A B C D E F G H I J K L M N O P 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.

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

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

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

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

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

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

Adds a new Instruction to the history of these Instances

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

addResultEntry(int, double) - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

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

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

afterCreateRun(MLExperiment, int) - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

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

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

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

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

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

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

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

Added some analytical functions to a learning curve.

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

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.

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

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

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

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

ASamplingAlgorithm<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

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

ASamplingAlgorithm(IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ASamplingAlgorithm

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

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

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

assignToStrati(IInstance) - 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.

associatedRunWithClassifier(int, Classifier) - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

AttributeBasedStratiAmountSelectorAndAssigner<I extends IInstance> - 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

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

beforeCreateRun(MLExperiment) - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

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

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.

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(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

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

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

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

calculateFinalInstanceBoundaries(Instances, 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

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

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

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

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

CaseControlLikeSampling<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

CaseControlLikeSampling(IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

CaseControlSampling<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

Case control sampling.

CaseControlSampling(Random, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlSampling

Constructor

CaseControlSamplingFactory<I extends IInstance> - 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.

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.

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.

ClassifierWeightedSampling<I extends IInstance> - 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, IDataset<I>) - 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

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

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

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

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

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 IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

ClusterSampling(long, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

ClusterSampling(long, DistanceMeasure, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.ClusterSampling

ClusterStratiAssigner<I extends IInstance> - 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

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.

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

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

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<IInstance, ASamplingAlgorithm<IInstance>>, IDataset<IInstance>, double, long, String, double[]) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.ConfigurationLearningCurveExtrapolationEvaluator

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

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

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

contains(double) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.Interval

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

countClassOccurrences(IDataset<I>) - 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.

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

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(IDataset<I>, 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 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.weka.WekaInstances

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.

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

createRunIfDoesNotExist(MLExperiment) - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

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

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

CVEvaluator(IMeasure<Double, Double>, int) - Constructor for class ai.libs.jaicore.ml.evaluation.evaluators.weka.CVEvaluator

D

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

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

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(IDataset<? extends IInstance>) - Static method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstancesUtil

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

DiscretizationHelper<I extends IInstance> - 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

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

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

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

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

E

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

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

Ensemble - Class in ai.libs.jaicore.ml.classification.multiclass

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

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.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.sampling.inmemory.stratified.sampling.Interval

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

equals(Object) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

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

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.

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

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.CVEvaluator

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

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

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

EXP_4 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants

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 IInstance> - Class in ai.libs.jaicore.ml.evaluation.evaluators.weka

For the classifier a learning curve will be extrapolated with a given set of anchorpoints.

ExtrapolatedSaturationPointEvaluator(int[], ISamplingAlgorithmFactory<I, ? extends ASamplingAlgorithm<I>>, IDataset<I>, double, LearningCurveExtrapolationMethod, long, IDataset<I>) - 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<IInstance, ? extends ASamplingAlgorithm<IInstance>>, 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

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

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.

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.

G

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).

get(int) - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema

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

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, IDataset<I>, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.CaseControlSamplingFactory

getAlgorithm(int, IDataset<I>, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.GmeansSamplingFactory

getAlgorithm(int, IDataset<I>, 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, IDataset<I>, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

getAlgorithm(int, IDataset<I>, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory

getAlgorithm(int, IDataset<I>, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory

getAlgorithm(int, IDataset<I>, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SimpleRandomSamplingFactory

getAlgorithm(int, IDataset<I>, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory

getAlgorithm(int, IDataset<I>, Random) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory

getAlgorithm() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

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

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.

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 class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getAsDoubleVector() - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

getAssumedMemoryOverheadPerProcess() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

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.IDataset

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.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, Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getAttributeValue(int, Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

getAvailableDatasets(File) - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

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

getBasicEvaluator() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.splitevaluation.AbstractSplitBasedClassifierEvaluator

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

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

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

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

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.

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

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

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

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

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

getClassifierOfRun(int) - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

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

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

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

Sets command name that specifies the type of instruction represented by the object.

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.

getConfiguredClassifier(int, String, String, int, int, int, EMulticlassMeasure) - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

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

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.CVEvaluator

getData() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

getDataset() - Method in class ai.libs.jaicore.ml.experiments.MLExperiment

getDataset() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getDatasetFolder() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

getDatasets() - Method in interface ai.libs.jaicore.ml.experiments.IMultiClassClassificationExperimentConfig

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.MonteCarloCrossValidationEvaluatorFactory

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

getDeclaredClasses() - Method in class ai.libs.jaicore.ml.core.WekaCompatibleInstancesImpl

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

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

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

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

getExperimentDescription(int, Classifier, int) - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

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

getExperimentsForWhichARunExists() - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

getExtrapolationMethod() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getExtrapolator() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolatedEvent

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

getFunctions() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationFunction

getGoalTester() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

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

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

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

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

Inputs are parameters of the instruction.

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

getIntervals() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy

getIntValOfClassName(Instance, String) - Static method in class ai.libs.jaicore.ml.WekaUtil

getKthInstances(File, int) - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

getLabel() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

getLabel() - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstance

getLearner() - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

getLogger() - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

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

getLowerBound() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.Interval

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

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

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

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

getNumberOfAllowedCPUs() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

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

Getter for the number of attributes (excluding target attribute).

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

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

getNumberOfCandidatesInSelectionPhase() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

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

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

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

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

getNumMCIterations() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

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

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

getPoint() - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance

getPortionOfDataForPhase2() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getPossibleClassValues(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

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

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

getRootGenerator() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

getRunIdOfClassifier(Classifier) - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

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

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

getSeed() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

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

getSeparability(String, String) - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.AllPairsTable

getSingleLabelMetrics() - Static method in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter

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.MonteCarloCrossValidationEvaluatorFactory

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

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

getSuccessorGenerator() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

getTargetType(Class<T>) - Method in interface ai.libs.jaicore.ml.core.dataset.IDataset

Returns the attribute type of the target attribute.

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

getTargetType(Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema

getTargetType() - Method in class ai.libs.jaicore.ml.core.dataset.InstanceSchema

getTargetType(Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

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

getTargetType(Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstances

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

getTargetValue(Class<T>) - Method in interface ai.libs.jaicore.ml.core.dataset.IInstance

Getter for the value of the target attribute.

getTargetValue(Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

getTargetValue(Class<T>) - Method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

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

getTimeoutForSolutionEvaluation() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

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

getTmpDir() - Method in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

getTrain() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.FixedSplitClassifierEvaluator

getTrainFoldSize() - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

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

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

getType() - Method in class ai.libs.jaicore.ml.core.dataset.attribute.AAttributeValue

getUpperBound() - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.Interval

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

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).

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 IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

Implementation of a sampling method using gmeans-clustering.

GmeansSampling(long, DistanceMeasure, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.GmeansSampling

GmeansSampling(long, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.GmeansSampling

GmeansSamplingFactory<I extends IInstance> - 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 IInstance> - 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.

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.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.sampling.inmemory.stratified.sampling.Interval

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

hashCode() - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

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

hasOnlyNumericAttributes(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

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

HILL_3 - Static variable in class ai.libs.jaicore.ml.learningcurve.extrapolation.lc.LinearCombinationConstants

I

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.

IBatchLearner<TARGET> - 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 IDataset as training input.

ICategoricalAttributeType - Interface in ai.libs.jaicore.ml.core.dataset.attribute.categorical

Interface for categorical attribute types.

IClassifierEvaluator - Interface in ai.libs.jaicore.ml.evaluation.evaluators.weka

IClassifierEvaluatorFactory - Interface in ai.libs.jaicore.ml.evaluation.evaluators.weka.factory

IDataset<I extends IInstance> - 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.

IDatasetSplitter - Interface in ai.libs.jaicore.ml.weka.dataset.splitter

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

The names of all the meta features that are computed by this characterizer

IInstance - Interface in ai.libs.jaicore.ml.core.dataset

Interface of an instance which consists of attributes and a target value.

IInstancesClassifier - Interface in ai.libs.jaicore.ml.evaluation

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.

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.

init(IDataset<I>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

init(IDataset<I>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeBasedStratiAmountSelectorAndAssigner

Initializes the algorithm for stratum assignment.

init(IDataset<I>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner

init(IDataset<I>, int) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAssigner

Initialize custom assigner if necessary.

init(IDataset<I>, int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.KMeansStratiAssigner

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.NoProbingCharacterizer

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

Initializes GlobalCharacterizer.ids.

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

InstanceSchema(List<IAttributeType<?>>, IAttributeType<?>) - 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

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

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

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<TARGET> - 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.

IPipelineEvaluationConf - Interface in ai.libs.jaicore.ml.experiments

IPredictiveModel<TARGET> - 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

IRerunnableSamplingAlgorithmFactory<I extends IInstance,A extends ASamplingAlgorithm<I>> - 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<I extends IInstance> - Interface in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

Interface for sampling algorithms.

ISamplingAlgorithm - Interface in ai.libs.jaicore.ml.core.dataset.sampling

Interface for sampling algorithms.

ISamplingAlgorithmFactory<I extends IInstance,A extends ASamplingAlgorithm<I>> - 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.

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

ISingleAttributeTransformer - Interface in ai.libs.jaicore.ml.core.dataset.attribute.transformer

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

isSelfContained() - Method in class ai.libs.jaicore.ml.classification.multiclass.reduction.reducer.ReductionGraphGenerator

IStratiAmountSelector<I extends IInstance> - 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 extends IInstance> - 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.

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

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

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

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

KmeansSampling<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

Implementation of a sampling method using kmeans-clustering.

KmeansSampling(long, int, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling

Implementation of a sampling method using kmeans-clustering.

KmeansSampling(long, DistanceMeasure, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling

Implementation of a sampling method using kmeans-clustering.

KmeansSampling(long, int, DistanceMeasure, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.KmeansSampling

Implementation of a sampling method using kmeans-clustering.

KmeansSamplingFactory<I extends IInstance> - 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 IInstance> - 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.

L

LabeledInstance<L> - Interface in ai.libs.jaicore.ml.interfaces

LabeledInstances<L> - Interface in ai.libs.jaicore.ml.interfaces

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

Evaluates a classifier by predicting its learning curve with a few anchorpoints.

LearningCurveExtrapolationEvaluator(int[], ISamplingAlgorithmFactory<IInstance, ? extends ASamplingAlgorithm<IInstance>>, IDataset<? extends IInstance>, 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<IInstance, ? extends ASamplingAlgorithm<IInstance>>, 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 IInstance> - Class in ai.libs.jaicore.ml.learningcurve.extrapolation

Abstract class for implementing a learning curve extrapolation method with some anchor points.

LearningCurveExtrapolator(LearningCurveExtrapolationMethod, Classifier, IDataset<I>, double, int[], ISamplingAlgorithmFactory<I, ? extends ASamplingAlgorithm<I>>, long) - Constructor for class ai.libs.jaicore.ml.learningcurve.extrapolation.LearningCurveExtrapolator

Create a learning curve extrapolator with a subsampling factory.

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.

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.

LocalCaseControlSampling<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

LocalCaseControlSampling(Random, int, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.LocalCaseControlSampling

LocalCaseControlSamplingFactory<I extends IInstance> - 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

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.classification.multiclass.reduction.PipelineOptimizer

main(String[]) - Static method in class ai.libs.jaicore.ml.openml.OpenMLHelper

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

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

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

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

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.

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

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

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

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

MULTI_LABEL_METRICS - Static variable in class ai.libs.jaicore.ml.core.evaluation.measure.ClassifierMetricGetter

Available metrics for multilabelclassifiers

MultiClassClassificationExperimentRunner - Class in ai.libs.jaicore.ml.experiments

MultiClassClassificationExperimentRunner(File, String[], Map<String, String[]>, int[], int, float, int, int, EMulticlassMeasure, IMultiClassClassificationExperimentDatabase) - Constructor for class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

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

MySQLExperimentDatabaseHandle - Class in ai.libs.jaicore.ml.experiments

MySQLExperimentDatabaseHandle(String, String, String, String) - Constructor for class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

N

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.

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

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.

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.

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

O

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

OSMAC<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

OSMAC(Random, int, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.OSMAC

OSMACSamplingFactory<I extends IInstance> - 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

PHASE2 - Static variable in interface ai.libs.jaicore.ml.experiments.IPipelineEvaluationConf

PilotEstimateSampling<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol

PilotEstimateSampling(IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling

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

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

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

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

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(IInstance) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel

Performs a prediction based on the given IInstance and returns the result.

predict(IDataset) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IPredictiveModel

Performs multiple predictions based on the IInstances contained in the given IDatasets and returns the result.

predict(int, double[], String) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetClient

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.

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

preSampleSize - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling

printClassSplitAssignments(List<Instances>) - Static method in class ai.libs.jaicore.ml.WekaUtil

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

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

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

probabilityBoundaries - Variable in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.CaseControlLikeSampling

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

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

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

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

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

removeClassAttribute(Instances) - Static method in class ai.libs.jaicore.ml.WekaUtil

removeClassAttribute(Instance) - Static method in class ai.libs.jaicore.ml.WekaUtil

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

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

runAll() - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

runAny() - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

runExperiment(int, int, int, int) - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

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

runSpecific(int) - Method in class ai.libs.jaicore.ml.experiments.MultiClassClassificationExperimentRunner

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

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

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

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

selectStratiAmount(IDataset<I>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.GMeansStratiAmountSelectorAndAssigner

selectStratiAmount(IDataset<I>) - Method in interface ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.IStratiAmountSelector

Select a suitable amount of strati for a Dataset.

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

setA(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

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.

setB(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

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

setC(double) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.ipl.InversePowerLawConfiguration

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.

setChosenInstance(I) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.casecontrol.PilotEstimateSampling

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.

setConfigurations(double[]) - Method in class ai.libs.jaicore.ml.learningcurve.extrapolation.lcnet.LCNetExtrapolationMethod

setData(Instances) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.CVEvaluator

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.

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.

setEpsilon(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.ExtrapolatedSaturationPointEvaluator

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

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.

setIntervals(List<Interval>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.AttributeDiscretizationPolicy

setK(int) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

Set how many clusters shall be created.

setLabel(String) - Method in class ai.libs.jaicore.ml.core.SimpleLabeledInstanceImpl

setLabel(L) - Method in interface ai.libs.jaicore.ml.interfaces.LabeledInstance

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

setLowerBound(double) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.Interval

setModelPath(File) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

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

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

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

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>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.CaseControlSamplingFactory

setPreviousRun(GmeansSampling<I>) - 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>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.KmeansSamplingFactory

setPreviousRun(LocalCaseControlSampling<I>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.LocalCaseControlSamplingFactory

setPreviousRun(OSMAC<I>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.OSMACSamplingFactory

setPreviousRun(StratifiedSampling<I>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory

setPreviousRun(SystematicSampling<I>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.SystematicSamplingFactory

setProbabilityBoundaries(List<SetUtil.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

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

setSchema(InstanceSchema) - Method in class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

setSeed(int) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator

setSortedDataset(IDataset<I>) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling

setStrati(IDataset<I>[]) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling

setTargets(int...) - Method in class ai.libs.jaicore.ml.scikitwrapper.ScikitLearnWrapper

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.

setTrainingPortion(float) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.SingleRandomSplitClassifierEvaluator

setUpperBound(double) - Method in class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.Interval

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

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

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

SimpleDataset - Class in ai.libs.jaicore.ml.core.dataset.standard

SimpleDataset(InstanceSchema) - Constructor for class ai.libs.jaicore.ml.core.dataset.standard.SimpleDataset

SimpleInstance - Class in ai.libs.jaicore.ml.core.dataset.standard

SimpleInstance(ArrayList<IAttributeValue<?>>, IAttributeValue<?>) - Constructor for class ai.libs.jaicore.ml.core.dataset.standard.SimpleInstance

SimpleInstance(InstanceSchema, ArrayList<IAttributeValue<?>>, IAttributeValue<?>) - 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 extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory

SimpleRandomSampling(Random, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SimpleRandomSampling

SimpleRandomSamplingFactory<I extends IInstance> - 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

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

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.

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

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

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 extends IInstance> - 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<I>, IStratiAssigner<I>, Random, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.stratified.sampling.StratifiedSampling

Constructor for Stratified Sampling.

StratifiedSamplingFactory<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories

StratifiedSamplingFactory(IStratiAmountSelector<I>, IStratiAssigner<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.factories.StratifiedSamplingFactory

StratifiedSplit<I extends IInstance> - Class in ai.libs.jaicore.ml.core.dataset.util

StratifiedSplit(IDataset<I>, 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

SubInstances - Class in ai.libs.jaicore.ml

SubInstances(Instances, int[]) - Constructor for class ai.libs.jaicore.ml.SubInstances

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 IInstance> - 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, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling

Simple constructor that uses the default datapoint comparator.

SystematicSampling(Random, Comparator<I>, IDataset<I>) - Constructor for class ai.libs.jaicore.ml.core.dataset.sampling.inmemory.SystematicSampling

Constructor for a custom datapoint comparator.

SystematicSamplingFactory<I extends IInstance> - 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

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

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

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

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.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.sampling.inmemory.stratified.sampling.Interval

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.SimpleLabeledInstanceImpl

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.

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

train(IDataset) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IBatchLearner

Trains this IBatchLearner using the given IDataset.

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

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

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

transformWEKAAttributeToAttributeType(Attribute) - Static method in class ai.libs.jaicore.ml.core.dataset.weka.WekaInstancesUtil

U

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.

update(Set<IInstance>) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IOnlineLearner

Updates this IOnlineLearner based on the given Set of IInstances.

update(IInstance) - Method in interface ai.libs.jaicore.ml.core.predictivemodel.IOnlineLearner

Updates this IOnlineLearner based on the given IInstance.

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

updateExperiment(MLExperiment, Map<String, String>) - Method in class ai.libs.jaicore.ml.experiments.MySQLExperimentDatabaseHandle

useFilterOnSingleInstance(Instance, Filter) - Static method in class ai.libs.jaicore.ml.WekaUtil

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

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.

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.

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

verbose - Variable in class ai.libs.jaicore.ml.scikitwrapper.DefaultProcessListener

Flag whether standard outputs are forwarded to the logger.

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

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

WekaInstance(Instance) - Constructor for class ai.libs.jaicore.ml.core.dataset.weka.WekaInstance

WekaInstances - 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

WekaInstancesUtil - Class in ai.libs.jaicore.ml.core.dataset.weka

WekaUtil - Class in ai.libs.jaicore.ml

WekaUtil() - Constructor for class ai.libs.jaicore.ml.WekaUtil

withData(Instances) - 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.MonteCarloCrossValidationEvaluatorFactory

withNumMCIterations(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.MonteCarloCrossValidationEvaluatorFactory

withSplitBasedEvaluator(ISplitBasedClassifierEvaluator<Double>) - 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.MonteCarloCrossValidationEvaluatorFactory

withTrainFoldSize(double) - Method in class ai.libs.jaicore.ml.evaluation.evaluators.weka.factory.MonteCarloCrossValidationEvaluatorFactory

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

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