImpl - Class in ai.libs.jaicore.ml.core

WekaCompatibleInstancesImpl(List<String>) - Constructor for class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl

WekaCompatibleInstancesImpl(String) - Constructor for class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl

WekaCompatibleInstancesImpl(JsonNode) - Constructor for class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl

WekaCompatibleInstancesImpl(File) - Constructor for class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl

WekaInstance<L> - Class in ai.libs.jaicore.ml.core.dataset.weka

WekaInstance(Instance) - Constructor for class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

WekaInstances<L> - Class in ai.libs.jaicore.ml.core.dataset.weka

WekaInstances(Instances) - Constructor for class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances

WekaInstancesFeatureUnion - Class in ai.libs.jaicore.ml.core

WekaInstancesFeatureUnion() - Constructor for class ai.libs.jaicore.ml.core.WekaInstancesFeatureUnion

wekaInstancesToDataset(Instances) - Static method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstancesUtil

wekaInstancesToINDArray(Instances, boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.WekaUtil

Converts Weka instances to an INDArray matrix.

WekaInstancesUtil - Class in ai.libs.jaicore.ml.core.dataset.weka

WekaUtil - Class in ai.libs.jaicore.ml.tsc.util

WekaUtil

WekaUtil - Class in ai.libs.jaicore.ml

windows - Variable in class ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleClassifier

Holds pairs of (number of correct predictions, window length) obtained in training phase.

windowSize() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

The size of the sliding window that is used over each instance and splits it into multiple smaller instances.

windowSizeMax() - Method in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig

windowSizeMin() - Method in interface ai.libs.jaicore.ml.tsc.classifier.neighbors.ShotgunEnsembleLearnerAlgorithm.IShotgunEnsembleLearnerConfig

withBoundaries - Variable in class ai.libs.jaicore.ml.tsc.filter.derivate.ADerivateFilter

Flag that states wheter the filter should add a padding to the derivate assure that is has the same length as the origin time series or not.

withData(Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Configures the dataset which is split into train and test data.

withData(Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

withData(Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

withDatasetSplitter(IDatasetSplitter) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Configures the evaluator to use the given dataset splitter.

withDatasetSplitter(IDatasetSplitter) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

withDatasetSplitter(IDatasetSplitter) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

withNumMCIterations(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Configures the number of monte carlo cross-validation iterations.

withNumMCIterations(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

withNumMCIterations(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

withSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Configures the evaluator to use the given random seed.

withSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

withSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<Double>) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Configures the evaluator to use the given classifier evaluator.

withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<Double>) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<Double>) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

withTimeoutForSolutionEvaluation(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Configures a timeout for evaluating a solution.

withTimeoutForSolutionEvaluation(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

withTimeoutForSolutionEvaluation(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

withTrainFoldSize(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.AMonteCarloCrossValidationBasedEvaluatorFactory

Configures the portion of the training data relative to the entire dataset size.

withTrainFoldSize(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

withTrainFoldSize(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.ProbabilisticMonteCarloCrossValidationEvaluatorFactory

wordLength() - Method in interface ai.libs.jaicore.ml.tsc.classifier.BOSSLearningAlgorithm.IBossAlgorithmConfig

The word length determines the number of used DFT-coefficients.

Y

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

Z

ZeroOneLoss - Class in ai.libs.jaicore.ml.core.evaluation.measure.singlelabel

ZeroOneLoss() - Constructor for class ai.libs.jaicore.ml.core.evaluation.measure.singlelabel.ZeroOneLoss

ZeroShotUtil - Class in ai.libs.jaicore.ml.dyadranking.zeroshot.util

A collection of utility methods used to map the results of a input optimization of PLNetInputOptimizer back to Weka options for the respective classifiers.

zNormalization() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

Indicator whether the z transformation should be used for the instances at training and prediction time.

zNormalize(double[], boolean) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

Z-normalizes a given dataVector.

zProportion() - Method in interface ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig

Proportion of the total time series length to be used for the subseries generation.

zTransform(double[]) - Static method in class ai.libs.jaicore.ml.tsc.util.TimeSeriesUtil

ZTransformer - Class in ai.libs.jaicore.ml.tsc.filter

ZTransformer() - Constructor for class ai.libs.jaicore.ml.tsc.filter.ZTransformer

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